saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-05-17 21:10:43 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
onnxruntime/docs/OperatorKernels.md

1341 lines
254 KiB
Markdown
Raw Normal View History

Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
## Supported Operators and Data Types
Replace 'master' branch ref to 'main' in the code (#12547)
2022-08-22 17:48:12 +00:00
*This file is automatically generated from the registered kernels by [this script](https://github.com/microsoft/onnxruntime/blob/main/tools/python/gen_opkernel_doc.py).
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
Do not modify directly.*
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
## Execution Providers
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
- [CPUExecutionProvider](#cpuexecutionprovider)
- [CUDAExecutionProvider](#cudaexecutionprovider)
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
- [DmlExecutionProvider](#dmlexecutionprovider)
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
---------------
<a name="cpuexecutionprovider"/>
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
## Operators implemented by CPUExecutionProvider
| Op Name | Parameters | OpSet Version | Types Supported |
|---------|------------|---------------|-----------------|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|**Operator Domain:** *ai.onnx*||||
|Abs|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Acos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|Acosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
|Add|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Affine|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
implement affinegrid cpu kernel (#17777)
2023-10-25 17:46:04 +00:00
|AffineGrid|*in* theta:**T1**<br> *in* size:**T2**<br> *out* grid:**T1**|20+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|And|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
Quantization of Argmax (#10213) This patch includes: * int8/uint8 support for Argmax * Quantization tool support for Argmax
2022-01-12 22:12:56 +00:00
|ArgMax|*in* data:**T**<br> *out* reduced:**tensor(int64)**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
int8/uint8 support for Argmax for opset 1, 11, 12 (#10296)
2022-01-18 22:37:34 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ArgMin|*in* data:**T**<br> *out* reduced:**tensor(int64)**|13+|**T** = tensor(double), tensor(float), tensor(int32)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(float), tensor(int32)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Asin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|Asinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
|Atan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|Atanh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
support AveragePool19 and Pad19 (#15597)
2023-05-15 17:46:24 +00:00
|AveragePool|*in* X:**T**<br> *out* Y:**T**|19+|**T** = tensor(float)|
|||[11, 18]|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||10|**T** = tensor(float)|
|||[7, 9]|**T** = tensor(float)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T1**<br> *in* B:**T1**<br> *in* input_mean:**T2**<br> *in* input_var:**T2**<br> *out* Y:**T**<br> *out* running_mean:**T2**<br> *out* running_var:**T2**|15+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(double), tensor(float)|
|||14|**T** = tensor(double), tensor(float)<br/> **U** = tensor(double), tensor(float)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 13]|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[7, 8]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|BitShift|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**|11+|**T** = tensor(uint32), tensor(uint64), tensor(uint8)|
cpu to support bitwise ops (#14197)
2023-01-24 00:42:18 +00:00
|BitwiseAnd|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseNot|*in* X:**T**<br> *out* Y:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseOr|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseXor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks https://github.com/microsoft/onnxruntime/issues/11640
2022-06-27 00:26:55 +00:00
|BlackmanWindow|*in* size:**T1**<br> *out* output:**T2**|17+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Cast|*in* input:**T1**<br> *out* output:**T2**|21+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[13, 18]|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[6, 12]|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add float64 kernels for Floor, Ceil, IsNaN (#13906) ### Description This PR adds support for `float64` kernels in the latest versions of operators: Floor, Ceil and IsNaN. ### Motivation and Context The lack of these kernels is non-trivial to work around and easily lead to performance losses when it is attempted. When equivalence with an existing implementation is required, precision is easily lost when casting to `float32` instead. IsNaN is common when cleaning up data in an ML pipeline. Floor and Ceil have uses for discretising values and single-precision floats are insufficient to round well when values get larger than a few million. According to my measurement this only increases the binary size by a few kilobytes (on the Python wheel of RelWithDebInfo). Closes #13673 (Round already has float64 support) Partially solves #8791 (Looks like there's parallel issues/PR open for Split, but it is also hard to work around and hence useful) Signed-off-by: jbachurski <kbachurski@gmail.com>
2022-12-14 22:57:14 +00:00
|Ceil|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Celu|*in* X:**T**<br> *out* Y:**T**|12+|**T** = tensor(float)|
Enable FP16 Clip and Handle Bias in FP16 Depthwise Conv (#21493) - Improved accuracy for face-detection, image-classification, and object-detection in the GeekBench ML benchmark on ARM64. - Fixed issue https://github.com/microsoft/onnxruntime/issues/18992
2024-07-30 10:49:14 +00:00
|Clip|*in* input:**T**<br> *in* min:**T**<br> *in* max:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||11|**T** = tensor(float)|
|||[6, 10]|**T** = tensor(float)|
Add Col2Im CPU op (#12311) **Description** This PR implements N-dimensional Col2Im as a contrib CPU Op as specified by ONNX's https://github.com/onnx/onnx/pull/3948 **Motivation and Context** - Col2Im enables models such as: - [SS-DCNet](https://github.com/xhp-hust-2018-2011/SS-DCNet) - [DSTT](https://github.com/ruiliu-ai/DSTT) - It also serves to document the ORT's obscure `math::Col2ImNd` utility Signed-off-by: Liqun Fu <liqfu@microsoft.com> Co-authored-by: Liqun Fu <liqfu@microsoft.com>
2023-01-25 20:23:00 +00:00
|Col2Im|*in* input:**T**<br> *in* image_shape:**tensor(int64)**<br> *in* block_shape:**tensor(int64)**<br> *out* output:**T**|18+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Compress|*in* input:**T**<br> *in* condition:**T1**<br> *out* output:**T**|11+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Concat|*in* inputs:**T**<br> *out* concat_result:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[4, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ConcatFromSequence|*in* input_sequence:**S**<br> *out* concat_result:**T**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|ConstantOfShape|*in* input:**T1**<br> *out* output:**T2**|21+|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||20|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
add bf16 support for few ops (#20385) ### Description Add bf16 support for below ops: ConstantOfShape Exp Erf convolution PythonOp ### Motivation and Context phimm model works on bf16, ORT need support bf16 on previous ops to work with phimm on bf16
2024-04-25 18:28:34 +00:00
|||[9, 19]|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ConvInteger|*in* x:**T1**<br> *in* w:**T2**<br> *in* x_zero_point:**T1**<br> *in* w_zero_point:**T2**<br> *out* y:**T3**|10+|**T1** = tensor(uint8)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(int32)|
|ConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Cos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|Cosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
|Crop|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|CumSum|*in* x:**T**<br> *in* axis:**T2**<br> *out* y:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 13]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)|
Implement dft(20) (#17821) ### Description dft is updated in opset20. implement it in ort ### Motivation and Context this is for ort 1.17.0 release Fixes #17723 --------- Signed-off-by: Liqun Fu <liqfu@microsoft.com>
2023-12-19 18:42:54 +00:00
|DFT|*in* input:**T1**<br> *in* dft_length:**T2**<br> *in* axis:**tensor(int64)**<br> *out* output:**T1**<br><br>or<br><br>*in* input:**T1**<br> *in* dft_length:**T2**<br> *out* output:**T1**|20+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int32), tensor(int64)|
|||[17, 19]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int32), tensor(int64)|
Support SpaceDepth ops in the CUDA and ROCM EPs (#7960)
2021-07-09 08:00:22 +00:00
|DepthToSpace|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
|||[11, 12]|**T** = tensor(double), tensor(float)|
|||[1, 10]|**T** = tensor(double), tensor(float)|
[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.
2024-05-31 01:56:24 +00:00
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**<br><br>or<br><br>*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|21+|**T1** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int8), tensor(uint16), tensor(uint4), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|||[19, 20]|**T1** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int32), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[13, 18]|**T** = tensor(int32), tensor(int8), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[10, 12]|**T** = tensor(int32), tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Det|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
|Div|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Dropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T1**|13+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
|||[10, 11]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|||[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|DynamicQuantizeLinear|*in* x:**T1**<br> *out* y:**T2**<br> *out* y_scale:**tensor(float)**<br> *out* y_zero_point:**T2**|11+|**T2** = tensor(uint8)|
|DynamicSlice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *out* output:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|Einsum|*in* Inputs:**T**<br> *out* Output:**T**|12+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|Elu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Equal|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|19+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)<br/> **T1** = tensor(bool)|
|||[13, 18]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|||[7, 10]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Erf|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Exp|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Expand|*in* input:**T**<br> *in* shape:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[8, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|EyeLike|*in* input:**T1**<br> *out* output:**T2**|9+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)<br/> **T2** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Flatten|*in* input:**T**<br> *out* output:**T**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 8]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add float64 kernels for Floor, Ceil, IsNaN (#13906) ### Description This PR adds support for `float64` kernels in the latest versions of operators: Floor, Ceil and IsNaN. ### Motivation and Context The lack of these kernels is non-trivial to work around and easily lead to performance losses when it is attempted. When equivalence with an existing implementation is required, precision is easily lost when casting to `float32` instead. IsNaN is common when cleaning up data in an ML pipeline. Floor and Ceil have uses for discretising values and single-precision floats are insufficient to round well when values get larger than a few million. According to my measurement this only increases the binary size by a few kilobytes (on the Python wheel of RelWithDebInfo). Closes #13673 (Round already has float64 support) Partially solves #8791 (Looks like there's parallel issues/PR open for Split, but it is also hard to work around and hence useful) Signed-off-by: jbachurski <kbachurski@gmail.com>
2022-12-14 22:57:14 +00:00
|Floor|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GRU|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|Gather|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GatherElements|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|GatherND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
|||12|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
|||11|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
ONNX Gelu Op in Opset 20 (#19560) ### ONNX Gelu Op in Opset 20 Refactor code to support MSDomain Gelu and ONNX Gelu-opset20 Op 1. Move CPU-GELU implmentation from `onnxruntime/contrib_ops/cpu/activations.h/cc` to `onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation for approximate attribute to be 'none'. 2. Dumplicate some logic from `onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc` to `onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation for approximate attribute to be 'tanh'. 3. Register ONNX domain Gelu CPU kernel from opset 20 in `onnxruntime/core/providers/cpu/cpu_execution_provider.cc`. 4. Move `onnxruntime/contrib_ops/cuda/bert/fast_gelu_impl.h/cu` to `onnxruntime/core/providers/cuda/tensor/gelu_impl.h` and `onnxruntime/core/providers/cuda/tensor/gelu_approximate_impl.cu` respectively, as the implementation for approximate attribute to be 'tanh'. 5. Implement the logic for approximate attribute to be 'none' in `onnxruntime/core/providers/cuda/tensor/gelu_impl.cu`. 6. Register ONNX domain Gelu CUDA kernel from opset 20 in `onnxruntime/core/providers/cuda/cuda_execution_provider.cc`. 7. ROCM ep related changes. 8. Enrich the tests for ONNX domain Gelu in `onnxruntime/test/providers/cpu/activation/activation_op_test.cc`.
2024-02-23 03:05:16 +00:00
|Gelu|*in* X:**T**<br> *out* Y:**T**|20+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Gemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float)|
|||[9, 10]|**T** = tensor(double), tensor(float)|
|||[7, 8]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|GlobalLpPool|*in* X:**T**<br> *out* Y:**T**|2+|**T** = tensor(float)|
|GlobalMaxPool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Greater|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|||[7, 8]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(bool)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|GreaterOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|||[12, 15]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
implement gridsample 20 (#17744)
2023-11-07 18:42:41 +00:00
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|20+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(double), tensor(float)|
|||[16, 19]|**T1** = tensor(float)<br/> **T2** = tensor(float)|
register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks https://github.com/microsoft/onnxruntime/issues/11640
2022-06-27 00:26:55 +00:00
|HammingWindow|*in* size:**T1**<br> *out* output:**T2**|17+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|HannWindow|*in* size:**T1**<br> *out* output:**T2**|17+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|HardSigmoid|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
|Hardmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|21+|**V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[16, 18]|**V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|||[14, 15]|**V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|If|*in* cond:**B**<br> *out* outputs:**V**|21+|**B** = tensor(bool)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**B** = tensor(bool)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[16, 18]|**B** = tensor(bool)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|||[13, 15]|**B** = tensor(bool)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ImageScaler|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|InstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|6+|**T** = tensor(float)|
Implement CUDA IsInf-10,20 (#19772) ### Description Implment IsInf-10,20 for CUDA. Add FP16 types also on CPU. ### Motivation and Context Certain models lag in performance due to IsInf not available on CUDA.
2024-03-05 21:33:01 +00:00
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
implement isinf20 and isnan20 (#17874)
2023-10-24 17:58:54 +00:00
|||[10, 19]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
Implement IsNaN-9,13,20 for CUDA along with tests (#19807) ### Description ### Motivation and Context Some models require IsNan CUDA along with training
2024-03-07 23:46:11 +00:00
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
implement isinf20 and isnan20 (#17874)
2023-10-24 17:58:54 +00:00
|||[13, 19]|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
Add float64 kernels for Floor, Ceil, IsNaN (#13906) ### Description This PR adds support for `float64` kernels in the latest versions of operators: Floor, Ceil and IsNaN. ### Motivation and Context The lack of these kernels is non-trivial to work around and easily lead to performance losses when it is attempted. When equivalence with an existing implementation is required, precision is easily lost when casting to `float32` instead. IsNaN is common when cleaning up data in an ML pipeline. Floor and Ceil have uses for discretising values and single-precision floats are insufficient to round well when values get larger than a few million. According to my measurement this only increases the binary size by a few kilobytes (on the Python wheel of RelWithDebInfo). Closes #13673 (Round already has float64 support) Partially solves #8791 (Looks like there's parallel issues/PR open for Split, but it is also hard to work around and hence useful) Signed-off-by: jbachurski <kbachurski@gmail.com>
2022-12-14 22:57:14 +00:00
|||[9, 12]|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LRN|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[1, 12]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LSTM|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|14+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
Update Attention operator to support separated Q/K/V inputs (#13410) ### Description Allow separated Q, K and V inputs to support cross attention: * Q: [batch_size, sequence_length, hidden_size] * K: [batch_size, kv_sequence_length, hidden_size] * V: [batch_size, kv_sequence_length, v_hidden_size] * Output: [batch_size, sequence_length, v_hidden_size] To use separated Q/K/V inputs, the input tensor is for query, and two optional inputs are added for key and value. Weights for input projection is not included for now, so the MatMul of input projection shall be done out of Attention operator, but Add bias is included for performance consideration.
2022-10-25 18:51:06 +00:00
|LayerNormalization|*in* X:**T**<br> *in* Scale:**T**<br> *in* B:**T**<br> *out* Y:**T**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**<br><br>or<br><br>*in* X:**T**<br> *in* Scale:**V**<br> *in* B:**V**<br> *out* Y:**V**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**|17+|**T** = tensor(double), tensor(float)<br/> **U** = tensor(float)|
|||[1, 16]|**T** = tensor(double), tensor(float)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(double), tensor(float)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|LeakyRelu|*in* X:**T**<br> *out* Y:**T**|16+|**T** = tensor(float)|
|||[6, 15]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Less|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|||[7, 8]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(bool)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|LessOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|||[12, 15]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Log|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LogSoftmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(double), tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Loop|*in* M:**I**<br> *in* cond:**B**<br> *in* v_initial:**V**<br> *out* v_final_and_scan_outputs:**V**|21+|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[16, 18]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|||[13, 15]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LpNormalization|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float)|
cpu support of LpPool(18) (#14205) Signed-off-by: Liqun Fu <liqfu@microsoft.com> ### Description To support LpPool (18) ### Motivation and Context for Ort 1.14 release Signed-off-by: Liqun Fu <liqfu@microsoft.com>
2023-01-26 07:14:56 +00:00
|LpPool|*in* X:**T**<br> *out* Y:**T**|18+|**T** = tensor(float)|
|||[11, 17]|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[2, 10]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 8]|**T** = tensor(double), tensor(float)|
add fallback of s8s8 support on x64 (#9995) * add fallback of s8s8 support on x64
2021-12-10 19:33:19 +00:00
|MatMulInteger|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *out* Y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int32)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Max|*in* data_0:**T**<br> *out* max:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[8, 11]|**T** = tensor(double), tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[6, 7]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MaxPool|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**<br> *out* Indices:**I**|12+|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(int8), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[8, 11]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float)|
|||[1, 7]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MaxRoiPool|*in* X:**T**<br> *in* rois:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|MaxUnpool|*in* X:**T1**<br> *in* I:**T2**<br> *in* output_shape:**T2**<br> *out* output:**T1**|11+|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[9, 10]|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Mean|*in* data_0:**T**<br> *out* mean:**T**|13+|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[8, 12]|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[6, 7]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MeanVarianceNormalization|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 8]|**T** = tensor(float)|
register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks https://github.com/microsoft/onnxruntime/issues/11640
2022-06-27 00:26:55 +00:00
|MelWeightMatrix|*in* num_mel_bins:**T1**<br> *in* dft_length:**T1**<br> *in* sample_rate:**T1**<br> *in* lower_edge_hertz:**T2**<br> *in* upper_edge_hertz:**T2**<br> *out* output:**T3**|17+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(float)<br/> **T3** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Min|*in* data_0:**T**<br> *out* min:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[8, 11]|**T** = tensor(double), tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[6, 7]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Mod|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[10, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Mul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Multinomial|*in* input:**T1**<br> *out* output:**T2**|7+|**T1** = tensor(float)<br/> **T2** = tensor(int32), tensor(int64)|
|Neg|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|NonZero|*in* X:**T**<br> *out* Y:**tensor(int64)**|13+|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|Not|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|OneHot|*in* indices:**T1**<br> *in* depth:**T2**<br> *in* values:**T3**<br> *out* output:**T3**|11+|**T1** = tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(float), tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[9, 10]|**T1** = tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(float), tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|Optional|*in* input:**V**<br> *out* output:**O**|15+|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|OptionalGetElement|*in* input:**O**<br> *out* output:**V**|18+|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[15, 17]|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|OptionalHasElement|*in* input:**O**<br> *out* output:**B**|18+|**B** = tensor(bool)<br/> **O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[15, 17]|**B** = tensor(bool)<br/> **O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Or|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|PRelu|*in* X:**T**<br> *in* slope:**T**<br> *out* Y:**T**|16+|**T** = tensor(float)|
|||[9, 15]|**T** = tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[7, 8]|**T** = tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *in* axes:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|21+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
support AveragePool19 and Pad19 (#15597)
2023-05-15 17:46:24 +00:00
|||18|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
support Pad(18) (#14219)
2023-01-23 20:14:35 +00:00
|||[13, 17]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[2, 10]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ParametricSoftplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|Pow|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**<br><br>or<br><br>*in* X:**T**<br> *in* Y:**T1**<br> *out* Z:**T**|15+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[13, 14]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 11]|**T** = tensor(double), tensor(float)|
add fallback of s8s8 support on x64 (#9995) * add fallback of s8s8 support on x64
2021-12-10 19:33:19 +00:00
|QLinearConv|*in* x:**T1**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T1**<br> *in* w:**T2**<br> *in* w_scale:**tensor(float)**<br> *in* w_zero_point:**T2**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T3**<br> *in* B:**T4**<br> *out* y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int8), tensor(uint8)<br/> **T4** = tensor(int32)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|QLinearMatMul|*in* a:**T1**<br> *in* a_scale:**TS**<br> *in* a_zero_point:**T1**<br> *in* b:**T2**<br> *in* b_scale:**TS**<br> *in* b_zero_point:**T2**<br> *in* y_scale:**TS**<br> *in* y_zero_point:**T3**<br> *out* y:**T3**<br><br>or<br><br>*in* a:**T1**<br> *in* a_scale:**tensor(float)**<br> *in* a_zero_point:**T1**<br> *in* b:**T2**<br> *in* b_scale:**tensor(float)**<br> *in* b_zero_point:**T2**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T3**<br> *out* y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int8), tensor(uint8)|
[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.
2024-05-31 01:56:24 +00:00
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**<br><br>or<br><br>*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|21+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int4), tensor(int8), tensor(uint16), tensor(uint4), tensor(uint8)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|||[19, 20]|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int8), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[13, 18]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[10, 12]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|RNN|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(float)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(float)<br/> **T1** = tensor(int32)|
|RandomNormal|*out* output:**T**|1+|**T** = tensor(double), tensor(float)|
|RandomNormalLike|*in* input:**T1**<br> *out* output:**T2**|1+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(double), tensor(float)|
|RandomUniform|*out* output:**T**|1+|**T** = tensor(double), tensor(float)|
|RandomUniformLike|*in* input:**T1**<br> *out* output:**T2**|1+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(double), tensor(float)|
|Range|*in* start:**T**<br> *in* limit:**T**<br> *in* delta:**T**<br> *out* output:**T**|11+|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)|
|Reciprocal|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Reduce operators support for int64 type (#15358)
2023-04-05 16:19:43 +00:00
|ReduceL1|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|ReduceL2|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|ReduceLogSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|ReduceLogSumExp|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
reduce max/min 20 (#17805) ### Description reducemax/min have been updated in onnx(20). implement it in ort ### Motivation and Context this is for ort1.17.0 release --------- Signed-off-by: Liqun Fu <liqfu@microsoft.com>
2024-01-05 01:41:01 +00:00
|ReduceMax|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|20+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||[18, 19]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|||[13, 17]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||11|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|ReduceMean|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(int32)|
|||[13, 17]|**T** = tensor(double), tensor(float), tensor(int32)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32)|
reduce max/min 20 (#17805) ### Description reducemax/min have been updated in onnx(20). implement it in ort ### Motivation and Context this is for ort1.17.0 release --------- Signed-off-by: Liqun Fu <liqfu@microsoft.com>
2024-01-05 01:41:01 +00:00
|ReduceMin|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|20+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||[18, 19]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|||[13, 17]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||11|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|ReduceProd|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(float), tensor(int32), tensor(int64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Reduce operators support for int64 type (#15358)
2023-04-05 16:19:43 +00:00
|ReduceSumSquare|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[13, 17]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
RegexFullMatch operator (#18002) ### Description <!-- Describe your changes. --> ### Motivation and Context Closes https://github.com/microsoft/onnxruntime/issues/17594.
2024-01-11 23:50:07 +00:00
|RegexFullMatch|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(string)<br/> **T2** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Relu|*in* X:**T**<br> *out* Y:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(double), tensor(float)|
|||[6, 12]|**T** = tensor(double), tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Reshape|*in* data:**T**<br> *in* shape:**tensor(int64)**<br> *out* reshaped:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reshaped:**T**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[19, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[14, 18]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[5, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[1, 4]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[Feature Request] Support Resize opset 19 (#15633)
2023-05-01 17:49:17 +00:00
|Resize|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T1**<br> *in* roi:**T2**<br> *in* scales:**tensor(float)**<br> *in* sizes:**tensor(int64)**<br> *out* Y:**T1**|19+|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||18|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|||[13, 17]|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
add int8_t for Resize (#10067) As we support quantization for format s8s8, we need Resize to support int8_t.
2021-12-17 23:36:09 +00:00
|||[11, 12]|**T1** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||10|**T** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReverseSequence|*in* input:**T**<br> *in* sequence_lens:**tensor(int64)**<br> *out* Y:**T**|10+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|RoiAlign|*in* X:**T1**<br> *in* rois:**T1**<br> *in* batch_indices:**T2**<br> *out* Y:**T1**|16+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int64)|
|||[10, 15]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Round|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
register signal ops for opset 17 (#11778) * Register signal ops for op set 17 Note code is mostly being moved, not added. These ops were previously only registered as Microsoft contrib ops and only built if `BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx standard op set in version 17. Main components of this change: * Move the kernels from the conrib_ops directory to the core directory. * Add function bodies for ms experimental ops. This will allow old models that use the contrib ops to continue to function. All the function bodies consist of a single op (the new standard op), so performance overhead should be minimal. Minor clean-up also in this change: * De-duplicate get_scalar_value_from_tensor: put it in a new utils.h. * Fix some bugs that caused compilation errors with the experimental ops. Tested with `build.sh --ms_experimental` * Fix some spelling errors and lint violations. * Replace a couple of switch statements with `MLTypeCallDispatcher`. * Use `InlineVector` instead of `std::vector`. Unblocks https://github.com/microsoft/onnxruntime/issues/11640
2022-06-27 00:26:55 +00:00
|STFT|*in* signal:**T1**<br> *in* frame_step:**T2**<br> *in* window:**T1**<br> *in* frame_length:**T2**<br> *out* output:**T1**|17+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Scale|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|ScaledTanh|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Scan|*in* initial_state_and_scan_inputs:**V**<br> *out* final_state_and_scan_outputs:**V**<br><br>or<br><br>*in* sequence_lens:**I**<br> *in* initial_state_and_scan_inputs:**V**<br> *out* final_state_and_scan_outputs:**V**|21+|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[19, 20]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[16, 18]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|||[11, 15]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|||[9, 10]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||8|**I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Scatter|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
support ScatterND(18) and ScatterElement(18) (#14224)
2023-01-19 21:54:20 +00:00
|ScatterElements|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[16, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|||[13, 15]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
support ScatterND(18) and ScatterElement(18) (#14224)
2023-01-19 21:54:20 +00:00
|ScatterND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *in* updates:**T**<br> *out* output:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[16, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|||[13, 15]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Selu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
|SequenceAt|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* tensor:**T**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceConstruct|*in* inputs:**T**<br> *out* output_sequence:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceEmpty|*out* output:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceErase|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceInsert|*in* input_sequence:**S**<br> *in* tensor:**T**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceLength|*in* input_sequence:**S**<br> *out* length:**I**|11+|**I** = tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[19, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|||[15, 18]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|||[13, 14]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Shrink|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sigmoid|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sign|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[9, 12]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Fuse LayerNormalization for Apex O2 (#10233)
2022-03-29 13:22:04 +00:00
|SimplifiedLayerNormalization|*in* X:**T**<br> *in* scale:**V**<br> *out* Y:**V**<br> *out* inv_std_var:**U**|1+|**T** = tensor(double), tensor(float)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(double), tensor(float)|
|Sinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Size|*in* data:**T**<br> *out* size:**T1**|21+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[19, 20]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
To support size opset 19 (#15689)
2023-08-11 21:48:53 +00:00
|||[13, 18]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[1, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Slice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *in* steps:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||10|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[1, 9]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Softmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Softplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Softsign|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
Support SpaceDepth ops in the CUDA and ROCM EPs (#7960)
2021-07-09 08:00:22 +00:00
|SpaceToDepth|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
|||[1, 12]|**T** = tensor(double), tensor(float)|
Split(18) (#14015) ### Description <!-- Describe your changes. --> Opset 18 Split changes. Adds ability to specify num_outputs which also allows uneven splitting. https://github.com/onnx/onnx/releases/tag/v1.13.0 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Support ONNX opset 18.
2023-01-11 22:14:10 +00:00
|Split|*in* input:**T**<br> *in* split:**T**<br> *out* outputs...:**T**<br><br>or<br><br>*in* input:**T**<br> *in* split:**tensor(int64)**<br> *out* outputs:**T**<br><br>or<br><br>*in* input:**T**<br> *out* outputs:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add support for other data types to Split CPU kernel. (#13900) Split copies data - we can add support for all data types without too much binary size impact by using data type size-based implementations. The DispatchStridedCopy() function used here does this.
2022-12-12 17:29:15 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[2, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add float16 type support to SplitToSequence and make code type independent (#18594) ### Description Add support for `float16` type to address the below issue. Re-work the code to make it type independent. This reduces binary size by ~11 K. ![image](https://github.com/microsoft/onnxruntime/assets/11303988/1a77c7bc-34a8-478c-a16a-abd94062c6c6) ### Motivation and Context This PR addresses https://github.com/microsoft/onnxruntime/issues/18481
2023-11-29 18:44:59 +00:00
|SplitToSequence|*in* input:**T**<br> *in* split:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(string)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sqrt|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Squeeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* squeezed:**T**<br><br>or<br><br>*in* data:**T**<br> *out* squeezed:**T**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
String concat operator (#17994) ### Description <!-- Describe your changes. --> ### Motivation and Context Closes https://github.com/microsoft/onnxruntime/issues/17595. --------- Signed-off-by: Aditya Goel <agoel4512@gmail.com>
2024-01-11 18:01:43 +00:00
|StringConcat|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**|20+|**T** = tensor(string)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|StringNormalizer|*in* X:**tensor(string)**<br> *out* Y:**tensor(string)**|10+|**X** = tensor(string)|
StringSplit operator (#18016) ### Description <!-- Describe your changes. --> ### Motivation and Context Closes https://github.com/microsoft/onnxruntime/issues/17596
2024-01-12 17:46:23 +00:00
|StringSplit|*in* X:**T1**<br> *out* Y:**T2**<br> *out* Z:**T3**|20+|**T1** = tensor(string)<br/> **T2** = tensor(string)<br/> **T3** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sub|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sum|*in* data_0:**T**<br> *out* sum:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[8, 12]|**T** = tensor(double), tensor(float)|
|||[6, 7]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Tan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|Tanh|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|TfIdfVectorizer|*in* X:**T**<br> *out* Y:**T1**|9+|**T** = tensor(int32), tensor(int64), tensor(string)<br/> **T1** = tensor(float)|
|ThresholdedRelu|*in* X:**T**<br> *out* Y:**T**|10+|**T** = tensor(float)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 9]|**T** = tensor(float)|
Add string support to tile op (#14686) ### Description Add std::string tensor type support to Tile operator ### Motivation and Context Multiple users are hitting this missing feature: https://github.com/microsoft/onnxruntime/issues/14511
2023-02-16 22:59:44 +00:00
|Tile|*in* input:**T**<br> *in* repeats:**T1**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *in* tiles:**T**<br> *in* axis:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[6, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|TopK|*in* X:**T**<br> *in* K:**tensor(int64)**<br> *out* Values:**T**<br> *out* Indices:**I**<br><br>or<br><br>*in* X:**T**<br> *out* Values:**T**<br> *out* Indices:**I**|11+|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||10|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float)|
|||[1, 9]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float)|
[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.
2024-05-31 01:56:24 +00:00
|Transpose|*in* data:**T**<br> *out* transposed:**T**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|||[13, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add support for Trilu<bool>. (#20917) ### Description <!-- Describe your changes. --> Trilu<bool> is used by phi-3 when exported with torch.onnx.export. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-06-06 05:21:34 +00:00
|Trilu|*in* input:**T**<br> *in* k:**tensor(int64)**<br> *out* output:**T**|14+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int64)|
Unique operator with double (#16359) ### Description The [ONNX standard](https://github.com/onnx/onnx/blob/main/docs/Operators.md#type-constraints-181) permits the `Unique` operator to have `double` input tensor element type, however this was not supported in onnxruntime. This PR enables this kernel. ### Motivation and Context The lack of support for `float64` forces users currently to cast to `float32` instead. This loss of precision can be severely problematic in feature engineering pipelines downstream of the `Unique` operator. It would be good to prevent this by updating ORT to reflect the standard and support `double` input tensors. --------- Signed-off-by: Aditya Goel <agoel4512@gmail.com>
2023-07-12 03:24:14 +00:00
|Unique|*in* X:**T**<br> *out* Y:**T**<br> *out* indices:**tensor(int64)**<br> *out* inverse_indices:**tensor(int64)**<br> *out* counts:**tensor(int64)**|11+|**T** = tensor(double), tensor(float), tensor(int64), tensor(int8), tensor(string)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|Unsqueeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* expanded:**T**<br><br>or<br><br>*in* data:**T**<br> *out* expanded:**T**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement qlinear concat and unit test. (#7341) * Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
2021-04-26 20:38:40 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
add int8_t for Resize (#10067) As we support quantization for format s8s8, we need Resize to support int8_t.
2021-12-17 23:36:09 +00:00
|Upsample|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**|9|**T** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
|||[7, 8]|**T** = tensor(float), tensor(int32), tensor(int8), tensor(uint8)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|Where|*in* condition:**B**<br> *in* X:**T**<br> *in* Y:**T**<br> *out* output:**T**|16+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint8)|
|||[9, 15]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Xor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
| |
| |
|**Operator Domain:** *ai.onnx.ml*||||
|ArrayFeatureExtractor|*in* X:**T**<br> *in* Y:**tensor(int64)**<br> *out* Z:**T**|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)|
|Binarizer|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|CastMap|*in* X:**T1**<br> *out* Y:**T2**|1+|**T1** = map(int64,tensor(float)), map(int64,tensor(string))<br/> **T2** = tensor(float), tensor(int64), tensor(string)|
|CategoryMapper|*in* X:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(int64), tensor(string)<br/> **T2** = tensor(int64), tensor(string)|
|DictVectorizer|*in* X:**T1**<br> *out* Y:**T2**|1+|**T1** = map(int64,tensor(double)), map(int64,tensor(float)), map(int64,tensor(string)), map(string,tensor(double)), map(string,tensor(float)), map(string,tensor(int64))<br/> **T2** = tensor(double), tensor(float), tensor(int64), tensor(string)|
|FeatureVectorizer|*in* X:**T1**<br> *out* Y:**tensor(float)**|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|Imputer|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(int64)|
Label encoder opset4 (#17977) ### Description <!-- Describe your changes. --> Implements LabelEncoder as per `ai.onnx.ml` opset 4 for the upcoming ONNX 1.15 release. ~~This currently depends on a new ONNX release candidate and so is marked as draft in the meantime.~~ ### Motivation and Context Closes https://github.com/microsoft/onnxruntime/issues/17602
2024-01-12 20:43:44 +00:00
|LabelEncoder|*in* X:**T1**<br> *out* Y:**T2**|4+|**T1** = tensor(double), tensor(float), tensor(int64), tensor(string)<br/> **T2** = tensor(double), tensor(float), tensor(int16), tensor(int64), tensor(string)|
|||[2, 3]|**T1** = tensor(float), tensor(int64), tensor(string)<br/> **T2** = tensor(float), tensor(int64), tensor(string)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|||1|**T1** = tensor(int64), tensor(string)<br/> **T2** = tensor(int64), tensor(string)|
|LinearClassifier|*in* X:**T1**<br> *out* Y:**T2**<br> *out* Z:**tensor(float)**|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|LinearRegressor|*in* X:**T**<br> *out* Y:**tensor(float)**|1+|**T** = tensor(float)|
|Normalizer|*in* X:**T**<br> *out* Y:**tensor(float)**|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|OneHotEncoder|*in* X:**T**<br> *out* Y:**tensor(float)**|1+|**T** = tensor(double), tensor(float), tensor(int64), tensor(string)|
|SVMClassifier|*in* X:**T1**<br> *out* Y:**T2**<br> *out* Z:**tensor(float)**|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|SVMRegressor|*in* X:**T**<br> *out* Y:**tensor(float)**|1+|**T** = tensor(float)|
|Scaler|*in* X:**T**<br> *out* Y:**tensor(float)**|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
Implement TreeEnsemble for opset(ai.onnx.ml)==3 (#10821) * Implement TreeEnsemble for opset(ai.onnx.ml)==3 * use of InlineVector * refactoring * improve attributes retrieval * avoid creating a temporary buffer * modifies onnx.ml.cpu.json * use unordered_map * update docs/OperatorKernels.md * address PR comments (TH -> ThresholdType, ORT_RETURN...) * add a python unit test to load a TreeEnsembleRegressor following ai.onnx.ml==3 specifications
2022-03-30 10:53:12 +00:00
|TreeEnsembleClassifier|*in* X:**T1**<br> *out* Y:**T2**<br> *out* Z:**tensor(float)**|3+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|||[1, 2]|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|TreeEnsembleRegressor|*in* X:**T**<br> *out* Y:**tensor(float)**|3+|**T** = tensor(double), tensor(float)|
|||[1, 2]|**T** = tensor(double), tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ZipMap|*in* X:**tensor(float)**<br> *out* Z:**T**|1+|**T** = seq(map(int64,tensor(float))), seq(map(string,tensor(float)))|
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
| |
| |
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|**Operator Domain:** *com.microsoft*||||
Some kernel changes for TULR (#14517) ### Description <!-- Describe your changes. --> 1. fix a bug in relative position bias kernel where seq_len > 32 2. rename extra_add_qk to relative_position_bias 3. support relative_position_bias in multihead attention (B, N, S, S*) 4. gru_gate support by Lei ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net> Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
2023-02-07 19:51:06 +00:00
|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|AttnLSTM|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *in* QW:**T**<br> *in* MW:**T**<br> *in* V:**T**<br> *in* M:**T**<br> *in* memory_seq_lens:**T1**<br> *in* AW:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|1+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
Add timestamp logits processor for whisper (#15853) Enable timestamp estimation and logits processing for Whisper model.
2023-05-17 04:40:00 +00:00
|BeamSearch|*in* input_ids:**F**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float)|
Bifurcation detector for aggressive decoding (#9432) ``` Component for aggressive decoding. Find the bifurcation index of predicted tokens, between source tokens, starting from previous suffix match index, and predicted tokens. Concat predicted tokens, starting from bifurcation index, to the back of current tokens. This forms the output tokens. Detect suffix match index in source tokens, between source tokens and output tokens. Detection is based on finding the appearances of last n-gram in output tokens in source tokens. A match is considered found if source tokens contain a single matching n-gram. Return the index of the start of the n-gram in source tokens. No matching if found if src tokens contain multiple or zero matching n-grams. Return -1. ```
2021-10-20 02:53:56 +00:00
|BifurcationDetector|*in* src_tokens:**T**<br> *in* cur_tokens:**T**<br> *in* prev_suffix_match_idx:**T**<br> *in* pred_tokens:**T**<br> *out* tokens:**T**<br> *out* suffix_match_idx:**T**|1+|**T** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|CDist|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(double), tensor(float)|
|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|CropAndResize|*in* X:**T1**<br> *in* rois:**T1**<br> *in* batch_indices:**T2**<br> *in* crop_size:**T2**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(int32)|
[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.
2024-05-31 01:56:24 +00:00
|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(int16), tensor(int32), tensor(int4), tensor(int8), tensor(uint16), tensor(uint4), tensor(uint8)<br/> **T2** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|DynamicQuantizeLSTM|*in* X:**T**<br> *in* W:**T2**<br> *in* R:**T2**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *in* W_scale:**T**<br> *in* W_zero_point:**T2**<br> *in* R_scale:**T**<br> *in* R_zero_point:**T2**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|1+|**T** = tensor(float)<br/> **T1** = tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
|DynamicQuantizeMatMul|*in* A:**T1**<br> *in* B:**T2**<br> *in* b_scale:**T1**<br> *in* b_zero_point:**T2**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
embed layer fusion gpt2 (#9336) * Changes to fuse embed layer for gpt2, kernal changes pending * verified add output and regular add match * Test added for additional output embedlayernorm, working on CUDA * Test passing on CPU * updated convert_to_onnx toll to check parity correctly * removed some debugs * couple of TODO left as in optimizer.py * removed changes to optimizer.py * fixing build * fixing build * updated order of initilization * added a test case for float16 * updating the docs * updating tests failing due to embed layer fusion * update unit tests * updating CUDA documentation in operatorkernels.md * addressing comments * OperatorKernels.md updated with CUDA * adding TODO to qembed_layer * minor edit * updated docs * addressing comments * adding position ids to embed layer gpt2 * updating fused gpt2 model * added extra test * remove comments * addressing comments * contrib_defs.cc updated * all tests passing * fixing a typo * minor edit * trigger build * qembedlayernorm checkinputs updated * fixing build error * fixing build error * fixing build error
2021-10-28 18:06:26 +00:00
|EmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding:**T**<br> *in* position_embedding:**T**<br> *in* segment_embedding:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* mask:**T1**<br> *in* position_ids:**T1**<br> *out* output:**T**<br> *out* mask_index:**T1**<br> *out* embedding_sum:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ExpandDims|*in* X:**T**<br> *in* axis:**tensor(int32)**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **axis** = tensor(int32)|
|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|FusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Z:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|FusedGemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|GatherND|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
A few fixes for generative model ops (#13363) ### Description <!-- Describe your changes. --> Fix a bug in GreedySearch Op when batch > 1 Support custom attention mask in GreedySearch and BeamSearch with GPT2 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2022-10-21 22:00:18 +00:00
|GreedySearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *out* sequences:**I**|1+|**T** = tensor(float)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
GQA 4 CPU (#20299) ### Description Support GQA operator on CPU with FP32. ### Motivation and Context Right now, models generated for CPU and GPU must be different. GQA CPU allows these models to be the same.
2024-04-23 02:57:05 +00:00
|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
Add MatMul FP4 and NF4 Support (#18066) ### Description Add a contrib op MatMulBnb4 (FP4 and NF4) and related toolchain to support quantization on weight. This PR adds: - schema for contrib op MatMulBnb4 which can support FP4 (4-bit floating point) and NF4 (4-bit NormalFloat) quantization on weight. - a naive implementation for MatMulBnb4 on CPU and GPU, i.e., implemented like MatMul(A, Dequantize(B)). - a special implementation for GemV for MatMulBnb4 and related benchmark tool. - tool to quantize model to FP4 or NF4.
2023-10-25 22:34:58 +00:00
|MatMulBnb4|*in* A:**T1**<br> *in* B:**T2**<br> *in* absmax:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)|
4b quantization for weights of LLMs (#16833) ### Description Blockwise 4b quantization for LLMs. 1. Introduce 4b block-wise quantization for linear layer weights. 2. Implements matrix multiplication kernel for fp32 x int4 3. Implements special operator MatMulFpQ4 4. Implements quantization tool, that convert MatMul operator to MatMulFpQ4, when the right hand side is 2D const tensor. ### Motivation and Context Compress and accelerate LLMs |Benchmark | Time(ns)| |-------------|----------| |Q4GEMM/Q4Sym/M:1/N:4096/K:4096/Threads:8| 218054| |Q4GEMM/Q4Sym/M:1024/N:4096/K:4096/Threads:8| 35830155| |Q4GEMM/Q4Sym/M:2048/N:4096/K:4096/Threads:8| 73479790| |Q4GEMM/Q4Zp8/M:1/N:4096/K:4096/Threads:8| 270152| |Q4GEMM/Q4Zp8/M:1024/N:4096/K:4096/Threads:8| 35826721| |Q4GEMM/Q4Zp8/M:2048/N:4096/K:4096/Threads:8| 73021200| |Q4GEMM/Q4Sym128/M:1/N:4096/K:4096/Threads:8| 213832| |Q4GEMM/Q4Sym128/M:1024/N:4096/K:4096/Threads:8| 36749874| |Q4GEMM/Q4Sym128/M:2048/N:4096/K:4096/Threads:8| 72618120| |Benchmark | Time(ns)| |-------------|----------| |SGEMM/LLM/M:1/N:4096/K:4096/Threads:8| 522610| |SGEMM/LLM/M:1024/N:4096/K:4096/Threads:8| 39237689| |SGEMM/LLM/M:2048/N:4096/K:4096/Threads:8| 75983467| --------- Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
2023-08-07 19:23:55 +00:00
|MatMulFpQ4|*in* A:**T1**<br> *in* B:**T2**<br> *in* B_shape:**T3**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MatMulInteger16|*in* A:**T1**<br> *in* B:**T2**<br> *out* Y:**T3**|1+|**T1** = tensor(int16)<br/> **T2** = tensor(int16)<br/> **T3** = tensor(int32)|
add fallback of s8s8 support on x64 (#9995) * add fallback of s8s8 support on x64
2021-12-10 19:33:19 +00:00
|MatMulIntegerToFloat|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_scale:**T3**<br> *in* b_scale:**T3**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *in* bias:**T3**<br> *out* Y:**T3**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float)|
MatMulNBits + Add fusion (#20587) - Add MatMulNBits Bias input - Add graph transformer to fuse MatMulNBits + Add
2024-05-16 18:00:59 +00:00
|MatMulNBits|*in* A:**T1**<br> *in* B:**T2**<br> *in* scales:**T1**<br> *in* zero_points:**T3**<br> *in* g_idx:**T4**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(float), tensor(uint8)<br/> **T4** = tensor(int32)|
Update kernel matching logic: decouple from op schemas and remove kernel def hashes (#12791) # Motivation Currently, ORT minimal builds use kernel def hashes to map from nodes to kernels to execute when loading the model. As the kernel def hashes must be known ahead of time, this works for statically registered kernels. This works well for the CPU EP. For this approach to work, the kernel def hashes must also be known at ORT format model conversion time, which means the EP with statically registered kernels must also be enabled then. This is not an issue for the always-available CPU EP. However, we do not want to require that any EP which statically registers kernels is always available too. Consequently, we explore another approach to match nodes to kernels that does not rely on kernel def hashes. An added benefit of this is the possibility of moving away from kernel def hashes completely, which would eliminate the maintenance burden of keeping the hashes stable. # Approach In a full build, ORT uses some information from the ONNX op schema to match a node to a kernel. We want to avoid including the ONNX op schema in a minimal build to reduce binary size. Essentially, we take the necessary information from the ONNX op schema and make it available in a minimal build. We decouple the ONNX op schema from the kernel matching logic. The kernel matching logic instead relies on per-op information which can either be obtained from the ONNX op schema or another source. This per-op information must be available in a minimal build when there are no ONNX op schemas. We put it in the ORT format model. Existing uses of kernel def hashes to look up kernels are replaced with the updated kernel matching logic. We no longer store kernel def hashes in the ORT format model’s session state and runtime optimization representations. We no longer keep the logic to generate and ensure stability of kernel def hashes.
2022-09-20 21:24:59 +00:00
|MaxpoolWithMask|*in* X:**T**<br> *in* M:**tensor(int32)**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Whisper Model Optimization (#15473) ### Description This PR contains fusion-level and kernel-level optimizations for [OpenAI's Whisper](https://github.com/openai/whisper). Some of the added optimizations include: - Pruning of duplicate/unnecessary inputs and outputs - Fusion support for Whisper models with or without these inputs/outputs (e.g. with these inputs/outputs if exporting with an older official Optimum version, without these inputs/outputs if exporting with Optimum from source) - Attention fusions - For Whisper's encoder and decoder - Modified symbolic shape inference for present output when no past input exists (for decoder) - Multi-head attention fusions - For Whisper's decoder and decoder with past - Packed MatMul for the 3 MatMuls excluded in multi-head attention fusion - Attention kernel changes - CPU: - Different Q and KV sequence lengths - Parallel memset for large sequence lengths - Convert broadcast add after MatMul of Q and K (add_qk) to element-wise add - Separate present key-value output into present key and present value (for multi-head attention spec) - CUDA: - Use memory efficient attention compute kernel with present state (for decoder) - Multi-head attention kernel changes - CPU: - Introduction of multi-head attention CPU kernel (previously did not exist) - Use AddBiasReshape instead of AddBiasTranspose when sequence length = 1 (for decoder with past) - Different Q, K, V input shapes - Pass past key and past value directly as key and value - CUDA: - Use memory efficient attention compute kernel with past and/or present state (for decoder with past) ### Usage To use the optimizations, run the ORT transformer optimizer script as follows: ``` $ cd onnxruntime/onnxruntime/python/tools/transformers/ $ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type bart --num_heads <number of attention heads, depends on the size of the whisper model used> --hidden_size <attention hidden size, depends on the size of the whisper model used> --use_external_data_format --use_multi_head_attention ``` Once optimized, here's an example of how to run Whisper with [Hugging Face's Optimum](https://github.com/huggingface/optimum): ``` from transformers.onnx.utils import get_preprocessor from optimum.onnxruntime import ORTModelForSpeechSeq2Seq from optimum.pipelines import pipeline as ort_pipeline import whisper # Installed from OpenAI's repo - setup instructions at https://github.com/openai/whisper/ directory = './whisper_opt' # Where the optimized ONNX models are located model_name = 'openai/whisper-tiny' device = 'cpu' # Get pipeline processor = get_preprocessor(model_name) model = ORTModelForSpeechSeq2Seq.from_pretrained( directory, use_io_binding=(device == 'cuda'), provider='CPUExecutionProvider', ).to(device) pipe = ort_pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=(-1 if device == 'cpu' else 0), ) # Load audio file and run pipeline audio = whisper.load_audio('tests/jfk.flac') audio = whisper.pad_or_trim(audio) outputs = pipe([audio]) print(outputs) ``` Note: In order to use these changes with Optimum, it is recommended to use Optimum from source to have the following changes: - https://github.com/huggingface/optimum/pull/872 - https://github.com/huggingface/optimum/pull/920 ### Motivation and Context This PR helps the following issues: - https://github.com/microsoft/onnxruntime/issues/15100 - https://github.com/microsoft/onnxruntime/issues/15235 - https://github.com/huggingface/optimum/issues/869 (work in progress) This PR can be used with the other currently merged Whisper PRs: - https://github.com/microsoft/onnxruntime/pull/15247 - https://github.com/microsoft/onnxruntime/pull/15339 - https://github.com/microsoft/onnxruntime/pull/15362 - https://github.com/microsoft/onnxruntime/pull/15365 - https://github.com/microsoft/onnxruntime/pull/15427 This PR uses changes from the following merged PRs: - https://github.com/microsoft/onnxruntime/pull/14198 - https://github.com/microsoft/onnxruntime/pull/14146 - https://github.com/microsoft/onnxruntime/pull/14201 - https://github.com/microsoft/onnxruntime/pull/14928 (this introduced the new multi-head attention spec)
2023-04-19 00:13:54 +00:00
|MultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MurmurHash3|*in* X:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(int32), tensor(uint32)|
Add NGramRepeatBlock contrib op (#8078) **Description**: Enforce no repetition of n-grams. Scores are set to `-inf` for tokens that form a repeated n-gram if added to the back of the input_ids. **Motivation and Context** Needed by transformer models in sequence generation algorithms (greedy search and beam search). This module has heavy impact on performance, and can be highly parallelized.
2021-06-21 17:21:48 +00:00
|NGramRepeatBlock|*in* input_ids:**Tid**<br> *in* scores:**T**<br> *out* scores_out:**T**|1+|**T** = tensor(float)<br/> **Tid** = tensor(int64)|
add int8_t support for pool operators (#9852) * add int8_t support for pool operators
2021-11-30 02:43:43 +00:00
|NhwcMaxPool|*in* x:**T**<br> *out* y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* value:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float)<br/> **T4** = tensor(int32)|
Create a quantized EmbedLayerNorm for ORT. (#8124) Create a quantized EmbedLayerNorm Op for ORT
2021-06-25 22:51:43 +00:00
|QEmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding_quant:**T2**<br> *in* position_embedding_quant:**T2**<br> *in* segment_embedding:**T2**<br> *in* gamma_quant:**T2**<br> *in* beta_quant:**T2**<br> *in* mask:**T1**<br> *in* word_embedding_scale:**T**<br> *in* position_embedding_scale:**T**<br> *in* segment_embedding_scale:**T**<br> *in* gamma_scale:**T**<br> *in* beta_scale:**T**<br> *in* word_embedding_zero_point:**T2**<br> *in* position_embedding_zero_point:**T2**<br> *in* segment_embedding_zero_point:**T2**<br> *in* gamma_zero_point:**T2**<br> *in* beta_zero_point:**T2**<br> *out* layernorm_out:**T**<br> *out* mask_index_out:**T1**|1+|**T** = tensor(float)|
add qdq support for QGemm (#10414) * add qgemm in quantization tool * add qdq support for QGemm * fix build break * fix OperatorKernels.md
2022-02-02 18:35:29 +00:00
|QGemm|*in* A:**TA**<br> *in* a_scale:**T**<br> *in* a_zero_point:**TA**<br> *in* B:**TB**<br> *in* b_scale:**T**<br> *in* b_zero_point:**TB**<br> *in* C:**TC**<br> *in* y_scale:**T**<br> *in* y_zero_point:**TYZ**<br> *out* Y:**TY**|1+|**T** = tensor(float)<br/> **TA** = tensor(int8), tensor(uint8)<br/> **TB** = tensor(int8), tensor(uint8)<br/> **TC** = tensor(int32)<br/> **TY** = tensor(float), tensor(int8), tensor(uint8)<br/> **TYZ** = tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|QLinearAdd|*in* A:**T**<br> *in* A_scale:**tensor(float)**<br> *in* A_zero_point:**T**<br> *in* B:**T**<br> *in* B_scale:**tensor(float)**<br> *in* B_zero_point:**T**<br> *in* C_scale:**tensor(float)**<br> *in* C_zero_point:**T**<br> *out* C:**T**|1+|**T** = tensor(int8), tensor(uint8)|
add fallback of s8s8 support on x64 (#9995) * add fallback of s8s8 support on x64
2021-12-10 19:33:19 +00:00
|QLinearConv|*in* x:**T1**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T1**<br> *in* w:**T2**<br> *in* w_scale:**tensor(float)**<br> *in* w_zero_point:**T2**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T3**<br> *in* B:**T4**<br> *out* y:**T3**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int8), tensor(uint8)<br/> **T4** = tensor(int32)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|QLinearLeakyRelu|*in* X:**T**<br> *in* X_scale:**tensor(float)**<br> *in* X_zero_point:**T**<br> *in* Y_scale:**tensor(float)**<br> *in* Y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
|QLinearMul|*in* A:**T**<br> *in* A_scale:**tensor(float)**<br> *in* A_zero_point:**T**<br> *in* B:**T**<br> *in* B_scale:**tensor(float)**<br> *in* B_zero_point:**T**<br> *in* C_scale:**tensor(float)**<br> *in* C_zero_point:**T**<br> *out* C:**T**|1+|**T** = tensor(int8), tensor(uint8)|
|QLinearSigmoid|*in* X:**T**<br> *in* X_scale:**tensor(float)**<br> *in* X_zero_point:**T**<br> *in* Y_scale:**tensor(float)**<br> *in* Y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
[Qlinearsoftmax] contrib cpu (#12177) * [Qlinearsoftmax] contrib cpu * int8 implementation * contrib operator md * qdq transformer test * new attribute: opset * doc * quantized tool * remove template to reduce Binary size * doc of contribe operators * enforce x_shape is valid * fix reduce_size if input-shape is dynamic * add UT * register one op for reducing binarysize * kernel hash update * docs/ContribOperators.md
2022-08-10 02:52:02 +00:00
|QLinearSoftmax|*in* X:**T**<br> *in* X_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
Cjian/where python operator (#12795) **Description**: This PR will enable the python tool to run QWhere and QDQWhere operation **Limitation**: s8s8 Where is still not supported.
2022-12-12 21:27:47 +00:00
|QLinearWhere|*in* condition:**B**<br> *in* X:**T**<br> *in* x_scale:**TF**<br> *in* x_zero_point:**T**<br> *in* Y:**T**<br> *in* y_scale:**TF**<br> *in* y_zero_point:**T**<br> *in* z_scale:**TF**<br> *in* z_zero_point:**T**<br> *out* Z:**T**|1+|**T** = tensor(int8), tensor(uint8)|
[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.
2024-05-31 01:56:24 +00:00
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(int16), tensor(int4), tensor(int8), tensor(uint16), tensor(uint4), tensor(uint8)|
QuickGelu Fusion (#12417) Some models have QuickGelu(x)=x*sigmoid(1.702x), which has 3 Ops for forward and 5 Ops for backward. The PR is to fuse this to a single Op named QuickGelu and its gradient QuickGeluGrad. For CUDA, tested in V100 using input tensor with shape [64,128,2048] and float16 type: Before, FW takes 335us, BW takes 614us ![image](https://user-images.githubusercontent.com/11661208/182291335-15188709-ffe7-44d1-9d14-0b544cbe5e55.png) After, FW takes 115us, BW takes 139us, which is much faster. ![image](https://user-images.githubusercontent.com/11661208/182291502-f0b5161c-b95c-45fc-90f8-ad0c592d2433.png) For CPU kernel, using same shape and float type: Before, FW takes 10us, BW takes 49us Mul: 3480[µs] Sigmoid: 1996[µs] Mul: 4789[µs] Mul: 4642[µs] Mul: 4195[µs] SigmoidGrad: 18328[µs] Mul: 2988[µs] Sum: 18576[µs] After, FW takes 4us, BW takes 5us, which is also much faster. QuickGelu: 3939[µs] QuickGeluGrad: 5089[µs] Co-authored-by: Vincent Wang <weicwang@microsoft.com>
2022-10-28 10:12:07 +00:00
|QuickGelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Range|*in* start:**T**<br> *in* limit:**T**<br> *in* delta:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)|
LLaMA Model Optimization (#18021) ### Description This PR contains fusion-level and kernel-level optimizations for [Meta's LLaMA-2](https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/). Some of the added optimizations include: - SimplifiedLayerNorm changes - Fusions for multiple variants - SkipSimplifiedLayerNorm changes - Kernel support for CPU - Rotary embeddings (previously did not exist) - Fusions for multiple variants - CPU and CUDA kernels - Supports interleaving and non-interleaving in the same kernels - Optimized cache that requires half of its originally exported sizes - Reduced from `(max_sequence_length, head_size)` to `(max_sequence_length, head_size / 2)` - Multi-head attention - Support for 2D and 3D attention masks - Group query attention (for FP16 CUDA and INT4 CUDA) - Integration with flash attention v2 and past-present buffer sharing - Removes need for `attention_mask` input as it is supported in the kernel - 4 bit quantization - `block_size` parameter is available for customizing - Support the new changes for [Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Support combinations of the below variants (ex: export ORT version and run with Optimum) Supported variants of LLaMA-2 include: - [ORT version](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/models/llama) - Produces one ONNX file that is already optimized (and quantized if requested) - Integrates with Optimum - [Another Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Already exported and available off-the-shelf - Faster versions of those models will be uploaded there soon - [Hugging Face version](https://huggingface.co/meta-llama) - Models that end with `-hf` - Some older and current versions of [`transformers`](https://github.com/huggingface/transformers) and [`optimum`](https://github.com/huggingface/optimum) that export the model to ONNX differently - Note that while some older versions are supported, it is recommended to use the latest package versions. ### Usage To use the optimizations, please see `README.md` for details. Please note the various `requirements.txt` files for the package versions recommended in order to use these changes. To run the ORT transformer optimizer separately, run the script as follows: ``` $ cd onnxruntime/onnxruntime/python/tools/transformers/ $ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type gpt2 --num_heads <number of attention heads> --hidden_size <attention hidden size> --use_external_data_format --opt_level 0 ``` ### Motivation and Context This PR helps the following issues: - https://github.com/microsoft/onnxruntime/issues/14997 - https://github.com/microsoft/onnxruntime/issues/16254 - https://github.com/microsoft/onnxruntime/issues/17681 - https://github.com/microsoft/onnxruntime/issues/17925 - https://github.com/microsoft/onnxruntime-inference-examples/issues/320 This PR uses changes from the following PRs: - https://github.com/pytorch/pytorch/pull/104468 - https://github.com/pytorch/pytorch/pull/109759 - https://github.com/microsoft/onnxruntime/pull/17020 - https://github.com/microsoft/onnxruntime/pull/17674 - https://github.com/microsoft/onnxruntime/pull/17890 - https://github.com/microsoft/onnxruntime/pull/17920 - https://github.com/huggingface/transformers/pull/26162 - https://github.com/huggingface/optimum/pull/1257 - https://github.com/huggingface/optimum/pull/1289 - https://github.com/huggingface/optimum/pull/1462 ### New TorchDynamo Exporter (experimental stage) This PR uses changes from the following issues and PRs to begin supporting the [new TorchDynamo exporter](https://pytorch.org/docs/stable/onnx.html#torchdynamo-based-onnx-exporter): - https://github.com/huggingface/transformers/pull/26307 - https://github.com/pytorch/pytorch/issues/104903 - https://github.com/pytorch/pytorch/pull/105040 - https://github.com/microsoft/onnxscript/pull/847 - https://github.com/microsoft/onnxscript/pull/862 - https://github.com/microsoft/onnxscript/issues/493
2023-10-23 20:00:56 +00:00
|RotaryEmbedding|*in* input:**T**<br> *in* position_ids:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**|1+|**M** = tensor(int64)<br/> **T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|SampleOp|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Some changes to Sampling Op (#14218) ### Description <!-- Describe your changes. --> 1. add an optional input to pass in seed 2. two UTs. one for top_p=0.5, another for top_p=0.01(create greedy search result, in convert_generation.py) 3. fix a bug in cpu kernel ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
2023-01-12 22:15:26 +00:00
|Sampling|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *in* presence_mask:**I**<br> *in* seed:**I**<br> *out* sequences:**I**<br> *out* filtered_logits:**T**|1+|**T** = tensor(float)|
Misc transformer fixes - 2 (#14156) ### Description 1. The graph pattern search introduced in https://github.com/microsoft/onnxruntime/pull/13914/ needs to be enhanced so that SkipLayerNormalization is supported 2. Fix fp32 parity for GPT-2 while using `SkipLayerNormalization` fusion. The optional output of SLN needs to also include the bias (if present) and the added output should be a sum of `input + skip + (bias)` ### Motivation and Context Fix some breaking tests
2023-01-06 15:27:10 +00:00
|SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(double), tensor(float)|
LLaMA Model Optimization (#18021) ### Description This PR contains fusion-level and kernel-level optimizations for [Meta's LLaMA-2](https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/). Some of the added optimizations include: - SimplifiedLayerNorm changes - Fusions for multiple variants - SkipSimplifiedLayerNorm changes - Kernel support for CPU - Rotary embeddings (previously did not exist) - Fusions for multiple variants - CPU and CUDA kernels - Supports interleaving and non-interleaving in the same kernels - Optimized cache that requires half of its originally exported sizes - Reduced from `(max_sequence_length, head_size)` to `(max_sequence_length, head_size / 2)` - Multi-head attention - Support for 2D and 3D attention masks - Group query attention (for FP16 CUDA and INT4 CUDA) - Integration with flash attention v2 and past-present buffer sharing - Removes need for `attention_mask` input as it is supported in the kernel - 4 bit quantization - `block_size` parameter is available for customizing - Support the new changes for [Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Support combinations of the below variants (ex: export ORT version and run with Optimum) Supported variants of LLaMA-2 include: - [ORT version](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/models/llama) - Produces one ONNX file that is already optimized (and quantized if requested) - Integrates with Optimum - [Another Microsoft version](https://github.com/microsoft/Llama-2-Onnx) - Already exported and available off-the-shelf - Faster versions of those models will be uploaded there soon - [Hugging Face version](https://huggingface.co/meta-llama) - Models that end with `-hf` - Some older and current versions of [`transformers`](https://github.com/huggingface/transformers) and [`optimum`](https://github.com/huggingface/optimum) that export the model to ONNX differently - Note that while some older versions are supported, it is recommended to use the latest package versions. ### Usage To use the optimizations, please see `README.md` for details. Please note the various `requirements.txt` files for the package versions recommended in order to use these changes. To run the ORT transformer optimizer separately, run the script as follows: ``` $ cd onnxruntime/onnxruntime/python/tools/transformers/ $ python3 optimizer.py --input <filename>.onnx --output <filename>.onnx --model_type gpt2 --num_heads <number of attention heads> --hidden_size <attention hidden size> --use_external_data_format --opt_level 0 ``` ### Motivation and Context This PR helps the following issues: - https://github.com/microsoft/onnxruntime/issues/14997 - https://github.com/microsoft/onnxruntime/issues/16254 - https://github.com/microsoft/onnxruntime/issues/17681 - https://github.com/microsoft/onnxruntime/issues/17925 - https://github.com/microsoft/onnxruntime-inference-examples/issues/320 This PR uses changes from the following PRs: - https://github.com/pytorch/pytorch/pull/104468 - https://github.com/pytorch/pytorch/pull/109759 - https://github.com/microsoft/onnxruntime/pull/17020 - https://github.com/microsoft/onnxruntime/pull/17674 - https://github.com/microsoft/onnxruntime/pull/17890 - https://github.com/microsoft/onnxruntime/pull/17920 - https://github.com/huggingface/transformers/pull/26162 - https://github.com/huggingface/optimum/pull/1257 - https://github.com/huggingface/optimum/pull/1289 - https://github.com/huggingface/optimum/pull/1462 ### New TorchDynamo Exporter (experimental stage) This PR uses changes from the following issues and PRs to begin supporting the [new TorchDynamo exporter](https://pytorch.org/docs/stable/onnx.html#torchdynamo-based-onnx-exporter): - https://github.com/huggingface/transformers/pull/26307 - https://github.com/pytorch/pytorch/issues/104903 - https://github.com/pytorch/pytorch/pull/105040 - https://github.com/microsoft/onnxscript/pull/847 - https://github.com/microsoft/onnxscript/pull/862 - https://github.com/microsoft/onnxscript/issues/493
2023-10-23 20:00:56 +00:00
|SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(double), tensor(float)|
[CPU] SparseAttention op (#21110) Add SparseAttention cpu implementation. - [x] Refactoring GQAAttentionBase - [x] Add SparseAttention implementation - [x] Add test cases This is unfused version. Flash attention version will be added later.
2024-07-04 04:51:57 +00:00
|SparseAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* block_row_indices:**M**<br> *in* block_col_indices:**M**<br> *in* total_sequence_length:**M**<br> *in* key_total_sequence_lengths:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float)|
Implement SparseTensor and infrastructure suppport and advance ONNX commit (#8038) SparseTensor support Implement Builder pattern Fix support for 1-D and 2-D COO indices Implement and test CSR support. Handle shape inference for SparseTensors Implement conversion for COO, CSR and tests. Address the case where constant sparse initializer is the output. Implement test infra for SparseTensors Implement SparseDenseMatMul for Csr and COO and tested it. Add hash for SparseToDenseMatMul Finish shared provider refactor Refactor GetOrCreate to Create Working on py interface Expose OrtDevice and use it in allocate_numpy Adjust Sparse interfaces, add support for string SparseTensor. Add tests. Add and test to_cuda() Add accessors to format specific indices Test values and indices views, read-only flag, after GC access Add sparse related methods to OrtValue Re-work SparseTensor wrapper, add OrtValue methods Rework numpy_array_to_cuda/to_cpu Add run_with_ort_values Add models and test sparse_mat_mul with run_with_ort_values Refactor sparse tensor to use a single buffer Ifdef x86 Eigen CSR sparse matmul implementation Exclude broken test, check for string type when copying cross device Split pybind schema, regenerate docs, add exclusion Conditionally exclude schema module Update docs fix cuda build Add test to a filter and renerate JS docs Add conversion and test string support for sparse tensors Exclude conversion utils from minimal build Add CUDA Memcpy and adjust provider interfaces
2021-07-22 22:24:36 +00:00
|SparseToDenseMatMul|*in* A:**T**<br> *in* B:**T1**<br> *out* Y:**T1**|1+|**T** = sparse_tensor(double), sparse_tensor(float), sparse_tensor(int32), sparse_tensor(int64), sparse_tensor(uint32), sparse_tensor(uint64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Tokenizer|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(string)|
|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Trilu|*in* X:**T**<br> *in* k:**tensor(int64)**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(int64)|
|Unique|*in* x:**T**<br> *out* y:**T**<br> *out* idx:**tensor(int64)**<br> *out* counts:**tensor(int64)**|1+|**T** = tensor(float)|
Add Temperature to WhisperBeamSearch input (#19188) ### Description <!-- Describe your changes. --> Add `temperature` as an input to WhisperBeamSearch op and initialize correctly in parameter setup. ### Motivation and Context Currently, temperature is included as an attribute to the BeamSearch op, which doesn't let the model act dynamically in a single inference session. By including this variable as an input, the temperature value can be altered in any inference call (important for 1P teams) --------- Co-authored-by: Peter McAughan <petermca@microsoft.com> Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
2024-01-23 21:44:34 +00:00
|WhisperBeamSearch|*in* input_ids:**F**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *in* cross_qk_layer_head:**I**<br> *in* extra_decoding_ids:**I**<br> *in* temperature:**T**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**<br> *out* cross_qk:**V**<br> *out* non_speech_probs:**T**|1+|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|WordConvEmbedding|*in* Sequence:**T**<br> *in* W:**T1**<br> *in* B:**T1**<br> *in* C:**T1**<br> *out* Y:**T1**|1+|**T** = tensor(int32)<br/> **T1** = tensor(float)|
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
| |
| |
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|**Operator Domain:** *com.microsoft.nchwc*||||
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|AveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Sum:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|GlobalMaxPool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|MaxPool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|ReorderInput|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|ReorderOutput|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Upsample|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Generate documentation from the registered operator kernels (#1395) - Added python script for generating markdown doc from the registered opkernels. - Made some conditional changes in the pybind to expose necessary python API - Added some missing type-constraints in the op kernel registrations
2019-08-15 01:12:24 +00:00
| |
| |
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
<a name="cudaexecutionprovider"/>
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
## Operators implemented by CUDAExecutionProvider
| Op Name | Parameters | OpSet Version | Types Supported |
|---------|------------|---------------|-----------------|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|**Operator Domain:** *ai.onnx*||||
|Abs|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Add|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Affine|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|And|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
enable reduce ops on opset18 (#18053) ### Description Opset 18 apply the "axes as input" change from ReduceSum to all the other reduce ops. Our cuda kernel actually support it, but we didn't enable it for opset18. This PR update the reduce ops' kernel registration to enable the "axes as input" behavior for opset18. As part of the fix, I also simplify the reduce op kernel registration part. ORT doesn't require the kernel definition need to be exactly the same as onnx op definition. For our case, which we share the same kernel for all the reduce ops (from version 1 to version 18), we don't need to maintain different version of kernel definitions. we can simplify it by just using a single kernel definition for multiple versions. Although for some cases, we might register more types for legacy versions, but it is harmless. Framework is using schema to validate the graph, not kernel definition. --------- Co-authored-by: Cheng Tang <chenta@a100.crj0ad2y1kku1j4yxl4sj10o4e.gx.internal.cloudapp.net> Co-authored-by: Cheng Tang <chenta@microsoft.com>
2023-10-26 23:57:21 +00:00
|ArgMax|*in* data:**T**<br> *out* reduced:**tensor(int64)**|[1, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|ArgMin|*in* data:**T**<br> *out* reduced:**tensor(int64)**|[1, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|AveragePool|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|||10|**T** = tensor(double), tensor(float), tensor(float16)|
|||[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T1**<br> *in* B:**T1**<br> *in* input_mean:**T2**<br> *in* input_var:**T2**<br> *out* Y:**T**<br> *out* running_mean:**T2**<br> *out* running_var:**T2**|15+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(double), tensor(float), tensor(float16)|
|||14|**T** = tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|||[9, 13]|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Cast|*in* input:**T1**<br> *out* output:**T2**|19+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 18]|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[9, 12]|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[6, 8]|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Ceil|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Clip|*in* input:**T**<br> *in* min:**T**<br> *in* max:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)|
|||11|**T** = tensor(float)|
|||[6, 10]|**T** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Compress|*in* input:**T**<br> *in* condition:**T1**<br> *out* output:**T**|11+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Concat|*in* inputs:**T**<br> *out* concat_result:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[4, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Implement Sequence Ops GPU (#7863)
2021-06-07 22:30:26 +00:00
|ConcatFromSequence|*in* input_sequence:**S**<br> *out* concat_result:**T**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ConstantOfShape|*in* input:**T1**<br> *out* output:**T2**|9+|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Cos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(double), tensor(float), tensor(float16)|
|Crop|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|CumSum|*in* x:**T**<br> *in* axis:**T2**<br> *out* y:**T**|14+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(int32), tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 13]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(int32), tensor(int64)|
Support SpaceDepth ops in the CUDA and ROCM EPs (#7960)
2021-07-09 08:00:22 +00:00
|DepthToSpace|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**<br><br>or<br><br>*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|19+|**T1** = tensor(float8e4m3fn), tensor(float8e5m2), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
|||[13, 18]|**T** = tensor(int8), tensor(uint8)|
|||[10, 12]|**T** = tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Div|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Dropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T1**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|||[10, 11]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
|||[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|DynamicSlice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *out* output:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|Einsum|*in* Inputs:**T**<br> *out* Output:**T**|12+|**T** = tensor(double), tensor(float), tensor(float16)|
|Elu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|Equal|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[7, 10]|**T** = tensor(bool), tensor(int32), tensor(int64)|
add bf16 support for few ops (#20385) ### Description Add bf16 support for below ops: ConstantOfShape Exp Erf convolution PythonOp ### Motivation and Context phimm model works on bf16, ORT need support bf16 on previous ops to work with phimm on bf16
2024-04-25 18:28:34 +00:00
|Erf|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
add bf16 support for few ops (#20385) ### Description Add bf16 support for below ops: ConstantOfShape Exp Erf convolution PythonOp ### Motivation and Context phimm model works on bf16, ORT need support bf16 on previous ops to work with phimm on bf16
2024-04-25 18:28:34 +00:00
|Exp|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Expand|*in* input:**T**<br> *in* shape:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[8, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|EyeLike|*in* input:**T1**<br> *out* output:**T2**|9+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)<br/> **T2** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)|
|Flatten|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 8]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Floor|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GRU|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|Gather|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GatherElements|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|GatherND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
|||12|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
|||11|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
ONNX Gelu Op in Opset 20 (#19560) ### ONNX Gelu Op in Opset 20 Refactor code to support MSDomain Gelu and ONNX Gelu-opset20 Op 1. Move CPU-GELU implmentation from `onnxruntime/contrib_ops/cpu/activations.h/cc` to `onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation for approximate attribute to be 'none'. 2. Dumplicate some logic from `onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc` to `onnxruntime/core/providers/cpu/tensor/gelu.h/cc`, as the implementation for approximate attribute to be 'tanh'. 3. Register ONNX domain Gelu CPU kernel from opset 20 in `onnxruntime/core/providers/cpu/cpu_execution_provider.cc`. 4. Move `onnxruntime/contrib_ops/cuda/bert/fast_gelu_impl.h/cu` to `onnxruntime/core/providers/cuda/tensor/gelu_impl.h` and `onnxruntime/core/providers/cuda/tensor/gelu_approximate_impl.cu` respectively, as the implementation for approximate attribute to be 'tanh'. 5. Implement the logic for approximate attribute to be 'none' in `onnxruntime/core/providers/cuda/tensor/gelu_impl.cu`. 6. Register ONNX domain Gelu CUDA kernel from opset 20 in `onnxruntime/core/providers/cuda/cuda_execution_provider.cc`. 7. ROCM ep related changes. 8. Enrich the tests for ONNX domain Gelu in `onnxruntime/test/providers/cpu/activation/activation_op_test.cc`.
2024-02-23 03:05:16 +00:00
|Gelu|*in* X:**T**<br> *out* Y:**T**|20+|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Gemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[9, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|GlobalMaxPool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|Greater|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|GreaterOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
|||[12, 15]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
Add support for NHWC GridSample in the CUDA EP and enable grid_sample_test for all EPs (#19562) I've added NHWC GridSample support to the CUDA EP to reduce the number of layout transforms. Also I've enabled the full set of GridSampleTests for all EPs. I've also added the GridSample OpSet 16 to the registered kernels. ### Motivation and Context This is the first PR is a series of enhancements of the CUDA EP improving NHWC support to avoid costly layout transforms between NWHC and NCHW nodes which are layout sensitive. Also testing was quite rudimentary for the CUDA EP while it was great for the CPU path. I've regenerated grid_sample_test.cc enabling tests for other platforms as well. Those tests resurfaced #10607 again which is fixed as well.
2024-02-23 03:47:15 +00:00
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|16+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|HardSigmoid|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|19+|**V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[14, 18]|**V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|||13|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|If|*in* cond:**B**<br> *out* outputs:**V**|19+|**B** = tensor(bool)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 18]|**B** = tensor(bool)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 10]|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ImageScaler|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|InstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|6+|**T** = tensor(double), tensor(float), tensor(float16)|
Implement CUDA IsInf-10,20 (#19772) ### Description Implment IsInf-10,20 for CUDA. Add FP16 types also on CPU. ### Motivation and Context Certain models lag in performance due to IsInf not available on CUDA.
2024-03-05 21:33:01 +00:00
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
|||[10, 19]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
Implement IsNaN-9,13,20 for CUDA along with tests (#19807) ### Description ### Motivation and Context Some models require IsNan CUDA along with training
2024-03-07 23:46:11 +00:00
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
|||[13, 19]|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|||[9, 12]|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LRN|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LSTM|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|14+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
Update Attention operator to support separated Q/K/V inputs (#13410) ### Description Allow separated Q, K and V inputs to support cross attention: * Q: [batch_size, sequence_length, hidden_size] * K: [batch_size, kv_sequence_length, hidden_size] * V: [batch_size, kv_sequence_length, v_hidden_size] * Output: [batch_size, sequence_length, v_hidden_size] To use separated Q/K/V inputs, the input tensor is for query, and two optional inputs are added for key and value. Weights for input projection is not included for now, so the MatMul of input projection shall be done out of Attention operator, but Add bias is included for performance consideration.
2022-10-25 18:51:06 +00:00
|LayerNormalization|*in* X:**T**<br> *in* Scale:**T**<br> *in* B:**T**<br> *out* Y:**T**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**<br><br>or<br><br>*in* X:**T**<br> *in* Scale:**V**<br> *in* B:**V**<br> *out* Y:**V**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**|17+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(float)|
|||[1, 16]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|LeakyRelu|*in* X:**T**<br> *out* Y:**T**|16+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[6, 15]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Less|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|LessOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
|||[12, 15]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Log|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|LogSoftmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Loop|*in* M:**I**<br> *in* cond:**B**<br> *in* v_initial:**V**<br> *out* v_final_and_scan_outputs:**V**|19+|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 18]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 10]|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[9, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MatMulInteger|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *out* Y:**T3**|10+|**T1** = tensor(int8)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(int32)|
|Max|*in* data_0:**T**<br> *out* max:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||12|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[6, 11]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|MaxPool|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**<br> *out* Indices:**I**|12+|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||11|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|||10|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|||[8, 9]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|||[1, 7]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|MemcpyFromHost|*in* X:**T**<br> *out* Y:**T**|1+|**T** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|MemcpyToHost|*in* X:**T**<br> *out* Y:**T**|1+|**T** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Min|*in* data_0:**T**<br> *out* min:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||12|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[6, 11]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
[CUDA] Mod Op Kernel (#12499) * mod for cuda and rocm * fix bfloat16 ut * change bf16 ut number * fix opset version * fix op kernel doc
2022-08-09 05:05:40 +00:00
|Mod|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||[10, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Mul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
add bfloat16 support for CUDA Neg kernel (#18306) ### Description <!-- Describe your changes. --> Registers BFloat16 datatype as valid input type for CUDA Neg Kernel. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Enabling `meta-llama/Llama-2-70b` to be finetuned with ONNX Runtime training. --------- Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
2023-11-09 02:32:12 +00:00
|Neg|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
Improve NonZero on CUDA/ROCM (#10307) * improve NonZero * fix megatron_fp16 optimzier, fix the doc * multi_tensor_applier * resolve comment * fix building warning * fix build error when enabling training and use tensorrt
2022-03-24 23:35:45 +00:00
|NonZero|*in* X:**T**<br> *out* Y:**tensor(int64)**|13+|**T** = tensor(bool), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
|||[9, 12]|**T** = tensor(bool), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|Not|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(bool)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|OneHot|*in* indices:**T1**<br> *in* depth:**T2**<br> *in* values:**T3**<br> *out* output:**T3**|11+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(float16), tensor(int64)|
|Or|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|PRelu|*in* X:**T**<br> *in* slope:**T**<br> *out* Y:**T**|16+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[9, 15]|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
Pad-18 Cuda implementation (#19211) ### Description Implement Pad-18 for Cuda. ### Motivation and Context Latest models converted by Dynamo fall back on CPU for Pad with performance degradation. This contributes to https://github.com/microsoft/onnx-rewriter/issues/126
2024-01-25 02:12:04 +00:00
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *in* axes:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|18+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16)|
|||[13, 17]|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[2, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ParametricSoftplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|Pow|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**<br><br>or<br><br>*in* X:**T**<br> *in* Y:**T1**<br> *out* Z:**T**|15+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[13, 14]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[7, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**<br><br>or<br><br>*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|19+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float8e4m3fn), tensor(float8e5m2), tensor(int8), tensor(uint8)|
|||[13, 18]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
|||[10, 12]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|RNN|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|||[7, 13]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
Add CUDA Kernels of RandomNormal[Like], RandomUniform[Like] (#9761)
2021-11-19 00:18:34 +00:00
|RandomNormal|*out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|RandomNormalLike|*in* input:**T1**<br> *out* output:**T2**|1+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(double), tensor(float), tensor(float16)|
|RandomUniform|*out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|RandomUniformLike|*in* input:**T1**<br> *out* output:**T2**|1+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Range|*in* start:**T**<br> *in* limit:**T**<br> *in* delta:**T**<br> *out* output:**T**|11+|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)|
|Reciprocal|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
enable reduce ops on opset18 (#18053) ### Description Opset 18 apply the "axes as input" change from ReduceSum to all the other reduce ops. Our cuda kernel actually support it, but we didn't enable it for opset18. This PR update the reduce ops' kernel registration to enable the "axes as input" behavior for opset18. As part of the fix, I also simplify the reduce op kernel registration part. ORT doesn't require the kernel definition need to be exactly the same as onnx op definition. For our case, which we share the same kernel for all the reduce ops (from version 1 to version 18), we don't need to maintain different version of kernel definitions. we can simplify it by just using a single kernel definition for multiple versions. Although for some cases, we might register more types for legacy versions, but it is harmless. Framework is using schema to validate the graph, not kernel definition. --------- Co-authored-by: Cheng Tang <chenta@a100.crj0ad2y1kku1j4yxl4sj10o4e.gx.internal.cloudapp.net> Co-authored-by: Cheng Tang <chenta@microsoft.com>
2023-10-26 23:57:21 +00:00
|ReduceL1|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|ReduceL2|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|ReduceLogSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16)|
|ReduceLogSumExp|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16)|
|ReduceMax|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|ReduceMean|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|ReduceMin|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|ReduceProd|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
enable reduce ops on opset18 (#18053) ### Description Opset 18 apply the "axes as input" change from ReduceSum to all the other reduce ops. Our cuda kernel actually support it, but we didn't enable it for opset18. This PR update the reduce ops' kernel registration to enable the "axes as input" behavior for opset18. As part of the fix, I also simplify the reduce op kernel registration part. ORT doesn't require the kernel definition need to be exactly the same as onnx op definition. For our case, which we share the same kernel for all the reduce ops (from version 1 to version 18), we don't need to maintain different version of kernel definitions. we can simplify it by just using a single kernel definition for multiple versions. Although for some cases, we might register more types for legacy versions, but it is harmless. Framework is using schema to validate the graph, not kernel definition. --------- Co-authored-by: Cheng Tang <chenta@a100.crj0ad2y1kku1j4yxl4sj10o4e.gx.internal.cloudapp.net> Co-authored-by: Cheng Tang <chenta@microsoft.com>
2023-10-26 23:57:21 +00:00
|||[1, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|ReduceSumSquare|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 17]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Relu|*in* X:**T**<br> *out* Y:**T**|14+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
|||13|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Reshape|*in* data:**T**<br> *in* shape:**tensor(int64)**<br> *out* reshaped:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reshaped:**T**|19+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[14, 18]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|||13|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[5, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **shape** = tensor(int64)|
|||[1, 4]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
CUDA Resize-18 implementation (#19595) ### Description Implement Resize-18 on CUDA. ### Motivation and Context Performance
2024-02-29 22:46:42 +00:00
|Resize|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T1**<br> *in* roi:**T2**<br> *in* scales:**tensor(float)**<br> *in* sizes:**tensor(int64)**<br> *out* Y:**T1**|18+|**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
|||[13, 17]|**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
|||10|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReverseSequence|*in* input:**T**<br> *in* sequence_lens:**tensor(int64)**<br> *out* Y:**T**|10+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|RoiAlign|*in* X:**T1**<br> *in* rois:**T1**<br> *in* batch_indices:**T2**<br> *out* Y:**T1**|10+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Round|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|ScaledTanh|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Scan|*in* initial_state_and_scan_inputs:**V**<br> *out* final_state_and_scan_outputs:**V**<br><br>or<br><br>*in* sequence_lens:**I**<br> *in* initial_state_and_scan_inputs:**V**<br> *out* final_state_and_scan_outputs:**V**|19+|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[16, 18]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|||[11, 15]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|||[9, 10]|**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||8|**I** = tensor(int64)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Scatter|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|[9, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Update ScatterElements to Support Opset 13, 15, 18 (#19198) `ScatterElements` in opset 18 has been around for a while. However, the highest opset supporting `ScatterElements` in ORT is 13. This PR implement this op in CUDA EP by replacing `assignment` in the current CDUA kernel with `atomic reduction` (e.g., atomic add, atomic max). A series of fundamental atomic functions (e.g., atomic max for int8_t and half) are implemented in `common.cuh`; the implementation is general enough to cover old CUDA and new CUDA versions. - The core changes are in `cuda/atomic/common.cuh` with very detailed documentation including `bit-wise operation's visualization`. They are also copied to `rocm/atomic/common.cuh` to support AMD GPU. - `/cuda/tensor/gather_elements_impl.cu` contains small changes to call the new atomic functions to support new `reduction` behavior in new `ScatterElements`. - New `ScatterElements` are defined in `rocm_execution_provider.cc` and `cuda_execution_provider.cc`.
2024-01-30 17:18:50 +00:00
|ScatterElements|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[16, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[13, 15]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Add implementation for ScatterND (#19540) ### Description onnxruntime switches to CPU for ScatterND after opset 13. This extends the implementation of higher opsets.
2024-04-24 12:08:50 +00:00
|ScatterND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *in* updates:**T**<br> *out* output:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[16, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 15]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Selu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(double), tensor(float), tensor(float16)|
Implement Sequence Ops GPU (#7863)
2021-06-07 22:30:26 +00:00
|SequenceAt|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* tensor:**T**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceConstruct|*in* inputs:**T**<br> *out* output_sequence:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceEmpty|*out* output:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceErase|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceInsert|*in* input_sequence:**S**<br> *in* tensor:**T**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceLength|*in* input_sequence:**S**<br> *out* length:**I**|11+|**I** = tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
Introduce float 8 types (#14731) ### Description The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA API to cast float/half to float8 if CUDA>=11.8, a custom implementation if CUDA<11.8. * It implements, Cast, QuantizeLinear, DequantizeLinear for all types on CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA. * It extends the supported types for control flow operator, Shape, Reshape, Identity, If, Loop, Scan, Reshape * It implements Equal(19). * Cast, QuantizeLinear, DequantizeLinear operators now support a parameter `saturate` only valid for float 8 types. It is true by default. In that case, any value out of range is converted into the maximum float 8 value. If false, it is infinite. * QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA (and ROCm by extension), scale = 1D tensor with one scale per channel ### Motivation and Context Supports latest onnx version. Fixes [AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395) --------- Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
2023-05-30 20:25:58 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|19+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[15, 18]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|||[13, 14]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Shrink|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sigmoid|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Sign CUDA Kernel (#17293)
2023-08-29 04:03:58 +00:00
|Sign|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
SimplifiedLayerNormalization Fusion BFloat16 support for Llama-v2 on A100 (#18898) ### Description <!-- Describe your changes. --> Adds bfloat16 as a supported dtype for SimplifiedLayerNormFusion which will provide speedup for Llama-v2 on A100 using bfloat16 numerical format. _layernorm_optimized_training.onnx exported in bfloat16 vs. float16:_ ![image](https://github.com/microsoft/onnxruntime/assets/31260940/8c0a5f0f-5fcb-4637-bcd9-f34272ec0284) ### Repro Instructions ```python from torch import nn from onnxruntime.training.ortmodule import ORTModule, DebugOptions, LogLevel import torch dtype = torch.bfloat16 # dtype = torch.float16 class Net(nn.Module): def __init__(self): super().__init__() self.fc = nn.Linear(784, 10, dtype=dtype) self.layernorm = nn.LayerNorm([784], dtype=dtype) def forward(self, x): x = x.view(x.shape[0], -1) x = self.layernorm(x) x = self.fc(x) return x model = Net() model = ORTModule(model, DebugOptions(save_onnx=True, onnx_prefix='layernorm', log_level=LogLevel.INFO)) model.to("cuda") images = torch.randn((8, 28, 28), dtype=dtype).to("cuda") output = model(images) ``` ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ONNX Runtime integration with Llama-v2 family of LLMs. --------- Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
2024-02-14 18:05:16 +00:00
|SimplifiedLayerNormalization|*in* X:**T**<br> *in* scale:**V**<br> *out* Y:**V**<br> *out* inv_std_var:**U**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(double), tensor(float), tensor(float16)|
|Size|*in* data:**T**<br> *out* size:**T1**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Fix incorrect type constraint registration for operator kernels. (#10489) * Fix incorrect type constraint registration for RoiAlign. This led to the input type not actually being checked when matching a kernel as the invalid constraint name is treated as a missing optional input. * fix missing dependency for the unit test exe. Whilst it doesn't link against the CUDA providers lib, without the dependency VS doesn't know it needs to rebuild the library if there are changes. * Add check for invalid type constraints. * Fix invalid registrations for other kernels. * Add hash replacement logic to provide backwards compatibility in ORT format models when the registration is fixed. * Add tests
2022-02-18 06:55:32 +00:00
|Slice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *in* steps:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||10|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[1, 9]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Softmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Softplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|Softsign|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
Support SpaceDepth ops in the CUDA and ROCM EPs (#7960)
2021-07-09 08:00:22 +00:00
|SpaceToDepth|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16)|
|||[1, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Split(18) (#14015) ### Description <!-- Describe your changes. --> Opset 18 Split changes. Adds ability to specify num_outputs which also allows uneven splitting. https://github.com/onnx/onnx/releases/tag/v1.13.0 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Support ONNX opset 18.
2023-01-11 22:14:10 +00:00
|Split|*in* input:**T**<br> *in* split:**T**<br> *out* outputs...:**T**<br><br>or<br><br>*in* input:**T**<br> *in* split:**tensor(int64)**<br> *out* outputs:**T**<br><br>or<br><br>*in* input:**T**<br> *out* outputs:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[2, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add BF16 to Sqrt (#19363) ### Description Sqrt does not have BF16 support yet. Adding that with this PR ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-02-15 02:07:51 +00:00
|Sqrt|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Squeeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* squeezed:**T**<br><br>or<br><br>*in* data:**T**<br> *out* squeezed:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sub|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Sum|*in* data_0:**T**<br> *out* sum:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[8, 12]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
|||[6, 7]|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Tanh|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ThresholdedRelu|*in* X:**T**<br> *out* Y:**T**|10+|**T** = tensor(double), tensor(float), tensor(float16)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||1+|**T** = tensor(double), tensor(float), tensor(float16)|
add bf16 for Tile CUDA executor (#20854) ### Description add bf16 for Tile CUDA executor ### Motivation and Context required change to support phimm model for ORT training
2024-06-17 12:52:13 +00:00
|Tile|*in* input:**T**<br> *in* repeats:**T1**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *in* tiles:**T**<br> *in* axis:**T**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)<br/> **T1** = tensor(int64)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[6, 12]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)<br/> **T1** = tensor(int64)|
reduce cuda library binary size (#14555) ### Description Reduce the cuda library size by: 1. refactoring beam_search_top_k to reduce template instantiation. It saves ~56MB 2. opt out TopK for type uint*, int8_t and int16_t. It saves ~50MB. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-02-07 17:03:14 +00:00
|TopK|*in* X:**T**<br> *in* K:**tensor(int64)**<br> *out* Values:**T**<br> *out* Indices:**I**<br><br>or<br><br>*in* X:**T**<br> *out* Values:**T**<br> *out* Indices:**I**|11+|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||10|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[1, 9]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Transpose|*in* data:**T**<br> *out* transposed:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Added Trilu CUDA kernel. (#9633) * Added Trilu CUDA kernel. * Added TriluGrad. * Added a training testcase for Trilu. * Added Trilu gradient checker test.
2021-11-09 19:20:17 +00:00
|Trilu|*in* input:**T**<br> *in* k:**tensor(int64)**<br> *out* output:**T**|14+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Unsqueeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* expanded:**T**<br><br>or<br><br>*in* data:**T**<br> *out* expanded:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[11, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[1, 10]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Upsample|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**|9|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
add bfloat16 support for where operator (#18118) ### Description <!-- Describe your changes. --> Adds bfloat16 as a valid input parameter type for where node for ONNX opset 16+. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Enabling `meta-llama/Llama-2-70b` to be finetuned with ONNX Runtime training. --------- Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
2023-11-02 19:23:20 +00:00
|Where|*in* condition:**B**<br> *in* X:**T**<br> *in* Y:**T**<br> *out* output:**T**|16+|**B** = tensor(bool)<br/> **T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +00:00
|||[9, 15]|**B** = tensor(bool)<br/> **T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|Xor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
| |
| |
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|**Operator Domain:** *com.microsoft*||||
Some kernel changes for TULR (#14517) ### Description <!-- Describe your changes. --> 1. fix a bug in relative position bias kernel where seq_len > 32 2. rename extra_add_qk to relative_position_bias 3. support relative_position_bias in multihead attention (B, N, S, S*) 4. gru_gate support by Lei ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net> Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
2023-02-07 19:51:06 +00:00
|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
Add timestamp logits processor for whisper (#15853) Enable timestamp estimation and logits processing for Whisper model.
2023-05-17 04:40:00 +00:00
|BeamSearch|*in* input_ids:**F**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float), tensor(float16)|
Stable Diffusion CUDA Optimizations Part 3 (#14646) The third part for stable diffusion CUDA optimizations (1) Add BiasAdd operator to replace two Add (bias and residual); Add fusion for BiasAdd (2) Add Attention fusion for VAE decoder. (3) Update float16 conversion to handle Resize and GroupNorm. This could reduce two Cast nodes for each Resize op in fp16 model. (4) Force inputs and outputs to be float16 to avoid data casts in the pipeline. (5) Add options --force_fp32_ops, --inspect etc in optimize script so that user could force some operator to run in float32 to potentially get better image quality (with cost of performance). Performance tests show slight improvement in T4. Average latency reduced 0.1 seconds (from 5.35s to 5.25s) for 512x512 in 50 steps.
2023-02-14 20:46:50 +00:00
|BiasAdd|*in* X:**T**<br> *in* bias:**T**<br> *in* skip:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|BiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
|BiasSoftmax|*in* data:**T**<br> *in* bias:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
Stable Diffusion CUDA Optimizations (#14428) ### Description Add stable diffusion CUDA kernel optimizations. The following are included: (1) GroupNorm operator. This kernel is from TensorRT 8.5. (2) BiasSplitGelu operator. This kernel is modified from SplitGelu of TensorRT 8.5. We added bias to the SplitGelu. (3) NhwcConv operator. This adds support of NHWC format (ONNX Conv operator uses NCHW format). (3) Update MultiHeadAttention (packed kv and no bias) for cross attention. This could avoid transpose of kv for TRT fused cross attention kernel. (4) Optimization and benchmark script Not included: (1) Script to convert Conv to NhwcConv in onnx graph. (2) Update symbolic shape inference for NhwcConv. (3) Add SeqLen2Spatial operator (4) Documents Limitations: GroupNorm, BiasSplitGelu and NhwcConv kernels are implemented based on stable diffusion usage. They might not be applicable to any input size or dimensions. For example, BiasSplitGelu requires hidden size to be 2560 | 5120 | 10240, and NhwcConv assumes 4D input/weight. There is minor increasement of binary size. For SM=75 only, python package wheel size adds (33757K - 33640K) = 117 KB. It is possible to move NHWC from template parameter to constructor to reduce binary size (with slight cost of performance). Note: for RTX 4090/4080/4070 Ti, need build with CUDA 11.8 and latest cuDNN to get best performance.
2023-02-03 07:43:51 +00:00
|BiasSplitGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|BitmaskBiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
|BitmaskDropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
|ComplexMul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
|ComplexMulConj|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|DecoderAttention|*in* query:**T**<br> *in* key:**T**<br> *in* q_weight:**T**<br> *in* kv_weight:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**B**<br> *in* key_cache:**T**<br> *in* value_cache:**T**<br> *in* static_kv:**B**<br> *in* use_past:**B**<br> *in* has_layer_state:**B**<br> *in* has_key_padding_mask:**B**<br> *out* output:**T**<br> *out* new_key_cache:**T**<br> *out* new_value_cache:**T**|1+|**T** = tensor(float), tensor(float16)|
Support output cross qk, dtw and more for whisper model (#17500) Support cross qk in beam search for whisper model and related features Make whisper exporting tools support cross qk and some related features, * extra_decoding_ids * no_speech_prob Implement DTW kernel, unfold tensor kernel with unit test Several fix related with multiple session running parallel, like: * guard multihead_attention, fused_fp16_runner_ * some memory allocation with stream awareness * add use_ep_level_unified_stream option
2023-10-13 18:47:15 +00:00
|DecoderMaskedMultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* mask_index:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* past_sequence_length:**M**<br> *in* beam_width:**M**<br> *in* cache_indirection:**M**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* qk:**V**|1+|**T** = tensor(float), tensor(float16)|
Rename DecoderMaskedMHA to DecoderMaskedSelfAttn (#15166) ### Description <!-- Describe your changes. --> As synced offline, rename this op and will create another op for mha that supports both self and cross attention. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
2023-03-23 19:31:38 +00:00
|DecoderMaskedSelfAttention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *in* beam_width:**M**<br> *in* cache_indirection:**M**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(float16)|
|DequantizeWithOrder|*in* input:**Q**<br> *in* scale_input:**S**<br> *out* output:**F**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
Support output cross qk, dtw and more for whisper model (#17500) Support cross qk in beam search for whisper model and related features Make whisper exporting tools support cross qk and some related features, * extra_decoding_ids * no_speech_prob Implement DTW kernel, unfold tensor kernel with unit test Several fix related with multiple session running parallel, like: * guard multihead_attention, fused_fp16_runner_ * some memory allocation with stream awareness * add use_ep_level_unified_stream option
2023-10-13 18:47:15 +00:00
|DynamicTimeWarping|*in* input:**F**<br> *out* output:**I**|1+|**F** = tensor(float)<br/> **I** = tensor(int32)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|EmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding:**T**<br> *in* position_embedding:**T**<br> *in* segment_embedding:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* mask:**T1**<br> *in* position_ids:**T1**<br> *out* output:**T**<br> *out* mask_index:**T1**<br> *out* embedding_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(float), tensor(float16)|
|FusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Z:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
[CUDA] RelativePositionBias supports input with padding removed (#16923) update RelativePositionBias to support input with padding removed. - [x] add bias transpose kernel - [x] add test - [x] update operator document
2023-08-01 23:39:09 +00:00
|GatedRelativePositionBias|*in* query_layer:**T**<br> *in* query_bias:**T**<br> *in* rel_pos:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* eco_a:**T**<br> *in* token_offset:**M**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
GemmFloat8 as a contrib ops (#16051) ### Description Add support for Gemm with float 8 as a contrib op. --------- Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Scott McKay <Scott.McKay@microsoft.com> Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
2023-10-27 12:33:55 +00:00
|GemmFloat8|*in* A:**TA**<br> *in* B:**TB**<br> *in* C:**TC**<br> *in* scaleA:**TS**<br> *in* scaleB:**TS**<br> *in* scaleY:**TS**<br> *out* Y:**TR**|1+|**TA** = tensor(bfloat16), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2)<br/> **TB** = tensor(bfloat16), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2)<br/> **TR** = tensor(bfloat16), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e5m2)<br/> **TS** = tensor(float)|
Add Gemma Rotary Embedding (#20267) ### Description Add GemmaRotaryEmbedding kernel which includes sin and cos in GemmaRotaryEmbedding forward and apply_rotary_pos_emb. See gemma_rotary_emb_impl.cu for subgraph details ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-04-16 22:31:56 +00:00
|GemmaRotaryEmbedding|*in* emb:**U**<br> *in* q:**T**<br> *in* q_rot:**T**<br> *in* k:**T**<br> *in* k_rot:**T**<br> *out* output1:**T**<br> *out* output2:**T**|1+|**T** = tensor(float16)<br/> **U** = tensor(float)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|GreedySearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *out* sequences:**I**|1+|**T** = tensor(float), tensor(float16)|
|GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
Stable Diffusion CUDA Optimizations (#14428) ### Description Add stable diffusion CUDA kernel optimizations. The following are included: (1) GroupNorm operator. This kernel is from TensorRT 8.5. (2) BiasSplitGelu operator. This kernel is modified from SplitGelu of TensorRT 8.5. We added bias to the SplitGelu. (3) NhwcConv operator. This adds support of NHWC format (ONNX Conv operator uses NCHW format). (3) Update MultiHeadAttention (packed kv and no bias) for cross attention. This could avoid transpose of kv for TRT fused cross attention kernel. (4) Optimization and benchmark script Not included: (1) Script to convert Conv to NhwcConv in onnx graph. (2) Update symbolic shape inference for NhwcConv. (3) Add SeqLen2Spatial operator (4) Documents Limitations: GroupNorm, BiasSplitGelu and NhwcConv kernels are implemented based on stable diffusion usage. They might not be applicable to any input size or dimensions. For example, BiasSplitGelu requires hidden size to be 2560 | 5120 | 10240, and NhwcConv assumes 4D input/weight. There is minor increasement of binary size. For SM=75 only, python package wheel size adds (33757K - 33640K) = 117 KB. It is possible to move NHWC from template parameter to constructor to reduce binary size (with slight cost of performance). Note: for RTX 4090/4080/4070 Ti, need build with CUDA 11.8 and latest cuDNN to get best performance.
2023-02-03 07:43:51 +00:00
|GroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
GQA Rotary and Packed QKV with Flash (#18906) ### Description These changes add rotary embedding and packed qkv input to gqa. As of now, the changes are only supported with Flash-Attention (SM >= 80) but should soon be supported with Memory Efficient Attention as well. ### Motivation and Context With the fusion of rotary embedding into this Attention op, we hope to observe some perf gain. The packed QKV should also provide some perf gain in the context of certain models, like Llama2, that would benefit from running ops on the fused QKV matrix, rather than the separate Q, K, and V. --------- Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
2024-01-24 00:34:26 +00:00
|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|Irfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|LongformerAttention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask:**T**<br> *in* global_weight:**T**<br> *in* global_bias:**T**<br> *in* global:**G**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Bfloat16 support for MatMulBnb4, Training support bitsandbytes>=0.41.2 (#18484) ### Description <!-- Describe your changes. --> Add bfloat16 support for `MatMulBnb4` contrib op. This is useful for QLoRA fine-tuning. - On GPUs with SM80+ (A100, etc), it uses the native cuda bfloat16 dtype, `nv_bfloat16`. On other GPUs, it uses the onnxruntime `BFloat16` type which uses float for compute. - I have validated the op in a llama2-7b training scenario. The losses match pytorch training and the training throughput is better. - Cannot add a bfloat16 case in the op unit test since casting BFloat16 to and from float multiple times during the test causes the required tolerances to be unachievable. The custom autograd function exporter in onnxruntime-training is updated to support the latest version of bitsandbytes. They changed how the `quant_state` is stored. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Enable QLoRA fine-tuning with bfloat16.
2023-11-20 17:52:58 +00:00
|MatMulBnb4|*in* A:**T1**<br> *in* B:**T2**<br> *in* absmax:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(bfloat16), tensor(float), tensor(float16)<br/> **T2** = tensor(uint8)|
MatMulNBits + Add fusion (#20587) - Add MatMulNBits Bias input - Add graph transformer to fuse MatMulNBits + Add
2024-05-16 18:00:59 +00:00
|MatMulNBits|*in* A:**T1**<br> *in* B:**T2**<br> *in* scales:**T1**<br> *in* zero_points:**T3**<br> *in* g_idx:**T4**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(uint8)|
[MoE] Add TP and Mixtral MoE (#19945) ### Description <!-- Describe your changes. --> 1.Support Tensor Parallelism in ShardedMoE. 2.Make necessary code changes to support Mixtral MoE. 3.Fix a bug related to using IOBinding in test script. 4.Fix the input size limitation ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-03-20 04:28:15 +00:00
|MoE|*in* input:**T**<br> *in* router_probs:**T**<br> *in* fc1_experts_weights:**T**<br> *in* fc1_experts_bias:**T**<br> *in* fc2_experts_weights:**T**<br> *in* fc2_experts_bias:**T**<br> *in* fc3_experts_weights:**T**<br> *in* fc3_experts_bias:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
[t5 optimization] kernel changes to t5 (#14928) ### Description <!-- Describe your changes. --> 1. support optional bias in Attention op (used in T5 encoder) 2. support broadcasting rel_pos_bias in attention_softmax.h 3. add scale in MHA op's attributes 4. support past_key/past_value and present_key/present_value in MHA 5. UT and parity tests are added 6. fix an issue: https://github.com/microsoft/onnxruntime/issues/14920 note: the fusions will be in another PR since mt5 needs to be tested and an issue from github will be investigated. Future works: 1. support shared buffer for past/present 2. enable trt kernels when possible and investigate (trt/cutlass)kernels with rel_pos_bias) 3. support KV/QKV packing with past/present ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
2023-03-13 21:29:16 +00:00
|MultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|NGramRepeatBlock|*in* input_ids:**Tid**<br> *in* scores:**T**<br> *out* scores_out:**T**|1+|**T** = tensor(float)<br/> **Tid** = tensor(int64)|
Stable Diffusion CUDA Optimizations (#14428) ### Description Add stable diffusion CUDA kernel optimizations. The following are included: (1) GroupNorm operator. This kernel is from TensorRT 8.5. (2) BiasSplitGelu operator. This kernel is modified from SplitGelu of TensorRT 8.5. We added bias to the SplitGelu. (3) NhwcConv operator. This adds support of NHWC format (ONNX Conv operator uses NCHW format). (3) Update MultiHeadAttention (packed kv and no bias) for cross attention. This could avoid transpose of kv for TRT fused cross attention kernel. (4) Optimization and benchmark script Not included: (1) Script to convert Conv to NhwcConv in onnx graph. (2) Update symbolic shape inference for NhwcConv. (3) Add SeqLen2Spatial operator (4) Documents Limitations: GroupNorm, BiasSplitGelu and NhwcConv kernels are implemented based on stable diffusion usage. They might not be applicable to any input size or dimensions. For example, BiasSplitGelu requires hidden size to be 2560 | 5120 | 10240, and NhwcConv assumes 4D input/weight. There is minor increasement of binary size. For SM=75 only, python package wheel size adds (33757K - 33640K) = 117 KB. It is possible to move NHWC from template parameter to constructor to reduce binary size (with slight cost of performance). Note: for RTX 4090/4080/4070 Ti, need build with CUDA 11.8 and latest cuDNN to get best performance.
2023-02-03 07:43:51 +00:00
|NhwcConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Add PackedAttention for packing mode (#14858) ### Description <!-- Describe your changes. --> Transformer models can handle batch of inputs at once. However, sequences in a batch usually have different length. Then we have to pad the short one to have same length as the longest. This is not efficient especially for large batch with high variance. This PR introduces a PackedAttention operator which can take in packed sequences (no padding) and also produces output in packing mode. There will be another PR to use the PackedAttention to implement the encoder in packing mode. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-03-21 19:59:29 +00:00
|PackedAttention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* token_offset:**M**<br> *in* cumulative_sequence_length:**M**<br> *in* relative_position_bias:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
[CUDA] PackedMultiHeadAttention support Bias and separated Q, K and V inputs (#16913) ### Description Follow-up change for PackedMultiHeadAttention added in https://github.com/microsoft/onnxruntime/pull/16779: - [x] Add Bias input - [x] Add CUDA kernels to support separated query, key and values inputs. - [x] Update operator documents - [x] Add unit tests
2023-08-01 22:30:41 +00:00
|PackedMultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* bias:**T**<br> *in* token_offset:**M**<br> *in* cumulative_sequence_length:**M**<br> *in* relative_position_bias:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(int8)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
add QMoE (#20108) ### Description <!-- Describe your changes. --> 1. Introduce latest cutlass extension from TRTLLM that gives us cutlass upgrade(to 3.4) opportunity from MoE side. 2. Fix Windows build issue 3. Add Int4 MoE op and ut ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-03-29 17:24:19 +00:00
|QMoE|*in* input:**T**<br> *in* router_probs:**T**<br> *in* fc1_experts_weights:**T1**<br> *in* fc1_scales:**T**<br> *in* fc1_experts_bias:**T**<br> *in* fc2_experts_weights:**T1**<br> *in* fc2_scales:**T**<br> *in* fc2_experts_bias:**T**<br> *in* fc3_experts_weights:**T1**<br> *in* fc3_scales:**T**<br> *in* fc3_experts_bias:**T**<br> *out* output:**T**|1+|**T** = tensor(float16)<br/> **T1** = tensor(uint8)|
Some kernel changes for TULR (#14517) ### Description <!-- Describe your changes. --> 1. fix a bug in relative position bias kernel where seq_len > 32 2. rename extra_add_qk to relative_position_bias 3. support relative_position_bias in multihead attention (B, N, S, S*) 4. gru_gate support by Lei ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net> Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
2023-02-07 19:51:06 +00:00
|QOrderedAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* scale_Q_gemm:**S**<br> *in* scale_K_gemm:**S**<br> *in* scale_V_gemm:**S**<br> *in* Q_weight:**Q**<br> *in* K_weight:**Q**<br> *in* V_weight:**Q**<br> *in* scale_Q_weight:**S**<br> *in* scale_K_weight:**S**<br> *in* scale_V_weight:**S**<br> *in* Q_bias:**S**<br> *in* K_bias:**S**<br> *in* V_bias:**S**<br> *in* scale_QKT_gemm:**S**<br> *in* scale_QKT_softmax:**S**<br> *in* scale_values_gemm:**S**<br> *in* mask_index:**G**<br> *in* past:**Q**<br> *in* relative_position_bias:**S**<br> *out* output:**Q**|1+|**G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|QOrderedGelu|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
|QOrderedLayerNormalization|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale:**F**<br> *in* B:**F**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|QOrderedLongformerAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* weight:**Q**<br> *in* scale_weight:**S**<br> *in* bias:**S**<br> *in* scale_bias:**S**<br> *in* scale_qkv_gemm:**S**<br> *in* mask:**F**<br> *in* global_weight:**Q**<br> *in* scale_global_weight:**S**<br> *in* global_bias:**S**<br> *in* scale_global_gemm:**S**<br> *in* global:**G**<br> *in* scale_output:**S**<br> *out* output:**Q**|1+|**F** = tensor(float16)<br/> **G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|QOrderedMatMul|*in* A:**Q**<br> *in* scale_A:**S**<br> *in* B:**Q**<br> *in* scale_B:**S**<br> *in* scale_Y:**S**<br> *in* bias:**S**<br> *in* C:**Q**<br> *in* scale_C:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float16)<br/> **T2** = tensor(int8), tensor(uint8)|
|QuantizeWithOrder|*in* input:**F**<br> *in* scale_input:**S**<br> *out* output:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
QuickGelu Fusion (#12417) Some models have QuickGelu(x)=x*sigmoid(1.702x), which has 3 Ops for forward and 5 Ops for backward. The PR is to fuse this to a single Op named QuickGelu and its gradient QuickGeluGrad. For CUDA, tested in V100 using input tensor with shape [64,128,2048] and float16 type: Before, FW takes 335us, BW takes 614us ![image](https://user-images.githubusercontent.com/11661208/182291335-15188709-ffe7-44d1-9d14-0b544cbe5e55.png) After, FW takes 115us, BW takes 139us, which is much faster. ![image](https://user-images.githubusercontent.com/11661208/182291502-f0b5161c-b95c-45fc-90f8-ad0c592d2433.png) For CPU kernel, using same shape and float type: Before, FW takes 10us, BW takes 49us Mul: 3480[µs] Sigmoid: 1996[µs] Mul: 4789[µs] Mul: 4642[µs] Mul: 4195[µs] SigmoidGrad: 18328[µs] Mul: 2988[µs] Sum: 18576[µs] After, FW takes 4us, BW takes 5us, which is also much faster. QuickGelu: 3939[µs] QuickGeluGrad: 5089[µs] Co-authored-by: Vincent Wang <weicwang@microsoft.com>
2022-10-28 10:12:07 +00:00
|QuickGelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
relational attention bias cuda op (#14149) ### Description This cuda op implements the compute_bias() method in T5 Attention including the permutation. note: 1. bias_table needs to be saved in col-major. be careful when implementing fusion script 2. second input(sequence length) is placed on cpu. (using Shape node's output should be good) 3. the first dimension of output is 1, so extra_add_qk in attention should support broadcasting 4. compute_bias() only used in self-attn in t5 TODO: docs change will be applied later ### Motivation and Context It's part of the process of optimizing t5 attention as well as t5 based generation model Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
2023-01-07 01:32:58 +00:00
|RelativePositionBias|*in* bias_table:**T**<br> *in* query_length:**U**<br> *in* key_length:**U**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Add RemovePadding and RestorePadding for BERT model (#13701) Add two operators RemovePadding and RestorePadding based on ideal of effective transformer (https://github.com/bytedance/effective_transformer) to improve large batch size inference for BERT model.
2022-11-22 18:00:23 +00:00
|RemovePadding|*in* input:**T**<br> *in* sequence_token_count:**M**<br> *out* output:**T**<br> *out* token_offset:**M**<br> *out* cumulated_seq_len:**M**<br> *out* max_seq_len:**M**|1+|**T** = tensor(float), tensor(float16)|
|RestorePadding|*in* input:**T**<br> *in* token_offset:**M**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|Rfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
phi2 contrib ops changes (#19112) ### Description <!-- Describe your changes. --> 1. support causal mask in MHA cpu 2. support custom rotary_dim in rotary_emb 3. add bf16 for rotary_emb 4. fix a bug in attention rotary ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-01-22 18:17:11 +00:00
|RotaryEmbedding|*in* input:**T**<br> *in* position_ids:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**|1+|**M** = tensor(int64)<br/> **T** = tensor(bfloat16), tensor(float), tensor(float16)|
Some changes to Sampling Op (#14218) ### Description <!-- Describe your changes. --> 1. add an optional input to pass in seed 2. two UTs. one for top_p=0.5, another for top_p=0.01(create greedy search result, in convert_generation.py) 3. fix a bug in cpu kernel ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
2023-01-12 22:15:26 +00:00
|Sampling|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *in* presence_mask:**I**<br> *in* seed:**I**<br> *out* sequences:**I**<br> *out* filtered_logits:**T**|1+|**T** = tensor(float), tensor(float16)|
[CUDA] Update GroupNorm and Add SkipGroupNorm (#18091) * Add a new operator SkipGroupNorm to support skip and bias inputs. * Update GroupNorm kernel to support number of channels used in SD XLrefiner. * Add epsilon in kernel * Add parity and performance test script * Remove many limitations including max batch size, max number of groups, c % cPerBlock ==0 etc. ### Motivation and Context Update GroupNorm to support SD XL Refiner and beyond.
2023-10-31 17:27:20 +00:00
|SkipGroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *in* skip:**T**<br> *in* bias:**T**<br> *out* Y:**T**<br> *out* S:**T**|1+|**T** = tensor(float), tensor(float16)|
Misc transformer fixes - 2 (#14156) ### Description 1. The graph pattern search introduced in https://github.com/microsoft/onnxruntime/pull/13914/ needs to be enhanced so that SkipLayerNormalization is supported 2. Fix fp32 parity for GPT-2 while using `SkipLayerNormalization` fusion. The optional output of SLN needs to also include the bias (if present) and the added output should be a sum of `input + skip + (bias)` ### Motivation and Context Fix some breaking tests
2023-01-06 15:27:10 +00:00
|SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
|SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
Update SparseAttention op spec to make it more flexible (#20625) ### Description Make the operator more flexible: (1) Decouple the max sequence length of rotary cache, kv cache and block mask. They are allowed to have different values. (2) Replace block_mask dense by CSR format (block_row_indices and block_col_indices) to improve performance. (3) Mark past_key and past_value as required inputs since we need them to compute the shape of present_key and present_value. ### Motivation and Context (1) LongRoPE has short and long rotary cache, which has different length. (2) Most users do not have enough GPU memory to run maximum sequence length 128K. This change allows user to use smaller kv cache length to test without hitting out of memory.
2024-05-10 05:15:21 +00:00
|SparseAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* block_row_indices:**M**<br> *in* block_col_indices:**M**<br> *in* total_sequence_length:**M**<br> *in* key_total_sequence_lengths:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
|Trilu|*in* X:**T**<br> *in* k:**tensor(int64)**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Support output cross qk, dtw and more for whisper model (#17500) Support cross qk in beam search for whisper model and related features Make whisper exporting tools support cross qk and some related features, * extra_decoding_ids * no_speech_prob Implement DTW kernel, unfold tensor kernel with unit test Several fix related with multiple session running parallel, like: * guard multihead_attention, fused_fp16_runner_ * some memory allocation with stream awareness * add use_ep_level_unified_stream option
2023-10-13 18:47:15 +00:00
|UnfoldTensor|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Add Temperature to WhisperBeamSearch input (#19188) ### Description <!-- Describe your changes. --> Add `temperature` as an input to WhisperBeamSearch op and initialize correctly in parameter setup. ### Motivation and Context Currently, temperature is included as an attribute to the BeamSearch op, which doesn't let the model act dynamically in a single inference session. By including this variable as an input, the temperature value can be altered in any inference call (important for 1P teams) --------- Co-authored-by: Peter McAughan <petermca@microsoft.com> Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
2024-01-23 21:44:34 +00:00
|WhisperBeamSearch|*in* input_ids:**F**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *in* cross_qk_layer_head:**I**<br> *in* extra_decoding_ids:**I**<br> *in* temperature:**T**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**<br> *out* cross_qk:**V**<br> *out* non_speech_probs:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
| |
| |
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
<a name="dmlexecutionprovider"/>
## Operators implemented by DmlExecutionProvider
| Op Name | Parameters | OpSet Version | Types Supported |
|---------|------------|---------------|-----------------|
|**Operator Domain:** *ai.onnx*||||
Update docs/OperatorKernels.md (#13485)
2022-10-28 03:11:49 +00:00
|Abs|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
|||6+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Acos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Acosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16)|
|Add|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Affine|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|And|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)|
|ArgMax|*in* data:**T**<br> *out* reduced:**tensor(int64)**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|ArgMin|*in* data:**T**<br> *out* reduced:**tensor(int64)**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Asin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Asinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16)|
|Atan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Atanh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16)|
Update OperatorKernels.md
2024-01-04 19:27:03 +00:00
|AveragePool|*in* X:**T**<br> *out* Y:**T**|19+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||10+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T1**<br> *in* B:**T1**<br> *in* input_mean:**T2**<br> *in* input_var:**T2**<br> *out* Y:**T**<br> *out* running_mean:**T2**<br> *out* running_var:**T2**|15+|**T** = tensor(float), tensor(float16)|
|||14+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
|BitShift|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**|11+|**T** = tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
DML EP Bitwise operators opset 18 (#15892) ### Description <!-- Describe your changes. --> Add dml registration for bitwise and, or, xor and not added in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-05-17 20:27:49 +00:00
|BitwiseAnd|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseNot|*in* X:**T**<br> *out* Y:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseOr|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|BitwiseXor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|18+|**T** = tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Cast|*in* input:**T1**<br> *out* output:**T2**|19+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||9+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||6+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|CastLike|*in* input:**T1**<br> *in* target_type:**T2**<br> *out* output:**T2**|19+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||15+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Ceil|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|Celu|*in* X:**T**<br> *out* Y:**T**|12+|**T** = tensor(float), tensor(float16)|
|Clip|*in* input:**T**<br> *in* min:**T**<br> *in* max:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
Update DirectML from 1.14.1 to 1.15.0 (#21323) Update DirectML from 1.14.1 to 1.15.0 --------- Co-authored-by: Sheil Kumar <sheilk@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2024-07-22 23:59:03 +00:00
|Col2Im|*in* input:**T**<br> *in* image_shape:**tensor(int64)**<br> *in* block_shape:**tensor(int64)**<br> *out* output:**T**|18+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Concat|*in* inputs:**T**<br> *out* concat_result:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||4+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|ConcatFromSequence|*in* input_sequence:**S**<br> *out* concat_result:**T**|11+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|ConstantOfShape|*in* input:**T1**<br> *out* output:**T2**|9+|**T1** = tensor(int64)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|ConvInteger|*in* x:**T1**<br> *in* w:**T2**<br> *in* x_zero_point:**T1**<br> *in* w_zero_point:**T2**<br> *out* y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int32)|
|ConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|Cos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Cosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16)|
|Crop|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|CumSum|*in* x:**T**<br> *in* axis:**T2**<br> *out* y:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
[DML EP] Register DFT-20 (#20341) ### Description <!-- Describe your changes. --> This PR registers DFT-20 to the DML EP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-05-02 18:08:39 +00:00
|DFT|*in* input:**T1**<br> *in* dft_length:**T2**<br> *in* axis:**tensor(int64)**<br> *out* output:**T1**<br><br>or<br><br>*in* input:**T1**<br> *in* dft_length:**T2**<br> *out* output:**T1**|20+|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
|||17+|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|DepthToSpace|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**<br><br>or<br><br>*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|19+|**T1** = tensor(int32), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
|||13+|**T** = tensor(int32), tensor(int8), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||10+|**T** = tensor(int32), tensor(int8), tensor(uint8)|
[DML EP] Register Div with int64 and NonZero with bool (#16276) [DML] Register Div with int64 and NonZero with bool These data types are supported by DML
2023-06-08 20:49:39 +00:00
|Div|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Dropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**<br> *out* mask:**T1**|7+|**T** = tensor(float), tensor(float16)|
Update OperatorKernels.md
2024-01-04 19:27:03 +00:00
|DynamicQuantizeLinear|*in* x:**T1**<br> *out* y:**T2**<br> *out* y_scale:**tensor(float)**<br> *out* y_zero_point:**T2**|11+|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Einsum|*in* Inputs:**T**<br> *out* Output:**T**|12+|**T** = tensor(float), tensor(float16)|
|Elu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float), tensor(float16)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Equal|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|19+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||7+|**T** = tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
|Erf|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|Exp|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|Expand|*in* input:**T**<br> *in* shape:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||8+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|EyeLike|*in* input:**T1**<br> *out* output:**T2**|9+|**T1** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Flatten|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||9+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Floor|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|GRU|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
|Gather|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|GatherElements|*in* data:**T**<br> *in* indices:**Tind**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|GatherND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Gemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|GlobalLpPool|*in* X:**T**<br> *out* Y:**T**|2+|**T** = tensor(float), tensor(float16)|
|GlobalMaxPool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|Greater|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||7+|**T** = tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
DML EP Register operators for Opset 16 (#14034) This PR Registers the following operators for opset 16 to the DML EP: - LeakyRelu-16 - PRelu-16 - Where-16 - GreaterOrEqual-16 - LessOrEqual-16 Identity-16 was not added in this PR due to pipeline failures Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2022-12-21 17:05:12 +00:00
|GreaterOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Add GridSample implementation to DirectML (#15788) Add GridSample implementation to DirectML EP. Temporary add HLSL shader in the DirectML EP to handle GridSample until officially added to DirectML.
2023-05-05 22:59:33 +00:00
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|16+|**T1** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|HardSigmoid|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float), tensor(float16)|
|Hardmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|19+|**V** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||16+|**V** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|||14+|**V** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML EP] Add I/O binding and `If` operator (#16859) Being able to leverage I/O binding for DML and registering `If` for the DML EP allows us to avoid copying the past/present key/values back and forth between the CPU and the GPU after every token. This gives us a 25% performance increase for Dolly V2 with 128 tokens on an RTX 4090.
2023-08-01 02:45:59 +00:00
|If|*in* cond:**B**<br> *out* outputs:**V**|19+|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||16+|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||7+|**B** = tensor(bool)<br/> **V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|ImageScaler|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|InstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|6+|**T** = tensor(float), tensor(float16)|
[DML EP] Register opset 20 operators (#20092) ### Description This PR registers the following opset 20 operators to the DML EP: -IsNaN-20 -IsInf-20 -ReduceMax-20 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-04-22 19:01:59 +00:00
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(float)<br/> **T2** = tensor(bool)|
|||10+|**T1** = tensor(float)<br/> **T2** = tensor(bool)|
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|||13+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||9+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|LRN|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|LSTM|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|14+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|LayerNormalization|*in* X:**T**<br> *in* Scale:**T**<br> *in* B:**T**<br> *out* Y:**T**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**<br><br>or<br><br>*in* X:**T**<br> *in* Scale:**V**<br> *in* B:**V**<br> *out* Y:**V**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**|17+|**T** = tensor(float), tensor(float16)<br/> **U** = tensor(float)|
[DML EP] Add SimplifiedLayerNorm and SkipSimplifiedLayerNorm (#20326)
2024-04-19 05:17:31 +00:00
|||1+|**T** = tensor(float), tensor(float16)<br/> **U** = tensor(float), tensor(float16)<br/> **V** = tensor(float), tensor(float16)|
DML EP Register operators for Opset 16 (#14034) This PR Registers the following operators for opset 16 to the DML EP: - LeakyRelu-16 - PRelu-16 - Where-16 - GreaterOrEqual-16 - LessOrEqual-16 Identity-16 was not added in this PR due to pipeline failures Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2022-12-21 17:05:12 +00:00
|LeakyRelu|*in* X:**T**<br> *out* Y:**T**|16+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Less|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||7+|**T** = tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
DML EP Register operators for Opset 16 (#14034) This PR Registers the following operators for opset 16 to the DML EP: - LeakyRelu-16 - PRelu-16 - Where-16 - GreaterOrEqual-16 - LessOrEqual-16 Identity-16 was not added in this PR due to pipeline failures Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2022-12-21 17:05:12 +00:00
|LessOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Log|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|LogSoftmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|LpNormalization|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
Update OperatorKernels.md
2024-01-04 19:27:03 +00:00
|LpPool|*in* X:**T**<br> *out* Y:**T**|18+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||2+|**T** = tensor(float), tensor(float16)|
|MatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|MatMulInteger|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *out* Y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int32)|
|Max|*in* data_0:**T**<br> *out* max:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||8+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|MaxPool|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**<br> *out* Indices:**I**|12+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
|||11+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
|||10+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
|||8+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
|||1+|**T** = tensor(float), tensor(float16)|
|MaxRoiPool|*in* X:**T**<br> *in* rois:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|MaxUnpool|*in* X:**T1**<br> *in* I:**T2**<br> *in* output_shape:**T2**<br> *out* output:**T1**|11+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int64)|
|||9+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int64)|
|Mean|*in* data_0:**T**<br> *out* mean:**T**|13+|**T** = tensor(float), tensor(float16)|
|||8+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|MeanVarianceNormalization|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|MemcpyFromHost|*in* X:**T**<br> *out* Y:**T**|1+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|MemcpyToHost|*in* X:**T**<br> *out* Y:**T**|1+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Min|*in* data_0:**T**<br> *out* min:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||8+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|Mod|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||10+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|Mul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Neg|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
|||6+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
[DML EP] Register Div with int64 and NonZero with bool (#16276) [DML] Register Div with int64 and NonZero with bool These data types are supported by DML
2023-06-08 20:49:39 +00:00
|NonZero|*in* X:**T**<br> *out* Y:**tensor(int64)**|13+|**T** = tensor(bool), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||9+|**T** = tensor(bool), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Not|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(bool)|
|OneHot|*in* indices:**T1**<br> *in* depth:**T2**<br> *in* values:**T3**<br> *out* output:**T3**|11+|**T1** = tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T3** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||9+|**T1** = tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T3** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Register CPU OptionalGetElement, OptionalHasElement on DirectML (#15926) Register CPU OptionalGetElement, OptionalHasElement on DirectML Graphs with OptionalGetElement and OptionalHasElement should work in a DML graph without extra memcpy operation on and off the GPU. CopyCpuTensor is swapped with DataTransferManager.CopyTensor() to make the CPU operator usable by other providers. --------- Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2023-05-15 16:53:35 +00:00
|OptionalGetElement|*in* input:**O**<br> *out* output:**V**|18+|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||15+|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|OptionalHasElement|*in* input:**O**<br> *out* output:**B**|18+|**B** = tensor(bool)<br/> **O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||15+|**B** = tensor(bool)<br/> **O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Or|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)|
DML EP Register operators for Opset 16 (#14034) This PR Registers the following operators for opset 16 to the DML EP: - LeakyRelu-16 - PRelu-16 - Where-16 - GreaterOrEqual-16 - LessOrEqual-16 Identity-16 was not added in this PR due to pipeline failures Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2022-12-21 17:05:12 +00:00
|PRelu|*in* X:**T**<br> *in* slope:**T**<br> *out* Y:**T**|16+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8)|
|||9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||7+|**T** = tensor(float), tensor(float16)|
Update DirectML from 1.14.1 to 1.15.0 (#21323) Update DirectML from 1.14.1 to 1.15.0 --------- Co-authored-by: Sheil Kumar <sheilk@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2024-07-22 23:59:03 +00:00
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *in* axes:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|19+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||18+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML EP] Register pad18 (#15985) ### Description <!-- Describe your changes. --> Pad18 adds the `axes` input, which is used to indicate what axes the padding values should be applied to. Add logic to manipulate paddings into DML padding operator inputs. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-05-24 01:25:36 +00:00
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||2+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|ParametricSoftplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|Pow|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**<br><br>or<br><br>*in* X:**T**<br> *in* Y:**T1**<br> *out* Z:**T**|15+|**T** = tensor(float), tensor(float16), tensor(int32)<br/> **T1** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int32)<br/> **T1** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||12+|**T** = tensor(float), tensor(float16), tensor(int32)<br/> **T1** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16)|
|QLinearConv|*in* x:**T1**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T1**<br> *in* w:**T2**<br> *in* w_scale:**tensor(float)**<br> *in* w_zero_point:**T2**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T3**<br> *in* B:**T4**<br> *out* y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int8), tensor(uint8)<br/> **T4** = tensor(int32)|
Integration with ONNX 1.16.0 (#19745) ### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
2024-04-12 16:46:49 +00:00
|QLinearMatMul|*in* a:**T1**<br> *in* a_scale:**TS**<br> *in* a_zero_point:**T1**<br> *in* b:**T2**<br> *in* b_scale:**TS**<br> *in* b_zero_point:**T2**<br> *in* y_scale:**TS**<br> *in* y_zero_point:**T3**<br> *out* y:**T3**<br><br>or<br><br>*in* a:**T1**<br> *in* a_scale:**tensor(float)**<br> *in* a_zero_point:**T1**<br> *in* b:**T2**<br> *in* b_scale:**tensor(float)**<br> *in* b_zero_point:**T2**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T3**<br> *out* y:**T3**|10+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int8), tensor(uint8)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**<br><br>or<br><br>*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|19+|**T1** = tensor(float), tensor(float16), tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
|||13+|**T1** = tensor(float), tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||10+|**T1** = tensor(float), tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
|RNN|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**|14+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
|Range|*in* start:**T**<br> *in* limit:**T**<br> *in* delta:**T**<br> *out* output:**T**|11+|**T** = tensor(float), tensor(int16), tensor(int32), tensor(int64)|
|Reciprocal|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceL1|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceL2|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceLogSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceLogSumExp|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
[DML EP] Register opset 20 operators (#20092) ### Description This PR registers the following opset 20 operators to the DML EP: -IsNaN-20 -IsInf-20 -ReduceMax-20 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-04-22 19:01:59 +00:00
|ReduceMax|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|20+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceMean|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
[DML EP] Register ReduceMin-20 (#20477) ### Description This PR registers the ReduceMin-20 operator to the DML EP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2024-07-26 00:06:30 +00:00
|ReduceMin|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|20+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||12+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceProd|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|ReduceSum|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
User/linneamay/reduce 18 (#15701) ### Description <!-- Describe your changes. --> Add registration for DML reduce functions in opset 18. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com>
2023-04-28 03:32:11 +00:00
|ReduceSumSquare|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* reduced:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reduced:**T**|18+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|||1+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|Relu|*in* X:**T**<br> *out* Y:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8)|
|||13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Reshape|*in* data:**T**<br> *in* shape:**tensor(int64)**<br> *out* reshaped:**T**<br><br>or<br><br>*in* data:**T**<br> *out* reshaped:**T**|19+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||14+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||5+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update DirectML from 1.14.1 to 1.15.0 (#21323) Update DirectML from 1.14.1 to 1.15.0 --------- Co-authored-by: Sheil Kumar <sheilk@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2024-07-22 23:59:03 +00:00
|Resize|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T1**<br> *in* roi:**T2**<br> *in* scales:**tensor(float)**<br> *in* sizes:**tensor(int64)**<br> *out* Y:**T1**|19+|**T1** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
|||18+|**T1** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
|||13+|**T1** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
Update OperatorKernels.md
2024-01-04 19:27:03 +00:00
|||11+|**T1** = tensor(float), tensor(float16), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||10+|**T** = tensor(float), tensor(float16)|
|ReverseSequence|*in* input:**T**<br> *in* sequence_lens:**tensor(int64)**<br> *out* Y:**T**|10+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
User/linneamay/roi align 16 (#15812) ### Description <!-- Describe your changes. --> Add registration for DML RoiAlign-16 and tests for new coordinate_transform_mode attribute. PR [7354](https://github.com/microsoft/onnxruntime/pull/7354) is still open to fix the CPU EP version, which is why there are skipped tests right now. That will be completed separately so that, for now, we can officially support opset16 with the next release. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Linnea May <linneamay@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2023-05-10 04:56:41 +00:00
|RoiAlign|*in* X:**T1**<br> *in* rois:**T1**<br> *in* batch_indices:**T2**<br> *out* Y:**T1**|16+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
|||10+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Round|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
STFT for DML EP (#14736) ### Description Implements the STFT operator for the DirectML execution provider. This is implemented as a custom op, just like the DFT kernel, because it's implemented as a composite of two operators (DML Mul/Identity + DFT). As such, this inherits the same restrictions as the existing DFT kernel (requires power-of-two window sizes for now). This change also adds a native FP16 shader to DFT so that both DFT/STFT kernels support float16 tensors. There is no typed UAV fallback or emulation path, so the HW _needs_ to support native float16. It also appears the stockham shader was compiled with all optimizations disabled and debug symbols (tsk tsk, Sheil), and this has been fixed. This is passing all existing STFT tests (i.e. all of 1). I'm adding some additional collateral in the Windows AI conformance tests in parallel to check some extra cases. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-24 05:12:22 +00:00
|STFT|*in* signal:**T1**<br> *in* frame_step:**T2**<br> *in* window:**T1**<br> *in* frame_length:**T2**<br> *out* output:**T1**|17+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|ScaledTanh|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|Scatter|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||9+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
[DML EP] Fix ScatterElements registration (#14560)
2023-02-06 18:01:02 +00:00
|ScatterElements|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|16+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Register ScatterElements-16 (#14425) This PR registers ScatterElements-16 to the DML EP - CPU fallback is added if the reduction attribute is in use, as this is not yet supported by DML. --------- Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2023-02-01 17:46:37 +00:00
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
DML EP Register ScatterND-16 (#14240) This PR registers ScatterND-16 to the DML EP - CPU fallback is added if the reduction attribute is in use, as this is not yet supported by DML. Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2023-01-12 18:39:25 +00:00
|ScatterND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *in* updates:**T**<br> *out* output:**T**|16+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Selu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float), tensor(float16)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|SequenceAt|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* tensor:**T**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceConstruct|*in* inputs:**T**<br> *out* output_sequence:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))<br/> **T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|SequenceEmpty|*out* output:**S**|11+|**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceErase|*in* input_sequence:**S**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceInsert|*in* input_sequence:**S**<br> *in* tensor:**T**<br> *in* position:**I**<br> *out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceLength|*in* input_sequence:**S**<br> *out* length:**I**|11+|**I** = tensor(int64)<br/> **S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|19+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||15+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|||13+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||1+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Shrink|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|Sigmoid|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
Update docs/OperatorKernels.md (#13485)
2022-10-28 03:11:49 +00:00
|Sign|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
[DML EP] Add SimplifiedLayerNorm and SkipSimplifiedLayerNorm (#20326)
2024-04-19 05:17:31 +00:00
|SimplifiedLayerNormalization|*in* X:**T**<br> *in* scale:**V**<br> *out* Y:**V**<br> *out* inv_std_var:**U**|1+|**T** = tensor(float), tensor(float16)<br/> **U** = tensor(float), tensor(float16)<br/> **V** = tensor(float), tensor(float16)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Sin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Sinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float), tensor(float16)|
[DML] Register DML operators for opset 19 (#16939) ### Description <!-- Describe your changes. --> Register DML operators for opset 19. - Cast19 - Castlike19 - Constant19 - Equal19 - Identity19 - QuantizeLinear19 - DequantizeLinear19 - Reshape19 - Shape19 - Size ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: linnealovespie <linneamay@microsoft.com>
2024-01-22 23:37:09 +00:00
|Size|*in* data:**T**<br> *out* size:**T1**|19+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||13+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP (#14442) Enable Opset11 Sequence Ops on DirectML, and make the CPU implementations agnostic to backend EP Opset 11 introduced the following sequence related operators: - SequenceAt - SequenceConstruct - SequenceEmpty - SequenceLength - SequenceErase - SequenceInsert - ConcatFromSequence With the exception of ConcatFromSequence, all of the above operators were implemented with CPU kernels that a) required all of the contained tensors to also be on CPU, and b) would clone each tensor into a new sequence as a side effect of each operator. The implementation of sequences are backend agnostic, as they dont affect actual tensor layout or manipulate the contents of the tensors. In addition, with the exception of SequenceAt, the other operators need not make copies of the underlying referenced tensors. Consequently, this change does the following: 1) Sequence* operators (except SequenceAt) no longer copies the contents of a sequence of tensors on every kernel execution. 2) SequenceAt uses the DataTransferManager to copy tensors agnostic to backend. 3) The internal container implemented by TensorSeq has changed from onnxruntime::Tensor to OrtValue. This is because onnxruntime::Tensor does not support copy or assignment construction, so it must have a singular owner. However, is same tensor participates in multiple containers it would have multiple container "owners" and this would not be possible. 4) Other code that accessed values from TensorSeq have associated changes to extract Tensors from OrtValues now. In addition, DirectML execution was very slow when the above Sequence operators were added to a graph, as this caused MemcpyToHost and MemcpyFromHost kernels to be inserted between the graph and the sequence operators. To optimize DirectML, 1) The CPU implementations for the Sequence* ops were registered as DML implementations. Since the above changes also includes making the CPU kernel implementations EP agnostic, the CPU kernels can be added as is. 2) The ConcatFromSequence operator needed to be implemented on DirectML. However, there was little DirectML EP operator framework support for operators that accept/output sequences of tensors. This change has modified the internal COM interfaces to include new apis to interrogate for sequence shapes, and extract the needed tensors from TensorSeq. --------- Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
2023-02-22 02:08:28 +00:00
|||1+|**T** = seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Slice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *in* steps:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||10+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **Tind** = tensor(int32), tensor(int64)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Softmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||11+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|Softplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|Softsign|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|SpaceToDepth|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
DML EP Register Split18 (#15931) Register Split18 for DirectML Split13 was previously implemented. Split18 adds a new attribute called "num_outputs" that must be used mutually exclusively with the "split" input. The "num_outputs" attribute wil split the tensor evenly (and handles odd uneven splits). To implement, the DML split tensor just needs to be overridden in the presence of the num_output attribute. --------- Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
2023-05-16 18:58:19 +00:00
|Split|*in* input:**T**<br> *in* split:**T**<br> *out* outputs...:**T**<br><br>or<br><br>*in* input:**T**<br> *in* split:**tensor(int64)**<br> *out* outputs:**T**<br><br>or<br><br>*in* input:**T**<br> *out* outputs:**T**|18+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||2+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sqrt|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|Squeeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* squeezed:**T**<br><br>or<br><br>*in* data:**T**<br> *out* squeezed:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sub|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sum|*in* data_0:**T**<br> *out* sum:**T**|13+|**T** = tensor(float), tensor(float16)|
|||8+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|Tan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float), tensor(float16)|
|Tanh|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float), tensor(float16)|
|||6+|**T** = tensor(float), tensor(float16)|
|ThresholdedRelu|*in* X:**T**<br> *out* Y:**T**|10+|**T** = tensor(float), tensor(float16)|
|||1+|**T** = tensor(float), tensor(float16)|
|Tile|*in* input:**T**<br> *in* repeats:**T1**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *in* tiles:**T**<br> *in* axis:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||6+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|TopK|*in* X:**T**<br> *in* K:**tensor(int64)**<br> *out* Values:**T**<br> *out* Indices:**I**<br><br>or<br><br>*in* X:**T**<br> *out* Values:**T**<br> *out* Indices:**I**|11+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||10+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**I** = tensor(int64)<br/> **T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Transpose|*in* data:**T**<br> *out* transposed:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Trilu|*in* input:**T**<br> *in* k:**tensor(int64)**<br> *out* output:**T**|14+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Unsqueeze|*in* data:**T**<br> *in* axes:**tensor(int64)**<br> *out* expanded:**T**<br><br>or<br><br>*in* data:**T**<br> *out* expanded:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Upsample|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**|10+|**T** = tensor(float), tensor(float16)|
|||9+|**T** = tensor(float), tensor(float16)|
|||7+|**T** = tensor(float), tensor(float16)|
DML EP Register operators for Opset 16 (#14034) This PR Registers the following operators for opset 16 to the DML EP: - LeakyRelu-16 - PRelu-16 - Where-16 - GreaterOrEqual-16 - LessOrEqual-16 Identity-16 was not added in this PR due to pipeline failures Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2022-12-21 17:05:12 +00:00
|Where|*in* condition:**B**<br> *in* X:**T**<br> *in* Y:**T**<br> *out* output:**T**|16+|**B** = tensor(bool)<br/> **T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|||9+|**B** = tensor(bool)<br/> **T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
Update operator kernel table to include DML operators (#12887) * Fix bug in pybind get_all_operator_schema due to premature reference dropping * Add updated operator kernels markdown table * Update build.py to include documentation generation for DML operators too * Update GPU pipeline to include DML in the build to so operators can be generated. * Use a separate pipeline stage, feedback from Changming and Scott * Appease annoying Python linter * Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
2022-09-09 17:21:25 +00:00
|Xor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)|
| |
| |
Add 4dmask support for attention cuda kernel (#7591) * checkin * add 4dmask support in attention cuda op * trim * add comments * fix build/test error * review comments and add tests * sync doc * review comments * minor change
2021-05-08 03:17:29 +00:00
|**Operator Domain:** *com.microsoft*||||
Some kernel changes for TULR (#14517) ### Description <!-- Describe your changes. --> 1. fix a bug in relative position bias kernel where seq_len > 32 2. rename extra_add_qk to relative_position_bias 3. support relative_position_bias in multihead attention (B, N, S, S*) 4. gru_gate support by Lei ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net> Co-authored-by: Lei Zhang <zhang.huanning@hotmail.com>
2023-02-07 19:51:06 +00:00
|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *out* output:**T**<br> *out* present:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
[DML EP] Add BiasAdd (#15211)
2023-04-10 21:46:33 +00:00
|BiasAdd|*in* X:**T**<br> *in* bias:**T**<br> *in* skip:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Add DML implementation for BiasGelu (#13795) ### Description Add DML implementation for BiasGelu
2022-12-01 17:23:19 +00:00
|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Add BiasSplitGelu (#15197)
2023-04-11 15:30:37 +00:00
|BiasSplitGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Fix MS domain QuantizeLinear and DequantizeLinear type registrations … (#16298) This fixes the type lists used to register DML kernels for Microsoft domain QuantizeLinear and DequantizeLinear. These previously did not include FP16 and incorrectly used the same type list for both operators. The new type lists are the same as opset 19 ONNX which aren't implemented yet in the DML EP.
2023-06-16 01:21:56 +00:00
|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(int32), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
[DML] DynamicQuantizeMatMul (#19763) ### Description DML Implementation for [com.microsoft.DynamicQuantizeMatMul ](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.DynamicQuantizeMatMul) ``` .\onnxruntime_test_all.exe --gtest_filter="*DynamicQuantizeMatMul.*" Note: Google Test filter = *DynamicQuantizeMatMul.* [==========] Running 10 tests from 1 test suite. [----------] Global test environment set-up. [----------] 10 tests from DynamicQuantizeMatMul [ RUN ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_S8 [ OK ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_S8 (635 ms) [ RUN ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_U8 [ OK ] DynamicQuantizeMatMul.HasZeroPoint_NoBias_test_U8 (514 ms) [ RUN ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_S8 [ OK ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_S8 (512 ms) [ RUN ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_U8 [ OK ] DynamicQuantizeMatMul.NoZeroPoint_HasBias_test_U8 (505 ms) [ RUN ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_S8 [ OK ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_S8 (526 ms) [ RUN ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_U8 [ OK ] DynamicQuantizeMatMul.NoZeroPoint_NoBias_test_U8 (504 ms) [ RUN ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_S8 [ OK ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_S8 (512 ms) [ RUN ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_U8 [ OK ] DynamicQuantizeMatMul.HasZeroPoint_HasBias_test_U8 (512 ms) [ RUN ] DynamicQuantizeMatMul.UInt8_test_with_empty_input [ OK ] DynamicQuantizeMatMul.UInt8_test_with_empty_input (112 ms) [ RUN ] DynamicQuantizeMatMul.B_PerColumn_ND [ OK ] DynamicQuantizeMatMul.B_PerColumn_ND (348 ms) [----------] 10 tests from DynamicQuantizeMatMul (4685 ms total) [----------] Global test environment tear-down [==========] 10 tests from 1 test suite ran. (4686 ms total) [ PASSED ] 10 tests. memleakdbg: ----- No memory leaks detected ----- ``` ### Motivation and Context - CalculateDynamicQuantizeMatMul to replace CPU EP run reference - Added more FP32 testcases to isolate all input datatype combinations --------- Co-authored-by: Xiang Zhang <xianz@microsoft.com>
2024-03-08 23:35:10 +00:00
|DynamicQuantizeMatMul|*in* A:**T1**<br> *in* B:**T2**<br> *in* b_scale:**T1**<br> *in* b_zero_point:**T2**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
[DML EP] Add EmbedLayerNorm (#13868) ### Description Add EmbedLayerNorm to the DML EP
2022-12-13 21:23:53 +00:00
|EmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding:**T**<br> *in* position_embedding:**T**<br> *in* segment_embedding:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* mask:**T1**<br> *in* position_ids:**T1**<br> *out* output:**T**<br> *out* mask_index:**T1**<br> *out* embedding_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML] Add FastGelu (#20066) Although DML doesn't have a "fast" gelu approximation operator, its standard GELU operator is still faster than having to combine all the separate elementwise operators from different ops.
2024-04-11 21:40:28 +00:00
|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Update DirectML version to 1.12.0 (#16011)
2023-05-19 02:37:12 +00:00
|FusedMatMulActivation|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Add GroupNorm (#15189) Comparison between the different normalization operations: ![](https://user-images.githubusercontent.com/1041752/106491728-73d40680-64b7-11eb-8769-3f758996e959.png)
2023-03-27 19:52:53 +00:00
|GroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *out* Y:**T**|1+|**M** = tensor(float), tensor(float16)<br/> **T** = tensor(float), tensor(float16)|
[DML EP] Add GroupQueryAttention (#20327)
2024-04-19 17:25:29 +00:00
|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
[DML] MatrixMultiplyIntegerToFloat (#19608) ### Description DML Implementation for [com.microsoft.MatMulIntegerToFloat](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.MatMulIntegerToFloat) ``` .\onnxruntime_test_all.exe --gtest_filter="*MatMulIntegerToFloat.*" Note: Google Test filter = *MatMulIntegerToFloat.* [==========] Running 22 tests from 1 test suite. [----------] Global test environment set-up. [----------] 22 tests from MatMulIntegerToFloat [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 (620 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 (497 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8 (488 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8 (503 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8 (495 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8 (488 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8 (492 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8 (502 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8 (452 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8 (454 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8 (446 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8 (508 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8 (456 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8 (455 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8 (447 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8 (465 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8 (111 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8 (115 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8 (114 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8 (110 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16 (112 ms) [ RUN ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint [ OK ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint (337 ms) [----------] 22 tests from MatMulIntegerToFloat (8679 ms total) [----------] Global test environment tear-down [==========] 22 tests from 1 test suite ran. (8680 ms total) [ PASSED ] 22 tests. memleakdbg: ----- No memory leaks detected ----- ``` ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * `CalculateMatMulIntegerToFloat` to replace CPU EP run reference * Added more FP32 testcases to isolate all input datatype combinations * Added fixed input to `MatMulIntegerToFloat_FP16*` test cases as for FP16 test cases. * onnxruntime/test/testdata/matmul_integer_to_float.py` is capable of generating FP16 models, but we do not produce any for now
2024-03-04 19:55:35 +00:00
|MatMulIntegerToFloat|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_scale:**T3**<br> *in* b_scale:**T3**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *in* bias:**T3**<br> *out* Y:**T3**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float), tensor(float16)|
MatMulNBits + Add fusion (#20587) - Add MatMulNBits Bias input - Add graph transformer to fuse MatMulNBits + Add
2024-05-16 18:00:59 +00:00
|MatMulNBits|*in* A:**T1**<br> *in* B:**T2**<br> *in* scales:**T1**<br> *in* zero_points:**T3**<br> *in* g_idx:**T4**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(uint8)|
[DML EP] Add MultiHeadAttention and fix Attention (#15727)
2023-05-19 22:07:14 +00:00
|MultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
[DML EP] Add NhwcConv (#15194)
2023-04-11 06:16:09 +00:00
|NhwcConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML] QAttention (#19766) ### Description DML Implementation for [com.microsoft.QAttention](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.QAttention) ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> --------- Co-authored-by: Xiang Zhang <xianz@microsoft.com>
2024-03-11 17:44:34 +00:00
|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|QLinearAdd|*in* A:**T**<br> *in* A_scale:**tensor(float)**<br> *in* A_zero_point:**T**<br> *in* B:**T**<br> *in* B_scale:**tensor(float)**<br> *in* B_zero_point:**T**<br> *in* C_scale:**tensor(float)**<br> *in* C_zero_point:**T**<br> *out* C:**T**|1+|**T** = tensor(int8), tensor(uint8)|
Update OperatorKernels.md
2024-01-04 19:27:03 +00:00
|QLinearAveragePool|*in* X:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
|QLinearConcat|*in* Y_scale:**TF**<br> *in* Y_zero_point:**T8**<br> *in* inputs:**TV**<br> *out* Y:**T8**|1+|**T8** = tensor(int8), tensor(uint8)<br/> **TF** = tensor(float)<br/> **TV** = tensor(float), tensor(int8), tensor(uint8)|
|QLinearGlobalAveragePool|*in* X:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
|QLinearSigmoid|*in* X:**T**<br> *in* X_scale:**tensor(float)**<br> *in* X_zero_point:**T**<br> *in* Y_scale:**tensor(float)**<br> *in* Y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
Fix MS domain QuantizeLinear and DequantizeLinear type registrations … (#16298) This fixes the type lists used to register DML kernels for Microsoft domain QuantizeLinear and DequantizeLinear. These previously did not include FP16 and incorrectly used the same type list for both operators. The new type lists are the same as opset 19 ONNX which aren't implemented yet in the DML EP.
2023-06-16 01:21:56 +00:00
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float), tensor(float16), tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
[DML EP] Add QuickGelu (#15220)
2023-04-05 17:49:34 +00:00
|QuickGelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Add RotaryEmbedding (#18158) This is a graph implementation of RotaryEmbedding since there's no time to add it to DML before 1.16.2, but it eventually should move into DirectML since we're bandwidth-bound.
2023-11-07 16:26:11 +00:00
|RotaryEmbedding|*in* input:**T**<br> *in* position_ids:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**|1+|**M** = tensor(int64)<br/> **T** = tensor(float), tensor(float16)|
Misc transformer fixes - 2 (#14156) ### Description 1. The graph pattern search introduced in https://github.com/microsoft/onnxruntime/pull/13914/ needs to be enhanced so that SkipLayerNormalization is supported 2. Fix fp32 parity for GPT-2 while using `SkipLayerNormalization` fusion. The optional output of SLN needs to also include the bias (if present) and the added output should be a sum of `input + skip + (bias)` ### Motivation and Context Fix some breaking tests
2023-01-06 15:27:10 +00:00
|SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
[DML EP] Add SimplifiedLayerNorm and SkipSimplifiedLayerNorm (#20326)
2024-04-19 05:17:31 +00:00
|SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
Document generation CI is broken (#13308) ### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
2022-10-27 21:20:48 +00:00
| |
| |
|**Operator Domain:** *com.microsoft.dml*||||
|DmlFusedAdd|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedBatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedGemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedInstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedMeanVarianceNormalization|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|DmlFusedSum|*in* data_0:**T**<br> *out* sum:**T**|1+|**T** = tensor(float), tensor(float16)|
Add nhwc support for QLinearAveragePool operator (#7656) * Add nhwc support for QLinearAveragePool operator * Update ContribOperators.md * Update OperatorKernels.md with cpu,dnnl and cuda enabled.
2021-05-14 05:05:30 +00:00
| |
| |
Reference in a new issue Copy permalink