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onnxruntime/docs/OperatorKernels.md

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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)|
|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)|
|AveragePool|*in* X:**T**<br> *out* Y:**T**|11+|**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)|
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)|
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
|Cast|*in* input:**T1**<br> *out* output:**T2**|13+|**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(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
|||[6, 12]|**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(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
|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)|
|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(int64), tensor(int8), 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
|||12|**T** = tensor(double), tensor(float), tensor(int64), tensor(int8), 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)|
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))|
|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)|
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)|
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
|DFT|*in* input:**T1**<br> *in* dft_length:**T2**<br> *out* output:**T1**|17+|**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)|
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
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**|13+|**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)|
|Equal|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**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)|
|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(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)|
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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +00:00
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|16+|**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)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|16+|**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)|
|||[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)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|If|*in* cond:**B**<br> *out* outputs:**V**|16+|**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)|
|||[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)|
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|10+|**T1** = tensor(double), tensor(float)<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
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|13+|**T1** = 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(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)|
Support optional type in ORT (#8339)
2021-11-04 22:01:42 +00:00
|Loop|*in* M:**I**<br> *in* cond:**B**<br> *in* v_initial:**V**<br> *out* v_final_and_scan_outputs:**V**|16+|**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)|
|||[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)|
|LpPool|*in* X:**T**<br> *out* Y:**T**|11+|**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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|13+|**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)|
|QLinearMatMul|*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)|
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
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|13+|**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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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)|
|||[13, 17]|**T** = 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(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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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(int32)|
|||[13, 17]|**T** = 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(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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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(int32)|
|||[13, 17]|**T** = 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(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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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(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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|18+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||[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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|18+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||[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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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)|
|||[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)|
Render Operator documentation as compliant markdown (#3658)
2020-09-02 22:07:50 +00:00
|||[1, 10]|**T** = tensor(double), 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
|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)|
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
|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**|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/> **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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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(float), tensor(int32), tensor(int8), tensor(uint8)|
|||[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)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +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**|16+|**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, 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)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|ScatterElements|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**T**<br> *out* output:**T**|16+|**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)|
|||[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)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|ScatterND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *in* updates:**T**<br> *out* output:**T**|16+|**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, 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))|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|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/> **T1** = tensor(int64)|
|||[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)|
|Size|*in* data:**T**<br> *out* size:**T1**|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)|
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)|
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
|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(int32), tensor(int64), tensor(string)|
|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)|
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(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)|
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)|
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)|
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
|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(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
|||[6, 12]|**T** = tensor(bool), tensor(double), tensor(float), 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
|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)|
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(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)|
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
|Trilu|*in* input:**T**<br> *in* k:**tensor(int64)**<br> *out* output:**T**|14+|**T** = tensor(double), tensor(float), tensor(int64)|
|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(float), tensor(int64), tensor(int8), tensor(string)|
|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(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)|
|LabelEncoder|*in* X:**T1**<br> *out* Y:**T2**|2+|**T1** = tensor(float), tensor(int64), tensor(string)<br/> **T2** = tensor(float), tensor(int64), tensor(string)|
|||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*||||
Add CrossAttention operator (#14146) Move separated Q, K and V (without input projection) from Attention to a new operator CrossAttention. The Attention operator is hard to maintain when we need support with and without input projection in one class. Add a new operator according to feedback. Some change might need in the future, but not in this PR: (1) bias could be optional (We will not proceed that route unless experiments show that fusing Add bias with MatMul instead of this op could improve performance). (2) support packed KV. There are two ways to support it: when key and value are same Tensor, they are packed; or we can make value as optional, and use packed mode when value is empty and the key has packed K/V. (3) support cached key and value, and other (like relative position bias), or more attention mask format. They can be added easily without breaking backward compatible. (4) ROCm/CPU implementation of this op.
2023-01-06 22:27:40 +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* extra_add:**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)|
apply zcode changes to the beam search op (#11880) * apply zcode changes to the beam search op * fix pipeline failure * add doc * workaround for C# * update * update * use name zcode * review comment * review comments * fix cpplint * review coments
2022-06-21 01:39:07 +00:00
|BeamSearch|*in* input_ids:**I**<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> *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)|
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
|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(float)|
|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)|
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)|
|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)|
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)|
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)|
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
|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(int8), 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)|
|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)|
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)|
|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)|
Update CUDA ArgMin/ArgMax op kernels to have end version 11 since opset 12+ is not supported yet. (#13983) ### Description Update CUDA ArgMin/ArgMax op kernels to have end version 11 since opset 12+ is not supported yet. With the way these kernels are currently registered, the documentation shows support for opset 11+. This is not accurate. ### Motivation and Context Fix #13781
2022-12-22 00:01:00 +00:00
|ArgMax|*in* data:**T**<br> *out* reduced:**tensor(int64)**|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 CUDA ArgMin/ArgMax op kernels to have end version 11 since opset 12+ is not supported yet. (#13983) ### Description Update CUDA ArgMin/ArgMax op kernels to have end version 11 since opset 12+ is not supported yet. With the way these kernels are currently registered, the documentation shows support for opset 11+. This is not accurate. ### Motivation and Context Fix #13781
2022-12-22 00:01:00 +00:00
|ArgMin|*in* data:**T**<br> *out* reduced:**tensor(int64)**|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
|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)|
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
|Cast|*in* input:**T1**<br> *out* output:**T2**|13+|**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(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
|||[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(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(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)|
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
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**|10+|**T** = tensor(int8), tensor(uint8)|
|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)|
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(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)|
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), 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)|
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)|
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)|
Implement Sequence Ops GPU (#7863)
2021-06-07 22:30:26 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|14+|**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)|
Optimize sequence type usage on CUDA [2/n] (#8720)
2021-08-24 17:40:28 +00:00
|If|*in* cond:**B**<br> *out* outputs:**V**|13+|**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)|
|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)|
Optimize sequence type usage on CUDA [2/n] (#8720)
2021-08-24 17:40:28 +00:00
|Loop|*in* M:**I**<br> *in* cond:**B**<br> *in* v_initial:**V**<br> *out* v_final_and_scan_outputs:**V**|13+|**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)|
Implement Sequence Ops GPU (#7863)
2021-06-07 22:30:26 +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(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)|
|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(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
|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)|
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
|Neg|*in* X:**T**<br> *out* Y:**T**|13+|**T** = 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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|13+|**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)|
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
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|10+|**T1** = tensor(float)<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(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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
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), tensor(int32)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
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), tensor(int32)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), 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
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||11|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), 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**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
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), tensor(int32)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|14+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
ReduceMin - add int64 cuda kernel support for opset12/13 (#8966) * ReduceMin - int64 support * fix doc Co-authored-by: Ethan Tao <ettao@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
2021-09-08 00:01:26 +00:00
|||13|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), 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|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
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**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
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), tensor(int32)|
|||[1, 10]|**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)|
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), tensor(int32), tensor(int64)|
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), 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
|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**|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)|
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)|
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
|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**|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/> **shape** = tensor(int64)|
|||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)|
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
|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**|13+|**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)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +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**|16+|**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)|
|||[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)|
|ScatterElements|*in* data:**T**<br> *in* indices:**Tind**<br> *in* updates:**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)|
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)|
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
|ScatterND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *in* updates:**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)|
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))|
Add opset 15 kernels for Pow, BatchNorm, and Shape (#8442)
2021-08-25 19:04:20 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|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/> **T1** = tensor(int64)|
|||[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)|
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), tensor(float16)<br/> **U** = tensor(double), tensor(float)<br/> **V** = 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)|
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), 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)|
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
|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(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)|
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(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
|||10|**I** = tensor(int64)<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)|
|||[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
|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)|
Register CPU, CUDA and ROCM opset-16 kernels for some operators (#10643)
2022-03-08 17:18:39 +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(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
|||[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*||||
Add CrossAttention operator (#14146) Move separated Q, K and V (without input projection) from Attention to a new operator CrossAttention. The Attention operator is hard to maintain when we need support with and without input projection in one class. Add a new operator according to feedback. Some change might need in the future, but not in this PR: (1) bias could be optional (We will not proceed that route unless experiments show that fusing Add bias with MatMul instead of this op could improve performance). (2) support packed KV. There are two ways to support it: when key and value are same Tensor, they are packed; or we can make value as optional, and use packed mode when value is empty and the key has packed K/V. (3) support cached key and value, and other (like relative position bias), or more attention mask format. They can be added easily without breaking backward compatible. (4) ROCm/CPU implementation of this op.
2023-01-06 22:27:40 +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* extra_add:**T**<br> *in* past_sequence_length:**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
|BeamSearch|*in* input_ids:**I**<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> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float), tensor(float16)|
|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)|
|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)|
|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)|
|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)|
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|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)|
|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)|
rename CrossAttention to MultiHeadAttention (#14201) ### Description <!-- Describe your changes. --> rename the CrossAttention to MultiheadAttention since this op can also be used as self 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>
2023-01-10 18:18:39 +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> *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
|NGramRepeatBlock|*in* input_ids:**Tid**<br> *in* scores:**T**<br> *out* scores_out:**T**|1+|**T** = tensor(float)<br/> **Tid** = tensor(int64)|
|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)|
|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* extra_add:**S**<br> *out* output:**Q**|1+|**G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|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)|
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)|
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)|
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)|
| |
| |
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)|
|AveragePool|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
|||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)|
|Cast|*in* input:**T1**<br> *out* output:**T2**|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)|
|||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)|
|CastLike|*in* input:**T1**<br> *in* target_type:**T2**<br> *out* output:**T2**|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)|
|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)|
|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)|
|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)|
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
|DFT|*in* input:**T1**<br> *in* dft_length:**T2**<br> *out* output:**T1**|17+|**T1** = tensor(float)<br/> **T2** = 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)|
|DequantizeLinear|*in* x:**T**<br> *in* x_scale:**tensor(float)**<br> *in* x_zero_point:**T**<br> *out* y:**tensor(float)**|13+|**T** = tensor(int32), tensor(int8), tensor(uint8)|
|||10+|**T** = tensor(int32), tensor(int8), tensor(uint8)|
|Div|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||7+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|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)|
|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(uint8)|
|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)|
|Equal|*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)|
|||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)|
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 EP Register Identity-16 (#14053) This PR Registers Identity-16 to the DML EP. ONNX Backend tests and optional type tests were skipped pending future additions. Co-authored-by: Numfor Mbiziwo-Tiapo <numform@microsoft.com>
2023-01-10 17:16:09 +00:00
|Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|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)|
|||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)|
|||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)|
|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)|
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|10+|**T1** = tensor(float)<br/> **T2** = tensor(bool)|
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|13+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|||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 mixed datatype support for DML's LayerNorm contrib op (#13734) ### Description Add mixed datatype support for DML's LayerNorm contrib op. ### Motivation and Context The fusion logic removes casts around LayerNorm in the graph because the contrib version of the op supports mixed datatypes. Scale, Bias and Output's datatypes must match, but input's datatype can be different.
2022-12-01 22:08:18 +00:00
|||1+|**T** = 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)|
|LpPool|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
|||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)|
|MemcpyFromHost|*in* X:**T**<br> *out* Y:**T**|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)|
|MemcpyToHost|*in* X:**T**<br> *out* Y:**T**|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)|
|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] Add an implementation for NonZero (#13768) ### Description Add the NonZero op for DML ### Motivation and Context NonZero is used in a few transformer models, so having a DML implementation will stop large tensors from being transferred to the CPU and back to the GPU
2022-12-03 02:39:21 +00:00
|NonZero|*in* X:**T**<br> *out* Y:**tensor(int64)**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
|||9+|**T** = 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)|
|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
|QLinearMatMul|*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)|
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**tensor(float)**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|13+|**T1** = tensor(float), tensor(int32)<br/> **T2** = tensor(int8), tensor(uint8)|
|||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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
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**|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)|
to work with onnx 1.13 rc, implement ver 18 reduce and optioanl ops, … (#13765)
2023-01-09 18:26:16 +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**|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)|
|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**|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)|
|||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)|
|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**|13+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float), tensor(float16)|
|||11+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float), tensor(float16)|
|||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)|
|RoiAlign|*in* X:**T1**<br> *in* rois:**T1**<br> *in* batch_indices:**T2**<br> *out* Y:**T1**|10+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(int32), tensor(int64)|
|Round|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float), tensor(float16)|
|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)|
|ScatterElements|*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)|
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)|
DML EP add a registration for Shape and Size (#13442) ### Description Add a DML registration for Shape to avoid copying back to the CPU just to get the shape of a GPU tensor. ### Motivation and Context When using free dimensions, many Transformers models extensively use the `Shape` operator. This causes hundreds of GPU->CPU copy that should be completely avoidable. Note that this change also uses the same heuristics as other providers (e.g. CUDA) to force some tensors on the CPU in certain situations. Co-authored-by: Patrice Vignola <pavignol@microsoft.com>
2022-11-09 03:29:37 +00:00
|Shape|*in* data:**T**<br> *out* shape:**T1**|15+|**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/> **T1** = tensor(int64)|
|||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/> **T1** = 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/> **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)|
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 EP add a registration for Shape and Size (#13442) ### Description Add a DML registration for Shape to avoid copying back to the CPU just to get the shape of a GPU tensor. ### Motivation and Context When using free dimensions, many Transformers models extensively use the `Shape` operator. This causes hundreds of GPU->CPU copy that should be completely avoidable. Note that this change also uses the same heuristics as other providers (e.g. CUDA) to force some tensors on the CPU in certain situations. Co-authored-by: Patrice Vignola <pavignol@microsoft.com>
2022-11-09 03:29:37 +00:00
|Size|*in* data:**T**<br> *out* size:**T1**|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/> **T1** = 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/> **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)|
|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**|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)|
|||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*||||
Add CrossAttention operator (#14146) Move separated Q, K and V (without input projection) from Attention to a new operator CrossAttention. The Attention operator is hard to maintain when we need support with and without input projection in one class. Add a new operator according to feedback. Some change might need in the future, but not in this PR: (1) bias could be optional (We will not proceed that route unless experiments show that fusing Add bias with MatMul instead of this op could improve performance). (2) support packed KV. There are two ways to support it: when key and value are same Tensor, they are packed; or we can make value as optional, and use packed mode when value is empty and the key has packed K/V. (3) support cached key and value, and other (like relative position bias), or more attention mask format. They can be added easily without breaking backward compatible. (4) ROCm/CPU implementation of this op.
2023-01-06 22:27:40 +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* extra_add:**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 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)|
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)|
|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = 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)|
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)|
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|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)|
|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)|
|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(uint8)|
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)|
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
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