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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
*This file is automatically generated from the registered kernels by [this script](https://github.com/microsoft/onnxruntime/blob/master/tools/python/gen_opkernel_doc.py).
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)
---------------
<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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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(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(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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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(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(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)|
update with onnx 1.11 release (#10441)
2022-03-08 05:10:55 +00:00
|GridSample|*in* X:**T1**<br> *in* grid:**T1**<br> *out* Y:**T2**|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)|
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|13+|**T1** = tensor(float), tensor(float16)<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
|||[9, 12]|**T1** = 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 ONNX to 1.12 (#11924) Follow-ups that need to happen after this and before the next ORT release: * Support SequenceMap with https://github.com/microsoft/onnxruntime/pull/11731 * Support signal ops with https://github.com/microsoft/onnxruntime/pull/11778 Follow-ups that need to happen after this but don't necessarily need to happen before the release: * Implement LayerNormalization kernel for opset version 17: https://github.com/microsoft/onnxruntime/issues/11916 Fixes #11640
2022-06-22 00:19:52 +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**|1+|**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)|
|OptionalGetElement|*in* input:**O**<br> *out* output:**V**|15+|**O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))<br/> **V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|OptionalHasElement|*in* input:**O**<br> *out* output:**B**|15+|**B** = tensor(bool)<br/> **O** = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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* 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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceL1|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceL2|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceLogSum|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceLogSumExp|*in* data:**T**<br> *out* reduced:**T**|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)|
|||[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
|ReduceMax|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceMean|*in* data:**T**<br> *out* reduced:**T**|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)|
|||[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
|ReduceMin|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceProd|*in* data:**T**<br> *out* reduced:**T**|13+|**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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceSumSquare|*in* data:**T**<br> *out* reduced:**T**|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(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)|
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
|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(int32), tensor(int8), tensor(uint8)|
|||[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)|
Support int8 for operator Split (#8615) * Support int8 for operator Split
2021-08-10 21:04:16 +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**|13+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint8)|
|||[11, 12]|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint8)|
|||[2, 10]|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), 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
|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*||||
Attention kernel update to handle different Q,K,V hidden sizes (#8039) * changes working to convert akv nodes * changes to replace nodes * changes to accomodate qkv hidden sizes as attributes * kernel to accept qkv_hidden_size attributes * Working till compute for varied dimension, todo applyattention() * changes to make all regression tests work * inference running successfully without prepack * success inference with pre-pack weights * add test for diff sizes * bias shape need not be a mul of 3 * get the output_hidden_size from input * infer output shape from input * merge with master * cleaning up files that got merged wrong * accurancy at accepted level * added unit test case for different dimensions * all unit tests passing * packed weights working for attention * prepacked weights working * added test case for newly added extra qk input * updated unit test to test only extra add qk * fixing build error * removing few debugs * reverting test changes * all python test passing * cleaning up * new unit test added, major clean up of code * removed extra code * minor * minor fix to tests * prepack weights code cleaned up * compacted compute() in attention.cc * reformat compute() * making a parameter T * adding 3 q,k,v buffers in all cases * fixing build * running tests only on cpu * Updating docs * trigger ci builds * Addressing comments in PR * addressing some more comments * get add_qk_str from add_qk node directly * updating docs, added extra check to verify attn inputs * Optimized the extra add by parallelizing * added attention_shape to symbolic_shape_infer.py * minor refactoring to address comments
2021-07-19 19:21:33 +00:00
|Attention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* extra_add:**T**<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)|
Support vocab_mask/prefix_vocab_mask/no_repeat_number in greedysearch op (#12327) * support more inputs for greedy search * fix docs * refactor test * lint * review comments
2022-08-03 17:10:08 +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> *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 the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|MaxpoolWithMask|*in* X:**T**<br> *in* M:**tensor(int32)**<br> *out* Y:**T**|1+|**X** = tensor(float)|
|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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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)|
|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)|
|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**|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)|
|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 the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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)**|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 ONNX to 1.12 (#11924) Follow-ups that need to happen after this and before the next ORT release: * Support SequenceMap with https://github.com/microsoft/onnxruntime/pull/11731 * Support signal ops with https://github.com/microsoft/onnxruntime/pull/11778 Follow-ups that need to happen after this but don't necessarily need to happen before the release: * Implement LayerNormalization kernel for opset version 17: https://github.com/microsoft/onnxruntime/issues/11916 Fixes #11640
2022-06-22 00:19:52 +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**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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)|
Support bool type for Pad Op and fix Unsqueeze in Tile grad for Opset 13 (#8602) * changes * tile grad unsqueeze fix for opset 13 * clean up * remove bool support for opset 2 to 12 for Pad as it is not supported. * Copy OperatorKernels.md from artifacts of Windows CI build.
2021-08-11 18:21:02 +00:00
|Pad|*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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceL1|*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
|ReduceL2|*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
|ReduceLogSum|*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
|ReduceLogSumExp|*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
|ReduceMax|*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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceMean|*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)|
Support int64 in ReduceMin cuda op for Opset 14 (#8307) * reducemin int64_t support * fix xxcuda.so load error * testtest * refactor * update doc * propagate types to opset14 * re-generate doc * rename macro
2021-07-13 23:18:06 +00:00
|ReduceMin|*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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceProd|*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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|ReduceSumSquare|*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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +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**|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)|
|||[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
| |
| |
|**Operator Domain:** *com.microsoft*||||
Attention kernel update to handle different Q,K,V hidden sizes (#8039) * changes working to convert akv nodes * changes to replace nodes * changes to accomodate qkv hidden sizes as attributes * kernel to accept qkv_hidden_size attributes * Working till compute for varied dimension, todo applyattention() * changes to make all regression tests work * inference running successfully without prepack * success inference with pre-pack weights * add test for diff sizes * bias shape need not be a mul of 3 * get the output_hidden_size from input * infer output shape from input * merge with master * cleaning up files that got merged wrong * accurancy at accepted level * added unit test case for different dimensions * all unit tests passing * packed weights working for attention * prepacked weights working * added test case for newly added extra qk input * updated unit test to test only extra add qk * fixing build error * removing few debugs * reverting test changes * all python test passing * cleaning up * new unit test added, major clean up of code * removed extra code * minor * minor fix to tests * prepack weights code cleaned up * compacted compute() in attention.cc * reformat compute() * making a parameter T * adding 3 q,k,v buffers in all cases * fixing build * running tests only on cpu * Updating docs * trigger ci builds * Addressing comments in PR * addressing some more comments * get add_qk_str from add_qk node directly * updating docs, added extra check to verify attn inputs * Optimized the extra add by parallelizing * added attention_shape to symbolic_shape_infer.py * minor refactoring to address comments
2021-07-19 19:21:33 +00:00
|Attention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* extra_add:**T**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
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), 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
|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)|
CUDA BFloat16 Refactor (#10085)
2022-01-14 11:38:56 +00:00
|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**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
|BiasSoftmax|*in* data:**T**<br> *in* bias:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
[CUDA] Implement BitmaskDropout, BitmaskBiasDropout and BitmaskDropoutGrad (#11534) * Implement BitmaskDropout and associated unit tests. * Implement BitmaskDropoutGrad and associated unit tests. * Implement Dropout -> BitmaskDropout rewrite rule and associated unit tests. * Implement (Dropout,DropoutGrad) -> (BitmaskDropout,BitmaskDropoutGrad) rewrite rule. This commit does not yet include unit tests for this rewrite rule. This commit also introduces improved documentation for all changes which will be grouped into this PR. * bitmask dropout * fix win build * bugfix for rocm * bugfix * fix code format * fix ut * fix build break * fix ut in win * resolve comments * fix ut in trt * resolve comments * fix rocm build error * fix typo Co-authored-by: Aidan Beggs <aidanbeggs@microsoft.com>
2022-05-27 09:24:47 +00:00
|BitmaskBiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
|BitmaskDropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|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)|
Decoder Attention CUDA Op (#9792) * add kernel interface * register kernel * add self/cross qkv projection without cache * add LaunchTransQkv2 for (S,B,X,N,H) -> (X,B,N,S,H) * refactor ConcatPastToPresent * DecoderQkvToContext interface * q,k,v buffer and cache as output * qk, pv and transctx * fix compiler error on linux machine * key_padding_mask * add test_parity file. However not runnable * add partial unittest * made partial attributes to inputs * --gen_doc * change kernel interface, add more tests * morre parity tests * fix test * fix typo * transpose optimizer has bug. remove it temporarily * add input shape checks * add type/shape inference * fix cache shape check * fix rocm build failure * fix rocm build error * review comments * review comments
2021-11-20 03:25:36 +00:00
|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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove 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(float16)|
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), 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
|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)|
Support vocab_mask/prefix_vocab_mask/no_repeat_number in greedysearch op (#12327) * support more inputs for greedy search * fix docs * refactor test * lint * review comments
2022-08-03 17:10:08 +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> *out* sequences:**I**|1+|**T** = 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
|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)|
|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)|
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)|
Update the operator documentation generation (#7787) * Update the operator documentation generation - Make layout a little nicer - Update to latest supported operators including training - Fix some links that are broken when the docs content is copied to github-pages - Fix incorrect usage of 'onnx.ai.ml' as the default domain - ML ops are now separated from the real default domain of 'onnx.ai' - Include CPU, CUDA and training kernels - exclude DNNL as it's not an EP we own * There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library. * Enable validation of both contrib ops and operator kernels in build Filter generation so it's deterministic Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs. Remove workarounds for github-pages as that will now link to the github docs which display correctly
2021-06-02 07:47:40 +00:00
|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(int8)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
|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)|
|Rfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|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**|1+|**T** = tensor(float), tensor(float16)|
|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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* 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)|
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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