mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-06-06 00:03:22 +00:00
Register opset 22 (#23344)
### Description Follw up #21897 To be compatible with onnx 17.0, Registering opset 22 is required in terms of the [updated operators (bfloat16)](https://github.com/onnx/onnx/releases/tag/v1.17.0) ### Motivation and Context Fix #23162 Fix #23161 Fix #23164 (Xnnpack) ### Remaining issue #23163 (QNN) See [the file](https://github.com/microsoft/onnxruntime/pull/23344/files#diff-04f5d6db0a6873f7299ed06ff1ec45a49e69f0865cb32f4397cd56db0cd0a784) ### Result of `find_optimizer_opset_version_updates_required.py (cpu only)` ``` [WARNING] - Newer opset found for kOnnxDomain.Conv. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/conv_add_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.IsInf. Latest:20 Optimizer support ends at 10. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/isinf_reducesum_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/isinf_reducesum_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/isinf_reducesum_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.HardSigmoid. Latest:22 Optimizer support ends at 6. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/conv_add_act_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/layer_norm_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Transpose. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.Conv. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.MaxPool. Latest:22 Optimizer support ends at 12. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.AveragePool. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.BatchNormalization. Latest:15 Optimizer support ends at 14. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.Transpose. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.Upsample. Latest:10 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.Resize. Latest:19 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.GlobalMaxPool. Latest:22 Optimizer support ends at 1. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.GlobalAveragePool. Latest:22 Optimizer support ends at 1. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/nchwc_transformer.cc [WARNING] - Newer opset found for kOnnxDomain.Shape. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pre_shape_node_elimination.cc [WARNING] - Newer opset found for kOnnxDomain.Conv. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/conv_bn_fusion.cc [ERROR] - Call/Declaration is split over multiple lines. Please check manually.File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/label_encoder_fusion.cc Line:49 [ERROR] - Failed to find version information for "ai.onnx.ml".LabelEncoder. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/label_encoder_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.HardSigmoid. Latest:22 Optimizer support ends at 6. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/conv_activation_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Dropout. Latest:22 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/dropout_elimination.cc [WARNING] - Newer opset found for kOnnxDomain.Transpose. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/gemm_transpose_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Transpose. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/gemm_transpose_fusion.cc [ERROR] - Symbolic name of 'ignorable_nodes[index].first' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/matmul_bn_fusion.cc [ERROR] - Symbolic name of 'dest.first' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/matmul_bn_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Conv. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pad_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.AveragePool. Latest:22 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pad_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.MaxPool. Latest:22 Optimizer support ends at 12. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pad_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Pad. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pad_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/pad_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Dropout. Latest:22 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/bias_dropout_fusion.cc [ERROR] - Failed to find version information for kMSDomain.BitmaskDropout. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/bias_dropout_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Clip. Latest:13 Optimizer support ends at 6. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/relu_clip_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/fast_gelu_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Cast. Latest:21 Optimizer support ends at 19. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/fast_gelu_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Reshape. Latest:21 Optimizer support ends at 14. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/reshape_fusion.cc [ERROR] - Failed to find version information for kMSDomain.ConcatTraining. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/reshape_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Where. Latest:16 Optimizer support ends at 9. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/not_where_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Where. Latest:16 Optimizer support ends at 9. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/not_where_fusion.cc [WARNING] - Newer opset found for kOnnxDomain.Conv. Latest:22 Optimizer support ends at 11. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/conv_mul_fusion.cc [ERROR] - Symbolic name of 'QOpName' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/qdq_util.cc [ERROR] - Symbolic name of 'QOpName' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/qdq_util.cc [ERROR] - Symbolic name of 'DQOpName' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/qdq_util.cc [ERROR] - Symbolic name of 'DQOpName' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/qdq_util.cc [ERROR] - Call/Declaration is split over multiple lines. Please check manually.File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/avx2_weight_s8_to_u8.cc Line:170 [WARNING] - Newer opset found for kOnnxDomain.MaxPool. Latest:22 Optimizer support ends at 12. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.cc [ERROR] - Symbolic name of 'current_node.OpType(' found for op. Please check manually. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/compute_optimizer/upstream_transformer_base.cc [WARNING] - Newer opset found for kOnnxDomain.Reshape. Latest:21 Optimizer support ends at 14. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/compute_optimizer/upstream_reshape.cc [WARNING] - Newer opset found for kOnnxDomain.Transpose. Latest:21 Optimizer support ends at 13. File:/home/titaiwang/onnxruntime/onnxruntime/core/optimizer/attention_fusion_helper.h ```
This commit is contained in:
Ti-Tai Wang
GitHub
parent
c7c8757a1c
commit
a08211febb
24 changed files with 591 additions and 283 deletions
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@ -36,10 +36,10 @@ This file should be generated. See [cgmanifests/README](/cgmanifests/README.md)
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1. If there is a build failure in stage "Check out of dated documents" in WebAssembly CI pipeline, update ONNX Runtime
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Web WebGL operator support document:
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- Make sure Node.js is installed (see [Prerequisites](../js/README.md#Prerequisites) for instructions).
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- Follow step 1 in [js/Build](../js/README.md#Build-2) to install dependencies).
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- Follow [js/Build](../js/README.md#Build-2) to install dependencies.
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- Follow instructions in [Generate document](../js/README.md#Generating-Document) to update document. Commit changes applied to file `docs/operators.md`.
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1. Usually some newly introduced tests will fail. Then you may need to update
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2. Usually some newly introduced tests will fail. Then you may need to update
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- [onnxruntime/test/onnx/main.cc](/onnxruntime/test/onnx/main.cc)
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- [onnxruntime/test/providers/cpu/model_tests.cc](/onnxruntime/test/providers/cpu/model_tests.cc)
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- [csharp/test/Microsoft.ML.OnnxRuntime.Tests.NetCoreApp/InferenceTest.netcore.cs](/csharp/test/Microsoft.ML.OnnxRuntime.Tests.NetCoreApp/InferenceTest.netcore.cs)
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@ -19,8 +19,10 @@ Do not modify directly.*
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|**Operator Domain:** *ai.onnx*||||
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|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)|
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|||[6, 12]|**T** = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|Acos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
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|Acosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
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|Acos|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[7, 21]|**T** = tensor(float)|
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|Acosh|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[9, 21]|**T** = tensor(float)|
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|Add|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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@ -33,11 +35,16 @@ Do not modify directly.*
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|ArgMin|*in* data:**T**<br> *out* reduced:**tensor(int64)**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
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|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
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|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
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|Asin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
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|Asinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
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|Atan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
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|Atanh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
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|AveragePool|*in* X:**T**<br> *out* Y:**T**|19+|**T** = tensor(float)|
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|Asin|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[7, 21]|**T** = tensor(float)|
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|Asinh|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[9, 21]|**T** = tensor(float)|
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|Atan|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[7, 21]|**T** = tensor(float)|
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|Atanh|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[9, 21]|**T** = tensor(float)|
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|AveragePool|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
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|||[19, 21]|**T** = tensor(float)|
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|||[11, 18]|**T** = tensor(float)|
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|||10|**T** = tensor(float)|
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|||[7, 9]|**T** = tensor(float)|
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@ -72,13 +79,17 @@ Do not modify directly.*
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|ConstantOfShape|*in* input:**T1**<br> *out* output:**T2**|21+|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|||20|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|||[9, 19]|**T1** = tensor(int64)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
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|Conv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
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|||[11, 21]|**T** = tensor(float)|
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|||[1, 10]|**T** = tensor(float)|
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|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)|
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|ConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
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|ConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
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|||[11, 21]|**T** = tensor(float)|
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|||[1, 10]|**T** = tensor(float)|
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|Cos|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
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|Cosh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
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|Cos|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[7, 21]|**T** = tensor(float)|
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|Cosh|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
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|||[9, 21]|**T** = tensor(float)|
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|Crop|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
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|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)|
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|||[11, 13]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)|
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@ -91,18 +102,21 @@ Do not modify directly.*
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|||[19, 20]|**T1** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int32), tensor(int8), tensor(uint8)<br/> **T2** = tensor(float), tensor(float16)|
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|||[13, 18]|**T** = tensor(int32), tensor(int8), tensor(uint8)|
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|||[10, 12]|**T** = tensor(int32), tensor(int8), tensor(uint8)|
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|Det|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
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|Det|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
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|||[11, 21]|**T** = tensor(float)|
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|Div|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|14+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|||13|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|||[7, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|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)|
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|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**|22+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
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|||[13, 21]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
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|||12|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
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|||[10, 11]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
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|||[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)|
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|DynamicQuantizeLinear|*in* x:**T1**<br> *out* y:**T2**<br> *out* y_scale:**tensor(float)**<br> *out* y_zero_point:**T2**|11+|**T2** = tensor(uint8)|
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|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)|
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|Einsum|*in* Inputs:**T**<br> *out* Output:**T**|12+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
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|Elu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
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|Elu|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
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|||[6, 21]|**T** = tensor(float)|
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|Equal|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|19+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)<br/> **T1** = tensor(bool)|
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|||[13, 18]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
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|||[11, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
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@ -113,7 +127,8 @@ Do not modify directly.*
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|||[6, 12]|**T** = tensor(double), tensor(float)|
|
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|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)|
|
||||
|||[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)|
|
||||
|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)|
|
||||
|EyeLike|*in* input:**T1**<br> *out* output:**T2**|22+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)<br/> **T2** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)|
|
||||
|||[9, 21]|**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**|21+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|||[13, 20]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|||[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)|
|
||||
|
|
@ -121,7 +136,8 @@ Do not modify directly.*
|
|||
|||[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)|
|
||||
|Floor|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
|
||||
|||[6, 12]|**T** = tensor(double), tensor(float)|
|
||||
|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)|
|
||||
|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**|22+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|||[14, 21]|**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)|
|
||||
|||[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)|
|
||||
|
|
@ -136,19 +152,23 @@ Do not modify directly.*
|
|||
|||[11, 12]|**T** = tensor(double), tensor(float)|
|
||||
|||[9, 10]|**T** = tensor(double), tensor(float)|
|
||||
|||[7, 8]|**T** = tensor(double), tensor(float)|
|
||||
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|
||||
|GlobalAveragePool|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[1, 21]|**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)|
|
||||
|GlobalMaxPool|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[1, 21]|**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)|
|
||||
|||[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)|
|
||||
|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)|
|
||||
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|20+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(double), tensor(float)|
|
||||
|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|22+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(double), tensor(float)|
|
||||
|||[20, 21]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(double), tensor(float)|
|
||||
|||[16, 19]|**T1** = tensor(float)<br/> **T2** = tensor(float)|
|
||||
|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)|
|
||||
|HardSigmoid|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
|
||||
|HardSigmoid|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[6, 21]|**T** = tensor(float)|
|
||||
|Hardmax|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(float)|
|
||||
|||[11, 12]|**T** = tensor(float)|
|
||||
|||[1, 10]|**T** = tensor(float)|
|
||||
|
|
@ -165,7 +185,8 @@ Do not modify directly.*
|
|||
|||[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)|
|
||||
|||[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)|
|
||||
|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)|
|
||||
|InstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
|
||||
|||[6, 21]|**T** = tensor(float)|
|
||||
|IsInf|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
|
||||
|||[10, 19]|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
|
||||
|IsNaN|*in* X:**T1**<br> *out* Y:**T2**|20+|**T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)<br/> **T2** = tensor(bool)|
|
||||
|
|
@ -173,7 +194,8 @@ Do not modify directly.*
|
|||
|||[9, 12]|**T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|
||||
|LRN|*in* X:**T**<br> *out* Y:**T**|13+|**T** = tensor(float)|
|
||||
|||[1, 12]|**T** = tensor(float)|
|
||||
|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)|
|
||||
|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**|22+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|||[14, 21]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|||[7, 13]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|LayerNormalization|*in* X:**T**<br> *in* Scale:**T**<br> *in* B:**T**<br> *out* Y:**T**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**<br><br>or<br><br>*in* X:**T**<br> *in* Scale:**V**<br> *in* B:**V**<br> *out* Y:**V**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**|17+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(float)|
|
||||
|||[1, 16]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(double), tensor(float), tensor(float16)<br/> **V** = tensor(double), tensor(float), tensor(float16)|
|
||||
|
|
@ -196,7 +218,8 @@ Do not modify directly.*
|
|||
|||[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)|
|
||||
|||[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)|
|
||||
|LpNormalization|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float)|
|
||||
|LpPool|*in* X:**T**<br> *out* Y:**T**|18+|**T** = tensor(float)|
|
||||
|LpPool|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[18, 21]|**T** = tensor(float)|
|
||||
|||[11, 17]|**T** = tensor(float)|
|
||||
|||[2, 10]|**T** = tensor(float)|
|
||||
|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)|
|
||||
|
|
@ -207,11 +230,13 @@ Do not modify directly.*
|
|||
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|||[8, 11]|**T** = tensor(double), tensor(float)|
|
||||
|||[6, 7]|**T** = tensor(float)|
|
||||
|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)|
|
||||
|MaxPool|*in* X:**T**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**<br> *out* Indices:**I**|22+|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(int8), tensor(uint8)|
|
||||
|||[12, 21]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(int8), tensor(uint8)|
|
||||
|||[8, 11]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float)|
|
||||
|||[1, 7]|**T** = tensor(float)|
|
||||
|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)|
|
||||
|MaxUnpool|*in* X:**T1**<br> *in* I:**T2**<br> *in* output_shape:**T2**<br> *out* output:**T1**|22+|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|||[11, 21]|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|||[9, 10]|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|Mean|*in* data_0:**T**<br> *out* mean:**T**|13+|**T** = tensor(float)|
|
||||
|||[8, 12]|**T** = tensor(float)|
|
||||
|
|
@ -264,7 +289,8 @@ Do not modify directly.*
|
|||
|||[19, 20]|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int8), tensor(uint8)|
|
||||
|||[13, 18]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
|
||||
|||[10, 12]|**T1** = tensor(float)<br/> **T2** = tensor(int8), tensor(uint8)|
|
||||
|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)|
|
||||
|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**|22+|**T** = tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|||[14, 21]|**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)|
|
||||
|
|
@ -334,7 +360,8 @@ Do not modify directly.*
|
|||
|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)|
|
||||
|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)|
|
||||
|Round|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Round|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[11, 21]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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)|
|
||||
|Scale|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|
||||
|ScaledTanh|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|
||||
|
|
@ -353,7 +380,8 @@ Do not modify directly.*
|
|||
|||[16, 17]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|||[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)|
|
||||
|||[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)|
|
||||
|Selu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float)|
|
||||
|Selu|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[6, 21]|**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))|
|
||||
|
|
@ -371,8 +399,10 @@ Do not modify directly.*
|
|||
|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)|
|
||||
|||[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)|
|
||||
|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), tensor(float16)<br/> **V** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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)|
|
||||
|Sin|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(double), tensor(float)|
|
||||
|||[7, 21]|**T** = tensor(double), tensor(float)|
|
||||
|Sinh|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
|
||||
|||[9, 21]|**T** = tensor(float)|
|
||||
|Size|*in* data:**T**<br> *out* size:**T1**|21+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|
||||
|||[19, 20]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|
||||
|||[13, 18]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|
||||
|
|
@ -384,8 +414,10 @@ Do not modify directly.*
|
|||
|Softmax|*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)|
|
||||
|Softplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|
||||
|Softsign|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float)|
|
||||
|Softplus|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[1, 21]|**T** = tensor(float)|
|
||||
|Softsign|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
|
||||
|||[1, 21]|**T** = tensor(float)|
|
||||
|SpaceToDepth|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
|
||||
|||[1, 12]|**T** = tensor(double), tensor(float)|
|
||||
|Split|*in* input:**T**<br> *in* split:**T**<br> *out* outputs...:**T**<br><br>or<br><br>*in* input:**T**<br> *in* split:**tensor(int64)**<br> *out* outputs:**T**<br><br>or<br><br>*in* input:**T**<br> *out* outputs:**T**|18+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|
|
@ -408,11 +440,13 @@ Do not modify directly.*
|
|||
|Sum|*in* data_0:**T**<br> *out* sum:**T**|13+|**T** = tensor(double), tensor(float)|
|
||||
|||[8, 12]|**T** = tensor(double), tensor(float)|
|
||||
|||[6, 7]|**T** = tensor(double), tensor(float)|
|
||||
|Tan|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(float)|
|
||||
|Tan|*in* input:**T**<br> *out* output:**T**|22+|**T** = tensor(float)|
|
||||
|||[7, 21]|**T** = tensor(float)|
|
||||
|Tanh|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(double), tensor(float)|
|
||||
|||[6, 12]|**T** = tensor(double), tensor(float)|
|
||||
|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)|
|
||||
|ThresholdedRelu|*in* X:**T**<br> *out* Y:**T**|22+|**T** = tensor(float)|
|
||||
|||[10, 21]|**T** = tensor(float)|
|
||||
|||[1, 9]|**T** = tensor(float)|
|
||||
|Tile|*in* input:**T**<br> *in* repeats:**T1**<br> *out* output:**T**<br><br>or<br><br>*in* input:**T**<br> *in* tiles:**T**<br> *in* axis:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|
||||
|||[6, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|
||||
|
|
|
|||
|
|
@ -33,8 +33,10 @@ namespace onnxruntime {
|
|||
op, since_version, type, \
|
||||
KernelDefBuilder().MayInplace(0, 0).TypeConstraint("T", DataTypeImpl::GetTensorType<type>()), op<type>);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Elu, 6);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(HardSigmoid, 6);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(Elu, 6, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Elu, 22);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(HardSigmoid, 6, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(HardSigmoid, 22);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(LeakyRelu, 6, 15);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Relu, 6, 12, float);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Relu, 6, 12, double);
|
||||
|
|
@ -52,19 +54,23 @@ REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(LeakyRelu, 6, 15, MLFloat16);
|
|||
REGISTER_UNARY_ELEMENTWISE_TYPED_KERNEL(LeakyRelu, 16, MLFloat16);
|
||||
#endif // MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Selu, 6);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(Selu, 6, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Selu, 22);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Sigmoid, 6, 12, float);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Sigmoid, 6, 12, double);
|
||||
REGISTER_UNARY_ELEMENTWISE_TYPED_KERNEL(Sigmoid, 13, float);
|
||||
REGISTER_UNARY_ELEMENTWISE_TYPED_KERNEL(Sigmoid, 13, double);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Softplus, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Softsign, 1);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(Softplus, 1, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Softplus, 22);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(Softsign, 1, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Softsign, 22);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Tanh, 6, 12, float);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_TYPED_KERNEL(Tanh, 6, 12, double);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(Celu, 12);
|
||||
REGISTER_UNARY_ELEMENTWISE_TYPED_KERNEL(Tanh, 13, float);
|
||||
REGISTER_UNARY_ELEMENTWISE_TYPED_KERNEL(Tanh, 13, double);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ThresholdedRelu, 10);
|
||||
REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL(ThresholdedRelu, 10, 21);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ThresholdedRelu, 22);
|
||||
|
||||
// Opset-16 adds BFloat16 to allowed types for the LeakyRelu operator
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(LeakyRelu, 16);
|
||||
|
|
|
|||
|
|
@ -40,8 +40,8 @@ std::vector<AllocatorPtr> CPUExecutionProvider::CreatePreferredAllocators() {
|
|||
|
||||
// Forward declarations of op kernels
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 10, Clip);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Elu);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, HardSigmoid);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, Elu);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, HardSigmoid);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 15, LeakyRelu);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, float, Relu);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, double, Relu);
|
||||
|
|
@ -49,11 +49,11 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, MLFloat16, Relu);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 15, MLFloat16, LeakyRelu);
|
||||
#endif
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Selu);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, Selu);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, float, Sigmoid);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, double, Sigmoid);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softplus);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softsign);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, Softplus);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, Softsign);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, float, Tanh);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, double, Tanh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, PRelu);
|
||||
|
|
@ -129,13 +129,13 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 10, double, Equal);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7, float, Mean);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12, float, Mean);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Sin);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, double, Sin);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Cos);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Tan);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Asin);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Acos);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Atan);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, float, Sin);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, double, Sin);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Cos);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Tan);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Asin);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Acos);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Atan);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, float, Gemm);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, double, Gemm);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, Hardmax);
|
||||
|
|
@ -154,7 +154,7 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, Conv);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, ConvTranspose);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 8, Flatten);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, InstanceNormalization);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, InstanceNormalization);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, LpNormalization);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double, LpNormalization);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 12, LRN);
|
||||
|
|
@ -166,11 +166,11 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
#endif
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 2, 10, LpPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 2, GlobalLpPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalAveragePool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, GlobalAveragePool);
|
||||
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MLFloat16, GlobalAveragePool);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, MLFloat16, GlobalAveragePool);
|
||||
#endif
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalMaxPool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, GlobalMaxPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MaxRoiPool);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceL1);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, ReduceL1);
|
||||
|
|
@ -286,7 +286,7 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, double, Less);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int32_t, Less);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int64_t, Less);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, EyeLike);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, EyeLike);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float, IsNaN);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, double, IsNaN);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, MLFloat16, IsNaN);
|
||||
|
|
@ -316,11 +316,11 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, int64_t_float_int32_t,
|
||||
OneHot);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, MaxUnpool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Asinh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Acosh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Atanh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Sinh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Cosh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Asinh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Acosh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Atanh);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Scan);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Scatter);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, TfIdfVectorizer);
|
||||
|
|
@ -362,7 +362,7 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, int32_t, Resize);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, int8_t, Resize);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, uint8_t, Resize);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ThresholdedRelu);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 21, ThresholdedRelu);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, uint8_t,
|
||||
DequantizeLinear);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, int8_t,
|
||||
|
|
@ -401,9 +401,9 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, int64_t, Equal);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, float, Equal);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, double, Equal);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, Round);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, Round);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MLFloat16, Round);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, float, Round);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, double, Round);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MLFloat16, Round);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, DynamicQuantizeLinear);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, float, ArgMax);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, double, ArgMax);
|
||||
|
|
@ -483,19 +483,19 @@ class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDoma
|
|||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, Split);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, Squeeze);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, Unsqueeze);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Det);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, Det);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, ScatterElements);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, NonMaxSuppression);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 18, AveragePool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MaxUnpool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MaxUnpool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 17, LpPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Conv);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, Conv);
|
||||
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MLFloat16, Conv);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MLFloat16, Conv);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 18, MLFloat16,
|
||||
AveragePool);
|
||||
#endif
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, ConvTranspose);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, ConvTranspose);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, If);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SequenceLength);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SequenceAt);
|
||||
|
|
@ -541,9 +541,9 @@ class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDoma
|
|||
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Min);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Max);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MaxPool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 21, MaxPool);
|
||||
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16, MaxPool);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 21, MLFloat16, MaxPool);
|
||||
#endif
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Pow);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, float, ReduceMax);
|
||||
|
|
@ -633,10 +633,10 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
|
|||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 20, Flatten);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, LRN);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MeanVarianceNormalization);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float_float, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float_double, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double_float, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double_double, Dropout);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, float_float, Dropout);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, float_double, Dropout);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, double_float, Dropout);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, double_double, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, ArgMax);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, ArgMax);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t, ArgMax);
|
||||
|
|
@ -844,9 +844,9 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
|
|||
BatchNormalization);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 14, double,
|
||||
BatchNormalization);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, GRU);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, LSTM);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, RNN);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, GRU);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, LSTM);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, RNN);
|
||||
|
||||
// Opset 15
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, Pow);
|
||||
|
|
@ -946,7 +946,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
|
|||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, double, ReduceSumSquare);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int32_t, ReduceSumSquare);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int64_t, ReduceSumSquare);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, LpPool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 21, LpPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Col2Im);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int8_t, BitwiseAnd);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int16_t, BitwiseAnd);
|
||||
|
|
@ -992,9 +992,9 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Op
|
|||
|
||||
// Opset 19
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20, Size);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, AveragePool);
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 21, AveragePool);
|
||||
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, MLFloat16, AveragePool);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 21, MLFloat16, AveragePool);
|
||||
#endif
|
||||
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20, Cast);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20, int32_t,
|
||||
|
|
@ -1062,8 +1062,8 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
|
|||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, int8_t, ReduceMin);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, uint8_t, ReduceMin);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, DFT);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float, GridSample);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, double, GridSample);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, 21, float, GridSample);
|
||||
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, 21, double, GridSample);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float, AffineGrid);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, double, AffineGrid);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float, IsNaN);
|
||||
|
|
@ -1125,6 +1125,56 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
|
|||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 21, Float8E5M2FNUZ, QuantizeLinear);
|
||||
#endif
|
||||
|
||||
// Opset 22
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Acos);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Cos);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Tan);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Asin);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Atan);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Sinh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Cosh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Asinh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Acosh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Atanh);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Conv);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, ConvTranspose);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Det);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float_float, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float_double, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double_float, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double_double, Dropout);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, GridSample);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, GridSample);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Elu);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, EyeLike);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GlobalAveragePool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GlobalMaxPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GRU);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, LSTM);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, RNN);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, HardSigmoid);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, InstanceNormalization);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, LpPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MaxPool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MaxUnpool);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Softplus);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, Round);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, Round);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16, Round);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Selu);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, Sin);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, Sin);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Softsign);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, ThresholdedRelu);
|
||||
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, AveragePool);
|
||||
|
||||
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16, Conv);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16, GlobalAveragePool);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16, MaxPool);
|
||||
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16, AveragePool);
|
||||
#endif
|
||||
|
||||
// !!PLEASE READ BELOW!! Following that, add new entries above this comment
|
||||
|
||||
/* *** IMPORTANT! ***
|
||||
|
|
@ -1168,21 +1218,21 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
static const BuildKernelCreateInfoFn function_table[] = {
|
||||
BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 10, Clip)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Elu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, HardSigmoid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, Elu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, HardSigmoid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 15,
|
||||
LeakyRelu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
float, Relu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
double, Relu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Selu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21, Selu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
float, Sigmoid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
double, Sigmoid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softplus)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softsign)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, Softplus)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, Softsign)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
float, Tanh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
|
|
@ -1320,13 +1370,13 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
float, Mean)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12,
|
||||
float, Mean)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, double, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Cos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Tan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Asin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Acos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Atan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, float, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, double, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Cos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Tan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Asin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Acos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 21, Atan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8,
|
||||
float, Gemm)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8,
|
||||
|
|
@ -1357,8 +1407,8 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10,
|
||||
ConvTranspose)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 8, Flatten)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6,
|
||||
InstanceNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 21,
|
||||
InstanceNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float,
|
||||
LpNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double,
|
||||
|
|
@ -1371,8 +1421,8 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 2, 10, LpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 2, GlobalLpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalAveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalMaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, GlobalAveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, GlobalMaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MaxRoiPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10,
|
||||
float, ReduceL1)>,
|
||||
|
|
@ -1567,7 +1617,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
int32_t, Less)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12,
|
||||
int64_t, Less)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, EyeLike)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, EyeLike)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12,
|
||||
float, IsNaN)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12,
|
||||
|
|
@ -1602,11 +1652,11 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
int64_t_float_int32_t, OneHot)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
|
||||
MaxUnpool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Asinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Acosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Atanh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Sinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Cosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Asinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Acosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 21, Atanh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Scan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
|
||||
Scatter)>,
|
||||
|
|
@ -1678,7 +1728,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
int8_t, Resize)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10,
|
||||
uint8_t, Resize)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ThresholdedRelu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 21, ThresholdedRelu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12,
|
||||
uint8_t, DequantizeLinear)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12,
|
||||
|
|
@ -1734,10 +1784,10 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
float, Equal)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12,
|
||||
double, Equal)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MLFloat16,
|
||||
Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, float, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, double, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MLFloat16,
|
||||
Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t,
|
||||
DynamicQuantizeLinear)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12,
|
||||
|
|
@ -1791,17 +1841,17 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
Squeeze)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12,
|
||||
Unsqueeze)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Det)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, Det)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12,
|
||||
ScatterElements)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, NonMaxSuppression)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 18,
|
||||
AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MaxUnpool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MaxUnpool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 17,
|
||||
LpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Conv)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, ConvTranspose)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, Conv)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, ConvTranspose)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, If)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SequenceLength)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SequenceAt)>,
|
||||
|
|
@ -1954,7 +2004,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Pow)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 21, MaxPool)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12,
|
||||
float, ReduceMax)>,
|
||||
|
|
@ -2081,14 +2131,14 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, LRN)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13,
|
||||
MeanVarianceNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, float_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, float_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, double_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 21, double_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, ArgMax)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double,
|
||||
ArgMax)>,
|
||||
|
|
@ -2389,9 +2439,9 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
float, BatchNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 14,
|
||||
double, BatchNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, GRU)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, LSTM)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, RNN)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, GRU)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, LSTM)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 21, RNN)>,
|
||||
|
||||
// Opset 15
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, Pow)>,
|
||||
|
|
@ -2556,7 +2606,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int64_t,
|
||||
ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, LpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, 21, LpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Col2Im)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int8_t,
|
||||
BitwiseAnd)>,
|
||||
|
|
@ -2633,7 +2683,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
|
||||
// Opset 19
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20, Size)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 21, AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20, Cast)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 20,
|
||||
uint8_t, DequantizeLinear)>,
|
||||
|
|
@ -2708,10 +2758,10 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, int8_t, ReduceMin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, uint8_t, ReduceMin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, DFT)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float,
|
||||
GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, double,
|
||||
GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, 21, float,
|
||||
GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, 21, double,
|
||||
GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float,
|
||||
AffineGrid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, double,
|
||||
|
|
@ -2803,6 +2853,54 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 21, Float8E5M2FNUZ,
|
||||
QuantizeLinear)>,
|
||||
#endif
|
||||
|
||||
// Opset 22
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Cos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Tan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Asin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Acos)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Atan)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Sinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Cosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Asinh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Acosh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Atanh)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Conv)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, ConvTranspose)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Det)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, EyeLike)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GlobalAveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GlobalMaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double_float,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double_double,
|
||||
Dropout)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, GridSample)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, GRU)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, LSTM)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, RNN)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Elu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, HardSigmoid)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, InstanceNormalization)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, LpPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MaxUnpool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Softplus)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16,
|
||||
Round)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Selu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, float, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, double, Sin)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, Softsign)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, ThresholdedRelu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, AveragePool)>,
|
||||
};
|
||||
|
||||
for (auto& function_table_entry : function_table) {
|
||||
|
|
@ -2819,18 +2917,26 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
Status RegisterFp16Kernels(KernelRegistry& kernel_registry) {
|
||||
static const BuildKernelCreateInfoFn function_table[] = {
|
||||
BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MLFloat16,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 21, MLFloat16,
|
||||
GlobalAveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16,
|
||||
GlobalAveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MLFloat16,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 21, MLFloat16,
|
||||
Conv)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16,
|
||||
Conv)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 18,
|
||||
MLFloat16, AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, MLFloat16,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, 21, MLFloat16,
|
||||
AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22, MLFloat16,
|
||||
AveragePool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 11,
|
||||
MLFloat16, MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16,
|
||||
MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 21, MLFloat16,
|
||||
MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 22,
|
||||
MLFloat16, MaxPool)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12,
|
||||
MLFloat16, Relu)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 13,
|
||||
|
|
|
|||
|
|
@ -596,9 +596,15 @@ Status FusedConvFp16::Compute(OpKernelContext* context) const {
|
|||
// Operator definitions
|
||||
//
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
Conv,
|
||||
11, 21, MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
FusedConvFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Conv,
|
||||
11,
|
||||
22,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
FusedConvFp16);
|
||||
|
|
|
|||
|
|
@ -224,9 +224,16 @@ ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
MaxPool,
|
||||
12, 21,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
MaxPool,
|
||||
12,
|
||||
22,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
|
@ -237,16 +244,30 @@ ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
AveragePool,
|
||||
19, 21,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
AveragePool,
|
||||
19,
|
||||
22,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
GlobalAveragePool,
|
||||
1, 21,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
GlobalAveragePool,
|
||||
1,
|
||||
22,
|
||||
MLFloat16,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()),
|
||||
PoolFp16);
|
||||
|
|
|
|||
|
|
@ -13,9 +13,17 @@ using namespace onnxruntime::common;
|
|||
|
||||
namespace onnxruntime {
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Det,
|
||||
11,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Det<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Det,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Det<float>);
|
||||
|
||||
|
|
|
|||
|
|
@ -1337,16 +1337,30 @@ class Sin final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
Sin,
|
||||
7, 21,
|
||||
float,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Sin<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
Sin,
|
||||
7, 21,
|
||||
double,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()),
|
||||
Sin<double>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Sin,
|
||||
7,
|
||||
22,
|
||||
float,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Sin<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
Sin,
|
||||
7,
|
||||
22,
|
||||
double,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()),
|
||||
Sin<double>);
|
||||
|
|
@ -1365,9 +1379,17 @@ class Cos final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Cos,
|
||||
7,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Cos<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Cos,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Cos<float>);
|
||||
|
||||
|
|
@ -1385,9 +1407,15 @@ class Tan final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Tan,
|
||||
7, 21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Tan<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Tan,
|
||||
7,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Tan<float>);
|
||||
|
||||
|
|
@ -1405,9 +1433,17 @@ class Asin final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Asin,
|
||||
7,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Asin<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Asin,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Asin<float>);
|
||||
|
||||
|
|
@ -1425,9 +1461,17 @@ class Acos final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Acos,
|
||||
7,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Acos<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Acos,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Acos<float>);
|
||||
|
||||
|
|
@ -1445,9 +1489,17 @@ class Atan final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Atan,
|
||||
7,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Atan<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Atan,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Atan<float>);
|
||||
|
||||
|
|
@ -1465,9 +1517,17 @@ class Sinh final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Sinh,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Sinh<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Sinh,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Sinh<float>);
|
||||
|
||||
|
|
@ -1485,9 +1545,17 @@ class Cosh final : public OpKernel {
|
|||
}
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Cosh,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Cosh<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Cosh,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Cosh<float>);
|
||||
|
||||
|
|
@ -1517,9 +1585,17 @@ class Asinh final : public OpKernel {
|
|||
ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(Asinh);
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Asinh,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Asinh<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Asinh,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Asinh<float>);
|
||||
|
||||
|
|
@ -1549,9 +1625,17 @@ class Acosh final : public OpKernel {
|
|||
ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(Acosh);
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Acosh,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Acosh<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Acosh,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Acosh<float>);
|
||||
|
||||
|
|
@ -1581,9 +1665,17 @@ class Atanh final : public OpKernel {
|
|||
ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(Atanh);
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Atanh,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Atanh<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Atanh,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Atanh<float>);
|
||||
|
||||
|
|
|
|||
|
|
@ -12,9 +12,13 @@
|
|||
|
||||
namespace onnxruntime {
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 11, MLFloat16, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()), Round<MLFloat16>);
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 11, float, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), Round<float>);
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 11, double, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), Round<double>);
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(Round, 11, 21, MLFloat16, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()), Round<MLFloat16>);
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(Round, 11, 21, float, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), Round<float>);
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(Round, 11, 21, double, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), Round<double>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 22, MLFloat16, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<MLFloat16>()), Round<MLFloat16>);
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 22, float, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), Round<float>);
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(Round, 22, double, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), Round<double>);
|
||||
|
||||
template <typename T>
|
||||
Status Round<T>::Compute(OpKernelContext* ctx) const {
|
||||
|
|
|
|||
|
|
@ -23,9 +23,17 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
MaxUnpool);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
MaxUnpool,
|
||||
11, 21,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<float>())
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
MaxUnpool);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
MaxUnpool,
|
||||
11,
|
||||
22,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<float>())
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
|
|
|
|||
|
|
@ -300,9 +300,17 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Conv<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Conv,
|
||||
11,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Conv<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Conv,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Conv<float>);
|
||||
|
||||
|
|
|
|||
|
|
@ -30,9 +30,17 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
ConvTranspose<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
ConvTranspose,
|
||||
11,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
ConvTranspose<float>);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ConvTranspose,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
ConvTranspose<float>);
|
||||
|
||||
|
|
|
|||
|
|
@ -19,12 +19,10 @@ namespace onnxruntime {
|
|||
Dropout<T1, T2>);
|
||||
|
||||
#define REGISTER_KERNEL_TYPED(OpName, VER, T1, T2) \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX( \
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL( \
|
||||
OpName, \
|
||||
kOnnxDomain, \
|
||||
VER, \
|
||||
T1##_##T2, \
|
||||
kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T", DataTypeImpl::GetTensorType<T1>()) \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<T2>()) \
|
||||
|
|
@ -42,8 +40,14 @@ REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 12, 12, float, double)
|
|||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 12, 12, double, float)
|
||||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 12, 12, double, double)
|
||||
|
||||
REGISTER_KERNEL_TYPED(Dropout, 13, float, float)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 13, float, double)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 13, double, float)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 13, double, double)
|
||||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 13, 21, float, float)
|
||||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 13, 21, float, double)
|
||||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 13, 21, double, float)
|
||||
REGISTER_KERNEL_VERSIONED_TYPED(Dropout, 13, 21, double, double)
|
||||
|
||||
// Opset 22 supports BFloat16
|
||||
REGISTER_KERNEL_TYPED(Dropout, 22, float, float)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 22, float, double)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 22, double, float)
|
||||
REGISTER_KERNEL_TYPED(Dropout, 22, double, double)
|
||||
} // namespace onnxruntime
|
||||
|
|
|
|||
|
|
@ -8,9 +8,16 @@ using namespace ::onnxruntime::common;
|
|||
|
||||
namespace onnxruntime {
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
InstanceNormalization,
|
||||
6,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
InstanceNorm<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
InstanceNormalization,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
InstanceNorm<float>);
|
||||
|
||||
|
|
|
|||
|
|
@ -399,29 +399,26 @@ Status LpPoolV18<T>::Compute(OpKernelContext* context) const {
|
|||
return Status::OK();
|
||||
}
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(AveragePool, 7, 9,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, AveragePool>);
|
||||
#define REGISTER_KERNEL_VERSIONED(OpName, START_VER, END_VER, ...) \
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL( \
|
||||
OpName, \
|
||||
START_VER, \
|
||||
END_VER, \
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), __VA_ARGS__);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(AveragePool, 10, 10,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, AveragePool>);
|
||||
#define REGISTER_KERNEL(OpName, VER, ...) \
|
||||
ONNX_CPU_OPERATOR_KERNEL( \
|
||||
OpName, \
|
||||
VER, \
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()), __VA_ARGS__);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(AveragePool, 11, 18,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, AveragePool>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(AveragePool, 19,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T",
|
||||
DataTypeImpl::GetTensorType<float>()),
|
||||
AveragePoolV19<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(MaxPool, 1, 7,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, MaxPool<1 /*VERSION*/>>);
|
||||
REGISTER_KERNEL_VERSIONED(AveragePool, 7, 9, Pool<float, AveragePool>);
|
||||
REGISTER_KERNEL_VERSIONED(AveragePool, 10, 10, Pool<float, AveragePool>);
|
||||
REGISTER_KERNEL_VERSIONED(AveragePool, 11, 18, Pool<float, AveragePool>);
|
||||
REGISTER_KERNEL_VERSIONED(AveragePool, 19, 21, AveragePoolV19<float>);
|
||||
REGISTER_KERNEL(AveragePool, 22, AveragePoolV19<float>);
|
||||
|
||||
REGISTER_KERNEL_VERSIONED(MaxPool, 1, 7, Pool<float, MaxPool<1 /*VERSION*/>>);
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(MaxPool, 8, 11,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
|
|
@ -430,7 +427,14 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(MaxPool, 8, 11,
|
|||
.TypeConstraint("I", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
MaxPoolV8);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(MaxPool, 12,
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(MaxPool, 12, 21,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T",
|
||||
BuildKernelDefConstraintsFromTypeList<EnabledMaxPool12DataTypes>())
|
||||
.TypeConstraint("I", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
MaxPoolV8);
|
||||
ONNX_CPU_OPERATOR_KERNEL(MaxPool, 22,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T",
|
||||
|
|
@ -438,29 +442,17 @@ ONNX_CPU_OPERATOR_KERNEL(MaxPool, 12,
|
|||
.TypeConstraint("I", DataTypeImpl::GetTensorType<int64_t>()),
|
||||
MaxPoolV8);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(LpPool, 2, 10,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, LpPool>);
|
||||
REGISTER_KERNEL_VERSIONED(LpPool, 2, 10, Pool<float, LpPool>);
|
||||
REGISTER_KERNEL_VERSIONED(LpPool, 11, 17, Pool<float, LpPool>);
|
||||
REGISTER_KERNEL_VERSIONED(LpPool, 18, 21, LpPoolV18<float>);
|
||||
REGISTER_KERNEL(LpPool, 22, LpPoolV18<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(LpPool, 11, 17,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, LpPool>);
|
||||
REGISTER_KERNEL(GlobalLpPool, 2, Pool<float, LpPool>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(LpPool, 18,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T",
|
||||
DataTypeImpl::GetTensorType<float>()),
|
||||
LpPoolV18<float>);
|
||||
REGISTER_KERNEL_VERSIONED(GlobalAveragePool, 1, 21, Pool<float, AveragePool>);
|
||||
REGISTER_KERNEL(GlobalAveragePool, 22, Pool<float, AveragePool>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(GlobalLpPool, 2, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, LpPool>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(GlobalAveragePool, 1,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, AveragePool>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(GlobalMaxPool, 1, KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Pool<float, MaxPool<1 /*VERSION*/>>);
|
||||
REGISTER_KERNEL_VERSIONED(GlobalMaxPool, 1, 21, Pool<float, MaxPool<1 /*VERSION*/>>);
|
||||
REGISTER_KERNEL(GlobalMaxPool, 22, Pool<float, MaxPool<1 /*VERSION*/>>);
|
||||
|
||||
} // namespace onnxruntime
|
||||
|
|
|
|||
|
|
@ -152,9 +152,18 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
DeepCpuGruOp);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
GRU,
|
||||
14,
|
||||
21,
|
||||
KernelDefBuilder().TypeConstraint("T", {DataTypeImpl::GetTensorType<float>(),
|
||||
DataTypeImpl::GetTensorType<double>()})
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
DeepCpuGruOp);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
GRU,
|
||||
22,
|
||||
KernelDefBuilder().TypeConstraint("T", {DataTypeImpl::GetTensorType<float>(),
|
||||
DataTypeImpl::GetTensorType<double>()})
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
|
|
|
|||
|
|
@ -163,7 +163,14 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(LSTM, 7, 13,
|
|||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
DeepCpuLstmOp);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(LSTM, 14,
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(LSTM, 14, 21,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", {DataTypeImpl::GetTensorType<float>(),
|
||||
DataTypeImpl::GetTensorType<double>()})
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
DeepCpuLstmOp);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(LSTM, 22,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", {DataTypeImpl::GetTensorType<float>(),
|
||||
DataTypeImpl::GetTensorType<double>()})
|
||||
|
|
|
|||
|
|
@ -24,9 +24,17 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int>()),
|
||||
RNN<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
RNN,
|
||||
14, 21,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", DataTypeImpl::GetTensorType<float>())
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int>()),
|
||||
RNN<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
RNN,
|
||||
14,
|
||||
22,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", DataTypeImpl::GetTensorType<float>())
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int>()),
|
||||
|
|
|
|||
|
|
@ -18,9 +18,23 @@ ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPES_ALL_OPSETS(
|
|||
using EnabledEyeLikeDataTypes = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST_ALL_OPSETS(
|
||||
kCpuExecutionProvider, kOnnxDomain, EyeLike, Output, 0);
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
EyeLike,
|
||||
9,
|
||||
21,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T1",
|
||||
BuildKernelDefConstraintsFromTypeList<EnabledEyeLikeDataTypes>())
|
||||
.TypeConstraint(
|
||||
"T2",
|
||||
BuildKernelDefConstraintsFromTypeList<EnabledEyeLikeDataTypes>()),
|
||||
EyeLike);
|
||||
|
||||
// Opset 22 starts to support bfloat16
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
EyeLike,
|
||||
22,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint(
|
||||
"T1",
|
||||
|
|
|
|||
|
|
@ -11,23 +11,29 @@
|
|||
|
||||
namespace onnxruntime {
|
||||
|
||||
#define REGISTER_KERNEL_TYPED(T) \
|
||||
ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX(GridSample, kOnnxDomain, 16, 19, T, kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<T>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<T>()), \
|
||||
GridSample<T>);
|
||||
#define REGISTER_VERSIONED_KERNEL_TYPED(START_VER, END_VER, T) \
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(GridSample, START_VER, END_VER, T, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<T>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<T>()), \
|
||||
GridSample<T>);
|
||||
|
||||
#define REGISTER_KERNEL_TYPED_20(T) \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX(GridSample, kOnnxDomain, 20, T, kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<T>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<T>()), \
|
||||
GridSample<T>);
|
||||
#define REGISTER_KERNEL_TYPED(VER, T) \
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(GridSample, VER, T, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<T>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<T>()), \
|
||||
GridSample<T>);
|
||||
|
||||
REGISTER_KERNEL_TYPED(float)
|
||||
REGISTER_KERNEL_TYPED_20(float)
|
||||
REGISTER_KERNEL_TYPED_20(double)
|
||||
REGISTER_VERSIONED_KERNEL_TYPED(16, 19, float)
|
||||
REGISTER_VERSIONED_KERNEL_TYPED(16, 19, double)
|
||||
|
||||
REGISTER_VERSIONED_KERNEL_TYPED(20, 21, float)
|
||||
REGISTER_VERSIONED_KERNEL_TYPED(20, 21, double)
|
||||
|
||||
// Opset 22 supports BFloat16
|
||||
REGISTER_KERNEL_TYPED(22, float)
|
||||
REGISTER_KERNEL_TYPED(22, double)
|
||||
|
||||
// Restore normalized location to actual image location
|
||||
// When align_corners is true:
|
||||
|
|
|
|||
|
|
@ -222,10 +222,9 @@ TEST(FusedMatMulOpTest, FloatTypeNoTranspose) {
|
|||
}
|
||||
|
||||
#if defined(USE_CUDA) || defined(USE_ROCM) // double support only implemented in CUDA/ROCM kernel
|
||||
// CUDAExecutionProvider cannot be used with this model due to its ONNX opset not being supported by the layout transformer.
|
||||
// TEST(FusedMatMulOpTest, DoubleTypeNoTranspose) {
|
||||
// RunFusedMatMulTest<double>("FusedMatMul", 1);
|
||||
// }
|
||||
TEST(FusedMatMulOpTest, DoubleTypeNoTranspose) {
|
||||
RunFusedMatMulTest<double>("FusedMatMul", 1);
|
||||
}
|
||||
#endif
|
||||
|
||||
TEST(FusedMatMulOpTest, FloatTypeTransposeA) {
|
||||
|
|
|
|||
|
|
@ -1396,10 +1396,10 @@ std::unique_ptr<std::set<BrokenTest>> GetBrokenTests(const std::string& provider
|
|||
broken_tests->insert({"resize_upsample_sizes_nearest", "result differs"});
|
||||
broken_tests->insert({"resize_upsample_sizes_nearest_axes_2_3", "result differs"});
|
||||
broken_tests->insert({"resize_upsample_sizes_nearest_axes_3_2", "result differs"});
|
||||
broken_tests->insert({"convtranspose_group_2", "group attribute (new of opset(22)) not supported"});
|
||||
broken_tests->insert({"convtranspose_group_2_image_3", "group attribute (new of opset(22)) not supported"});
|
||||
broken_tests->insert({"resize_upsample_sizes_nearest_not_larger",
|
||||
"output=Y:expected 1 (3f800000), got 4 (40800000), diff: 3, tol=0.002 idx=24. 13 of 49 differ. CPU test passed."});
|
||||
broken_tests->insert({"convtranspose_group_2", "Segmentation fault (core dumped). CPU test passed."});
|
||||
broken_tests->insert({"convtranspose_group_2_image_3", "Segmentation fault (core dumped). CPU test passed."});
|
||||
}
|
||||
|
||||
#ifdef DISABLE_CONTRIB_OPS
|
||||
|
|
|
|||
|
|
@ -266,7 +266,7 @@ TEST(XnnpackEP, TestQDQConvS8S8_per_channel) {
|
|||
RunModelTestWithPath(ort_model_path, "xnnpack_qdq_test_graph_conv_s8s8_perchannel", graph_verify, 0.2f);
|
||||
}
|
||||
|
||||
TEST(XnnpackEP, DISABLED_TestAveragePool) { // [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for AveragePool(19) node with name 'node'
|
||||
TEST(XnnpackEP, TestAveragePool) {
|
||||
const std::vector<int64_t> input_shape = {1, 2, 3, 3};
|
||||
auto modelBuilder = [&input_shape](ModelTestBuilder& builder) {
|
||||
auto* input_arg = builder.MakeInput<float>(input_shape, -1.f, 1.f);
|
||||
|
|
@ -295,7 +295,7 @@ TEST(XnnpackEP, DISABLED_TestQDQAveragePool) { // [ONNXRuntimeError] : 9 : NOT
|
|||
});
|
||||
}
|
||||
|
||||
TEST(XnnpackEP, DISABLED_TestMaxPool) { // NOT_IMPLEMENTED : Could not find an implementation for MaxPool(22) node with name 'node'
|
||||
TEST(XnnpackEP, TestMaxPool) {
|
||||
const std::vector<int64_t> input_shape = {1, 2, 13, 13};
|
||||
auto modelBuilder = [&input_shape](ModelTestBuilder& builder) {
|
||||
auto* input_arg = builder.MakeInput<float>(input_shape, -1.f, 1.f);
|
||||
|
|
@ -395,7 +395,7 @@ TEST(XnnpackEP, TestConvTranspose) {
|
|||
RunModelTestWithPath(ort_model_path, "test_conv_follow_convtrans", nullptr);
|
||||
}
|
||||
|
||||
TEST(XnnpackEP, DISABLED_TestConvTranspose_With_Outputpadding) { // NOT_IMPLEMENTED : Could not find an implementation for ConvTranspose(22) node with name 'node'
|
||||
TEST(XnnpackEP, TestConvTranspose_With_Outputpadding) {
|
||||
const std::vector<int64_t> input_shape = {1, 4, 15, 15};
|
||||
auto modelBuilder = [&input_shape](ModelTestBuilder& builder) {
|
||||
auto* input_arg = builder.MakeInput<float>(input_shape, -127.f, 127.f);
|
||||
|
|
@ -415,7 +415,7 @@ TEST(XnnpackEP, DISABLED_TestConvTranspose_With_Outputpadding) { // NOT_IMPLEME
|
|||
});
|
||||
}
|
||||
|
||||
TEST(XnnpackEP, DISABLED_TestConvTranspose_With_OutputShape) { // NOT_IMPLEMENTED : Could not find an implementation for ConvTranspose(22) node with name 'node'
|
||||
TEST(XnnpackEP, TestConvTranspose_With_OutputShape) {
|
||||
const std::vector<int64_t> input_shape = {1, 4, 15, 15};
|
||||
auto modelBuilder = [&input_shape](ModelTestBuilder& builder) {
|
||||
auto* input_arg = builder.MakeInput<float>(input_shape, -127.f, 127.f);
|
||||
|
|
|
|||
|
|
@ -323,46 +323,7 @@
|
|||
"^test_dequantizelinear_int4",
|
||||
"^test_dequantizelinear_uint4",
|
||||
"^test_quantizelinear_int4",
|
||||
"^test_quantizelinear_uint4",
|
||||
// onnx 1.17.0 op tests: skip until implemented in ORT
|
||||
"^test_acos*", // Could not find an implementation for Acos(22)
|
||||
"^test_acosh*", // Could not find an implementation for Acosh(22)
|
||||
"^test_asin*", // Could not find an implementation for Asin(22)
|
||||
"^test_asinh*", // Could not find an implementation for Asinh(22)
|
||||
"^test_atan*", // Could not find an implementation for Atan(22)
|
||||
"^test_atanh*", // Could not find an implementation for Atanh(22)
|
||||
"^test_basic_conv_with_padding*", // Could not find an implementation for Conv(22)
|
||||
"^test_basic_conv_without_padding*", // Could not find an implementation for Conv(22)
|
||||
"^test_conv*", // Could not find an implementation for Conv(22)
|
||||
"^test_convtranspose*", // Could not find an implementation for ConvTranspose(22)
|
||||
"^test_cos*", // Could not find an implementation for Cos(22)
|
||||
"^test_cosh*", // Could not find an implementation for Cosh(22)
|
||||
"^test_det*", // Could not find an implementation for Det(22)
|
||||
"^test_dropout*", // Could not find an implementation for Dropout(22)
|
||||
"^test_elu*", // Could not find an implementation for Elu(22)
|
||||
"^test_eyelike*", // Could not find an implementation for EyeLike(22)
|
||||
"^test_globalaveragepool*", // Could not find an implementation for GlobalAveragePool(22)
|
||||
"^test_globalmaxpool*", // Could not find an implementation for GlobalMaxPool(22)
|
||||
"^test_gridsample*", // Could not find an implementation for GridSample(22)
|
||||
"^test_gru*", // Could not find an implementation for GRU(22)
|
||||
"^test_hardsigmoid*", // Could not find an implementation for HardSigmoid(22)
|
||||
"^test_hardswish*", // Could not find an implementation for HardSigmoid(22)
|
||||
"^test_instancenorm*", // Could not find an implementation for InstanceNormalization(22)
|
||||
"^test_lppool*", // Could not find an implementation for LpPool(22)
|
||||
"^test_lstm*", // Could not find an implementation for LSTM(22)
|
||||
"^test_maxpool*", // Could not find an implementation for MaxPool(22)
|
||||
"^test_maxunpool*", // Could not find an implementation for MaxUnpool(22)
|
||||
"^test_mish*", // Could not find an implementation for Softplus(22)
|
||||
"^test_rnn*", // Could not find an implementation for RNN(22)
|
||||
"^test_round*", // Could not find an implementation for Round(22)
|
||||
"^test_selu*", // Could not find an implementation for Selu(22)
|
||||
"^test_simple_rnn*", // Could not find an implementation for RNN(22)
|
||||
"^test_sin*", // Could not find an implementation for Sin(22)
|
||||
"^test_sinh*", // Could not find an implementation for Sinh(22)
|
||||
"^test_softplus*", // Could not find an implementation for Softplus(22)
|
||||
"^test_softsign*", // Could not find an implementation for Softsign(22)
|
||||
"^test_tan*", // Could not find an implementation for Tan(22)
|
||||
"^test_thresholdedrelu*" // Could not find an implementation for ThresholdedRelu(22)
|
||||
"^test_quantizelinear_uint4"
|
||||
],
|
||||
"current_failing_tests_x86": [
|
||||
"^test_vgg19",
|
||||
|
|
|
|||
Loading…
Reference in a new issue