mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-27 20:02:15 +00:00
Update onnx (#8458)
* updates for picking pnnx commit * add tests filter to c# tests * plus test fixes * fix versioning for contrib ops * fix tests * test filter for optional ops * more versioning related updates * fix test * fix layernorm spec * more updates * update docs * add more test filters * more filters * update binary size threshold * update docs * plus more fixes * updates per review * update to release commit * add filters for optional type tests * plus updates
This commit is contained in:
Ashwini Khade
GitHub
parent
9d88b1de78
commit
96eb9810ba
20 changed files with 180 additions and 101 deletions
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@ -305,7 +305,7 @@
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"component": {
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"type": "git",
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"git": {
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"commitHash": "d75fb0502c9d8fef817d82c15223b4aaae8e8b6e",
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"commitHash": "a57bc99daa6ddeef2ad535f8f78d1847f57216f0",
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"repositoryUrl": "https://github.com/onnx/onnx"
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},
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"comments": "git submodule at cmake/external/onnx"
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2
cmake/external/onnx
vendored
2
cmake/external/onnx
vendored
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@ -1 +1 @@
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Subproject commit d75fb0502c9d8fef817d82c15223b4aaae8e8b6e
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Subproject commit 1f63dcb7fcc3a8bf5c3c8e326867ecd6f5c43f35
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@ -837,9 +837,7 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_max_uint16", "node test error"},
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{ "test_resize_downsample_scales_linear_align_corners", "node test error"},
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{ "test_strnormalizer_nostopwords_nochangecase", "node test error"},
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{ "test_cast_STRING_to_FLOAT", "node test error"},
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{ "test_cumsum_2d_negative_axis", "node test error"},
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{ "test_cast_FLOAT16_to_DOUBLE", "node test error"},
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{ "test_adagrad_multiple", "node test error"},
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{ "test_einsum_inner_prod", "node test error"},
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{ "test_clip_default_int8_min", "node test error"},
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@ -852,7 +850,6 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_clip_default_int8_max", "node test error"},
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{ "test_einsum_sum", "node test error"},
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{ "test_min_int16", "node test error"},
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{ "test_cast_FLOAT_to_DOUBLE", "node test error"},
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{ "test_adagrad", "node test error"},
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{ "test_min_float64", "node test error"},
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{ "test_max_int16", "node test error"},
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@ -860,7 +857,6 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_sequence_insert_at_front", "node test error"},
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{ "test_cumsum_1d_exclusive", "node test error"},
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{ "test_training_dropout_default", "node test error"},
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{ "test_cast_BFLOAT16_to_FLOAT", "node test error"},
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{ "test_training_dropout", "node test error"},
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{ "test_adam", "node test error"},
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{ "test_training_dropout_mask", "node test error"},
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@ -869,7 +865,24 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_cumsum_1d", "node test error"},
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{ "test_conv_with_autopad_same", "node test error"},
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{ "test_cumsum_1d_reverse_exclusive", "node test error"},
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{ "test_cast_STRING_to_FLOAT", "node test error"},
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{ "test_cast_FLOAT16_to_DOUBLE", "node test error"},
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{ "test_cast_FLOAT_to_DOUBLE", "node test error"},
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{ "test_cast_BFLOAT16_to_FLOAT", "node test error"},
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{ "test_cast_FLOAT_to_BFLOAT16", "node test error"},
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{ "test_cast_FLOAT_to_STRING", "node test error"},
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{ "test_castlike_STRING_to_FLOAT", "node test error"},
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{ "test_castlike_STRING_to_FLOAT_expanded", "node test error"},
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{ "test_castlike_FLOAT16_to_DOUBLE", "node test error"},
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{ "test_castlike_FLOAT16_to_DOUBLE_expanded", "node test error"},
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{ "test_castlike_FLOAT_to_DOUBLE", "node test error"},
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{ "test_castlike_FLOAT_to_DOUBLE_expanded", "node test error"},
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{ "test_castlike_BFLOAT16_to_FLOAT", "node test error"},
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{ "test_castlike_BFLOAT16_to_FLOAT_expanded", "node test error"},
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{ "test_castlike_FLOAT_to_BFLOAT16", "node test error"},
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{ "test_castlike_FLOAT_to_BFLOAT16_expanded", "node test error"},
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{ "test_castlike_FLOAT_to_STRING", "node test error"},
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{ "test_castlike_FLOAT_to_STRING_expanded", "node test error"},
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{ "test_bitshift_right_uint16", "node test error"},
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{ "test_bitshift_left_uint16", "node test error"},
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{ "test_pow_types_float32_uint64", "node test error"},
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@ -891,7 +904,6 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_convtranspose_autopad_same", "node test error"},
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{ "test_training_dropout_default_mask", "node test error"},
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{ "test_min_int8", "node test error"},
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{ "test_cast_FLOAT_to_STRING", "node test error"},
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{ "test_identity_sequence", "data type not supported"},
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{ "test_gru_batchwise", "batchwise operations not supported"},
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{ "test_lstm_batchwise", "batchwise operations not supported"},
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@ -907,6 +919,24 @@ namespace Microsoft.ML.OnnxRuntime.Tests
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{ "test_bernoulli", "random generator"},
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{ "test_bernoulli_seed", "random generator"},
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{ "test_bernoulli_double", "random generator"},
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{ "test_bernoulli_expanded", "random generator"},
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{ "test_bernoulli_seed_expanded", "random generator"},
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{ "test_bernoulli_double_expanded", "random generator"},
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{ "test_shape", "opset15 version not implemented yet"},
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{ "test_shape_clip_end", "opset15 version not implemented yet"},
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{ "test_shape_clip_start", "opset15 version not implemented yet"},
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{ "test_shape_end_1", "opset15 version not implemented yet"},
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{ "test_shape_end_negative", "opset15 version not implemented yet"},
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{ "test_shape_example", "opset15 version not implemented yet"},
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{ "test_shape_start_1", "opset15 version not implemented yet"},
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{ "test_shape_start_negative_1", "opset15 version not implemented yet"},
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{ "test_shape_start_1_end_2", "opset15 version not implemented yet"},
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{ "test_shape_start_1_end_negative_1", "opset15 version not implemented yet"},
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{ "test_shape_end_negative_1", "opset15 version not implemented yet"},
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{ "test_optional_get_element", "not implemented yet"},
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{ "test_optional_get_element_sequence", "not implemented yet"},
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{ "test_optional_has_element", "not implemented yet"},
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{ "test_optional_has_element_empty", "not implemented yet"},
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};
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// The following models fails on nocontribops win CI
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@ -38,7 +38,7 @@ Do not modify directly.*
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|AveragePool|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(float)|
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|||10|**T** = tensor(float)|
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|||[7, 9]|**T** = tensor(float)|
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|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**|14+|**T** = tensor(double), tensor(float)|
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|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T1**<br> *in* B:**T1**<br> *in* input_mean:**T2**<br> *in* input_var:**T2**<br> *out* Y:**T**<br> *out* running_mean:**T2**<br> *out* running_var:**T2**|14+|**T** = tensor(double), tensor(float)|
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|||[9, 13]|**T** = tensor(double), tensor(float)|
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|||[7, 8]|**T** = tensor(double), tensor(float)|
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|BitShift|*in* X:**T**<br> *in* Y:**T**<br> *out* Z:**T**|11+|**T** = tensor(uint32), tensor(uint64), tensor(uint8)|
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@ -445,7 +445,7 @@ Do not modify directly.*
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|AveragePool|*in* X:**T**<br> *out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
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|||10|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
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|||[7, 9]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
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|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**|14+|**T** = tensor(double), tensor(float), tensor(float16)|
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|BatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* input_mean:**U**<br> *in* input_var:**U**<br> *out* Y:**T**<br> *out* running_mean:**U**<br> *out* running_var:**U**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**<br><br>or<br><br>*in* X:**T**<br> *in* scale:**T1**<br> *in* B:**T1**<br> *in* input_mean:**T2**<br> *in* input_var:**T2**<br> *out* Y:**T**<br> *out* running_mean:**T2**<br> *out* running_var:**T2**|14+|**T** = tensor(double), tensor(float), tensor(float16)|
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|||[9, 13]|**T** = tensor(double), tensor(float), tensor(float16)|
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|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
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|Cast|*in* input:**T1**<br> *out* output:**T2**|13+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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@ -20,10 +20,11 @@ See [Compatibility](../README.md#Compatibility) for a list of the supported plat
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| [Atan](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Atan) | [7+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Atan-7) |
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| [Atanh](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Atanh) | |
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| [AveragePool](https://github.com/onnx/onnx/blob/master/docs/Operators.md#AveragePool) | [7-9](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#AveragePool-7), [10](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#AveragePool-10) |
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| [BatchNormalization](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization) | [7-8](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-7), [9-13](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-9), [14+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-14) |
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| [BatchNormalization](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization) | [7-8](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-7), [9-13](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-9), [14](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-14), [15+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#BatchNormalization-15) |
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| [Bernoulli](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Bernoulli) | |
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| [BitShift](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BitShift) | |
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| [Cast](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Cast) | |
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| [CastLike](https://github.com/onnx/onnx/blob/master/docs/Operators.md#CastLike) | |
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| [Ceil](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Ceil) | [6-12](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Ceil-6), [13+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Ceil-13) |
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| [Celu](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Celu) | |
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| [Clip](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Clip) | [6-10](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Clip-6) |
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@ -99,6 +100,9 @@ See [Compatibility](../README.md#Compatibility) for a list of the supported plat
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| [NonZero](https://github.com/onnx/onnx/blob/master/docs/Operators.md#NonZero) | |
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| [Not](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Not) | [1+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Not-1) |
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| [OneHot](https://github.com/onnx/onnx/blob/master/docs/Operators.md#OneHot) | |
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| [Optional](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Optional) | |
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| [OptionalGetElement](https://github.com/onnx/onnx/blob/master/docs/Operators.md#OptionalGetElement) | |
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| [OptionalHasElement](https://github.com/onnx/onnx/blob/master/docs/Operators.md#OptionalHasElement) | |
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| [Or](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Or) | [7+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Or-7) |
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| [PRelu](https://github.com/onnx/onnx/blob/master/docs/Operators.md#PRelu) | [7-8](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#PRelu-7), [9+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#PRelu-9) |
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| [Pad](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Pad) | [2-10](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Pad-2) |
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@ -140,7 +144,7 @@ See [Compatibility](../README.md#Compatibility) for a list of the supported plat
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| [SequenceErase](https://github.com/onnx/onnx/blob/master/docs/Operators.md#SequenceErase) | |
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| [SequenceInsert](https://github.com/onnx/onnx/blob/master/docs/Operators.md#SequenceInsert) | |
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| [SequenceLength](https://github.com/onnx/onnx/blob/master/docs/Operators.md#SequenceLength) | |
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| [Shape](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Shape) | [1-12](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Shape-1), [13+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Shape-13) |
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| [Shape](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Shape) | [1-12](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Shape-1), [13-14](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Shape-13), [15+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Shape-15) |
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| [Shrink](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Shrink) | |
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| [Sigmoid](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sigmoid) | [6-12](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Sigmoid-6), [13+](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Sigmoid-13) |
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| [Sign](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sign) | |
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@ -790,7 +790,7 @@ GELU (Gaussian Error Linear Unit) approximation: Y=0.5*X*(1+tanh(0.797885*X+0.03
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onnx_opset_13.set_domain("");
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onnx_opset_13.set_version(13);
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return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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});
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ONNX_CONTRIB_OPERATOR_SCHEMA(SkipLayerNormalization)
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@ -2507,10 +2507,14 @@ Example 4:
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{{"Deviation"}, "Sub", {"XU", "Mean2D"}},
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{{"Normalized"}, "Div", {"Deviation", "StdDev"}},
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{{"NormalizedT"}, "Cast", {"Normalized"}, {{"to", T}}},
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{{"Scaled"}, "Mul", {"NormalizedT", "Scale"}},
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{{"Biased"}, "Add", {"Scaled", "B"}},
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{{"Y"}, "Reshape", {"Biased", "XShape"}},
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{{"InvStdDev2D"}, "Reciprocal", {"StdDev"}}};
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{{"Scaled"}, "Mul", {"NormalizedT", "Scale"}}};
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if (ctx.hasInput(2)) {
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body.push_back({{"Biased"}, "Add", {"Scaled", "B"}});
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} else {
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body.push_back({{"Biased"}, "Identity", {"Scaled"}});
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}
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body.push_back({{"Y"}, "Reshape", {"Biased", "XShape"}});
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body.push_back({{"InvStdDev2D"}, "Reciprocal", {"StdDev"}});
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if (ctx.hasOutput(1))
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body.push_back({{"Mean"}, "Reshape", {"Mean2D", "ReducedShape"}});
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if (ctx.hasOutput(2))
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@ -2520,7 +2524,7 @@ Example 4:
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onnx_opset_13.set_domain("");
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onnx_opset_13.set_version(13);
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return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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});
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ONNX_CONTRIB_OPERATOR_SCHEMA(SimplifiedLayerNormalization)
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@ -2613,7 +2617,7 @@ inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.)D
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onnx_opset_13.set_domain("");
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onnx_opset_13.set_version(13);
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return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
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});
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static const char* BiasGelu_ver1_doc =
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@ -94,6 +94,9 @@ class NhwcInferenceContext : public InferenceContext {
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return nullptr;
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}
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const TensorShapeProto* getSymbolicInput(size_t) const override {
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return nullptr;
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}
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private:
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InferenceContext& ctx_;
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@ -79,31 +79,4 @@ TensorProto ToTensor(double value, TensorProto_DataType elem_type) {
|
|||
|
||||
return t;
|
||||
}
|
||||
|
||||
void BuildNodes(FunctionProto& functionProto, const std::vector<FunctionBodyHelper::NodeDef>& node_defs) {
|
||||
for (size_t i = 0; i < node_defs.size(); i++) {
|
||||
const FunctionBodyHelper::NodeDef& node = node_defs[i];
|
||||
auto* np = functionProto.add_node();
|
||||
|
||||
np->set_op_type(node.op_type);
|
||||
for (const auto& inp : node.inputs) {
|
||||
np->add_input(inp);
|
||||
}
|
||||
for (const auto& o : node.outputs) {
|
||||
np->add_output(o);
|
||||
}
|
||||
for (const auto& attr : node.attributes) {
|
||||
*(np->add_attribute()) = attr.proto;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool BuildFunctionProto(FunctionProto& functionProto, const OpSchema& schema,
|
||||
const std::vector<FunctionBodyHelper::NodeDef>& node_defs,
|
||||
const std::vector<OperatorSetIdProto>& relied_opsets) {
|
||||
BuildNodes(functionProto, node_defs);
|
||||
schema.BuildFunction(functionProto, relied_opsets);
|
||||
return true;
|
||||
}
|
||||
|
||||
} // namespace ONNX_NAMESPACE
|
||||
|
|
@ -20,12 +20,4 @@ inline static FunctionBodyHelper::NodeDef Const(const std::string& name, double
|
|||
return FunctionBodyHelper::NodeDef{
|
||||
{name}, "Constant", {}, {{"value", ToTensor(value, elem_type)}}};
|
||||
}
|
||||
|
||||
// Utility function to construct a FunctionProto from an opschema (for the signature information),
|
||||
// a sequence of NodeDefs (for the function body), and the relied opsets.
|
||||
bool BuildFunctionProto(FunctionProto& functionProto,
|
||||
const OpSchema& schema,
|
||||
const std::vector<FunctionBodyHelper::NodeDef>& node_defs,
|
||||
const std::vector<OperatorSetIdProto>& relied_opsets = {});
|
||||
|
||||
} // namespace ONNX_NAMESPACE
|
||||
|
|
@ -8,6 +8,17 @@
|
|||
#include "onnx/shape_inference/implementation.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
|
||||
// Utilify function to get the imported version of domain from opset imports
|
||||
// Returns -1 if requested domain is not found in the opset_imports
|
||||
static int GetVersionForDomain(const std::string& domain, const std::unordered_map<std::string, int>& opset_imports) {
|
||||
auto it = opset_imports.find(domain);
|
||||
if (it == opset_imports.end()) {
|
||||
return -1;
|
||||
}
|
||||
return it->second;
|
||||
}
|
||||
|
||||
// Auto inferred and generate an opschema for stand-alone functions
|
||||
// TODO: revisit to see if we can eliminate typeconstraint step
|
||||
void IOTypeConstraintHelper(const ONNX_NAMESPACE::FunctionProto& onnx_func_proto_,
|
||||
|
|
@ -19,9 +30,15 @@ void IOTypeConstraintHelper(const ONNX_NAMESPACE::FunctionProto& onnx_func_proto
|
|||
std::unordered_map<std::string, std::vector<std::string>> type_constraint_map;
|
||||
std::unordered_map<std::string, ONNX_NAMESPACE::AttributeProto_AttributeType> attribute_type_map;
|
||||
auto schema_registry = ONNX_NAMESPACE::OpSchemaRegistry::Instance();
|
||||
std::unordered_map<std::string, int> opset_imports;
|
||||
for (auto& relied_opset : onnx_func_proto_.opset_import()) {
|
||||
opset_imports[relied_opset.domain()] = static_cast<int>(relied_opset.version());
|
||||
}
|
||||
for (auto& node : onnx_func_proto_.node()) {
|
||||
int domain_version = GetVersionForDomain(node.domain(), opset_imports);
|
||||
ORT_ENFORCE(domain_version != -1, "No opset registered for domain " + node.domain() + " in function opset imports.");
|
||||
const auto node_op_schema =
|
||||
schema_registry->GetSchema(node.op_type(), static_cast<int>(onnx_func_proto_.since_version()), node.domain());
|
||||
schema_registry->GetSchema(node.op_type(), domain_version, node.domain());
|
||||
for (int i = 0; i < node.input_size(); ++i) {
|
||||
auto& in_name = node.input().Get(i);
|
||||
auto iter = input_name_idx_map.find(in_name);
|
||||
|
|
@ -177,22 +194,14 @@ static std::unique_ptr<ONNX_NAMESPACE::OpSchema> CreateSchema(const Graph& graph
|
|||
return op_schema;
|
||||
}
|
||||
|
||||
// Creates domain to version map for onnx function by merging graph level opset imports with opset imports from
|
||||
// funtion proto
|
||||
static std::unordered_map<std::string, int> CreateOpsetImportsForFunction(const ONNX_NAMESPACE::FunctionProto& func_proto,
|
||||
const std::unordered_map<std::string, int>& graph_opset_imports) {
|
||||
// function inherits all graph level opset imports
|
||||
std::unordered_map<std::string, int> function_opset_imports{graph_opset_imports};
|
||||
// merge with opset imports in function proto
|
||||
// Creates domain to version map for onnx function
|
||||
static std::unordered_map<std::string, int> GetFunctionOpsetImports(const ONNX_NAMESPACE::FunctionProto& func_proto, const std::unordered_map<std::string, int>& graph_imports) {
|
||||
std::unordered_map<std::string, int> function_opset_imports{graph_imports};
|
||||
for (const auto& opset_import : func_proto.opset_import()) {
|
||||
auto opset_version = static_cast<int>(opset_import.version());
|
||||
auto result = function_opset_imports.insert({opset_import.domain(), opset_version});
|
||||
ORT_ENFORCE((result.first->second == opset_version),
|
||||
"ONNX model does not support multiple opset versions for a domain. Model imports opset version ",
|
||||
result.first->second, " for domain ", result.first->first, " and function is trying to import opset version ",
|
||||
opset_version, " for the same domain");
|
||||
// If graph imports does not contain opset_import then insert it otherwise the one in graph imports overrides.
|
||||
// If the opset imports are not compatible then this will be caught during function body inline.
|
||||
function_opset_imports.insert({opset_import.domain(), static_cast<int>(opset_import.version())});
|
||||
}
|
||||
|
||||
return function_opset_imports;
|
||||
}
|
||||
|
||||
|
|
@ -273,7 +282,7 @@ FunctionImpl::FunctionImpl(const onnxruntime::Graph& graph,
|
|||
: parent_graph_(&graph),
|
||||
body_(onnx_func_proto.name(), false, onnxruntime::ModelMetaData(),
|
||||
graph.ModelPath().ToPathString(), IOnnxRuntimeOpSchemaRegistryList(),
|
||||
CreateOpsetImportsForFunction(onnx_func_proto, graph.DomainToVersionMap()),
|
||||
onnx_func_proto.opset_import_size() != 0 ? GetFunctionOpsetImports(onnx_func_proto, graph.DomainToVersionMap()) : graph.DomainToVersionMap(),
|
||||
{}, logger),
|
||||
onnx_func_proto_(onnx_func_proto) {
|
||||
// Make a copy of the FunctionProto.
|
||||
|
|
@ -282,11 +291,17 @@ FunctionImpl::FunctionImpl(const onnxruntime::Graph& graph,
|
|||
// as we might make some modifications to the FunctionProto along the way
|
||||
|
||||
const auto* node_in_parent_graph = parent_graph_->GetNode(node_index);
|
||||
// For schema defined functions get the version from the node in parent graph.
|
||||
// For the functions which do not have schema defined (model local functions)
|
||||
// get the since version from the version in opset imports using the domain.
|
||||
auto since_version = node_in_parent_graph->SinceVersion() == -1
|
||||
? GetVersionForDomain(node_in_parent_graph->Domain(), body_.MainGraph().DomainToVersionMap())
|
||||
: node_in_parent_graph->SinceVersion();
|
||||
op_schema_ = std::make_unique<ONNX_NAMESPACE::OpSchema>();
|
||||
op_schema_->SetName(onnx_func_proto_.name());
|
||||
op_schema_->SetDomain(node_in_parent_graph->Domain());
|
||||
op_schema_->SetDoc(onnx_func_proto_.doc_string());
|
||||
op_schema_->SinceVersion(static_cast<ONNX_NAMESPACE::OperatorSetVersion>(onnx_func_proto_.since_version()));
|
||||
op_schema_->SinceVersion(static_cast<ONNX_NAMESPACE::OperatorSetVersion>(since_version));
|
||||
std::unordered_map<std::string, int> input_name_idx_map;
|
||||
std::unordered_map<std::string, int> output_name_idx_map;
|
||||
for (int i = 0; i < onnx_func_proto_.input_size(); ++i) {
|
||||
|
|
@ -328,7 +343,8 @@ FunctionImpl::FunctionImpl(const onnxruntime::Graph& graph,
|
|||
op_schema_->TypeAndShapeInferenceFunction(
|
||||
[this](ONNX_NAMESPACE::InferenceContext& ctx) {
|
||||
auto schema_registry = ONNX_NAMESPACE::OpSchemaRegistry::Instance();
|
||||
ONNX_NAMESPACE::shape_inference::InferShapeForFunctionNode(&onnx_func_proto_, body_.MainGraph().DomainToVersionMap(), schema_registry, ctx);
|
||||
ONNX_NAMESPACE::ShapeInferenceOptions options {true, 1, false};
|
||||
ONNX_NAMESPACE::shape_inference::InferShapeForFunctionNode(&onnx_func_proto_, body_.MainGraph().DomainToVersionMap(), schema_registry, ctx, options);
|
||||
});
|
||||
} else {
|
||||
op_schema_->TypeAndShapeInferenceFunction(cached_op_schema->GetTypeAndShapeInferenceFunction());
|
||||
|
|
|
|||
|
|
@ -1902,6 +1902,11 @@ class InferenceContextImpl : public ONNX_NAMESPACE::InferenceContext {
|
|||
return initializer;
|
||||
}
|
||||
|
||||
// ORT does not implement partial data propagation yet so just return nullptr.
|
||||
const TensorShapeProto* getSymbolicInput(size_t) const override {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
GraphInferencer* getGraphAttributeInferencer(const std::string& attribute_name) override {
|
||||
GraphInferencer* graph_inferencer = nullptr;
|
||||
|
||||
|
|
@ -2352,8 +2357,12 @@ Status Graph::VerifyNodeAndOpMatch(const ResolveOptions& options) {
|
|||
auto maxInclusiveVersion = DomainToVersionMap().find(domain)->second;
|
||||
node.op_ = schema_registry_->GetSchema(node.OpType(), maxInclusiveVersion, node.Domain());
|
||||
|
||||
if (node.op_ && node.op_->Deprecated()) {
|
||||
node.op_ = nullptr;
|
||||
if (node.op_) {
|
||||
node.since_version_ = node.op_->since_version();
|
||||
|
||||
if (node.op_->Deprecated()) {
|
||||
node.op_ = nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
InitFunctionBodyForNode(node);
|
||||
|
|
@ -2362,7 +2371,11 @@ Status Graph::VerifyNodeAndOpMatch(const ResolveOptions& options) {
|
|||
return Status(ONNXRUNTIME, FAIL, "Fatal error: " + node.OpType() + " is not a registered function/op");
|
||||
}
|
||||
|
||||
node.since_version_ = node.op_->since_version();
|
||||
// For ops without schema (like model local functions set the since version after constructing the schema.
|
||||
// schema construction will happen during function body initialization.
|
||||
if (node.since_version_ == -1) {
|
||||
node.since_version_ = node.op_->since_version();
|
||||
}
|
||||
}
|
||||
|
||||
ORT_RETURN_IF_ERROR(node.UpdateInputArgCount());
|
||||
|
|
@ -2429,19 +2442,17 @@ void Graph::InitFunctionBodyForNode(Node& node) {
|
|||
onnx_function_proto = *(node.op_->GetFunction());
|
||||
}
|
||||
|
||||
// Check function's opset requirements are compatible with model's opset.
|
||||
auto& graphImports = DomainToVersionMap();
|
||||
for (const auto& fn_import : onnx_function_proto.opset_import()) {
|
||||
auto it = graphImports.find(fn_import.domain());
|
||||
if ((it != graphImports.end()) && (it->second != fn_import.version()))
|
||||
return; // Incompatible. Do not use this function expansion.
|
||||
ORT_TRY {
|
||||
auto func_ptr = std::make_unique<onnxruntime::FunctionImpl>(*this, node.Index(), onnx_function_proto,
|
||||
logger_);
|
||||
function_container_.emplace_back(std::move(func_ptr));
|
||||
node.SetFunctionBody(*function_container_.back());
|
||||
}
|
||||
ORT_CATCH(const std::exception& ) {
|
||||
// Return without using this function op's expansion. No need to fail just yet.
|
||||
// If ORT has a specialized kernel for this op then execution will proceed
|
||||
return;
|
||||
}
|
||||
|
||||
auto func_ptr = std::make_unique<onnxruntime::FunctionImpl>(*this, node.Index(), onnx_function_proto,
|
||||
logger_);
|
||||
|
||||
function_container_.emplace_back(std::move(func_ptr));
|
||||
node.SetFunctionBody(*function_container_.back());
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -145,7 +145,19 @@ static bool RegisterCustomSchemas() {
|
|||
}
|
||||
}
|
||||
return nodes;
|
||||
}());
|
||||
}(),
|
||||
[]() {
|
||||
std::vector<OperatorSetIdProto> operator_sets(2);
|
||||
auto& onnx_opset = operator_sets[0];
|
||||
onnx_opset.set_domain("");
|
||||
onnx_opset.set_version(11);
|
||||
|
||||
auto& test_opset = operator_sets[1];
|
||||
test_opset.set_domain(kMSNchwcDomain);
|
||||
test_opset.set_version(1);
|
||||
|
||||
return operator_sets;
|
||||
}());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -583,11 +583,38 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
|
|||
{"momentum", "not a registered function/op", {}}, // Op not registered.
|
||||
{"momentum_multiple", "not a registered function/op", {}}, // Op not registered.
|
||||
{"nesterov_momentum", "not a registered function/op", {}}, // Op not registered.
|
||||
{"cast_FLOAT_to_BFLOAT16", "onnx generate bfloat tensor as uint16 type", {}},
|
||||
{"cast_BFLOAT16_to_FLOAT", "onnx generate bfloat tensor as uint16 type", {}},
|
||||
{"sequence_insert_at_back", "onnx currently not supporting loading segment", {}},
|
||||
{"sequence_insert_at_front", "onnx currently not supporting loading segment", {}},
|
||||
{"loop13_seq", "ORT api does not currently support creating empty sequences (needed for this test)", {}},
|
||||
{"cast_FLOAT_to_BFLOAT16", "onnx generate bfloat tensor as uint16 type", {}},
|
||||
{"cast_BFLOAT16_to_FLOAT", "onnx generate bfloat tensor as uint16 type", {}},
|
||||
{"castlike_FLOAT_to_BFLOAT16", "Depends on cast.", {}},
|
||||
{"castlike_BFLOAT16_to_FLOAT", "Depends on cast", {}},
|
||||
{"castlike_FLOAT_to_BFLOAT16_expanded", "Depends on cast.", {}},
|
||||
{"castlike_BFLOAT16_to_FLOAT_expanded", "Depends on cast", {}},
|
||||
{"castlike_FLOAT_to_STRING", "Numpy float to string has unexpected rounding for some results.", {}},
|
||||
{"castlike_FLOAT_to_STRING_expanded", "Numpy float to string has unexpected rounding for some results.", {}},
|
||||
{"bernoulli", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"bernoulli_double", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"bernoulli_double_expanded", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"bernoulli_seed", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"bernoulli_seed_expanded", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"bernoulli_expanded", "By design. Test data is for informational purpose because the generator is non deterministic."},
|
||||
{"shape", "opset15 updates not supported yet."},
|
||||
{"shape_clip_end", "opset15 updates not supported yet."},
|
||||
{"shape_clip_start", "opset15 updates not supported yet."},
|
||||
{"shape_end_1", "opset15 updates not supported yet."},
|
||||
{"shape_end_negative_1", "opset15 updates not supported yet."},
|
||||
{"shape_example", "opset15 updates not supported yet."},
|
||||
{"shape_start_1", "opset15 updates not supported yet."},
|
||||
{"shape_start_1_end_2", "opset15 updates not supported yet."},
|
||||
{"shape_start_1_end_negative_1", "opset15 updates not supported yet."},
|
||||
{"shape_start_negative_1", "opset15 updates not supported yet."},
|
||||
{"test_optional_get_element", "opset15 updates not supported yet."},
|
||||
{"test_optional_get_element_sequence", "opset15 updates not supported yet."},
|
||||
{"test_optional_has_element", "opset15 updates not supported yet."},
|
||||
{"test_optional_has_element_empty", "opset15 updates not supported yet."},
|
||||
|
||||
};
|
||||
|
||||
#ifdef DISABLE_ML_OPS
|
||||
|
|
|
|||
|
|
@ -275,7 +275,7 @@ TEST(SoftmaxOperator, InvalidAxis_opset13) {
|
|||
// In opset-13, Softmax is composed as afunction of several other ops,
|
||||
// and hence it breaks differently to the test above but the most important thing
|
||||
// is that it breaks and this is the right behavior
|
||||
"[ShapeInferenceError] axis must be in [-rank, rank-1]. input rank was 2");
|
||||
"[ShapeInferenceError]");
|
||||
}
|
||||
TEST(SoftmaxOperator, DimWithZero) {
|
||||
std::vector<float> x_vals = {};
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ inline void TestShapeInference(const std::string& op_type,
|
|||
|
||||
node->add_output("Output");
|
||||
|
||||
ONNX_NAMESPACE::shape_inference::InferShapes(model, true, schema_registry);
|
||||
ONNX_NAMESPACE::shape_inference::InferShapes(model, schema_registry);
|
||||
ONNX_NAMESPACE::checker::check_model(model);
|
||||
|
||||
auto inferredGraph = model.graph();
|
||||
|
|
|
|||
|
|
@ -66,8 +66,9 @@
|
|||
"^test_mul_uint8_cpu",
|
||||
"^test_add_uint8_cpu",
|
||||
"^test_div_uint8_cpu",
|
||||
"^test_batchnorm_*",
|
||||
"^test_bernoulli_*",
|
||||
// Following tests are for opset 15 ops and are not yet implemented in ORT
|
||||
"^test_shape_*",
|
||||
"^test_optional_*",
|
||||
//GPU failures
|
||||
"^test_batchnorm_epsilon_training_mode_cuda",
|
||||
"^test_batchnorm_example_training_mode_cuda",
|
||||
|
|
@ -311,7 +312,13 @@
|
|||
// Numpy float to string has unexpected rounding for some results given numpy default precision is meant to be 8.
|
||||
// e.g. 0.296140194 -> "0.2961402" not "0.29614019". ORT produces the latter with precision set to 8, which
|
||||
// doesn"t match the expected output that was generated with numpy.
|
||||
"^test_cast_FLOAT_to_STRING"
|
||||
"^test_cast_FLOAT_to_STRING",
|
||||
"^test_castlike_FLOAT_to_STRING",
|
||||
"^test_castlike_FLOAT_to_STRING_expanded",
|
||||
// The test cases for Bernoulli op are for informational purpose. The generator operator is
|
||||
// non-deterministic and may not produce the same values in different implementations
|
||||
// even if a seed is specified.
|
||||
"^test_bernoulli_*"
|
||||
],
|
||||
"test_with_types_disabled_due_to_binary_size_concerns": [
|
||||
"^test_bitshift_right_uint16",
|
||||
|
|
|
|||
|
|
@ -649,7 +649,7 @@ void RegisterTrainingOpSchemas() {
|
|||
onnx_opset_13.set_domain("");
|
||||
onnx_opset_13.set_version(13);
|
||||
|
||||
return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
});
|
||||
|
||||
ONNX_CONTRIB_OPERATOR_SCHEMA(LogSoftmaxGrad)
|
||||
|
|
@ -1711,7 +1711,7 @@ Example 4:
|
|||
{{"scaled_dy"}, "Div", {"dy", "scale"}},
|
||||
{{"dx"}, "Where", {"mask", "scaled_dy", "C0"}}};
|
||||
|
||||
return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
} else {
|
||||
// ratio not specified. Use a value of 0.5
|
||||
std::vector<FunctionBodyHelper::NodeDef> body{
|
||||
|
|
@ -1722,7 +1722,7 @@ Example 4:
|
|||
{{"scaled_dy"}, "Div", {"dy", "scale"}},
|
||||
{{"dx"}, "Where", {"mask", "scaled_dy", "C0"}}};
|
||||
|
||||
return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
}
|
||||
});
|
||||
|
||||
|
|
@ -2079,7 +2079,7 @@ Example 4:
|
|||
onnx_opset_13.set_domain("");
|
||||
onnx_opset_13.set_version(13);
|
||||
|
||||
return ONNX_NAMESPACE::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
return ONNX_NAMESPACE::FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {onnx_opset_13});
|
||||
});
|
||||
|
||||
ONNX_CONTRIB_OPERATOR_SCHEMA(LayerNormalizationGrad)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ mypy
|
|||
pytest
|
||||
setuptools>=41.4.0
|
||||
wheel
|
||||
git+http://github.com/onnx/onnx.git@d75fb0502c9d8fef817d82c15223b4aaae8e8b6e#egg=onnx
|
||||
git+http://github.com/onnx/onnx.git@1f63dcb7fcc3a8bf5c3c8e326867ecd6f5c43f35#egg=onnx
|
||||
protobuf
|
||||
sympy==1.1.1
|
||||
flake8
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ mypy
|
|||
pytest
|
||||
setuptools>=41.4.0
|
||||
wheel
|
||||
git+http://github.com/onnx/onnx.git@d75fb0502c9d8fef817d82c15223b4aaae8e8b6e#egg=onnx
|
||||
git+http://github.com/onnx/onnx.git@1f63dcb7fcc3a8bf5c3c8e326867ecd6f5c43f35#egg=onnx
|
||||
argparse
|
||||
sympy==1.1.1
|
||||
flake8
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ python3 /onnxruntime_src/tools/ci_build/build.py \
|
|||
--include_ops_by_config /home/onnxruntimedev/.test_data/include_no_operators.config
|
||||
|
||||
# set current size limit to BINARY_SIZE_LIMIT_IN_BYTES.
|
||||
BINARY_SIZE_LIMIT_IN_BYTES=1220000
|
||||
BINARY_SIZE_LIMIT_IN_BYTES=1235000
|
||||
echo "The current preset binary size limit is $BINARY_SIZE_LIMIT_IN_BYTES"
|
||||
python3 /onnxruntime_src/tools/ci_build/github/linux/ort_minimal/check_build_binary_size.py \
|
||||
--threshold=$BINARY_SIZE_LIMIT_IN_BYTES \
|
||||
|
|
|
|||
Loading…
Reference in a new issue