Fix corner case where output size need to reduce by one in MaxPool #5741

liqunfu · 2023-11-08T05:38:57Z

Description

fix #5711 where output size shall not increase fully to padding part of the input region in ceil mode

Motivation and Context

bug fix

Signed-off-by: Liqun Fu <[email protected]>

onnx/defs/nn/defs.cc

In #106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. [ghstack-poisoned]

Signed-off-by: Liqun Fu <[email protected]>

onnx/reference/ops/op_max_pool.py

codecov · 2023-11-09T00:53:34Z

Codecov Report

Attention: 6 lines in your changes are missing coverage. Please review.

Comparison is base (8a6b70f) 56.45% compared to head (d5ee359) 56.44%.

Files	Patch %	Lines
onnx/backend/test/case/node/maxpool.py	0.00%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #5741      +/-   ##
==========================================
- Coverage   56.45%   56.44%   -0.01%     
==========================================
  Files         504      504              
  Lines       29872    29881       +9     
  Branches     4489     4491       +2     
==========================================
+ Hits        16863    16866       +3     
- Misses      12191    12197       +6     
  Partials      818      818

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In #106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. [ghstack-poisoned]

… inputs" In #106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. [ghstack-poisoned]

In #106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. [ghstack-poisoned]

In #106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. Pull Request resolved: #113318 Approved by: https://github.com/thiagocrepaldi

In pytorch#106270, the solution managed to solve the [`ceil_model` corner issue](onnx/onnx#5711) with the usage of `get_pool_ceil_padding`. However, padding the ceil in converter side only works when we already know the input shapes, therefore, a regression happens when users want to do dynamic inputs. This PR provides (1) refactor codes with torchlib implementation, (2) add dynamic shapes test, and (3) disable the corner tests with comments saying re-enable it when the [real fix from ONNX](onnx/onnx#5741) is merged. Pull Request resolved: pytorch#113318 Approved by: https://github.com/thiagocrepaldi

Signed-off-by: Liqun Fu <[email protected]>

justinchuby · 2024-01-04T17:29:34Z

@fatcat-z do you know this is the expected behavior for tf2onnx?

fatcat-z · 2024-01-05T02:19:11Z

@fatcat-z do you know this is the expected behavior for tf2onnx?

In TF op MaxPool, there is no parameter to determine ceil_mode. At this moment, I don't see the impact to tf2onnx.

justinchuby · 2024-01-05T16:28:48Z

Probably need to skip the test_maxpool_2d_ceil_output_size_reduce_by_one test

…into liqun/max_pool_outsize

Signed-off-by: Liqun Fu <[email protected]>

onnx/backend/test/case/node/maxpool.py

onnx/reference/ops/op_max_pool.py

Signed-off-by: Liqun Fu <[email protected]>

### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context  ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](#18377) is merged. Refer to original [ONNX PR](onnx/onnx#5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <[email protected]> Signed-off-by: Ganesan Ramalingam <[email protected]> Co-authored-by: Ganesan Ramalingam <[email protected]> Co-authored-by: George Wu <[email protected]> Co-authored-by: adrianlizarraga <[email protected]>

### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context  ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](microsoft#18377) is merged. Refer to original [ONNX PR](onnx/onnx#5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <[email protected]> Signed-off-by: Ganesan Ramalingam <[email protected]> Co-authored-by: Ganesan Ramalingam <[email protected]> Co-authored-by: George Wu <[email protected]> Co-authored-by: adrianlizarraga <[email protected]>

…nnx#5741) ### Description fix onnx#5711 where output size shall not increase fully to padding part of the input region in ceil mode ### Motivation and Context bug fix --------- Signed-off-by: Liqun Fu <[email protected]> Co-authored-by: Justin Chu <[email protected]> Co-authored-by: G. Ramalingam <[email protected]> Signed-off-by: Linsho Kaku <[email protected]>

### Description Pooling in reference currently has two bugs: (1) it has ["pads required"](https://github.com/onnx/onnx/blob/e292b4ae6d016c3231a801bfeb26f802ba95d82a/onnx/reference/ops/op_pool_common.py#L53) to make sure sliding window does not go out-of-bound, but it does not exclude pads required from pooling caculations. This causes microsoft/onnxruntime#16203 (2) In `ceil_mode`, the reference pooling pads the input image, but does not check if sliding window starts on pads. When we reach the end of the image, the window should stop working. This causes pytorch/pytorch#131272. Not directly, but related fix on MaxPooling: #5741. Detail: pytorch/pytorch#116420 (comment) ### Motivation and Context This PR fixes the two bugs and update their tests accordingly. I also drafted a PR to onnxruntime to have this update in CPU provider: microsoft/onnxruntime#16752 --------- Signed-off-by: titaiwangms <[email protected]> Signed-off-by: Justin Chu <[email protected]> Signed-off-by: Andreas Fehlner <[email protected]> Co-authored-by: Justin Chu <[email protected]> Co-authored-by: Andreas Fehlner <[email protected]>

### Description Pooling in reference currently has two bugs: (1) it has ["pads required"](https://github.com/onnx/onnx/blob/e292b4ae6d016c3231a801bfeb26f802ba95d82a/onnx/reference/ops/op_pool_common.py#L53) to make sure sliding window does not go out-of-bound, but it does not exclude pads required from pooling caculations. This causes microsoft/onnxruntime#16203 (2) In `ceil_mode`, the reference pooling pads the input image, but does not check if sliding window starts on pads. When we reach the end of the image, the window should stop working. This causes pytorch/pytorch#131272. Not directly, but related fix on MaxPooling: #5741. Detail: pytorch/pytorch#116420 (comment) ### Motivation and Context This PR fixes the two bugs and update their tests accordingly. I also drafted a PR to onnxruntime to have this update in CPU provider: microsoft/onnxruntime#16752 --------- Signed-off-by: titaiwangms <[email protected]> Signed-off-by: Justin Chu <[email protected]> Signed-off-by: Andreas Fehlner <[email protected]> Co-authored-by: Justin Chu <[email protected]> Co-authored-by: Andreas Fehlner <[email protected]> Signed-off-by: Andreas Fehlner <[email protected]>

### Description Pooling in reference currently has two bugs: (1) it has ["pads required"](https://github.com/onnx/onnx/blob/e292b4ae6d016c3231a801bfeb26f802ba95d82a/onnx/reference/ops/op_pool_common.py#L53) to make sure sliding window does not go out-of-bound, but it does not exclude pads required from pooling caculations. This causes microsoft/onnxruntime#16203 (2) In `ceil_mode`, the reference pooling pads the input image, but does not check if sliding window starts on pads. When we reach the end of the image, the window should stop working. This causes pytorch/pytorch#131272. Not directly, but related fix on MaxPooling: onnx#5741. Detail: pytorch/pytorch#116420 (comment) ### Motivation and Context This PR fixes the two bugs and update their tests accordingly. I also drafted a PR to onnxruntime to have this update in CPU provider: microsoft/onnxruntime#16752 --------- Signed-off-by: titaiwangms <[email protected]> Signed-off-by: Justin Chu <[email protected]> Signed-off-by: Andreas Fehlner <[email protected]> Co-authored-by: Justin Chu <[email protected]> Co-authored-by: Andreas Fehlner <[email protected]> Signed-off-by: seungwoo-ji <[email protected]>

### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### Motivation and Context  ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](microsoft/onnxruntime#18377) is merged. Refer to original [ONNX PR](onnx/onnx#5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <[email protected]> Signed-off-by: Ganesan Ramalingam <[email protected]> Co-authored-by: Ganesan Ramalingam <[email protected]> Co-authored-by: George Wu <[email protected]> Co-authored-by: adrianlizarraga <[email protected]>

…ode=True (#163186) # Summary - Add a note to each `nn.LPPool*d` docstring explaining how `ceil_mode=True` interacts with right padding. - Mirror the same clarification in the `torch.nn.functional.lp_pool*` docstrings so the rendered functional docs stay in sync. # Motivation The current PyTorch spec for **LPPool** does not fully match runtime behavior, which has led to downstream confusion in other specs (e.g., ONNX) and runtimes (e.g., [onnxruntime issue #25848](microsoft/onnxruntime#25848)). A corresponding clarification was also made in the ONNX spec: [onnx/onnx#5741](onnx/onnx#5741). PyTorch’s **LPPool** implementation calls into **AvgPool**, which enforces the rule that windows starting entirely in the right padded region are ignored when `ceil_mode=True`. As a result, **LPPool** inherits the same behavior. This is an edge case where the output size formula shown in the LPPool docs/spec is not sufficient on its own. Without the added caveat, the documentation is technically incorrect. This PR brings the LPPool docs in line with actual behavior. Note that this is a trivial fix to the spec as all major implementers of the spec adhere to this caveat. For comparison, both **MaxPool** and **AvgPool** already include this clarification in their spec. Their docstrings explicitly state: > *When `ceil_mode=True`, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.* Adding the same note to LPPool ensures consistency across all pooling operators. Pull Request resolved: #163186 Approved by: https://github.com/mikaylagawarecki

fix corner case where output size need to reduce by one

e0488c8

Signed-off-by: Liqun Fu <[email protected]>

liqunfu requested a review from a team as a code owner November 8, 2023 05:38

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[onnx] Fix output shape mismatch issue of max_pool pytorch/pytorch#106270

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justinchuby changed the title ~~fix corner case where output size need to reduce by one~~ Fix corner case where output size need to reduce by one Nov 8, 2023

justinchuby added the module: shape inference Issues related to shape inference label Nov 8, 2023

titaiwangms mentioned this pull request Nov 9, 2023

[ONNX] Refactor MaxPool to support dynamic inputs pytorch/pytorch#113318

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fix op_max_pool, update md

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Signed-off-by: Liqun Fu <[email protected]>

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liqunfu added 3 commits December 23, 2023 21:50

update doc

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Signed-off-by: Liqun Fu <[email protected]>

lint

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Signed-off-by: Liqun Fu <[email protected]>

Merge branch 'main' into liqun/max_pool_outsize

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justinchuby added this to the 1.16 milestone Jan 4, 2024

justinchuby added the review needed: operators approvers Require reviews from members of operators-approvers label Jan 4, 2024

Merge branch 'main' into liqun/max_pool_outsize

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justinchuby changed the title ~~Fix corner case where output size need to reduce by one~~ Fix corner case where output size need to reduce by one in MaxPool Jan 17, 2024

justinchuby mentioned this pull request Jan 17, 2024

Ambiguity in Pool spec (AveragePool and MaxPool) #1922

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justinchuby requested a review from xadupre January 17, 2024 04:18

Merge branch 'main' into liqun/max_pool_outsize

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liqunfu added 2 commits January 18, 2024 10:02

Merge branch 'liqun/max_pool_outsize' of https://github.com/onnx/onnx …

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…into liqun/max_pool_outsize

skip ort test for the new test case until Ort has the corresponding fix

e1b5dc6

Signed-off-by: Liqun Fu <[email protected]>

liqunfu requested a review from a team as a code owner January 18, 2024 19:54

bumpup lintrunner-adapter version

ef609cb

Signed-off-by: Liqun Fu <[email protected]>

justinchuby approved these changes Jan 18, 2024

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update with reviewer's comments

b328fe4

Signed-off-by: Liqun Fu <[email protected]>

justinchuby enabled auto-merge January 19, 2024 00:25

justinchuby and others added 2 commits January 18, 2024 16:26

Merge branch 'main' into liqun/max_pool_outsize

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Merge branch 'main' into liqun/max_pool_outsize

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gramalingam approved these changes Jan 29, 2024

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Merged via the queue into main with commit c5d91f6 Jan 29, 2024

justinchuby deleted the liqun/max_pool_outsize branch January 29, 2024 23:30

justinchuby mentioned this pull request Feb 8, 2024

operator MaxPool output tensor shape is ambiguously documented. #2927

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adrianlizarraga mentioned this pull request Apr 10, 2024

Integration with ONNX 1.16.0 microsoft/onnxruntime#19745

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titaiwangms mentioned this pull request Jan 23, 2025

Fix pooling pads issues #6650

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This was referenced Sep 17, 2025

Update LPPool docs to clarify ceil_mode padding semantics when ceil_mode=True pytorch/pytorch#163186

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Align LpPool ceil-mode docs and tests with AveragePool, MaxPool #7305

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Fix corner case where output size need to reduce by one in MaxPool #5741

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