From 1932d87a269fad3f29453e3bf0b8ffdc73268fc8 Mon Sep 17 00:00:00 2001 From: yuchaojie Date: Mon, 18 Jan 2021 16:26:51 +0800 Subject: [PATCH] update some op's attr name --- config/op_info.config | 44 ++-- .../gpu/math/bias_add_gpu_kernel.h | 2 +- .../gpu/nn/bias_add_grad_gpu_kenel.h | 2 +- .../gpu/nn/conv2d_gpu_kernel.h | 2 +- .../gpu/nn/conv2d_grad_filter_gpu_kernel.h | 2 +- .../gpu/nn/conv2d_grad_input_gpu_kernel.h | 2 +- .../gpu/nn/fused_batch_norm_ex_gpu_kernel.h | 2 +- .../gpu/nn/fused_batch_norm_gpu_kernel.h | 2 +- .../nn/fused_batch_norm_grad_ex_gpu_kernel.h | 2 +- .../gpu/nn/pooling_gpu_kernel.h | 2 +- .../gpu/nn/pooling_grad_gpu_kernel.h | 2 +- .../ascend/mindir/conv2d_unify_mindir.cc | 4 +- .../gpu/batch_norm_add_relu_fusion.cc | 2 +- .../gpu/batch_norm_add_relu_grad_fusion.cc | 4 +- .../optimizer/gpu/batch_norm_relu_fusion.cc | 2 +- .../gpu/batch_norm_relu_grad_fusion.cc | 2 +- mindspore/ccsrc/pybind_api/ir/primitive_py.cc | 7 + .../runtime/device/gpu/kernel_info_setter.cc | 8 +- .../elewise_calculation_ops_declare.cc | 2 +- .../op_declare/nn_batch_norm_ops_declare.cc | 4 +- .../op_declare/nn_calculation_ops_declare.cc | 10 +- .../op_declare/nn_norm_ops_declare.cc | 4 +- .../op_declare/nn_pooling_ops_declare.cc | 8 +- mindspore/ccsrc/utils/utils.h | 2 +- .../ops/_op_impl/_custom_op/batchnorm_fold.py | 2 +- mindspore/ops/_op_impl/aicpu/topk.py | 2 +- mindspore/ops/_op_impl/tbe/avg_pool.py | 2 +- mindspore/ops/_op_impl/tbe/avg_pool_grad.py | 2 +- .../ops/_op_impl/tbe/avg_pool_grad_vm.py | 2 +- mindspore/ops/_op_impl/tbe/batchnorm.py | 2 +- mindspore/ops/_op_impl/tbe/batchnorm_grad.py | 2 +- mindspore/ops/_op_impl/tbe/bias_add.py | 2 +- mindspore/ops/_op_impl/tbe/bias_add_grad.py | 2 +- mindspore/ops/_op_impl/tbe/conv2d.py | 2 +- .../_op_impl/tbe/conv2d_backprop_filter.py | 2 +- .../ops/_op_impl/tbe/conv2d_backprop_input.py | 2 +- mindspore/ops/_op_impl/tbe/conv3d.py | 2 +- .../_op_impl/tbe/conv3d_backprop_filter.py | 2 +- .../ops/_op_impl/tbe/conv3d_backprop_input.py | 2 +- .../ops/_op_impl/tbe/conv3d_transpose.py | 2 +- .../ops/_op_impl/tbe/depthwise_conv2d.py | 2 +- .../tbe/depthwise_conv2d_backprop_filter.py | 2 +- .../tbe/depthwise_conv2d_backprop_input.py | 2 +- mindspore/ops/_op_impl/tbe/max_pool.py | 2 +- .../ops/_op_impl/tbe/max_pool_grad_grad.py | 2 +- mindspore/ops/_op_impl/tbe/smooth_l1_loss.py | 2 +- .../ops/_op_impl/tbe/smooth_l1_loss_grad.py | 2 +- mindspore/ops/operations/_grad_ops.py | 8 +- mindspore/ops/operations/array_ops.py | 8 +- mindspore/ops/operations/nn_ops.py | 3 +- tests/ut/cpp/transform/convert_test.cc | 196 +++++++++--------- 51 files changed, 196 insertions(+), 188 deletions(-) diff --git a/config/op_info.config b/config/op_info.config index cc5fce23d07..bd29086395e 100644 --- a/config/op_info.config +++ b/config/op_info.config @@ -12,7 +12,7 @@ {"op_name": "DropoutGenMask", "inputs": [{"index": 0, "name": "x1", "param_type": "required"}, {"index": 1, "name": "x2", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [{"name": "Seed0", "type": "int"}, {"name": "Seed1", "type": "int"}], "fusion_type": "OPAQUE", "dtype_format": [[["int32", "NCHW"], ["float16", "NCHW"], ["uint8", "NCHW"]]], "imply_type": "AiCPU"} {"op_name": "GetNext", "inputs": [], "outputs": [{"index": 0, "name": "y", "param_type": "dynamic"}], "attr": [{"name": "shared_name", "type": "str"}], "fusion_type": "OPAQUE", "dtype_format": [[["bool", "DefaultFormat"]], [["int8", "DefaultFormat"]], [["int16", "DefaultFormat"]], [["int32", "DefaultFormat"]], [["int64", "DefaultFormat"]], [["float16", "DefaultFormat"]], [["uint8", "DefaultFormat"]], [["uint16", "DefaultFormat"]], [["uint32", "DefaultFormat"]], [["uint64", "DefaultFormat"]], [["float32", "DefaultFormat"]]], "imply_type": "AiCPU"} {"op_name": "Print", "inputs": [{"index": 0, "name": "x", "param_type": "dynamic"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["bool", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int16", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["uint64", "DefaultFormat"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"]]], "imply_type": "AiCPU"} -{"op_name": "TopK", "inputs": [{"index": 0, "name": "intput", "param_type": "required"}, {"index": 1, "name": "k", "param_type": "required"}], "outputs": [{"index": 0, "name": "values", "param_type": "required"}, {"index": 1, "name": "indices", "param_type": "required"}], "attr": [{"name": "sorted", "type": "bool"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"], ["int32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]]], "imply_type": "AiCPU"} +{"op_name": "TopK", "inputs": [{"index": 0, "name": "input", "param_type": "required"}, {"index": 1, "name": "k", "param_type": "required"}], "outputs": [{"index": 0, "name": "values", "param_type": "required"}, {"index": 1, "name": "indices", "param_type": "required"}], "attr": [{"name": "sorted", "type": "bool"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "DefaultFormat"], ["int32", "DefaultFormat"]], [["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]]], "imply_type": "AiCPU"} {"op_name": "IsFinite", "inputs": [{"index": 0, "name": "x", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["bool", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int8", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int16", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int32", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int64", "DefaultFormat"], ["bool", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["bool", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["bool", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["bool", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["bool", "DefaultFormat"]], [["float16", "DefaultFormat"], ["bool", "DefaultFormat"]], [["float32", "DefaultFormat"], ["bool", "DefaultFormat"]], [["float64", "DefaultFormat"], ["bool", "DefaultFormat"]], [["bool", "NCHW"], ["bool", "NCHW"]], [["int8", "NCHW"], ["bool", "NCHW"]], [["int16", "NCHW"], ["bool", "NCHW"]], [["int32", "NCHW"], ["bool", "NCHW"]], [["int64", "NCHW"], ["bool", "NCHW"]], [["uint8", "NCHW"], ["bool", "NCHW"]], [["uint16", "NCHW"], ["bool", "NCHW"]], [["uint32", "NCHW"], ["bool", "NCHW"]], [["uint64", "NCHW"], ["bool", "NCHW"]], [["float16", "NCHW"], ["bool", "NCHW"]], [["float32", "NCHW"], ["bool", "NCHW"]], [["float64", "NCHW"], ["bool", "NCHW"]]], "imply_type": "AiCPU"} {"op_name": "Reshape", "inputs": [{"index": 0, "name": "x", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["bool", "DefaultFormat"], ["bool", "DefaultFormat"]], [["int8", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int16", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["uint64", "DefaultFormat"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float64", "DefaultFormat"], ["float64", "DefaultFormat"]], [["bool", "NCHW"], ["bool", "NCHW"]], [["int8", "NCHW"], ["int8", "NCHW"]], [["int16", "NCHW"], ["int16", "NCHW"]], [["int32", "NCHW"], ["int32", "NCHW"]], [["int64", "NCHW"], ["int64", "NCHW"]], [["uint8", "NCHW"], ["uint8", "NCHW"]], [["uint16", "NCHW"], ["uint16", "NCHW"]], [["uint32", "NCHW"], ["uint32", "NCHW"]], [["uint64", "NCHW"], ["uint64", "NCHW"]], [["float16", "NCHW"], ["float16", "NCHW"]], [["float32", "NCHW"], ["float32", "NCHW"]], [["float64", "NCHW"], ["float64", "NCHW"]]], "imply_type": "AiCPU"} {"op_name": "Flatten", "inputs": [{"index": 0, "name": "x", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int16", "DefaultFormat"], ["int16", "DefaultFormat"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint16", "DefaultFormat"], ["uint16", "DefaultFormat"]], [["uint32", "DefaultFormat"], ["uint32", "DefaultFormat"]], [["uint64", "DefaultFormat"], ["uint64", "DefaultFormat"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["int8", "NCHW"], ["int8", "NCHW"]], [["int16", "NCHW"], ["int16", "NCHW"]], [["int32", "NCHW"], ["int32", "NCHW"]], [["int64", "NCHW"], ["int64", "NCHW"]], [["uint8", "NCHW"], ["uint8", "NCHW"]], [["uint16", "NCHW"], ["uint16", "NCHW"]], [["uint32", "NCHW"], ["uint32", "NCHW"]], [["uint64", "NCHW"], ["uint64", "NCHW"]], [["float16", "NCHW"], ["float16", "NCHW"]], [["float32", "NCHW"], ["float32", "NCHW"]]], "imply_type": "AiCPU"} @@ -136,22 +136,22 @@ {"op_name": "AssignAdd", "inputs": [{"index": 0, "name": "ref", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "value", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "ref", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["int8", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "NC1HWC0"], ["int8", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "C1HWNCoC0"], ["int8", "C1HWNCoC0"], ["int8", "C1HWNCoC0"]], [["int8", "FracZ"], ["int8", "FracZ"], ["int8", "FracZ"]], 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false, "param_type": "optional", "shape": "all"}, {"index": 3, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "offset_x", "param_type": "optional", "type": "int", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d.so", "compute_cost": 10, "kernel_name": "conv3d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""} +{"op_name": "Conv3DBackpropInput", "inputs": [{"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "FRACTAL_Z_3D"], ["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_input_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_input_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""} +{"op_name": "Conv3DBackpropFilter", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "out_backprop", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "filter_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "NDC1HWC0"], ["float32", "FRACTAL_Z_3D"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_backprop_filter_d.so", "compute_cost": 10, "kernel_name": "conv3d_backprop_filter_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""} +{"op_name": "Conv3DTranspose", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 0, "name": "filter", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 0, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 1, "name": "offset_w", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": true, "param_type": "required", "shape": "all"}], "attr": [{"name": "input_size", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "strides", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "pads", "param_type": "required", "type": "listInt", "value": "all"}, {"name": "dilations", "param_type": "optional", "type": "listInt", "value": "all"}, {"name": "groups", "param_type": "optional", "type": "int", "value": "all"}, {"name": "format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "output_padding", "param_type": "optional", "type": "listInt", "value": "all"}], "fusion_type": "CONVLUTION", "dtype_format": [[["float16", "NDC1HWC0"], ["float16", "FRACTAL_Z_3D"], ["float16", "DefaultFormat"], ["int8", "DefaultFormat"], ["float16", "NDC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "conv3d_transpose_d.so", "compute_cost": 10, "kernel_name": "conv3d_transpose_d", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""} diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h index 00d7be70378..aff53bf9643 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h @@ -83,7 +83,7 @@ class BiasAddGpuKernel : public GpuKernel { MS_LOG(EXCEPTION) << "input dims must be at least 2, but got " << num_dims; } - std::string format = GetAttr(kernel_node, "data_format"); + std::string format = GetAttr(kernel_node, "format"); string::size_type pos = format.find("C"); if (pos == std::string::npos || pos >= num_dims) { MS_LOG(EXCEPTION) << "format '" << format << "' invalid"; diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h index 935ade363b4..a5097e55449 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h @@ -78,7 +78,7 @@ class BiasAddGradGpuKernel : public GpuKernel { MS_LOG(EXCEPTION) << "input dims must be at least 2, but got " << num_dims; } - std::string format = GetAttr(kernel_node, "data_format"); + std::string format = GetAttr(kernel_node, "format"); string::size_type pos = format.find("C"); if (pos == std::string::npos || pos >= num_dims) { MS_LOG(EXCEPTION) << "format '" << format << "' invalid"; diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h index 9d5aa0f412e..668ee801f5a 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h @@ -86,7 +86,7 @@ class Conv2dGpuFwdKernel : public GpuKernel { } cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { data_format_ = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h index 8423b0a0b7f..24c3f4a75b3 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h @@ -119,7 +119,7 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel { return true; } data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0); - format_attr_ = GetAttr(kernel_node, "data_format"); + format_attr_ = GetAttr(kernel_node, "format"); if (format_attr_ == kOpFormat_NHWC) { data_format_ = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h index 6057fb9e0ab..56a08d0c3b4 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h @@ -109,7 +109,7 @@ class ConvGradInputGpuBkwKernel : public GpuKernel { } cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { data_format_ = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h index 07f7eb7a143..ddba8b1c767 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h @@ -133,7 +133,7 @@ class FusedBatchNormExGpuKernel : public GpuKernel { return true; } auto format = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { format = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h index 8fb9a931397..91b78a86993 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h @@ -104,7 +104,7 @@ class FusedBatchNormGpuKernel : public GpuKernel { } cudnnTensorFormat_t cudnn_format = CUDNN_TENSOR_NCHW; auto format = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { format = kOpFormat_NHWC; cudnn_format = CUDNN_TENSOR_NHWC; diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h index de06ba7d96f..9c77c050025 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h @@ -143,7 +143,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel { return true; } std::string format = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { format = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h index a4659227333..9e5060f9518 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h @@ -80,7 +80,7 @@ class PoolingGpuFwdKernel : public GpuKernel { } cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0))); data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0); - auto format_attr = GetAttr(kernel_node, "data_format"); + auto format_attr = GetAttr(kernel_node, "format"); if (format_attr == kOpFormat_NHWC) { data_format_ = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h index b22119282d8..d92cac8f44e 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h +++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h @@ -85,7 +85,7 @@ class PoolingGradGpuKernel : public GpuKernel { auto dout_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 2); auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0); auto data_format = AnfAlgo::GetInputFormat(kernel_node, 0); - format_attr_ = GetAttr(kernel_node, "data_format"); + format_attr_ = GetAttr(kernel_node, "format"); if (format_attr_ == kOpFormat_NHWC) { data_format = kOpFormat_NHWC; } diff --git a/mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc b/mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc index 335783c6e28..4c9047b387e 100644 --- a/mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc +++ b/mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc @@ -45,7 +45,7 @@ bool NeedUpdate(const CNodePtr &conv2d, std::vector in_shape, std::vecto if (group == 1) { return false; } - auto data_format = AnfAlgo::GetNodeAttr(conv2d, kAttrDataFormat); + auto data_format = AnfAlgo::GetNodeAttr(conv2d, kAttrFormat); if (data_format != "NCHW") { MS_LOG(EXCEPTION) << "Conv2D only supports NCHW when group > 1, but got " << data_format; } @@ -199,7 +199,7 @@ CNodePtr CreateDepthwiseConv2DBackpropFilter(const FuncGraphPtr &graph, const CN void SetCommonAttrs(const CNodePtr &conv2d, const CNodePtr &depth_conv) { AnfAlgo::CopyNodeAttr(kAttrKernelSize, conv2d, depth_conv); AnfAlgo::CopyNodeAttr(kAttrDilation, conv2d, depth_conv); - AnfAlgo::CopyNodeAttr(kAttrDataFormat, conv2d, depth_conv); + AnfAlgo::CopyNodeAttr(kAttrFormat, conv2d, depth_conv); AnfAlgo::CopyNodeAttr(kAttrPadList, kAttrPads, conv2d, depth_conv); AnfAlgo::CopyNodeAttr(kAttrPadMode, conv2d, depth_conv); AnfAlgo::CopyNodeAttr(kAttrPad, conv2d, depth_conv); diff --git a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc index 0e231576cc4..2cd97161128 100644 --- a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc +++ b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc @@ -46,7 +46,7 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons MS_EXCEPTION_IF_NULL(tuple_get_item); auto batch_norm_ex = AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); MS_EXCEPTION_IF_NULL(batch_norm_ex); - auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("data_format"); + auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") { diff --git a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc index 3745b280062..ebde54d9360 100644 --- a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc +++ b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc @@ -97,7 +97,7 @@ void ReplaceOutput(const FuncGraphPtr &graph, const AnfNodePtr &bn_grad, const A return; } - // Replace orignal output + // Replace original output auto manager = graph->manager(); MS_EXCEPTION_IF_NULL(manager); sort(bn_outputs.begin(), bn_outputs.end(), CompareTupleGetitem); @@ -114,7 +114,7 @@ void ReplaceOutput(const FuncGraphPtr &graph, const AnfNodePtr &bn_grad, const A bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("data_format"); + auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(node, 0) != kOpFormat_NHWC && format != "NHWC") { diff --git a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc index 92faf0f325a..501f2e6f0f5 100644 --- a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc +++ b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc @@ -43,7 +43,7 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A MS_EXCEPTION_IF_NULL(tuple_get_item); auto batch_norm_ex = AnfAlgo::GetInputNode(utils::cast(tuple_get_item), 0); MS_EXCEPTION_IF_NULL(batch_norm_ex); - auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("data_format"); + auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") { diff --git a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc index b179b9c6fba..b75c27ea913 100644 --- a/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc +++ b/mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc @@ -39,7 +39,7 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con const EquivPtr &equiv) const { MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(node); - auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("data_format"); + auto format_attr = AnfAlgo::GetCNodePrimitive(node)->GetAttr("format"); MS_EXCEPTION_IF_NULL(format_attr); auto format = GetValue(format_attr); auto ms_context = MsContext::GetInstance(); diff --git a/mindspore/ccsrc/pybind_api/ir/primitive_py.cc b/mindspore/ccsrc/pybind_api/ir/primitive_py.cc index c35ce5ae696..bc12da790bc 100644 --- a/mindspore/ccsrc/pybind_api/ir/primitive_py.cc +++ b/mindspore/ccsrc/pybind_api/ir/primitive_py.cc @@ -17,6 +17,7 @@ #include "pybind_api/ir/primitive_py.h" #include +#include #include "ir/signature.h" #include "pipeline/jit/parse/data_converter.h" #include "pipeline/jit/parse/python_adapter.h" @@ -36,6 +37,9 @@ namespace { constexpr auto kBpropAttrName = "bprop"; constexpr auto kCellHookAttrName = "cell_hook"; constexpr auto kCellIDAttrName = "cell_id"; +std::map kOpAttrNameReplaceMap = { + {"data_format", "format"}, +}; void SyncData(const py::object &arg) { if (py::isinstance(arg)) { @@ -273,6 +277,9 @@ void PrimitivePy::AddPyAttr(const py::str &name, const py::object &obj) { if (!converted) { MS_LOG(EXCEPTION) << "Attribute convert error with type: " << std::string(py::str(obj)); } + if (kOpAttrNameReplaceMap.find(attr_name) != kOpAttrNameReplaceMap.end()) { + attr_name = kOpAttrNameReplaceMap[attr_name]; + } (void)this->AddAttr(attr_name, converted_ret); } diff --git a/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc b/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc index 5307cb44bd6..21a08868bc8 100644 --- a/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc +++ b/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc @@ -247,8 +247,8 @@ void UpdateKernelFormatInfo(const CNodePtr &kernel_node, const std::vectorHasAttr("data_format")) { - *origin_data_format = AnfAlgo::GetNodeAttr(kernel_node, "data_format"); + if (prim->HasAttr("format")) { + *origin_data_format = AnfAlgo::GetNodeAttr(kernel_node, "format"); } } @@ -342,8 +342,8 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptrGetAttr("data_format") != nullptr && - GetValue(value->GetAttr("data_format")) == kOpFormat_NHWC) { + if (value != nullptr && value->GetAttr("format") != nullptr && + GetValue(value->GetAttr("format")) == kOpFormat_NHWC) { format_transform_ = false; return; } diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc index 5da114d8bba..b015c944bce 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc @@ -193,7 +193,7 @@ REG_ADPT_DESC(Exp, kNameExp, ADPT_DESC(Exp)) // BiasAdd INPUT_MAP(BiasAdd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(bias)}}; -ATTR_MAP(BiasAdd) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}}; +ATTR_MAP(BiasAdd) = {{"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(BiasAdd) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(BiasAdd, kNameBiasAdd, ADPT_DESC(BiasAdd)) diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_batch_norm_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_batch_norm_ops_declare.cc index 72f935cb96d..8c3856bcb12 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_batch_norm_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_batch_norm_ops_declare.cc @@ -24,7 +24,7 @@ INPUT_MAP(BatchNorm) = {{1, INPUT_DESC(x)}, {3, INPUT_DESC(offset)}, {4, INPUT_DESC(mean)}, {5, INPUT_DESC(variance)}}; -ATTR_MAP(BatchNorm) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}, +ATTR_MAP(BatchNorm) = {{"format", ATTR_DESC(data_format, AnyTraits())}, {"epsilon", ATTR_DESC(epsilon, AnyTraits())}, {"is_training", ATTR_DESC(is_training, AnyTraits())}}; OUTPUT_MAP(BatchNorm) = {{0, OUTPUT_DESC(y)}, @@ -40,7 +40,7 @@ INPUT_MAP(BatchNormGrad) = {{1, INPUT_DESC(y_backprop)}, {3, INPUT_DESC(scale)}, {4, INPUT_DESC(reserve_space_1)}, {5, INPUT_DESC(reserve_space_2)}}; -ATTR_MAP(BatchNormGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}, +ATTR_MAP(BatchNormGrad) = {{"format", ATTR_DESC(data_format, AnyTraits())}, {"epsilon", ATTR_DESC(epsilon, AnyTraits())}, {"is_training", ATTR_DESC(is_training, AnyTraits())}}; OUTPUT_MAP(BatchNormGrad) = {{0, OUTPUT_DESC(x_backprop)}, diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_calculation_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_calculation_ops_declare.cc index 42b2e99d0cb..32755a6408b 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_calculation_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_calculation_ops_declare.cc @@ -20,7 +20,7 @@ namespace mindspore::transform { // BiasAddGrad INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}}; -ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits())}}; +ATTR_MAP(BiasAddGrad) = {{"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(BiasAddGrad) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(BiasAddGrad, prim::kPrimBiasAddGrad->name(), ADPT_DESC(BiasAddGrad)) @@ -30,7 +30,7 @@ ATTR_MAP(Conv2D) = { {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"format", ATTR_DESC(data_format, AnyTraits())}, {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}}; @@ -44,7 +44,7 @@ ATTR_MAP(Conv2DBackpropInputD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"format", ATTR_DESC(data_format, AnyTraits())}, {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}}; @@ -58,7 +58,7 @@ ATTR_MAP(Conv2DBackpropFilterD) = { {"pad_list", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"stride", ATTR_DESC(strides, "pad", AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"format", ATTR_DESC(data_format, AnyTraits())}, {"group", ATTR_DESC(groups, AnyTraits())}, }; OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}}; @@ -70,7 +70,7 @@ ATTR_MAP(DepthwiseConv2D) = { {"stride", ATTR_DESC(strides, AnyTraits>(), AnyTraits>())}, {"pads", ATTR_DESC(pads, AnyTraits>(), AnyTraits>())}, {"dilation", ATTR_DESC(dilations, AnyTraits>(), AnyTraits>())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}, + {"format", ATTR_DESC(data_format, AnyTraits())}, }; OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(DepthwiseConv2D, prim::kPrimDepthwiseConv2dNative->name(), ADPT_DESC(DepthwiseConv2D)) diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_norm_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_norm_ops_declare.cc index fc3ddd4d34a..b25a594f1ac 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_norm_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_norm_ops_declare.cc @@ -41,13 +41,13 @@ REG_ADPT_DESC(SoftmaxCrossEntropyWithLogits, prim::kPrimSoftmaxCrossEntropyWithL // SmoothL1Loss INPUT_MAP(SmoothL1Loss) = {{1, INPUT_DESC(predict)}, {2, INPUT_DESC(label)}}; -ATTR_MAP(SmoothL1Loss) = {{"sigma", ATTR_DESC(sigma, AnyTraits())}}; +ATTR_MAP(SmoothL1Loss) = {{"beta", ATTR_DESC(sigma, AnyTraits())}}; OUTPUT_MAP(SmoothL1Loss) = {{0, OUTPUT_DESC(loss)}}; REG_ADPT_DESC(SmoothL1Loss, kNameSmoothL1Loss, ADPT_DESC(SmoothL1Loss)) // SmoothL1LossGrad INPUT_MAP(SmoothL1LossGrad) = {{1, INPUT_DESC(predict)}, {2, INPUT_DESC(label)}, {3, INPUT_DESC(dout)}}; -ATTR_MAP(SmoothL1LossGrad) = {{"sigma", ATTR_DESC(sigma, AnyTraits())}}; +ATTR_MAP(SmoothL1LossGrad) = {{"beta", ATTR_DESC(sigma, AnyTraits())}}; OUTPUT_MAP(SmoothL1LossGrad) = {{0, OUTPUT_DESC(gradient)}}; REG_ADPT_DESC(SmoothL1LossGrad, kNameSmoothL1LossGrad, ADPT_DESC(SmoothL1LossGrad)) diff --git a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.cc b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.cc index 57e7ff5f119..d35fa6e01c3 100644 --- a/mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.cc +++ b/mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.cc @@ -23,7 +23,7 @@ INPUT_MAP(MaxPool) = {{1, INPUT_DESC(x)}}; ATTR_MAP(MaxPool) = {{"kernel_size", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"pad_mode", ATTR_DESC(padding, AnyTraits())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}}; + {"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(MaxPool) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(MaxPool, kNameMaxPool, ADPT_DESC(MaxPool)) @@ -32,7 +32,7 @@ INPUT_MAP(AvgPool) = {{1, INPUT_DESC(x)}}; ATTR_MAP(AvgPool) = {{"kernel_size", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"pad_mode", ATTR_DESC(padding, AnyTraits())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}}; + {"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(AvgPool) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(AvgPool, kNameAvgPool, ADPT_DESC(AvgPool)) @@ -41,7 +41,7 @@ INPUT_MAP(MaxPoolGrad) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}, {3, INPUT_DE ATTR_MAP(MaxPoolGrad) = {{"kernel_size", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"pad_mode", ATTR_DESC(padding, AnyTraits())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}}; + {"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(MaxPoolGrad) = {{0, OUTPUT_DESC(y)}}; REG_ADPT_DESC(MaxPoolGrad, kNameMaxPoolGrad, ADPT_DESC(MaxPoolGrad)) @@ -50,7 +50,7 @@ INPUT_MAP(AvgPoolGrad) = {{1, INPUT_DESC(orig_input_shape)}, {2, INPUT_DESC(inpu ATTR_MAP(AvgPoolGrad) = {{"kernel_size", ATTR_DESC(ksize, AnyTraits(), AnyTraits>())}, {"strides", ATTR_DESC(strides, AnyTraits(), AnyTraits>())}, {"pad_mode", ATTR_DESC(padding, AnyTraits())}, - {"data_format", ATTR_DESC(data_format, AnyTraits())}}; + {"format", ATTR_DESC(data_format, AnyTraits())}}; OUTPUT_MAP(AvgPoolGrad) = {{0, OUTPUT_DESC(out_grad)}}; REG_ADPT_DESC(AvgPoolGrad, kNameAvgPoolGrad, ADPT_DESC(AvgPoolGrad)) diff --git a/mindspore/ccsrc/utils/utils.h b/mindspore/ccsrc/utils/utils.h index 430e6271106..dad954657d3 100644 --- a/mindspore/ccsrc/utils/utils.h +++ b/mindspore/ccsrc/utils/utils.h @@ -292,7 +292,7 @@ constexpr auto kAttrEpsilon = "epsilon"; constexpr auto kAttrFactor = "factor"; constexpr auto kAttrIsRef = "isRef"; constexpr auto kAttrDataShape = "data_shape"; -constexpr auto kAttrDataFormat = "data_format"; +constexpr auto kAttrFormat = "format"; constexpr auto kAttrAxis = "axis"; constexpr auto kAttrKeepDims = "keep_dims"; constexpr auto kAttrShapeGamma = "shape_gamma"; diff --git a/mindspore/ops/_op_impl/_custom_op/batchnorm_fold.py b/mindspore/ops/_op_impl/_custom_op/batchnorm_fold.py index 11434223d35..dcf86745a8b 100644 --- a/mindspore/ops/_op_impl/_custom_op/batchnorm_fold.py +++ b/mindspore/ops/_op_impl/_custom_op/batchnorm_fold.py @@ -32,7 +32,7 @@ batch_norm_op_info = TBERegOp("BatchNormFoldD") \ .attr("epsilon", "optional", "float", "all") \ .attr("is_training", "optional", "bool", "all") \ .attr("freeze_bn", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "x_sum", False, "required", "all") \ .input(2, "x_square_sum", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/aicpu/topk.py b/mindspore/ops/_op_impl/aicpu/topk.py index 80cf1c5203f..80366bc4001 100644 --- a/mindspore/ops/_op_impl/aicpu/topk.py +++ b/mindspore/ops/_op_impl/aicpu/topk.py @@ -19,7 +19,7 @@ from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataTyp top_k_op_info = AiCPURegOp("TopK") \ .fusion_type("OPAQUE") \ .attr("sorted", "bool")\ - .input(0, "intput", "required") \ + .input(0, "input", "required") \ .input(1, "k", "required") \ .output(0, "values", "required") \ .output(1, "indices", "required") \ diff --git a/mindspore/ops/_op_impl/tbe/avg_pool.py b/mindspore/ops/_op_impl/tbe/avg_pool.py index b2816f1e865..ef3b2e87f06 100644 --- a/mindspore/ops/_op_impl/tbe/avg_pool.py +++ b/mindspore/ops/_op_impl/tbe/avg_pool.py @@ -26,7 +26,7 @@ avg_pool_op_info = TBERegOp("AvgPool") \ .attr("kernel_size", "required", "listInt", "all") \ .attr("strides", "required", "listInt", "all") \ .attr("pad_mode", "required", "str", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "filter", False, "optional", "all") \ .input(2, "bias", False, "optional", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/avg_pool_grad.py b/mindspore/ops/_op_impl/tbe/avg_pool_grad.py index cea48b470d1..89183a4766b 100644 --- a/mindspore/ops/_op_impl/tbe/avg_pool_grad.py +++ b/mindspore/ops/_op_impl/tbe/avg_pool_grad.py @@ -27,7 +27,7 @@ avg_pool_grad_op_info = TBERegOp("AvgPoolGrad") \ .attr("kernel_size", "required", "listInt", "all") \ .attr("strides", "required", "listInt", "all") \ .attr("pad_mode", "required", "str", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "input_grad", False, "required", "all") \ .input(1, "mean_matrix", False, "optional", "all") \ .input(2, "kernel_matrix", False, "optional", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/avg_pool_grad_vm.py b/mindspore/ops/_op_impl/tbe/avg_pool_grad_vm.py index 1a64210e288..9dcf8a9a7e2 100644 --- a/mindspore/ops/_op_impl/tbe/avg_pool_grad_vm.py +++ b/mindspore/ops/_op_impl/tbe/avg_pool_grad_vm.py @@ -27,7 +27,7 @@ avg_pool_grad_vm_op_info = TBERegOp("AvgPoolGradVm") \ .attr("kernel_size", "required", "listInt", "all") \ .attr("strides", "required", "listInt", "all") \ .attr("pad_mode", "required", "str", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "input_grad", False, "required", "all") \ .input(1, "mean_matrix", False, "optional", "all") \ .input(2, "kernel_matrix", False, "optional", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/batchnorm.py b/mindspore/ops/_op_impl/tbe/batchnorm.py index ddb24ac3e7d..ecbf979f3be 100644 --- a/mindspore/ops/_op_impl/tbe/batchnorm.py +++ b/mindspore/ops/_op_impl/tbe/batchnorm.py @@ -24,7 +24,7 @@ batch_norm_op_info = TBERegOp("BatchNorm") \ .kernel_name("batch_norm") \ .partial_flag(True) \ .attr("epsilon", "optional", "float", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .attr("is_training", "optional", "bool", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "scale", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/batchnorm_grad.py b/mindspore/ops/_op_impl/tbe/batchnorm_grad.py index 286fb273010..bad8e050090 100644 --- a/mindspore/ops/_op_impl/tbe/batchnorm_grad.py +++ b/mindspore/ops/_op_impl/tbe/batchnorm_grad.py @@ -24,7 +24,7 @@ batch_norm_grad_op_info = TBERegOp("BatchNormGrad") \ .kernel_name("batch_norm_grad") \ .partial_flag(True) \ .attr("epsilon", "optional", "float", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .attr("is_training", "optional", "bool", "all") \ .input(0, "y_backprop", False, "required", "all") \ .input(1, "x", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/bias_add.py b/mindspore/ops/_op_impl/tbe/bias_add.py index ef17fa6f881..35d3774f84b 100644 --- a/mindspore/ops/_op_impl/tbe/bias_add.py +++ b/mindspore/ops/_op_impl/tbe/bias_add.py @@ -23,7 +23,7 @@ bias_add_grad_op_info = TBERegOp("BiasAdd") \ .compute_cost(10) \ .kernel_name("bias_add") \ .partial_flag(True) \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "bias", False, "required", "all") \ .output(0, "y", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/bias_add_grad.py b/mindspore/ops/_op_impl/tbe/bias_add_grad.py index 2205bc6dcb8..482ed706ff1 100644 --- a/mindspore/ops/_op_impl/tbe/bias_add_grad.py +++ b/mindspore/ops/_op_impl/tbe/bias_add_grad.py @@ -23,7 +23,7 @@ bias_add_grad_op_info = TBERegOp("BiasAddGrad") \ .compute_cost(10) \ .kernel_name("bias_add_grad") \ .partial_flag(True) \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .input(0, "output_backprop", False, "required", "all") \ .output(0, "output", False, "required", "all") \ .dtype_format(DataType.F16_Default, DataType.F16_Default) \ diff --git a/mindspore/ops/_op_impl/tbe/conv2d.py b/mindspore/ops/_op_impl/tbe/conv2d.py index 1773b5f1106..f262eb9b9d3 100644 --- a/mindspore/ops/_op_impl/tbe/conv2d.py +++ b/mindspore/ops/_op_impl/tbe/conv2d.py @@ -28,7 +28,7 @@ conv2d_op_info = TBERegOp("Conv2D") \ .attr("pad_list", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "filter", False, "required", "all") \ .input(2, "bias", False, "optional", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py b/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py index c309a4f2abd..0569bbfe05e 100644 --- a/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py +++ b/mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py @@ -28,7 +28,7 @@ conv2d_backprop_filter_op_info = TBERegOp("Conv2DBackpropFilter") \ .attr("pad_list", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "out_backprop", False, "required", "all") \ .input(1, "x", False, "required", "all") \ .output(0, "y", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py b/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py index 76b5715d8e6..3adaf9f2943 100644 --- a/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py +++ b/mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py @@ -28,7 +28,7 @@ conv2d_backprop_input_op_info = TBERegOp("Conv2DBackpropInput") \ .attr("pad_list", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "out_backprop", False, "required", "all") \ .input(1, "filter", False, "required", "all") \ .output(0, "y", True, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv3d.py b/mindspore/ops/_op_impl/tbe/conv3d.py index b7f2a0ec872..8f906702d67 100644 --- a/mindspore/ops/_op_impl/tbe/conv3d.py +++ b/mindspore/ops/_op_impl/tbe/conv3d.py @@ -27,7 +27,7 @@ conv3d_op_info = TBERegOp("Conv3D") \ .attr("pads", "required", "listInt", "all") \ .attr("dilations", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .attr("offset_x", "optional", "int", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "filter", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv3d_backprop_filter.py b/mindspore/ops/_op_impl/tbe/conv3d_backprop_filter.py index fd0b2677404..415f20d3abc 100644 --- a/mindspore/ops/_op_impl/tbe/conv3d_backprop_filter.py +++ b/mindspore/ops/_op_impl/tbe/conv3d_backprop_filter.py @@ -28,7 +28,7 @@ conv3d_backprop_filter_op_info = TBERegOp("Conv3DBackpropFilter") \ .attr("pads", "required", "listInt", "all") \ .attr("dilations", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "out_backprop", False, "required", "all") \ .output(0, "y", True, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv3d_backprop_input.py b/mindspore/ops/_op_impl/tbe/conv3d_backprop_input.py index b5957c54f8c..9b684db4797 100644 --- a/mindspore/ops/_op_impl/tbe/conv3d_backprop_input.py +++ b/mindspore/ops/_op_impl/tbe/conv3d_backprop_input.py @@ -28,7 +28,7 @@ conv3d_backprop_input_op_info = TBERegOp("Conv3DBackpropInput") \ .attr("pads", "required", "listInt", "all") \ .attr("dilations", "required", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "filter", False, "required", "all") \ .input(1, "out_backprop", False, "required", "all") \ .output(0, "y", True, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/conv3d_transpose.py b/mindspore/ops/_op_impl/tbe/conv3d_transpose.py index cfca109f896..585a8af109d 100644 --- a/mindspore/ops/_op_impl/tbe/conv3d_transpose.py +++ b/mindspore/ops/_op_impl/tbe/conv3d_transpose.py @@ -28,7 +28,7 @@ conv3d_transpose_op_info = TBERegOp("Conv3DTranspose") \ .attr("pads", "required", "listInt", "all") \ .attr("dilations", "optional", "listInt", "all") \ .attr("groups", "optional", "int", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .attr("output_padding", "optional", "listInt", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "filter", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py index fdafcd3fa40..23e5a8f06e9 100644 --- a/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py +++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d.py @@ -26,7 +26,7 @@ depthwise_conv2d_op_info = TBERegOp("DepthwiseConv2dNative") \ .attr("stride", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("pads", "required", "listInt", "all") \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .attr("offset_a", "optional", "int", "all") \ .input(0, "x", False, "required", "all") \ .input(1, "filter", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py index f4d8069b12d..1aeb7e87177 100644 --- a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py +++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py @@ -27,7 +27,7 @@ depthwise_conv2d_backprop_filter_op_info = TBERegOp("DepthwiseConv2dNativeBackpr .attr("stride", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("pads", "required", "listInt", "all") \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .input(0, "input", False, "required", "all") \ .input(1, "out_backprop", False, "required", "all") \ .output(0, "filter_grad", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py index 61c1406b32a..ed57a574eaa 100644 --- a/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py +++ b/mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py @@ -27,7 +27,7 @@ depthwise_conv2d_backprop_input_op_info = TBERegOp("DepthwiseConv2dNativeBackpro .attr("stride", "required", "listInt", "all") \ .attr("dilation", "required", "listInt", "all") \ .attr("pads", "required", "listInt", "all") \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .input(0, "filter", False, "required", "all") \ .input(1, "out_backprop", False, "required", "all") \ .output(0, "input_grad", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/max_pool.py b/mindspore/ops/_op_impl/tbe/max_pool.py index 7d425cb72b8..6c5653ca542 100644 --- a/mindspore/ops/_op_impl/tbe/max_pool.py +++ b/mindspore/ops/_op_impl/tbe/max_pool.py @@ -26,7 +26,7 @@ max_pool_op_info = TBERegOp("MaxPool") \ .attr("kernel_size", "required", "listInt", "all") \ .attr("strides", "required", "listInt", "all") \ .attr("pad_mode", "required", "str", "all") \ - .attr("data_format", "required", "str", "all") \ + .attr("format", "required", "str", "all") \ .input(0, "input_data", False, "required", "all") \ .output(0, "output_data", False, "required", "all") \ .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \ diff --git a/mindspore/ops/_op_impl/tbe/max_pool_grad_grad.py b/mindspore/ops/_op_impl/tbe/max_pool_grad_grad.py index 112e7c56a58..ce7cf39bff5 100644 --- a/mindspore/ops/_op_impl/tbe/max_pool_grad_grad.py +++ b/mindspore/ops/_op_impl/tbe/max_pool_grad_grad.py @@ -26,7 +26,7 @@ max_pool_grad_grad_op_info = TBERegOp("MaxPoolGradGrad") \ .attr("kernel_size", "required", "listInt", "all") \ .attr("strides", "required", "listInt", "all") \ .attr("pad_mode", "required", "str", "all") \ - .attr("data_format", "optional", "str", "all") \ + .attr("format", "optional", "str", "all") \ .input(0, "x1", False, "required", "all") \ .input(1, "x2", False, "required", "all") \ .input(2, "grad", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py b/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py index 3723b30c043..4159346b7e7 100644 --- a/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py +++ b/mindspore/ops/_op_impl/tbe/smooth_l1_loss.py @@ -23,7 +23,7 @@ smooth_l1_loss_op_info = TBERegOp("SmoothL1Loss") \ .compute_cost(10) \ .kernel_name("smooth_l1_loss") \ .partial_flag(True) \ - .attr("sigma", "required", "float", "all") \ + .attr("beta", "required", "float", "all") \ .input(0, "predict", False, "required", "all") \ .input(1, "label", False, "required", "all") \ .output(0, "loss", False, "required", "all") \ diff --git a/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py b/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py index fa1ae1ec34d..db7be64b418 100644 --- a/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py +++ b/mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py @@ -23,7 +23,7 @@ smooth_l1_loss_grad_op_info = TBERegOp("SmoothL1LossGrad") \ .compute_cost(10) \ .kernel_name("smooth_l1_loss_grad") \ .partial_flag(True) \ - .attr("sigma", "required", "float", "all") \ + .attr("beta", "required", "float", "all") \ .input(0, "predict", False, "required", "all") \ .input(1, "label", False, "required", "all") \ .input(2, "dout", False, "required", "all") \ diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py index deccfab5467..49f48c71e23 100644 --- a/mindspore/ops/operations/_grad_ops.py +++ b/mindspore/ops/operations/_grad_ops.py @@ -501,10 +501,10 @@ class DepthwiseConv2dNativeBackpropFilter(PrimitiveWithInfer): Refer to class DepthwiseConv2dNative for more details. Args: - channel_multiplier (int): The multipiler for the original output conv. + channel_multiplier (int): The multiplier for the original output conv. kernel_size (int or tuple): The size of the conv kernel. mode (int): Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution, - 2 deconvolution,3 depthwise convolution. Defaul: 3. + 2 deconvolution,3 depthwise convolution. Default: 3. pad_mode (str): The mode to fill padding which can be: "valid", "same" or "pad". Default: "valid". pad (int): The pad value to be filled. Default: 0. pads (tuple): The pad list like (top, bottom, left, right). Default: (0, 0, 0, 0). @@ -562,7 +562,7 @@ class DepthwiseConv2dNativeBackpropInput(PrimitiveWithInfer): Applies depthwise conv2d for the input, which will generate more channels with channel_multiplier. Args: - channel_multiplier (int): The multipiler for the original output conv. + channel_multiplier (int): The multiplier for the original output conv. kernel_size (int or tuple): The size of the conv kernel. mode (int): Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution , 2 deconvolution,3 depthwise convolution. Default: 3. @@ -1799,7 +1799,7 @@ class SmoothL1LossGrad(PrimitiveWithInfer): @prim_attr_register def __init__(self, beta=1.0): - self.add_prim_attr('sigma', beta) + pass def infer_shape(self, prediction, target, dloss): validator.check('prediction shape', prediction, 'target shape', target, Rel.EQ, self.name) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 7aae306b33f..a033e0edc33 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -986,6 +986,8 @@ class Split(PrimitiveWithCheck): if output_valid_check != 0: raise ValueError(f"x_shape[{self.axis}] {x_shape[self.axis]} must be divide exactly by" f" output_num {self.output_num}") + size_splits = [x_shape[self.axis] / self.output_num] * self.output_num + self.add_prim_attr('size_splits', size_splits) class Rank(PrimitiveWithInfer): @@ -2403,7 +2405,7 @@ class Slice(PrimitiveWithInfer): validator.check_positive_int(size_v[i], f'input size[{i}]') if x_shape[i] < begin_v[i] + size_v[i]: y = begin_v[i] + size_v[i] - raise ValueError("For '%s' slice shape can not bigger than orign shape %d, %d." % + raise ValueError("For '%s' slice shape can not bigger than origin shape %d, %d." % (self.name, x_shape[i], y)) return {'shape': size_v, 'dtype': x['dtype'], @@ -3658,6 +3660,7 @@ class SpaceToDepth(PrimitiveWithInfer): validator.check_value_type('block_size', block_size, [int], self.name) validator.check('block_size', block_size, '', 2, Rel.GE) self.block_size = block_size + self.add_prim_attr("data_format", "NCHW") def infer_shape(self, x_shape): validator.check('x dimension', len(x_shape), '', 4, Rel.EQ) @@ -3719,6 +3722,7 @@ class DepthToSpace(PrimitiveWithInfer): validator.check_value_type('block_size', block_size, [int], self.name) validator.check('block_size', block_size, '', 2, Rel.GE, self.name) self.block_size = block_size + self.add_prim_attr("data_format", "NCHW") def infer_shape(self, x_shape): validator.check('x dimension', len(x_shape), '', 4, Rel.EQ) @@ -4118,7 +4122,7 @@ class BroadcastTo(PrimitiveWithInfer): Raises: ValueError: Given a shape tuple, if it has several -1; or if the -1 is in an invalid position such as one that does not have a opposing dimension in an input tensor; or if the target and - input shapes are incompatiable. + input shapes are incompatible. Supported Platforms: ``Ascend`` ``GPU`` diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 6613b0b3b9f..6fb7f400a0b 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -1338,7 +1338,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer): :math:`\text{in_channels} * \text{channel_multiplier}` channels. Args: - channel_multiplier (int): The multipiler for the original output convolution. Its value must be greater than 0. + channel_multiplier (int): The multiplier for the original output convolution. Its value must be greater than 0. kernel_size (Union[int, tuple[int]]): The size of the convolution kernel. mode (int): Modes for different convolutions. 0 Math convolution, 1 cross-correlation convolution , 2 deconvolution, 3 depthwise convolution. Default: 3. @@ -2272,7 +2272,6 @@ class SmoothL1Loss(PrimitiveWithInfer): validator.check_value_type('beta', beta, [float], self.name) validator.check('beta', beta, '', 0, Rel.GT, self.name) self.init_prim_io_names(inputs=['prediction', 'target'], outputs=['output']) - self.add_prim_attr('sigma', beta) def infer_shape(self, prediction, target): validator.check('prediction shape', prediction, 'target shape', target, Rel.EQ, self.name) diff --git a/tests/ut/cpp/transform/convert_test.cc b/tests/ut/cpp/transform/convert_test.cc index bd3346ecc40..9ff2fa68ece 100644 --- a/tests/ut/cpp/transform/convert_test.cc +++ b/tests/ut/cpp/transform/convert_test.cc @@ -59,9 +59,9 @@ AnfGraphPtr createAnfGraph() { return std::make_shared(); } TEST_F(TestConvert, TestConstruct) { AnfGraphPtr func_graph = std::make_shared(); - DfGraphConvertor convertor(func_graph); - convertor.ConvertAllNode().GetComputeGraph(); - ASSERT_NE(convertor.ErrCode(), SUCCESS); + DfGraphConvertor converter(func_graph); + converter.ConvertAllNode().GetComputeGraph(); + ASSERT_NE(converter.ErrCode(), SUCCESS); } #if (!defined ENABLE_GE) @@ -75,11 +75,11 @@ bool MakeDfGraph(PrimitivePtr prim, unsigned int nparam) { draw::Draw("ut_prim_" + prim->name() + ".dot", anf_graph); DumpIR("ut_prim_" + prim->name() + ".ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph(prim->name() + ".dot"); - if (convertor.ErrCode() != 0) { - MS_LOG(ERROR) << "DfGraphConvertor convert " << prim->name() << " error, error code is: " << convertor.ErrCode(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph(prim->name() + ".dot"); + if (converter.ErrCode() != 0) { + MS_LOG(ERROR) << "DfGraphConvertor convert " << prim->name() << " error, error code is: " << converter.ErrCode(); return false; } if (df_graph == nullptr) { @@ -103,10 +103,10 @@ TEST_F(TestConvert, TestConvertConv2d) { draw::Draw("ut_prim_conv2d1.dot", anf_graph); DumpIR("ut_prim_conv2d1.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("conv2d.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("conv2d.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -118,10 +118,10 @@ TEST_F(TestConvert, TestConvertMaxpooling) { draw::Draw("ut_prim_maxpooling.dot", anf_graph); DumpIR("ut_prim_maxpooling.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("maxpooling.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("maxpooling.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -141,9 +141,9 @@ TEST_F(TestConvert, TestReluOps) { // draw graph auto anfGraph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anfGraph); - convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anfGraph); + converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + ASSERT_EQ(converter.ErrCode(), 0); } TEST_F(TestConvert, TestConvertBatchNorm) { @@ -180,10 +180,10 @@ TEST_F(TestConvert, TestConvertBatchNorm) { draw::Draw("ut_prim_batchnorm.dot", anf_graph); DumpIR("ut_prim_batchnorm.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("batchnrom.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("batchnrom.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -209,11 +209,11 @@ TEST_F(TestConvert, TestConvertConvBackpropInput) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("Conv2DBackpropInput.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("Conv2DBackpropInput.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -239,11 +239,11 @@ TEST_F(TestConvert, TestConvertConvBackpropFilter) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("Conv2DBackpropFilter.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("Conv2DBackpropFilter.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -264,11 +264,11 @@ TEST_F(TestConvert, TestConvertReluGrad) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("ReluGrad.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("ReluGrad.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -276,7 +276,6 @@ TEST_F(TestConvert, TestConvertBiasAdd) { auto prim = std::make_shared("BiasAdd"); prim->AddAttr("alpha", MakeValue(0.0f)); prim->AddAttr("beta", MakeValue(1.0f)); - prim->AddAttr("format", MakeValue(static_cast(1))); auto func_graph = MakeFuncGraph(prim, 2); ASSERT_NE(func_graph, nullptr); @@ -289,11 +288,11 @@ TEST_F(TestConvert, TestConvertBiasAdd) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("BiasAdd.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("BiasAdd.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -301,7 +300,6 @@ TEST_F(TestConvert, TestConvertBiasAddGrad) { auto prim = prim::kPrimBiasAddGrad; prim->AddAttr("alpha", MakeValue(0.0f)); prim->AddAttr("beta", MakeValue(1.0f)); - prim->AddAttr("format", MakeValue(static_cast(1))); auto func_graph = MakeFuncGraph(prim, 2); ASSERT_NE(func_graph, nullptr); @@ -314,11 +312,11 @@ TEST_F(TestConvert, TestConvertBiasAddGrad) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("BiasAddGrad.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("BiasAddGrad.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -344,11 +342,11 @@ TEST_F(TestConvert, TestConvertMaxPoolGradWithArgmax) { // draw graph auto anf_graph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("MaxPoolGradWithArgmax.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + converter.DrawComputeGraph("MaxPoolGradWithArgmax.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -361,10 +359,10 @@ TEST_F(TestConvert, TestConcat) { draw::Draw("ut_prim_concat.dot", anf_graph); DumpIR("ut_prim_concat.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("concat.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("concat.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -377,10 +375,10 @@ TEST_F(TestConvert, TestGatherV2) { draw::Draw("ut_prim_gatherv2.dot", anf_graph); DumpIR("ut_prim_gatherv2.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("gatherv2.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("gatherv2.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -393,10 +391,10 @@ TEST_F(TestConvert, TestCast) { draw::Draw("ut_prim_cast.dot", anf_graph); DumpIR("ut_prim_cast.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("cast.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("cast.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -409,10 +407,10 @@ TEST_F(TestConvert, TestExp) { draw::Draw("ut_prim_exp.dot", anf_graph); DumpIR("ut_prim_exp.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("exp.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("exp.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -425,10 +423,10 @@ TEST_F(TestConvert, TestFloor) { draw::Draw("ut_prim_floor.dot", anf_graph); DumpIR("ut_prim_floor.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("floor.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("floor.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -441,10 +439,10 @@ TEST_F(TestConvert, TestGreaterEqual) { draw::Draw("ut_prim_greater_equal.dot", anf_graph); DumpIR("ut_prim_greater_equal.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("greater_equal.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("greater_equal.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -458,10 +456,10 @@ TEST_F(TestConvert, TestLess) { draw::Draw("ut_prim_less.dot", anf_graph); DumpIR("ut_prim_less.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("less.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("less.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -474,10 +472,10 @@ TEST_F(TestConvert, TestLessEqual) { draw::Draw("ut_prim_less_equal.dot", anf_graph); DumpIR("ut_prim_less_equal.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("less_equal.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("less_equal.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -490,10 +488,10 @@ TEST_F(TestConvert, TestLogicalNot) { draw::Draw("ut_prim_logical_not.dot", anf_graph); DumpIR("ut_prim_logical_not.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("logical_not.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("logical_not.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -507,10 +505,10 @@ TEST_F(TestConvert, TestAssignAdd) { draw::Draw("ut_prim_assign_add.dot", anf_graph); DumpIR("ut_prim_assign_add.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("assign_add.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("assign_add.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -524,10 +522,10 @@ TEST_F(TestConvert, LogSoftmax) { draw::Draw("ut_prim_log_softmax.dot", anf_graph); DumpIR("ut_prim_log_softmax.ir", anf_graph); - DfGraphConvertor convertor(anf_graph); - auto df_graph = convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - convertor.DrawComputeGraph("log_softmax.dot"); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anf_graph); + auto df_graph = converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + converter.DrawComputeGraph("log_softmax.dot"); + ASSERT_EQ(converter.ErrCode(), 0); ASSERT_NE(df_graph, nullptr); } @@ -693,9 +691,9 @@ TEST_F(TestConvert, TestAddOps) { // draw graph auto anfGraph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anfGraph); - convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anfGraph); + converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + ASSERT_EQ(converter.ErrCode(), 0); } TEST_F(TestConvert, TestConvertTensor) { @@ -827,9 +825,9 @@ TEST_F(TestConvert, TestConvertMakeTuple) { // draw graph auto anfGraph = *(manager->func_graphs().begin()); - DfGraphConvertor convertor(anfGraph); - convertor.ConvertAllNode().BuildGraph().GetComputeGraph(); - ASSERT_EQ(convertor.ErrCode(), 0); + DfGraphConvertor converter(anfGraph); + converter.ConvertAllNode().BuildGraph().GetComputeGraph(); + ASSERT_EQ(converter.ErrCode(), 0); } TEST_F(TestConvert, TestConvertInputTensors) {