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update some op's attr name

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yuchaojie 2021-01-18 16:26:51 +08:00
parent 60feffad20
commit 1932d87a26
51 changed files with 196 additions and 188 deletions
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config/op_info.config
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@ -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"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"]], [["uint8", "FracZ"], ["uint8", "FracZ"], ["uint8", "FracZ"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"]], [["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["int64", "NC1HWC0"], ["int64", "NC1HWC0"], ["int64", "NC1HWC0"]], [["int64", "C1HWNCoC0"], ["int64", "C1HWNCoC0"], ["int64", "C1HWNCoC0"]], [["int64", "FracZ"], ["int64", "FracZ"], ["int64", "FracZ"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"]], [["float16", "FracZ"], ["float16", "FracZ"], ["float16", "FracZ"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"]], [["float32", "FracZ"], ["float32", "FracZ"], ["float32", "FracZ"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "assign_add.so", "compute_cost": 10, "kernel_name": "assign_add", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""}
{"op_name": "AssignSub", "inputs": [{"index": 0, "name": "var", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "value", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "var", "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"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"]], [["uint8", "FracZ"], ["uint8", "FracZ"], ["uint8", "FracZ"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"]], [["int32", "FracZ"], ["int32", "FracZ"], ["int32", "FracZ"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"]], [["float16", "FracZ"], ["float16", "FracZ"], ["float16", "FracZ"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"]], [["float32", "FracZ"], ["float32", "FracZ"], ["float32", "FracZ"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "assign_sub.so", "compute_cost": 10, "kernel_name": "assign_sub", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""}
{"op_name": "BatchMatMul", "inputs": [{"index": 0, "name": "x1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x2", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "bias", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}], "attr": [{"name": "transpose_x1", "param_type": "required", "type": "bool", "value": "all"}, {"name": "transpose_x2", "param_type": "required", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["int32", ""], ["int32", ""], ["int32", ""], ["int32", ""]], [["float16", ""], ["float16", ""], ["float16", ""], ["float16", ""]], [["float32", ""], ["float32", ""], ["float32", ""], ["float32", ""]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "batch_matmul.so", "compute_cost": 10, "kernel_name": "batch_matmul", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": "dynamicFormat"}
{"op_name": "BatchNorm", "inputs": [{"index": 0, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "scale", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "offset", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "mean", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 4, "name": "variance", "need_compile": false, "param_type": "optional", "shape": "all"}], "outputs": [{"index": 0, "name": "y", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "batch_mean", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "batch_variance", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "reserve_space_1", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 4, "name": "reserve_space_2", "need_compile": false, "param_type": "optional", "shape": "all"}], "attr": [{"name": "epsilon", "param_type": "optional", "type": "float", "value": "all"}, {"name": "data_format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "is_training", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float16", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float16", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float16", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "batch_norm.so", "compute_cost": 10, "kernel_name": "batch_norm", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""}
{"op_name": "BatchNormGrad", "inputs": [{"index": 0, "name": "y_backprop", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "x", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "scale", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "reserve_space_1", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 4, "name": "reserve_space_2", "need_compile": false, "param_type": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "x_backprop", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 1, "name": "scale_backprop", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 2, "name": "offset_backprop", "need_compile": false, "param_type": "required", "shape": "all"}, {"index": 3, "name": "reserve_space_4", "need_compile": false, "param_type": "optional", "shape": "all"}, {"index": 4, "name": "reserve_space_5", "need_compile": false, "param_type": "optional", "shape": "all"}], "attr": [{"name": "epsilon", "param_type": "optional", "type": "float", "value": "all"}, {"name": "data_format", "param_type": "optional", "type": "str", "value": "all"}, {"name": "is_training", "param_type": "optional", "type": "bool", "value": "all"}], "fusion_type": "OPAQUE", "dtype_format": [[["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float16", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float16", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]]], "imply_type": "TBE", "async_flag": false, "binfile_name": "batch_norm_grad.so", "compute_cost": 10, "kernel_name": "batch_norm_grad", "partial_flag": true, "reshape_type": "", "dynamic_format": false, "dynamic_shape": false, "op_pattern": ""}
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@ -298,21 +298,21 @@
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@ -420,7 +420,7 @@
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@ -437,7 +437,7 @@
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{"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": ""}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
std::string format = GetAttr<std::string>(kernel_node, "format");
string::size_type pos = format.find("C");
if (pos == std::string::npos || pos >= num_dims) {
MS_LOG(EXCEPTION) << "format '" << format << "' invalid";

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h
View File

@ -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<std::string>(kernel_node, "data_format");
std::string format = GetAttr<std::string>(kernel_node, "format");
string::size_type pos = format.find("C");
if (pos == std::string::npos || pos >= num_dims) {
MS_LOG(EXCEPTION) << "format '" << format << "' invalid";

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
data_format_ = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h
View File

@ -119,7 +119,7 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel {
return true;
}
data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0);
format_attr_ = GetAttr<std::string>(kernel_node, "data_format");
format_attr_ = GetAttr<std::string>(kernel_node, "format");
if (format_attr_ == kOpFormat_NHWC) {
data_format_ = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
data_format_ = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h
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@ -133,7 +133,7 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
return true;
}
auto format = AnfAlgo::GetInputFormat(kernel_node, 0);
auto format_attr = GetAttr<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
format = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
format = kOpFormat_NHWC;
cudnn_format = CUDNN_TENSOR_NHWC;

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h
View File

@ -143,7 +143,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
return true;
}
std::string format = AnfAlgo::GetInputFormat(kernel_node, 0);
auto format_attr = GetAttr<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
format = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
data_format_ = kOpFormat_NHWC;
}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h
View File

@ -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<std::string>(kernel_node, "data_format");
format_attr_ = GetAttr<std::string>(kernel_node, "format");
if (format_attr_ == kOpFormat_NHWC) {
data_format = kOpFormat_NHWC;
}

4
mindspore/ccsrc/backend/optimizer/ascend/mindir/conv2d_unify_mindir.cc
View File

@ -45,7 +45,7 @@ bool NeedUpdate(const CNodePtr &conv2d, std::vector<size_t> in_shape, std::vecto
if (group == 1) {
return false;
}
auto data_format = AnfAlgo::GetNodeAttr<std::string>(conv2d, kAttrDataFormat);
auto data_format = AnfAlgo::GetNodeAttr<std::string>(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);

2
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc
View File

@ -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<CNodePtr>(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<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") {

4
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc
View File

@ -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<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(node, 0) != kOpFormat_NHWC && format != "NHWC") {

2
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc
View File

@ -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<CNodePtr>(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<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") {

2
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc
View File

@ -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<std::string>(format_attr);
auto ms_context = MsContext::GetInstance();

7
mindspore/ccsrc/pybind_api/ir/primitive_py.cc
View File

@ -17,6 +17,7 @@
#include "pybind_api/ir/primitive_py.h"
#include <mutex>
#include <map>
#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<std::string, std::string> kOpAttrNameReplaceMap = {
{"data_format", "format"},
};
void SyncData(const py::object &arg) {
if (py::isinstance<py::tuple>(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);
}

8
mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc
View File

@ -247,8 +247,8 @@ void UpdateKernelFormatInfo(const CNodePtr &kernel_node, const std::vector<TypeI
}
auto prim = AnfAlgo::GetCNodePrimitive(kernel_node);
MS_EXCEPTION_IF_NULL(prim);
if (prim->HasAttr("data_format")) {
*origin_data_format = AnfAlgo::GetNodeAttr<std::string>(kernel_node, "data_format");
if (prim->HasAttr("format")) {
*origin_data_format = AnfAlgo::GetNodeAttr<std::string>(kernel_node, "format");
}
}
@ -342,8 +342,8 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptr<s
return;
}
auto value = AnfAlgo::GetCNodePrimitive(kernel);
if (value != nullptr && value->GetAttr("data_format") != nullptr &&
GetValue<std::string>(value->GetAttr("data_format")) == kOpFormat_NHWC) {
if (value != nullptr && value->GetAttr("format") != nullptr &&
GetValue<std::string>(value->GetAttr("format")) == kOpFormat_NHWC) {
format_transform_ = false;
return;
}

2
mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc
View File

@ -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<std::string>())}};
ATTR_MAP(BiasAdd) = {{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
OUTPUT_MAP(BiasAdd) = {{0, OUTPUT_DESC(y)}};
REG_ADPT_DESC(BiasAdd, kNameBiasAdd, ADPT_DESC(BiasAdd))

4
mindspore/ccsrc/transform/graph_ir/op_declare/nn_batch_norm_ops_declare.cc
View File

@ -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<std::string>())},
ATTR_MAP(BatchNorm) = {{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())},
{"is_training", ATTR_DESC(is_training, AnyTraits<bool>())}};
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<std::string>())},
ATTR_MAP(BatchNormGrad) = {{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())},
{"is_training", ATTR_DESC(is_training, AnyTraits<bool>())}};
OUTPUT_MAP(BatchNormGrad) = {{0, OUTPUT_DESC(x_backprop)},

10
mindspore/ccsrc/transform/graph_ir/op_declare/nn_calculation_ops_declare.cc
View File

@ -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<std::string>())}};
ATTR_MAP(BiasAddGrad) = {{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
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<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"group", ATTR_DESC(groups, AnyTraits<int64_t>())},
};
OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}};
@ -44,7 +44,7 @@ ATTR_MAP(Conv2DBackpropInputD) = {
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"group", ATTR_DESC(groups, AnyTraits<int64_t>())},
};
OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}};
@ -58,7 +58,7 @@ ATTR_MAP(Conv2DBackpropFilterD) = {
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"group", ATTR_DESC(groups, AnyTraits<int64_t>())},
};
OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}};
@ -70,7 +70,7 @@ ATTR_MAP(DepthwiseConv2D) = {
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())},
};
OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}};
REG_ADPT_DESC(DepthwiseConv2D, prim::kPrimDepthwiseConv2dNative->name(), ADPT_DESC(DepthwiseConv2D))

4
mindspore/ccsrc/transform/graph_ir/op_declare/nn_norm_ops_declare.cc
View File

@ -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<float>())}};
ATTR_MAP(SmoothL1Loss) = {{"beta", ATTR_DESC(sigma, AnyTraits<float>())}};
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<float>())}};
ATTR_MAP(SmoothL1LossGrad) = {{"beta", ATTR_DESC(sigma, AnyTraits<float>())}};
OUTPUT_MAP(SmoothL1LossGrad) = {{0, OUTPUT_DESC(gradient)}};
REG_ADPT_DESC(SmoothL1LossGrad, kNameSmoothL1LossGrad, ADPT_DESC(SmoothL1LossGrad))

8
mindspore/ccsrc/transform/graph_ir/op_declare/nn_pooling_ops_declare.cc
View File

@ -23,7 +23,7 @@ INPUT_MAP(MaxPool) = {{1, INPUT_DESC(x)}};
ATTR_MAP(MaxPool) = {{"kernel_size", ATTR_DESC(ksize, AnyTraits<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"strides", ATTR_DESC(strides, AnyTraits<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
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<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"strides", ATTR_DESC(strides, AnyTraits<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
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<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"strides", ATTR_DESC(strides, AnyTraits<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
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<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"strides", ATTR_DESC(strides, AnyTraits<int64_t>(), AnyTraits<std::vector<int64_t>>())},
{"pad_mode", ATTR_DESC(padding, AnyTraits<std::string>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
{"format", ATTR_DESC(data_format, AnyTraits<std::string>())}};
OUTPUT_MAP(AvgPoolGrad) = {{0, OUTPUT_DESC(out_grad)}};
REG_ADPT_DESC(AvgPoolGrad, kNameAvgPoolGrad, ADPT_DESC(AvgPoolGrad))

2
mindspore/ccsrc/utils/utils.h
View File

@ -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";

2
mindspore/ops/_op_impl/_custom_op/batchnorm_fold.py
View File

@ -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") \

2
mindspore/ops/_op_impl/aicpu/topk.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/avg_pool.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/avg_pool_grad.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/avg_pool_grad_vm.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/batchnorm.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/batchnorm_grad.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/bias_add.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/bias_add_grad.py
View File

@ -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) \

2
mindspore/ops/_op_impl/tbe/conv2d.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv3d.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv3d_backprop_filter.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv3d_backprop_input.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/conv3d_transpose.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/depthwise_conv2d.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_filter.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/depthwise_conv2d_backprop_input.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/max_pool.py
View File

@ -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) \

2
mindspore/ops/_op_impl/tbe/max_pool_grad_grad.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/smooth_l1_loss.py
View File

@ -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") \

2
mindspore/ops/_op_impl/tbe/smooth_l1_loss_grad.py
View File

@ -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") \

8
mindspore/ops/operations/_grad_ops.py
View File

@ -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)

8
mindspore/ops/operations/array_ops.py
View File

@ -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``

3
mindspore/ops/operations/nn_ops.py
View File

@ -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)

196
tests/ut/cpp/transform/convert_test.cc
View File

@ -59,9 +59,9 @@ AnfGraphPtr createAnfGraph() { return std::make_shared<AnfGraph>(); }
TEST_F(TestConvert, TestConstruct) {
AnfGraphPtr func_graph = std::make_shared<AnfGraph>();
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<Primitive>("BiasAdd");
prim->AddAttr("alpha", MakeValue(0.0f));
prim->AddAttr("beta", MakeValue(1.0f));
prim->AddAttr("format", MakeValue(static_cast<int64_t>(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<int64_t>(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) {