# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle.fluid.core as core import numpy as np from functools import reduce __all__ = [] registerd_op = { # forwards "elementwise_add": "AddParser", "matmul": "MatMulParser", "mul": "MulParser", "relu": "ReluParser", "softmax_with_cross_entropy": "SoftmaxWithCrossEntropyParser", "shape": "ShapeParser", "fill_constant": "FillConstantParser", "reduce_sum": "ReduceSumParser", "elementwise_mul": "DotMulParser", "elementwise_div": "DotDivParser", "elementwise_pow": "DotPowParser", "elementwise_max": "MaxParser", "elementwise_min": "MinParser", "elementwise_sub": "DotSubParser", "pow": "PowParser", "gelu": "GeluParser", "sqrt": "SqrtParser", "log": "LogParser", "sum": "SumParser", "logical_not": "LogicalNotParser", "gather": "GatherParser", "scatter": "ScatterParser", "cast": "CastParser", "tanh": "TanhParser", "stack": "StackParser", "square": "SquareParser", "unsqueeze2": "UnSqueezeParser", "assign": "AssignParser", "softmax": "SoftMaxParser", "reshape2": "ReshapeParser", "transpose2": "TransposeParser", "layer_norm": "LayerNormParser", "less_than": "LessParser", "mean": "MeanParser", "scale": "ScaleParser", "slice": "SliceParser", "top_k": "TopkParser", "accuracy": "AccuracyParser", # "increment": "IncrementParser", "lookup_table": "LookupTableParser", "truncated_gaussian_random": "TruncatedNormalParser", "c_allgather": "AllGatherParser", "c_allreduce_sum": "AllReduceSumParser", "c_allreduce_max": "AllReduceMaxParser", "c_broadcast": "BroadcastParser", "c_reduce_scatter": "ReduceScatterParser", "c_send": "SendParser", "c_receive": "ReceiveParser", "uniform_random": "UniformRandomParser", "range": "RangeParser", "equal": "EqualParser", "expand": "ExpandParser", "squeeze2": "SqueezeParser", ## backwords "matmul_grad": "MatMulGradParser", "mul_grad": "MulGradParser", "relu_grad": "ReluGradParser", "reduce_sum_grad": "ReduceSumGradParser", "softmax_with_cross_entropy_grad": "SoftmaxWithCrossEntropyGradParser", "tanh_grad": "TanhGradParser", "log_grad": "LogGradParser", "pow_grad": "PowGradParser", "sqrt_grad": "SqrtGradParser", "gelu_grad": "GeluGradParser", "mean_grad": "MeanGradParser", 'lookup_table_grad': "LookUpTableGradParser", "elementwise_mul_grad": "DotMulGradParser", "elementwise_add_grad": "DotAddGradParser", "elementwise_div_grad": "DotDivGradParser", "softmax_grad": "SoftmaxGradParser", "slice_grad": "SliceGradParser", "reshape2_grad": "ReshapeGradParser", "gather_grad": "GatherGradParser", "transpose2_grad": "TransposeGradParser", "layer_norm_grad": "LayerNormGradParser", ## opt "sgd": "SGDParser", # "adam": "AdamParser", } global_cnt = -1 global_input_cnt = -1 class AscendHelper(object): def __init__(self): self.dtype2ge_map = { 0: core.GEDataType.DT_BOOL, 1: core.GEDataType.DT_INT16, 2: core.GEDataType.DT_INT32, 3: core.GEDataType.DT_INT64, 4: core.GEDataType.DT_FLOAT16, 5: core.GEDataType.DT_FLOAT, 6: core.GEDataType.DT_DOUBLE, } self.dtype2np_map = { 0: "bool", 1: "int16", 2: "int32", 3: "int64", 4: "float16", 5: "float32", 6: "float64", } self.dtype2paddle_inv_map = {"VarType.FP32": 0, "VarType.FP16": 1} def dtype2ge(self, dtype): assert dtype in self.dtype2ge_map, "dtype[%d] is not supported %d" % ( dtype ) return self.dtype2ge_map[dtype] def dtype2np(self, index): assert index in self.dtype2np_map, "index[%d] is not supported %d" % ( index ) return self.dtype2np_map[index] class AscendParserFactory(object): def __init__(self, graph, var2geop): self.graph = graph self.var2geop = var2geop def create_parse(self, parser_class): try: parser = globals()[parser_class](self.graph, self.var2geop) return parser except: raise ValueError("parser class %s does not exist" % parser_class) class AscendParserBase(object): def __init__(self, graph, var2geop): self.graph = graph self.var2geop = var2geop self.op = None self.ascend_helper = AscendHelper() def _get_ge_input(self, input_var_name): assert input_var_name in self.var2geop, "var %s not created before" % ( input_var_name ) return self.var2geop[input_var_name] def update_output(self, geop_list, index_list): output_num = len(self.op.output_names) assert output_num == len(index_list), ( "Parser[%s]'s output number[%d] is not equal to parameters number[%d]" % (self.parser_name, len(index_list), output_num) ) for output_id in range(output_num): arguments = self.op.output(self.op.output_names[output_id]) if len(arguments) > 0: assert len(arguments) == len(index_list[output_id]), ( "Parser[%s]'s %dth argument number[%d] is not equal to paddle's number[%d]" % ( self.parser_name, output_id, len(index_list[output_id]), len(arguments), ) ) for i in range(len(arguments)): self.var2geop[arguments[i]] = geop_list[ index_list[output_id][i] ] for geop in geop_list: self.graph.add_op(geop) def apply(self, op): self.op = op assert ( self.op.type == self.parser_name ), "op [%s] != parser_name[%s]" % (self.op.type, self.parser_name) # print("begin to parse op %s" % (self.parser_name)) geop_list, index_list = self._apply() self.update_output(geop_list, index_list) def _mark_as_input(self, ge_tensor): global global_input_cnt global_input_cnt += 1 self.var2geop["geinput." + str(global_input_cnt)] = ge_tensor def _accumulated_op_id(self): global global_cnt global_cnt += 1 name = "." + str(global_cnt) return name def _create_ge_tensor(self, shape, dtype, value): tensor_desc = core.GETensorDesc( core.GEShape(shape), core.GEFormat.FORMAT_ND, self.ascend_helper.dtype2ge(dtype), ) tensor = core.GETensor(tensor_desc) data = ( (value * np.ones((shape))) .reshape(shape) .astype(self.ascend_helper.dtype2np(dtype)) ) buf = data.tobytes() data_8 = np.frombuffer(buf, dtype=np.uint8) tensor.set_data(data_8) return tensor def _get_ge_tensor(self, shape, dtype, value_list): tensor_desc = core.GETensorDesc( core.GEShape(shape), core.GEFormat.FORMAT_ND, self.ascend_helper.dtype2ge(dtype), ) tensor = core.GETensor(tensor_desc) data = ( np.array(value_list) .reshape(shape) .astype(self.ascend_helper.dtype2np(dtype)) ) buf = data.tobytes() data_8 = np.frombuffer(buf, dtype=np.uint8) tensor.set_data(data_8) tensor_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) return tensor_const def _get_variable(self, shape, dtype, tensor): if dtype == "int32": type = core.GEDataType.DT_INT32 elif dtype == "float32": type = core.GEDataType.DT_FLOAT var = core.GEOperatorFactory.create_operator( "variable" + self._accumulated_op_id(), "Variable" ) var.update_output_desc( "y", core.GETensorDesc( core.GEShape(shape), core.GEFormat.FORMAT_ND, type ), ) assign = ( core.GEOperatorFactory.create_operator( "assign" + self._accumulated_op_id(), "Assign" ) .set_input("value", tensor) .set_input("ref", var) ) return assign def _create_shape_tensor(self): tensor_desc = core.GETensorDesc( core.GEShape([2]), core.GEFormat.FORMAT_ND, core.GEDataType.DT_INT32 ) tensor = core.GETensor(tensor_desc) data = np.ones((2)).astype("int32").reshape([2]) data[0] = 64 buf = data.tobytes() data_8 = np.frombuffer(buf, dtype=np.uint8) tensor.set_data(data_8) return tensor def _get_GEtensor_shape(self, tensor): tensor_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", tensor) tensor_shape = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", tensor_shape) .set_attr_int32("dst_type", 0) ) return tensor_shape class AddParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_add" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) add = ( core.GEOperatorFactory.create_operator( "add" + self._accumulated_op_id(), "Add" ) .set_input("x1", x) .set_input("x2", y) ) return [add], [[0]] class DotSubParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_sub" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) sub = ( core.GEOperatorFactory.create_operator( "sub" + self._accumulated_op_id(), "Sub" ) .set_input("x1", x) .set_input("x2", y) ) return [sub], [[0]] class DotMulParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_mul" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) mul = ( core.GEOperatorFactory.create_operator( "dotmul" + self._accumulated_op_id(), "Mul" ) .set_input("x1", x) .set_input("x2", y) ) return [mul], [[0]] class DotDivParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_div" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) div = ( core.GEOperatorFactory.create_operator( "dotdiv" + self._accumulated_op_id(), "Div" ) .set_input("x1", x) .set_input("x2", y) ) return [div], [[0]] class DotPowParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_pow" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) pow = ( core.GEOperatorFactory.create_operator( "dotpow" + self._accumulated_op_id(), "Pow" ) .set_input("x1", x) .set_input("x2", y) ) return [pow], [[0]] class LessParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "less_than" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) less_than = ( core.GEOperatorFactory.create_operator( "less_than" + self._accumulated_op_id(), "Less" ) .set_input("x1", x) .set_input("x2", y) ) return [less_than], [[0]] class MaxParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_max" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) max_out = ( core.GEOperatorFactory.create_operator( "max" + self._accumulated_op_id(), "Maximum" ) .set_input("x1", x) .set_input("x2", y) ) return [max_out], [[0]] class MinParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_min" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) min_out = ( core.GEOperatorFactory.create_operator( "min" + self._accumulated_op_id(), "Minimum" ) .set_input("x1", x) .set_input("x2", y) ) return [min_out], [[0]] ## cal class LogParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "log" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) log = core.GEOperatorFactory.create_operator( "log" + self._accumulated_op_id(), "Log" ).set_input("x", x) return [log], [[0]] class SqrtParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "sqrt" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) sqrt = core.GEOperatorFactory.create_operator( "sqrt" + self._accumulated_op_id(), "Sqrt" ).set_input("x", x) return [sqrt], [[0]] class PowParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "pow" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) factor = self.op.attr("factor") pow_value = ( core.GEOperatorFactory.create_operator( "pow" + self._accumulated_op_id(), "Power" ) .set_input("x", x) .set_attr_float("power", factor) .set_attr_float("scale", 1.0) .set_attr_float("shift", 0.0) ) return [pow_value], [[0]] class SquareParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "square" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) square = core.GEOperatorFactory.create_operator( "square" + self._accumulated_op_id(), "Square" ).set_input("x", x) return [square], [[0]] class SumParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "sum" def _apply(self): len_list = len(self.op.input_arg_names) if len_list < 2: assert False, "the size of input list must large or equal 2" x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) sum = ( core.GEOperatorFactory.create_operator( "sum" + self._accumulated_op_id(), "Add" ) .set_input("x1", x) .set_input("x2", y) ) for i in range(2, len_list): y = self._get_ge_input(self.op.input_arg_names[i]) sum = ( core.GEOperatorFactory.create_operator( "sum" + self._accumulated_op_id(), "Add" ) .set_input("x1", sum) .set_input("x2", y) ) return [sum], [[0]] class LogicalNotParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "logical_not" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) logical_not = core.GEOperatorFactory.create_operator( "logical_not" + self._accumulated_op_id(), "LogicalNot" ).set_input("x", x) return [logical_not], [[0]] class MeanParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "mean" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) mean = ( core.GEOperatorFactory.create_operator( "mean" + self._accumulated_op_id(), "ReduceMeanD" ) .set_input("x", x) .set_attr_bool("keep_dims", False) .set_attr_vec_int32("axes", []) ) return [mean], [[0]] class ReduceSumParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "reduce_sum" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) axes = self.op.attr("dim") keep_dims = self.op.attr("keep_dim") reduce_all = self.op.attr("reduce_all") x_shape = self.op.block.var(self.op.input_arg_names[0]).shape if reduce_all: axes = list(range(len(x_shape))) reduce_sum = ( core.GEOperatorFactory.create_operator( "reduce_sum" + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", x, 0) .set_attr_vec_int32("axes", axes) .set_attr_bool("keep_dims", keep_dims) ) return [reduce_sum], [[0]] # class IncrementParser(AscendParserBase): # def __init__(self, graph, var2geop): # super().__init__(graph, var2geop) # self.parser_name = "increment" # # def _apply(self): # x = self._get_ge_input(self.op.input_arg_names[0]) # step = self.op.attr("step") #self._get_ge_input(self.op.input_arg_names[1]) # print("step: ", step) # # increment = core.GEOperatorFactory.create_operator("adds" + self._accumulated_op_id(), "Adds").set_input("x", x).set_attr_float("value", step) #set_input("x2", bias) # # return [increment] ## matrix cal class MatMulParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "matmul" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) transpose_x = self.op.attr("transpose_X") transpose_y = self.op.attr("transpose_Y") x1_shape = self.op.block.var(self.op.input_arg_names[0]).shape x2_shape = self.op.block.var(self.op.input_arg_names[1]).shape if len(x1_shape) > 2: matmul = ( core.GEOperatorFactory.create_operator( "matmul" + self._accumulated_op_id(), "BatchMatMul" ) .set_input("x1", x) .set_input("x2", y) .set_attr_bool("adj_x1", transpose_x) .set_attr_bool("adj_x2", transpose_y) ) elif len(x1_shape) == 2: matmul = ( core.GEOperatorFactory.create_operator( "matmul" + self._accumulated_op_id(), "MatMul" ) .set_input("x1", x) .set_input("x2", y) .set_attr_bool("transpose_x1", transpose_x) .set_attr_bool("transpose_x2", transpose_y) ) else: assert False, "not support" return [matmul], [[0]] class MulParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "mul" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) y = self._get_ge_input(self.op.input_arg_names[1]) x_num_col_dims = self.op.attr("x_num_col_dims") y_num_col_dims = self.op.attr("y_num_col_dims") shape_x1 = self.op.block.var(self.op.input_arg_names[0]).shape shape_x2 = self.op.block.var(self.op.input_arg_names[1]).shape if x_num_col_dims == 1 and y_num_col_dims == 1: if len(shape_x1) == 2 and len(shape_x2) == 2: matmul = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "MatMul" ) .set_input("x1", x) .set_input("x2", y) ) elif len(shape_x1) == 3 and len(shape_x2) == 2: flatten_x1 = core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "Flatten" ).set_input("x", x) matmul = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "MatMul" ) .set_input("x1", flatten_x1, 0) .set_input("x2", y, 0) ) else: assert False, "not support" else: if len(shape_x1) == 3 and len(shape_x2) == 2: assert x_num_col_dims == 2, "only support 2" flatten_x1 = ( core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "FlattenV2" ) .set_input("x", x) .set_attr_int32("axis", 0) .set_attr_int32("end_axis", 1) ) matmul_m = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "MatMul" ) .set_input("x1", flatten_x1, 0) .set_input("x2", y, 0) ) matmul_transpose = ( core.GEOperatorFactory.create_operator( "transpose" + self._accumulated_op_id(), "TransposeD" ) .set_input("x", matmul_m) .set_attr_vec_int32("perm", [1, 0]) ) tensor = self._create_ge_tensor( [3], 2, [shape_x2[1], shape_x1[0], shape_x1[1]] ) const_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) reshape_matmul = ( core.GEOperatorFactory.create_operator( "reshape" + self._accumulated_op_id(), "Reshape" ) .set_input("x", matmul_transpose) .set_input("shape", const_shape) .set_attr_int32("axis", 0) ) matmul = ( core.GEOperatorFactory.create_operator( "transpose" + self._accumulated_op_id(), "TransposeD" ) .set_input("x", reshape_matmul) .set_attr_vec_int32("perm", [1, 2, 0]) ) else: assert False, "not support" return [matmul], [[0]] class LayerNormParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "layer_norm" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[2]) scale = self._get_ge_input(self.op.input_arg_names[1]) bias = self._get_ge_input(self.op.input_arg_names[0]) epsilon = self.op.attr("epsilon") begin_norm_axis = self.op.attr("begin_norm_axis") x_dtype = self.op.block.var(self.op.input_arg_names[2]).dtype shape_tensor = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", x) scale_expand = ( core.GEOperatorFactory.create_operator( "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo" ) .set_input("x", scale) .set_input("shape", shape_tensor) ) bias_expand = ( core.GEOperatorFactory.create_operator( "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo" ) .set_input("x", bias) .set_input("shape", shape_tensor) ) layer_norm = ( core.GEOperatorFactory.create_operator( "layer_norm" + self._accumulated_op_id(), "LayerNorm" ) .set_input("x", x) .set_input("gamma", scale_expand) .set_input("beta", bias_expand) .set_attr_int32("begin_norm_axis", begin_norm_axis) .set_attr_int32("begin_params_axis", begin_norm_axis) .set_attr_float("epsilon", epsilon) ) cast_dtype = ( 0 if self.ascend_helper.dtype2paddle_inv_map[str(x_dtype)] == 0 else 1 ) y = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", layer_norm, 0) .set_attr_int32("dst_type", cast_dtype) ) mean = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", layer_norm, 1) .set_attr_int32("dst_type", cast_dtype) ) variance = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", layer_norm, 2) .set_attr_int32("dst_type", cast_dtype) ) return [y, mean, variance], [[1], [2], [0]] ## activate function class ReluParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "relu" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) relu = core.GEOperatorFactory.create_operator( "relu" + self._accumulated_op_id(), "Relu" ).set_input("x", x) return [relu], [[0]] class GeluParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "gelu" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) gelu = core.GEOperatorFactory.create_operator( "gelu" + self._accumulated_op_id(), "Gelu" ).set_input("x", x) return [gelu], [[0]] class TanhParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "tanh" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) tanh = core.GEOperatorFactory.create_operator( "tanh" + self._accumulated_op_id(), "Tanh" ).set_input("x", x) return [tanh], [[0]] ## loss function class SoftmaxWithCrossEntropyParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "softmax_with_cross_entropy" def _apply(self): label = self._get_ge_input(self.op.input_arg_names[0]) logits = self._get_ge_input(self.op.input_arg_names[1]) cls_num = self.op.block.var(self.op.input_arg_names[1]).shape[1] softmax = core.GEOperatorFactory.create_operator( "softmax" + self._accumulated_op_id(), "SoftmaxV2" ).set_input("x", logits) label = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", label) .set_attr_int32("dst_type", 3) ) tensoron = self._create_ge_tensor([1], 5, 1) on = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensoron) tensoroff = self._create_ge_tensor([1], 5, 0) off = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensoroff) self._mark_as_input(on) self._mark_as_input(off) onehot = ( core.GEOperatorFactory.create_operator( "onehot" + self._accumulated_op_id(), "OneHotD" ) .set_input("x", label) .set_input("on_value", on) .set_input("off_value", off) .set_attr_int32("depth", cls_num) ) squeeze = core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Squeeze" ).set_input("x", onehot) loss_all = ( core.GEOperatorFactory.create_operator( "loss" + self._accumulated_op_id(), "SoftmaxCrossEntropyWithLogits", ) .set_input("features", logits) .set_input("labels", squeeze) ) loss = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", loss_all, 0) .set_attr_int32("dst_type", 0) ) loss_expand = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self._accumulated_op_id(), "Unsqueeze" ) .set_input("x", loss) .set_attr_vec_int32("axes", [1]) ) return [label, softmax, loss_expand], [[2], [1]] class SoftMaxParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "softmax" def _apply(self): logits = self._get_ge_input(self.op.input_arg_names[0]) axes = self.op.attr("axis") softmax = ( core.GEOperatorFactory.create_operator( "softmax" + self._accumulated_op_id(), "SoftmaxV2" ) .set_input("x", logits) .set_attr_vec_int32("axes", [axes]) ) return [softmax], [[0]] ## general class ShapeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "shape" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", x) return [shape], [[0]] class FillConstantParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "fill_constant" def _apply(self): shape = self.op.attr("shape") dtype = self.op.attr("dtype") value = self.op.attr("value") tensor = self._create_ge_tensor(shape, dtype, value) const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) self._mark_as_input(const) if self.op.block.var(self.op.output('Out')[0]).persistable: # print("%s is Persistable in fill_constant" % # (self.op.output('Out')[0])) var = core.GEOperatorFactory.create_operator( self.op.output('Out')[0], "Variable" ) var.update_output_desc( "y", core.GETensorDesc( core.GEShape(shape), core.GEFormat.FORMAT_ND, core.GEDataType.DT_FLOAT, ), ) assign = ( core.GEOperatorFactory.create_operator( "assign" + self._accumulated_op_id(), "Assign" ) .set_input("value", const) .set_input("ref", var) ) return [const], [[0]] return [const], [[0]] class TruncatedNormalParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "truncated_gaussian_random" def _apply(self): shape = self.op.attr("shape") dtype = self.op.attr("dtype") mean = self.op.attr("mean") std = self.op.attr("std") seed = self.op.attr("seed") tensor1 = self._create_ge_tensor([len(shape)], 2, shape) shape_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor1) tensor2 = self._create_ge_tensor([1], dtype, mean) mean_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor2) tensor3 = self._create_ge_tensor([1], dtype, std) std_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor3) tensor4 = self._create_ge_tensor([1], dtype, mean - 2 * std) min_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor4) tensor5 = self._create_ge_tensor([1], dtype, mean + 2 * std) max_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor5) self._mark_as_input(shape_tensor) self._mark_as_input(mean_tensor) self._mark_as_input(std_tensor) self._mark_as_input(min_tensor) self._mark_as_input(max_tensor) truncated_normal = ( core.GEOperatorFactory.create_operator( "truncated_normal" + self._accumulated_op_id(), "ParameterizedTruncatedNormal", ) .set_input("shape", shape_tensor) .set_input("means", mean_tensor) .set_input("stdevs", std_tensor) .set_input("min", min_tensor) .set_input("max", max_tensor) .set_attr_int32("seed", 0) ) ## wirte the output of truncatedNormal from startup_program to main_program if self.op.block.var(self.op.output('Out')[0]).persistable: # print("%s is Persistable in truncated_normal" % # (self.op.output('Out')[0])) var = core.GEOperatorFactory.create_operator( self.op.output('Out')[0], "Variable" ) var.update_output_desc( "y", core.GETensorDesc( core.GEShape(shape), core.GEFormat.FORMAT_ND, core.GEDataType.DT_FLOAT, ), ) assign = ( core.GEOperatorFactory.create_operator( "assign" + self._accumulated_op_id(), "Assign" ) .set_input("value", truncated_normal) .set_input("ref", var) ) return [ shape_tensor, mean_tensor, std_tensor, min_tensor, max_tensor, truncated_normal, ], [[-1]] # else: # print( # "self.op.output('Out')[0] is not persistable in truncated_noraml" # ) return [truncated_normal], [[0]] class GatherParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "gather" def _apply(self): index = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) clo = self.op.block.var(self.op.input_arg_names[1]).shape[-1] gather = ( core.GEOperatorFactory.create_operator( "gather" + self._accumulated_op_id(), "Gather" ) .set_input("x", x) .set_input("indices", index) .set_attr_bool("validate_indices", True) ) return [gather], [[0]] class ScatterParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "scatter" def _apply(self): index = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) updates = self._get_ge_input(self.op.input_arg_names[2]) overwrite = self.op.attr("overwrite") index_shape = self.op.block.var(self.op.input_arg_names[0]).shape if len(index_shape) == 1: index = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self.getid(), "Unsqueeze" ) .set_input("x", index) .set_attr_vec_int32("axes", [1]) ) if not overwrite: scatter_value = ( core.GEOperatorFactory.create_operator( "scatter" + self._accumulated_op_id(), "TensorScatterAdd" ) .set_input("x", x) .set_input("indices", index) .set_input("updates", updates) ) else: scatter_value = ( core.GEOperatorFactory.create_operator( "scatter" + self._accumulated_op_id(), "TensorScatterUpdate" ) .set_input("x", x) .set_input("indices", index) .set_input("updates", updates) ) return [x, index, updates, scatter_value], [[-1]] class CastParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "cast" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) dtype = self.op.attr("out_dtype") cast = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x) .set_attr_int32("dst_type", dtype) ) return [cast], [[0]] class AssignParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "assign" def _apply(self): const = self._get_ge_input(self.op.input_arg_names[0]) var = self._get_ge_input(self.op.input_arg_names[1]) assign = ( core.GEOperatorFactory.create_operator( "assign" + self._accumulated_op_id(), "Assign" ) .set_input("value", const) .set_input("ref", var) ) return [assign], [[0]] class ScaleParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "scale" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) scale = self.op.attr("scale") bias = self.op.attr("bias") bias_after_scale = self.op.attr("bias_after_scale") if bias_after_scale: scale_value = ( core.GEOperatorFactory.create_operator( "scale" + self._accumulated_op_id(), "Power" ) .set_input("x", x) .set_attr_float("power", 1.0) .set_attr_float("scale", scale) .set_attr_float("shift", bias) ) else: x_add_bias = ( core.GEOperatorFactory.create_operator( "adds" + self._accumulated_op_id(), "Adds" ) .set_input("x", x) .set_attr_float("value", bias) ) scale_value = ( core.GEOperatorFactory.create_operator( "scale" + self._accumulated_op_id(), "Power" ) .set_input("x", x_add_bias) .set_attr_float("power", 1.0) .set_attr_float("scale", scale) .set_attr_float("shift", 0.0) ) return [scale_value], [[0]] class SliceParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "slice" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) axes = self.op.attr("axes") starts = self.op.attr("starts") ends = self.op.attr("ends") x_shape = self.op.block.var(self.op.input_arg_names[0]).shape len_shape = len(x_shape) axes_cor = list(range(len_shape)) starts_cor, ends_cor = [], [] cnt = 0 for i in range(len_shape): starts_cor.append(starts[cnt] if i in axes else 0) if i in axes and ends[cnt] <= x_shape[i]: ends_cor.append(ends[cnt]) else: ends_cor.append(x_shape[i]) if i in axes: cnt += 1 size = [ends_cor[i] - starts_cor[i] for i in range(len(axes_cor))] assert ( len(axes_cor) == len(starts_cor) == len(ends_cor) ), "the three fields must have same size" slice_value = ( core.GEOperatorFactory.create_operator( "slice" + self._accumulated_op_id(), "SliceD" ) .set_input("x", x) .set_attr_vec_int32("offsets", starts_cor) .set_attr_vec_int32("size", size) ) return [slice_value], [[0]] class ReshapeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "reshape2" def _apply(self): org_shape = self.op.block.var(self.op.input_arg_names[0]).shape assert org_shape.count(-1) == 0, "do not allow the dim is -1" shape = self.op.attr("shape") for cnt in range(len(shape)): if shape[cnt] == 0: shape[cnt] = org_shape[cnt] if -1 in shape: assert shape.count(-1) == 1, "only allow one dim is -1" mul_res_org = reduce(lambda x, y: x * y, org_shape) mul_res_refine = reduce(lambda x, y: x * y, shape) * -1 idx = shape.index(-1) shape[idx] = mul_res_org // mul_res_refine x = self._get_ge_input(self.op.input_arg_names[0]) tensor = self._create_ge_tensor([len(shape)], 2, shape) const_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) reshape = ( core.GEOperatorFactory.create_operator( "reshape" + self._accumulated_op_id(), "Reshape" ) .set_input("x", x) .set_input("shape", const_shape) .set_attr_int32("axis", 0) ) x_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", x) return [x_shape, reshape], [[1], [0]] class TransposeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "transpose2" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) perm = self.op.attr("axis") transpose = ( core.GEOperatorFactory.create_operator( "transpose" + self._accumulated_op_id(), "TransposeD" ) .set_input("x", x) .set_attr_vec_int32("perm", perm) ) x_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", x) return [x_shape, transpose], [[1], [0]] class AccuracyParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "accuracy" def _apply(self): pred = self._get_ge_input(self.op.input_arg_names[0]) label = self._get_ge_input(self.op.input_arg_names[1]) logits = self._get_ge_input(self.op.input_arg_names[2]) pred = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", pred) .set_attr_int32("dst_type", 3) ) label = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", label) .set_attr_int32("dst_type", 3) ) equal = ( core.GEOperatorFactory.create_operator( "equal" + self._accumulated_op_id(), "Equal" ) .set_input("x1", pred) .set_input("x2", label) ) cast = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", equal) .set_attr_int32("dst_type", 0) ) acc = ( core.GEOperatorFactory.create_operator( "mean" + self._accumulated_op_id(), "ReduceMeanD" ) .set_input("x", cast) .set_attr_bool("keep_dims", False) .set_attr_vec_int32("axes", []) ) correct = ( core.GEOperatorFactory.create_operator( "sum" + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", cast) .set_attr_bool("keep_dims", False) .set_attr_vec_int32("axes", []) ) ones_tensor = core.GEOperatorFactory.create_operator( "oneslike" + self._accumulated_op_id(), "OnesLike" ).set_input("x", label) ones_tensor = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", ones_tensor) .set_attr_int32("dst_type", 0) ) total = ( core.GEOperatorFactory.create_operator( "sum" + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", ones_tensor) .set_attr_bool("keep_dims", False) .set_attr_vec_int32("axes", []) ) return [acc, correct, total], [[0], [1], [2]] class TopkParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "top_k" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) k = self.op.attr("k") tensor = self._create_ge_tensor([1], 2, k) const_k = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) cast_x = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x) .set_attr_int32("dst_type", 1) ) topk = ( core.GEOperatorFactory.create_operator( "topk" + self._accumulated_op_id(), "TopK" ) .set_input("x", cast_x) .set_input("k", const_k) ) value = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", topk, 0) .set_attr_int32("dst_type", 0) ) index = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", topk, 1) .set_attr_int32("dst_type", 0) ) return [value, index], [[1], [0]] class LookupTableParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "lookup_table" def _apply(self): ids = self._get_ge_input(self.op.input_arg_names[0]) w = self._get_ge_input(self.op.input_arg_names[1]) ids_squeeze = ( core.GEOperatorFactory.create_operator( "squeeze" + self._accumulated_op_id(), "Squeeze" ) .set_input("x", ids) .set_attr_vec_int32("axes", [-1]) ) out = ( core.GEOperatorFactory.create_operator( "lookup" + self._accumulated_op_id(), "Gather" ) .set_input("x", w) .set_input("indices", ids_squeeze) ) return [out], [[0]] class StackParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "stack" def _apply(self): tiles = len(self.op.input_arg_names) data_x_lst = [] for index in range(tiles): data_x_lst.append( self._get_ge_input(self.op.input_arg_names[index]) ) axis = self.op.attr("axis") data_x = data_x_lst[0] tensor = self._create_ge_tensor([1], 2, axis) tensor_axis = core.GEOperatorFactory.create_operator( "axis" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) expand = ( core.GEOperatorFactory.create_operator( "expand" + self._accumulated_op_id(), "ExpandDims" ) .set_input("x", data_x) .set_input("axis", tensor_axis) ) stack = ( core.GEOperatorFactory.create_operator( "stack" + self._accumulated_op_id(), "TileWithAxis" ) .set_input("x", expand) .set_attr_int32("axis", axis) .set_attr_int32("tiles", tiles) ) return [stack], [[0]] class UnSqueezeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "unsqueeze2" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) axes = self.op.attr('axes') output = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self._accumulated_op_id(), "Unsqueeze" ) .set_input("x", x) .set_attr_vec_int32("axes", axes) ) shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", output) return [shape, output], [[1], [0]] ## parallel class AllGatherParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "c_allgather" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) rank_size = self.op.attr("rank_size") group = self.op.attr("group") allgather = ( core.GEOperatorFactory.create_operator( "allgather" + self._accumulated_op_id(), "HcomAllGather" ) .set_input("x", x) .set_attr_int32("rank_size", rank_size) .set_attr_string("group", group) ) return [allgather], [[0]] class AllReduceParser(AscendParserBase): def __init__(self, graph, var2geop, reduction): super().__init__(graph, var2geop) self.parser_name = "c_allreduce_" + reduction self.reduction = reduction def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) reduction = self.reduction ring_id = self.op.attr("ring_id") group = "hcom_group_" + str(ring_id) fusion = None # self.op.attr("fusion") fusion_id = None # self.op.attr("fusion_id") allreduce = ( core.GEOperatorFactory.create_operator( "allreduce" + self._accumulated_op_id(), "HcomAllReduce" ) .set_input("x", x) .set_attr_string("reduction", reduction) .set_attr_string("group", group) ) if fusion is not None: allreduce.set_attr_int32("fusion", fusion) if fusion_id is not None: allreduce.set_attr_int32("fusion_id", fusion_id) return [allreduce], [[0]] class AllReduceSumParser(AllReduceParser): def __init__(self, graph, var2geop): super().__init__(graph, var2geop, 'sum') class AllReduceMaxParser(AllReduceParser): def __init__(self, graph, var2geop): super().__init__(graph, var2geop, 'max') class BroadcastParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "c_broadcast" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) root_rank = self.op.attr("root_rank") group = self.op.attr("group") broadcast = ( core.GEOperatorFactory.create_operator( "broadcast" + self._accumulated_op_id(), "HcomBroadcast" ) .set_input("x", x) .set_attr_int32("root_rank", root_rank) .set_attr_string("group", group) ) return [broadcast], [[0]] class ReduceScatterParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "c_reduce_scatter" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) reduction = self.op.attr("reduction") group = self.op.attr("group") rank_size = self.op.attr("rank_size") reduce_scatter = ( core.GEOperatorFactory.create_operator( "reducescatter" + self._accumulated_op_id(), "HcomReduceScatter" ) .set_input("x", x) .set_attr_string("reduction", reduction) .set_attr_string("group", group) .set_attr_int32("rank_size", rank_size) ) return [reduce_scatter], [[0]] class SendParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "c_send" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) sr_tag = self.op.attr("sr_tag") dest_rank = self.op.attr("dest_rank") group = self.op.attr("group") send = ( core.GEOperatorFactory.create_operator( "send" + self._accumulated_op_id(), "HcomSend" ) .set_input("x", x) .set_attr_int32("sr_tag", sr_tag) .set_attr_int32("dest_rank", dest_rank) .set_attr_string("group", group) ) return [send], [[0]] class ReceiveParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "c_receive" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) sr_tag = self.op.attr("sr_tag") src_rank = self.op.attr("src_rank") group = self.op.attr("group") shape = self.op.attr("shape") dtype = self.op.attr("dtype") receive = ( core.GEOperatorFactory.create_operator( "receive" + self._accumulated_op_id(), "HcomReceive" ) .set_input("x", x) .set_attr_int32("sr_tag", sr_tag) .set_attr_int32("src_rank", src_rank) .set_attr_string("group", group) .set_attr_vec_int32("shape", shape) .set_attr_int32("dtype", dtype) ) return [receive], [[0]] class RangeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "range" def _apply(self): # TODO not support range type yet start = self._get_ge_input(self.op.input_arg_names[0]) end = self._get_ge_input(self.op.input_arg_names[1]) delta = self._get_ge_input(self.op.input_arg_names[2]) ge_range = ( core.GEOperatorFactory.create_operator( "range" + self._accumulated_op_id(), "Range" ) .set_input("start", end) .set_input("limit", start) .set_input("delta", delta) ) return [ge_range], [[0]] class UniformRandomParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "uniform_random" def _apply(self): shape = self.op.attr("shape") min_v = self.op.attr("min") max_v = self.op.attr("max") seed = self.op.attr("seed") dtype = self.op.attr("dtype") assert max_v > min_v, ( "assert max_v > min_v, but received " + "as max_v={}, min_v={} ".format(max_v, min_v) ) tensor1 = self._create_ge_tensor([len(shape)], 2, shape) shape_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor1) ge_ur = ( core.GEOperatorFactory.create_operator( "uniform_random" + self._accumulated_op_id(), "RandomUniform" ) .set_input("shape", shape_tensor) .set_attr_dtype("dtype", self.ascend_helper.dtype2ge(dtype)) .set_attr_int32("seed", seed) .set_attr_int32("seed2", seed) ) scale = max_v - min_v scale_value = ( core.GEOperatorFactory.create_operator( "scale" + self._accumulated_op_id(), "Power" ) .set_input("x", ge_ur) .set_attr_float("power", 1.0) .set_attr_float("scale", scale) .set_attr_float("shift", min_v) ) return [scale_value], [[0]] class EqualParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "equal" def _apply(self): data_x1 = self._get_ge_input(self.op.input_arg_names[0]) data_x2 = self._get_ge_input(self.op.input_arg_names[1]) equal = ( core.GEOperatorFactory.create_operator( "equal" + self._accumulated_op_id(), "Equal" ) .set_input("x1", data_x1) .set_input("x2", data_x2) ) return [equal], [[0]] class ExpandParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "expand" def _apply(self): data_x1_shape = self._get_ge_input(self.op.input_arg_names[0]) expand_times = self.op.attr('expand_times') tensor = self._create_ge_tensor([len(expand_times)], 2, expand_times) expand_tensor = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) assign = ( core.GEOperatorFactory.create_operator( "tile" + self._accumulated_op_id(), "Tile" ) .set_input("x", data_x1_shape) .set_input("multiples", expand_tensor) ) return [assign], [[0]] class SqueezeParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "squeeze2" def _apply(self): tensor = self._get_ge_input(self.op.input_arg_names[0]) axes = self.op.attr("axes") data_squeezed = ( core.GEOperatorFactory.create_operator( "squeeze" + self._accumulated_op_id(), "Squeeze" ) .set_input("x", tensor) .set_attr_vec_int32("axes", axes) ) shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", data_squeezed) return [shape, data_squeezed], [[1], [0]] # ****************************************************************# # *************************** *************************# # *************************** *************************# # *************************** GradParser *************************# # *************************** *************************# # *************************** *************************# # ****************************************************************# ## grad class ReduceSumGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "reduce_sum_grad" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) input = self._get_ge_input(self.op.input_arg_names[1]) shape_tensor = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", input, 0) tensoron = self._create_ge_tensor([1], 2, -1) const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensoron) self._mark_as_input(const) reduce_sum = ( core.GEOperatorFactory.create_operator( "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo" ) .set_input("x", x) .set_input("shape", shape_tensor) ) # reduce_sum = core.GEOperatorFactory.create_operator("expand" + self._accumulated_op_id(), "ExpandDims").set_input("x", reduce_sum).set_input("axis", const) return [reduce_sum], [[0]] class MatMulGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "matmul_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) y = self._get_ge_input(self.op.input_arg_names[2]) transpose_x = self.op.attr("transpose_X") transpose_y = self.op.attr("transpose_Y") out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape x_shape = self.op.block.var(self.op.input_arg_names[1]).shape y_shape = self.op.block.var(self.op.input_arg_names[2]).shape if len(x_shape) > 2: if transpose_y: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "BatchMatMul", ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("adj_x1", False) .set_attr_bool("adj_x2", False) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "BatchMatMul", ) .set_input("x1", out_grad) .set_input("x2", x) .set_attr_bool("adj_x1", True) .set_attr_bool("adj_x2", False) ) else: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "BatchMatMul", ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("adj_x1", False) .set_attr_bool("adj_x2", True) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "BatchMatMul", ) .set_input("x1", x) .set_input("x2", out_grad) .set_attr_bool("adj_x1", True) .set_attr_bool("adj_x2", False) ) else: if transpose_y: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("transpose_x1", False) .set_attr_bool("transpose_x2", False) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", out_grad) .set_input("x2", x) .set_attr_bool("transpose_x1", True) .set_attr_bool("transpose_x2", False) ) else: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("transpose_x1", False) .set_attr_bool("transpose_x2", True) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", x) .set_input("x2", out_grad) .set_attr_bool("transpose_x1", True) .set_attr_bool("transpose_x2", False) ) return [x_grad, y_grad], [[0], [1]] class MulGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "mul_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) y = self._get_ge_input(self.op.input_arg_names[2]) x_num_col_dims = self.op.attr("x_num_col_dims") y_num_col_dims = self.op.attr("y_num_col_dims") shape_out_grad = self.op.block.var(self.op.input_arg_names[0]).shape shape_x = self.op.block.var(self.op.input_arg_names[1]).shape shape_y = self.op.block.var(self.op.input_arg_names[2]).shape if x_num_col_dims == 1 and y_num_col_dims == 1: if len(shape_x) == 2 and len(shape_y) == 2: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("transpose_x1", False) .set_attr_bool("transpose_x2", True) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", x) .set_input("x2", out_grad) .set_attr_bool("transpose_x1", True) .set_attr_bool("transpose_x2", False) ) elif len(shape_x) == 3 and len(shape_y) == 2: flatten_x = core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "Flatten" ).set_input("x", x) x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", out_grad) .set_input("x2", y) .set_attr_bool("transpose_x1", False) .set_attr_bool("transpose_x2", True) ) if len(shape_out_grad) == 2: x_grad = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self._accumulated_op_id(), "Unsqueeze" ) .set_input("x", x_grad) .set_attr_vec_int32("axes", [1]) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", flatten_x) .set_input("x2", out_grad) .set_attr_bool("transpose_x1", True) .set_attr_bool("transpose_x2", False) ) else: if len(shape_x) == 3 and len(shape_y) == 2: assert x_num_col_dims == 2, "only support 2" flatten_x = ( core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "FlattenV2" ) .set_input("x", x) .set_attr_int32("axis", 0) .set_attr_int32("end_axis", 1) ) flatten_out_grad = ( core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "FlattenV2" ) .set_input("x", out_grad) .set_attr_int32("axis", 0) .set_attr_int32("end_axis", 1) ) y_unsqueeze = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self._accumulated_op_id(), "Unsqueeze" ) .set_input("x", y) .set_attr_vec_int32("axes", [0]) ) y_stack = ( core.GEOperatorFactory.create_operator( "stack" + self._accumulated_op_id(), "TileWithAxis" ) .set_input("x", y_unsqueeze) .set_attr_int32("axis", 0) .set_attr_int32("tiles", shape_out_grad[0]) ) x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "BatchMatMul", ) .set_input("x1", out_grad) .set_input("x2", y_stack) .set_attr_bool("adj_x1", False) .set_attr_bool("adj_x2", True) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "MatMul" ) .set_input("x1", flatten_x) .set_input("x2", flatten_out_grad) .set_attr_bool("transpose_x1", True) .set_attr_bool("transpose_x2", False) ) return [x_grad, y_grad], [[0], [1]] class ReluGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "relu_grad" def _apply(self): out = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) relu_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "ReluGrad" ) .set_input("gradients", out_grad) .set_input("features", out) ) return [relu_grad], [[0]] class SoftmaxWithCrossEntropyGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "softmax_with_cross_entropy_grad" def _apply(self): label = self._get_ge_input(self.op.input_arg_names[0]) loss_grad = self._get_ge_input(self.op.input_arg_names[1]) softmax = self._get_ge_input(self.op.input_arg_names[2]) cls_num = self.op.block.var(self.op.input_arg_names[2]).shape[1] label_shape = self.op.block.var(self.op.input_arg_names[0]).shape loss_grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape softmax_shape = self.op.block.var(self.op.input_arg_names[2]).shape tensoron = self._create_ge_tensor([1], 5, 1) on = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensoron) tensoroff = self._create_ge_tensor([1], 5, 0) off = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensoroff) self._mark_as_input(on) self._mark_as_input(off) label = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", label) .set_attr_int32("dst_type", 3) ) onehot = ( core.GEOperatorFactory.create_operator( "onehot" + self._accumulated_op_id(), "OneHotD" ) .set_input("x", label) .set_input("on_value", on) .set_input("off_value", off) .set_attr_int32("depth", cls_num) ) squeeze = core.GEOperatorFactory.create_operator( "suqeeze" + self._accumulated_op_id(), "Squeeze" ).set_input("x", onehot) sub = ( core.GEOperatorFactory.create_operator( "sub" + self._accumulated_op_id(), "Sub" ) .set_input("x1", softmax) .set_input("x2", squeeze) ) grad = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Mul" ) .set_input("x1", loss_grad) .set_input("x2", sub) ) return [on, off, label, onehot, grad], [[-1]] class DotMulGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_mul_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) out_1 = self._get_ge_input(self.op.input_arg_names[1]) out_2 = self._get_ge_input(self.op.input_arg_names[2]) x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "Mul" ) .set_input("x1", out_grad) .set_input("x2", out_2) ) y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "Mul" ) .set_input("x1", out_1) .set_input("x2", out_grad) ) return [x_grad, y_grad], [[0], [1]] class DotAddGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_add_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) out_1 = self._get_ge_input(self.op.input_arg_names[1]) out_2 = self._get_ge_input(self.op.input_arg_names[2]) out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape out_1_shape = self.op.block.var(self.op.input_arg_names[1]).shape out_2_shape = self.op.block.var(self.op.input_arg_names[2]).shape x_grad = out_grad cur_time_x = len(out_grad_shape) - len(out_1_shape) for i in range(cur_time_x): x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", x_grad) .set_attr_vec_int32("axes", [0]) .set_attr_bool("keep_dims", False) ) for axis, size in enumerate(out_1_shape): if size == 1: x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "ReduceSumD", ) .set_input("x", x_grad) .set_attr_vec_int32("axes", [axis]) .set_attr_bool("keep_dims", True) ) y_grad = out_grad cur_time_y = len(out_grad_shape) - len(out_2_shape) for i in range(cur_time_y): y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", y_grad) .set_attr_vec_int32("axes", [0]) .set_attr_bool("keep_dims", False) ) for axis, size in enumerate(out_2_shape): if size == 1: y_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "ReduceSumD", ) .set_input("x", y_grad) .set_attr_vec_int32("axes", [axis]) .set_attr_bool("keep_dims", True) ) return [x_grad, y_grad], [[0], [1]] class DotDivGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "elementwise_div_grad" def _apply(self): out = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) x = self._get_ge_input(self.op.input_arg_names[2]) y = self._get_ge_input(self.op.input_arg_names[3]) y_power = ( core.GEOperatorFactory.create_operator( "power" + self._accumulated_op_id(), "Power" ) .set_input("x", y) .set_attr_float("power", -1) ) tensor_zeros = core.GEOperatorFactory.create_operator( "zeroslike" + self._accumulated_op_id(), "ZerosLike" ).set_input("x", x) x_zero = ( core.GEOperatorFactory.create_operator( "equal" + self._accumulated_op_id(), "Equal" ) .set_input("x1", x) .set_input("x2", tensor_zeros) ) x_nozero = core.GEOperatorFactory.create_operator( "logical_not" + self._accumulated_op_id(), "LogicalNot" ).set_input("x", x_zero) x_nozero_f = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x_nozero) .set_attr_int32("dst_type", 0) ) x_grad_w = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Mul" ) .set_input("x1", x_nozero_f) .set_input("x2", y_power) ) x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "Mul" ) .set_input("x1", x_grad_w) .set_input("x2", out_grad) ) y_grad_w = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Mul" ) .set_input("x1", out) .set_input("x2", y_power) ) y_grad = ( core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Mul" ) .set_input("x1", y_grad_w) .set_input("x2", out_grad) ) return [x_grad, y_grad], [[0], [1]] class SoftmaxGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "softmax_grad" def _apply(self): out = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "SoftmaxGrad" ) .set_input("softmax", out) .set_input("grad_softmax", out_grad) ) return [x_grad], [[0]] class ReshapeGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "reshape2_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) x_shape = self._get_ge_input(self.op.input_arg_names[1]) x_shape_list = self.op.block.var(self.op.input_arg_names[1]).shape if x_shape_list[0] == 0: x_shape_delzero = x_shape_list[1:] tensor = self._create_ge_tensor( [len(x_shape_delzero)], 2, x_shape_delzero ) const_shape = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", tensor) x_grad = ( core.GEOperatorFactory.create_operator( "reshape" + self._accumulated_op_id(), "Reshape" ) .set_input("x", out_grad) .set_input("shape", const_shape) ) return [x_grad], [[0]] class GatherGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "gather_grad" def _apply(self): index = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) x = self._get_ge_input(self.op.input_arg_names[2]) index_shape = self.op.block.var(self.op.input_arg_names[0]).shape out_grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape x_shape = self.op.block.var(self.op.input_arg_names[2]).shape if len(index_shape) == 1: index = ( core.GEOperatorFactory.create_operator( "unsqueeze" + self._accumulated_op_id(), "Unsqueeze" ) .set_input("x", index) .set_attr_vec_int32("axes", [1]) ) tensor_zeros = core.GEOperatorFactory.create_operator( "zeroslike" + self._accumulated_op_id(), "ZerosLike" ).set_input("x", x) x_grad = ( core.GEOperatorFactory.create_operator( "scatter" + self._accumulated_op_id(), "TensorScatterUpdate" ) .set_input("x", tensor_zeros) .set_input("indices", index) .set_input("updates", out_grad) ) return [tensor_zeros, x_grad], [[-1]] class TransposeGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "transpose2_grad" def _apply(self): out_grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) perm = self.op.attr("axis") x_shape = self.op.block.var(self.op.input_arg_names[1]).shape[1:] out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape assert list(map(lambda x: out_grad_shape[x], perm)) == list(x_shape) x_grad = ( core.GEOperatorFactory.create_operator( "transpose" + self._accumulated_op_id(), "TransposeD" ) .set_input("x", out_grad) .set_attr_vec_int32("perm", perm) ) return [x_grad], [[0]] class LayerNormGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "layer_norm_grad" def _apply(self): bias = self._get_ge_input(self.op.input_arg_names[0]) mean = self._get_ge_input(self.op.input_arg_names[1]) scale = self._get_ge_input(self.op.input_arg_names[2]) variance = self._get_ge_input(self.op.input_arg_names[3]) x = self._get_ge_input(self.op.input_arg_names[4]) out_grad = self._get_ge_input(self.op.input_arg_names[5]) x_dtype = self.op.block.var(self.op.input_arg_names[4]).dtype x_grad = ( core.GEOperatorFactory.create_operator( self.parser_name + self._accumulated_op_id(), "LayerNormGrad" ) .set_input("dy", out_grad) .set_input("x", x) .set_input("variance", variance) .set_input("mean", mean) .set_input("gamma", scale) ) cast_dtype = ( 0 if self.ascend_helper.dtype2paddle_inv_map[str(x_dtype)] == 0 else 1 ) out_x_grad = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x_grad, 0) .set_attr_int32("dst_type", cast_dtype) ) out_scale_grad = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x_grad, 1) .set_attr_int32("dst_type", cast_dtype) ) out_bias_grad = ( core.GEOperatorFactory.create_operator( "cast" + self._accumulated_op_id(), "Cast" ) .set_input("x", x_grad, 2) .set_attr_int32("dst_type", cast_dtype) ) return [out_x_grad, out_scale_grad, out_bias_grad], [[2], [1], [0]] class TanhGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = 'tanh_grad' def _apply(self): y = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) tanh_grad = ( core.GEOperatorFactory.create_operator( "tanh_grad" + self._accumulated_op_id(), "TanhGrad" ) .set_input("y", y) .set_input("dy", out_grad) ) return [tanh_grad], [[0]] class LogGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = 'log_grad' def _apply(self): grad = self._get_ge_input(self.op.input_arg_names[0]) input = self._get_ge_input(self.op.input_arg_names[1]) log_grad = ( core.GEOperatorFactory.create_operator( "log_grad" + self._accumulated_op_id(), "DivNoNan" ) .set_input("x1", grad) .set_input("x2", input) ) return [log_grad], [[0]] class SqrtGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "sqrt_grad" def _apply(self): y = self._get_ge_input(self.op.input_arg_names[0]) out_grad = self._get_ge_input(self.op.input_arg_names[1]) sqrt_grad = ( core.GEOperatorFactory.create_operator( "sqrt_grad" + self._accumulated_op_id(), "SqrtGrad" ) .set_input("y", y) .set_input("dy", out_grad) ) return [sqrt_grad] class PowGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "pow_grad" def _apply(self): grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) factor = self.op.attr("factor") shape_tensor = self._create_shape_tensor() shape_tensor = core.GEOperatorFactory.create_operator( "shape" + self._accumulated_op_id(), "Shape" ).set_input("x", x) factor_scale = self._create_ge_tensor([1], 5, factor) factor_scale = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", factor_scale) factor_tensor = ( core.GEOperatorFactory.create_operator( "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo" ) .set_input("x", factor_scale) .set_input("shape", shape_tensor) ) x_power = ( core.GEOperatorFactory.create_operator( "x_power" + self._accumulated_op_id(), "Power" ) .set_input("x", x) .set_attr_float("power", factor - 1) ) x_power_mul_factor = ( core.GEOperatorFactory.create_operator( "x_power_mul_factor" + self._accumulated_op_id(), "Mul" ) .set_input("x1", x) .set_input("x2", factor_tensor) ) x_power_mul_factor_grad = ( core.GEOperatorFactory.create_operator( "x_power_mul_factor_grad" + self._accumulated_op_id(), "Mul" ) .set_input("x1", x_power_mul_factor) .set_input("x2", grad) ) return [x_power_mul_factor_grad], [[0]] class GeluGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "gelu_grad" def _apply(self): grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) y = core.GEOperatorFactory.create_operator( "gelu" + self._accumulated_op_id(), "Gelu" ).set_input("x", x) gelu_grad = ( core.GEOperatorFactory.create_operator( "gelu_grad" + self._accumulated_op_id(), "GeluGrad" ) .set_input("x", x) .set_input("dy", grad) .set_input("y", y) ) return [gelu_grad], [[0]] class MeanGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "mean_grad" def _apply(self): grad = self._get_ge_input(self.op.input_arg_names[0]) x = self._get_ge_input(self.op.input_arg_names[1]) ones_tensor = core.GEOperatorFactory.create_operator( "one_tensor" + self._accumulated_op_id(), "OnesLike" ).set_input("x", x) sum = ( core.GEOperatorFactory.create_operator( "mean" + self._accumulated_op_id(), "ReduceSumD" ) .set_input("x", ones_tensor) .set_attr_bool("keep_dims", False) .set_attr_vec_int32("axes", []) ) mean = ( core.GEOperatorFactory.create_operator( "x_power" + self._accumulated_op_id(), "Power" ) .set_input("x", sum) .set_attr_float("power", -1) ) mean_grad = ( core.GEOperatorFactory.create_operator( "mean_grad" + self._accumulated_op_id(), "Mul" ) .set_input("x1", mean) .set_input("x2", grad) ) return [mean_grad], [[0]] class SliceGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "slice_grad" def _apply(self): x = self._get_ge_input(self.op.input_arg_names[0]) grad = self._get_ge_input(self.op.input_arg_names[1]) axes = self.op.attr("axes") starts = self.op.attr("starts") ends = self.op.attr("ends") x_shape = self.op.block.var(self.op.input_arg_names[0]).shape grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape len_shape = len(x_shape) axes_cor = list(range(len_shape)) starts_cor, ends_cor = [], [] cnt = 0 for i in range(len_shape): starts_cor.append(starts[cnt] if i in axes else 0) if i in axes and ends[cnt] <= x_shape[i]: ends_cor.append(x_shape[i] - ends[cnt]) else: ends_cor.append(0) if i in axes: cnt += 1 starts_cor[0] = 0 ends_cor[0] = 0 paddings = [[s, e] for (s, e) in zip(starts_cor, ends_cor)] slice_value = ( core.GEOperatorFactory.create_operator( "slice_grad" + self._accumulated_op_id(), "PadD" ) .set_input("x", grad) .set_attr_vec_vec_int64("paddings", paddings) ) return [slice_value], [[0]] class LookUpTableGradParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "lookup_table_grad" def _apply(self): ids = self._get_ge_input(self.op.input_arg_names[0]) grad = self._get_ge_input(self.op.input_arg_names[1]) embedding = self._get_ge_input(self.op.input_arg_names[2]) shape_ids = self.op.block.var(self.op.input_arg_names[0]).shape shape_grad = self.op.block.var(self.op.input_arg_names[1]).shape shape_embedding = self.op.block.var(self.op.input_arg_names[2]).shape ids_flatten = ( core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "FlattenV2" ) .set_input("x", ids) .set_attr_int32("axis", 0) .set_attr_int32("end_axis", 1) ) grad_flatten = ( core.GEOperatorFactory.create_operator( "flatten" + self._accumulated_op_id(), "FlattenV2" ) .set_input("x", grad) .set_attr_int32("axis", 0) .set_attr_int32("end_axis", 1) ) tensor_zeros = core.GEOperatorFactory.create_operator( "zeroslike" + self._accumulated_op_id(), "ZerosLike" ).set_input("x", embedding) embedding_grad = ( core.GEOperatorFactory.create_operator( "scatteradd" + self._accumulated_op_id(), "TensorScatterAdd" ) .set_input("x", tensor_zeros) .set_input("indices", ids_flatten) .set_input("updates", grad_flatten) ) return [embedding_grad], [[0]] class SGDParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "sgd" def _apply(self): grad = self._get_ge_input(self.op.input_arg_names[0]) lr = self._get_ge_input(self.op.input_arg_names[1]) param = self._get_ge_input(self.op.input_arg_names[2]) sgd = ( core.GEOperatorFactory.create_operator( "momentum" + self._accumulated_op_id(), "ApplyGradientDescent" ) .set_input("var", param) .set_input("alpha", lr) .set_input("delta", grad) ) return [sgd], [[0]] class AdamParser(AscendParserBase): def __init__(self, graph, var2geop): super().__init__(graph, var2geop) self.parser_name = "adam" def _apply(self): beta1_power = self._get_ge_input(self.op.input_arg_names[0]) beta2_power = self._get_ge_input(self.op.input_arg_names[1]) grad = self._get_ge_input(self.op.input_arg_names[2]) lr = self._get_ge_input(self.op.input_arg_names[3]) moment1 = self._get_ge_input(self.op.input_arg_names[4]) moment2 = self._get_ge_input(self.op.input_arg_names[5]) param = self._get_ge_input(self.op.input_arg_names[6]) beta1 = self.op.attr('beta1') beta2 = self.op.attr('beta2') epsilon = self.op.attr('epsilon') beta1 = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", self._create_ge_tensor([1], 5, beta1)) beta2 = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", self._create_ge_tensor([1], 5, beta2)) epsilon = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const" ).set_attr_tensor("value", self._create_ge_tensor([1], 5, epsilon)) adam = ( core.GEOperatorFactory.create_operator( "adam" + self._accumulated_op_id(), "ApplyAdam" ) .set_input("var", param) .set_input("m", moment1) .set_input("v", moment2) .set_input("beta1_power", beta1_power) .set_input("beta2_power", beta2_power) .set_input("lr", lr) .set_input("beta1", beta1) .set_input("beta2", beta2) .set_input("epsilon", epsilon) .set_input("grad", grad) ) return [adam], [[0]]