# 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.framework as framework from paddle.fluid.optimizer import Optimizer import paddle.fluid.core as core import numpy as np registerd_op = { "elementwise_add": "AddParser", "matmul": "MatMulParser", "mul": "MulParser", "relu": "ReluParser", "softmax_with_cross_entropy": "SoftmaxWithCrossEntropyParser", "shape": "ShapeParser", "fill_constant": "FillConstantParser", "reduce_sum": "ReduceSumParser", "reduce_sum_grad": "ReduceSumGradParser", "matmul_grad": "MatMulGradParser", "mul_grad": "MulGradParser", "reshape2": "ReshapeParser", "scale": "ScaleParser", "relu_grad": "ReluGradParser", "softmax_with_cross_entropy_grad": "SoftmaxWithCrossEntropyGradParser", "truncated_gaussian_random": "TruncatedNormalParser", "sgd": "SGDParser" } 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" } 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" % (dtype) 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]) print("%d argument: %s" % (output_id, str(arguments))) 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)): print("assgin index_list[%d][%d] to %s" % (output_id, i, arguments[i])) 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 return "." + str(global_cnt) 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 class AddParser(AscendParserBase): def __init__(self, graph, var2geop): super(AddParser, self).__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 ReduceSumParser(AscendParserBase): def __init__(self, graph, var2geop): super(ReduceSumParser, self).__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_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 ReduceSumGradParser(AscendParserBase): def __init__(self, graph, var2geop): super(ReduceSumGradParser, self).__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) axis_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const").set_attr_tensor( "value", self._create_ge_tensor([1], 2, -1)) self._mark_as_input(axis_const) broadcast = core.GEOperatorFactory.create_operator( "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo").set_input("x", x).set_input("shape", shape_tensor) # unsqueeze cannot get right result, but ExpandDims seems have the same functionality. reduce_sum_grad = core.GEOperatorFactory.create_operator( "expand" + self._accumulated_op_id(), "ExpandDims").set_input( "x", broadcast).set_input("axis", axis_const) return [shape_tensor, axis_const, broadcast, reduce_sum_grad], [[3]] class MatMulParser(AscendParserBase): def __init__(self, graph, var2geop): super(MatMulParser, self).__init__(graph, var2geop) self.parser_name = "matmul" def _apply(self): x1 = self._get_ge_input(self.op.input_arg_names[0]) x2 = self._get_ge_input(self.op.input_arg_names[1]) matmul = core.GEOperatorFactory.create_operator( "matmul" + self._accumulated_op_id(), "MatMul").set_input( "x1", x1).set_input("x2", x2) return [matmul], [[0]] class MatMulGradParser(AscendParserBase): def __init__(self, graph, var2geop): super(MatMulGradParser, self).__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]) 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(MulGradParser, self).__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_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 MulParser(AscendParserBase): def __init__(self, graph, var2geop): super(MulParser, self).__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]) matmul = core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "MatMul").set_input( "x1", x).set_input("x2", y) return [matmul], [[0]] class ReluParser(AscendParserBase): def __init__(self, graph, var2geop): super(ReluParser, self).__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 ReluGradParser(AscendParserBase): def __init__(self, graph, var2geop): super(ReluGradParser, self).__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 SoftmaxWithCrossEntropyParser(AscendParserBase): def __init__(self, graph, var2geop): super(SoftmaxWithCrossEntropyParser, self).__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_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const").set_attr_tensor( "value", tensoron) self._mark_as_input(on_const) tensoroff = self._create_ge_tensor([1], 5, 0) off_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const").set_attr_tensor( "value", tensoroff) self._mark_as_input(off_const) onehot = core.GEOperatorFactory.create_operator( "onehot" + self._accumulated_op_id(), "OneHotD").set_input( "x", label).set_input("on_value", on_const).set_input( "off_value", off_const).set_attr_int32("depth", cls_num) squeeze = core.GEOperatorFactory.create_operator( "mul" + self._accumulated_op_id(), "Squeeze").set_input("x", onehot) loss = core.GEOperatorFactory.create_operator( "loss" + self._accumulated_op_id(), "SoftmaxCrossEntropyWithLogits").set_input( "features", logits).set_input("labels", squeeze) return [label, softmax, on_const, off_const, onehot, squeeze, loss], [[6], [1]] class SoftmaxWithCrossEntropyGradParser(AscendParserBase): def __init__(self, graph, var2geop): super(SoftmaxWithCrossEntropyGradParser, self).__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] tensoron = self._create_ge_tensor([1], 5, 1) on_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const").set_attr_tensor( "value", tensoron) self._mark_as_input(on_const) tensoroff = self._create_ge_tensor([1], 5, 0) off_const = core.GEOperatorFactory.create_operator( "const" + self._accumulated_op_id(), "Const").set_attr_tensor( "value", tensoroff) self._mark_as_input(off_const) 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_const).set_input( "off_value", off_const).set_attr_int32("depth", cls_num) # the fuck onehot will add a demension, so must call squeeze afterward squeeze = core.GEOperatorFactory.create_operator( "mul" + 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_const, off_const, label, onehot, squeeze, sub, grad], [[-1]] class ShapeParser(AscendParserBase): def __init__(self, graph, var2geop): super(ShapeParser, self).__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(FillConstantParser, self).__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") print("shape: ", shape) print("dtype: ", dtype) print("value: ", 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 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]] else: print( "self.op.output('Out')[0] is not persistable in fill_constant") return [const], [[0]] class SGDParser(AscendParserBase): def __init__(self, graph, var2geop): super(SGDParser, self).__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 TruncatedNormalParser(AscendParserBase): def __init__(self, graph, var2geop): super(TruncatedNormalParser, self).__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").set_input("x", truncated_normal) 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]] #[assign] class ScaleParser(AscendParserBase): def __init__(self, graph, var2geop): super(ScaleParser, self).__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") #self.get_ge_input(self.op.input_arg_names[1]) 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) #set_input("x2", 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) #tensor_zeros = core.GEOperatorFactory.create_operator("zeroslike" + self.getid(), "ZerosLike").set_input("x", x) #bias_ = self.create_ge_tensor([1], 5, bias) #const_bias = core.GEOperatorFactory.create_operator("const" + self.getid(), "Const").set_attr_tensor("value", tensor_bias) return [scale_value],[[0]] class ReshapeParser(AscendParserBase): def __init__(self, graph, var2geop): super(ReshapeParser, self).__init__(graph, var2geop) self.parser_name = "reshape2" def _apply(self): print("swbuf:", self.op.input_arg_names) shape = self.op.attr("shape") axis = 0 if shape[0] == -1: axis = 1 shape = shape[1:] print("shape: ", shape) data_x1_shape = 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", data_x1_shape).set_input("shape", const_shape).set_attr_int32("axis", axis) return [reshape, reshape], [[0],[1]]