# Copyright (c) 2020 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 copy import sys import numpy as np from x2paddle.optimizer.pattern_matcher import FuseBase from x2paddle.core.program import PaddleGraph, PaddleLayer from x2paddle.core.util import * class TransposeElimination(FuseBase): def __init__(self): super(TransposeElimination, self).__init__() self.direct_layers = [ 'paddle.nn.ReLU', 'paddle.nn.ReLU6', 'paddle.abs', 'paddle.nn.Sigmoid', 'paddle.exp', 'paddle.rsqrt', 'paddle.nn.Swish', 'paddle.nn.Tanh', 'paddle.nn.Softplus', 'paddle.nn.LeakyReLU', 'paddle.floor', 'paddle.erf', 'paddle.square' ] self.elementwise_layers = [ 'paddle.add', 'fluid.layers.elementwise_sub', 'paddle.multiply', 'paddle.divide' ] self.reduce_layers = [ 'paddle.mean', 'paddle.all', 'paddle.max', 'paddle.any', 'paddle.sum', 'paddle.prod' ] def get_transpose_num(self, graph): count = 0 for layer_id, layer in graph.layers.items(): if layer.kernel == "paddle.transpose": count += 1 return count def operate(self, graph): total_layer_num = len(graph.layers) scanned_layers = set() optimized_transpose_layers = list() optimized_reduce_layers = list() optimized_concat_layers = list() optimized_elementwise_layers = list() def get_index(layer): if layer.kernel.startswith( "paddle.nn") and "functional" not in layer.kernel: return 1 else: return 0 def strip_transpose(_graph): layers = copy.deepcopy(_graph.layers) for layer_id, layer in layers.items(): if layer_id in scanned_layers: continue scanned_layers.add(layer_id) percent = round(len(scanned_layers) / total_layer_num * 100, 2) sys.stderr.write("\rOptimize Transpose Layers...{}%".format( percent)) if layer.kernel != "paddle.transpose": continue if layer.attrs["perm"] != [0, 2, 3, 1]: continue transpose_layers = list() propagate_layers = list() reduce_layers = list() concat_layers = list() # 此elementwise_layers专用于存储shape(4) + shape(1)的形式layer elementwise_layers = list() can_be_optimized = True for out in _graph.edges_out.get(layer_id, []): if _graph.layers[out].kernel == "paddle.transpose": if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]: can_be_optimized = False break transpose_layers.append(out) elif _graph.layers[out].kernel in self.elementwise_layers: propagate_layers.append(out) elif _graph.layers[out].kernel in self.direct_layers: ouput_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ ouput_index] in _graph.outputs: can_be_optimized = False break propagate_layers.append(out) elif _graph.layers[out].kernel in self.reduce_layers: ouput_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ ouput_index] in _graph.outputs: can_be_optimized = False break if not _graph.layers[out].attrs.get('keepdim', False): can_be_optimized = False break propagate_layers.append(out) reduce_layers.append(out) elif _graph.layers[out].kernel == "paddle.concat": ouput_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ ouput_index] in _graph.outputs: can_be_optimized = False break propagate_layers.append(out) concat_layers.append(out) else: can_be_optimized = False break visited_layers = set() while len(propagate_layers) > 0 and can_be_optimized: current_id = propagate_layers.pop(0) visited_layers.add(current_id) for out in _graph.edges_out.get(current_id, []): if _graph.layers[out].kernel == "paddle.transpose": if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]: can_be_optimized = False break transpose_layers.append(out) elif _graph.layers[ out].kernel in self.elementwise_layers: output_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if out not in visited_layers: propagate_layers.append(out) elif _graph.layers[out].kernel in self.direct_layers: output_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if out not in visited_layers: propagate_layers.append(out) elif _graph.layers[out].kernel in self.reduce_layers: output_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if not _graph.layers[out].attrs.get('keepdim', False): can_be_optimized = False break if out not in visited_layers: propagate_layers.append(out) reduce_layers.append(out) elif _graph.layers[out].kernel == "paddle.concat": output_index = get_index(_graph.layers[out]) if _graph.layers[out].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if out not in visited_layers: propagate_layers.append(out) concat_layers.append(out) else: can_be_optimized = False break for ipt in _graph.edges_in.get(current_id, []): if _graph.layers[ current_id].kernel in self.elementwise_layers: try: x_shape = _graph.layers[ current_id].input_shapes['x'] y_shape = _graph.layers[ current_id].input_shapes['y'] output_index = get_index(_graph.layers[ipt]) if _graph.layers[ipt].outputs[ output_index] == _graph.layers[ current_id].inputs['x']: if list(x_shape) == [1] or len(x_shape) < 1: elementwise_layers.append(current_id) continue elif _graph.layers[ipt].outputs[ output_index] == _graph.layers[ current_id].inputs['y']: if list(y_shape) == [1] or len(y_shape) < 1: elementwise_layers.append(current_id) continue else: raise Exception( "Unexcepted situation happend while optimizing transpose" ) except Exception as e: can_be_optimized = False break output_index = get_index(_graph.layers[ipt]) if _graph.layers[ipt].kernel == "paddle.transpose": if _graph.layers[ipt].attrs["perm"] != [0, 2, 3, 1]: can_be_optimized = False break if ipt not in visited_layers: transpose_layers.append(ipt) elif _graph.layers[ ipt].kernel in self.elementwise_layers: if _graph.layers[ipt].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if ipt not in visited_layers: propagate_layers.append(ipt) elif _graph.layers[ipt].kernel in self.direct_layers: if _graph.layers[ipt].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if ipt not in visited_layers: propagate_layers.append(ipt) elif _graph.layers[ipt].kernel in self.reduce_layers: if _graph.layers[ipt].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if not _graph.layers[ipt].attrs.get('keepdim', False): can_be_optimized = False break if ipt not in visited_layers: propagate_layers.append(ipt) reduce_layers.append(ipt) elif _graph.layers[ipt].kernel == "paddle.concat": if _graph.layers[ipt].outputs[ output_index] in _graph.outputs: can_be_optimized = False break if ipt not in visited_layers: propagate_layers.append(ipt) concat_layers.append(ipt) else: can_be_optimized = False break if not can_be_optimized: break if not can_be_optimized: continue transpose_layers.append(layer_id) transpose_layers = list(set(transpose_layers)) for l in transpose_layers: output_index = get_index(graph.layers[l]) if graph.layers[l].outputs[output_index] in graph.outputs: can_be_optimized = False break if not can_be_optimized: continue for l in transpose_layers: _graph.del_layer(l) optimized_transpose_layers.extend(transpose_layers) optimized_reduce_layers.extend(reduce_layers) optimized_concat_layers.extend(concat_layers) optimized_elementwise_layers.extend(elementwise_layers) return True return False before_transpose_num = self.get_transpose_num(graph) opt_graph = copy.deepcopy(graph) total_layer_num = len(opt_graph.layers) while strip_transpose(opt_graph): pass for layer_id in list(set(optimized_transpose_layers)): graph.del_layer(layer_id) for layer_id in list(set(optimized_reduce_layers)): dim = graph.layers[layer_id].attrs.get('axis', None) if dim is not None: for i in range(len(dim)): dim[i] = [0, 2, 3, 1][dim[i]] graph.layers[layer_id].attrs['axis'] = dim for layer_id in list(set(optimized_concat_layers)): axis = graph.layers[layer_id].attrs.get('axis', 0) graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis] current_transpose_num = self.get_transpose_num(graph) print( "\nTranspose layers optimized, before: transpose_num={}, after: transpose_num={}". format(before_transpose_num, current_transpose_num))