# 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 numpy as np from x2paddle.core.util import * from x2paddle.core.program import PaddleLayer, PaddleGraph from x2paddle.optimizer.passes import Pass, Matcher, PyTorchMatcher class LinearPass(Pass): def __init__(self): super(LinearPass, self).__init__() def build_pattern(self): """ 构造fc层的模式。 fc层模式python实现代码示例: x149 = 2 x151 = x146.shape x151 = len(x151) x152 = x151 == x149 if x152 : x147 = self.x147 x154 = fluid.layers.transpose(x=x147, perm=[1, 0]) x148 = self.x148 x155 = fluid.layers.addmm(input=x148, x=x146, y=x154, beta=1, alpha=1) x153 = x155 else: x147 = self.x147 x157 = fluid.layers.transpose(x=x147, perm=[1, 0]) x158 = fluid.layers.matmul(x=x146, y=x157) x159 = True if x159 : x148 = self.x148 x161 = x158 + 1 * x148 x160 = x161 else: x160 = x158 x153 = x160 """ def gen_name(id): return "x" + str(id) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(0)], value=2) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(1)], value=1) self.pattern.add_layer( "prim.shape", inputs={'input': "fc-input-0"}, outputs=[gen_name(2)]) self.pattern.add_layer( "prim.len", inputs={'input': gen_name(2)}, outputs=[gen_name(2)]) self.pattern.add_layer( "prim.eq", inputs={"eq0": gen_name(2), "eq1": gen_name(0)}, outputs=[gen_name(3)]) self.pattern.add_layer("prim.if", {'input': gen_name(3)}, [gen_name(4)]) self.pattern.outputs.append(gen_name(4)) if_layer_a = self.pattern.layers[list(self.pattern.layers.keys())[-1]] pattern_block0 = PaddleGraph(if_layer_a) pattern_block0.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[gen_name(5)], value="params[{}]".format(string(gen_name(5)))) pattern_block0.add_layer( "fluid.layers.transpose", inputs={"x": gen_name(5)}, outputs=[gen_name(6)], perm=[1, 0]) pattern_block0.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[gen_name(7)], value="params[{}]".format(string(gen_name(7)))) pattern_block0.add_layer( "fluid.layers.addmm", inputs={"input": gen_name(7), "x": "fc-input-0", "y": gen_name(6)}, outputs=[gen_name(8)], beta=1, alpha=1) if_layer_a.inputs["input-0"] = "fc-input-0" self.pattern.inputs.append("fc-input-0") pattern_block0.add_layer( "prim.equal", inputs={'input': gen_name(8)}, outputs=[gen_name(4)]) if_layer_a.add_block(pattern_block0) pattern_block1 = PaddleGraph(if_layer_a) pattern_block1.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[gen_name(5)], value="params[{}]".format(string(gen_name(5)))) pattern_block1.add_layer( "fluid.layers.transpose", inputs={"x": gen_name(5)}, outputs=[gen_name(6)], perm=[1, 0]) pattern_block1.add_layer( "fluid.layers.matmul", inputs={"x": "fc-input-0", "y": gen_name(6)}, outputs=[gen_name(9)]) if_layer_a.inputs["input-1"] = "fc-input-0" pattern_block1.add_layer( "prim.constant", inputs={}, outputs=[gen_name(10)], value=True) pattern_block1.add_layer("prim.if", {'input': gen_name(10)}, [gen_name(11)]) if_layer_b = pattern_block1.layers[list(pattern_block1.layers.keys())[ -1]] pattern_block1_block0 = PaddleGraph(if_layer_b) pattern_block1_block0.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[gen_name(12)], value="params[{}]".format(string(gen_name(12)))) pattern_block1_block0.add_layer( "prim.add", inputs={"x": gen_name(9), "y": gen_name(12)}, outputs=[gen_name(13)], alpha=1) if_layer_b.inputs["input-0"] = gen_name(9) pattern_block1_block0.add_layer( "prim.equal", inputs={'input': gen_name(13)}, outputs=[gen_name(11)]) if_layer_b.add_block(pattern_block1_block0) pattern_block1_block1 = PaddleGraph(if_layer_b) pattern_block1_block1.add_layer( "prim.equal", inputs={'input': gen_name(9)}, outputs=[gen_name(11)]) if_layer_b.inputs["input-1"] = gen_name(9) pattern_block1.add_layer( "prim.equal", inputs={'input': gen_name(11)}, outputs=[gen_name(4)]) if_layer_b.add_block(pattern_block1_block1) if_layer_a.add_block(pattern_block1) self.pattern.build( inputs={"input-0": "fc-input-0", "input-1": "fc-input-0"}) class LinearMatcher(PyTorchMatcher): def __init__(self): self.linear_index = 0 super(LinearMatcher, self).__init__() def replace_layer(self, graph, subgraph_global_layers): subgraph_global_layers_id = list(subgraph_global_layers.keys()) layer = subgraph_global_layers[subgraph_global_layers_id[2]] input_name = layer.inputs["input"] layer = subgraph_global_layers[subgraph_global_layers_id[5]] output_name = layer.outputs[0] layer = subgraph_global_layers[subgraph_global_layers_id[6]] weight_name = layer.attrs["value"][8:-2] layer = subgraph_global_layers[subgraph_global_layers_id[8]] bias_name = layer.attrs["value"][8:-2] attrs = {} attrs["input_dim"] = graph.parameters[weight_name].shape[1] attrs["output_dim"] = graph.parameters[weight_name].shape[0] linear_name = "linear{}".format(self.linear_index) self.linear_index += 1 graph.parameters["{}.weight".format(linear_name)] = graph.parameters[ weight_name].transpose((1, 0)) graph.parameters["{}.bias".format(linear_name)] = np.squeeze( graph.parameters[bias_name]) graph.parameters.pop(weight_name) graph.parameters.pop(bias_name) for i, layer_id in enumerate(subgraph_global_layers): if layer_id in graph.layers: layer = graph.layers[layer_id] if i == 0: new_layer = PaddleLayer( layer_id, "fluid.dygraph.Linear", inputs={"input": input_name}, outputs=[linear_name, output_name], **attrs) graph.layers[layer_id] = new_layer else: graph.layers.pop(layer_id) graph.build() return graph