# 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.optimizer.pattern_matcher import FuseBase from x2paddle.core.program import PaddleGraph, PaddleLayer from x2paddle.core.util import * class BatchNorm2dFuser(FuseBase): def __init__(self): super(BatchNorm2dFuser, self).__init__(graph_type="dygraph") def build_pattern(self): """ 描述需要替换的batchnorm2d图结构。 batchnorm2d层模式python实现代码示例: x2209 = 1 x2212 = 'Exception' x2213 = 4 x2214 = x2207.shape x2214 = len(x2214) x2215 = x2214 != x2213 if x2215 : raise RaiseException(x2212) x2218 = False if x2218 : x2220 = self.x2220 x2221 = x2220 + x2209 self.x2220 = x2221 x2227 = False if x2227 : x2230 = x2207.shape x2231 = 'Exception' x2233 = 0 x2234 = 2 x2235 = 1 x2236 = x2230[x2233] x2237 = len(x2230) x2238 = x2237 - x2234 x2241 = x2236 for _x2240 in range(x2238): x2242 = _x2240 + x2234 x2243 = x2230[x2242] x2244 = x2241 * x2243 x2239 = x2244 x2245 = x2239 == x2235 if x2245 : raise RaiseException(x2231) x2248 = self.batchnorm41(x2207) """ def gen_name(id): return "x" + str(id) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(0)], value=1) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(1)], value=0.1) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(2)], value=0.001) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(3)], value="Exception") self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(4)], value=4) self.pattern.add_layer( "prim.shape", inputs={'input': "bn-input-0"}, outputs=[gen_name(5)]) self.pattern.add_layer( "prim.len", inputs={'input': gen_name(5)}, outputs=[gen_name(5)]) self.pattern.add_layer( "prim.ne", inputs={"x": gen_name(5), "y": gen_name(4)}, outputs=[gen_name(6)]) self.pattern.add_layer("prim.if", {'input': gen_name(6)}, [gen_name(7)]) if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] pattern_block0 = PaddleGraph(if_layer1, graph_type="dygraph") pattern_block0.add_layer( "prim.exception", inputs={"input": gen_name(3)}, outputs=[gen_name(8)]) if_layer1.inputs["input-0"] = gen_name(3) if_layer1.add_block(pattern_block0) pattern_block1 = PaddleGraph(if_layer1, graph_type="dygraph") if_layer1.add_block(pattern_block1) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(9)], value=False) self.pattern.add_layer("prim.if", {'input': gen_name(9)}, [gen_name(10)]) if_layer2 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] pattern_block0 = PaddleGraph(if_layer2, graph_type="dygraph") pattern_block0.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[gen_name(11)], value="params[{}]".format(string(gen_name(11)))) pattern_block0.add_layer( "prim.add", inputs={"x": gen_name(11), "y": gen_name(0)}, outputs=[gen_name(12)]) pattern_block0.add_layer( "prim.set_attr", inputs={"input": gen_name(12)}, outputs=["self." + gen_name(11)]) if_layer2.inputs["input-0"] = gen_name(0) if_layer2.add_block(pattern_block0) pattern_block1 = PaddleGraph(if_layer2, graph_type="dygraph") if_layer2.add_block(pattern_block1) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(13)], value=True) self.pattern.add_layer( "prim.constant", inputs={}, outputs=[gen_name(14)], value=False) self.pattern.add_layer("prim.if", {'input': gen_name(14)}, [gen_name(15)]) if_layer3 = self.pattern.layers[list(self.pattern.layers.keys())[-1]] pattern_block0 = PaddleGraph(if_layer3, graph_type="dygraph") pattern_block0.add_layer( "prim.shape", inputs={'input': "bn-input-0"}, outputs=[gen_name(16)]) pattern_block0.add_layer( "prim.constant", inputs={}, outputs=[gen_name(17)], value="Exception") pattern_block0.add_layer( "prim.constant", inputs={}, outputs=[gen_name(18)], value=True) pattern_block0.add_layer( "prim.constant", inputs={}, outputs=[gen_name(19)], value=0) pattern_block0.add_layer( "prim.constant", inputs={}, outputs=[gen_name(20)], value=2) pattern_block0.add_layer( "prim.constant", inputs={}, outputs=[gen_name(21)], value=1) pattern_block0.add_layer( "prim.getitem", inputs={"list": gen_name(16), "index": gen_name(19)}, outputs=[gen_name(22)]) pattern_block0.add_layer( "prim.len", inputs={"input": gen_name(16)}, outputs=[gen_name(23)]) pattern_block0.add_layer( "prim.sub", inputs={"x": gen_name(23), "y": gen_name(20)}, outputs=[gen_name(24)]) pattern_block0.add_layer( "prim.equal", inputs={"input": gen_name(22)}, outputs=[gen_name(25)]) pattern_block0.add_layer( "prim.loop", inputs={"input": gen_name(24)}, outputs=[gen_name(26), gen_name(27)]) loop_layer = pattern_block0.layers[list(pattern_block0.layers.keys())[ -1]] pattern_block0_block0 = PaddleGraph(loop_layer, graph_type="dygraph") pattern_block0_block0.add_layer( "prim.add", inputs={"x": gen_name(27), "y": gen_name(20)}, outputs=[gen_name(28)]) pattern_block0_block0.add_layer( "prim.getitem", inputs={"list": gen_name(16), "index": gen_name(28)}, outputs=[gen_name(29)]) pattern_block0_block0.add_layer( "prim.mul", inputs={"x": gen_name(25), "y": gen_name(29)}, outputs=[gen_name(30)]) pattern_block0_block0.add_layer( "prim.equal", inputs={"input": gen_name(30)}, outputs=[gen_name(26)]) loop_layer.inputs["input-1"] = gen_name(20) loop_layer.inputs["input-2"] = gen_name(16) loop_layer.inputs["input-3"] = gen_name(25) loop_layer.add_block(pattern_block0_block0) pattern_block0.add_layer( "prim.eq", inputs={"x": gen_name(26), "y": gen_name(21)}, outputs=[gen_name(31)]) pattern_block0.add_layer( "prim.if", inputs={"input": gen_name(31)}, outputs=[gen_name(32)]) if_layer31 = pattern_block0.layers[list(pattern_block0.layers.keys())[ -1]] pattern_block0_block0 = PaddleGraph(if_layer31, graph_type="dygraph") pattern_block0_block0.add_layer( "prim.exception", inputs={"input": gen_name(17)}, outputs=[gen_name(33)]) if_layer31.inputs["input-0"] = gen_name(17) if_layer31.add_block(pattern_block0_block0) pattern_block0_block1 = PaddleGraph(if_layer31, graph_type="dygraph") if_layer31.add_block(pattern_block0_block1) if_layer3.inputs["input-0"] = "bn-input-0" if_layer3.add_block(pattern_block0) pattern_block1 = PaddleGraph(if_layer3, graph_type="dygraph") if_layer3.add_block(pattern_block1) self.pattern.add_layer( "paddle.nn.BatchNorm", inputs={"input": "bn-input-0"}, outputs=[gen_name(34), gen_name(35)], is_test=True, num_channels=160, momentum=0.1, epsilon=0.001) self.pattern.build(inputs={"input-0": "bn-input-0"}) def insert_new_layer(self, graph, parameters, matches): new_layer = self.gen_new_layer(parameters, matches) new_layer_id = list(matches.keys())[0] graph.layers[new_layer_id] = new_layer matches.pop(new_layer_id) def gen_new_layer(self, parameters, matches): layers_id = list(matches.keys()) layer = matches[layers_id[-1]] return layer