diff --git a/paddle/fluid/framework/ir/conv_bn_fuse_pass.cc b/paddle/fluid/framework/ir/conv_bn_fuse_pass.cc index 455b647f3b487d3e71f25c256dca62fcb54da7ec..b6c410dc957fd1603d2503f6d271963a534d1b0e 100644 --- a/paddle/fluid/framework/ir/conv_bn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/conv_bn_fuse_pass.cc @@ -744,3 +744,8 @@ REGISTER_PASS_CAPABILITY(conv_transpose_eltwiseadd_bn_fuse_pass) .LE("conv2d_transpose", 2) .LE("elementwise_add", 1) .EQ("batch_norm", 0)); +REGISTER_PASS_CAPABILITY(conv_transpose_bn_fuse_pass) + .AddCombination( + paddle::framework::compatible::OpVersionComparatorCombination() + .LE("conv2d_transpose", 2) + .EQ("batch_norm", 0)); diff --git a/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt b/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt index 2a91b96aaef68cdedf24c14db2517118c386bafc..cf2d42d8ffc536e4c2bb9e2071e65a0edcc68fae 100755 --- a/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt @@ -89,5 +89,6 @@ if (WITH_MKLDNN) set_tests_properties(test_mkldnn_depthwise_conv_pass PROPERTIES TIMEOUT 120) set_tests_properties(test_mkldnn_prelu_op PROPERTIES TIMEOUT 300) set_tests_properties(test_conv_transpose_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 250) + set_tests_properties(test_conv_transpose_bn_fuse_pass PROPERTIES TIMEOUT 300) endif() endif() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_transpose_bn_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_transpose_bn_fuse_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..62515fc2177b8ff9faa4995340481ca527ad7f2f --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_transpose_bn_fuse_pass.py @@ -0,0 +1,224 @@ +# 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. + +from auto_scan_test import PassAutoScanTest, IgnoreReasons +from program_config import TensorConfig, ProgramConfig, OpConfig +import numpy as np +import copy as cp +import paddle.inference as paddle_infer +from functools import partial +from typing import Optional, List, Callable, Dict, Any, Set +import unittest + +import hypothesis +from hypothesis import given, settings, seed, example, assume, reproduce_failure +import hypothesis.strategies as st + + +class TestConvTransposeBnFusePass(PassAutoScanTest): + ''' + conv_input conv_weight_var(persistable) + \ / + conv_op + | + conv_out_var (bn_scale_var, bn_bias_var, bn_mean_var,bn_variance_var) + | / + batch_norm_op + | \ + bn_out_var (bn_mean_out_var, bn_variance_out_var,bn_saved_mean_var, bn_saved_variance_var) + ''' + + def test(self): + self.run_and_statis( + quant=False, + max_examples=150, + max_duration=250, + passes=["conv_transpose_bn_fuse_pass"]) + + def sample_program_config(self, draw): + # generate random number + random_batch_size = draw(st.integers(min_value=1, max_value=3)) + random_channel = draw(st.integers(min_value=2, max_value=10)) + random_input_dim1 = draw(st.integers(min_value=20, max_value=50)) + random_input_dim2 = draw(st.integers(min_value=20, max_value=50)) + random_groups = draw(st.integers(min_value=1, max_value=2)) + random_dilations = draw( + st.lists( + st.integers( + min_value=1, max_value=3), min_size=2, max_size=2)) + random_strides = draw( + st.lists( + st.integers( + min_value=1, max_value=4), min_size=2, max_size=2)) + random_paddings = draw( + st.lists( + st.integers( + min_value=0, max_value=4), min_size=2, max_size=2)) + random_padding_algorithm = draw( + st.sampled_from(["EXPLICIT", "SAME", "VALID"])) + random_data_layout = draw(st.sampled_from(["NCHW", "NHWC"])) + random_use_mkldnn = draw(st.booleans()) + random_output_size = [] + random_filter = draw( + st.lists( + st.integers( + min_value=1, max_value=4), min_size=2, max_size=2)) + random_out_channel = draw(st.integers(min_value=10, max_value=25)) + random_epsilon = draw(st.floats(min_value=0.0, max_value=0.001)) + + def generate_conv2d_Input(): + shape = [random_input_dim1, random_input_dim2] + if random_data_layout == "NCHW": + shape.insert(0, random_channel * random_groups) + shape.insert(0, random_batch_size) + else: + shape.append(random_channel) + shape.insert(0, random_batch_size) + return np.random.random(shape).astype(np.float32) + + def generate_conv2d_Filter(): + shape = cp.copy(random_filter) + shape.insert(0, random_out_channel * random_groups) + shape.insert(0, random_channel * random_groups) + return np.random.random(shape).astype(np.float32) + + def generate_batch_norm_Scale(): + return np.random.random( + [random_out_channel * random_groups * random_groups]).astype( + np.float32) + + def generate_batch_norm_Bias(): + return np.random.random( + [random_out_channel * random_groups * random_groups]).astype( + np.float32) + + def generate_batch_norm_Mean(): + return np.random.random( + [random_out_channel * random_groups * random_groups]).astype( + np.float32) + + def generate_batch_norm_Variance(): + return np.random.random( + [random_out_channel * random_groups * random_groups]).astype( + np.float32) + + # define op + conv2d_op = OpConfig( + type="conv2d_transpose", + inputs={ + "Input": ["conv2d_Input"], + "Filter": ["conv2d_Filter"], + #"Bias": ["conv2d_Bias"], + }, + outputs={"Output": ["conv2d_Out"], }, + attrs={ + 'groups': random_groups, + 'dilations': random_dilations, + 'strides': random_strides, + 'paddings': random_paddings, + 'padding_algorithm': random_padding_algorithm, + 'data_format': random_data_layout, + 'output_size': random_output_size, + 'output_padding': random_output_size, + 'use_mkldnn': random_use_mkldnn, + 'is_test': True, + }) + + batch_norm_op = OpConfig( + type="batch_norm", + inputs={ + "X": ["conv2d_Out"], + "Scale": ["batch_norm_Scale"], + "Bias": ["batch_norm_Bias"], + "Mean": ["batch_norm_Mean"], + "Variance": ["batch_norm_Variance"], + }, + outputs={ + "Y": ["batch_norm_Y"], + "MeanOut": ["batch_norm_Mean"], + "VarianceOut": ["batch_norm_Variance"], + "SavedMean": ["batch_norm_SavedMean"], + "SavedVariance": ["batch_norm_SavedVariance"], + "ReserveSpace": ["batch_norm_ReserveSpace"], + }, + attrs={ + 'epsilon': random_epsilon, + 'is_test': True, + 'trainable_statistics': False, + 'data_layout': random_data_layout, + 'use_mkldnn': random_use_mkldnn, + }) + + # define model_net + model_net = [conv2d_op, batch_norm_op] + # set tensor + program_config = ProgramConfig( + ops=model_net, + inputs={ + "conv2d_Input": TensorConfig(data_gen=generate_conv2d_Input), + }, + weights={ + "conv2d_Filter": TensorConfig(data_gen=generate_conv2d_Filter), + "batch_norm_Scale": + TensorConfig(data_gen=generate_batch_norm_Scale), + "batch_norm_Bias": + TensorConfig(data_gen=generate_batch_norm_Bias), + "batch_norm_Mean": + TensorConfig(data_gen=generate_batch_norm_Mean), + "batch_norm_Variance": + TensorConfig(data_gen=generate_batch_norm_Variance), + }, + outputs=["batch_norm_Y"]) + + return program_config + + def sample_predictor_configs(self, program_config): + # for mkldnn + config = self.create_inference_config() + if program_config.ops[0].attrs['use_mkldnn']: + config.enable_mkldnn() + yield config, ['conv2d_transpose'], (1e-5, 1e-5) + # for cpu + else: + yield config, ['conv2d_transpose', 'elementwise_add'], (1e-5, 1e-5) + + def is_program_valid(self, program_config: ProgramConfig) -> bool: + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + + if attrs[0]['data_format'] == "NHWC": + return False + + return True + + def add_ignore_pass_case(self): + def teller1(program_config, predictor_config): + if program_config.ops[0].attrs['data_format'] == "NHWC": + return True + return False + + def teller2(program_config, predictor_config): + if program_config.ops[0].attrs['groups'] != 1: + return True + return False + + self.add_ignore_check_case( + teller1, IgnoreReasons.PASS_ACCURACY_ERROR, + "The output format of conv2d_transpose is wrong when data_format attribute is NHWC" + ) + + self.add_ignore_check_case(teller2, IgnoreReasons.PASS_ACCURACY_ERROR, + "there is diff when group >1 in this pass")