diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_bn_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_bn_fuse_pass.py index ffe177e59d86e4451b3da1e40fd62e1d398b03b2..434b89135be76cacc3d9b08d19d99b75a0e7bb9b 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_bn_fuse_pass.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_bn_fuse_pass.py @@ -1,4 +1,4 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# 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. @@ -12,165 +12,199 @@ # See the License for the specific language governing permissions and # limitations under the License. -import unittest +from auto_scan_test import PassAutoScanTest, IgnoreReasons +from program_config import TensorConfig, ProgramConfig, OpConfig import numpy as np -from inference_pass_test import InferencePassTest -import paddle.fluid as fluid -import paddle.fluid.core as core -from paddle.fluid.core import PassVersionChecker - - -class ConvBnFusePassExplicitPaddingTest(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding=[1, 1, 1, 1], - bias_attr=False, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass')) - - -class ConvBnFusePassValidPaddingTest(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding='VALID', - bias_attr=False, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass')) - - -class ConvBnFusePassSamePaddingTest(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding='SAME', - bias_attr=False, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass')) - - -class ConvEltwiseAddBnFuseExplicitPaddingPass(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding=[1, 1, 1, 1], - bias_attr=None, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue( - PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass')) - - -class ConvEltwiseAddBnFuseValidPaddingPass(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding='VALID', - bias_attr=None, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue( - PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass')) - - -class ConvEltwiseAddBnFuseSamePaddingPass(InferencePassTest): - def setUp(self): - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name="data", shape=[-1, 3, 64, 64], dtype="float32") - conv_out = fluid.layers.conv2d( - input=data, - num_filters=6, - filter_size=6, - groups=3, - padding='SAME', - bias_attr=None, - act=None) - bn_out = fluid.layers.batch_norm(conv_out, is_test=True) - - self.feeds = { - "data": np.random.random([1, 3, 64, 64]).astype("float32"), - } - self.fetch_list = [bn_out] - - def test_check_output(self): - self.check_output() - self.assertTrue( - PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass')) +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 TestConvBnFusePass(PassAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + # mainly for TRT, which is invalid for current pass test framework!! + if attrs[0]['data_format'] == "NHWC": + return False + + return True + + def sample_program_config(self, draw): + padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"])) + groups = draw(st.integers(min_value=1, max_value=3)) + data_format = draw(st.sampled_from(["NCHW", "NHWC"])) + axis = draw(st.sampled_from([1])) + filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4 + filter_size = draw(st.integers(min_value=1, max_value=4)) + in_channel = groups * filter_channel + out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4 + out_channel = groups * out_channel_factor + batch_size = draw(st.integers(min_value=1, max_value=4)) + dilations = draw( + st.lists( + st.integers( + min_value=1, max_value=2), min_size=2, max_size=2)) + paddings = draw( + st.lists( + st.integers( + min_value=0, max_value=2), min_size=2, max_size=2)) + strides = draw( + st.lists( + st.integers( + min_value=1, max_value=2), min_size=2, max_size=2)) + has_bias = draw(st.booleans()) + use_mkldnn = draw(st.booleans()) + epsilon = draw(st.floats(min_value=0.0, max_value=0.001)) + + x_shape = [ + batch_size, in_channel, 64, 64 + ] if data_format == "NCHW" else [batch_size, 64, 64, in_channel] + w_shape = [out_channel, filter_channel, filter_size, filter_size] + scale_shape = [out_channel] + bias_shape = [out_channel] + var_shape = [out_channel] + mean_shape = [out_channel] + + def generate_conv2d_Input(): + return np.random.random(x_shape).astype(np.float32) + + def generate_conv2d_Filter(): + return np.random.random(w_shape).astype(np.float32) + + def generate_conv2d_Bias(): + return np.random.random(bias_shape).astype(np.float32) + + def generate_bn_Scale(): + return np.random.random(scale_shape).astype(np.float32) + + def generate_bn_Bias(): + return np.random.random(bias_shape).astype(np.float32) + + def generate_bn_Mean(): + return np.random.random(mean_shape).astype(np.float32) + + def generate_bn_Var(): + return np.random.random(var_shape).astype(np.float32) + + conv2d_op = OpConfig( + "conv2d", + inputs={ + "Input": ["conv2d_input"], + "Filter": ["conv2d_weight"], + }, + outputs={"Output": ["conv2d_out"]}, + data_format=data_format, + dilations=dilations, + padding_algorithm=padding_algorithm, + groups=groups, + paddings=paddings, + strides=strides, + use_mkldnn=use_mkldnn, + has_bias=has_bias, + is_test=True) + bn_op = OpConfig( + "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"], + }, + epsilon=epsilon, + trainable_statistics=False, + data_layout=data_format, + is_test=True) + if has_bias == True: + conv2d_op.inputs["Bias"] = ["conv2d_bias"] + ops = [conv2d_op, bn_op] + + program_config = ProgramConfig( + ops=ops, + inputs={ + "conv2d_input": + TensorConfig(data_gen=partial(generate_conv2d_Input)), + }, + weights={ + "conv2d_weight": + TensorConfig(data_gen=partial(generate_conv2d_Filter)), + "batch_norm_Scale": TensorConfig(data_gen=generate_bn_Scale), + "batch_norm_Bias": TensorConfig(data_gen=generate_bn_Bias), + "batch_norm_Mean": TensorConfig(data_gen=generate_bn_Mean), + "batch_norm_Variance": TensorConfig(data_gen=generate_bn_Var), + }, + outputs=["batch_norm_Y"]) + if has_bias == True: + program_config.weights["conv2d_bias"] = TensorConfig( + data_gen=partial(generate_conv2d_Bias)) + return program_config + + def sample_predictor_configs(self, program_config): + # for mkldnn + if program_config.ops[0].attrs['use_mkldnn']: + config = self.create_inference_config() + config.enable_mkldnn() + yield config, ['conv2d'], (1e-5, 1e-5) + else: + config = self.create_inference_config() + yield config, ['conv2d', 'elementwise_add'], (1e-5, 1e-5) + + config = self.create_inference_config(use_gpu=True) + yield config, ['conv2d', 'elementwise_add'], (1e-5, 1e-5) + + config = self.create_trt_inference_config() + config.enable_tensorrt_engine( + workspace_size=1 << 20, + max_batch_size=4, + min_subgraph_size=1, + precision_mode=paddle_infer.PrecisionType.Float32, + use_static=False, + use_calib_mode=False) + if program_config.ops[0].attrs['has_bias']: + yield config, ['conv2d', 'elementwise_add'], (1e-5, 1e-5) + else: # it will enter conv_elementwise_add_fuse_pass + yield config, ['conv2d_fusion'], (1e-5, 1e-5) + + 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 + + # mkldnn Output has diff with bias! + def teller2(program_config, predictor_config): + return predictor_config.mkldnn_enabled() and program_config.ops[ + 0].attrs['has_bias'] == True + + self.add_ignore_check_case( + teller1, IgnoreReasons.PASS_ACCURACY_ERROR, + "The output format of conv2d is wrong when data_format attribute is NHWC" + ) + + self.add_ignore_check_case( + teller2, IgnoreReasons.PASS_ACCURACY_ERROR, + "Currently mkldnn Output has diff with bias!") + + def test(self): + self.run_and_statis( + quant=False, + passes=["conv_bn_fuse_pass"], ) if __name__ == "__main__":