From fc06be9dbd82da832c8eed8cac8573d0166638ba Mon Sep 17 00:00:00 2001 From: wenbin Date: Tue, 1 Mar 2022 15:08:27 +0800 Subject: [PATCH] remove conv_affine_channel_fuse_pass (#39817) * remove * pass * more pass --- paddle/fluid/framework/ir/CMakeLists.txt | 1 - .../ir/conv_affine_channel_fuse_pass.cc | 420 ------------------ .../ir/conv_affine_channel_fuse_pass.h | 54 --- .../inference/api/paddle_pass_builder.cc | 56 ++- .../quantization/quant2_int8_mkldnn_pass.py | 3 - .../test_conv_affine_channel_fuse_pass.py | 160 ------- ...onv_eltwiseadd_affine_channel_fuse_pass.py | 183 -------- tools/parallel_UT_rule.py | 2 - tools/static_mode_white_list.py | 1 - 9 files changed, 25 insertions(+), 855 deletions(-) delete mode 100644 paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc delete mode 100644 paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h delete mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py delete mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_fuse_pass.py diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt index dad5358590..0d53a54ff8 100755 --- a/paddle/fluid/framework/ir/CMakeLists.txt +++ b/paddle/fluid/framework/ir/CMakeLists.txt @@ -78,7 +78,6 @@ pass_library(is_test_pass base) pass_library(conv_elementwise_add_act_fuse_pass inference) pass_library(conv_elementwise_add2_act_fuse_pass inference) pass_library(conv_elementwise_add_fuse_pass inference) -pass_library(conv_affine_channel_fuse_pass inference) pass_library(transpose_flatten_concat_fuse_pass inference) pass_library(identity_scale_op_clean_pass base) pass_library(sync_batch_norm_pass base) diff --git a/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc b/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc deleted file mode 100644 index f28c9988bd..0000000000 --- a/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc +++ /dev/null @@ -1,420 +0,0 @@ -// 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. - -#include "paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h" - -#include - -#include "paddle/fluid/framework/convert_utils.h" -#include "paddle/fluid/framework/op_version_registry.h" - -namespace phi { -class DenseTensor; -} // namespace phi - -namespace paddle { -namespace framework { -class Scope; -} // namespace framework -} // namespace paddle - -namespace paddle { -namespace framework { -namespace ir { - -class Node; - -#define GET_CONV_BN_NODES(pattern_name) \ - /* OPERATORS */ \ - GET_IR_NODE_FROM_SUBGRAPH(conv, conv, pattern_name); \ - GET_IR_NODE_FROM_SUBGRAPH(affine_channel, affine_channel, pattern_name); \ - /* CONV inputs */ \ - GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight, pattern_name); \ - /* CONV outputs */ \ - GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, pattern_name); \ - /* Affine Channel inputs */ \ - GET_IR_NODE_FROM_SUBGRAPH(ac_scale, ac_scale, pattern_name); \ - GET_IR_NODE_FROM_SUBGRAPH(ac_bias, ac_bias, pattern_name); \ - /* Affine channel outputs */ \ - GET_IR_NODE_FROM_SUBGRAPH(ac_out, ac_out, pattern_name); /* Out */ - -void recompute_bias_and_weights(const Scope* scope, ir::Node* conv_weight, - const ir::Node& ac_scale, - const LoDTensor& ac_bias_tensor, - LoDTensor* eltwise_y_in_tensor) { - using EigenVectorArrayMap = - Eigen::Map>; - using ConstEigenVectorArrayMap = - Eigen::Map>; - using EigenMatrixArrayMap = Eigen::Map< - Eigen::Array>; - - // Re-compute bias of conv2d from AffineChannel - PADDLE_ENFORCE_EQ( - eltwise_y_in_tensor->dims(), ac_bias_tensor.dims(), - platform::errors::InvalidArgument( - "Tensor elementwise y(%d) and activation bias(%d) must have same " - "dimension.", - eltwise_y_in_tensor->dims().size(), ac_bias_tensor.dims().size())); - - auto* scale_tensor = scope->FindVar(ac_scale.Name())->GetMutable(); - - ConstEigenVectorArrayMap scale_array(scale_tensor->data(), - scale_tensor->numel(), 1); - ConstEigenVectorArrayMap ac_bias_array(ac_bias_tensor.data(), - ac_bias_tensor.numel(), 1); - - EigenVectorArrayMap eltwise_y_in_array( - eltwise_y_in_tensor->mutable_data(platform::CPUPlace()), - eltwise_y_in_tensor->numel(), 1); - - eltwise_y_in_array = (eltwise_y_in_array * scale_array) + ac_bias_array; - - // Re-compute weight of conv2d from AffineChannel - auto* weights = scope->FindVar(conv_weight->Name())->GetMutable(); - auto weights_shape = weights->dims(); - auto weights_shape_2d = phi::flatten_to_2d(weights_shape, 1); - auto* weights_data = weights->mutable_data(platform::CPUPlace()); - - EigenMatrixArrayMap weights_array_2d(weights_data, weights_shape_2d[0], - weights_shape_2d[1]); - - weights_array_2d.colwise() *= scale_array; - - // Check for subnormal values that slows down convolution execution - for (int i = 0; i < weights->numel(); ++i) { - if (std::fpclassify(weights_data[i]) == FP_SUBNORMAL) weights_data[i] = 0; - } -} - -ConvAffineChannelFusePass::ConvAffineChannelFusePass() { - AddOpCompat(OpCompat("conv2d")) - .AddInput("Input") - .IsTensor() - .End() - .AddInput("Filter") - .IsTensor() - .End() - .AddInput("Bias") - .IsTensor() - .IsOptional() - .End() - .AddInput("ResidualData") - .IsTensor() - .IsOptional() - .End() - .AddOutput("Output") - .IsTensor() - .End() - .AddAttr("strides") - .IsType>() - .End() - .AddAttr("paddings") - .IsType>() - .End() - .AddAttr("padding_algorithm") - .IsOptional() - .IsStringIn({"EXPLICIT", "SAME", "VALID"}) - .End() - .AddAttr("groups") - .IsNumGE(1) - .End() - .AddAttr("dilations") - .IsType>() - .End() - .AddAttr("data_format") - .IsStringIn({"NCHW", "AnyLayout"}) - .End(); - - AddOpCompat(OpCompat("affine_channel")) - .AddInput("X") - .IsTensor() - .End() - .AddInput("Scale") - .IsTensor() - .End() - .AddInput("Bias") - .IsTensor() - .IsOptional() - .End() - .AddOutput("Out") - .IsTensor() - .End() - .AddAttr("data_layout") - .IsStringIn({"NCHW", "AnyLayout"}) - .End(); - - AddOpCompat(OpCompat("elementwise_add")) - .AddInput("X") - .IsTensor() - .End() - .AddInput("Y") - .IsTensor() - .End() - .AddOutput("Out") - .IsTensor() - .End() - .AddAttr("axis") - .IsNumEQ(1) - .End(); -} - -void ConvAffineChannelFusePass::ApplyImpl(ir::Graph* graph) const { - PADDLE_ENFORCE_NOT_NULL( - graph, platform::errors::InvalidArgument("Graph cannot be nullptr.")); - FusePassBase::Init(name_scope_, graph); - - auto* scope = param_scope(); - PADDLE_ENFORCE_NOT_NULL( - scope, platform::errors::InvalidArgument("Scope cannot be nullptr.")); - - GraphPatternDetector gpd; - auto* conv_input = - gpd.mutable_pattern() - ->NewNode(patterns::PDNodeName(name_scope_, "conv_input")) - ->AsInput() - ->assert_is_op_input("conv2d", "Input"); - patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(), - name_scope_); - conv_ac_pattern(conv_input, false /*with_eltwise_add*/); - - int found_conv_ac_count = 0; - auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph, - Graph* g) { - if (!IsCompat(subgraph, g)) { - LOG(WARNING) << "ConvAffineChannelFusePass in op compat failed."; - return; - } - - VLOG(4) << "handle ConvAffineChannel fuse"; - - GET_CONV_BN_NODES(conv_ac_pattern); - - auto data_format = conv->Op()->GetAttrIfExists("data_format"); - if (data_format == "AnyLayout") { - LOG_FIRST_N(WARNING, 1) << "conv_affine_channel_fuse_pass is enabled, " - "it's wrong if data_format of conv is not " - "NCHW."; - } - - // Get affine_channel bias for resizing eltwise_y! - auto* ac_bias_tensor = - scope->FindVar(ac_bias->Name())->GetMutable(); - - // Create eltwise_y (conv bias) variable - VarDesc eltwise_y_in_desc( - patterns::PDNodeName(name_scope_, "eltwise_y_in")); - // Set shape && datatype manually - eltwise_y_in_desc.SetShape(phi::vectorize(ac_bias_tensor->dims())); - eltwise_y_in_desc.SetDataType( - framework::TransToProtoVarType(ac_bias_tensor->dtype())); - eltwise_y_in_desc.SetLoDLevel(ac_bias->Var()->GetLoDLevel()); - eltwise_y_in_desc.SetPersistable(true); - - // Initialize eltwise_y - auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc); - auto* eltwise_y_in_tensor = - scope->Var(eltwise_y_in_node->Name())->GetMutable(); - eltwise_y_in_tensor->Resize(ac_bias_tensor->dims()); - std::fill_n(eltwise_y_in_tensor->mutable_data(platform::CPUPlace()), - eltwise_y_in_tensor->numel(), 0.0f); - - // update weights and biases - recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor, - eltwise_y_in_tensor); - - // create an elementwise add node. - OpDesc desc; - desc.SetInput("X", std::vector({conv_out->Name()})); - desc.SetInput("Y", std::vector({eltwise_y_in_node->Name()})); - desc.SetOutput("Out", std::vector({ac_out->Name()})); - desc.SetType("elementwise_add"); - desc.SetAttr("axis", 1); - desc.SetAttr("use_mkldnn", conv->Op()->GetAttrIfExists("use_mkldnn")); - - auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied. - - GraphSafeRemoveNodes(graph, {ac_scale, ac_bias, affine_channel}); - - IR_NODE_LINK_TO(conv_out, eltwise_op); - IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op); - IR_NODE_LINK_TO(eltwise_op, ac_out); - found_conv_ac_count++; - }; - - gpd(graph, handler); - - AddStatis(found_conv_ac_count); -} - -ConvEltwiseAddAffineChannelFusePass::ConvEltwiseAddAffineChannelFusePass() { - AddOpCompat(OpCompat("conv2d")) - .AddInput("Input") - .IsTensor() - .End() - .AddInput("Filter") - .IsTensor() - .End() - .AddInput("Bias") - .IsTensor() - .IsOptional() - .End() - .AddInput("ResidualData") - .IsTensor() - .IsOptional() - .End() - .AddOutput("Output") - .IsTensor() - .End() - .AddAttr("strides") - .IsType>() - .End() - .AddAttr("paddings") - .IsType>() - .End() - .AddAttr("padding_algorithm") - .IsOptional() - .IsStringIn({"EXPLICIT", "SAME", "VALID"}) - .End() - .AddAttr("groups") - .IsNumGE(1) - .End() - .AddAttr("dilations") - .IsType>() - .End() - .AddAttr("data_format") - .IsStringIn({"NCHW", "AnyLayout"}) - .End(); - AddOpCompat(OpCompat("affine_channel")) - .AddInput("X") - .IsTensor() - .End() - .AddInput("Scale") - .IsTensor() - .End() - .AddInput("Bias") - .IsTensor() - .IsOptional() - .End() - .AddOutput("Out") - .IsTensor() - .End() - .AddAttr("data_layout") - .IsStringIn({"NCHW", "AnyLayout"}) - .End(); - AddOpCompat(OpCompat("elementwise_add")) - .AddInput("X") - .IsTensor() - .End() - .AddInput("Y") - .IsTensor() - .End() - .AddOutput("Out") - .IsTensor() - .End() - .AddAttr("axis") - .IsNumEQ(1) - .End(); -} - -void ConvEltwiseAddAffineChannelFusePass::ApplyImpl(ir::Graph* graph) const { - PADDLE_ENFORCE_NOT_NULL( - graph, platform::errors::InvalidArgument("Graph cannot be nullptr.")); - FusePassBase::Init(name_scope_, graph); - - auto* scope = param_scope(); - PADDLE_ENFORCE_NOT_NULL( - scope, platform::errors::InvalidArgument("Scope cannot be nullptr.")); - - GraphPatternDetector gpd; - auto* conv_input = - gpd.mutable_pattern() - ->NewNode(patterns::PDNodeName(name_scope_, "conv_input")) - ->AsInput() - ->assert_is_op_input("conv2d", "Input"); - patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(), - name_scope_); - conv_ac_pattern(conv_input, true /*with_eltwise_add*/); - - int found_conv_ac_count = 0; - auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph, - Graph* g) { - if (!IsCompat(subgraph, g)) { - LOG(WARNING) - << "ConvEltwiseAddAffineChannelFusePass in op compat failed."; - return; - } - - VLOG(4) << "handle ConvBN fuse"; - - GET_CONV_BN_NODES(conv_ac_pattern); - auto data_format = conv->Op()->GetAttrIfExists("data_format"); - if (data_format == "AnyLayout") { - LOG_FIRST_N(WARNING, 1) << "conv_eltwiseadd_affine_channel_fuse_pass is " - "enabled, it's wrong if data_format of conv " - "is not NCHW."; - } - // OPERATORS - GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_ac_pattern); - // BIAS inputs - GET_IR_NODE_FROM_SUBGRAPH(eltwise_y_in, eltwise_y_in, conv_ac_pattern); - // BIAS outputs - GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_ac_pattern); - - // Get eltwise_y (conv bias) variable - auto* eltwise_y_in_tensor = - scope->FindVar(eltwise_y_in->Name())->GetMutable(); - - // Get batch norm bias - auto* ac_bias_tensor = - scope->FindVar(ac_bias->Name())->GetMutable(); - - recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor, - eltwise_y_in_tensor); - - // Update the elementwise_add node - eltwise->Op()->SetAttr("axis", 1); - eltwise->Op()->SetOutput("Out", std::vector({ac_out->Name()})); - - GraphSafeRemoveNodes(graph, - {ac_scale, ac_bias, affine_channel, eltwise_out}); - - IR_NODE_LINK_TO(eltwise, ac_out); - - found_conv_ac_count++; - }; - - gpd(graph, handler); - AddStatis(found_conv_ac_count); -} - -} // namespace ir -} // namespace framework -} // namespace paddle - -REGISTER_PASS(conv_affine_channel_fuse_pass, - paddle::framework::ir::ConvAffineChannelFusePass); -REGISTER_PASS(conv_eltwiseadd_affine_channel_fuse_pass, - paddle::framework::ir::ConvEltwiseAddAffineChannelFusePass); -REGISTER_PASS_CAPABILITY(conv_affine_channel_fuse_pass) - .AddCombination( - paddle::framework::compatible::OpVersionComparatorCombination() - .LE("conv2d", 1) - .EQ("affine_channel", 0)); -REGISTER_PASS_CAPABILITY(conv_eltwiseadd_affine_channel_fuse_pass) - .AddCombination( - paddle::framework::compatible::OpVersionComparatorCombination() - .LE("conv2d", 1) - .LE("elementwise_add", 1) - .EQ("affine_channel", 0)); diff --git a/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h b/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h deleted file mode 100644 index 8cfaf5c6a8..0000000000 --- a/paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h +++ /dev/null @@ -1,54 +0,0 @@ -// Copyright (c) 2018 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. - -#pragma once - -#include - -#include "paddle/fluid/framework/ir/fuse_pass_base.h" -#include "paddle/fluid/framework/ir/graph.h" -#include "paddle/fluid/framework/ir/graph_pattern_detector.h" - -namespace paddle { -namespace framework { -namespace ir { - -/* - * Fuse the Conv and ConvAffineChannel. - */ -class Graph; - -class ConvAffineChannelFusePass : public FusePassBase { - public: - ConvAffineChannelFusePass(); - virtual ~ConvAffineChannelFusePass() {} - - protected: - void ApplyImpl(ir::Graph*) const override; - const std::string name_scope_{"conv_affine_channel_fuse"}; -}; - -class ConvEltwiseAddAffineChannelFusePass : public FusePassBase { - public: - ConvEltwiseAddAffineChannelFusePass(); - virtual ~ConvEltwiseAddAffineChannelFusePass() {} - - protected: - void ApplyImpl(ir::Graph*) const override; - const std::string name_scope_{"conv_eltwiseadd_affine_channel_fuse"}; -}; - -} // namespace ir -} // namespace framework -} // namespace paddle diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc index 313e1f2fae..f5f36d805b 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.cc +++ b/paddle/fluid/inference/api/paddle_pass_builder.cc @@ -75,13 +75,11 @@ void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) { void PaddlePassBuilder::ClearPasses() { passes_.clear(); } const std::vector kTRTSubgraphPasses({ - "conv_affine_channel_fuse_pass", // - "adaptive_pool2d_convert_global_pass", - "conv_eltwiseadd_affine_channel_fuse_pass", // - "shuffle_channel_detect_pass", // - "quant_conv2d_dequant_fuse_pass", // - "delete_quant_dequant_op_pass", // - "delete_quant_dequant_filter_op_pass", // + "adaptive_pool2d_convert_global_pass", + "shuffle_channel_detect_pass", // + "quant_conv2d_dequant_fuse_pass", // + "delete_quant_dequant_op_pass", // + "delete_quant_dequant_filter_op_pass", // // "fc_fuse_pass", // "simplify_with_basic_ops_pass", // "embedding_eltwise_layernorm_fuse_pass", // @@ -134,22 +132,20 @@ const std::vector kLiteSubgraphPasses({ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) { passes_.assign({ // "identity_scale_op_clean_pass", // - "is_test_pass", // - "simplify_with_basic_ops_pass", // - "conv_affine_channel_fuse_pass", // - "conv_eltwiseadd_affine_channel_fuse_pass", // - "conv_bn_fuse_pass", // - "conv_eltwiseadd_bn_fuse_pass", // - "embedding_eltwise_layernorm_fuse_pass", // - "multihead_matmul_fuse_pass_v2", // - "gpu_cpu_squeeze2_matmul_fuse_pass", // - "gpu_cpu_reshape2_matmul_fuse_pass", // - "gpu_cpu_flatten2_matmul_fuse_pass", // - "gpu_cpu_map_matmul_v2_to_mul_pass", // - "gpu_cpu_map_matmul_v2_to_matmul_pass", // - "gpu_cpu_map_matmul_to_mul_pass", // - "fc_fuse_pass", // - "fc_elementwise_layernorm_fuse_pass", // + "is_test_pass", // + "simplify_with_basic_ops_pass", // + "conv_bn_fuse_pass", // + "conv_eltwiseadd_bn_fuse_pass", // + "embedding_eltwise_layernorm_fuse_pass", // + "multihead_matmul_fuse_pass_v2", // + "gpu_cpu_squeeze2_matmul_fuse_pass", // + "gpu_cpu_reshape2_matmul_fuse_pass", // + "gpu_cpu_flatten2_matmul_fuse_pass", // + "gpu_cpu_map_matmul_v2_to_mul_pass", // + "gpu_cpu_map_matmul_v2_to_matmul_pass", // + "gpu_cpu_map_matmul_to_mul_pass", // + "fc_fuse_pass", // + "fc_elementwise_layernorm_fuse_pass", // #if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be // guaranteed at least v7 // cudnn8.0 has memory leak problem in conv + eltwise + act, so we @@ -236,14 +232,12 @@ void CpuPassStrategy::EnableMKLDNN() { passes_.insert(passes_.begin(), "mkldnn_placement_pass"); for (auto &pass : std::vector({ - "depthwise_conv_mkldnn_pass", // - "conv_bn_fuse_pass", // Execute BN passes again to - "conv_eltwiseadd_bn_fuse_pass", // preserve correct pass order - "conv_affine_channel_fuse_pass", // - "conv_eltwiseadd_affine_channel_fuse_pass", // - "conv_transpose_bn_fuse_pass", // - "conv_transpose_eltwiseadd_bn_fuse_pass", // - "conv_bias_mkldnn_fuse_pass", // + "depthwise_conv_mkldnn_pass", // + "conv_bn_fuse_pass", // Execute BN passes again to + "conv_eltwiseadd_bn_fuse_pass", // preserve correct pass order + "conv_transpose_bn_fuse_pass", // + "conv_transpose_eltwiseadd_bn_fuse_pass", // + "conv_bias_mkldnn_fuse_pass", // "conv_transpose_bias_mkldnn_fuse_pass", // TODO(baoachun): Need to support 5-dimensional input. // "conv3d_bias_mkldnn_fuse_pass", // diff --git a/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py b/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py index d5bc2e6b53..9d9fbd39a5 100644 --- a/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py +++ b/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py @@ -426,9 +426,6 @@ class Quant2Int8MkldnnPass(object): graph = self._apply_pass(graph, 'depthwise_conv_mkldnn_pass') graph = self._apply_pass(graph, 'conv_bn_fuse_pass') graph = self._apply_pass(graph, 'conv_eltwiseadd_bn_fuse_pass') - graph = self._apply_pass(graph, 'conv_affine_channel_fuse_pass') - graph = self._apply_pass(graph, - 'conv_eltwiseadd_affine_channel_fuse_pass') graph = self._apply_pass(graph, 'conv_transpose_bn_fuse_pass') graph = self._apply_pass(graph, 'conv_transpose_eltwiseadd_bn_fuse_pass') diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py deleted file mode 100644 index 5afaf08eec..0000000000 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py +++ /dev/null @@ -1,160 +0,0 @@ -# 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 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 TestConvAffineChannelFusePass(PassAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: - 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()) - - 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] - - def generate_input(): - return np.random.random(x_shape).astype(np.float32) - - def generate_weight(): - return np.random.random(w_shape).astype(np.float32) - - def generate_bias(): - return np.random.random(bias_shape).astype(np.float32) - - def generate_scale_bias(): - return np.random.random(bias_shape).astype(np.float32) - - conv2d_op = OpConfig( - "conv2d", - inputs={ - "Input": ["input_data"], - "Filter": ["conv2d_weight"], - }, - outputs={"Output": ["conv_output"]}, - data_format=data_format, - dilations=dilations, - padding_algorithm=padding_algorithm, - groups=groups, - paddings=paddings, - strides=strides, - has_bias=has_bias, - is_test=True) - ac_op = OpConfig( - "affine_channel", - inputs={ - "X": ["conv_output"], - "Scale": ["affine_channel_scale"], - "Bias": ["affine_channel_bias"] - }, - outputs={"Out": ["affine_channel_ouput"]}, - data_layout=data_format) - if has_bias == True: - conv2d_op.inputs["Bias"] = ["conv2d_bias"] - ops = [conv2d_op, ac_op] - - program_config = ProgramConfig( - ops=ops, - inputs={ - "input_data": TensorConfig(data_gen=partial(generate_input)), - }, - weights={ - "conv2d_weight": - TensorConfig(data_gen=partial(generate_weight)), - "affine_channel_scale": - TensorConfig(data_gen=partial(generate_scale_bias)), - "affine_channel_bias": - TensorConfig(data_gen=partial(generate_scale_bias)), - }, - outputs=["affine_channel_ouput"]) - if has_bias == True: - program_config.weights["conv2d_bias"] = TensorConfig( - data_gen=partial(generate_bias)) - return program_config - - def sample_predictor_configs(self, program_config): - config = self.create_inference_config(use_gpu=True) - yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4) - - config = self.create_inference_config(use_mkldnn=True) - yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4) - - def add_ignore_pass_case(self): - # If the problem has been fixed, the judgment - # in is_program_valid needs to be deleted!!! - 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, \ - because currently its fused op (Conv2DFusion) only supports data format of channel first (NCHW)." - ) - - 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_affine_channel_fuse_pass"], ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_fuse_pass.py deleted file mode 100644 index a8bfdb79ca..0000000000 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_fuse_pass.py +++ /dev/null @@ -1,183 +0,0 @@ -# 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 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 -import hypothesis.strategies as st - - -class TestConvEltwiseAddAffineChannelFusePass(PassAutoScanTest): - 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" and attrs[1]['axis'] != 3: - 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()) - - 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] - - def generate_input(): - return np.random.random(x_shape).astype(np.float32) - - def generate_weight(): - return np.random.random(w_shape).astype(np.float32) - - def generate_bias(): - return np.random.random(bias_shape).astype(np.float32) - - def generate_scale_bias(): - return np.random.random(bias_shape).astype(np.float32) - - conv2d_op = OpConfig( - "conv2d", - inputs={ - "Input": ["input_data"], - "Filter": ["conv2d_weight"], - }, - outputs={"Output": ["conv_output"]}, - data_format=data_format, - dilations=dilations, - padding_algorithm=padding_algorithm, - groups=groups, - paddings=paddings, - strides=strides, - has_bias=has_bias, - is_test=True) - eltwise_op = OpConfig( - "elementwise_add", - inputs={"X": ["conv_output"], - "Y": ["conv2d_bias"]}, - outputs={"Out": ["elementwise_output"]}, - axis=axis) - ac_op = OpConfig( - "affine_channel", - inputs={ - "X": ["elementwise_output"], - "Scale": ["affine_channel_scale"], - "Bias": ["affine_channel_bias"] - }, - outputs={"Out": ["affine_channel_ouput"]}, - data_layout=data_format) - if has_bias == True: - conv2d_op.inputs["Bias"] = ["conv2d_bias"] - ops = [conv2d_op, eltwise_op, ac_op] - program_config = ProgramConfig( - ops=ops, - inputs={ - "input_data": TensorConfig(data_gen=partial(generate_input)), - }, - weights={ - "conv2d_weight": - TensorConfig(data_gen=partial(generate_weight)), - "conv2d_bias": TensorConfig(data_gen=partial(generate_bias)), - "affine_channel_scale": - TensorConfig(data_gen=partial(generate_scale_bias)), - "affine_channel_bias": - TensorConfig(data_gen=partial(generate_scale_bias)), - }, - outputs=["affine_channel_ouput"]) - return program_config - - def sample_predictor_configs(self, program_config): - config = self.create_inference_config(use_gpu=True) - yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4) - - config = self.create_inference_config(use_mkldnn=True) - yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4) - - # TRT - 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) - yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4) - - def add_ignore_pass_case(self): - # If the problem has been fixed, the judgment - # in is_program_valid needs to be deleted!!! - 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, \ - it will trigger Broadcast dimension mismatch bug \ - when data_format attribute is NHWC and axis of eltwise op is 1 for this pass." - ) - - 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_eltwiseadd_affine_channel_fuse_pass"], ) - - -if __name__ == "__main__": - unittest.main() diff --git a/tools/parallel_UT_rule.py b/tools/parallel_UT_rule.py index 4df27bfe4e..7f8e516496 100755 --- a/tools/parallel_UT_rule.py +++ b/tools/parallel_UT_rule.py @@ -958,7 +958,6 @@ FOURTH_HIGH_PARALLEL_JOB_NEW = [ 'test_dynamic_rnn_stop_gradient', 'test_raw_program_optimizer', 'test_pow', 'test_inplace_softmax_with_cross_entropy', 'test_transforms', 'test_unfold_op', 'test_assign_op', 'test_isinstance', - 'test_conv_affine_channel_fuse_pass', 'auto_growth_best_fit_allocator_facade_test', 'test_cholesky_op', 'test_adaptive_avg_pool3d', 'test_paddle_save_load_binary', 'test_fused_fc_elementwise_layernorm_op', 'test_sequence_enumerate_op', @@ -1873,7 +1872,6 @@ TETRAD_PARALLEL_JOB = [ 'test_dataloader_unkeep_order', 'test_parallel_executor_profiler', 'test_correlation', - 'test_conv_affine_channel_fuse_pass', 'test_ir_inplace_pass', 'test_moving_average_abs_max_scale_op', 'test_flatten_contiguous_range_op', diff --git a/tools/static_mode_white_list.py b/tools/static_mode_white_list.py index 694283264c..7356f0c8db 100755 --- a/tools/static_mode_white_list.py +++ b/tools/static_mode_white_list.py @@ -578,7 +578,6 @@ STATIC_MODE_TESTING_LIST = [ 'test_ir_embedding_eltwise_layernorm_fuse_pass', 'test_ir_fc_fuse_pass', 'test_ir_skip_layernorm_pass', - 'test_conv_affine_channel_fuse_pass', 'test_conv_bias_mkldnn_fuse_pass', 'test_conv_bn_fuse_pass', 'test_conv_elementwise_add2_act_fuse_pass', -- GitLab