xiaoli.liu@intel.com
6 years ago
commit
086f892225
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h"
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#include <functional>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/operators/math/cpu_vec.h"
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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#define GET_CONV_BN_NODES(pattern_name) \
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/* OPERATORS */ \
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GET_IR_NODE_FROM_SUBGRAPH(conv, conv, pattern_name); \
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GET_IR_NODE_FROM_SUBGRAPH(affine_channel, affine_channel, pattern_name); \
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/* CONV inputs */ \
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GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight, pattern_name); \
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/* CONV outputs */ \
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GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, pattern_name); \
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/* Affine Channel inputs */ \
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GET_IR_NODE_FROM_SUBGRAPH(ac_scale, ac_scale, pattern_name); \
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GET_IR_NODE_FROM_SUBGRAPH(ac_bias, ac_bias, pattern_name); \
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/* Affine channel outputs */ \
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GET_IR_NODE_FROM_SUBGRAPH(ac_out, ac_out, pattern_name); /* Out */
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void recompute_bias_and_weights(const Scope* scope, ir::Node* conv_weight,
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const ir::Node& ac_scale,
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const LoDTensor& ac_bias_tensor,
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LoDTensor* eltwise_y_in_tensor) {
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using EigenVectorArrayMap =
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Eigen::Map<Eigen::Array<float, Eigen::Dynamic, 1>>;
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using ConstEigenVectorArrayMap =
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Eigen::Map<const Eigen::Array<float, Eigen::Dynamic, 1>>;
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using EigenMatrixArrayMap = Eigen::Map<
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Eigen::Array<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
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// Re-compute bias of conv2d from AffineChannel
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PADDLE_ENFORCE_EQ(eltwise_y_in_tensor->dims(), ac_bias_tensor.dims());
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auto* scale_tensor = scope->FindVar(ac_scale.Name())->GetMutable<LoDTensor>();
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ConstEigenVectorArrayMap scale_array(scale_tensor->data<float>(),
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scale_tensor->numel(), 1);
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ConstEigenVectorArrayMap ac_bias_array(ac_bias_tensor.data<float>(),
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ac_bias_tensor.numel(), 1);
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EigenVectorArrayMap eltwise_y_in_array(
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eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
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eltwise_y_in_tensor->numel(), 1);
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eltwise_y_in_array = (eltwise_y_in_array * scale_array) + ac_bias_array;
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// Re-compute weight of conv2d from AffineChannel
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auto* weights = scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();
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auto weights_shape = weights->dims();
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auto weights_shape_2d = flatten_to_2d(weights_shape, 1);
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EigenMatrixArrayMap weights_array_2d(
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weights->mutable_data<float>(platform::CPUPlace()), weights_shape_2d[0],
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weights_shape_2d[1]);
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weights_array_2d.colwise() *= scale_array;
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}
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std::unique_ptr<ir::Graph> ConvAffineChannelFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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PADDLE_ENFORCE(graph.get());
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FusePassBase::Init(name_scope_, graph.get());
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auto* scope = param_scope();
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PADDLE_ENFORCE(scope);
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GraphPatternDetector gpd;
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auto* conv_input =
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gpd.mutable_pattern()
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->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
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->AsInput()
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->assert_is_op_input("conv2d", "Input");
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patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
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name_scope_);
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conv_ac_pattern(conv_input, false /*with_eltwise_add*/);
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int found_conv_ac_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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VLOG(4) << "handle ConvAffineChannel fuse";
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GET_CONV_BN_NODES(conv_ac_pattern);
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// check if fuse can be done and if MKL-DNN should be used
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FuseOptions fuse_option = FindFuseOption(*conv, *affine_channel);
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if (fuse_option == DO_NOT_FUSE) {
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VLOG(3) << "do not perform conv+affinechannel fuse";
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return;
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}
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// Create eltwise_y (conv bias) variable
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VarDesc eltwise_y_in_desc(
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patterns::PDNodeName(name_scope_, "eltwise_y_in"));
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eltwise_y_in_desc.SetPersistable(true);
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auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
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auto* eltwise_y_in_tensor =
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scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
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// Get affine_channel bias
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auto* ac_bias_tensor =
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scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
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// Initialize eltwise_y
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eltwise_y_in_tensor->Resize(ac_bias_tensor->dims());
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std::fill_n(eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
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eltwise_y_in_tensor->numel(), 0.0f);
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// update weights and biases
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recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
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eltwise_y_in_tensor);
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// create an elementwise add node.
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OpDesc desc;
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desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
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desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
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desc.SetOutput("Out", std::vector<std::string>({ac_out->Name()}));
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desc.SetType("elementwise_add");
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desc.SetAttr("axis", 1);
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auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
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GraphSafeRemoveNodes(graph.get(), {ac_scale, ac_bias, affine_channel});
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IR_NODE_LINK_TO(conv_out, eltwise_op);
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IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
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IR_NODE_LINK_TO(eltwise_op, ac_out);
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found_conv_ac_count++;
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};
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gpd(graph.get(), handler);
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AddStatis(found_conv_ac_count);
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return graph;
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}
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std::unique_ptr<ir::Graph> ConvEltwiseAddAffineChannelFusePass::ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const {
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PADDLE_ENFORCE(graph.get());
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FusePassBase::Init(name_scope_, graph.get());
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auto* scope = param_scope();
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PADDLE_ENFORCE(scope);
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GraphPatternDetector gpd;
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auto* conv_input =
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gpd.mutable_pattern()
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->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
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->AsInput()
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->assert_is_op_input("conv2d", "Input");
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patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
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name_scope_);
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conv_ac_pattern(conv_input, true /*with_eltwise_add*/);
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int found_conv_ac_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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VLOG(4) << "handle ConvBN fuse";
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GET_CONV_BN_NODES(conv_ac_pattern);
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// OPERATORS
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GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_ac_pattern);
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// BIAS inputs
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GET_IR_NODE_FROM_SUBGRAPH(eltwise_y_in, eltwise_y_in, conv_ac_pattern);
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// BIAS outputs
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GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_ac_pattern);
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// Get eltwise_y (conv bias) variable
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auto* eltwise_y_in_tensor =
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scope->FindVar(eltwise_y_in->Name())->GetMutable<LoDTensor>();
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// Get batch norm bias
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auto* ac_bias_tensor =
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scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
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recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
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eltwise_y_in_tensor);
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// Update the elementwise_add node
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eltwise->Op()->SetAttr("axis", 1);
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eltwise->Op()->SetOutput("Out", std::vector<std::string>({ac_out->Name()}));
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GraphSafeRemoveNodes(graph.get(),
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{ac_scale, ac_bias, affine_channel, eltwise_out});
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IR_NODE_LINK_TO(eltwise, ac_out);
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found_conv_ac_count++;
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};
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gpd(graph.get(), handler);
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AddStatis(found_conv_ac_count);
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return graph;
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(conv_affine_channel_fuse_pass,
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paddle::framework::ir::ConvAffineChannelFusePass);
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REGISTER_PASS(conv_eltwiseadd_affine_channel_fuse_pass,
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paddle::framework::ir::ConvEltwiseAddAffineChannelFusePass);
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@ -0,0 +1,49 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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/*
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* Fuse the Conv and ConvAffineChannel.
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*/
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class ConvAffineChannelFusePass : public FusePassBase {
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public:
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virtual ~ConvAffineChannelFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"conv_affine_channel_fuse"};
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};
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class ConvEltwiseAddAffineChannelFusePass : public FusePassBase {
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public:
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virtual ~ConvEltwiseAddAffineChannelFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"conv_eltwiseadd_affine_channel_fuse"};
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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