refine trt first run add quant dequant fuse pass omit simplify_anakin_priorbox_detection template omit transpose_flatten_concat_fuse template test=developrevert-16807-engine2-interface
nhzlx
6 years ago
parent
ed61d67c73
commit
d065b5bf2b
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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 <memory>
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#include <string>
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#include <unordered_set>
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#include <vector>
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#include "paddle/fluid/framework/ir/graph_viz_pass.h"
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#include "paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void RunQuantDequant(ir::Graph* graph, Scope* scope, int times,
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std::string op_type) {
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const std::string pattern_name = "quant_dequant_fuse";
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// FusePassBase::Init(pattern_name, graph);
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const int kNumFields = 5;
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const int kQuantizedWeightOffset = 0;
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const int kQuantizedOpOffset = 1;
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const int kQuantizedOpOutOffset = 2;
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const int kDequantOpOffset = 3;
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const int kDequantOpOutOffset = 4;
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GraphPatternDetector gpd;
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auto* x = gpd.mutable_pattern()
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->NewNode("x")
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->assert_is_op_input("fake_quantize_range_abs_max", "X")
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->AsInput();
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std::string quantized_op_type = "";
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std::string weight_name = "";
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if (op_type == "conv2d") {
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quantized_op_type = "conv2d";
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weight_name = "Filter";
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} else if (op_type == "conv2d_fusion") {
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quantized_op_type = "conv2d_fusion";
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weight_name = "Filter";
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} else if (op_type == "mul") {
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quantized_op_type = "mul";
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weight_name = "Y";
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} else if (op_type == "fc") {
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quantized_op_type = "fc";
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weight_name = "W";
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} else {
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PADDLE_ENFORCE(
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"QuantDequantFuse: We only support conv2d, conv2d_fusion, fc, mul for "
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"now.");
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}
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patterns::QuantDequantOpFuse pattern(gpd.mutable_pattern(), pattern_name);
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pattern(x, quantized_op_type, weight_name, times);
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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PADDLE_ENFORCE(subgraph.count(x));
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auto* input_node = subgraph.at(x);
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Node* quant_op_in_scale =
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subgraph.at(pattern.GetPDNode("quant_op_in_scale"));
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Node* quant_op = subgraph.at(pattern.GetPDNode("quant_op"));
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Node* quant_op_out_scale =
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subgraph.at(pattern.GetPDNode("quant_op_out_scale"));
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Node* quant_op_out = subgraph.at(pattern.GetPDNode("quant_op_out"));
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std::vector<Node*> nodes;
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for (int i = 0; i < times; i++) {
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nodes.push_back(subgraph.at(
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pattern.GetPDNode("quantized_op_weight" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("quantized_op" + std::to_string(i))));
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nodes.push_back(subgraph.at(
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pattern.GetPDNode("quantized_op_out" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("dequant_op" + std::to_string(i))));
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nodes.push_back(
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subgraph.at(pattern.GetPDNode("dequant_op_out" + std::to_string(i))));
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}
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int bit_length = boost::get<int>(quant_op->Op()->GetAttr("bit_length"));
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int range = ((1 << (bit_length - 1)) - 1);
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// Prepare input scale
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std::string input_scale_var_name = quant_op->Op()->Input("InScale").front();
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PADDLE_ENFORCE(scope);
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const LoDTensor& input_scale_tensor =
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scope->FindVar(input_scale_var_name)->Get<LoDTensor>();
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PADDLE_ENFORCE(paddle::platform::is_cpu_place(input_scale_tensor.place()));
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const float* input_scale_data = input_scale_tensor.data<float>();
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float input_scale = input_scale_data[0];
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std::unordered_set<const Node*> delete_nodes;
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for (int i = 0; i < times; i++) {
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// max_range = (range * range) / weight_scale
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float max_range = boost::get<float>(
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nodes[i * kNumFields + kDequantOpOffset]->Op()->GetAttr("max_range"));
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float weight_scale = (range * range) / max_range;
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auto base_op_desc =
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*nodes[i * kNumFields + kQuantizedOpOffset]->Op()->Proto();
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std::string new_input = input_node->Name();
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std::string new_output =
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nodes[i * kNumFields + kDequantOpOutOffset]->Name();
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framework::OpDesc new_op_desc(base_op_desc, nullptr);
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new_op_desc.SetType(quantized_op_type);
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if (quantized_op_type == "conv2d" ||
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quantized_op_type == "conv2d_fusion") {
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new_op_desc.SetInput("Input", {new_input});
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new_op_desc.SetOutput("Output", {new_output});
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} else if (quantized_op_type == "fc") {
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new_op_desc.SetInput("Input", {new_input});
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new_op_desc.SetOutput("Out", {new_output});
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} else if (quantized_op_type == "mul") {
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new_op_desc.SetInput("X", {new_input});
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new_op_desc.SetOutput("Out", {new_output});
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}
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new_op_desc.SetAttr("enable_int8", true);
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new_op_desc.SetAttr("input_scale", input_scale);
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new_op_desc.SetAttr("weight_scale", weight_scale);
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new_op_desc.Flush();
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auto* new_op = graph->CreateOpNode(&new_op_desc);
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IR_NODE_LINK_TO(input_node, new_op);
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IR_NODE_LINK_TO(nodes[i * kNumFields + kQuantizedWeightOffset], new_op);
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IR_NODE_LINK_TO(new_op, nodes[i * kNumFields + kDequantOpOutOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kQuantizedOpOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kQuantizedOpOutOffset]);
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delete_nodes.insert(nodes[i * kNumFields + kDequantOpOffset]);
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}
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delete_nodes.insert(quant_op_in_scale);
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delete_nodes.insert(quant_op);
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delete_nodes.insert(quant_op_out);
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delete_nodes.insert(quant_op_out_scale);
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// Delete the unneeded nodes.
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GraphSafeRemoveNodes(graph, delete_nodes);
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};
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gpd(graph, handler);
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}
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void QuantDequantFusePass::ApplyImpl(ir::Graph* graph) const {
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const std::string pattern_name = "quant_dequant_fuse";
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FusePassBase::Init(pattern_name, graph);
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std::unordered_set<std::string> quantized_op_types = {"conv2d", "mul"};
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auto* scope = param_scope();
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for (auto& op_type : quantized_op_types) {
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for (int i = 1; i <= 6; i++) {
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RunQuantDequant(graph, scope, i, op_type);
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}
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}
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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(quant_conv2d_dequant_fuse_pass,
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paddle::framework::ir::QuantDequantFusePass);
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@ -0,0 +1,35 @@
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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 <memory>
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#include "paddle/fluid/framework/ir/fuse_pass_base.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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class QuantDequantFusePass : public FusePassBase {
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public:
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virtual ~QuantDequantFusePass() {}
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protected:
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void ApplyImpl(ir::Graph* graph) const override;
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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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