sneaxiy
6 years ago
commit
ad5f0e6018
@ -1,32 +0,0 @@
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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 "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/pass.h"
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namespace paddle {
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namespace framework {
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namespace details {
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class EagerDeletionPass : public ir::Pass {
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const override;
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,62 @@
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// Copyright (c) 2019 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/details/computation_op_handle.h"
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#include "paddle/fluid/framework/details/multi_devices_helper.h"
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#include "paddle/fluid/framework/ir/graph_helper.h"
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#include "paddle/fluid/operators/controlflow/while_op_helper.h"
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namespace paddle {
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namespace framework {
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namespace details {
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class WhileOpEagerDeletionPass : public ir::Pass {
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const override {
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auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
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// Find all while_op and while_grad_op
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std::unordered_map<size_t, std::pair<std::vector<OperatorBase *>,
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std::vector<OperatorBase *>>>
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target_ops;
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for (auto *op : all_ops) {
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auto compute_op = dynamic_cast<ComputationOpHandle *>(op);
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if (compute_op == nullptr) continue;
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if (compute_op->Name() == "while") {
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target_ops[compute_op->GetScopeIdx()].first.emplace_back(
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compute_op->GetOp());
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} else if (compute_op->Name() == "while_grad") {
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target_ops[compute_op->GetScopeIdx()].second.emplace_back(
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compute_op->GetOp());
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}
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}
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for (auto &ops_pair : target_ops) {
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auto &while_ops = ops_pair.second.first;
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auto &while_grad_ops = ops_pair.second.second;
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operators::PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(
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while_ops, while_grad_ops);
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}
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return graph;
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}
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};
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} // namespace details
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(while_op_eager_deletion_pass,
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paddle::framework::details::WhileOpEagerDeletionPass);
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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 eint8_outcept 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 eint8_outpress or
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// 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/cpu_quantize_squash_pass.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/platform/enforce.h"
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#include "paddle/fluid/string/pretty_log.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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using string::PrettyLogDetail;
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void CPUQuantizeSquashPass::FindNodesToKeep(
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Graph* graph,
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std::unordered_map<const Node*, int>* nodes_keep_counter) const {
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GraphPatternDetector gpd;
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patterns::DequantAny deq_any_pattern{gpd.mutable_pattern(), "deqant_any"};
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deq_any_pattern();
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int found_count = 0;
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auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
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Graph* g) {
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GET_IR_NODE_FROM_SUBGRAPH(dequant_out, dequant_out, deq_any_pattern);
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if (nodes_keep_counter->find(dequant_out) == nodes_keep_counter->end())
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(*nodes_keep_counter)[dequant_out] = 1;
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else
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(*nodes_keep_counter)[dequant_out] += 1;
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found_count++;
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};
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gpd(graph, handler);
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AddStatis(found_count);
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}
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void CPUQuantizeSquashPass::Squash(
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Graph* graph,
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std::unordered_map<const Node*, int>* nodes_keep_counter) const {
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GraphPatternDetector gpd;
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patterns::DequantQuantAny squash_pattern{gpd.mutable_pattern(), "squash"};
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squash_pattern();
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int found_squash_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) << "squash requantize-quantize ops pair";
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GET_IR_NODE_FROM_SUBGRAPH(dequant_in, dequant_in, squash_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(dequant_op, dequant_op, squash_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(dequant_out, dequant_out, squash_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(quant_op, quant_op, squash_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(quant_out, quant_out, squash_pattern);
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GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, squash_pattern);
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auto* next_op_desc = next_op->Op();
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float dequant_scale = boost::get<float>(dequant_op->Op()->GetAttr("Scale"));
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float quant_scale = boost::get<float>(quant_op->Op()->GetAttr("Scale"));
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PADDLE_ENFORCE(nodes_keep_counter->find(dequant_out) !=
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nodes_keep_counter->end());
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// check if dequantize op should be kept or removed, decrease the counter
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bool keep_dequant = (*nodes_keep_counter)[dequant_out]-- > 1;
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if (dequant_scale == quant_scale) {
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// squash dequantize-quantize to nothing
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auto quant_out_var_name = quant_out->Name();
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auto next_op_inputs = next_op_desc->InputNames();
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for (const auto& name : next_op_inputs) {
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auto var_name = next_op_desc->Input(name)[0];
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if (var_name.compare(quant_out_var_name) == 0) {
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next_op_desc->SetInput(
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name, std::vector<std::string>({dequant_in->Name()}));
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break;
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}
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}
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if (keep_dequant)
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GraphSafeRemoveNodes(graph, {quant_op, quant_out});
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else
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GraphSafeRemoveNodes(graph,
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{dequant_op, quant_op, dequant_out, quant_out});
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IR_NODE_LINK_TO(dequant_in, next_op);
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found_squash_count++;
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} else {
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// squash dequantize-quantize to requantize op
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OpDesc desc;
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desc.SetType("requantize");
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desc.SetInput("Input", std::vector<std::string>({dequant_in->Name()}));
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desc.SetOutput("Output", std::vector<std::string>({quant_out->Name()}));
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desc.SetAttr("Scale_in", dequant_scale);
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desc.SetAttr("Scale_out", quant_scale);
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auto requant_op = g->CreateOpNode(&desc);
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if (keep_dequant)
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GraphSafeRemoveNodes(graph, {quant_op});
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else
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GraphSafeRemoveNodes(graph, {dequant_op, quant_op, dequant_out});
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IR_NODE_LINK_TO(dequant_in, requant_op);
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IR_NODE_LINK_TO(requant_op, quant_out);
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found_squash_count++;
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}
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};
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gpd(graph, handler);
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AddStatis(found_squash_count);
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PrettyLogDetail("--- squashed %d dequantize-quantize pairs",
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found_squash_count);
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}
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std::unique_ptr<ir::Graph> CPUQuantizeSquashPass::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("cpu_quantize_squash_pass", graph.get());
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std::unordered_map<const Node*, int> nodes_keep_counter;
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FindNodesToKeep(graph.get(), &nodes_keep_counter);
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Squash(graph.get(), &nodes_keep_counter);
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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(cpu_quantize_squash_pass,
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paddle::framework::ir::CPUQuantizeSquashPass);
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@ -0,0 +1,58 @@
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// Copyright (c) 2019 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 <string>
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#include <unordered_map>
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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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#include "paddle/fluid/framework/ir/pass.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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* Squash dequantize->quantize pair pattern into requantize op
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*/
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class CPUQuantizeSquashPass : public FusePassBase {
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public:
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virtual ~CPUQuantizeSquashPass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const override;
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/*
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* For each dequantize's output find the number of operators it is an input to
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*/
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void FindNodesToKeep(
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Graph* graph,
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std::unordered_map<const Node*, int>* nodes_keep_counter) const;
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/*
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* Squash dequantize-quantize ops pairs into requantize or nothing
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*/
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void Squash(Graph* graph,
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std::unordered_map<const Node*, int>* nodes_keep_counter) const;
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const std::string name_scope_{"squash"};
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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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@ -0,0 +1,179 @@
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// Copyright (c) 2019 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/cpu_quantize_squash_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/naive_executor.h"
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#include "paddle/fluid/platform/place.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
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const std::vector<std::string>& inputs,
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const std::vector<std::string>& outputs, bool use_mkldnn,
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float scale = 0) {
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auto* op = prog->MutableBlock(0)->AppendOp();
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op->SetType(type);
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op->SetAttr("use_mkldnn", use_mkldnn);
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op->SetAttr("name", name);
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if (type == "conv2d") {
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op->SetInput("Input", {inputs[0]});
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if (inputs.size() > 1) op->SetInput("Filter", {inputs[1]});
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if (inputs.size() > 2) op->SetInput("Bias", {inputs[2]});
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op->SetOutput("Output", {outputs[0]});
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} else if (type == "quantize") {
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op->SetInput("Input", {inputs[0]});
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op->SetOutput("Output", {outputs[0]});
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op->SetAttr("Scale", scale);
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} else if (type == "dequantize") {
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op->SetInput("Input", {inputs[0]});
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op->SetOutput("Output", {outputs[0]});
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op->SetAttr("Scale", scale);
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}
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}
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// (a,w1,b1)->Conv1->d
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// d->Dequant->e
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// e->Quant->f
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// (f,w2,b2)->Conv2->i
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ProgramDesc BuildProgramDesc(bool use_mkldnn, float scale1, float scale2) {
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ProgramDesc prog;
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for (auto& v : std::initializer_list<std::string>(
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{"a", "w1", "b1", "d", "e", "f", "w2", "b2", "i"})) {
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auto* var = prog.MutableBlock(0)->Var(v);
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if (v.find("w") == 0 || v.find("b") == 0) {
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var->SetPersistable(true);
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}
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}
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SetOp(&prog, "conv2d", "Conv1", {"a", "w1", "b1"}, {"d"}, use_mkldnn);
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SetOp(&prog, "dequantize", "Dequant", {"d"}, {"e"}, use_mkldnn, scale1);
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SetOp(&prog, "quantize", "Quant", {"e"}, {"f"}, use_mkldnn, scale2);
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SetOp(&prog, "conv2d", "Conv2", {"f", "w2", "b2"}, {"i"}, use_mkldnn);
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return prog;
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}
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static const std::initializer_list<std::string> variable_names{
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"a", "b", "c", "d", "e", "f", "g", "h"};
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// a->Conv1->b
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// b->Dequant->c
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//
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// c->Quant1->d and d->Conv2->e
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//
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// c->Conv3->f
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//
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// c->Quant2->g and g->Conv4->h
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//
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ProgramDesc BuildProgramDesc2(bool use_mkldnn, float scale1, float scale2,
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float scale3) {
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ProgramDesc prog;
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for (auto& v : variable_names) {
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prog.MutableBlock(0)->Var(v);
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}
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SetOp(&prog, "conv2d", "Conv1", {"a"}, {"b"}, use_mkldnn);
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SetOp(&prog, "dequantize", "Dequant", {"b"}, {"c"}, use_mkldnn, scale1);
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SetOp(&prog, "quantize", "Quant1", {"c"}, {"d"}, use_mkldnn, scale2);
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SetOp(&prog, "conv2d", "Conv2", {"d"}, {"e"}, use_mkldnn);
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SetOp(&prog, "conv2d", "Conv3", {"c"}, {"f"}, use_mkldnn);
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SetOp(&prog, "quantize", "Quant2", {"c"}, {"g"}, use_mkldnn, scale3);
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SetOp(&prog, "conv2d", "Conv4", {"g"}, {"h"}, use_mkldnn);
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return prog;
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}
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void InitTensorHolder(Scope* scope, const paddle::platform::Place& place,
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const char* var_name) {
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auto x = scope->Var(var_name);
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auto tensor = x->GetMutable<LoDTensor>();
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tensor->mutable_data(place, proto::VarType::FP32,
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::paddle::memory::Allocator::kDefault, 1);
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}
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void MainTest(const ProgramDesc& prog, int removed_nodes_num) {
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std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
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// Init scope, as it is used in pass
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auto place = paddle::platform::CPUPlace();
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NaiveExecutor exe{place};
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Scope scope;
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exe.CreateVariables(prog, 0, true, &scope);
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for (auto& v : variable_names) {
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InitTensorHolder(&scope, place, v.c_str());
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}
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graph->Set(kParamScopeAttr, new framework::Scope*(&scope));
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auto pass = PassRegistry::Instance().Get("cpu_quantize_squash_pass");
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int original_nodes_num = graph->Nodes().size();
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graph = pass->Apply(std::move(graph));
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int current_nodes_num = graph->Nodes().size();
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EXPECT_EQ(original_nodes_num - removed_nodes_num, current_nodes_num);
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}
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TEST(CpuQuantizeSquashPass, equal_scales) {
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auto scale = 1.2345f;
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auto use_mkldnn = true;
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// Remove 4 nodes: Dequant, Quant, e, f
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auto remove_nodes = 4;
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MainTest(BuildProgramDesc(use_mkldnn, scale, scale), remove_nodes);
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use_mkldnn = !use_mkldnn;
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MainTest(BuildProgramDesc(use_mkldnn, scale, scale), remove_nodes);
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}
|
||||
|
||||
TEST(CpuQuantizeSquashPass, inequal_scales) {
|
||||
auto scale1 = 1.2345f;
|
||||
auto scale2 = 21.0f;
|
||||
auto use_mkldnn = true;
|
||||
// Remove 3 nodes: Dequant, Quant, e
|
||||
// Insert 1 node: requantize
|
||||
auto remove_nodes = 2;
|
||||
MainTest(BuildProgramDesc(use_mkldnn, scale1, scale2), remove_nodes);
|
||||
|
||||
use_mkldnn = !use_mkldnn;
|
||||
MainTest(BuildProgramDesc(use_mkldnn, scale1, scale2), remove_nodes);
|
||||
}
|
||||
|
||||
TEST(CpuQuantizeSquashPass, branch_to_equal_inequal_and_fp32) {
|
||||
// Delete both quantize ops,
|
||||
// bypass dequantize in both branches,
|
||||
// insert requantize on one branch
|
||||
auto scale = 1.2345f;
|
||||
auto scale2 = 21.0f;
|
||||
auto use_mkldnn = true;
|
||||
// Remove 3 nodes: Quant1, Quant2, g
|
||||
// Insert 1 node: requantize
|
||||
auto remove_nodes = 2;
|
||||
MainTest(BuildProgramDesc2(use_mkldnn, scale, scale, scale2), remove_nodes);
|
||||
|
||||
use_mkldnn = !use_mkldnn;
|
||||
MainTest(BuildProgramDesc2(use_mkldnn, scale, scale, scale2), remove_nodes);
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
USE_PASS(cpu_quantize_squash_pass);
|
||||
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Reference in new issue