wehub-resource-sync
118 lines
3.8 KiB
C++
118 lines
3.8 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <glog/logging.h>
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#include <gtest/gtest.h>
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#include <fstream>
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#include <iostream>
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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/pass.h"
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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#include "paddle/fluid/framework/op_desc.h"
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#include "paddle/fluid/framework/program_desc.h"
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#include "paddle/fluid/platform/enforce.h"
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using std::pair;
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using std::string;
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using std::unordered_map;
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PD_DEFINE_bool(enable_onednn, true, "Enable ONEDNN");
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namespace paddle {
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namespace pass {
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using VarQuantScale =
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std::unordered_map<std::string, std::pair<bool, phi::DenseTensor>>;
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static float const SCALE = 2.f;
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const std::vector<std::string> PreGraphPasses({
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"conv_activation_onednn_fuse_pass",
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"cpu_quantize_placement_pass",
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"cpu_quantize_pass",
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});
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TEST(cpuQuantizePass, ConvReLU6) {
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paddle::framework::ProgramDesc prog;
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auto* block = prog.MutableBlock(0);
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auto* conv2d_op = block->AppendOp();
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conv2d_op->SetType("conv2d");
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conv2d_op->SetInput("Input", {"conv2d-X"});
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conv2d_op->SetInput("Filter", {"conv2d-Y"});
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conv2d_op->SetOutput("Output", {"conv2d-Out"});
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const std::vector<int> strides({1, 1});
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const std::vector<int> paddings({1, 1});
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const std::vector<int> dilations({1, 1});
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const int groups = 1;
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conv2d_op->SetAttr("strides", strides);
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conv2d_op->SetAttr("paddings", paddings);
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conv2d_op->SetAttr("dilations", dilations);
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conv2d_op->SetAttr("groups", groups);
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auto* relu6_op = block->AppendOp();
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relu6_op->SetType("relu6");
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relu6_op->SetAttr("threshold", 6.f);
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relu6_op->SetInput("X", {"conv2d-Out"});
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relu6_op->SetOutput("Out", {"relu-Out"});
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auto place = phi::CPUPlace();
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VarQuantScale* scales = new VarQuantScale();
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phi::DenseTensor scale_input_tensor;
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phi::DenseTensor scale_weight_tensor;
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scale_input_tensor.Resize({1});
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scale_weight_tensor.Resize({1});
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auto* ptr_scale_input = scale_input_tensor.mutable_data<double>(place);
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auto* ptr_scale_weight = scale_weight_tensor.mutable_data<double>(place);
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ptr_scale_input[0] = SCALE;
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ptr_scale_weight[0] = SCALE;
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(*scales)["conv2d-X"] = std::make_pair(false, std::move(scale_input_tensor));
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(*scales)["conv2d-Y"] = std::make_pair(false, std::move(scale_weight_tensor));
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paddle::framework::Scope scope;
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std::unique_ptr<paddle::framework::ir::Graph> graph(
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new paddle::framework::ir::Graph(prog));
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(graph)->SetNotOwned(paddle::framework::ir::kParamScopeAttr, &scope);
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for (const auto& pass : PreGraphPasses) {
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auto pass_ = paddle::framework::ir::PassRegistry::Instance().Get(pass);
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if (pass == "cpu_quantize_pass") {
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pass_->Set("quant_var_scales", scales);
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}
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graph.reset(pass_->Apply(graph.release()));
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}
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int fused_conv2d_num = 0;
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for (auto* node : graph->Nodes()) {
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if (node->IsOp() && node->Op() && node->Op()->Type() == "fused_conv2d") {
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PADDLE_ENFORCE_EQ(
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node->Op()->GetAttrIfExists<float>("fuse_beta"),
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6,
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common::errors::InvalidArgument("Attr fuse_beta must equal to 6. "));
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fused_conv2d_num++;
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}
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}
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PADDLE_ENFORCE_GT(
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fused_conv2d_num,
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0,
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common::errors::InvalidArgument("Graph must contain fused_conv2d. "));
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}
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} // namespace pass
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} // namespace paddle
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