wehub-resource-sync
228 lines
9.2 KiB
C++
228 lines
9.2 KiB
C++
// Copyright (c) 2021 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/generate_pass.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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REGISTER_GENERATE_PASS(generate_fc_fuse) {
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paddle::framework::ir::PassPairs pass_pairs;
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for (bool with_relu : {true, false}) {
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// pattern
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SUBGRAPH_(pattern) = [subgraph = &pattern, with_relu](
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VAR_(x), VAR_(y), VAR_(z)) {
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VLOG(3) << "exec lambda func.";
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auto mul = OP_(mul)({{"X", x}, {"Y", y}}).Out("Out");
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auto ewadd = OP_(elementwise_add)({{"X", mul}, {"Y", z}}).Out("Out");
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if (with_relu) { // NOLINT
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return OP_(relu)({"X", ewadd}).Out("Out");
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} else {
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return ewadd;
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}
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};
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// replace
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SUBGRAPH_(replace) = [subgraph = &replace](VAR_(x), VAR_(y), VAR_(z)) {
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auto& fc = OP_(fc)({{"Input", x}, {"W", y}, {"Bias", z}});
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return fc.Out("Out");
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};
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pass_pairs.AddPassDesc(pattern, replace);
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}
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return pass_pairs;
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}
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REGISTER_GENERATE_PASS(generate_multi_add_to_addn) {
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// pattern
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SUBGRAPH_(pattern) = [subgraph = &pattern](VAR_(x), VAR_(y), VAR_(z)) {
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auto ewadd1 = OP_(elementwise_add)({{"X", x}, {"Y", y}}).Out("Out");
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auto ewadd2 = OP_(elementwise_add)({{"X", ewadd1}, {"Y", z}}).Out("Out");
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return ewadd2;
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};
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// replace
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SUBGRAPH_(replace) = [subgraph = &replace](VAR_(x), VAR_(y), VAR_(z)) {
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return OP_(sum)({"X", {x, y, z}}).Out("Out");
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};
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return {pattern, replace};
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}
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REGISTER_GENERATE_PASS(generate_combine_matmul) {
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// pattern
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SUBGRAPH_(pattern) = [subgraph = &pattern](VAR_(x), VAR_(y), VAR_(z)) {
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auto matmul1 = OP_(matmul)({{"X", x}, {"Y", y}}).Out("Out");
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auto matmul2 = OP_(matmul)({{"X", x}, {"Y", z}}).Out("Out");
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return std::make_tuple(matmul1, matmul2);
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};
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// replace
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SUBGRAPH_(replace) = [subgraph = &replace](VAR_(x), VAR_(y), VAR_(z)) {
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auto concat = OP_(concat)({"X", {y, z}}).Out("Out");
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auto matmul = OP_(matmul)({{"X", x}, {"Y", concat}}).Out("Out");
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auto slice1 = OP_(slice)({"X", matmul}).Out("Out");
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auto slice2 = OP_(slice)({"X", matmul}).Out("Out");
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return std::make_tuple(slice1, slice2);
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};
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return {pattern, replace};
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}
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namespace paddle {
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namespace framework {
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namespace ir {
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TEST(GeneratePass, construct_with_string) {
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std::string binary_str;
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register_generate_fc_fuse().MultiPassDesc().SerializeToString(&binary_str);
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GeneratePass generate_pass(binary_str);
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}
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TEST(GeneratePass, generate_fc_fuse) {
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// inputs operator output
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// --------------------------------------------------------
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// (a, filters_0 bias_0) conv2d -> conv2d_out
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// conv2d_out relu -> relu_out_0
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// (relu_out_0, weights_0) mul -> mul_out_0
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// (mul_out_0, bias_1) elementwise_add -> add_out_0
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// add_out_0 relu -> relu_out_1
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// (relu_out_1, weights_1) mul -> mul_out_1
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// (mul_out_1, bias_2) elementwise_add -> add_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* filters_0 = layers.data("conv2d_filters_0", {}, true);
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auto* bias_0 = layers.data("conv2d_bias_0", {}, true);
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auto* conv2d_out = layers.conv2d(a, filters_0, bias_0, false);
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auto* relu_out_0 = layers.relu(conv2d_out);
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auto* weights_0 = layers.data("weights_0", {}, true);
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auto* mul_out_0 = layers.mul(relu_out_0, weights_0);
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auto* bias_1 = layers.data("bias_1", {}, true);
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auto* add_out_0 = layers.elementwise_add(mul_out_0, bias_1, nullptr, 1);
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auto* relu_out_1 = layers.relu(add_out_0);
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auto* weights_1 = layers.data("weights_1", {}, true);
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auto* mul_out_1 = layers.mul(relu_out_1, weights_1);
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auto* bias_2 = layers.data("bias_2", {}, true);
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auto* add_out_1 = layers.elementwise_add(mul_out_1, bias_2, nullptr, 1);
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VLOG(4) << add_out_1;
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass = PassRegistry::Instance().Get("generate_fc_fuse");
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int num_nodes_before = static_cast<int>(graph->Nodes().size());
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int num_mul_nodes_before = GetNumOpNodes(graph, "mul");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = static_cast<int>(graph->Nodes().size());
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int num_fc_nodes_after = GetNumOpNodes(graph, "fc");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before,
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num_nodes_after + 6,
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common::errors::InvalidArgument(
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"num_nodes_before=%d, num_nodes_after=%d.",
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num_nodes_before,
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num_nodes_after));
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PADDLE_ENFORCE_EQ(num_fc_nodes_after,
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2,
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common::errors::InvalidArgument("num_fc_nodes_after=%d.",
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num_fc_nodes_after));
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PADDLE_ENFORCE_EQ(num_mul_nodes_before,
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num_fc_nodes_after,
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common::errors::InvalidArgument(
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"num_mul_nodes_before=%d, num_fc_nodes_after=%d.",
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num_mul_nodes_before,
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num_fc_nodes_after));
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}
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TEST(GeneratePass, generate_multi_add_to_addn) {
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// inputs operator output
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// --------------------------------------------------------
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// (a, b) elementwise_add -> add_out_0
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// (add_out_0, c) elementwise_add -> add_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* b = layers.data("b");
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auto* c = layers.data("c");
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auto* add_out_0 = layers.elementwise_add(a, b);
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layers.elementwise_add(add_out_0, c);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass = PassRegistry::Instance().Get("generate_multi_add_to_addn");
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int num_nodes_before = static_cast<int>(graph->Nodes().size());
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int num_add_nodes_before = GetNumOpNodes(graph, "elementwise_add");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = static_cast<int>(graph->Nodes().size());
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int num_addn_nodes_after = GetNumOpNodes(graph, "sum");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before,
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num_nodes_after + 2,
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common::errors::InvalidArgument(
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"num_nodes_before=%d, num_nodes_after=%d.",
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num_nodes_before,
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num_nodes_after));
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PADDLE_ENFORCE_EQ(num_addn_nodes_after,
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1,
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common::errors::InvalidArgument("num_addn_nodes_after=%d.",
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num_addn_nodes_after));
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PADDLE_ENFORCE_EQ(num_add_nodes_before,
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num_addn_nodes_after + 1,
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common::errors::InvalidArgument(
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"num_add_nodes_before=%d, num_addn_nodes_after=%d.",
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num_add_nodes_before,
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num_addn_nodes_after));
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}
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TEST(GeneratePass, generate_combine_matmul) {
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// inputs operator output
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// --------------------------------------------------------
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// (a, b) matmul -> matmul_out_0
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// (a, c) matmul -> matmul_out_1
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Layers layers;
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auto* a = layers.data("a");
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auto* b = layers.data("b");
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auto* c = layers.data("c");
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layers.matmul(a, b);
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layers.matmul(a, c);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass = PassRegistry::Instance().Get("generate_combine_matmul");
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int num_nodes_before = static_cast<int>(graph->Nodes().size());
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int num_matmul_nodes_before = GetNumOpNodes(graph, "matmul");
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = static_cast<int>(graph->Nodes().size());
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int num_matmul_nodes_after = GetNumOpNodes(graph, "matmul");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before,
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num_nodes_after - 4,
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common::errors::InvalidArgument(
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"num_nodes_before=%d, num_nodes_after=%d.",
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num_nodes_before,
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num_nodes_after));
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PADDLE_ENFORCE_EQ(num_matmul_nodes_after,
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1,
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common::errors::InvalidArgument(
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"num_matmul_nodes_after=%d.", num_matmul_nodes_after));
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PADDLE_ENFORCE_EQ(
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num_matmul_nodes_before,
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num_matmul_nodes_after + 1,
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common::errors::InvalidArgument(
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"num_matmul_nodes_before=%d, num_matmul_nodes_after=%d.",
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num_matmul_nodes_before,
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num_matmul_nodes_after));
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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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