sneaxiy
6 years ago
commit
2f54d9f995
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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 <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
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#include "paddle/fluid/framework/details/container_cast.h"
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#include "paddle/fluid/framework/details/fused_all_reduce_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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namespace paddle {
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namespace framework {
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namespace details {
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class FuseAllReduceOpPass : 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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ir::Graph &result = *graph;
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auto &places = Get<const std::vector<platform::Place>>(kPlaces);
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auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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auto *nccl_ctxs = &Get<platform::NCCLContextMap>(kNCCLCtxs);
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#endif
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std::unordered_set<std::string> grads;
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auto ¶ms_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
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size_t num_of_all_reduce = params_grads.size();
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grads.reserve(num_of_all_reduce);
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for (auto p_g : params_grads) {
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grads.insert(p_g.second);
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}
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size_t num_place = places.size();
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std::unordered_map<std::string, ir::Node *> all_reduce_ops;
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all_reduce_ops.reserve(grads.size());
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for (auto &node : result.Nodes()) {
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if (node->IsOp()) {
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PADDLE_ENFORCE(node->IsWrappedBy<OpHandleBase>());
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auto *all_reduce_op_handle =
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dynamic_cast<AllReduceOpHandle *>(&node->Wrapper<OpHandleBase>());
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if (all_reduce_op_handle) {
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auto inputs = DynamicCast<VarHandle>(all_reduce_op_handle->Inputs());
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PADDLE_ENFORCE_EQ(inputs.size(), num_place);
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// The inputs' name should be the same.
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auto &grad_name = inputs[0]->name();
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for (size_t i = 1; i < inputs.size(); ++i) {
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PADDLE_ENFORCE_EQ(inputs[i]->name(), grad_name,
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"The input name should be the same.");
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}
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PADDLE_ENFORCE_NE(grads.count(grad_name), static_cast<size_t>(0));
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all_reduce_ops.emplace(grad_name, node);
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}
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}
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}
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VLOG(10) << "Find all_reduce_ops: " << all_reduce_ops.size();
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if (all_reduce_ops.size() == 0) {
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return std::move(graph);
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}
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PADDLE_ENFORCE_EQ(all_reduce_ops.size(), grads.size(),
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"The number of all_reduce OpHandle is not equal to the "
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"number of grads. Maybe some gradients are sparse type, "
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"it is not supported currently.");
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VLOG(10) << "Insert fused_all_reduce";
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auto &group_grads_params =
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graph->Get<GroupGradsAndParams>(kGroupGradsAndParams);
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for (auto &group_g_p : group_grads_params) {
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size_t group_size = group_g_p.size();
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PADDLE_ENFORCE_GT(group_size, static_cast<size_t>(0));
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std::vector<ir::Node *> group_all_reduce_ops;
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group_all_reduce_ops.reserve(group_size);
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for (auto &g_p : group_g_p) {
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group_all_reduce_ops.emplace_back(all_reduce_ops.at(g_p.first));
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}
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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InsertFusedAllReduce(places, local_scopes, group_size,
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group_all_reduce_ops, nccl_ctxs, &result);
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#else
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InsertFusedAllReduce(places, local_scopes, group_size,
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group_all_reduce_ops, &result);
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#endif
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}
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return std::move(graph);
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}
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void InsertFusedAllReduce(const std::vector<platform::Place> &places,
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const std::vector<Scope *> &local_scopes,
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const size_t num_of_all_reduce,
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const std::vector<ir::Node *> &all_reduce_ops,
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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const platform::NCCLContextMap *nccl_ctxs,
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#endif
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ir::Graph *result) const {
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std::vector<VarHandleBase *> inputs;
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std::vector<VarHandleBase *> outputs;
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for (auto &op : all_reduce_ops) {
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auto &op_handle = op->Wrapper<OpHandleBase>();
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inputs.insert(inputs.end(), op_handle.Inputs().begin(),
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op_handle.Inputs().end());
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// Remove output
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for_each(op_handle.Inputs().begin(), op_handle.Inputs().end(),
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[&op_handle](VarHandleBase *var_handle) {
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var_handle->RemoveOutput(&op_handle, op_handle.Node());
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});
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outputs.insert(outputs.end(), op_handle.Outputs().begin(),
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op_handle.Outputs().end());
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// Remove Input
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for_each(
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op_handle.Outputs().begin(), op_handle.Outputs().end(),
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[](VarHandleBase *var_handle) { var_handle->ClearGeneratedOp(); });
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result->RemoveNode(op_handle.Node());
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}
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
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local_scopes, nccl_ctxs, result);
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#else
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CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
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local_scopes, result);
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#endif
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}
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private:
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void CreateFusedAllReduceOp(const std::vector<VarHandleBase *> &inputs,
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const std::vector<VarHandleBase *> &outputs,
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const size_t num_of_all_reduce,
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const std::vector<platform::Place> &places,
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const std::vector<Scope *> &local_scopes,
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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const platform::NCCLContextMap *nccl_ctxs,
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#endif
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ir::Graph *result) const {
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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auto *op_handle = new FusedAllReduceOpHandle(
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result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
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local_scopes, places, num_of_all_reduce, nccl_ctxs);
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#else
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auto *op_handle = new FusedAllReduceOpHandle(
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result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
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local_scopes, places, num_of_all_reduce);
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#endif
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for (auto in : inputs) {
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op_handle->AddInput(in);
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}
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for (auto out : outputs) {
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op_handle->AddOutput(out);
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}
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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if (!nccl_ctxs) {
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SetCommunicationContext(places, op_handle);
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}
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#else
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SetCommunicationContext(places, op_handle);
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#endif
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}
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void SetCommunicationContext(const std::vector<platform::Place> &places,
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FusedAllReduceOpHandle *op_handle) const {
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for (size_t i = 0; i < places.size(); ++i) {
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op_handle->SetDeviceContext(
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places[i], platform::DeviceContextPool::Instance().Get(places[i]));
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}
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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(fuse_all_reduce_op_pass,
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paddle::framework::details::FuseAllReduceOpPass);
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@ -0,0 +1,248 @@
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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/fused_all_reduce_op_handle.h"
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#include <algorithm>
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#include <utility>
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#include "paddle/fluid/framework/details/container_cast.h"
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#include "paddle/fluid/framework/details/reduce_and_gather.h"
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#include "paddle/fluid/framework/details/variable_visitor.h"
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#include "paddle/fluid/platform/profiler.h"
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DEFINE_bool(skip_fused_all_reduce_check, false, "");
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namespace paddle {
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namespace framework {
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namespace details {
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typedef std::vector<std::vector<std::pair<std::string, const LoDTensor *>>>
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GradientAndLoDTensor;
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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FusedAllReduceOpHandle::FusedAllReduceOpHandle(
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ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
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const platform::NCCLContextMap *ctxs)
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: OpHandleBase(node),
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local_scopes_(local_scopes),
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places_(places),
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num_of_all_reduce_(num_of_all_reduce),
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nccl_ctxs_(ctxs) {
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if (nccl_ctxs_) {
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for (auto &p : places_) {
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this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
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}
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}
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PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
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}
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#else
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FusedAllReduceOpHandle::FusedAllReduceOpHandle(
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ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places, const size_t num_of_all_reduce)
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: OpHandleBase(node),
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local_scopes_(local_scopes),
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places_(places),
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num_of_all_reduce_(num_of_all_reduce) {
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PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
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}
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#endif
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void FusedAllReduceOpHandle::RunImpl() {
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platform::RecordEvent record_event(Name());
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VLOG(4) << this->DebugString();
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WaitInputVarGenerated();
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// The input: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
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// The output: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
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auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
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auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
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size_t place_num = places_.size();
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PADDLE_ENFORCE_EQ(
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in_var_handles.size(), place_num * num_of_all_reduce_,
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"The NoDummyInputSize should be equal to the number of places.");
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PADDLE_ENFORCE_EQ(
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in_var_handles.size(), out_var_handles.size(),
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"The NoDummyInputSize and NoDummyOutputSize should be equal.");
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GradientAndLoDTensor grads_tensor;
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grads_tensor.resize(place_num);
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int64_t numel = -1;
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auto dtype = static_cast<framework::proto::VarType::Type>(0);
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for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
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auto &g_tensor = grads_tensor.at(scope_idx);
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g_tensor.reserve(num_of_all_reduce_);
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GetGradLoDTensor(scope_idx, in_var_handles, out_var_handles, &g_tensor);
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int64_t element_num = 0;
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framework::proto::VarType::Type ele_dtype =
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static_cast<framework::proto::VarType::Type>(0);
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GetDTypeAndNumel(g_tensor, &ele_dtype, &element_num);
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if (numel == -1) {
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numel = element_num;
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}
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if (dtype == static_cast<framework::proto::VarType::Type>(0)) {
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dtype = ele_dtype;
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PADDLE_ENFORCE_NE(ele_dtype,
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static_cast<framework::proto::VarType::Type>(0));
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}
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PADDLE_ENFORCE_EQ(ele_dtype, dtype);
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// Check whether the address space is contiguous.
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std::sort(
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g_tensor.begin(), g_tensor.end(),
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[](const std::pair<std::string, const LoDTensor *> &grad1,
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const std::pair<std::string, const LoDTensor *> &grad2) -> bool {
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return grad1.second->data<void>() < grad2.second->data<void>();
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});
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for (size_t k = 1; k < g_tensor.size(); ++k) {
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const void *pre_address = g_tensor.at(k - 1).second->data<void>();
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int64_t len = g_tensor.at(k - 1).second->numel();
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auto offset = len * framework::SizeOfType(dtype);
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void *next_address = reinterpret_cast<void *>(
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reinterpret_cast<uintptr_t>(pre_address) + offset);
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const void *cur_address = g_tensor.at(k).second->data<void>();
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VLOG(10) << k << ", "
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<< " pre_address(" << g_tensor.at(k - 1).first
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<< "): " << pre_address << ", cur_address("
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<< g_tensor.at(k).first << "): " << cur_address
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<< ", offset:" << offset << ", " << next_address << ", "
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<< cur_address;
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PADDLE_ENFORCE_EQ(next_address, cur_address);
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}
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}
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if (!FLAGS_skip_fused_all_reduce_check) {
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for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
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for (size_t j = 1; j < num_of_all_reduce_; ++j) {
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PADDLE_ENFORCE_EQ(grads_tensor.at(0).at(j).first,
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grads_tensor.at(scope_idx).at(j).first);
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}
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}
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}
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std::vector<const void *> lod_tensor_data;
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for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
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auto data = grads_tensor.at(scope_idx).at(0).second->data<void>();
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lod_tensor_data.emplace_back(data);
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}
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if (platform::is_gpu_place(places_[0])) {
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
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int nccl_dtype = platform::ToNCCLDataType(dtype);
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std::vector<std::function<void()>> all_reduce_calls;
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for (size_t i = 0; i < local_scopes_.size(); ++i) {
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auto &p = places_[i];
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void *buffer = const_cast<void *>(lod_tensor_data.at(i));
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int dev_id = boost::get<platform::CUDAPlace>(p).device;
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auto &nccl_ctx = nccl_ctxs_->at(dev_id);
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auto stream = nccl_ctx.stream();
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auto comm = nccl_ctx.comm_;
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all_reduce_calls.emplace_back([=] {
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PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
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buffer, buffer, numel, static_cast<ncclDataType_t>(nccl_dtype),
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ncclSum, comm, stream));
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});
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}
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this->RunAndRecordEvent([&] {
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if (all_reduce_calls.size() == 1UL) {
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// Do not use NCCLGroup when manage NCCL by per thread per device
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all_reduce_calls[0]();
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} else {
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platform::NCCLGroupGuard guard;
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for (auto &call : all_reduce_calls) {
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call();
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}
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}
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});
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#else
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PADDLE_THROW("Not compiled with CUDA");
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#endif
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} else {
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// Special handle CPU only Operator's gradient. Like CRF
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auto grad_name = grads_tensor.at(0).at(0).first;
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auto &trg = *this->local_scopes_[0]
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->FindVar(kLocalExecScopeName)
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->Get<Scope *>()
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->FindVar(grad_name)
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->GetMutable<framework::LoDTensor>();
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// Reduce All data to trg in CPU
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ReduceBufferData func(lod_tensor_data, trg.data<void>(), numel);
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VisitDataType(trg.type(), func);
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for (size_t i = 1; i < local_scopes_.size(); ++i) {
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auto &scope =
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*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
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auto &p = places_[i];
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auto *var = scope.FindVar(grad_name);
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auto *dev_ctx = dev_ctxes_.at(p);
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size_t size = numel * SizeOfType(trg.type());
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RunAndRecordEvent(p, [&trg, var, dev_ctx, p, size] {
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auto dst_ptr = var->GetMutable<framework::LoDTensor>()->data<void>();
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platform::CPUPlace cpu_place;
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memory::Copy(cpu_place, dst_ptr, cpu_place, trg.data<void>(), size);
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});
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}
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}
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}
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void FusedAllReduceOpHandle::GetGradLoDTensor(
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const size_t &scope_idx, const std::vector<VarHandle *> &in_var_handles,
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const std::vector<VarHandle *> &out_var_handles,
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std::vector<std::pair<std::string, const LoDTensor *>> *grad_tensor) const {
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auto *local_scope =
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local_scopes_.at(scope_idx)->FindVar(kLocalExecScopeName)->Get<Scope *>();
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size_t place_num = places_.size();
|
||||
|
||||
for (size_t j = 0; j < in_var_handles.size(); j += place_num) {
|
||||
auto var_name = in_var_handles[j]->name();
|
||||
PADDLE_ENFORCE_EQ(var_name, out_var_handles[j]->name());
|
||||
auto &lod_tensor = local_scope->FindVar(var_name)->Get<LoDTensor>();
|
||||
PADDLE_ENFORCE_EQ(lod_tensor.place(), places_.at(scope_idx));
|
||||
grad_tensor->emplace_back(std::make_pair(var_name, &lod_tensor));
|
||||
}
|
||||
}
|
||||
|
||||
void FusedAllReduceOpHandle::GetDTypeAndNumel(
|
||||
const std::vector<std::pair<std::string, const LoDTensor *>> &grad_tensor,
|
||||
proto::VarType::Type *dtype, int64_t *numel) const {
|
||||
*numel = 0;
|
||||
for (size_t i = 0; i < grad_tensor.size(); ++i) {
|
||||
// Get element number
|
||||
int64_t len = grad_tensor.at(i).second->numel();
|
||||
PADDLE_ENFORCE_GT(len, 0);
|
||||
*numel += len;
|
||||
|
||||
// Get dtype
|
||||
auto ele_type = grad_tensor.at(i).second->type();
|
||||
if (i == 0) {
|
||||
*dtype = ele_type;
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(ele_type, *dtype);
|
||||
}
|
||||
}
|
||||
|
||||
std::string FusedAllReduceOpHandle::Name() const { return "fused_all_reduce"; }
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,76 @@
|
||||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <vector>
|
||||
#include "paddle/fluid/framework/details/op_handle_base.h"
|
||||
#include "paddle/fluid/framework/lod_tensor.h"
|
||||
#include "paddle/fluid/framework/scope.h"
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
#include "paddle/fluid/platform/nccl_helper.h"
|
||||
#endif
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
struct FusedAllReduceOpHandle : public OpHandleBase {
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
FusedAllReduceOpHandle(ir::Node *node,
|
||||
const std::vector<Scope *> &local_scopes,
|
||||
const std::vector<platform::Place> &places,
|
||||
const size_t num_of_all_reduce,
|
||||
const platform::NCCLContextMap *ctxs);
|
||||
#else
|
||||
FusedAllReduceOpHandle(ir::Node *node,
|
||||
const std::vector<Scope *> &local_scopes,
|
||||
const std::vector<platform::Place> &places,
|
||||
const size_t num_of_all_reduce);
|
||||
#endif
|
||||
std::string Name() const override;
|
||||
|
||||
// Delay and buffer nccl_all_reduce together can significantly increase
|
||||
// performance. Disable this feature by returning false.
|
||||
bool IsMultiDeviceTransfer() override { return true; };
|
||||
|
||||
protected:
|
||||
void RunImpl() override;
|
||||
|
||||
private:
|
||||
std::vector<Scope *> local_scopes_;
|
||||
std::vector<platform::Place> places_;
|
||||
size_t num_of_all_reduce_;
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
const platform::NCCLContextMap *nccl_ctxs_;
|
||||
#endif
|
||||
|
||||
// Check the dtype of the input
|
||||
void GetDTypeAndNumel(
|
||||
const std::vector<std::pair<std::string, const LoDTensor *>> &g_tensor,
|
||||
proto::VarType::Type *dtype, int64_t *total_num) const;
|
||||
|
||||
// Get gradient's name and LoDTensor
|
||||
void GetGradLoDTensor(const size_t &scope_idx,
|
||||
const std::vector<VarHandle *> &in_var_handles,
|
||||
const std::vector<VarHandle *> &out_var_handles,
|
||||
std::vector<std::pair<std::string, const LoDTensor *>>
|
||||
*grad_tensor) const;
|
||||
};
|
||||
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,58 @@
|
||||
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#include "paddle/fluid/framework/ir/cpu_quantize_placement_pass.h"
|
||||
#include <string>
|
||||
#include <unordered_set>
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
|
||||
std::unique_ptr<ir::Graph> CPUQuantizePlacementPass::ApplyImpl(
|
||||
std::unique_ptr<ir::Graph> graph) const {
|
||||
VLOG(3) << "Marks operators which are to be quantized.";
|
||||
const auto& excluded_ids_list =
|
||||
Get<std::unordered_set<int>>("quantize_excluded_op_ids");
|
||||
const auto& op_types_list =
|
||||
Get<std::unordered_set<std::string>>("quantize_enabled_op_types");
|
||||
for (const Node* n : graph->Nodes()) {
|
||||
if (n->IsOp()) {
|
||||
if (std::find(excluded_ids_list.begin(), excluded_ids_list.end(),
|
||||
n->id()) != excluded_ids_list.end())
|
||||
continue;
|
||||
auto* op = n->Op();
|
||||
if (op->HasAttr("use_quantizer") || op->HasProtoAttr("use_quantizer")) {
|
||||
if (op_types_list.empty()) {
|
||||
op->SetAttr("use_quantizer", true);
|
||||
} else if (std::find(op_types_list.begin(), op_types_list.end(),
|
||||
n->Name()) != op_types_list.end()) {
|
||||
op->SetAttr("use_quantizer", true);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return graph;
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
REGISTER_PASS(cpu_quantize_placement_pass,
|
||||
paddle::framework::ir::CPUQuantizePlacementPass)
|
||||
// a vector of operator type names to be quantized ("conv2d" etc.)
|
||||
.RequirePassAttr("quantize_enabled_op_types")
|
||||
// a vector of operator ids that are to be excluded from quantization
|
||||
.RequirePassAttr("quantize_excluded_op_ids");
|
||||
@ -0,0 +1,34 @@
|
||||
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <memory>
|
||||
#include "paddle/fluid/framework/ir/pass.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
/*
|
||||
* Specifies which operators should be quantized.
|
||||
*/
|
||||
class CPUQuantizePlacementPass : public Pass {
|
||||
protected:
|
||||
std::unique_ptr<ir::Graph> ApplyImpl(
|
||||
std::unique_ptr<ir::Graph> graph) const override;
|
||||
};
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,129 @@
|
||||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "paddle/fluid/framework/ir/cpu_quantize_placement_pass.h"
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
#include <boost/logic/tribool.hpp>
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace ir {
|
||||
|
||||
void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
|
||||
const std::vector<std::string>& inputs,
|
||||
const std::vector<std::string>& outputs,
|
||||
boost::tribool use_quantizer) {
|
||||
auto* op = prog->MutableBlock(0)->AppendOp();
|
||||
|
||||
op->SetType(type);
|
||||
|
||||
if (!boost::indeterminate(use_quantizer))
|
||||
op->SetAttr("use_quantizer", use_quantizer);
|
||||
|
||||
if (type == "conv2d") {
|
||||
op->SetAttr("name", name);
|
||||
op->SetInput("Input", {inputs[0]});
|
||||
op->SetInput("Filter", {inputs[1]});
|
||||
op->SetInput("Bias", {inputs[2]});
|
||||
} else if (type == "relu") {
|
||||
op->SetInput("X", inputs);
|
||||
} else if (type == "concat") {
|
||||
op->SetAttr("axis", 1);
|
||||
op->SetInput("X", {inputs[0], inputs[1]});
|
||||
} else if (type == "pool2d") {
|
||||
op->SetInput("X", {inputs[0]});
|
||||
} else {
|
||||
FAIL() << "Unexpected operator type.";
|
||||
}
|
||||
op->SetOutput("Out", {outputs[0]});
|
||||
}
|
||||
|
||||
// operator use_quantizer
|
||||
// ---------------------------------------
|
||||
// (a,b)->concat->c none
|
||||
// (c,weights,bias)->conv->f false
|
||||
// f->relu->g none
|
||||
// g->pool->h false
|
||||
// (h,weights2,bias2)->conv->k false
|
||||
// k->pool->l false
|
||||
ProgramDesc BuildProgramDesc() {
|
||||
ProgramDesc prog;
|
||||
|
||||
for (auto& v :
|
||||
std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
|
||||
"h", "weights2", "bias2", "k", "l"})) {
|
||||
auto* var = prog.MutableBlock(0)->Var(v);
|
||||
var->SetType(proto::VarType::SELECTED_ROWS);
|
||||
if (v == "weights" || v == "bias") {
|
||||
var->SetPersistable(true);
|
||||
}
|
||||
}
|
||||
|
||||
SetOp(&prog, "concat", "concat1", {"a", "b"}, {"c"}, boost::indeterminate);
|
||||
SetOp(&prog, "conv2d", "conv1", {"c", "weights", "bias"}, {"f"}, false);
|
||||
SetOp(&prog, "relu", "relu1", {"f"}, {"g"}, boost::indeterminate);
|
||||
SetOp(&prog, "pool2d", "pool1", {"g"}, {"h"}, false);
|
||||
SetOp(&prog, "conv2d", "conv2", {"h", "weights2", "bias2"}, {"k"}, false);
|
||||
SetOp(&prog, "pool2d", "pool2", {"k"}, {"l"}, false);
|
||||
|
||||
return prog;
|
||||
}
|
||||
|
||||
void MainTest(std::initializer_list<std::string> quantize_enabled_op_types,
|
||||
std::initializer_list<int> quantize_excluded_op_ids,
|
||||
unsigned expected_use_quantizer_true_count) {
|
||||
auto prog = BuildProgramDesc();
|
||||
|
||||
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
|
||||
|
||||
auto pass = PassRegistry::Instance().Get("cpu_quantize_placement_pass");
|
||||
pass->Set("quantize_enabled_op_types",
|
||||
new std::unordered_set<std::string>(quantize_enabled_op_types));
|
||||
pass->Set("quantize_excluded_op_ids",
|
||||
new std::unordered_set<int>(quantize_excluded_op_ids));
|
||||
|
||||
graph = pass->Apply(std::move(graph));
|
||||
|
||||
unsigned use_quantizer_true_count = 0;
|
||||
|
||||
for (auto* node : graph->Nodes()) {
|
||||
if (node->IsOp()) {
|
||||
auto* op = node->Op();
|
||||
if (op->HasAttr("use_quantizer") &&
|
||||
boost::get<bool>(op->GetAttr("use_quantizer"))) {
|
||||
++use_quantizer_true_count;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
EXPECT_EQ(use_quantizer_true_count, expected_use_quantizer_true_count);
|
||||
}
|
||||
|
||||
TEST(QuantizerPlacementPass, enabled_pool) { MainTest({"pool2d"}, {}, 2); }
|
||||
|
||||
TEST(QuantizerPlacementPass, enabled_conv_excluded_one) {
|
||||
MainTest({"conv2d"}, {4}, 1);
|
||||
}
|
||||
|
||||
TEST(QuantizerPlacementPass, excluded_none) {
|
||||
// 2 conv + 2 pool
|
||||
MainTest({}, {}, 4);
|
||||
}
|
||||
|
||||
} // namespace ir
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
USE_PASS(cpu_quantize_placement_pass);
|
||||
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Reference in new issue