Qiao Longfei
6 years ago
commit
7cd2417fe2
@ -0,0 +1,46 @@
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INCLUDE(ExternalProject)
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set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash)
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set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
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set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
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IF(WITH_STATIC_LIB)
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SET(BUILD_CMD make lib)
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ELSE()
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SET(BUILD_CMD sed -i "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
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ENDIF()
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ExternalProject_Add(
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extern_xxhash
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${EXTERNAL_PROJECT_LOG_ARGS}
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GIT_REPOSITORY "https://github.com/Cyan4973/xxHash"
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GIT_TAG "v0.6.5"
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PREFIX ${XXHASH_SOURCE_DIR}
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DOWNLOAD_NAME "xxhash"
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UPDATE_COMMAND ""
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CONFIGURE_COMMAND ""
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BUILD_IN_SOURCE 1
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PATCH_COMMAND
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BUILD_COMMAND ${BUILD_CMD}
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INSTALL_COMMAND export PREFIX=${XXHASH_INSTALL_DIR}/ && make install
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TEST_COMMAND ""
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)
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set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
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INCLUDE_DIRECTORIES(${XXHASH_INCLUDE_DIR})
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add_library(xxhash STATIC IMPORTED GLOBAL)
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set_property(TARGET xxhash PROPERTY IMPORTED_LOCATION ${XXHASH_LIBRARIES})
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include_directories(${XXHASH_INCLUDE_DIR})
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add_dependencies(xxhash extern_xxhash)
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LIST(APPEND external_project_dependencies xxhash)
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IF(WITH_C_API)
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INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
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IF(ANDROID)
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INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
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ELSE()
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INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
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ENDIF()
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ENDIF()
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@ -0,0 +1,50 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
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namespace paddle {
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namespace details {
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// Should be called after the parameters are loaded.
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void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
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if (flag_) {
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for (auto &var_name : scope->LocalVarNames()) {
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auto *var = scope->FindVar(var_name);
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// TODO(Superjomn) should avoid the case when a TensorArray is a
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// parameter.
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if (var_name == "feed" || var_name == "fetch") continue;
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if (var->Type() == typeid(framework::LoDTensorArray)) {
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VLOG(4) << "collect " << var_name;
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arrays_.push_back(var->GetMutable<framework::LoDTensorArray>());
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}
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}
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for (auto *kid : scope->kids()) {
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CollectTensorArrays(kid);
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}
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VLOG(3) << "Collect " << arrays_.size() << " arrays";
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flag_ = false;
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}
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}
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// Should be called when `Run` finished.
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void TensorArrayBatchCleaner::ResetTensorArray() {
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for (auto *arr : arrays_) {
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arr->clear();
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}
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}
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} // namespace details
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} // namespace paddle
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@ -0,0 +1,37 @@
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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 <vector>
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#include "paddle/fluid/framework/lod_tensor_array.h"
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#include "paddle/fluid/framework/scope.h"
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namespace paddle {
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namespace details {
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// Clean the TensorArray each batch to make the behavior the same with the
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// training phase.
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struct TensorArrayBatchCleaner {
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// Fix the tensor array not clear in the inference scenarios.
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void CollectTensorArrays(framework::Scope *scope);
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void ResetTensorArray();
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private:
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bool flag_{true};
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std::vector<framework::LoDTensorArray *> arrays_;
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};
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} // namespace details
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} // namespace paddle
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@ -0,0 +1,74 @@
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/* Copyright (c) 2016 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 "paddle/fluid/operators/hash_op.h"
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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class HashOp : public framework::OperatorWithKernel {
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public:
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HashOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of HashOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of HashOp should not be null.");
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auto dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(dims.size(), 2UL,
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"The input of hash_op's dimensions must be 2");
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std::vector<int64_t> out_dims;
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out_dims.reserve(dims.size() + 1);
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// copy all dims except the last one
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for (size_t i = 0u; i != dims.size() - 1; ++i) {
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out_dims.emplace_back(dims[i]);
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}
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int num_hash = ctx->Attrs().Get<int>("num_hash");
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out_dims.emplace_back(num_hash);
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// keep the last dim to 1
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out_dims.emplace_back(1);
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class HashOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) Input tensor of scale operator.");
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AddOutput("Out", "(Tensor) Output tensor of scale operator.");
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AddComment(R"DOC(
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**Hash Operator**
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$$Out = scale * X$$
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)DOC");
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AddAttr<int>("num_hash", "").SetDefault(1);
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AddAttr<int>("mod_by", "").SetDefault(100000);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
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REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);
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@ -0,0 +1,56 @@
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/* Copyright (c) 2016 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
|
||||||
|
|
||||||
|
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,
|
||||||
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
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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extern "C" {
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#include <xxhash.h>
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}
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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// template <typename DeviceContext, typename T>
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template <typename T>
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class HashKerel : public framework::OpKernel<T> {
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public:
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virtual void Compute(const framework::ExecutionContext& context) const {
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auto* out_t = context.Output<framework::LoDTensor>("Out");
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auto* in_t = context.Input<framework::LoDTensor>("X");
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int mod_by = context.Attr<int>("mod_by");
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int num_hash = context.Attr<int>("num_hash");
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auto* output = out_t->mutable_data<T>(context.GetPlace());
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auto in_dims = in_t->dims();
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auto in_lod = in_t->lod();
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PADDLE_ENFORCE_EQ(
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static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
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"The actual input data's size mismatched with LoD information.");
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auto seq_length = in_dims[0];
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auto last_dim = in_dims[in_dims.size() - 1];
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auto* input = in_t->data<T>();
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for (int idx = 0; idx < seq_length; ++idx) {
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for (int ihash = 0; ihash != num_hash; ++ihash) {
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output[idx * num_hash + ihash] =
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XXH64(input, sizeof(int) * last_dim, ihash) % mod_by;
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}
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input += last_dim;
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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