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未验证 提交 95089204 编写于 作者: S ShenLiang 提交者： GitHub
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fix conflict, test=develop (#24238)

上级 c3c61d34
隐藏空白更改
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Showing with 790 addition and 168 deletion
paddle/fluid/framework/fleet/box_wrapper.cc
浏览文件 @ 95089204
......@@ -28,6 +28,8 @@ std::shared_ptr<BoxWrapper> BoxWrapper::s_instance_ = nullptr;
cudaStream_t BoxWrapper::stream_list_[8];
std::shared_ptr<boxps::BoxPSBase> BoxWrapper::boxps_ptr_ = nullptr;
AfsManager* BoxWrapper::afs_manager = nullptr;
int BoxWrapper::embedx_dim_ = 8;
int BoxWrapper::expand_embed_dim_ = 0;
void BasicAucCalculator::compute() {
double* table[2] = {&_table[0][0], &_table[1][0]};
......@@ -57,6 +59,94 @@ void BasicAucCalculator::compute() {
_size = fp + tp;
}
void BoxWrapper::CheckEmbedSizeIsValid(int embedx_dim, int expand_embed_dim) {
PADDLE_ENFORCE_EQ(
embedx_dim_, embedx_dim,
platform::errors::InvalidArgument("SetInstance(): invalid embedx_dim. "
"When embedx_dim = %d, but got %d.",
embedx_dim_, embedx_dim));
PADDLE_ENFORCE_EQ(expand_embed_dim_, expand_embed_dim,
platform::errors::InvalidArgument(
"SetInstance(): invalid expand_embed_dim. When "
"expand_embed_dim = %d, but got %d.",
expand_embed_dim_, expand_embed_dim));
}
void BoxWrapper::PullSparse(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<float*>& values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int expand_embed_dim) {
#define EMBEDX_CASE(i, ...) \
case i: { \
constexpr size_t EmbedxDim = i; \
switch (expand_embed_dim) { \
__VA_ARGS__ \
default: \
PADDLE_THROW(platform::errors::InvalidArgument( \
"Unsupport this expand embedding size [%d]", expand_embed_dim)); \
} \
} break
#define PULLSPARSE_CASE(i, ...) \
case i: { \
constexpr size_t ExpandDim = i; \
PullSparseCase<EmbedxDim, ExpandDim>(place, keys, values, slot_lengths, \
hidden_size, expand_embed_dim); \
} break
CheckEmbedSizeIsValid(hidden_size - 3, expand_embed_dim);
switch (hidden_size - 3) {
EMBEDX_CASE(8, PULLSPARSE_CASE(0); PULLSPARSE_CASE(8);
PULLSPARSE_CASE(64););
EMBEDX_CASE(16, PULLSPARSE_CASE(0););
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Unsupport this embedding size [%d]", hidden_size - 3));
}
#undef PULLSPARSE_CASE
#undef EMBEDX_CASE
}
void BoxWrapper::PushSparseGrad(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<const float*>& grad_values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size,
const int expand_embed_dim,
const int batch_size) {
#define EMBEDX_CASE(i, ...) \
case i: { \
constexpr size_t EmbedxDim = i; \
switch (expand_embed_dim) { \
__VA_ARGS__ \
default: \
PADDLE_THROW(platform::errors::InvalidArgument( \
"Unsupport this expand embedding size [%d]", expand_embed_dim)); \
} \
} break
#define PUSHSPARSE_CASE(i, ...) \
case i: { \
constexpr size_t ExpandDim = i; \
PushSparseGradCase<EmbedxDim, ExpandDim>(place, keys, grad_values, \
slot_lengths, hidden_size, \
expand_embed_dim, batch_size); \
} break
CheckEmbedSizeIsValid(hidden_size - 3, expand_embed_dim);
switch (hidden_size - 3) {
EMBEDX_CASE(8, PUSHSPARSE_CASE(0); PUSHSPARSE_CASE(8);
PUSHSPARSE_CASE(64););
EMBEDX_CASE(16, PUSHSPARSE_CASE(0););
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Unsupport this embedding size [%d]", hidden_size - 3));
}
#undef PUSHSPARSE_CASE
#undef EMBEDX_CASE
}
void BasicAucCalculator::calculate_bucket_error() {
double last_ctr = -1;
double impression_sum = 0;
......@@ -128,134 +218,6 @@ void BoxWrapper::EndPass(bool need_save_delta) const {
ret, 0, platform::errors::PreconditionNotMet("EndPass failed in BoxPS."));
}
void BoxWrapper::PullSparse(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<float*>& values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size) {
VLOG(3) << "Begin PullSparse";
platform::Timer all_timer;
platform::Timer pull_boxps_timer;
all_timer.Start();
int64_t total_length =
std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
auto buf =
memory::AllocShared(place, total_length * sizeof(boxps::FeatureValueGpu));
boxps::FeatureValueGpu* total_values_gpu =
reinterpret_cast<boxps::FeatureValueGpu*>(buf->ptr());
if (platform::is_cpu_place(place)) {
PADDLE_THROW(platform::errors::Unimplemented(
"Warning:: CPUPlace is not supported in PaddleBox now."));
} else if (platform::is_gpu_place(place)) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
VLOG(3) << "Begin copy keys, key_num[" << total_length << "]";
int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
LoDTensor& total_keys_tensor = keys_tensor[device_id];
uint64_t* total_keys = reinterpret_cast<uint64_t*>(
total_keys_tensor.mutable_data<int64_t>({total_length, 1}, place));
// construct slot_level lod info
auto slot_lengths_lod = slot_lengths;
for (size_t i = 1; i < slot_lengths_lod.size(); i++) {
slot_lengths_lod[i] += slot_lengths_lod[i - 1];
}
auto buf_key = memory::AllocShared(place, keys.size() * sizeof(uint64_t*));
auto buf_length =
memory::AllocShared(place, slot_lengths.size() * sizeof(int64_t));
uint64_t** gpu_keys = reinterpret_cast<uint64_t**>(buf_key->ptr());
int64_t* gpu_len = reinterpret_cast<int64_t*>(buf_length->ptr());
cudaMemcpy(gpu_keys, keys.data(), keys.size() * sizeof(uint64_t*),
cudaMemcpyHostToDevice);
cudaMemcpy(gpu_len, slot_lengths_lod.data(),
slot_lengths.size() * sizeof(int64_t), cudaMemcpyHostToDevice);
this->CopyKeys(place, gpu_keys, total_keys, gpu_len,
static_cast<int>(slot_lengths.size()),
static_cast<int>(total_length));
VLOG(3) << "Begin call PullSparseGPU in BoxPS";
pull_boxps_timer.Start();
int ret =
boxps_ptr_->PullSparseGPU(total_keys, total_values_gpu,
static_cast<int>(total_length), device_id);
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"PullSparseGPU failed in BoxPS."));
pull_boxps_timer.Pause();
VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length
<< "]";
this->CopyForPull(place, gpu_keys, values, total_values_gpu, gpu_len,
static_cast<int>(slot_lengths.size()), hidden_size,
total_length);
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Please compile WITH_GPU option, because NCCL doesn't support "
"windows."));
#endif
} else {
PADDLE_THROW(platform::errors::PreconditionNotMet(
"PaddleBox: PullSparse Only Support CPUPlace or CUDAPlace Now."));
}
all_timer.Pause();
VLOG(1) << "PullSparse total costs: " << all_timer.ElapsedSec()
<< " s, of which BoxPS costs: " << pull_boxps_timer.ElapsedSec()
<< " s";
VLOG(3) << "End PullSparse";
}
void BoxWrapper::PushSparseGrad(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<const float*>& grad_values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int batch_size) {
VLOG(3) << "Begin PushSparseGrad";
platform::Timer all_timer;
platform::Timer push_boxps_timer;
all_timer.Start();
int64_t total_length =
std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
auto buf = memory::AllocShared(
place, total_length * sizeof(boxps::FeaturePushValueGpu));
boxps::FeaturePushValueGpu* total_grad_values_gpu =
reinterpret_cast<boxps::FeaturePushValueGpu*>(buf->ptr());
if (platform::is_cpu_place(place)) {
PADDLE_THROW(platform::errors::Unimplemented(
"Warning:: CPUPlace is not supported in PaddleBox now."));
} else if (platform::is_gpu_place(place)) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
LoDTensor& cached_total_keys_tensor = keys_tensor[device_id];
uint64_t* total_keys =
reinterpret_cast<uint64_t*>(cached_total_keys_tensor.data<int64_t>());
VLOG(3) << "Begin copy grad tensor to boxps struct";
this->CopyForPush(place, grad_values, total_grad_values_gpu, slot_lengths,
hidden_size, total_length, batch_size);
VLOG(3) << "Begin call PushSparseGPU in BoxPS";
push_boxps_timer.Start();
int ret = boxps_ptr_->PushSparseGPU(
total_keys, total_grad_values_gpu, static_cast<int>(total_length),
BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId());
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"PushSparseGPU failed in BoxPS."));
push_boxps_timer.Pause();
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Please compile WITH_GPU option, because NCCL doesn't support "
"windows."));
#endif
} else {
PADDLE_THROW(platform::errors::PreconditionNotMet(
"PaddleBox: PushSparseGrad Only Support CPUPlace or CUDAPlace Now."));
}
all_timer.Pause();
VLOG(1) << "PushSparseGrad total cost: " << all_timer.ElapsedSec()
<< " s, of which BoxPS cost: " << push_boxps_timer.ElapsedSec()
<< " s";
VLOG(3) << "End PushSparseGrad";
}
void BoxWrapper::GetRandomReplace(const std::vector<Record>& pass_data) {
VLOG(0) << "Begin GetRandomReplace";
size_t ins_num = pass_data.size();
......
paddle/fluid/framework/fleet/box_wrapper.cu
浏览文件 @ 95089204
......@@ -27,9 +27,12 @@ namespace framework {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
__global__ void PullCopy(float** dest, const boxps::FeatureValueGpu* src,
const int64_t* len, int hidden, int slot_num,
int total_len, uint64_t** keys) {
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
__global__ void PullCopy(
float** dest,
const boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* src,
const int64_t* len, int hidden, int expand_dim, int slot_num, int total_len,
uint64_t** keys) {
CUDA_KERNEL_LOOP(i, total_len) {
int low = 0;
int high = slot_num - 1;
......@@ -52,15 +55,28 @@ __global__ void PullCopy(float** dest, const boxps::FeatureValueGpu* src,
*(dest[x] + y * hidden + 2) = (src + i)->embed_w;
}
if ((src + i)->embedding_size == 0 || *(keys[x] + y) == 0) {
for (int j = 0; j < 8; j++) {
for (int j = 0; j < hidden - 3; j++) {
*(dest[x] + y * hidden + 3 + j) = 0;
}
} else {
for (int j = 0; j < 8; j++) {
for (int j = 0; j < hidden - 3; j++) {
*(dest[x] + y * hidden + 3 + j) = (src + i)->embedx[1 + j];
}
}
}
// process embed_expand
if (expand_dim > 0) {
int z = x + slot_num;
if ((src + i)->embed_expand_size[0] == 0 || *(keys[x] + y) == 0) {
for (int j = 0; j < expand_dim; j++) {
*(dest[z] + y * expand_dim + j) = 0;
}
} else {
for (int j = 0; j < expand_dim; j++) {
*(dest[z] + y * expand_dim + j) = (src + i)->embed_expand[1 + j];
}
}
}
} // end kernel loop
}
__global__ void CopyKeysKernel(uint64_t** src_keys, uint64_t* dest_total_keys,
......@@ -82,9 +98,11 @@ __global__ void CopyKeysKernel(uint64_t** src_keys, uint64_t* dest_total_keys,
}
}
__global__ void PushCopy(boxps::FeaturePushValueGpu* dest, float** src,
int64_t* len, int hidden, int slot_num, int total_len,
int bs, int* slot_vector) {
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
__global__ void PushCopy(
boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* dest, float** src,
int64_t* len, int hidden, int expand_dim, int slot_num, int total_len,
int bs, int* slot_vector) {
CUDA_KERNEL_LOOP(i, total_len) {
int low = 0;
int high = slot_num - 1;
......@@ -101,18 +119,25 @@ __global__ void PushCopy(boxps::FeaturePushValueGpu* dest, float** src,
(dest + i)->show = *(src[x] + y * hidden);
(dest + i)->clk = *(src[x] + y * hidden + 1);
(dest + i)->embed_g = *(src[x] + y * hidden + 2) * -1. * bs;
for (int j = 0; j < 8; j++) {
for (int j = 0; j < hidden - 3; j++) {
(dest + i)->embedx_g[j] = *(src[x] + y * hidden + 3 + j) * -1. * bs;
}
if (expand_dim > 0) {
int z = x + slot_num;
for (int j = 0; j < expand_dim; j++) {
(dest + i)->embed_expand_g[j] =
*(src[z] + y * expand_dim + j) * -1. * bs;
}
}
}
}
void BoxWrapper::CopyForPull(const paddle::platform::Place& place,
uint64_t** gpu_keys,
const std::vector<float*>& values,
const boxps::FeatureValueGpu* total_values_gpu,
const int64_t* gpu_len, const int slot_num,
const int hidden_size,
void* total_values_gpu, const int64_t* gpu_len,
const int slot_num, const int hidden_size,
const int expand_embed_dim,
const int64_t total_length) {
auto stream = dynamic_cast<platform::CUDADeviceContext*>(
platform::DeviceContextPool::Instance().Get(
......@@ -122,11 +147,40 @@ void BoxWrapper::CopyForPull(const paddle::platform::Place& place,
float** gpu_values = reinterpret_cast<float**>(buf_value->ptr());
cudaMemcpy(gpu_values, values.data(), values.size() * sizeof(float*),
cudaMemcpyHostToDevice);
#define EMBEDX_CASE(i, ...) \
case i: { \
constexpr size_t EmbedxDim = i; \
switch (expand_embed_dim) { \
__VA_ARGS__ \
default: \
PADDLE_THROW(platform::errors::InvalidArgument( \
"Unsupport this expand embedding size [%d]", expand_embed_dim)); \
} \
} break
#define EXPAND_EMBED_PULL_CASE(i, ...) \
case i: { \
constexpr size_t ExpandDim = i; \
PullCopy<EmbedxDim, \
ExpandDim><<<(total_length + 512 - 1) / 512, 512, 0, stream>>>( \
gpu_values, \
reinterpret_cast<boxps::FeatureValueGpu<EmbedxDim, ExpandDim>*>( \
total_values_gpu), \
gpu_len, hidden_size, expand_embed_dim, slot_num, total_length, \
gpu_keys); \
} break
PullCopy<<<(total_length + 512 - 1) / 512, 512, 0, stream>>>(
gpu_values, total_values_gpu, gpu_len, hidden_size, slot_num,
total_length, gpu_keys);
switch (hidden_size - 3) {
EMBEDX_CASE(8, EXPAND_EMBED_PULL_CASE(0); EXPAND_EMBED_PULL_CASE(8);
EXPAND_EMBED_PULL_CASE(64););
EMBEDX_CASE(16, EXPAND_EMBED_PULL_CASE(0););
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Unsupport this embedding size [%d]", hidden_size - 3));
}
cudaStreamSynchronize(stream);
#undef EXPAND_EMBED_PULL_CASE
#undef EMBEDX_CASE
}
void BoxWrapper::CopyKeys(const paddle::platform::Place& place,
......@@ -143,10 +197,10 @@ void BoxWrapper::CopyKeys(const paddle::platform::Place& place,
void BoxWrapper::CopyForPush(const paddle::platform::Place& place,
const std::vector<const float*>& grad_values,
boxps::FeaturePushValueGpu* total_grad_values_gpu,
void* total_grad_values_gpu,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int64_t total_length,
const int batch_size) {
const int hidden_size, const int expand_embed_dim,
const int64_t total_length, const int batch_size) {
auto stream = dynamic_cast<platform::CUDADeviceContext*>(
platform::DeviceContextPool::Instance().Get(
BOOST_GET_CONST(platform::CUDAPlace, place)))
......@@ -173,11 +227,42 @@ void BoxWrapper::CopyForPush(const paddle::platform::Place& place,
cudaMemcpy(d_slot_vector, slot_vector_.data(),
slot_lengths_lod.size() * sizeof(int), cudaMemcpyHostToDevice);
PushCopy<<<(total_length + 512 - 1) / 512, 512, 0, stream>>>(
total_grad_values_gpu, gpu_values, gpu_len, hidden_size,
slot_lengths.size(), total_length, batch_size, d_slot_vector);
#define EMBEDX_CASE(i, ...) \
case i: { \
constexpr size_t EmbedxDim = i; \
switch (expand_embed_dim) { \
__VA_ARGS__ \
default: \
PADDLE_THROW(platform::errors::InvalidArgument( \
"Unsupport this expand embedding size [%d]", expand_embed_dim)); \
} \
} break
#define EXPAND_EMBED_PUSH_CASE(i, ...) \
case i: { \
constexpr size_t ExpandDim = i; \
PushCopy<EmbedxDim, \
ExpandDim><<<(total_length + 512 - 1) / 512, 512, 0, stream>>>( \
reinterpret_cast<boxps::FeaturePushValueGpu<EmbedxDim, ExpandDim>*>( \
total_grad_values_gpu), \
gpu_values, gpu_len, hidden_size, expand_embed_dim, \
slot_lengths.size(), total_length, batch_size, d_slot_vector); \
} break
switch (hidden_size - 3) {
EMBEDX_CASE(8, EXPAND_EMBED_PUSH_CASE(0); EXPAND_EMBED_PUSH_CASE(8);
EXPAND_EMBED_PUSH_CASE(64););
EMBEDX_CASE(16, EXPAND_EMBED_PUSH_CASE(0););
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Unsupport this embedding size [%d]", hidden_size - 3));
}
cudaStreamSynchronize(stream);
#undef EXPAND_EMBED_PUSH_CASE
#undef EMBEDX_CASE
}
} // end namespace framework
} // end namespace paddle
#endif
paddle/fluid/framework/fleet/box_wrapper.h
浏览文件 @ 95089204
......@@ -341,30 +341,54 @@ class BoxWrapper {
void BeginPass() const;
void EndPass(bool need_save_delta) const;
void SetTestMode(bool is_test) const;
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM = 0>
void PullSparseCase(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<float*>& values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int expand_embed_dim);
void PullSparse(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<float*>& values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size);
const int hidden_size, const int expand_embed_dim);
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM = 0>
void PushSparseGradCase(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<const float*>& grad_values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int expand_embed_dim,
const int batch_size);
void PushSparseGrad(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<const float*>& grad_values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int batch_size);
const int hidden_size, const int expand_embed_dim,
const int batch_size);
void CopyForPull(const paddle::platform::Place& place, uint64_t** gpu_keys,
const std::vector<float*>& values,
const boxps::FeatureValueGpu* total_values_gpu,
const std::vector<float*>& values, void* total_values_gpu,
const int64_t* gpu_len, const int slot_num,
const int hidden_size, const int64_t total_length);
const int hidden_size, const int expand_embed_dim,
const int64_t total_length);
void CopyForPush(const paddle::platform::Place& place,
const std::vector<const float*>& grad_values,
boxps::FeaturePushValueGpu* total_grad_values_gpu,
void* total_grad_values_gpu,
const std::vector<int64_t>& slot_lengths,
const int hidden_size, const int64_t total_length,
const int batch_size);
const int hidden_size, const int expand_embed_dim,
const int64_t total_length, const int batch_size);
void CopyKeys(const paddle::platform::Place& place, uint64_t** origin_keys,
uint64_t* total_keys, const int64_t* gpu_len, int slot_num,
int total_len);
void CheckEmbedSizeIsValid(int embedx_dim, int expand_embed_dim);
boxps::PSAgentBase* GetAgent() { return p_agent_; }
void InitializeGPUAndLoadModel(
const char* conf_file, const std::vector<int>& slot_vector,
......@@ -442,6 +466,15 @@ class BoxWrapper {
}
static std::shared_ptr<BoxWrapper> GetInstance() {
PADDLE_ENFORCE_EQ(
s_instance_ == nullptr, false,
platform::errors::PreconditionNotMet(
"GetInstance failed in BoxPs, you should use SetInstance firstly"));
return s_instance_;
}
static std::shared_ptr<BoxWrapper> SetInstance(int embedx_dim = 8,
int expand_embed_dim = 0) {
if (nullptr == s_instance_) {
// If main thread is guaranteed to init this, this lock can be removed
static std::mutex mutex;
......@@ -449,8 +482,13 @@ class BoxWrapper {
if (nullptr == s_instance_) {
VLOG(3) << "s_instance_ is null";
s_instance_.reset(new paddle::framework::BoxWrapper());
s_instance_->boxps_ptr_.reset(boxps::BoxPSBase::GetIns());
s_instance_->boxps_ptr_.reset(
boxps::BoxPSBase::GetIns(embedx_dim, expand_embed_dim));
embedx_dim_ = embedx_dim;
expand_embed_dim_ = expand_embed_dim;
}
} else {
LOG(WARNING) << "You have already used SetInstance() before";
}
return s_instance_;
}
......@@ -776,6 +814,9 @@ class BoxWrapper {
const int feedpass_thread_num_ = 30; // magic number
static std::shared_ptr<BoxWrapper> s_instance_;
std::unordered_set<std::string> slot_name_omited_in_feedpass_;
// EMBEDX_DIM and EXPAND_EMBED_DIM
static int embedx_dim_;
static int expand_embed_dim_;
// Metric Related
int phase_ = 1;
......@@ -1009,3 +1050,5 @@ class BoxHelper {
} // end namespace framework
} // end namespace paddle
#include "paddle/fluid/framework/fleet/box_wrapper_impl.h"
paddle/fluid/framework/fleet/box_wrapper_impl.h 0 → 100644
浏览文件 @ 95089204
/* Copyright (c) 2020 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
#ifdef PADDLE_WITH_BOX_PS
#include <vector>
namespace paddle {
namespace framework {
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
void BoxWrapper::PullSparseCase(const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<float*>& values,
const std::vector<int64_t>& slot_lengths,
const int hidden_size,
const int expand_embed_dim) {
VLOG(3) << "Begin PullSparse";
platform::Timer all_timer;
platform::Timer pull_boxps_timer;
all_timer.Start();
int64_t total_length =
std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
auto buf = memory::AllocShared(
place, total_length *
sizeof(boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>));
boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>* total_values_gpu =
reinterpret_cast<boxps::FeatureValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*>(
buf->ptr());
if (platform::is_cpu_place(place)) {
PADDLE_THROW(platform::errors::Unimplemented(
"Warning:: CPUPlace is not supported in PaddleBox now."));
} else if (platform::is_gpu_place(place)) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
VLOG(3) << "Begin copy keys, key_num[" << total_length << "]";
int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
LoDTensor& total_keys_tensor = keys_tensor[device_id];
uint64_t* total_keys = reinterpret_cast<uint64_t*>(
total_keys_tensor.mutable_data<int64_t>({total_length, 1}, place));
// construct slot_level lod info
auto slot_lengths_lod = slot_lengths;
for (size_t i = 1; i < slot_lengths_lod.size(); i++) {
slot_lengths_lod[i] += slot_lengths_lod[i - 1];
}
auto buf_key = memory::AllocShared(place, keys.size() * sizeof(uint64_t*));
auto buf_length =
memory::AllocShared(place, slot_lengths.size() * sizeof(int64_t));
uint64_t** gpu_keys = reinterpret_cast<uint64_t**>(buf_key->ptr());
int64_t* gpu_len = reinterpret_cast<int64_t*>(buf_length->ptr());
cudaMemcpy(gpu_keys, keys.data(), keys.size() * sizeof(uint64_t*),
cudaMemcpyHostToDevice);
cudaMemcpy(gpu_len, slot_lengths_lod.data(),
slot_lengths.size() * sizeof(int64_t), cudaMemcpyHostToDevice);
this->CopyKeys(place, gpu_keys, total_keys, gpu_len,
static_cast<int>(slot_lengths.size()),
static_cast<int>(total_length));
VLOG(3) << "Begin call PullSparseGPU in BoxPS";
pull_boxps_timer.Start();
int ret = boxps_ptr_->PullSparseGPU(
total_keys, reinterpret_cast<void*>(total_values_gpu),
static_cast<int>(total_length), device_id);
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"PullSparseGPU failed in BoxPS."));
pull_boxps_timer.Pause();
VLOG(3) << "Begin Copy result to tensor, total_length[" << total_length
<< "]";
this->CopyForPull(place, gpu_keys, values,
reinterpret_cast<void*>(total_values_gpu), gpu_len,
static_cast<int>(slot_lengths.size()), hidden_size,
expand_embed_dim, total_length);
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Please compile WITH_GPU option, because NCCL doesn't support "
"windows."));
#endif
} else {
PADDLE_THROW(platform::errors::PreconditionNotMet(
"PaddleBox: PullSparse Only Support CPUPlace or CUDAPlace Now."));
}
all_timer.Pause();
VLOG(1) << "PullSparse total costs: " << all_timer.ElapsedSec()
<< " s, of which BoxPS costs: " << pull_boxps_timer.ElapsedSec()
<< " s";
VLOG(3) << "End PullSparse";
}
template <size_t EMBEDX_DIM, size_t EXPAND_EMBED_DIM>
void BoxWrapper::PushSparseGradCase(
const paddle::platform::Place& place,
const std::vector<const uint64_t*>& keys,
const std::vector<const float*>& grad_values,
const std::vector<int64_t>& slot_lengths, const int hidden_size,
const int expand_embed_dim, const int batch_size) {
VLOG(3) << "Begin PushSparseGrad";
platform::Timer all_timer;
platform::Timer push_boxps_timer;
all_timer.Start();
int64_t total_length =
std::accumulate(slot_lengths.begin(), slot_lengths.end(), 0UL);
auto buf = memory::AllocShared(
place,
total_length *
sizeof(boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>));
boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*
total_grad_values_gpu = reinterpret_cast<
boxps::FeaturePushValueGpu<EMBEDX_DIM, EXPAND_EMBED_DIM>*>(
buf->ptr());
if (platform::is_cpu_place(place)) {
PADDLE_THROW(platform::errors::Unimplemented(
"Warning:: CPUPlace is not supported in PaddleBox now."));
} else if (platform::is_gpu_place(place)) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
int device_id = BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId();
LoDTensor& cached_total_keys_tensor = keys_tensor[device_id];
uint64_t* total_keys =
reinterpret_cast<uint64_t*>(cached_total_keys_tensor.data<int64_t>());
VLOG(3) << "Begin copy grad tensor to boxps struct";
this->CopyForPush(place, grad_values, total_grad_values_gpu, slot_lengths,
hidden_size, expand_embed_dim, total_length, batch_size);
VLOG(3) << "Begin call PushSparseGPU in BoxPS";
push_boxps_timer.Start();
int ret = boxps_ptr_->PushSparseGPU(
total_keys, reinterpret_cast<void*>(total_grad_values_gpu),
static_cast<int>(total_length),
BOOST_GET_CONST(platform::CUDAPlace, place).GetDeviceId());
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"PushSparseGPU failed in BoxPS."));
push_boxps_timer.Pause();
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Please compile WITH_GPU option, because NCCL doesn't support "
"windows."));
#endif
} else {
PADDLE_THROW(platform::errors::PreconditionNotMet(
"PaddleBox: PushSparseGrad Only Support CPUPlace or CUDAPlace Now."));
}
all_timer.Pause();
VLOG(1) << "PushSparseGrad total cost: " << all_timer.ElapsedSec()
<< " s, of which BoxPS cost: " << push_boxps_timer.ElapsedSec()
<< " s";
VLOG(3) << "End PushSparseGrad";
}
} // namespace framework
} // namespace paddle
#endif
paddle/fluid/operators/pull_box_extended_sparse_op.cc 0 → 100644
浏览文件 @ 95089204
// Copyright (c) 2020 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/operators/pull_box_extended_sparse_op.h"
namespace paddle {
namespace operators {
class PullBoxExtendedSparseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_GE(
ctx->Inputs("Ids").size(), 1UL,
platform::errors::InvalidArgument(
"Inputs(Ids) of PullBoxExtendedSparseOp should not be empty."));
PADDLE_ENFORCE_GE(
ctx->Outputs("Out").size(), 1UL,
platform::errors::InvalidArgument(
"Outputs(Out) of PullBoxExtendedSparseOp should not be empty."));
PADDLE_ENFORCE_GE(ctx->Outputs("OutExtend").size(), 1UL,
platform::errors::InvalidArgument(
"Outputs(OutExtend) of PullBoxExtendedSparseOp "
"should not be empty."));
auto emb_size = static_cast<int64_t>(ctx->Attrs().Get<int>("emb_size"));
auto emb_extended_size =
static_cast<int64_t>(ctx->Attrs().Get<int>("emb_extended_size"));
auto all_ids_dim = ctx->GetInputsDim("Ids");
const size_t n_ids = all_ids_dim.size();
std::vector<framework::DDim> outs_dims;
std::vector<framework::DDim> outs_extended_dims;
outs_dims.resize(n_ids);
outs_extended_dims.resize(n_ids);
for (size_t i = 0; i < n_ids; ++i) {
const auto ids_dims = all_ids_dim[i];
int ids_rank = ids_dims.size();
PADDLE_ENFORCE_EQ(ids_dims[ids_rank - 1], 1,
platform::errors::InvalidArgument(
"Shape error in %lu id, the last dimension of the "
"'Ids' tensor must be 1.",
i));
auto out_dim = framework::vectorize(
framework::slice_ddim(ids_dims, 0, ids_rank - 1));
out_dim.push_back(emb_size);
outs_dims[i] = framework::make_ddim(out_dim);
auto out_extended_dim = framework::vectorize(
framework::slice_ddim(ids_dims, 0, ids_rank - 1));
out_extended_dim.push_back(emb_extended_size);
outs_extended_dims[i] = framework::make_ddim(out_extended_dim);
}
ctx->SetOutputsDim("Out", outs_dims);
ctx->SetOutputsDim("OutExtend", outs_extended_dims);
for (size_t i = 0; i < n_ids; ++i) {
ctx->ShareLoD("Ids", "Out", i, i);
ctx->ShareLoD("Ids", "OutExtend", i, i);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(framework::proto::VarType::FP32,
ctx.device_context());
}
};
class PullBoxExtendedSparseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Ids",
"Input tensors with type int32 or int64 "
"contains the ids to be looked up in BoxPS. "
"The last dimension size must be 1.")
.AsDuplicable();
AddOutput("Out", "The lookup results tensors.").AsDuplicable();
AddOutput("OutExtend", "The lookup extended results tensors.")
.AsDuplicable();
AddAttr<int>("emb_size", "(int, the embedding hidden size").SetDefault(1);
AddAttr<int>("emb_extended_size",
"(int, the extended_embedding hidden size")
.SetDefault(128);
AddComment(R"DOC(
Pull Box Extended Sparse Operator.
This operator is used to perform lookups on the BoxPS,
then concatenated into a dense tensor.
The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.
)DOC");
}
};
template <typename T>
class PushBoxExtendedSparseOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> op) const override {
op->SetType("push_box_extended_sparse");
op->SetInput("Ids", this->Input("Ids"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetInput(framework::GradVarName("OutExtend"),
this->OutputGrad("OutExtend"));
op->SetOutput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetAttrMap(this->Attrs());
}
};
class PushBoxExtendedSparseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out")),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(
pull_box_extended_sparse, ops::PullBoxExtendedSparseOp,
ops::PullBoxExtendedSparseOpMaker,
ops::PushBoxExtendedSparseOpMaker<paddle::framework::OpDesc>,
ops::PushBoxExtendedSparseOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(push_box_extended_sparse, ops::PushBoxExtendedSparseOp);
REGISTER_OP_CPU_KERNEL(pull_box_extended_sparse,
ops::PullBoxExtendedSparseCPUKernel<float>,
ops::PullBoxExtendedSparseCPUKernel<double>);
REGISTER_OP_CPU_KERNEL(push_box_extended_sparse,
ops::PushBoxExtendedSparseCPUKernel<float>,
ops::PushBoxExtendedSparseCPUKernel<double>);
paddle/fluid/operators/pull_box_extended_sparse_op.cu 0 → 100644
浏览文件 @ 95089204
// Copyright (c) 2020 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/operators/pull_box_extended_sparse_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
namespace operators {
template <typename T>
class PullBoxExtendedSparseCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
PullBoxExtendedSparseFunctor<T>(ctx);
}
};
template <typename T>
class PushBoxExtendedSparseCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
PushBoxExtendedSparseFunctor<T>(ctx);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(pull_box_extended_sparse,
ops::PullBoxExtendedSparseCUDAKernel<float>,
ops::PullBoxExtendedSparseCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(push_box_extended_sparse,
ops::PushBoxExtendedSparseCUDAKernel<float>,
ops::PushBoxExtendedSparseCUDAKernel<double>);
paddle/fluid/operators/pull_box_extended_sparse_op.h 0 → 100644
浏览文件 @ 95089204
// Copyright (c) 2020 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 <vector>
#include "paddle/fluid/framework/fleet/box_wrapper.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
template <typename T>
static void PullBoxExtendedSparseFunctor(
const framework::ExecutionContext &ctx) {
auto inputs = ctx.MultiInput<framework::Tensor>("Ids");
auto outputs = ctx.MultiOutput<framework::Tensor>("Out");
auto outputs_extend = ctx.MultiOutput<framework::Tensor>("OutExtend");
const auto slot_size = inputs.size();
std::vector<const uint64_t *> all_keys(slot_size);
// BoxPS only supports float now
std::vector<float *> all_values(slot_size * 2);
std::vector<int64_t> slot_lengths(slot_size);
for (size_t i = 0; i < slot_size; i++) {
const auto *slot = inputs[i];
const uint64_t *single_slot_keys =
reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
all_keys[i] = single_slot_keys;
slot_lengths[i] = slot->numel();
auto *output = outputs[i]->mutable_data<T>(ctx.GetPlace());
auto *output_extend = outputs_extend[i]->mutable_data<T>(ctx.GetPlace());
all_values[i] = reinterpret_cast<float *>(output);
all_values[i + slot_size] = reinterpret_cast<float *>(output_extend);
}
#ifdef PADDLE_WITH_BOX_PS
auto emb_size = ctx.Attr<int>("emb_size");
auto emb_extended_size = ctx.Attr<int>("emb_extended_size");
auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
box_ptr->PullSparse(ctx.GetPlace(), all_keys, all_values, slot_lengths,
emb_size, emb_extended_size);
#endif
}
template <typename T>
static void PushBoxExtendedSparseFunctor(
const framework::ExecutionContext &ctx) {
auto inputs = ctx.MultiInput<framework::LoDTensor>("Ids");
auto d_output =
ctx.MultiInput<framework::Tensor>(framework::GradVarName("Out"));
auto d_output_extend =
ctx.MultiInput<framework::Tensor>(framework::GradVarName("OutExtend"));
const auto slot_size = inputs.size();
std::vector<const uint64_t *> all_keys(slot_size);
std::vector<const float *> all_grad_values(slot_size * 2);
std::vector<int64_t> slot_lengths(slot_size);
int batch_size = -1;
for (size_t i = 0; i < slot_size; i++) {
const auto *slot = inputs[i];
const uint64_t *single_slot_keys =
reinterpret_cast<const uint64_t *>(slot->data<int64_t>());
all_keys[i] = single_slot_keys;
slot_lengths[i] = slot->numel();
int cur_batch_size =
slot->lod().size() ? slot->lod()[0].size() - 1 : slot->dims()[0];
if (batch_size == -1) {
batch_size = cur_batch_size;
} else {
PADDLE_ENFORCE_EQ(batch_size, cur_batch_size,
platform::errors::PreconditionNotMet(
"The batch size of all input slots should be same,"
"please cheack"));
}
const float *grad_value = d_output[i]->data<float>();
const float *grad_value_extend = d_output_extend[i]->data<float>();
all_grad_values[i] = reinterpret_cast<const float *>(grad_value);
all_grad_values[i + slot_size] =
reinterpret_cast<const float *>(grad_value_extend);
}
#ifdef PADDLE_WITH_BOX_PS
auto emb_size = ctx.Attr<int>("emb_size");
auto emb_extended_size = ctx.Attr<int>("emb_extended_size");
auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
box_ptr->PushSparseGrad(ctx.GetPlace(), all_keys, all_grad_values,
slot_lengths, emb_size, emb_extended_size,
batch_size);
#endif
}
using LoDTensor = framework::LoDTensor;
template <typename T>
class PullBoxExtendedSparseCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
PullBoxExtendedSparseFunctor<T>(ctx);
}
};
template <typename T>
class PushBoxExtendedSparseCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
PushBoxExtendedSparseFunctor<T>(ctx);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/pull_box_sparse_op.h
浏览文件 @ 95089204
......@@ -44,7 +44,7 @@ static void PullBoxSparseFunctor(const framework::ExecutionContext &ctx) {
auto hidden_size = ctx.Attr<int>("size");
auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
box_ptr->PullSparse(ctx.GetPlace(), all_keys, all_values, slot_lengths,
hidden_size);
hidden_size, 0);
#endif
}
......@@ -81,7 +81,7 @@ static void PushBoxSparseFunctor(const framework::ExecutionContext &ctx) {
auto hidden_size = ctx.Attr<int>("size");
auto box_ptr = paddle::framework::BoxWrapper::GetInstance();
box_ptr->PushSparseGrad(ctx.GetPlace(), all_keys, all_grad_values,
slot_lengths, hidden_size, batch_size);
slot_lengths, hidden_size, 0, batch_size);
#endif
}
......
paddle/fluid/pybind/box_helper_py.cc
浏览文件 @ 95089204
......@@ -63,9 +63,9 @@ void BindBoxHelper(py::module* m) {
void BindBoxWrapper(py::module* m) {
py::class_<framework::BoxWrapper, std::shared_ptr<framework::BoxWrapper>>(
*m, "BoxWrapper")
.def(py::init([]() {
.def(py::init([](int embedx_dim, int expand_embed_dim) {
// return std::make_shared<paddle::framework::BoxHelper>(dataset);
return framework::BoxWrapper::GetInstance();
return framework::BoxWrapper::SetInstance(embedx_dim, expand_embed_dim);
}))
.def("save_base", &framework::BoxWrapper::SaveBase,
py::call_guard<py::gil_scoped_release>())
......
python/paddle/fluid/contrib/layers/nn.py
浏览文件 @ 95089204
......@@ -34,7 +34,8 @@ __all__ = [
'fused_elemwise_activation', 'sequence_topk_avg_pooling', 'var_conv_2d',
'match_matrix_tensor', 'tree_conv', 'fused_embedding_seq_pool',
'multiclass_nms2', 'search_pyramid_hash', 'shuffle_batch', 'partial_concat',
'partial_sum', 'tdm_child', 'rank_attention', 'tdm_sampler', 'batch_fc'
'partial_sum', 'tdm_child', 'rank_attention', 'tdm_sampler', 'batch_fc',
'_pull_box_extended_sparse'
]
......@@ -1361,3 +1362,50 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
"Bias": b},
outputs={"Out": pre_act})
return helper.append_activation(pre_act)
def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
"""
**Pull Box Extended Sparse Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
BoxPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
contains the IDs information.
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively.
extend_size(int): The embedding size parameter in extended dim,
which indicates the size of each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Variable|list of Variable: The tensor variable storing the embeddings of the \
supplied inputs.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1)
emb, emb_ex = fluid.contrib.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128)
"""
helper = LayerHelper('pull_box_extended_sparse', **locals())
helper.input_dtype()
inputs = helper.multiple_input()
outs = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
outs_extend = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
helper.append_op(
type='pull_box_extended_sparse',
inputs={'Ids': inputs},
outputs={'Out': outs,
'OutExtend': outs_extend},
attrs={'emb_size': size,
'emb_extended_size': extend_size})
if len(outs) == 1:
return outs[0], outs_extend[0]
return outs, outs_extend
python/paddle/fluid/tests/unittests/test_paddlebox_datafeed.py
浏览文件 @ 95089204
......@@ -17,7 +17,6 @@ import paddle.fluid.core as core
import os
import unittest
import paddle.fluid.layers as layers
from paddle.fluid.layers.nn import _pull_box_sparse
class TestDataFeed(unittest.TestCase):
......@@ -57,9 +56,9 @@ class TestDataFeed(unittest.TestCase):
lod_level=0,
append_batch_size=False)
emb_x, emb_y = _pull_box_sparse([x, y], size=2)
emb_xp = _pull_box_sparse(x, size=2)
concat = layers.concat([emb_x, emb_y], axis=1)
emb_x, emb_y = fluid.contrib.layers._pull_box_extended_sparse(
[x, y], size=2, extend_size=128)
concat = layers.concat([emb_x[0], emb_x[1], emb_y[0], emb_y[1]], axis=1)
fc = layers.fc(input=concat,
name="fc",
size=1,
......
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