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提交 5ff1ef36 编写于 作者: C chengduoZH
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update sparse parameter

上级 7c90d7a3
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Showing with 453 addition and 106 deletion
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 5ff1ef36
......@@ -15,12 +15,14 @@ if(WITH_GPU)
dynload_cuda)
set(multi_devices_graph_builder_deps nccl_all_reduce_op_handle)
nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim dynload_cuda)
nv_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor dynload_cuda)
else()
set(multi_devices_graph_builder_deps)
cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim)
cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
endif()
cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
......
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 5ff1ef36
......@@ -19,11 +19,9 @@
namespace paddle {
namespace framework {
namespace details {
BroadcastOpHandle::BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places)
: local_scopes_(local_scopes), places_(places) {}
void BroadcastOpHandle::RunImpl() {
if (places_.size() == 1) return;
// the input and output may have dummy var.
VarHandle *in_var_handle;
......@@ -55,27 +53,95 @@ void BroadcastOpHandle::RunImpl() {
Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
for (auto *out : out_var_handles) {
if (*out == *in_var_handle) {
continue;
if (platform::is_cpu_place(in_tensor.place())) {
for (auto *out : out_var_handles) {
if (*out == *in_var_handle) {
continue;
}
auto &out_p = out->place_;
auto *out_var = var_scopes.at(out->scope_idx_)->FindVar(out->name_);
PADDLE_ENFORCE_NOT_NULL(out_var);
PADDLE_ENFORCE_EQ(out_p.which(), in_tensor.place().which(),
"Places must be all on CPU or all on CUDA.");
VariableVisitor::ShareDimsAndLoD(*in_var, out_var);
VariableVisitor::GetMutableTensor(out_var).mutable_data(out_p,
in_tensor.type());
auto dev_ctx = dev_ctxes_.at(out_p);
RunAndRecordEvent(out_p, [in_tensor, out_var, dev_ctx, out_p] {
paddle::framework::TensorCopy(
in_tensor, out_p, *dev_ctx,
&VariableVisitor::GetMutableTensor(out_var));
});
}
} else {
#ifdef PADDLE_WITH_CUDA
PADDLE_ENFORCE(platform::is_gpu_place(in_tensor.place()));
VarHandle *out_handle;
int root = boost::get<platform::CUDAPlace>(in_tensor.place()).device;
std::vector<std::function<void()>> broadcast_calls;
for (size_t j = 0; j < out_var_handles.size(); ++j) {
VarHandle *out_var_handle = out_var_handles[j];
Variable *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
if (*out_var_handle != *in_var_handle) {
PADDLE_ENFORCE_NOT_NULL(out_var);
PADDLE_ENFORCE_EQ(out_var_handle->place_.which(),
in_tensor.place().which(),
"Places must be all on CPU or all on CUDA.");
VariableVisitor::ShareDimsAndLoD(*in_var, out_var);
VariableVisitor::GetMutableTensor(out_var).mutable_data(
out_var_handle->place_, in_tensor.type());
}
auto out_p = out_var_handle->place_;
int dev_id = boost::get<platform::CUDAPlace>(out_p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
void *send_recv_buffer = nullptr;
if (root == dev_id) {
send_recv_buffer = const_cast<void *>(in_tensor.data<void>());
out_handle = out_var_handle;
} else {
send_recv_buffer =
VariableVisitor::GetMutableTensor(out_var).mutable_data(
out_var_handle->place_);
}
int type = platform::ToNCCLDataType(in_tensor.type());
broadcast_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclBcast(
send_recv_buffer, in_tensor.numel(),
static_cast<ncclDataType_t>(type), root, comm, stream));
});
}
auto &out_p = out->place_;
auto *out_var = var_scopes.at(out->scope_idx_)->FindVar(out->name_);
PADDLE_ENFORCE_NOT_NULL(out_var);
PADDLE_ENFORCE_EQ(out_p.which(), in_var_handle->place_.which(),
"Places must be all on CPU or all on CUDA.");
VariableVisitor::ShareDimsAndLoD(*in_var, out_var);
VariableVisitor::GetMutableTensor(out_var).mutable_data(out_p,
in_tensor.type());
auto dev_ctx = dev_ctxes_.at(out_p);
RunAndRecordEvent(out_p, [in_tensor, out_var, dev_ctx, out_p] {
paddle::framework::TensorCopy(
in_tensor, out_p, *(dev_ctx),
&VariableVisitor::GetMutableTensor(out_var));
this->RunAndRecordEvent([&] {
{
platform::NCCLGroupGuard guard;
for (auto &call : broadcast_calls) {
call();
}
}
if (*out_handle != *in_var_handle) {
auto out_var = var_scopes.at(in_var_handle->scope_idx_)
->FindVar(out_var_handles[0]->name_);
paddle::framework::TensorCopy(
in_tensor, in_var_handle->place_,
*(dev_ctxes_.at(in_var_handle->place_)),
&VariableVisitor::GetMutableTensor(out_var));
}
});
#else
PADDLE_THROW("CUDA is not support.");
#endif
}
}
......
paddle/fluid/framework/details/broadcast_op_handle.h
浏览文件 @ 5ff1ef36
......@@ -24,14 +24,32 @@
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/platform/device_context.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
namespace details {
struct BroadcastOpHandle : public OpHandleBase {
public:
#ifdef PADDLE_WITH_CUDA
BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *nccl_ctxs)
: local_scopes_(local_scopes), places_(places), nccl_ctxs_(nccl_ctxs) {
if (nccl_ctxs_) {
for (auto &p_ctx : nccl_ctxs_->contexts_) {
dev_ctxes_[platform::CUDAPlace(p_ctx.first)] = p_ctx.second.ctx_.get();
}
}
}
#else
BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
const std::vector<platform::Place> &places)
: local_scopes_(local_scopes), places_(places) {}
#endif
std::string Name() const override;
......@@ -44,6 +62,9 @@ struct BroadcastOpHandle : public OpHandleBase {
private:
const std::vector<Scope *> &local_scopes_;
const std::vector<platform::Place> &places_;
#ifdef PADDLE_WITH_CUDA
const platform::NCCLContextMap *nccl_ctxs_;
#endif
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/broadcast_op_handle_test.cc
浏览文件 @ 5ff1ef36
......@@ -35,15 +35,25 @@ struct TestBroadcastOpHandle {
std::unique_ptr<OpHandleBase> op_handle_;
std::vector<std::unique_ptr<VarHandleBase>> vars_;
std::vector<p::Place> gpu_list_;
bool use_gpu_;
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
#endif
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
#ifdef PADDLE_WITH_CUDA
if (nccl_ctxs_) {
nccl_ctxs_->WaitAll();
}
#endif
}
void InitCtxOnGpu(bool use_gpu) {
if (use_gpu) {
use_gpu_ = use_gpu;
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
int count = p::GetCUDADeviceCount();
if (count <= 1) {
......@@ -57,6 +67,7 @@ struct TestBroadcastOpHandle {
gpu_list_.push_back(p);
ctxs_.emplace_back(new p::CUDADeviceContext(p));
}
nccl_ctxs_.reset(new platform::NCCLContextMap(gpu_list_));
#else
PADDLE_THROW("CUDA is not support.");
#endif
......@@ -67,6 +78,9 @@ struct TestBroadcastOpHandle {
gpu_list_.push_back(p);
ctxs_.emplace_back(new p::CPUDeviceContext(p));
}
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_.reset(nullptr);
#endif
}
}
......@@ -82,7 +96,21 @@ struct TestBroadcastOpHandle {
}
param_scopes_[input_scope_idx]->Var("input");
op_handle_.reset(new BroadcastOpHandle(local_scopes_, gpu_list_));
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(
new BroadcastOpHandle(local_scopes_, gpu_list_, nccl_ctxs_.get()));
#else
PADDLE_THROW("CUDA is not support.");
#endif
} else {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(
new BroadcastOpHandle(local_scopes_, gpu_list_, nccl_ctxs_.get()));
#else
op_handle_.reset(new BroadcastOpHandle(local_scopes_, gpu_list_));
#endif
}
auto* in_var_handle =
new VarHandle(1, input_scope_idx, "input", gpu_list_[input_scope_idx]);
......@@ -97,7 +125,9 @@ struct TestBroadcastOpHandle {
op_handle_->AddInput(dummy_var_handle);
for (size_t j = 0; j < gpu_list_.size(); ++j) {
op_handle_->SetDeviceContext(gpu_list_[j], ctxs_[j].get());
if (!use_gpu_) {
op_handle_->SetDeviceContext(gpu_list_[j], ctxs_[j].get());
}
VarHandle* out_var_handle = new VarHandle(2, j, "out", gpu_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
......
paddle/fluid/framework/details/gather_op_handle.cc
浏览文件 @ 5ff1ef36
......@@ -25,6 +25,7 @@ GatherOpHandle::GatherOpHandle(const std::vector<Scope *> &local_scopes,
: local_scopes_(local_scopes), places_(places) {}
void GatherOpHandle::RunImpl() {
if (places_.size() == 1) return;
// the input and output may have dummy var.
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
......@@ -53,55 +54,53 @@ void GatherOpHandle::RunImpl() {
PADDLE_ENFORCE(pre_in_var->IsType<framework::SelectedRows>(),
"Currently, gather_op only can gather SelectedRows.");
auto pre_place = in_0_handle->place_;
PADDLE_ENFORCE_EQ(out_var_handle->place_.which(), pre_place.which(),
"The place of input and output should be the same.");
// Wait input done, this Wait is asynchronous operation
WaitInputVarGenerated(in_var_handles);
std::vector<int64_t> out_rows;
std::vector<Tensor> in_tensors;
std::vector<platform::Place> in_places;
auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
auto &pre_in_value = pre_in_var->Get<framework::SelectedRows>();
// gather the inputs
for (auto *in_handle : in_var_handles) {
auto in_p = in_handle->place_;
in_places.push_back(in_p);
PADDLE_ENFORCE_EQ(in_p.which(), pre_place.which(),
"Places must be all on CPU or all on CUDA.");
auto *in_var =
var_scopes.at(in_handle->scope_idx_)->FindVar(in_handle->name_);
auto &in_sr = in_var->Get<framework::SelectedRows>();
PADDLE_ENFORCE_NOT_NULL(in_var);
auto &in_sr_value = in_var->Get<framework::SelectedRows>();
PADDLE_ENFORCE_EQ(in_sr.value().type(), pre_in.value().type(),
PADDLE_ENFORCE_EQ(in_sr_value.place().which(), pre_in_value.place().which(),
"Places must be all on CPU or all on GPU.");
PADDLE_ENFORCE_EQ(in_sr_value.value().type(), pre_in_value.value().type(),
"The type of input is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(),
PADDLE_ENFORCE_EQ(in_sr_value.height(), pre_in_value.height(),
"The height of inputs is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(),
PADDLE_ENFORCE_EQ(in_sr_value.GetCompleteDims(),
pre_in_value.GetCompleteDims(),
"The dims of inputs is not consistent.");
auto &in_sr_rows = in_sr.rows();
auto &in_sr_rows = in_sr_value.rows();
out_rows.insert(out_rows.end(), in_sr_rows.begin(), in_sr_rows.end());
in_tensors.emplace_back(in_sr.value());
in_tensors.emplace_back(in_sr_value.value());
}
// write the output
auto &out_place = out_var_handle->place_;
auto out_scope_idx = out_var_handle->scope_idx_;
auto out_var = var_scopes.at(out_scope_idx)->FindVar(out_var_handle->name_);
auto out = out_var->GetMutable<framework::SelectedRows>();
out->set_height(pre_in.height());
out->set_rows(out_rows);
PADDLE_ENFORCE_EQ(out_place.which(), pre_in_value.place().which(),
"Places must be all on CPU or all on GPU.");
auto out_var =
var_scopes.at(out_var_handle->scope_idx_)->FindVar(out_var_handle->name_);
PADDLE_ENFORCE_NOT_NULL(out_var);
auto out_value = out_var->GetMutable<framework::SelectedRows>();
out_value->set_height(pre_in_value.height());
out_value->set_rows(out_rows);
size_t rows = out_rows.size();
DDim out_dim = pre_in.GetCompleteDims();
DDim out_dim = pre_in_value.GetCompleteDims();
out_dim[0] = static_cast<int64_t>(rows);
out->mutable_value()->Resize(out_dim);
out->mutable_value()->mutable_data(out_place, pre_in.value().type());
Tensor *out_tensor = out->mutable_value();
out_value->mutable_value()->Resize(out_dim);
out_value->mutable_value()->mutable_data(out_place,
pre_in_value.value().type());
Tensor *out_tensor = out_value->mutable_value();
// copy
auto dev_ctx = dev_ctxes_[out_place];
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 5ff1ef36
......@@ -11,9 +11,11 @@
// 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/details/multi_devices_graph_builder.h"
#include <utility>
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
#include "paddle/fluid/framework/details/send_op_handle.h"
#include "paddle/fluid/framework/scope.h"
......@@ -34,21 +36,26 @@ MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes, bool use_default_grad_scale,
platform::NCCLContextMap *nccl_ctxs)
const std::vector<Scope *> &local_scopes,
platform::NCCLContextMap *nccl_ctxs, bool use_default_grad_scale,
bool use_nccl_allreduce)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
nccl_ctxs_(nccl_ctxs) {
nccl_ctxs_(nccl_ctxs),
use_nccl_allreduce_(use_nccl_allreduce) {
#else
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes, bool use_default_grad_scale)
const std::vector<Scope *> &local_scopes, bool use_default_grad_scale,
bool use_nccl_allreduce)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes) {
local_scopes_(local_scopes),
use_nccl_allreduce_(use_nccl_allreduce) {
#endif
for (auto &p : params) {
grad_names_.insert(GradVarName(p));
......@@ -114,6 +121,14 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
std::unordered_map<std::string, std::vector<std::unique_ptr<VarHandle>>>>(
places_.size());
size_t cur_device_id = 0;
std::vector<std::unordered_set<std::string>> var_name_on_devices;
std::vector<std::unordered_set<std::string>> bcast_var_name_set;
var_name_on_devices.resize(places_.size());
bcast_var_name_set.resize(places_.size());
// Find "send" op first for split is in front of send.
OpDesc *send_op = GetSendOpDesc(program);
......@@ -132,19 +147,44 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
is_forwarding = false;
} else {
CreateComputationalOps(&result, *op, places_.size());
if (!is_forwarding) {
int op_dev_id = GetOpDeviceID(var_name_on_devices, *op);
if (op_dev_id == -1) { // var on all device
CreateComputationalOps(&result, *op, places_.size());
} else {
CreateComputationalOp(&result, *op, op_dev_id);
for (auto &var_name : op->OutputArgumentNames()) {
var_name_on_devices[op_dev_id].emplace(var_name);
}
}
if (!is_forwarding && places_.size() > 1) {
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
for (auto &og : op->OutputArgumentNames()) {
if (IsParameterGradientOnce(og, &og_has_been_broadcast)) {
InsertNCCLAllReduceOp(&result, og);
if (use_nccl_allreduce_) {
InsertNCCLAllReduceOp(&result, og);
} else {
CreateReduceOp(&result, cur_device_id, og);
var_name_on_devices[cur_device_id].emplace(og);
bcast_var_name_set[cur_device_id].emplace(
og.substr(0, og.size() - strlen(kGradVarSuffix)));
cur_device_id = (cur_device_id + 1) % places_.size();
}
}
}
}
}
}
// Insert BCast Ops
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
}
}
/*
Dependency graph has been constructed. However, there are still data
harzaeds need to be handled.
......@@ -165,6 +205,60 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
return std::unique_ptr<SSAGraph>(graph);
}
int MultiDevSSAGraphBuilder::GetOpDeviceID(
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const {
if (use_nccl_allreduce_) {
return -1;
}
int var_dev_id = -1;
for (auto &var_name : op.InputArgumentNames()) {
if (var_dev_id != -1) break;
for (size_t i = 0; i < var_name_on_devices.size(); ++i) {
if (var_name_on_devices[i].count(var_name)) {
var_dev_id = static_cast<int>(i);
break;
}
}
}
return var_dev_id;
}
void MultiDevSSAGraphBuilder::CreateBroadcastOp(SSAGraph *result,
const std::string &p_name,
size_t dev_id) const {
#ifdef PADDLE_WITH_CUDA
auto *op_handle = new BroadcastOpHandle(local_scopes_, places_, nccl_ctxs_);
#else
auto *op_handle = new BroadcastOpHandle(local_scopes_, places_);
#endif
result->ops_.emplace_back(op_handle);
auto *in = result->vars_.at(dev_id).at(p_name).back().get();
op_handle->AddInput(in);
for (size_t i = 0; i < places_.size(); ++i) {
auto &vars = result->vars_.at(dev_id).at(p_name);
auto &p = places_[i];
auto *out_var = new VarHandle(vars.size(), i, p_name, p);
vars.emplace_back(out_var);
op_handle->AddOutput(out_var);
#ifndef ADDLE_WITH_CUDA
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
#endif
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOp(SSAGraph *result,
const OpDesc &op,
int dev_id) const {
result->ops_.emplace_back(
new ComputationOpHandle(op, local_scopes_[dev_id], places_[dev_id]));
CreateOpHandleIOs(result, op, dev_id);
}
OpDesc *MultiDevSSAGraphBuilder::GetSendOpDesc(
const ProgramDesc &program) const {
for (auto *op : program.Block(0).AllOps()) {
......@@ -174,7 +268,6 @@ OpDesc *MultiDevSSAGraphBuilder::GetSendOpDesc(
}
return nullptr;
}
void MultiDevSSAGraphBuilder::InsertNCCLAllReduceOp(
SSAGraph *result, const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
......@@ -247,6 +340,35 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(SSAGraph *result,
}
}
VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(
SSAGraph *result, int dst_dev_id, const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
result->ops_.emplace_back(
new ReduceOpHandle(local_scopes_, places_, nccl_ctxs_));
#else
result->ops_.emplace_back(new ReduceOpHandle(local_scopes_, places_));
#endif
auto *op_handle = result->ops_.back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &vars = result->vars_[i][og];
#ifndef PADDLE_WITH_CUDA
auto &p = places_[i];
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
#endif
PADDLE_ENFORCE(!vars.empty());
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
}
auto &vars = result->vars_[dst_dev_id][og];
auto var =
new VarHandle(vars.size() - 1, dst_dev_id, og, places_[dst_dev_id]);
vars.emplace_back(var);
op_handle->AddOutput(var);
return var;
}
void MultiDevSSAGraphBuilder::CreateSendOp(SSAGraph *result,
const OpDesc &op) const {
auto &p = places_[0];
......@@ -263,6 +385,7 @@ bool MultiDevSSAGraphBuilder::IsScaleLossOp(const OpDesc &op) const {
return op.OutputArgumentNames().size() == 1 &&
op.OutputArgumentNames()[0] == GradVarName(loss_var_name_);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ 5ff1ef36
......@@ -13,8 +13,8 @@
// limitations under the License.
#pragma once
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
......@@ -27,6 +27,7 @@ class NCCLContextMap;
namespace framework {
class Scope;
namespace details {
class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
public:
#ifdef PADDLE_WITH_CUDA
......@@ -34,14 +35,14 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
bool skip_scale_loss,
platform::NCCLContextMap *nccl_ctxs);
platform::NCCLContextMap *nccl_ctxs,
bool use_default_grad_scale, bool use_nccl_allreduce);
#else
MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
bool use_default_grad_scale);
bool use_default_grad_scale, bool use_nccl_allreduce);
#endif
std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
......@@ -59,6 +60,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nccl_ctxs_;
#endif
bool use_nccl_allreduce_;
bool use_default_grad_scale_;
bool IsScaleLossOp(const OpDesc &op) const;
......@@ -74,6 +76,10 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
size_t num_places) const;
void CreateScaleLossGradOp(SSAGraph *result) const;
VarHandle *CreateReduceOp(SSAGraph *result, int dst_dev_id,
const std::string &og) const;
void CreateComputationalOp(SSAGraph *result, const OpDesc &op,
int dev_id) const;
bool IsParameterGradientOnce(
const std::string &og,
......@@ -81,6 +87,13 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
void InsertNCCLAllReduceOp(SSAGraph *result, const std::string &og) const;
void CreateBroadcastOp(SSAGraph *result, const std::string &p_name,
size_t dev_id) const;
int GetOpDeviceID(
const std::vector<std::unordered_set<std::string>> &var_name_on_devices,
const OpDesc &op) const;
/**
* Get send op in the global block of program.
* nullptr if not found.
......
paddle/fluid/framework/details/reduce_op_handle.cc
浏览文件 @ 5ff1ef36
......@@ -22,6 +22,7 @@ namespace framework {
namespace details {
void ReduceOpHandle::RunImpl() {
if (places_.size() == 1) return;
// the input and output may have dummy var.
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
......@@ -52,19 +53,18 @@ void ReduceOpHandle::RunImpl() {
// Wait input done, this Wait is asynchronous operation
WaitInputVarGenerated(in_var_handles);
auto pre_place = in_0_handle->place_;
std::vector<platform::Place> in_places;
std::vector<platform::Place> in_places; // used to get dev_ctx
auto pre_in_tensor = VariableVisitor::GetMutableTensor(pre_in_var);
for (auto *in_handle : in_var_handles) {
auto in_p = in_handle->place_;
PADDLE_ENFORCE_EQ(in_p.which(), pre_place.which(),
"Places must be all on CPU or all on CUDA.");
in_places.emplace_back(in_p);
in_places.emplace_back(in_handle->place_);
auto in_var =
var_scopes.at(in_handle->scope_idx_)->FindVar(in_handle->name_);
PADDLE_ENFORCE_NOT_NULL(in_var);
auto in_tensor = VariableVisitor::GetMutableTensor(in_var);
PADDLE_ENFORCE_EQ(pre_in_tensor.place().which(), in_tensor.place().which(),
"Places must be all on CPU or all on GPU.");
PADDLE_ENFORCE_EQ(in_tensor.type(), pre_in_tensor.type(),
"The type of input is not consistent.");
}
......@@ -84,11 +84,11 @@ void ReduceOpHandle::RunImpl() {
std::vector<const LoDTensor *> lod_tensors =
GetInputValues<LoDTensor>(in_var_handles, var_scopes);
if (paddle::platform::is_cpu_place(pre_place)) {
if (paddle::platform::is_cpu_place(lod_tensors[0]->place())) {
ReduceLoDTensor func(lod_tensors,
out_var->GetMutable<framework::LoDTensor>());
VisitDataType(ToDataType(lod_tensors[0]->type()), func);
} else if (paddle::platform::is_gpu_place(pre_place)) {
} else if (paddle::platform::is_gpu_place(lod_tensors[0]->place())) {
#ifdef PADDLE_WITH_CUDA
auto pre_in = pre_in_var->Get<framework::LoDTensor>();
VariableVisitor::ShareDimsAndLoD(*pre_in_var, out_var);
......
paddle/fluid/framework/details/reduce_op_handle.h
浏览文件 @ 5ff1ef36
......@@ -55,7 +55,7 @@ struct ReduceOpHandle : public OpHandleBase {
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
bool IsMultiDeviceTransfer() override { return true; };
protected:
void RunImpl() override;
......
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @ 5ff1ef36
......@@ -47,6 +47,17 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(SSAGraph *graph) {
}
}
VarHandle *SSAGraphBuilder::GetLatestVarHandle(SSAGraph *graph,
const std::string &each_var_name,
size_t place_offset) {
auto &var_holders = graph->vars_[place_offset];
auto &var_holder = var_holders[each_var_name];
if (var_holder.empty()) {
return nullptr;
}
return var_holder.rbegin()->get();
}
VarHandle *SSAGraphBuilder::CreateOrGetLatestVarHandle(
SSAGraph *graph, const std::string &each_var_name,
const platform::Place &place, size_t place_offset) {
......
paddle/fluid/framework/details/ssa_graph_builder.h
浏览文件 @ 5ff1ef36
......@@ -48,6 +48,10 @@ class SSAGraphBuilder {
const platform::Place &place,
size_t place_offset);
static VarHandle *GetLatestVarHandle(SSAGraph *graph,
const std::string &each_var_name,
size_t place_offset);
// Add an output variable (each_var_name, place, place_offset) to op_handle,
// which belongs to graph
static void CreateOpOutput(SSAGraph *graph, OpHandleBase *op_handle,
......
paddle/fluid/framework/details/var_handle.h
浏览文件 @ 5ff1ef36
......@@ -66,6 +66,8 @@ struct VarHandle : public VarHandleBase {
return o.generated_op_ == generated_op_ && o.name_ == name_ &&
o.scope_idx_ == scope_idx_;
}
bool operator!=(const VarHandle& o) const { return !this->operator==(o); }
};
// Dummy Variable. It is used to represent dependencies between operators
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 5ff1ef36
......@@ -58,7 +58,7 @@ ParallelExecutor::ParallelExecutor(
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, const std::vector<Scope *> &local_scopes, bool allow_op_delay,
bool use_default_grad_scale)
bool use_default_grad_scale, bool use_nccl_allreduce)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
......@@ -93,11 +93,11 @@ ParallelExecutor::ParallelExecutor(
#ifdef PADDLE_WITH_CUDA
details::MultiDevSSAGraphBuilder builder(
member_->places_, loss_var_name, params, member_->local_scopes_,
use_default_grad_scale, member_->nccl_ctxs_.get());
member_->nccl_ctxs_.get(), use_default_grad_scale, use_nccl_allreduce);
#else
details::MultiDevSSAGraphBuilder builder(member_->places_, loss_var_name,
params, member_->local_scopes_,
use_default_grad_scale);
details::MultiDevSSAGraphBuilder builder(
member_->places_, loss_var_name, params, member_->local_scopes_,
use_default_grad_scale, use_nccl_allreduce);
#endif
auto graph = builder.Build(main_program);
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 5ff1ef36
......@@ -40,7 +40,8 @@ class ParallelExecutor {
const ProgramDesc& main_program,
const std::string& loss_var_name, Scope* scope,
const std::vector<Scope*>& local_scopes,
bool allow_op_delay, bool use_default_grad_scale);
bool allow_op_delay, bool use_default_grad_scale,
bool use_nccl_allreduce);
~ParallelExecutor();
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 5ff1ef36
......@@ -502,11 +502,12 @@ All parameter, weight, gradient are variables in Paddle.
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, std::vector<Scope *> &local_scopes,
bool allow_op_delay, bool use_default_grad_scale) {
bool allow_op_delay, bool use_default_grad_scale,
bool use_nccl_allreduce) {
new (&self) ParallelExecutor(
num_threads, use_event, places, params, bcast_vars,
main_program, loss_var_name, scope, local_scopes,
allow_op_delay, use_default_grad_scale);
allow_op_delay, use_default_grad_scale, use_nccl_allreduce);
})
.def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 5ff1ef36
......@@ -30,7 +30,8 @@ class ParallelExecutor(object):
num_threads=None,
allow_op_delay=False,
share_vars_from=None,
use_default_grad_scale=True):
use_default_grad_scale=True,
use_nccl_allreduce=True):
"""
ParallelExecutor can run program in parallel.
......@@ -43,9 +44,17 @@ class ParallelExecutor(object):
training.
allow_op_delay(bool, default False): Whether to delay and buffer
some operators together for scheduling or not, which may
improve performance in some cases, defalut False.
improve performance in some cases, default False.
share_vars_from(ParallelExecutor, default None): If provied,
it will share variables from the specified ParallelExecutor.
use_nccl_allreduce(bool, default True): Whether to use nccl_allreduce
or not, if set True, the communication between different
devices by nccl allReduce, which doesn't support updating sparse
parameter, if set False, the communication between different
devices by reduce_op and broadcast_op, which will distribute all
the parameter gradients evenly to different device and updates
the parameters, and finally broadcast to other device, this method
support updating sparse parameter. Default True.
use_default_grad_scale(bool, default True): If set True, a default
scale value equal to `1./device_count` would be multiplied to
gradients of each device and scaled gradients would be
......@@ -93,7 +102,7 @@ class ParallelExecutor(object):
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads = len(self._places)
num_threads = len(self._places) * 2
else:
num_threads = min(
len(self._places) * 2, multiprocessing.cpu_count())
......@@ -129,7 +138,9 @@ class ParallelExecutor(object):
scope,
local_scopes,
allow_op_delay,
use_default_grad_scale)
use_default_grad_scale,
use_nccl_allreduce)
self.scope = scope
def run(self, fetch_list, feed=None, feed_dict=None):
......
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @ 5ff1ef36
......@@ -205,7 +205,8 @@ class TestParallelExecutorBase(unittest.TestCase):
allow_op_delay=False,
feed_dict=None,
seed=None,
use_parallel_executor=True):
use_parallel_executor=True,
use_nccl_allreduce=True):
def run_executor(exe, feed, fetch_list, program=None):
if isinstance(exe, fluid.ParallelExecutor):
res = exe.run(fetch_list=fetch_list, feed=feed)
......@@ -234,7 +235,10 @@ class TestParallelExecutorBase(unittest.TestCase):
if use_parallel_executor:
exe = fluid.ParallelExecutor(
True, loss_name=loss.name, allow_op_delay=allow_op_delay)
True,
loss_name=loss.name,
allow_op_delay=allow_op_delay,
use_nccl_allreduce=use_nccl_allreduce)
else:
exe = fluid.Executor(place=place)
......@@ -280,17 +284,25 @@ class TestMNIST(TestParallelExecutorBase):
fluid.recordio_writer.convert_reader_to_recordio_file(
'./mnist.recordio', reader, feeder)
def test_simple_fc(self):
def check_simple_fc_convergence(self, use_nccl_allreduce=True):
self.check_network_convergence(simple_fc_net)
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
simple_fc_net, feed_dict={"image": img,
"label": label})
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_nccl_allreduce=use_nccl_allreduce)
def test_simple_fc_with_nccl_allreduce(self):
self.check_simple_fc_convergence(True)
def test_simple_fc_parallel_accuracy(self):
def test_simple_fc_with_reduce_op(self):
self.check_simple_fc_convergence(False)
def check_simple_fc_parallel_accuracy(self, use_nccl_allreduce=True):
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
single_first_loss, single_last_loss = self.check_network_convergence(
......@@ -304,20 +316,35 @@ class TestMNIST(TestParallelExecutorBase):
seed=1000,
feed_dict={"image": img,
"label": label},
use_parallel_executor=True)
use_parallel_executor=True,
use_nccl_allreduce=use_nccl_allreduce)
for p_f in parallel_first_loss:
self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6)
for p_l in parallel_last_loss:
self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6)
def test_batchnorm_fc(self):
def test_simple_fc_parallel_accuracy_with_nccl_allreduce(self):
self.check_simple_fc_parallel_accuracy(True)
def test_simple_fc_parallel_accuracy_with_reduce_op(self):
self.check_simple_fc_parallel_accuracy(False)
def check_batchnorm_fc_convergence(self, use_nccl_allreduce):
self.check_network_convergence(fc_with_batchnorm)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
fc_with_batchnorm, feed_dict={"image": img,
"label": label})
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_nccl_allreduce=use_nccl_allreduce)
def test_batchnorm_fc_with_nccl_allreduce(self):
self.check_batchnorm_fc_convergence(True)
def test_batchnorm_fc_with_reduce_op(self):
self.check_batchnorm_fc_convergence(False)
class TestResnet(TestParallelExecutorBase):
......@@ -339,14 +366,21 @@ class TestResnet(TestParallelExecutorBase):
# fluid.recordio_writer.convert_reader_to_recordio_file(
# "./flowers.recordio", reader, feeder, compressor=fluid.core.RecordIOWriter.Compressor.NoCompress)
def test_resnet(self):
def check_resnet_convergence(self, use_nccl_allreduce):
import functools
batch_size = 2
self.check_network_convergence(
functools.partial(
SE_ResNeXt50Small, batch_size=batch_size),
iter=20,
batch_size=batch_size)
batch_size=batch_size,
use_nccl_allreduce=use_nccl_allreduce)
def test_resnet_with_nccl_allreduce(self):
self.check_resnet_convergence(True)
def test_resnet_with_reduce_op(self):
self.check_resnet_convergence(False)
class ModelHyperParams(object):
......@@ -510,7 +544,7 @@ class TestTransformer(TestParallelExecutorBase):
class ParallelExecutorTestingDuringTraining(unittest.TestCase):
def test_parallel_testing(self):
def check_network_convergence(self, use_nccl_allreduce):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -531,12 +565,16 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
feed_dict = {'image': image, 'label': label}
train_exe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name, main_program=main)
use_cuda=True,
loss_name=loss.name,
main_program=main,
use_nccl_allreduce=use_nccl_allreduce)
test_exe = fluid.ParallelExecutor(
use_cuda=True,
main_program=test_program,
share_vars_from=train_exe)
share_vars_from=train_exe,
use_nccl_allreduce=use_nccl_allreduce)
for i in xrange(5):
test_loss, = test_exe.run([loss.name], feed=feed_dict)
......@@ -550,6 +588,12 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
"Train loss: " + str(train_loss) + "\n Test loss:" +
str(test_loss))
def test_parallel_testing_with_nccl_allreduce(self):
self.check_network_convergence(use_nccl_allreduce=True)
def test_parallel_testing_with_reduce_op(self):
self.check_network_convergence(use_nccl_allreduce=False)
import paddle.dataset.conll05 as conll05
import paddle.fluid as fluid
......@@ -568,21 +612,26 @@ embedding_name = 'emb'
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
**ignored):
is_sparse, use_nccl_allreduce, **ignored):
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
is_sparse=is_sparse,
size=[pred_dict_len, word_dim],
dtype='float32',
param_attr='vemb')
mark_embedding = fluid.layers.embedding(
input=mark, size=[mark_dict_len, mark_dim], dtype='float32')
input=mark,
is_sparse=is_sparse,
size=[mark_dict_len, mark_dim],
dtype='float32')
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
is_sparse=is_sparse,
input=x,
param_attr=fluid.ParamAttr(
name=embedding_name, trainable=False)) for x in word_input
......@@ -632,7 +681,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
class TestCRFModel(unittest.TestCase):
def test_all(self):
def check_network_convergence(self, is_sparse, use_nccl_allreduce):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -652,6 +701,7 @@ class TestCRFModel(unittest.TestCase):
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
feature_out = db_lstm(**locals())
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1)
......@@ -679,7 +729,10 @@ class TestCRFModel(unittest.TestCase):
exe = fluid.Executor(place)
exe.run(startup)
pe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
pe = fluid.ParallelExecutor(
use_cuda=True,
loss_name=avg_cost.name,
use_nccl_allreduce=use_nccl_allreduce)
feeder = fluid.DataFeeder(
feed_list=[
......@@ -694,3 +747,13 @@ class TestCRFModel(unittest.TestCase):
print map(numpy.array,
pe.run(feed=feeder.feed(cur_batch),
fetch_list=[avg_cost.name]))[0]
def test_update_sparse_parameter(self):
self.check_network_convergence(is_sparse=True, use_nccl_allreduce=False)
def test_update_dense_parameter_with_nccl_allreduce(self):
self.check_network_convergence(is_sparse=False, use_nccl_allreduce=True)
def test_update_dense_parameter_with_reduce_op(self):
self.check_network_convergence(
is_sparse=False, use_nccl_allreduce=False)
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