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P
Paddle
提交 3dd01823 编写于 作者: Y Yu Yang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/clean_matmul

上级 c6a6d87f dce0732d
隐藏空白更改
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Showing with 1327 addition and 431 deletion
benchmark/cluster/vgg16/vgg16_fluid.py
浏览文件 @ 3dd01823
......@@ -80,6 +80,8 @@ parser.add_argument(
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument(
"--profile", action='store_true', help="If set, profile a few steps.")
# Flags for defining the tf.train.Server
parser.add_argument(
......@@ -183,8 +185,8 @@ def main():
start_time = time.time()
num_samples = 0
train_pass_acc.reset()
for batch_id, data in enumerate(train_reader()):
ts = time.time()
def run_step(batch_id, data):
img_data = np.array(
map(lambda x: x[0].reshape(data_shape), data)).astype(
"float32")
......@@ -196,14 +198,28 @@ def main():
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost, batch_acc, batch_size])
return loss, acc, b_size
if args.profile and args.task_index == 0:
# warmup.
for batch_id, data in enumerate(train_reader()):
if batch_id > 5: break
run_step(batch_id, data)
with profiler.profiler('All', 'total', '/tmp/profile_vgg'):
for batch_id, data in enumerate(train_reader()):
if batch_id > 5: break
run_step(batch_id, data)
for batch_id, data in enumerate(train_reader()):
ts = time.time()
loss, acc, b_size = run_step(batch_id, data)
iters += 1
num_samples += len(data)
train_pass_acc.add(value=acc, weight=b_size)
print(
"Task:%d Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, "
"Speed = %.2f img/s " % (args.task_index, pass_id, iters,
loss, acc,
len(data) / (time.time() - ts))
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, "
"Speed = %.2f img/s" % (pass_id, iters, loss, acc,
len(data) / (time.time() - ts))
) # The accuracy is the accumulation of batches, but not the current batch.
pass_elapsed = time.time() - start_time
......
doc/fluid/design/dist_train/distributed_traing_review.md
浏览文件 @ 3dd01823
......@@ -42,7 +42,3 @@ Codistillation is a technique that tries to scale the training further. A few tr
[3] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation.
[4] LARGE SCALE DISTRIBUTED NEURAL NETWORK TRAINING THROUGH ONLINE DISTILLATION
paddle/fluid/framework/block_desc.cc
浏览文件 @ 3dd01823
......@@ -143,7 +143,7 @@ OpDesc *BlockDesc::InsertOp(size_t index) {
}
void BlockDesc::RemoveOp(size_t s, size_t e) {
if (ops_.begin() + s == ops_.end() || ops_.begin() + e == ops_.end()) {
if (ops_.begin() + s >= ops_.end() || ops_.begin() + e > ops_.end()) {
return;
}
need_update_ = true;
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 3dd01823
......@@ -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 @ 3dd01823
......@@ -19,14 +19,12 @@
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() {
// the input and output may have dummy var.
VarHandle *in_var_handle;
if (places_.size() == 1) return;
// The input and output may have dummy vars.
VarHandle *in_var_handle;
{
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
PADDLE_ENFORCE_EQ(in_var_handles.size(), 1,
......@@ -55,27 +53,97 @@ void BroadcastOpHandle::RunImpl() {
Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
for (auto *out : out_var_handles) {
if (*out == *in_var_handle) {
// NOTE: The tensors' Place of input and output must be all on GPU or all on
// CPU.
for (auto *out_var_handle : out_var_handles) {
if (out_var_handle->IsTheSameVar(*in_var_handle)) {
continue;
}
auto &out_p = out->place_;
auto *out_var = var_scopes.at(out->scope_idx_)->FindVar(out->name_);
auto t_out_p = out_var_handle->place_;
auto *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->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.");
if (platform::is_gpu_place(in_tensor.place())) {
PADDLE_ENFORCE(platform::is_gpu_place(t_out_p),
"Places of input and output must be all on GPU.");
} else {
t_out_p = platform::CPUPlace();
}
VariableVisitor::ShareDimsAndLoD(*in_var, out_var);
VariableVisitor::GetMutableTensor(out_var).mutable_data(out_p,
VariableVisitor::GetMutableTensor(out_var).mutable_data(t_out_p,
in_tensor.type());
}
if (platform::is_cpu_place(in_tensor.place())) {
for (auto *out_var_handle : out_var_handles) {
if (out_var_handle->IsTheSameVar(*in_var_handle)) {
continue;
}
auto &out_p = out_var_handle->place_;
auto *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
RunAndRecordEvent(out_p, [in_tensor, out_var] {
paddle::framework::TensorCopy(
in_tensor, platform::CPUPlace(),
&VariableVisitor::GetMutableTensor(out_var));
});
}
} else {
#ifdef PADDLE_WITH_CUDA
VarHandle *out_handle = nullptr;
int root_id = boost::get<platform::CUDAPlace>(in_tensor.place()).device;
std::vector<std::function<void()>> broadcast_calls;
for (auto out_var_handle : out_var_handles) {
Variable *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
int dst_id =
boost::get<platform::CUDAPlace>(out_var_handle->place_).device;
auto &nccl_ctx = nccl_ctxs_->at(dst_id);
void *send_recv_buffer = nullptr;
if (root_id == dst_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());
size_t numel = static_cast<size_t>(in_tensor.numel());
broadcast_calls.emplace_back(
[send_recv_buffer, numel, type, root_id, &nccl_ctx] {
PADDLE_ENFORCE(platform::dynload::ncclBcast(
send_recv_buffer, numel, static_cast<ncclDataType_t>(type),
root_id, nccl_ctx.comm_, nccl_ctx.stream()));
});
}
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->IsTheSameVar(*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 enabled.");
#endif
}
}
......
paddle/fluid/framework/details/broadcast_op_handle.h
浏览文件 @ 3dd01823
......@@ -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 @ 3dd01823
......@@ -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 @ 3dd01823
......@@ -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_);
......@@ -35,7 +36,6 @@ void GatherOpHandle::RunImpl() {
VarHandle *out_var_handle;
{
auto out_var_handles = DynamicCast<VarHandle>(outputs_);
PADDLE_ENFORCE_EQ(out_var_handles.size(), 1,
"The number of output should be one.");
out_var_handle = out_var_handles.front();
......@@ -50,68 +50,62 @@ void GatherOpHandle::RunImpl() {
auto pre_in_var =
var_scopes.at(in_0_handle->scope_idx_)->FindVar(in_0_handle->name_);
PADDLE_ENFORCE_NOT_NULL(pre_in_var);
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);
auto &pre_in_value = pre_in_var->Get<framework::SelectedRows>();
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>();
// gather the inputs
// 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);
VariableVisitor::EnforceShapeAndDTypeEQ(*in_var, *pre_in_var);
PADDLE_ENFORCE_EQ(in_sr.value().type(), pre_in.value().type(),
"The type of input is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(),
"The height of inputs is not consistent.");
PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(),
"The dims of inputs is not consistent.");
auto &in_sr_value = in_var->Get<framework::SelectedRows>();
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_);
// NOTE: The Places of all input tensor must be all on CPU or all on GPU.
platform::Place t_out_p = out_var_handle->place_;
if (platform::is_gpu_place(pre_in_value.place())) {
PADDLE_ENFORCE(platform::is_gpu_place(t_out_p),
"Places of input and output must be all on GPU.");
} else {
t_out_p = platform::CPUPlace();
}
auto out = out_var->GetMutable<framework::SelectedRows>();
out->set_height(pre_in.height());
out->set_rows(out_rows);
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).mutable_data(
t_out_p, pre_in_value.value().type());
Tensor *out_tensor = out_value->mutable_value();
// copy
auto dev_ctx = dev_ctxes_[out_place];
RunAndRecordEvent(out_place, [in_tensors, out_tensor, dev_ctx, out_place] {
auto dev_ctx = dev_ctxes_[out_var_handle->place_];
RunAndRecordEvent(out_var_handle->place_, [in_tensors, out_tensor, &dev_ctx,
t_out_p] {
int s = 0, e = 0;
for (size_t j = 0; j < in_tensors.size(); ++j) {
e += in_tensors[j].dims()[0];
auto sub_out = out_tensor->Slice(s, e);
paddle::framework::TensorCopy(in_tensors[j], out_place, *(dev_ctx),
&sub_out);
paddle::framework::TensorCopy(in_tensors[j], t_out_p, *dev_ctx, &sub_out);
s = e;
}
});
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 3dd01823
......@@ -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"
......@@ -34,8 +36,8 @@ 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)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
......@@ -105,6 +107,11 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
const ProgramDesc &program) const {
std::unordered_map<std::string, proto::VarType::Type> var_types;
for (auto *var : program.Block(0).AllVars()) {
var_types[var->Name()] = var->GetType();
}
auto graph = new SSAGraph();
SSAGraph &result = *graph;
std::unordered_set<std::string> og_has_been_broadcast;
......@@ -133,12 +140,17 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
is_forwarding = false;
} else {
CreateComputationalOps(&result, *op, places_.size());
if (!is_forwarding) {
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 (IsSparseGradient(var_types, og)) {
CreateReduceOp(&result, og, 0);
CreateBroadcastOp(&result, og, 0);
} else {
InsertNCCLAllReduceOp(&result, og);
}
}
}
}
......@@ -165,6 +177,50 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
return std::unique_ptr<SSAGraph>(graph);
}
bool MultiDevSSAGraphBuilder::IsSparseGradient(
const std::unordered_map<std::string, proto::VarType::Type> &var_types,
const std::string &og) const {
PADDLE_ENFORCE(var_types.count(og) != 0);
if (var_types.at(og) == proto::VarType::SELECTED_ROWS) {
return true;
}
return false;
}
void MultiDevSSAGraphBuilder::CreateBroadcastOp(SSAGraph *result,
const std::string &p_name,
size_t src_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(src_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(i).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 +230,6 @@ OpDesc *MultiDevSSAGraphBuilder::GetSendOpDesc(
}
return nullptr;
}
void MultiDevSSAGraphBuilder::InsertNCCLAllReduceOp(
SSAGraph *result, const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
......@@ -247,6 +302,36 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(SSAGraph *result,
}
}
VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(SSAGraph *result,
const std::string &og,
int dst_dev_id) 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 +348,7 @@ 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 @ 3dd01823
......@@ -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
......@@ -34,8 +35,8 @@ 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);
#else
MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
const std::string &loss_var_name,
......@@ -74,6 +75,10 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
size_t num_places) const;
void CreateScaleLossGradOp(SSAGraph *result) const;
VarHandle *CreateReduceOp(SSAGraph *result, const std::string &og,
int dst_dev_id) const;
void CreateComputationalOp(SSAGraph *result, const OpDesc &op,
int dev_id) const;
bool IsParameterGradientOnce(
const std::string &og,
......@@ -81,11 +86,18 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
void InsertNCCLAllReduceOp(SSAGraph *result, const std::string &og) const;
void CreateBroadcastOp(SSAGraph *result, const std::string &p_name,
size_t src_dev_id) const;
/**
* Get send op in the global block of program.
* nullptr if not found.
*/
OpDesc *GetSendOpDesc(const ProgramDesc &program) const;
bool IsSparseGradient(
const std::unordered_map<std::string, proto::VarType::Type> &var_types,
const std::string &og) const;
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/reduce_op_handle.cc
浏览文件 @ 3dd01823
......@@ -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_);
......@@ -51,44 +52,48 @@ 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;
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);
// NOTE: The Places of all input tensor must be all on CPU or all on GPU.
std::vector<platform::Place> in_places; // used to get dev_ctx
for (auto *in_handle : in_var_handles) {
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(in_tensor.type(), pre_in_tensor.type(),
"The type of input is not consistent.");
VariableVisitor::EnforceShapeAndDTypeEQ(*pre_in_var, *in_var);
}
auto out_var =
var_scopes.at(out_var_handle->scope_idx_)->FindVar(out_var_handle->name_);
PADDLE_ENFORCE_NOT_NULL(out_var);
// NOTE: The tensors' Place of input and output must be all on GPU or all on
// CPU.
auto in_p = VariableVisitor::GetMutableTensor(pre_in_var).place();
platform::Place t_out_p;
if (platform::is_gpu_place(in_p)) {
PADDLE_ENFORCE(platform::is_gpu_place(out_var_handle->place_),
"Places of input and output must be all on GPU.");
t_out_p = out_var_handle->place_;
} else {
t_out_p = platform::CPUPlace();
}
if (pre_in_var->IsType<framework::SelectedRows>()) {
std::vector<const SelectedRows *> in_selected_rows =
GetInputValues<SelectedRows>(in_var_handles, var_scopes);
GatherSelectedRows(in_selected_rows, in_places, dev_ctxes_,
out_var_handle->place_,
GatherSelectedRows(in_selected_rows, in_places, dev_ctxes_, t_out_p,
out_var->GetMutable<framework::SelectedRows>());
} else {
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);
......@@ -96,7 +101,7 @@ void ReduceOpHandle::RunImpl() {
out_var_handle->place_, pre_in.type());
auto out_p = out_var_handle->place_;
int root = boost::get<platform::CUDAPlace>(out_p).device;
int root_id = boost::get<platform::CUDAPlace>(out_p).device;
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < var_scopes.size(); ++i) {
auto &p = in_places[i];
......@@ -104,23 +109,23 @@ void ReduceOpHandle::RunImpl() {
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
void *buffer = const_cast<void *>(lod_tensor.data<void>());
void *recvbuffer = nullptr;
if (root == dev_id) {
if (root_id == dev_id) {
recvbuffer =
out_var->GetMutable<framework::LoDTensor>()->mutable_data(
out_var_handle->place_);
}
int type = platform::ToNCCLDataType(lod_tensor.type());
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclReduce(
buffer, recvbuffer, static_cast<size_t>(lod_tensor.numel()),
static_cast<ncclDataType_t>(type), ncclSum, root, comm, stream));
});
size_t numel = static_cast<size_t>(lod_tensor.numel());
all_reduce_calls.emplace_back(
[buffer, recvbuffer, type, numel, root_id, &nccl_ctx] {
PADDLE_ENFORCE(platform::dynload::ncclReduce(
buffer, recvbuffer, numel, static_cast<ncclDataType_t>(type),
ncclSum, root_id, nccl_ctx.comm_, nccl_ctx.stream()));
});
}
this->RunAndRecordEvent([&] {
......@@ -130,7 +135,7 @@ void ReduceOpHandle::RunImpl() {
}
});
#else
PADDLE_THROW("CUDA is not support.");
PADDLE_THROW("CUDA is not enabled.");
#endif
} else {
PADDLE_THROW("Place should be CPUPlace or CUDAPlace.");
......
paddle/fluid/framework/details/reduce_op_handle.h
浏览文件 @ 3dd01823
......@@ -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/var_handle.h
浏览文件 @ 3dd01823
......@@ -62,7 +62,7 @@ struct VarHandle : public VarHandleBase {
std::string name_;
platform::Place place_;
bool operator==(const VarHandle& o) const {
bool IsTheSameVar(const VarHandle& o) const {
return o.generated_op_ == generated_op_ && o.name_ == name_ &&
o.scope_idx_ == scope_idx_;
}
......
paddle/fluid/framework/details/variable_visitor.cc
浏览文件 @ 3dd01823
......@@ -88,6 +88,52 @@ void VariableVisitor::ShareDimsAndLoD(const Variable& src, Variable* trg) {
VisitVariable(src, &visitor);
}
struct EnforceShapeAndDTypeEQVisitor {
const Variable* trg_;
void operator()(const LoDTensor& src) {
auto& tensor = trg_->Get<LoDTensor>();
PADDLE_ENFORCE_EQ(
src.place().which(), tensor.place().which(),
"The Places of the two Variable must be all on CPU or all on GPU.");
PADDLE_ENFORCE_EQ(src.type(), tensor.type(),
"The dtype of the two Variable is not equal.");
PADDLE_ENFORCE_EQ(src.dims(), tensor.dims(),
"The dims of the two Variable is not equal.");
PADDLE_ENFORCE_EQ(src.lod(), tensor.lod(),
"The lod of the two Variable is not equal.");
PADDLE_ENFORCE_EQ(src.layout(), tensor.layout(),
"The layout of the two Variable's tensor is not equal.");
}
void operator()(const SelectedRows& src) {
auto& selected_rows = trg_->Get<SelectedRows>();
PADDLE_ENFORCE_EQ(
src.place().which(), selected_rows.place().which(),
"The Places of the two Variable must be all on CPU or all on GPU.");
PADDLE_ENFORCE_EQ(src.value().type(), selected_rows.value().type(),
"The dtype of the two Variable is not equal.");
PADDLE_ENFORCE_EQ(src.value().layout(), selected_rows.value().layout(),
"The layout of the two Variable's tensor is not equal.");
PADDLE_ENFORCE_EQ(src.height(), selected_rows.height(),
"The height of the two Variable is not equal.");
PADDLE_ENFORCE_EQ(src.GetCompleteDims(), selected_rows.GetCompleteDims(),
"The dims of the two Variable is not equal.");
}
template <typename T>
void operator()(const T&) {
PADDLE_ENFORCE("EnforceShapeAndDTypeEQ is not supported by type %s",
typeid(T).name());
}
};
void VariableVisitor::EnforceShapeAndDTypeEQ(const Variable& var1,
const Variable& var2) {
EnforceShapeAndDTypeEQVisitor visitor{&var1};
VisitVariable(var2, &visitor);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/variable_visitor.h
浏览文件 @ 3dd01823
......@@ -26,6 +26,9 @@ class VariableVisitor {
static Tensor &GetMutableTensor(Variable *var);
static void ShareDimsAndLoD(const Variable &src, Variable *trg);
static void EnforceShapeAndDTypeEQ(const Variable &var1,
const Variable &var2);
};
} // namespace details
......
paddle/fluid/framework/executor.cc
浏览文件 @ 3dd01823
......@@ -348,8 +348,12 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
}
}
platform::DeviceContextPool::Instance().Get(place_)->Wait();
if (create_vars && create_local_scope) {
scope->DeleteScope(local_scope);
} else {
// Delete the local scopes created in operators.
scope->DropKids();
}
if (FLAGS_benchmark) {
VLOG(2) << "-------------------------------------------------------";
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 3dd01823
......@@ -93,7 +93,7 @@ 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);
#else
details::MultiDevSSAGraphBuilder builder(member_->places_, loss_var_name,
params, member_->local_scopes_,
......
paddle/fluid/inference/tensorrt/CMakeLists.txt
浏览文件 @ 3dd01823
nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
nv_test(test_tensorrt_engine SRCS test_engine.cc engine.cc DEPS dynload_cuda)
nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
set(ENGINE_FILE ${CMAKE_CURRENT_SOURCE_DIR}/engine.cc)
add_subdirectory(convert)
paddle/fluid/inference/tensorrt/io_converter.cc 0 → 100644
浏览文件 @ 3dd01823
/* Copyright (c) 2018 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/inference/tensorrt/io_converter.h"
#include <cuda.h>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace inference {
namespace tensorrt {
using platform::is_gpu_place;
using platform::is_cpu_place;
class DefaultInputConverter : public EngineInputConverter {
public:
DefaultInputConverter() {}
// NOTE out is GPU memory.
virtual void operator()(const LoDTensor& in, void* out,
size_t max_size) override {
PADDLE_ENFORCE(out != nullptr);
PADDLE_ENFORCE_LE(in.memory_size(), max_size);
const auto& place = in.place();
if (is_cpu_place(place)) {
PADDLE_ENFORCE(stream_ != nullptr);
PADDLE_ENFORCE_EQ(0,
cudaMemcpyAsync(out, in.data<float>(), in.memory_size(),
cudaMemcpyHostToDevice, *stream_));
} else if (is_gpu_place(place)) {
PADDLE_ENFORCE_EQ(0,
cudaMemcpyAsync(out, in.data<float>(), in.memory_size(),
cudaMemcpyHostToHost, *stream_));
} else {
PADDLE_THROW("Unknown device for converter");
}
cudaStreamSynchronize(*stream_);
}
};
REGISTER_TENSORRT_INPUT_CONVERTER(mul, DefaultInputConverter);
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/io_converter.h 0 → 100644
浏览文件 @ 3dd01823
/* Copyright (c) 2018 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 <unordered_map>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/utils/singleton.h"
namespace paddle {
namespace inference {
namespace tensorrt {
using framework::LoDTensor;
/*
* Convert Input from Fluid to an Engine.
* TensorRT's ITensor follows row major, NCHW. Fluid is also row major, so in
* most cases just need to copy the data.
*/
class EngineInputConverter {
public:
EngineInputConverter() {}
virtual void operator()(const LoDTensor& in, void* out, size_t max_size) {}
void SetStream(cudaStream_t* stream) { stream_ = stream; }
static void Run(const std::string& in_op_type, const LoDTensor& in, void* out,
size_t max_size, cudaStream_t* stream) {
PADDLE_ENFORCE(stream != nullptr);
auto* converter = Registry<EngineInputConverter>::Lookup(in_op_type);
PADDLE_ENFORCE_NOT_NULL(converter);
converter->SetStream(stream);
(*converter)(in, out, max_size);
}
virtual ~EngineInputConverter() {}
protected:
cudaStream_t* stream_{nullptr};
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
#define REGISTER_TENSORRT_INPUT_CONVERTER(in_op_type__, Converter__) \
struct trt_input_##in_op_type__##_converter { \
trt_input_##in_op_type__##_converter() { \
::paddle::inference::Registry<EngineInputConverter>::Register< \
Converter__>(#in_op_type__); \
} \
}; \
trt_input_##in_op_type__##_converter trt_input_##in_op_type__##_converter__;
paddle/fluid/inference/tensorrt/test_io_converter.cc 0 → 100644
浏览文件 @ 3dd01823
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/tensorrt/io_converter.h"
#include <gtest/gtest.h>
namespace paddle {
namespace inference {
namespace tensorrt {
class EngineInputConverterTester : public ::testing::Test {
public:
void SetUp() override { tensor.Resize({10, 10}); }
framework::LoDTensor tensor;
};
TEST_F(EngineInputConverterTester, DefaultCPU) {
void* buffer;
tensor.mutable_data<float>(platform::CPUPlace());
ASSERT_EQ(cudaMalloc(&buffer, tensor.memory_size()), 0);
cudaStream_t stream;
EngineInputConverter::Run("mul", tensor, buffer, tensor.memory_size(),
&stream);
}
TEST_F(EngineInputConverterTester, DefaultGPU) {
void* buffer;
tensor.mutable_data<float>(platform::CUDAPlace());
ASSERT_EQ(cudaMalloc(&buffer, tensor.memory_size()), 0);
cudaStream_t stream;
EngineInputConverter::Run("mul", tensor, buffer, tensor.memory_size(),
&stream);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @ 3dd01823
......@@ -24,6 +24,11 @@ function(inference_test TARGET_NAME)
endforeach()
endfunction(inference_test)
####################
# Inference tests here depend on fluid/tests/book. If users want to run
# individual test with ctest, they need to run tests in fluid/tests/book
# first to generate saved model.
####################
# This unittest is buggy!
#inference_test(fit_a_line)
inference_test(image_classification ARGS vgg resnet)
......
paddle/fluid/inference/utils/singleton.h 0 → 100644
浏览文件 @ 3dd01823
/* Copyright (c) 2018 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 <unordered_map>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace inference {
// NOTE not thread-safe.
template <typename T>
struct Singleton {
static T& Global() {
static T* x = new T;
return *x;
}
Singleton() = delete;
Singleton& operator=(const Singleton&) = delete;
};
/*
* An registor for any type.
* NOTE not thread-safe.
*/
template <typename ItemParent>
struct Registry {
static Registry& Global() {
static auto* x = new Registry<ItemParent>;
return *x;
}
template <typename ItemChild>
static void Register(const std::string& name) {
PADDLE_ENFORCE_EQ(items_.count(name), 0);
items_[name] = new ItemChild;
}
static ItemParent* Lookup(const std::string& name) {
auto it = items_.find(name);
if (it == items_.end()) return nullptr;
return it->second;
}
~Registry() {
for (auto& item : items_) {
delete item.second;
}
}
private:
Registry() = default;
static std::unordered_map<std::string, ItemParent*> items_;
};
template <typename ItemParent>
std::unordered_map<std::string, ItemParent*> Registry<ItemParent>::items_;
} // namespace inference
} // namespace paddle
paddle/fluid/operators/adadelta_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class AdadeltaOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -55,6 +56,12 @@ class AdadeltaOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("AvgSquaredGradOut", param_dim);
ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/adagrad_op.cc
浏览文件 @ 3dd01823
......@@ -23,6 +23,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class AdagradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -56,6 +57,12 @@ class AdagradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dims);
ctx->SetOutputDim("MomentOut", param_dims);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class AdamOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -69,6 +70,12 @@ class AdamOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("Moment1Out", param_dims);
ctx->SetOutputDim("Moment2Out", param_dims);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/adamax_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class AdamaxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -63,6 +64,12 @@ class AdamaxOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("MomentOut", param_dims);
ctx->SetOutputDim("InfNormOut", param_dims);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 3dd01823
......@@ -366,7 +366,8 @@ REGISTER_OP_KERNEL(conv2d_grad, CUDNN, plat::CUDAPlace,
REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvOpKernel<float>,
paddle::operators::CUDNNConvOpKernel<double>);
paddle::operators::CUDNNConvOpKernel<double>,
paddle::operators::CUDNNConvOpKernel<plat::float16>);
REGISTER_OP_KERNEL(conv3d_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvGradOpKernel<float>,
paddle::operators::CUDNNConvGradOpKernel<double>);
paddle/fluid/operators/decayed_adagrad_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class DecayedAdagradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -51,6 +52,12 @@ class DecayedAdagradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dims);
ctx->SetOutputDim("MomentOut", param_dims);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class DecayedAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/detail/send_recv.proto
浏览文件 @ 3dd01823
......@@ -69,6 +69,10 @@ message VariableMessage {
bytes rows = 9;
// Look up table block execution output variable name.
string out_varname = 10;
// If true, the ps server will start profiling, the ps
// server stops profiling and generates a profile to /tmp/profile_ps_*
// when profile switches from true to false.
bool profile = 11;
}
message VoidMessage {}
paddle/fluid/operators/detail/sendrecvop_utils.cc
浏览文件 @ 3dd01823
......@@ -23,6 +23,7 @@ limitations under the License. */
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
#include "paddle/fluid/operators/detail/proto_encoder_helper.h"
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
......@@ -45,6 +46,13 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
void* payload = nullptr;
size_t payload_size;
ProtoEncodeHelper e(static_cast<char*>(buf), 1024);
// Note: normally the profiler is enabled in 1 trainer, hence only
// 1 trainer returns true for ShouldSendProfileState(). It tells PS
// servers the trainer's profiling state so that PS can follow the
// trainer.
if (platform::ShouldSendProfileState()) {
e.WriteBool(VarMsg::kProfileFieldNumber, platform::IsProfileEnabled());
}
e.WriteString(VarMsg::kVarnameFieldNumber, name);
if (var->IsType<framework::LoDTensor>()) {
e.WriteUint64(VarMsg::kTypeFieldNumber, 0);
......
paddle/fluid/operators/detail/variable_response.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
......@@ -427,7 +428,26 @@ int VariableResponse::Parse(Source* source) {
meta_.set_out_varname(temp);
break;
}
case sendrecv::VariableMessage::kProfileFieldNumber: {
bool profiling;
if (!input.ReadRaw(reinterpret_cast<void*>(&profiling), 1)) {
return tag;
}
meta_.set_profile(profiling);
int64_t listener_id = platform::ListenerId();
if (listener_id <= 0) {
break;
}
if (profiling && !platform::IsProfileEnabled()) {
platform::EnableProfiler(platform::ProfilerState::kCPU);
} else if (!profiling && platform::IsProfileEnabled()) {
// TODO(panyx0718): Should we allow to customize file dir.
platform::DisableProfiler(
platform::EventSortingKey::kDefault,
string::Sprintf("/tmp/profile_ps_%lld", listener_id));
}
break;
}
default: {
// Unknown tag, return unknown error.
return -1;
......
paddle/fluid/operators/ftrl_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class FTRLOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -53,6 +54,12 @@ class FTRLOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("SquaredAccumOut", param_dim);
ctx->SetOutputDim("LinearAccumOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class FTRLOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 3dd01823
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
......@@ -294,6 +295,8 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
void ListenAndServOp::RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const {
// Mark this as PS that it should decide profiling by listening from trainer.
platform::SetProfileListener();
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
framework::Scope &recv_scope = scope.NewScope();
......@@ -328,9 +331,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
rpc_service_->WaitServerReady();
// Write to a file of server selected port for python use.
std::string file_path =
string::Sprintf("/tmp/paddle.%d.selected_port",
static_cast<int>(::getpid()));
std::string file_path = string::Sprintf("/tmp/paddle.%d.selected_port",
static_cast<int>(::getpid()));
SavePort(file_path);
if (sync_mode) {
RunSyncLoop(&executor, program, &recv_scope, prefetch_block);
......
paddle/fluid/operators/lod_reset_op.h
浏览文件 @ 3dd01823
......@@ -46,8 +46,7 @@ class LoDResetKernel : public framework::OpKernel<T> {
auto* lod = lod_t->data<int>();
if (platform::is_gpu_place(ctx.GetPlace())) {
framework::Tensor lod_cpu;
framework::TensorCopy(*lod_t, platform::CPUPlace(),
ctx.device_context(), &lod_cpu);
framework::TensorCopySync(*lod_t, platform::CPUPlace(), &lod_cpu);
lod = lod_cpu.data<int>();
}
level0 = std::vector<int>(lod, lod + lod_t->numel());
......
paddle/fluid/operators/lookup_sparse_table_op.cc
浏览文件 @ 3dd01823
......@@ -62,7 +62,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
auto w_t = w_var->GetMutable<framework::SelectedRows>();
std::vector<int64_t> keys;
keys.resize(ids_t.numel());
for (size_t i = 0; i < ids_t.numel(); ++i) {
for (int64_t i = 0; i < ids_t.numel(); ++i) {
keys[i] = ids_t.data<int64_t>()[i];
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 3dd01823
......@@ -41,7 +41,7 @@ math_library(depthwise_conv)
math_library(gru_compute DEPS activation_functions math_function)
math_library(im2col)
math_library(lstm_compute DEPS activation_functions)
cc_library(blas SRCS blas.cc DEPS cblas framework_proto)
cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
math_library(math_function DEPS blas)
math_library(maxouting)
math_library(pooling)
......
paddle/fluid/operators/math/concat_test.cc
浏览文件 @ 3dd01823
......@@ -69,8 +69,8 @@ void testConcat() {
}
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(input_a_cpu, Place(), *context, &input_a);
paddle::framework::TensorCopy(input_b_cpu, Place(), *context, &input_b);
paddle::framework::TensorCopySync(input_a_cpu, Place(), &input_a);
paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b);
}
std::vector<paddle::framework::Tensor> input;
......@@ -86,8 +86,8 @@ void testConcat() {
int* out_ptr;
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(out, paddle::platform::CPUPlace(), *context,
&out_cpu);
paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(),
&out_cpu);
out_ptr = out_cpu.data<int>();
} else {
out_ptr = out.data<int>();
......@@ -142,8 +142,8 @@ void testConcat() {
}
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(input_a_cpu, Place(), *context, &input_a);
paddle::framework::TensorCopy(input_b_cpu, Place(), *context, &input_b);
paddle::framework::TensorCopySync(input_a_cpu, Place(), &input_a);
paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b);
}
input.clear();
......@@ -157,8 +157,8 @@ void testConcat() {
PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(out, paddle::platform::CPUPlace(), *context,
&out_cpu);
paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(),
&out_cpu);
out_ptr = out_cpu.data<int>();
} else {
out_ptr = out.data<int>();
......@@ -215,8 +215,8 @@ void testConcat() {
}
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(input_a_cpu, Place(), *context, &input_a);
paddle::framework::TensorCopy(input_b_cpu, Place(), *context, &input_b);
paddle::framework::TensorCopySync(input_a_cpu, Place(), &input_a);
paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b);
}
input.clear();
......@@ -230,8 +230,8 @@ void testConcat() {
PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(out, paddle::platform::CPUPlace(), *context,
&out_cpu);
paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(),
&out_cpu);
out_ptr = out_cpu.data<int>();
} else {
out_ptr = out.data<int>();
......@@ -290,8 +290,8 @@ void testConcat() {
}
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(input_a_cpu, Place(), *context, &input_a);
paddle::framework::TensorCopy(input_b_cpu, Place(), *context, &input_b);
paddle::framework::TensorCopySync(input_a_cpu, Place(), &input_a);
paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b);
}
input.clear();
......@@ -305,8 +305,8 @@ void testConcat() {
PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);
if (paddle::platform::is_gpu_place(Place())) {
paddle::framework::TensorCopy(out, paddle::platform::CPUPlace(), *context,
&out_cpu);
paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(),
&out_cpu);
out_ptr = out_cpu.data<int>();
} else {
out_ptr = out.data<int>();
......
paddle/fluid/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ 3dd01823
......@@ -59,9 +59,9 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
r_prev_state = value.prev_state_value[i];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_prev_state, r_state,
r_state_atv, r_out, r_checkI, r_checkF, r_checkO, active_node,
active_gate, active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -125,11 +125,11 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
r_prev_state = value.prev_state_value[i];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_grad_in, r_grad_ig,
r_grad_fg, r_grad_og, r_prev_state, r_prev_state_grad, r_state,
r_state_grad, r_state_atv, r_output_grad, r_checkI, r_checkF, r_checkO,
r_checkIGrad, r_checkFGrad, r_checkOGrad, active_node, active_gate,
active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_grad_in,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......@@ -186,9 +186,9 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
r_prev_state = (reinterpret_cast<__m256 *>(value.prev_state_value))[i];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_prev_state, r_state,
r_state_atv, r_out, r_checkI, r_checkF, r_checkO, active_node,
active_gate, active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -258,11 +258,11 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
r_prev_state = (reinterpret_cast<__m256 *>(value.prev_state_value))[i];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_grad_in, r_grad_ig,
r_grad_fg, r_grad_og, r_prev_state, r_prev_state_grad, r_state,
r_state_grad, r_state_atv, r_output_grad, r_checkI, r_checkF, r_checkO,
r_checkIGrad, r_checkFGrad, r_checkOGrad, active_node, active_gate,
active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_grad_in,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......
paddle/fluid/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ 3dd01823
......@@ -70,9 +70,9 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
r_prev_state = value.prev_state_value[frame_idx];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_prev_state, r_state,
r_state_atv, r_out, r_checkI, r_checkF, r_checkO, active_node, active_gate,
active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
value.gate_value[frame_idx] = r_value_in;
value.gate_value[frame_idx + frame_size] = r_value_ig;
......@@ -145,11 +145,11 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
r_prev_state = value.prev_state_value[frame_idx];
}
op(r_value_in, r_value_ig, r_value_fg, r_value_og, r_grad_in, r_grad_ig,
r_grad_fg, r_grad_og, r_prev_state, r_prev_state_grad, r_state,
r_state_grad, r_state_atv, r_output_grad, r_checkI, r_checkF, r_checkO,
r_checkIGrad, r_checkFGrad, r_checkOGrad, active_node, active_gate,
active_state);
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_grad_in, &r_grad_ig,
&r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad, &r_state,
&r_state_grad, &r_state_atv, &r_output_grad, &r_checkI, &r_checkF,
&r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad, active_node,
active_gate, active_state);
grad.gate_grad[frame_idx] = r_grad_in;
grad.gate_grad[frame_idx + frame_size] = r_grad_ig;
......
paddle/fluid/operators/math/detail/lstm_kernel.h
浏览文件 @ 3dd01823
......@@ -12,11 +12,11 @@ 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. */
#pragma once
#include <type_traits>
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/platform/hostdevice.h"
#include <type_traits>
namespace paddle {
namespace operators {
namespace math {
......@@ -27,19 +27,19 @@ namespace forward {
template <class T>
class lstm {
public:
HOSTDEVICE void operator()(T &value_in, T &value_ig, T &value_fg, T &value_og,
T &prev_state, T &state, T &state_atv, T &output,
T &checkI, T &checkF, T &checkO,
HOSTDEVICE void operator()(T *value_in, T *value_ig, T *value_fg, T *value_og,
T *prev_state, T *state, T *state_atv, T *output,
T *checkI, T *checkF, T *checkO,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
value_in = activation(value_in, active_node);
value_ig = activation(value_ig + prev_state * checkI, active_gate);
value_fg = activation(value_fg + prev_state * checkF, active_gate);
state = value_in * value_ig + prev_state * value_fg;
value_og = activation(value_og + state * checkO, active_gate);
state_atv = activation(state, active_state);
output = value_og * state_atv;
*value_in = activation(*value_in, active_node);
*value_ig = activation(*value_ig + (*prev_state) * (*checkI), active_gate);
*value_fg = activation(*value_fg + (*prev_state) * (*checkF), active_gate);
*state = (*value_in) * (*value_ig) + (*prev_state) * (*value_fg);
*value_og = activation(*value_og + (*state) * (*checkO), active_gate);
*state_atv = activation(*state, active_state);
*output = (*value_og) * (*state_atv);
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -48,27 +48,27 @@ class lstm {
// Only float support AVX optimization
static const bool avx = std::is_same<T, float>::value;
HOSTDEVICE void operator()(__m256 &value_in, __m256 &value_ig,
__m256 &value_fg, __m256 &value_og,
__m256 &prev_state, __m256 &state,
__m256 &state_atv, __m256 &output, __m256 &checkI,
__m256 &checkF, __m256 &checkO,
HOSTDEVICE void operator()(__m256 *value_in, __m256 *value_ig,
__m256 *value_fg, __m256 *value_og,
__m256 *prev_state, __m256 *state,
__m256 *state_atv, __m256 *output, __m256 *checkI,
__m256 *checkF, __m256 *checkO,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
value_in = activation(value_in, active_node);
value_ig =
activation(_mm256_add_ps(value_ig, _mm256_mul_ps(prev_state, checkI)),
active_gate);
value_fg =
activation(_mm256_add_ps(value_fg, _mm256_mul_ps(prev_state, checkF)),
active_gate);
state = _mm256_add_ps(_mm256_mul_ps(value_in, value_ig),
_mm256_mul_ps(prev_state, value_fg));
value_og = activation(_mm256_add_ps(value_og, _mm256_mul_ps(state, checkO)),
active_gate);
state_atv = activation(state, active_state);
output = _mm256_mul_ps(value_og, state_atv);
*value_in = activation(*value_in, active_node);
*value_ig = activation(
_mm256_add_ps(*value_ig, _mm256_mul_ps(*prev_state, *checkI)),
active_gate);
*value_fg = activation(
_mm256_add_ps(*value_fg, _mm256_mul_ps(*prev_state, *checkF)),
active_gate);
*state = _mm256_add_ps(_mm256_mul_ps(*value_in, *value_ig),
_mm256_mul_ps(*prev_state, *value_fg));
*value_og = activation(
_mm256_add_ps(*value_og, _mm256_mul_ps(*state, *checkO)), active_gate);
*state_atv = activation(*state, active_state);
*output = _mm256_mul_ps(*value_og, *state_atv);
}
#endif
#endif
......@@ -81,26 +81,29 @@ namespace backward {
template <class T>
class lstm {
public:
HOSTDEVICE void operator()(T &value_in, T &value_ig, T &value_fg, T &value_og,
T &grad_in, T &grad_ig, T &grad_fg, T &grad_og,
T &prev_state, T &prev_state_grad, T &state,
T &state_grad, T &state_atv, T &output_grad,
T &checkI, T &checkF, T &checkO, T &checkIGrad,
T &checkFGrad, T &checkOGrad,
HOSTDEVICE void operator()(T *value_in, T *value_ig, T *value_fg, T *value_og,
T *grad_in, T *grad_ig, T *grad_fg, T *grad_og,
T *prev_state, T *prev_state_grad, T *state,
T *state_grad, T *state_atv, T *output_grad,
T *checkI, T *checkF, T *checkO, T *checkIGrad,
T *checkFGrad, T *checkOGrad,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
grad_og = activation(output_grad * state_atv, value_og, active_gate);
state_grad += activation(output_grad * value_og, state_atv, active_state) +
grad_og * checkO;
grad_in = activation(state_grad * value_ig, value_in, active_node);
grad_ig = activation(state_grad * value_in, value_ig, active_gate);
grad_fg = activation(state_grad * prev_state, value_fg, active_gate);
prev_state_grad =
grad_ig * checkI + grad_fg * checkF + state_grad * value_fg;
checkIGrad = grad_ig * prev_state;
checkFGrad = grad_fg * prev_state;
checkOGrad = grad_og * state;
*grad_og =
activation((*output_grad) * (*state_atv), *value_og, active_gate);
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
*grad_in = activation((*state_grad) * (*value_ig), *value_in, active_node);
*grad_ig = activation((*state_grad) * (*value_in), *value_ig, active_gate);
*grad_fg =
activation((*state_grad) * (*prev_state), *value_fg, active_gate);
*prev_state_grad = (*grad_ig) * (*checkI) + (*grad_fg) * (*checkF) +
(*state_grad) * (*value_fg);
*checkIGrad = (*grad_ig) * (*prev_state);
*checkFGrad = (*grad_fg) * (*prev_state);
*checkOGrad = (*grad_og) * (*state);
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -109,32 +112,33 @@ class lstm {
// Only float support AVX optimization
static const bool avx = std::is_same<T, float>::value;
HOSTDEVICE void operator()(
__m256 &value_in, __m256 &value_ig, __m256 &value_fg, __m256 &value_og,
__m256 &grad_in, __m256 &grad_ig, __m256 &grad_fg, __m256 &grad_og,
__m256 &prev_state, __m256 &prev_state_grad, __m256 &state,
__m256 &state_grad, __m256 &state_atv, __m256 &output_grad,
__m256 &checkI, __m256 &checkF, __m256 &checkO, __m256 &checkIGrad,
__m256 &checkFGrad, __m256 &checkOGrad, ActivationType active_node,
__m256 *value_in, __m256 *value_ig, __m256 *value_fg, __m256 *value_og,
__m256 *grad_in, __m256 *grad_ig, __m256 *grad_fg, __m256 *grad_og,
__m256 *prev_state, __m256 *prev_state_grad, __m256 *state,
__m256 *state_grad, __m256 *state_atv, __m256 *output_grad,
__m256 *checkI, __m256 *checkF, __m256 *checkO, __m256 *checkIGrad,
__m256 *checkFGrad, __m256 *checkOGrad, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
grad_og = activation(_mm256_mul_ps(output_grad, state_atv), value_og,
active_gate);
state_grad = _mm256_add_ps(activation(_mm256_mul_ps(output_grad, value_og),
state_atv, active_state),
state_grad);
state_grad = _mm256_add_ps(_mm256_mul_ps(grad_og, checkO), state_grad);
grad_in =
activation(_mm256_mul_ps(state_grad, value_ig), value_in, active_node);
grad_ig =
activation(_mm256_mul_ps(state_grad, value_in), value_ig, active_gate);
grad_fg = activation(_mm256_mul_ps(state_grad, prev_state), value_fg,
active_gate);
prev_state_grad = _mm256_add_ps(_mm256_mul_ps(grad_ig, checkI),
_mm256_mul_ps(grad_fg, checkF));
prev_state_grad =
_mm256_add_ps(_mm256_mul_ps(state_grad, value_fg), prev_state_grad);
checkIGrad = _mm256_mul_ps(grad_ig, prev_state);
checkFGrad = _mm256_mul_ps(grad_fg, prev_state);
checkOGrad = _mm256_mul_ps(grad_og, state);
*grad_og = activation(_mm256_mul_ps(*output_grad, *state_atv), *value_og,
active_gate);
*state_grad =
_mm256_add_ps(activation(_mm256_mul_ps(*output_grad, *value_og),
*state_atv, active_state),
*state_grad);
*state_grad = _mm256_add_ps(_mm256_mul_ps(*grad_og, *checkO), *state_grad);
*grad_in = activation(_mm256_mul_ps(*state_grad, *value_ig), *value_in,
active_node);
*grad_ig = activation(_mm256_mul_ps(*state_grad, *value_in), *value_ig,
active_gate);
*grad_fg = activation(_mm256_mul_ps(*state_grad, *prev_state), *value_fg,
active_gate);
*prev_state_grad = _mm256_add_ps(_mm256_mul_ps(*grad_ig, *checkI),
_mm256_mul_ps(*grad_fg, *checkF));
*prev_state_grad =
_mm256_add_ps(_mm256_mul_ps(*state_grad, *value_fg), *prev_state_grad);
*checkIGrad = _mm256_mul_ps(*grad_ig, *prev_state);
*checkFGrad = _mm256_mul_ps(*grad_fg, *prev_state);
*checkOGrad = _mm256_mul_ps(*grad_og, *state);
}
#endif
#endif
......
paddle/fluid/operators/math/sequence_padding_test.cc
浏览文件 @ 3dd01823
......@@ -41,7 +41,7 @@ void TestSequencePadding(const paddle::framework::LoD& lod,
if (paddle::platform::is_cpu_place(*place)) {
seq = cpu_seq;
} else {
TensorCopy(cpu_seq, *place, *context, &seq);
TensorCopySync(cpu_seq, *place, &seq);
seq.set_lod(lod);
}
......@@ -64,7 +64,7 @@ void TestSequencePadding(const paddle::framework::LoD& lod,
if (paddle::platform::is_cpu_place(*place)) {
cpu_seq_back = seq_back;
} else {
TensorCopy(seq_back, paddle::platform::CPUPlace(), *context, &cpu_seq_back);
TensorCopySync(seq_back, paddle::platform::CPUPlace(), &cpu_seq_back);
cpu_seq_back.set_lod(lod);
}
......
paddle/fluid/operators/multiplex_op.cu
浏览文件 @ 3dd01823
......@@ -33,7 +33,7 @@ class MultiplexGPUKernel : public framework::OpKernel<T> {
auto cols = ins[0]->numel() / rows;
// copy index to cpu
Tensor index_t_cpu;
TensorCopy(*ids, platform::CPUPlace(), ctx.device_context(), &index_t_cpu);
TensorCopySync(*ids, platform::CPUPlace(), &index_t_cpu);
auto* index = index_t_cpu.data<int32_t>();
auto stream = ctx.cuda_device_context().stream();
platform::CUDAPlace place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
......@@ -69,7 +69,7 @@ class MultiplexGradGPUKernel : public framework::OpKernel<T> {
auto cols = ins[0]->numel() / rows;
// copy index to cpu
Tensor index_t_cpu;
TensorCopy(*ids, platform::CPUPlace(), ctx.device_context(), &index_t_cpu);
TensorCopySync(*ids, platform::CPUPlace(), &index_t_cpu);
auto* index = index_t_cpu.data<int32_t>();
auto stream = ctx.cuda_device_context().stream();
......
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ 3dd01823
......@@ -174,7 +174,8 @@ REGISTER_OP_KERNEL(pool2d_grad, CUDNN, plat::CUDAPlace,
REGISTER_OP_KERNEL(pool3d, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNOpKernel<float>,
ops::PoolCUDNNOpKernel<double>);
ops::PoolCUDNNOpKernel<double>,
ops::PoolCUDNNOpKernel<plat::float16>);
REGISTER_OP_KERNEL(pool3d_grad, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNGradOpKernel<float>,
ops::PoolCUDNNGradOpKernel<double>);
paddle/fluid/operators/proximal_adagrad_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class ProximalAdagradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -55,6 +56,12 @@ class ProximalAdagradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("MomentOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class ProximalAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/proximal_gd_op.cc
浏览文件 @ 3dd01823
......@@ -17,6 +17,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class ProximalGDOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -43,6 +44,12 @@ class ProximalGDOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class ProximalGDOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/row_conv_op.cu
浏览文件 @ 3dd01823
......@@ -224,7 +224,7 @@ __global__ void RowConvGradFilterImproved(const T *in, const T *dout,
for (int offset = 16; offset > 0;
offset = offset / 2) { // blockDim.x is 32.
val += platform::__shfl_down_sync(mask, val, offset);
val += platform::CudaShuffleDownSync(mask, val, offset);
}
__syncthreads();
......@@ -284,7 +284,7 @@ __global__ void RowConvGradFilter(const T *in, const T *dout, int num_sequence,
for (int offset = 16; offset > 0;
offset = offset / 2) { // blockDim.x is 32.
val += platform::__shfl_down_sync(mask, val, offset);
val += platform::CudaShuffleDownSync(mask, val, offset);
}
__syncthreads();
......
paddle/fluid/operators/sequence_slice_op.h
浏览文件 @ 3dd01823
......@@ -66,13 +66,11 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
framework::TensorCopy(*offset, platform::CPUPlace(), ctx.device_context(),
&offset_cpu);
framework::TensorCopySync(*offset, platform::CPUPlace(), &offset_cpu);
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
framework::TensorCopy(*length, platform::CPUPlace(), ctx.device_context(),
&length_cpu);
framework::TensorCopySync(*length, platform::CPUPlace(), &length_cpu);
length_data = length_cpu.data<int64_t>();
}
......@@ -127,13 +125,11 @@ class SequenceSliceGradOpKernel : public framework::OpKernel<T> {
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
framework::TensorCopy(*offset, platform::CPUPlace(), ctx.device_context(),
&offset_cpu);
framework::TensorCopySync(*offset, platform::CPUPlace(), &offset_cpu);
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
framework::TensorCopy(*length, platform::CPUPlace(), ctx.device_context(),
&length_cpu);
framework::TensorCopySync(*length, platform::CPUPlace(), &length_cpu);
length_data = length_cpu.data<int64_t>();
}
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ 3dd01823
......@@ -241,7 +241,8 @@ __device__ __forceinline__ void BlockReduce(Pair<T>* sh_topk, int* maxid,
CREATE_SHFL_MASK(mask, true);
if (maxid[0] / 32 == warp) {
if (platform::__shfl_sync(mask, *beam, (maxid[0]) % 32, 32) == MaxLength)
if (platform::CudaShuffleSync(mask, *beam, (maxid[0]) % 32, 32) ==
MaxLength)
break;
}
}
......
paddle/fluid/platform/cuda_device_function.h
浏览文件 @ 3dd01823
......@@ -18,34 +18,33 @@ limitations under the License. */
namespace paddle {
namespace platform {
// __shfl_down and __shfl have been deprecated as of CUDA 9.0.
#if CUDA_VERSION < 9000
template <typename T>
__forceinline__ __device__ T __shfl_down_sync(unsigned, T val, int delta) {
return __shfl_down(val, delta);
}
template <typename T>
__forceinline__ __device__ T __shfl_sync(unsigned, T val, int src_line,
int width) {
return __shfl(val, src_line, width);
}
#define CREATE_SHFL_MASK(mask, predicate) mask = 0u;
#else
#define FULL_WARP_MASK 0xFFFFFFFF
#define CREATE_SHFL_MASK(mask, predicate) \
mask = __ballot_sync(FULL_WARP_MASK, (predicate))
#endif
template <typename T>
__forceinline__ __device__ T __shfl_down_sync(unsigned mask, T val, int delta) {
return __shfl_down_sync(mask, val, delta);
__forceinline__ __device__ T CudaShuffleDownSync(unsigned mask, T val,
int delta, int width = 32) {
#if CUDA_VERSION < 9000
return __shfl_down(val, delta, width);
#else
return __shfl_down_sync(mask, val, delta, width);
#endif
}
template <typename T>
__forceinline__ __device__ T __shfl_sync(unsigned mask, T val, int src_line,
int width) {
__forceinline__ __device__ T CudaShuffleSync(unsigned mask, T val, int src_line,
int width = 32) {
#if CUDA_VERSION < 9000
return __shfl(val, src_line, width);
#else
return __shfl_sync(mask, val, src_line, width);
}
#endif
}
template <typename T>
__device__ T reduceSum(T val, int tid, int len) {
......@@ -61,7 +60,7 @@ __device__ T reduceSum(T val, int tid, int len) {
CREATE_SHFL_MASK(mask, tid < len);
for (int offset = warpSize / 2; offset > 0; offset /= 2)
val += platform::__shfl_down_sync(mask, val, offset);
val += platform::CudaShuffleDownSync(mask, val, offset);
if (tid < warpSize) shm[tid] = 0;
......@@ -75,7 +74,7 @@ __device__ T reduceSum(T val, int tid, int len) {
if (tid < warpSize) {
val = shm[tid];
for (int offset = warpSize / 2; offset > 0; offset /= 2)
val += platform::__shfl_down_sync(mask, val, offset);
val += platform::CudaShuffleDownSync(mask, val, offset);
}
return val;
}
......
paddle/fluid/platform/profiler.cc
浏览文件 @ 3dd01823
......@@ -13,12 +13,15 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/profiler.h"
#include <sys/time.h>
#include <time.h>
#include <algorithm>
#include <iomanip>
#include <limits>
#include <map>
#include <mutex> // NOLINT
#include <random>
#include <string>
#ifdef PADDLE_WITH_CUDA
#include <cuda.h>
......@@ -33,6 +36,9 @@ namespace platform {
struct EventList;
static int64_t profiler_lister_id = 0;
static bool should_send_profile_state = false;
// The profiler state, the initial value is ProfilerState::kDisabled
static ProfilerState g_state = ProfilerState::kDisabled;
// The thread local event list only can be accessed by the specific thread
......@@ -219,13 +225,12 @@ void EnableProfiler(ProfilerState state) {
PADDLE_ENFORCE(state != ProfilerState::kDisabled,
"Can't enbale profling, since the input state is ",
"ProfilerState::kDisabled");
PADDLE_ENFORCE(g_state == ProfilerState::kDisabled,
"The profiling state should be disabled when calling ",
"EnableProfiler.");
g_state = state;
if (g_state == ProfilerState::kAll) {
GetDeviceTracer()->Enable();
if (state == g_state) {
return;
}
g_state = state;
should_send_profile_state = true;
GetDeviceTracer()->Enable();
#ifdef PADDLE_WITH_CUDA
if (g_state == ProfilerState::kCUDA) {
// Generate some dummy events first to reduce the startup overhead.
......@@ -435,8 +440,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
void DisableProfiler(EventSortingKey sorted_key,
const std::string& profile_path) {
PADDLE_ENFORCE(g_state != ProfilerState::kDisabled,
"Can't disable profiling, since it's not starting.");
if (g_state == ProfilerState::kDisabled) return;
// Mark the profiling stop.
Mark("_stop_profiler_", nullptr);
......@@ -444,12 +448,25 @@ void DisableProfiler(EventSortingKey sorted_key,
ParseEvents(all_events, sorted_key);
ResetProfiler();
DeviceTracer* tracer = GetDeviceTracer();
if (g_state == ProfilerState::kAll && tracer && tracer->IsEnabled()) {
if (tracer->IsEnabled()) {
tracer->Disable();
tracer->GenProfile(profile_path);
}
g_state = ProfilerState::kDisabled;
should_send_profile_state = true;
}
bool IsProfileEnabled() { return g_state != ProfilerState::kDisabled; }
bool ShouldSendProfileState() { return should_send_profile_state; }
void SetProfileListener() {
std::mt19937 rng;
rng.seed(std::random_device()());
std::uniform_int_distribution<std::mt19937::result_type> dist6(
1, std::numeric_limits<int64_t>::max());
profiler_lister_id = dist6(rng);
}
int64_t ListenerId() { return profiler_lister_id; }
} // namespace platform
} // namespace paddle
paddle/fluid/platform/profiler.h
浏览文件 @ 3dd01823
......@@ -114,5 +114,13 @@ void ResetProfiler();
void DisableProfiler(EventSortingKey sorted_key,
const std::string& profile_path);
// Test if the profiler is currently enabled.
bool IsProfileEnabled();
// Whether the trainer should send profiling state to PS.
bool ShouldSendProfileState();
// Mark current process as PS by assigning a lister id.
void SetProfileListener();
int64_t ListenerId();
} // namespace platform
} // namespace paddle
python/paddle/fluid/__init__.py
浏览文件 @ 3dd01823
......@@ -40,16 +40,14 @@ import backward
import regularizer
import average
import metrics
import transpiler
from param_attr import ParamAttr, WeightNormParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
from distribute_transpiler import DistributeTranspiler
from distribute_transpiler_simple import SimpleDistributeTranspiler
from transpiler import DistributeTranspiler, SimpleDistributeTranspiler, InferenceTranspiler, memory_optimize, release_memory
from concurrency import (Go, make_channel, channel_send, channel_recv,
channel_close, Select)
from inference_transpiler import InferenceTranspiler
import clip
from memory_optimization_transpiler import memory_optimize, release_memory
import profiler
import unique_name
import recordio_writer
......@@ -58,7 +56,7 @@ from parallel_executor import ParallelExecutor
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ +\
trainer.__all__ + inferencer.__all__ + [
trainer.__all__ + inferencer.__all__ + transpiler.__all__ + [
'io',
'initializer',
'layers',
......@@ -76,11 +74,6 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ +\
'WeightNormParamAttr',
'DataFeeder',
'clip',
'SimpleDistributeTranspiler',
'DistributeTranspiler',
'InferenceTranspiler',
'memory_optimize',
'release_memory',
'profiler',
'unique_name',
'recordio_writer',
......
python/paddle/fluid/inferencer.py
浏览文件 @ 3dd01823
......@@ -13,7 +13,9 @@
# limitations under the License.
import core
import framework
import executor
import io
__all__ = ['Inferencer', ]
......@@ -29,6 +31,15 @@ class Inferencer(object):
# 4. load params from param_path into scope
self.scope = core.Scope()
self.place = place
self.startup_program = framework.Program()
# TODO: generate the startup_program with network_func
exe = executor.Executor(place)
exe.run(self.startup_program, scope=self.scope)
if param_path:
# load params from param_path into scope
io.load_persistables(exe, dirname=param_path)
def infer(self, inputs):
# run self.program
......
python/paddle/fluid/metrics.py
浏览文件 @ 3dd01823
......@@ -251,7 +251,7 @@ class EditDistance(MetricBase):
self.instance_error += seq_num - seq_right_count
self.total_distance += total_distance
def eval():
def eval(self):
if self.seq_num == 0:
raise ValueError(
"There is no data in EditDistance Metric. Please check layers.edit_distance output has been added to EditDistance."
......@@ -280,6 +280,7 @@ class DetectionMAP(MetricBase):
super(DetectionMAP, self).__init__(name)
# the current map value
self.value = .0
self.weight = .0
def update(self, value, weight):
if not _is_number_or_matrix_(value):
......@@ -340,8 +341,8 @@ class Auc(MetricBase):
raise ValueError("The 'predictions' must be a numpy ndarray.")
kepsilon = 1e-7 # to account for floating point imprecisions
thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
for i in range(num_thresholds - 2)]
thresholds = [(i + 1) * 1.0 / (self._num_thresholds - 1)
for i in range(self._num_thresholds - 2)]
thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
# caculate TP, FN, TN, FP count
......@@ -358,19 +359,20 @@ class Auc(MetricBase):
fp += 1
else:
tn += 1
tp_list[idx_thresh] += tp
fn_list[idx_thresh] += fn
tn_list[idx_thresh] += tn
fp_list[idx_thresh] += fp
self.tp_list[idx_thresh] += tp
self.fn_list[idx_thresh] += fn
self.tn_list[idx_thresh] += tn
self.fp_list[idx_thresh] += fp
def eval(self):
epsilon = self._epsilon
num_thresholds = self._num_thresholds
tpr = (tp_list.astype("float32") + epsilon) / (
tp_list + fn_list + epsilon)
fpr = fp_list.astype("float32") / (fp_list + tn_list + epsilon)
rec = (tp_list.astype("float32") + epsilon) / (
tp_list + fp_list + epsilon)
tpr = (self.tp_list.astype("float32") + epsilon) / (
self.tp_list + self.fn_list + epsilon)
fpr = self.fp_list.astype("float32") / (
self.fp_list + self.tn_list + epsilon)
rec = (self.tp_list.astype("float32") + epsilon) / (
self.tp_list + self.fp_list + epsilon)
x = fpr[:num_thresholds - 1] - fpr[1:]
y = (tpr[:num_thresholds - 1] + tpr[1:]) / 2.0
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 3dd01823
......@@ -43,7 +43,7 @@ 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_default_grad_scale(bool, default True): If set True, a default
......@@ -93,7 +93,7 @@ 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())
......@@ -130,6 +130,7 @@ class ParallelExecutor(object):
local_scopes,
allow_op_delay,
use_default_grad_scale)
self.scope = scope
def run(self, fetch_list, feed=None, feed_dict=None):
......
python/paddle/fluid/tests/unittests/test_conv3d_op.py
浏览文件 @ 3dd01823
......@@ -70,9 +70,11 @@ def conv3d_forward_naive(input, filter, group, conv_param):
class TestConv3dOp(OpTest):
def setUp(self):
self.op_type = "conv3d"
self.use_cudnn = False
self.dtype = np.float32
self.init_kernel_type()
self.init_group()
self.init_op_type()
self.init_dilation()
self.init_test_case()
......@@ -80,20 +82,24 @@ class TestConv3dOp(OpTest):
'stride': self.stride,
'pad': self.pad,
'dilations': self.dilations,
'use_cudnn': self.use_cudnn,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}
input = np.random.random(self.input_size).astype("float32")
filter = np.random.random(self.filter_size).astype("float32")
input = np.random.random(self.input_size).astype(self.dtype)
filter = np.random.random(self.filter_size).astype(self.dtype)
output = conv3d_forward_naive(input, filter, self.groups,
conv3d_param).astype("float32")
conv3d_param).astype(self.dtype)
self.inputs = {'Input': input, 'Filter': filter}
self.inputs = {
'Input': OpTest.np_dtype_to_fluid_dtype(input),
'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
}
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
'groups': self.groups,
'dilations': self.dilations
'dilations': self.dilations,
'use_cudnn': self.use_cudnn
}
self.outputs = {'Output': output}
......@@ -108,6 +114,8 @@ class TestConv3dOp(OpTest):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -120,6 +128,8 @@ class TestConv3dOp(OpTest):
set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
def test_check_grad_no_filter(self):
if self.dtype == np.float16:
return
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -135,6 +145,8 @@ class TestConv3dOp(OpTest):
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
if self.dtype == np.float16:
return
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -163,8 +175,8 @@ class TestConv3dOp(OpTest):
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv3d"
def init_kernel_type(self):
pass
class TestCase1(TestConv3dOp):
......@@ -235,34 +247,90 @@ class TestWithDilation(TestConv3dOp):
self.groups = 3
#----------------Conv3dCUDNN----------------
class TestCUDNN(TestConv3dOp):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv3d"
class TestFP16CUDNN(TestConv3dOp):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestWithGroup1CUDNN(TestWithGroup1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv3d"
class TestFP16WithGroup1CUDNN(TestWithGroup1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestWithGroup2CUDNN(TestWithGroup2):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv3d"
class TestFP16WithGroup2CUDNN(TestWithGroup2):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestWith1x1CUDNN(TestWith1x1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv3d"
class TestFP16With1x1CUDNN(TestWith1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestWithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv3d"
class TestFP16WithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
# FIXME(typhoonzero): find a way to determine if
......
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @ 3dd01823
......@@ -280,7 +280,7 @@ 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):
self.check_network_convergence(simple_fc_net)
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
......@@ -290,7 +290,10 @@ class TestMNIST(TestParallelExecutorBase):
simple_fc_net, feed_dict={"image": img,
"label": label})
def test_simple_fc_parallel_accuracy(self):
def test_simple_fc(self):
self.check_simple_fc_convergence()
def check_simple_fc_parallel_accuracy(self):
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(
......@@ -311,7 +314,10 @@ class TestMNIST(TestParallelExecutorBase):
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(self):
self.check_simple_fc_parallel_accuracy()
def check_batchnorm_fc_convergence(self):
self.check_network_convergence(fc_with_batchnorm)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
......@@ -319,6 +325,9 @@ class TestMNIST(TestParallelExecutorBase):
fc_with_batchnorm, feed_dict={"image": img,
"label": label})
def test_batchnorm_fc(self):
self.check_batchnorm_fc_convergence()
class TestResnet(TestParallelExecutorBase):
# @classmethod
......@@ -339,7 +348,7 @@ 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):
import functools
batch_size = 2
self.check_network_convergence(
......@@ -348,6 +357,9 @@ class TestResnet(TestParallelExecutorBase):
iter=20,
batch_size=batch_size)
def test_resnet(self):
self.check_resnet_convergence()
class ModelHyperParams(object):
# Dictionary size for source and target language. This model directly uses
......@@ -510,7 +522,7 @@ class TestTransformer(TestParallelExecutorBase):
class ParallelExecutorTestingDuringTraining(unittest.TestCase):
def test_parallel_testing(self):
def check_network_convergence(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -550,6 +562,9 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
"Train loss: " + str(train_loss) + "\n Test loss:" +
str(test_loss))
def test_parallel(self):
self.check_network_convergence()
import paddle.dataset.conll05 as conll05
import paddle.fluid as fluid
......@@ -568,21 +583,26 @@ embedding_name = 'emb'
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
**ignored):
is_sparse, **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 +652,7 @@ 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):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -652,6 +672,7 @@ 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)
......@@ -694,3 +715,9 @@ 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)
def test_update_dense_parameter(self):
self.check_network_convergence(is_sparse=False)
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @ 3dd01823
......@@ -90,20 +90,22 @@ def avg_pool3D_forward_naive(x,
class TestPool3d_Op(OpTest):
def setUp(self):
self.op_type = "pool3d"
self.use_cudnn = False
self.dtype = np.float32
self.init_test_case()
self.init_global_pool()
self.init_op_type()
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool3D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype("float32")
self.inputs = {'X': input}
self.ceil_mode).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
'strides': self.strides,
......@@ -116,7 +118,7 @@ class TestPool3d_Op(OpTest):
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}
self.outputs = {'Out': output.astype('float32')}
self.outputs = {'Out': output}
def testcudnn(self):
return core.is_compiled_with_cuda() and self.use_cudnn
......@@ -129,6 +131,8 @@ class TestPool3d_Op(OpTest):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.testcudnn() and self.pool_type != "max":
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -142,8 +146,8 @@ class TestPool3d_Op(OpTest):
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
def init_op_type(self):
self.op_type = "pool3d"
def init_kernel_type(self):
pass
def init_pool_type(self):
self.pool_type = "avg"
......@@ -158,15 +162,11 @@ class TestPool3d_Op(OpTest):
class TestCase1(TestPool3d_Op):
def init_test_case(self):
self.op_type = "pool3d"
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
......@@ -182,9 +182,6 @@ class TestCase2(TestPool3d_Op):
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
......@@ -194,27 +191,18 @@ class TestCase2(TestPool3d_Op):
class TestCase3(TestPool3d_Op):
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
class TestCase4(TestCase1):
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
class TestCase5(TestCase2):
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
......@@ -222,39 +210,105 @@ class TestCase5(TestCase2):
#--------------------test pool3d--------------------
class TestCUDNNCase1(TestPool3d_Op):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase1(TestPool3d_Op):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase2(TestCase1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase2(TestCase1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase3(TestCase2):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase3(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase4(TestCase3):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase4(TestCase3):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase5(TestCase4):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase5(TestCase4):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase6(TestCase5):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool3d"
class TestFP16CUDNNCase6(TestCase5):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCeilModeCase1(TestCUDNNCase1):
......
python/paddle/fluid/trainer.py
浏览文件 @ 3dd01823
......@@ -12,14 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import core
import framework
import executor
import data_feeder
import contextlib
import io
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
import optimizer as opt_module
from transpiler import distribute_transpiler
__all__ = [
'Trainer',
......@@ -76,22 +79,60 @@ class Trainer(object):
raise TypeError(
"The optimizer should be an instance of Optimizer")
optimizer.minimize(loss)
optimize_ops, params_grads = optimizer.minimize(loss)
self.place = Trainer._check_and_get_place(place)
self.dist_transpile_if_necessary(optimize_ops, params_grads)
# 2. move the default_main_program to self.program and run the
# default_startup program on an empty core.Scope()
# Run startup program
exe = executor.Executor(place)
exe.run(self.startup_program, scope=self.scope)
with self._prog_and_scope_guard():
exe = executor.Executor(place)
exe.run(self.startup_program)
if param_path:
# load params from param_path into scope
# TODO(yuyang): This depends on parameters implementation.
pass
# TODO(helin): support distributed training
io.load_persistables(exe, dirname=param_path)
def dist_transpile_if_necessary(self, optimize_ops, params_grads):
if "PADDLE_TRAINING_ROLE" not in os.environ:
return
# the port of all pservers, needed by both trainer and pserver
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
# comma separated ips of all pservers, needed by trainer and
# pserver
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
# total number of workers/trainers in the job, needed by
# trainer and pserver
trainers = int(os.getenv("PADDLE_TRAINERS"))
# the IP of the local machine, needed by pserver only
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
# the unique trainer id, starting from 0, needed by trainer
# only
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
# the role, should be either PSERVER or TRAINER
training_role = os.getenv("PADDLE_TRAINING_ROLE")
with self._prog_and_scope_guard():
t = distribute_transpiler.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
self.train_program = t.get_pserver_program(current_endpoint)
self.startup_program = t.get_startup_program(current_endpoint,
self.train_program)
elif training_role == "TRAINER":
self.train_program = t.get_trainer_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def train(self,
num_epochs,
......@@ -117,6 +158,13 @@ class Trainer(object):
raise NotImplementedError(
"Parallel Executor version of trainer is not implemented")
training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
if training_role == "PSERVER":
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
exe.run()
return
self._train_by_executor(num_epochs, event_handler, reader, feed_order)
def test(self, reader):
......@@ -124,7 +172,9 @@ class Trainer(object):
def save_params(self, param_path):
# reference: save_persistables in io.py
pass
exe = executor.Executor(self.place)
io.save_persistables(
exe, dirname=param_path, main_program=self.startup_program)
@staticmethod
def _check_and_get_place(place):
......
python/paddle/fluid/transpiler/__init__.py 0 → 100644
浏览文件 @ 3dd01823
# Copyright (c) 2018 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.
from distribute_transpiler import DistributeTranspiler
from inference_transpiler import InferenceTranspiler
from memory_optimization_transpiler import memory_optimize, release_memory
from distribute_transpiler_simple import SimpleDistributeTranspiler
__all__ = [
"DistributeTranspiler", "InferenceTranspiler", "SimpleDistributeTranspiler",
"memory_optimize", "release_memory"
]
python/paddle/fluid/distribute_transpiler.py → python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 3dd01823
......@@ -17,9 +17,8 @@ from __future__ import print_function
import math
import distributed_splitter as splitter
import framework
from framework import Program, default_main_program, Variable, Parameter
from . import core
from .. import core
from ..framework import Program, default_main_program, Variable, Parameter
LOOKUP_TABLE_TYPE = "lookup_table"
LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
......@@ -135,6 +134,16 @@ def split_dense_variable(var_list,
return blocks
def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block.remove_op(start) for _ in xrange(end - start + 1)]
except Exception, e:
raise e
block.program.sync_with_cpp()
class DistributeTranspiler:
def transpile(self,
trainer_id,
......@@ -317,7 +326,7 @@ class DistributeTranspiler:
def get_trainer_program(self):
# remove optimize ops and add a send op to main_program
self.delete_ops(self.origin_program.global_block(), self.optimize_ops)
delete_ops(self.origin_program.global_block(), self.optimize_ops)
# FIXME(typhoonzero): serialize once will fix error occurs when clone.
self.origin_program.__str__()
return self.origin_program
......@@ -601,7 +610,7 @@ class DistributeTranspiler:
attrs={"axis": 0})
# delete lookup_table_op
self.delete_ops(program.global_block(), [op])
delete_ops(program.global_block(), [op])
# break for loop
break
......@@ -1164,12 +1173,3 @@ class DistributeTranspiler:
in_name.startswith("beta2_pow_acc"):
return True
return False
def delete_ops(self, block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block.remove_op(start) for _ in xrange(end - start + 1)]
except Exception, e:
raise e
block.program.sync_with_cpp()
python/paddle/fluid/distribute_transpiler_simple.py → python/paddle/fluid/transpiler/distribute_transpiler_simple.py
浏览文件 @ 3dd01823
......@@ -12,10 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import framework
from framework import Program, default_main_program, Parameter, Variable
import optimizer
from layer_helper import LayerHelper
from ..framework import Program, default_main_program, Parameter, Variable
from ..layer_helper import LayerHelper
def hash_name_to_server(params_grads, pserver_endpoints):
......
python/paddle/fluid/inference_transpiler.py → python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ 3dd01823
......@@ -13,9 +13,9 @@
# limitations under the License.
import numpy as np
from framework import Program
from executor import global_scope
from . import core
from .. import core
from ..framework import Program
from ..executor import global_scope
class InferenceTranspiler:
......
python/paddle/fluid/memory_optimization_transpiler.py → python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 3dd01823
......@@ -13,11 +13,9 @@
# limitations under the License.
from collections import defaultdict
import framework
from framework import Program, default_main_program, Parameter, Variable
import backward
from backward import _rename_arg_
from . import core
from .. import core
from ..framework import Program, default_main_program, Parameter, Variable
from ..backward import _rename_arg_
dtype_to_size = {
core.VarDesc.VarType.FP16: 2,
......
tools/timeline.py
浏览文件 @ 3dd01823
......@@ -22,7 +22,11 @@ import paddle.fluid.proto.profiler.profiler_pb2 as profiler_pb2
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
'--profile_path', type=str, default='', help='Input profile file name.')
'--profile_path',
type=str,
default='',
help='Input profile file name. If there are multiple file, the format '
'should be trainer1=file1,trainer2=file2,ps=file3')
parser.add_argument(
'--timeline_path', type=str, default='', help='Output timeline file name.')
args = parser.parse_args()
......@@ -108,8 +112,8 @@ class _ChromeTraceFormatter(object):
class Timeline(object):
def __init__(self, profile_pb):
self._profile_pb = profile_pb
def __init__(self, profile_dict):
self._profile_dict = profile_dict
self._pid = 0
self._devices = dict()
self._chrome_trace = _ChromeTraceFormatter()
......@@ -120,35 +124,37 @@ class Timeline(object):
return cur_pid
def _allocate_pids(self):
for event in self._profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
if (event.device_id, "CPU") not in self._devices:
pid = self._allocate_pid()
self._devices[(event.device_id, "CPU")] = pid
self._chrome_trace.emit_pid("cpu:block:%d" %
(event.device_id), pid)
elif event.type == profiler_pb2.Event.GPUKernel:
if (event.device_id, "GPUKernel") not in self._devices:
pid = self._allocate_pid()
self._devices[(event.device_id, "GPUKernel")] = pid
self._chrome_trace.emit_pid("gpu:%d" % (event.device_id),
pid)
for k, profile_pb in self._profile_dict.iteritems():
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
if (k, event.device_id, "CPU") not in self._devices:
pid = self._allocate_pid()
self._devices[(k, event.device_id, "CPU")] = pid
self._chrome_trace.emit_pid("%s:cpu:block:%d" %
(k, event.device_id), pid)
elif event.type == profiler_pb2.Event.GPUKernel:
if (k, event.device_id, "GPUKernel") not in self._devices:
pid = self._allocate_pid()
self._devices[(k, event.device_id, "GPUKernel")] = pid
self._chrome_trace.emit_pid("%s:gpu:%d" %
(k, event.device_id), pid)
def _allocate_events(self):
for event in self._profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
type = "CPU"
elif event.type == profiler_pb2.Event.GPUKernel:
type = "GPUKernel"
pid = self._devices[(event.device_id, type)]
args = {'name': event.name}
if event.memcopy.bytes > 0:
args = {'mem_bytes': event.memcopy.bytes}
# TODO(panyx0718): Chrome tracing only handles ms. However, some
# ops takes micro-seconds. Hence, we keep the ns here.
self._chrome_trace.emit_region(
event.start_ns, (event.end_ns - event.start_ns) / 1.0, pid,
event.sub_device_id, 'Op', event.name, args)
for k, profile_pb in self._profile_dict.iteritems():
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
type = "CPU"
elif event.type == profiler_pb2.Event.GPUKernel:
type = "GPUKernel"
pid = self._devices[(k, event.device_id, type)]
args = {'name': event.name}
if event.memcopy.bytes > 0:
args = {'mem_bytes': event.memcopy.bytes}
# TODO(panyx0718): Chrome tracing only handles ms. However, some
# ops takes micro-seconds. Hence, we keep the ns here.
self._chrome_trace.emit_region(
event.start_ns, (event.end_ns - event.start_ns) / 1.0, pid,
event.sub_device_id, 'Op', event.name, args)
def generate_chrome_trace(self):
self._allocate_pids()
......@@ -163,11 +169,23 @@ timeline_path = '/tmp/timeline'
if args.timeline_path:
timeline_path = args.timeline_path
with open(profile_path, 'r') as f:
profile_s = f.read()
profile_pb = profiler_pb2.Profile()
profile_pb.ParseFromString(profile_s)
tl = Timeline(profile_pb)
profile_paths = profile_path.split(',')
profile_dict = dict()
if len(profile_path) == 1:
with open(profile_path, 'r') as f:
profile_s = f.read()
profile_pb = profiler_pb2.Profile()
profile_pb.ParseFromString(profile_s)
profile_dict['trainer'] = profile_pb
else:
for profile_path in profile_paths:
k, v = profile_path.split('=')
with open(v, 'r') as f:
profile_s = f.read()
profile_pb = profiler_pb2.Profile()
profile_pb.ParseFromString(profile_s)
profile_dict[k] = profile_pb
tl = Timeline(profile_dict)
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace())
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