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提交 629c9210 编写于 作者: G guosheng
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into add-python-pad

上级 4c8eef57 09fcf5f2
隐藏空白更改
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Showing with 448 addition and 153 deletion
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ 629c9210
......@@ -2,7 +2,7 @@ if(WITH_DISTRIBUTE)
grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(serde_test.cc grpc_server_test PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
......
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 629c9210
......@@ -12,8 +12,10 @@ 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 "grpc_client.h"
#include <sys/time.h>
#include "paddle/fluid/operators/detail/grpc_client.h"
#include <limits>
#include "paddle/fluid/framework/threadpool.h"
namespace paddle {
......@@ -52,7 +54,7 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
});
req_count_++;
......@@ -70,8 +72,7 @@ void ProcGetResponse(const VarHandle& var_h,
template <typename T>
void RequestToByteBuffer(const T& proto, ::grpc::ByteBuffer* result) {
::grpc::Slice slice(proto.ByteSizeLong());
proto.SerializeWithCachedSizesToArray(
const_cast<uint8_t*>(reinterpret_cast<const uint8_t*>(slice.begin())));
proto.SerializeWithCachedSizesToArray(const_cast<uint8_t*>(slice.begin()));
::grpc::ByteBuffer tmp(&slice, 1);
result->Swap(&tmp);
}
......@@ -109,7 +110,7 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
});
req_count_++;
......@@ -153,7 +154,7 @@ bool RPCClient::AsyncPrefetchVariable(const std::string& ep,
s->context_.get(), "/sendrecv.SendRecvService/PrefetchVariable", req,
&cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
});
req_count_++;
......@@ -169,7 +170,7 @@ void RPCClient::AsyncSendBatchBarrier(const std::string& ep, int64_t time_out) {
sendrecv::VariableMessage req;
req.set_varname(BATCH_BARRIER_MESSAGE);
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
rpc->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
req_count_++;
}
......@@ -181,7 +182,7 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
sendrecv::VariableMessage req;
req.set_varname(FETCH_BARRIER_MESSAGE);
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
rpc->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
req_count_++;
}
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ 629c9210
......@@ -14,6 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/detail/grpc_server.h"
#include <limits>
#include <string>
using ::grpc::ServerAsyncResponseWriter;
namespace paddle {
......@@ -156,6 +159,8 @@ class RequestPrefetch final : public RequestBase {
::grpc::ByteBuffer reply;
// TODO(Yancey1989): execute the Block which containers prefetch ops
VLOG(3) << "RequestPrefetch Process in";
responder_.Finish(reply, ::grpc::Status::OK, this);
status_ = FINISH;
}
......@@ -221,6 +226,7 @@ void AsyncGRPCServer::ShutdownQueue() {
std::unique_lock<std::mutex> lock(cq_mutex_);
cq_send_->Shutdown();
cq_get_->Shutdown();
cq_prefetch_->Shutdown();
}
// This URL explains why shutdown is complicate:
......@@ -233,6 +239,7 @@ void AsyncGRPCServer::ShutDown() {
void AsyncGRPCServer::TryToRegisterNewSendOne() {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewSendOne";
return;
}
RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_,
......@@ -243,6 +250,7 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() {
void AsyncGRPCServer::TryToRegisterNewGetOne() {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewGetOne";
return;
}
RequestGet* get = new RequestGet(&service_, cq_get_.get(), scope_, dev_ctx_,
......@@ -253,6 +261,7 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() {
void AsyncGRPCServer::TryToRegisterNewPrefetchOne() {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewPrefetchOne";
return;
}
RequestPrefetch* prefetch =
......@@ -270,25 +279,28 @@ void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq,
void* tag = NULL;
bool ok = false;
while (true) {
VLOG(3) << "HandleRequest for " << cq_name << " while in";
if (!cq->Next(&tag, &ok)) {
LOG(INFO) << cq_name << " CompletionQueue shutdown!";
break;
}
VLOG(3) << "HandleRequest for " << cq_name << " while after Next";
PADDLE_ENFORCE(tag);
// FIXME(typhoonzero): de-couple the barriers with recv_op
if (!is_shut_down_ && cq_name == "cq_get") WaitCond(1);
if (!is_shut_down_ && cq_name == "cq_send") WaitCond(0);
RequestBase* base = (RequestBase*)tag;
RequestBase* base = reinterpret_cast<RequestBase*>(tag);
// reference:
// https://github.com/tensorflow/tensorflow/issues/5596
// https://groups.google.com/forum/#!topic/grpc-io/xftlRy-IQwM
// https://groups.google.com/forum/#!topic/grpc-io/ywATt88Ef_I
if (!ok) {
LOG(WARNING) << cq_name << " recv no regular event:argument name"
<< base->GetReqName();
LOG(WARNING) << cq_name << " recv no regular event:argument name["
<< base->GetReqName() << "]";
TryToRegisterNewOne();
delete base;
continue;
......
paddle/fluid/operators/detail/grpc_server.h
浏览文件 @ 629c9210
......@@ -15,7 +15,8 @@ limitations under the License. */
#pragma once
#include <grpc++/grpc++.h>
#include <thread>
#include <string>
#include <utility>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
......@@ -93,6 +94,7 @@ class AsyncGRPCServer final {
// received variable from RPC, operators fetch variable from this queue.
SimpleBlockQueue<MessageWithName> var_get_queue_;
// client send variable to this queue.
ReceivedQueue var_recv_queue_;
// condition of the sub program
......
paddle/fluid/operators/detail/grpc_server_test.cc
浏览文件 @ 629c9210
......@@ -28,6 +28,7 @@ std::unique_ptr<detail::AsyncGRPCServer> rpc_service_;
void StartServer(const std::string& endpoint) {
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
rpc_service_->RunSyncUpdate();
}
TEST(PREFETCH, CPU) {
......@@ -39,13 +40,23 @@ TEST(PREFETCH, CPU) {
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// create var on local scope
std::string var_name("tmp_0");
auto var = scope.Var(var_name);
auto tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize({10, 10});
std::string in_var_name("in");
std::string out_var_name("out");
auto* in_var = scope.Var(in_var_name);
auto* in_tensor = in_var->GetMutable<framework::LoDTensor>();
in_tensor->Resize({10, 10});
VLOG(3) << "before mutable_data";
in_tensor->mutable_data<int>(place);
scope.Var(out_var_name);
VLOG(3) << "before fetch";
detail::RPCClient client;
client.AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, var_name, "");
client.AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, in_var_name,
out_var_name);
client.Wait();
rpc_service_->ShutDown();
server_thread.join();
rpc_service_.reset(nullptr);
}
paddle/fluid/operators/detail/grpc_service.h
浏览文件 @ 629c9210
......@@ -80,7 +80,7 @@ enum class GrpcMethod {
};
static const int kGrpcNumMethods =
static_cast<int>(GrpcMethod::kGetVariable) + 1;
static_cast<int>(GrpcMethod::kPrefetchVariable) + 1;
inline const char* GrpcMethodName(GrpcMethod id) {
switch (id) {
......@@ -89,7 +89,7 @@ inline const char* GrpcMethodName(GrpcMethod id) {
case GrpcMethod::kGetVariable:
return "/sendrecv.SendRecvService/GetVariable";
case GrpcMethod::kPrefetchVariable:
return "/sendrecv.SendREcvService/PrefetchVariable";
return "/sendrecv.SendRecvService/PrefetchVariable";
}
// Shouldn't be reached.
......@@ -117,5 +117,5 @@ class GrpcService final {
};
} // namespace detail
} // namespace operator
} // namespace operators
} // namespace paddle
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 629c9210
......@@ -13,22 +13,13 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <stdint.h>
#include <sys/stat.h>
#include <ostream>
#include <thread>
#include <unistd.h>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/proto_desc.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/simple_block_queue.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace operators {
......@@ -111,6 +102,11 @@ class ListenAndServOp : public framework::OperatorBase {
framework::Executor executor(dev_place);
// TODO(qiao) set proper fields for table lookup and update
rpc_service_->SetExecutor(&executor);
rpc_service_->SetPrefetchBlkdId(0);
rpc_service_->SetProgram(program);
// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
// Record received sparse variables, so that
......@@ -173,7 +169,8 @@ class ListenAndServOp : public framework::OperatorBase {
}
ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope);
VLOG(2) << "run all blocks spent (ms) " << detail::GetTimestamp() - ts;
VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts
<< "(ms)";
// Reset the received sparse variables, the sum operator would not
// sum the input sparse variables which rows is empty at the next
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 629c9210
......@@ -17,90 +17,66 @@ limitations under the License. */
namespace paddle {
namespace operators {
class ReshapeOp : public framework::OperatorWithKernel {
public:
ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
// input check
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ReshapeOp should not be null.");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
auto x_dims = ctx->GetInputDim("X");
std::vector<size_t> neg_dims_idx;
// set some dimension to -1 if it is unknown
const int unknown_size = -1;
for (size_t i = 0; i < shape.size(); ++i) {
PADDLE_ENFORCE(shape[i] > 0 || shape[i] == unknown_size,
"Each dimension of Attr(shape) must be positive or %d.",
unknown_size);
if (shape[i] == unknown_size) {
neg_dims_idx.push_back(i);
PADDLE_ENFORCE(neg_dims_idx.size() <= 1,
"Only one dimension of Attr(shape) can be unknown.");
}
}
int64_t capacity =
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
int64_t in_size = framework::product(x_dims);
if (neg_dims_idx.size() == 1) {
// dim infer
shape[neg_dims_idx[0]] = in_size / (-capacity);
// recalculate capacity
capacity = shape[neg_dims_idx[0]] * (-capacity);
}
// capacity check
PADDLE_ENFORCE(capacity == in_size,
"The size of Input(X) mismatches with Attr(shape).");
// resize output
std::vector<int64_t> shape_int64(shape.size(), 0);
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto out_dims = framework::make_ddim(shape_int64);
ctx->SetOutputDim("Out", out_dims);
if (shape[0] == x_dims[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};
class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ReshapeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of reshape operator.");
AddOutput("Out", "The output tensor of reshape operator.");
AddAttr<std::vector<int>>("shape",
"(vector<int>) "
"Target shape of reshape operator.");
AddInput("X", "(Tensor). The input tensor of reshape operator.");
AddInput("Shape",
"(Tensor<int32>, optional). If provided, reshape according to "
"this given shape. That is to say it has a higher priority than "
"the shape attribute, while the shape attribute still should be "
"set correctly to gurantee shape inference in compile time.")
.AsDispensable();
AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
AddAttr<std::vector<int>>(
"shape", "(std::vector<int>) Target shape of reshape operator.");
AddAttr<bool>("inplace",
"Change the source tensor's shape without copy memory.")
.SetDefault(true);
"(default: false) Change the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x).")
.SetDefault(false);
AddComment(R"DOC(
Reshape Operator.
Reshape Input(X) into the shape specified by Attr(shape).
Reshape Input(X) into the shape specified by Attr(shape) or Input(Shape). The
data in Input(X) are unchanged.
Examples:
An example:
Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]
1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
and target shape = [1, 4], the reshape operator will transform
the tensor X into a 2-D tensor: [[1, 2, 3, 4]]
2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
the value of this dimension is inferred from the total element number of
Input(X) and remaining dimensions.
3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
unchanged. In this case, besides -1, 0 means the actual dimension value is going
to be copied from the corresponding dimension of Input(X).
Note:
1. One and only one dimension in Attr(shape) can be set -1. In this case,
the actual dimension value will be infered from the total element number of
Input(X) and remaining dimensions.
2. More than one dimensions in Attr(shape) can be set to 0, which means the real
dimension value will be copied from Input(X) at runtime. Note that the index of
0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape
[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
Attr(shape) still should be set correctly to gurantee shape inference in
compile-time.
One dimension in the target shape can be set -1, representing that its
size is unknown. In this case, the real dimension will be infered from
the original shape of Input(X) and other dimensions in the target shape.
)DOC");
}
};
......@@ -119,6 +95,14 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
"Input(Out@GRAD) shouldn't be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
} // namespace operators
......
paddle/fluid/operators/reshape_op.h
浏览文件 @ 629c9210
......@@ -20,17 +20,129 @@ limitations under the License. */
namespace paddle {
namespace operators {
class ReshapeOp : public framework::OperatorWithKernel {
public:
ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ReshapeOp should not be null.");
const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE(!shape.empty(),
"The shape information must be set by Attr(shape).");
if (ctx->HasInput("Shape") && ctx->IsRuntime()) {
// If true, set the shape of Output(Out) according to Input(Shape) in
// ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
ctx->ShareLoD("X", /*->*/ "Out");
return;
}
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ValidateShape(shape, x_dims);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", /*->*/ "Out");
}
}
static framework::DDim ValidateShape(const std::vector<int> shape,
const framework::DDim &in_dims) {
const int64_t in_size = framework::product(in_dims);
// only one dimension canbe set to -1, whose size will be automatically
// infered.
const int64_t unk_dim_val = -1;
const int64_t copy_dim_val = 0;
std::vector<int64_t> output_shape(shape.size(), 0);
int64_t capacity = 1;
int unk_dim_idx = -1;
for (size_t i = 0; i < shape.size(); ++i) {
if (shape[i] == unk_dim_val) {
PADDLE_ENFORCE(
unk_dim_idx == -1,
"Only one input dimension of Attr(shape) can be unknown.");
unk_dim_idx = i;
} else if (shape[i] == copy_dim_val) {
PADDLE_ENFORCE(
static_cast<int>(i) < in_dims.size(),
"The index of dimension to copy from input shape must be less "
"than the size of input shape.");
} else {
PADDLE_ENFORCE(
shape[i] > 0,
"Each input dimension of Attr(shape) must not be negtive except "
"one unknown dimension.");
}
capacity *= (shape[i] ? shape[i] : in_dims[i]);
output_shape[i] =
(shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
}
if (unk_dim_idx != -1) {
output_shape[unk_dim_idx] = -in_size / capacity;
PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
"Invalid shape is given.");
} else {
PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
}
return framework::make_ddim(output_shape);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
template <typename DeviceContext, typename T>
class ReshapeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out = ctx.Output<framework::Tensor>("Out");
auto* in = ctx.Input<framework::Tensor>("X");
void Compute(const framework::ExecutionContext &ctx) const {
auto *out = ctx.Output<framework::LoDTensor>("Out");
auto *in = ctx.Input<framework::LoDTensor>("X");
auto *shape_tensor = ctx.Input<framework::LoDTensor>("Shape");
framework::DDim out_dims = out->dims();
if (shape_tensor) {
auto *shape_data = shape_tensor->data<int>();
if (platform::is_gpu_place(ctx.GetPlace())) {
framework::Tensor cpu_shape_tensor;
TensorCopy(*shape_tensor, platform::CPUPlace(), ctx.device_context(),
&cpu_shape_tensor);
shape_data = cpu_shape_tensor.data<int>();
}
auto shape =
std::vector<int>(shape_data, shape_data + shape_tensor->numel());
out_dims = ReshapeOp::ValidateShape(shape, in->dims());
}
if (!in->lod().empty()) {
PADDLE_ENFORCE_EQ(
out_dims[0], in->dims()[0],
"Reshape operator cannot reshape an input sequence batch "
"into an output sequence batch that has a different "
"number of time steps. Please consider using "
"sequence_reshape op.");
}
bool inplace = ctx.Attr<bool>("inplace");
auto out_dims = out->dims();
out->Resize(out_dims);
if (!inplace) {
out->mutable_data<T>(ctx.GetPlace());
framework::TensorCopy(*in, ctx.GetPlace(), ctx.device_context(), out);
// TensorCopy will resize to in_dims.
out->Resize(out_dims);
} else {
out->ShareDataWith(*in);
......@@ -42,9 +154,10 @@ class ReshapeKernel : public framework::OpKernel<T> {
template <typename DeviceContext, typename T>
class ReshapeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
void Compute(const framework::ExecutionContext &ctx) const {
auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_x->mutable_data<T>(ctx.GetPlace());
bool inplace = ctx.Attr<bool>("inplace");
......
python/paddle/fluid/distribute_transpiler.py
浏览文件 @ 629c9210
......@@ -276,20 +276,25 @@ class DistributeTranspiler:
suff_idx = v.name.find(".trainer_")
if suff_idx >= 0:
orig_var_name = v.name[:suff_idx]
pserver_program.global_block().create_var(
else:
orig_var_name = v.name
single_trainer_var = pserver_program.global_block().create_var(
name=orig_var_name,
persistable=True,
type=v.type,
dtype=v.dtype,
shape=v.shape)
for trainer_id in xrange(self.trainers):
var = pserver_program.global_block().create_var(
name="%s.trainer_%d" % (orig_var_name, trainer_id),
persistable=False,
type=v.type,
dtype=v.dtype,
shape=v.shape)
recv_inputs.append(var)
if self.trainers > 1:
for trainer_id in xrange(self.trainers):
var = pserver_program.global_block().create_var(
name="%s.trainer_%d" % (orig_var_name, trainer_id),
persistable=False,
type=v.type,
dtype=v.dtype,
shape=v.shape)
recv_inputs.append(var)
else:
recv_inputs.append(single_trainer_var)
# step3
optimize_block = pserver_program.create_block(0)
......@@ -511,8 +516,11 @@ class DistributeTranspiler:
def _append_split_op(self, program, gradblocks):
# Split variables that need to be split and append respective ops
add_suffix = False
if self.trainers > 1:
add_suffix = True
var_mapping = self._create_vars_from_blocklist(
program, gradblocks, add_trainer_suffix=True)
program, gradblocks, add_trainer_suffix=add_suffix)
for varname, splited_vars in var_mapping.iteritems():
# variable that don't need to split have empty splited_vars
if len(splited_vars) <= 1:
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 629c9210
......@@ -19,7 +19,6 @@ from layer_function_generator import generate_layer_fn
from layer_function_generator import autodoc
from ..layer_helper import LayerHelper
import tensor
import ops
import nn
import math
......@@ -58,7 +57,7 @@ def detection_output(loc,
This operation is to get the detection results by performing following
two steps:
1. Decode input bounding box predictions according to the prior boxes.
2. Get the final detection results by applying multi-class non maximum
suppression (NMS).
......@@ -130,9 +129,9 @@ def detection_output(loc,
target_box=loc,
code_type='decode_center_size')
old_shape = scores.shape
scores = ops.reshape(x=scores, shape=(-1, old_shape[-1]))
scores = nn.reshape(x=scores, shape=(-1, old_shape[-1]))
scores = nn.softmax(input=scores)
scores = ops.reshape(x=scores, shape=old_shape)
scores = nn.reshape(x=scores, shape=old_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
......@@ -463,7 +462,7 @@ def ssd_loss(location,
num, num_prior, num_class = confidence.shape
def __reshape_to_2d(var):
return ops.reshape(x=var, shape=[-1, var.shape[-1]])
return nn.reshape(x=var, shape=[-1, var.shape[-1]])
# 1. Find matched boundding box by prior box.
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
......@@ -474,7 +473,7 @@ def ssd_loss(location,
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
gt_label = ops.reshape(x=gt_label, shape=gt_label.shape + (1, ))
gt_label = nn.reshape(x=gt_label, shape=gt_label.shape + (1, ))
gt_label.stop_gradient = True
target_label, _ = target_assign(
gt_label, matched_indices, mismatch_value=background_label)
......@@ -487,7 +486,7 @@ def ssd_loss(location,
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
conf_loss = ops.reshape(x=conf_loss, shape=(num, num_prior))
conf_loss = nn.reshape(x=conf_loss, shape=(num, num_prior))
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
dtype = matched_indices.dtype
......@@ -556,7 +555,7 @@ def ssd_loss(location,
# 5.3 Compute overall weighted loss.
loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
# reshape to [N, Np], N is the batch size and Np is the prior box number.
loss = ops.reshape(x=loss, shape=[-1, num_prior])
loss = nn.reshape(x=loss, shape=[-1, num_prior])
loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
if normalize:
normalizer = nn.reduce_sum(target_loc_weight)
......@@ -709,7 +708,7 @@ def multi_box_head(inputs,
new_shape = [
-1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
]
out = ops.reshape(x=input, shape=new_shape)
out = nn.reshape(x=input, shape=new_shape)
return out
def _is_list_or_tuple_(data):
......@@ -803,7 +802,7 @@ def multi_box_head(inputs,
mbox_loc.shape[0],
mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3] / 4, 4
]
mbox_loc_flatten = ops.reshape(mbox_loc, shape=new_shape)
mbox_loc_flatten = nn.reshape(mbox_loc, shape=new_shape)
mbox_locs.append(mbox_loc_flatten)
# get conf
......@@ -819,7 +818,7 @@ def multi_box_head(inputs,
conf_loc.shape[0], conf_loc.shape[1] * conf_loc.shape[2] *
conf_loc.shape[3] / num_classes, num_classes
]
conf_loc_flatten = ops.reshape(conf_loc, shape=new_shape)
conf_loc_flatten = nn.reshape(conf_loc, shape=new_shape)
mbox_confs.append(conf_loc_flatten)
if len(box_results) == 1:
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 629c9210
......@@ -73,6 +73,7 @@ __all__ = [
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'lod_reset',
'lrn',
'pad',
......@@ -3266,6 +3267,8 @@ def one_hot(input, depth):
The one-hot tensor or LodTensor, same as input.
Examples:
.. code-block:: python
X is a LoDTensor:
X.lod = [[0, 1, 4]]
X.shape = [4, 1]
......@@ -3320,6 +3323,101 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
return counter
def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"""
Gives a new shape to the input Tensor without changing its data.
The target shape can be given by :attr:`shape` or :attr:`actual_shape`.
:attr:`shape` is a list of integer while :attr:`actual_shape` is a tensor
variable. :attr:`actual_shape` has a higher priority than :attr:`shape`
if it is provided, while :attr:`shape` still should be set correctly to
gurantee shape inference in compile-time.
Some tricks exist when specifying the target shape.
1. -1 means the value of this dimension is inferred from the total element
number of x and remaining dimensions. Thus one and only one dimension can
be set -1.
2. 0 means the actual dimension value is going to be copied from the
corresponding dimension of x. The indice of 0s in shape can not exceed
Rank(X).
Here are some examples to explain it.
1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [6, 8], the reshape operator will transform x into a 2-D tensor with
shape [6, 8] and leaving x's data unchanged.
2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
specified is [2, 3, -1, 2], the reshape operator will transform x into a
4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
case, one dimension of the target shape is set to -1, the value of this
dimension is inferred from the total element number of x and remaining
dimensions.
3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
besides -1, 0 means the actual dimension value is going to be copied from
the corresponding dimension of x.
Args:
input(variable): The input tensor.
shape(list): The new shape. At most one dimension of the new shape can
be -1.
actual_shape(variable): An optional input. If provided, reshape
according to this given shape rather than
:attr:`shape` specifying shape. That is to
say :attr:`actual_shape` has a higher priority
than :attr:`shape`.
act (str): The non-linear activation to be applied to output variable.
inplace(bool): If this flag is set true, a new output tensor is created
whose data is copied from input x, otherwise the output
shares data with input without copying.
Returns(variable): The output tensor.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.reshape(
x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True)
"""
if not (isinstance(shape, list) or isinstance(shape, tuple)):
raise ValueError("Input shape must be a python lsit or tuple.")
# Validate the shape
unk_dim_idx = -1
for dim_idx, dim_size in enumerate(shape):
if dim_size == -1:
assert unk_dim_idx == -1, (
"Only one dimension in shape can be unknown.")
unk_dim_idx = dim_idx
elif dim_size == 0:
assert dim_idx < len(x.shape), (
"The indice of 0s in shape can not exceed Rank(X).")
else:
assert dim_size > 0, (
"Each dimension size given in shape must not be negtive "
"except one unknown dimension.")
helper = LayerHelper("reshape", **locals())
reshaped = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape",
inputs={"X": x,
"Shape": actual_shape}
if isinstance(actual_shape, Variable) else {"X": x},
attrs={"shape": shape,
"inplace": inplace},
outputs={"Out": reshaped})
return helper.append_activation(reshaped)
def lod_reset(x, y=None, target_lod=None):
"""
LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 629c9210
......@@ -49,7 +49,6 @@ __activations__ = [
__all__ = [
'mean',
'mul',
'reshape',
'scale',
'sigmoid_cross_entropy_with_logits',
'elementwise_add',
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 629c9210
......@@ -334,7 +334,7 @@ class OpTest(unittest.TestCase):
np.allclose(
actual_t, expect_t, atol=atol),
"Output (" + out_name + ") has diff at " + str(place) +
str(actual_t) + str(expect_t))
str(actual_t) + "\n" + str(expect_t))
if isinstance(expect, tuple):
self.assertListEqual(actual.lod(), expect[1],
"Output (" + out_name +
......@@ -568,6 +568,6 @@ class OpTest(unittest.TestCase):
fetch_list = [g for p, g in param_grad_list]
executor = Executor(place)
return map(
np.array,
executor.run(prog, feed_dict, fetch_list, return_numpy=False))
return map(np.array,
executor.run(prog, feed_dict, fetch_list,
return_numpy=False))
python/paddle/fluid/tests/unittests/test_reshape_op.py
浏览文件 @ 629c9210
......@@ -14,15 +14,19 @@
import unittest
import numpy as np
from op_test import OpTest
class TestReshapeOp(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
self.attrs = {'shape': [10 * 20]}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
......@@ -31,12 +35,33 @@ class TestReshapeOp(OpTest):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer(OpTest):
class TestReshapeOpDimInfer1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.op_type = "reshape"
self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
self.attrs = {'shape': [4, -1, 5]}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
def test_check_output(self):
self.check_output()
......@@ -47,10 +72,30 @@ class TestReshapeOpDimInfer(OpTest):
class TestReshapeOpInplace(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.op_type = "reshape"
self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
self.attrs = {'shape': [10 * 20], 'inplace': True}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
......@@ -59,12 +104,38 @@ class TestReshapeOpInplace(OpTest):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace(OpTest):
class TestReshapeOpDimInferInplace2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpWithInputShape(OpTest):
def setUp(self):
ori_shape = (6, 5)
new_shape = (0, -1, 5)
actual_shape = (2, 3, 5)
self.op_type = "reshape"
self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
self.attrs = {'shape': [4, -1, 5], 'inplace': True}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
self.inputs = {
"X": np.random.random(ori_shape).astype("float32"),
"Shape": np.array(
actual_shape, dtype="int32")
}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(actual_shape)}
def test_check_output(self):
self.check_output()
......@@ -73,5 +144,5 @@ class TestReshapeOpDimInferInplace(OpTest):
self.check_grad(["X"], "Out")
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_target_assign_op.py 100755 → 100644
浏览文件 @ 629c9210
文件模式从 100755 更改为 100644
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