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提交 018e2f3a 编写于 作者: T tangwei12
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Merge branch 'dis_ckpt_fix' of github.com:seiriosPlus/Paddle into dis_ckpt_fix

上级 74c5476f 04b1df2a
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Showing with 513 addition and 487 deletion
paddle/fluid/API.spec
浏览文件 @ 018e2f3a
paddle.fluid.Variable.__init__ ArgSpec(args=['self', 'block', 'type', 'name', 'shape', 'dtype', 'lod_level', 'capacity', 'persistable', 'error_clip', 'stop_gradient', 'is_data'], varargs=None, keywords='kwargs', defaults=(VarType.LOD_TENSOR, None, None, None, None, None, None, None, False, False))
paddle.fluid.Variable.astype ArgSpec(args=['self', 'dtype'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Variable.set_desc ArgSpec(args=['self', 'input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Variable.set_error_clip ArgSpec(args=['self', 'error_clip'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Variable.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.Program.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.block ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.clone ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,))
......@@ -33,8 +28,6 @@ paddle.fluid.Operator.set_attr ArgSpec(args=['self', 'name', 'val'], varargs=Non
paddle.fluid.Operator.to_string ArgSpec(args=['self', 'throw_on_error'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Parameter.__init__ ArgSpec(args=['self', 'block', 'shape', 'dtype'], varargs=None, keywords='kwargs', defaults=None)
paddle.fluid.Parameter.astype ArgSpec(args=['self', 'dtype'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Parameter.set_desc ArgSpec(args=['self', 'input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Parameter.set_error_clip ArgSpec(args=['self', 'error_clip'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Parameter.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
......@@ -42,8 +35,7 @@ paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', def
paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.as_lodtensor ArgSpec(args=['self', 'data'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.begin_pass ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.end_pass ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
......@@ -207,31 +199,23 @@ paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None
paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.split_lod_tensor ArgSpec(args=['input', 'mask', 'level'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.merge_lod_tensor ArgSpec(args=['in_true', 'in_false', 'x', 'mask', 'level'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.complete ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.lod_rank_table ArgSpec(args=['x', 'level'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.max_sequence_len ArgSpec(args=['rank_table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.lod_tensor_to_array ArgSpec(args=['x', 'table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.array_to_lod_tensor ArgSpec(args=['x', 'table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.increment ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True))
paddle.fluid.layers.array_write ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.create_array ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.less_than ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None))
paddle.fluid.layers.equal ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords='ignored', defaults=(None,))
paddle.fluid.layers.array_read ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shrink_memory ArgSpec(args=['x', 'i', 'table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.array_length ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.IfElse.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.IfElse.false_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
paddle.fluid.layers.IfElse.parent_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.DynamicRNN.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
......@@ -240,9 +224,6 @@ paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs',
paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.update_memory ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ConditionalBlock.__init__ ArgSpec(args=['self', 'inputs', 'is_scalar_condition', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.ConditionalBlock.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ConditionalBlock.complete ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.StaticRNN.complete_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.memory ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1))
......
paddle/fluid/framework/executor.cc
浏览文件 @ 018e2f3a
......@@ -45,19 +45,13 @@ ExecutorPrepareContext::~ExecutorPrepareContext() {
Executor::Executor(const platform::Place& place) : place_(place) {}
void Executor::Close() {
#ifdef PADDLE_WITH_DISTRIBUTE
void Executor::BeginPass() {
::paddle::operators::distributed::RPCClient::GetInstance<
::paddle::operators::distributed::GRPCClient>()
->SendBeginPass();
}
void Executor::EndPass() {
::paddle::operators::distributed::RPCClient::GetInstance<
::paddle::operators::distributed::GRPCClient>()
->SendEndPass();
}
->SendComplete();
#endif
}
void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
if (var_type == proto::VarType::LOD_TENSOR) {
......
paddle/fluid/framework/executor.h
浏览文件 @ 018e2f3a
......@@ -44,17 +44,11 @@ class Executor {
explicit Executor(const platform::Place& place);
#ifdef PADDLE_WITH_DISTRIBUTE
/*
* Sending signal to pserver to mark current pass started.
* Close this Executor.
* Calling this method will send complete messages to all pserver instances.
*/
void BeginPass();
/*
* Sending signal to pserver to mark current pass finished.
*/
void EndPass();
#endif
void Close();
/* @Brief
* Runtime evaluation of the given ProgramDesc under certain Scope
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 018e2f3a
......@@ -137,6 +137,7 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
executor_->RunPreparedContext(
ctx_.get(), sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
&feed_targets, &fetch_targets,
false, /* don't create local scope each time*/
false /* don't create variable eatch time */);
VLOG(4) << "Finish prepared context";
if (!GetFetch(fetchs, output_data)) {
......
paddle/fluid/inference/tensorrt/convert/fc_op.cc
浏览文件 @ 018e2f3a
......@@ -32,11 +32,11 @@ void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides,
for (int h = 0; h < shape.h(); ++h) {
for (int w = 0; w < shape.w(); ++w) {
odata[h * ostrides.h() + w * ostrides.w()] =
idata[h * ostrides.h() + w * ostrides.w()];
idata[h * istrides.h() + w * istrides.w()];
}
}
}
// indata c * k
// Reorder the data layout from CK to KC.
void ReorderCKtoKC(TensorRTEngine::Weight& iweights,
TensorRTEngine::Weight* oweights) {
......@@ -79,9 +79,8 @@ class FcOpConverter : public OpConverter {
framework::LoDTensor tmp;
tmp.Resize(Y_t->dims());
memcpy(tmp.mutable_data<float>(platform::CPUPlace()), Y_t->data<float>(),
Y_t->dims()[0] * Y_t->dims()[1]);
memcpy(tmp.mutable_data<float>(platform::CPUPlace()), weight_data,
Y_t->dims()[0] * Y_t->dims()[1] * sizeof(float));
TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
static_cast<void*>(weight_data),
Y_t->memory_size() / sizeof(float)};
......@@ -93,7 +92,7 @@ class FcOpConverter : public OpConverter {
// The data layout of TRT FC layer's weight is different from fluid's FC,
// need to reorder the elements.
ReorderCKtoKC(tmp_weight, &weight);
ReorderCKtoKC(weight, &tmp_weight);
// Currently, the framework can only handle one fluid op -> one TRT layer,
// but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just
......@@ -103,7 +102,7 @@ class FcOpConverter : public OpConverter {
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected,
*const_cast<nvinfer1::ITensor*>(X),
n_output, weight.get(), bias.get());
n_output, tmp_weight.get(), bias.get());
auto output_name = op_desc.Output("Out").front();
engine_->SetITensor(output_name, layer->getOutput(0));
......@@ -118,4 +117,3 @@ class FcOpConverter : public OpConverter {
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(fc, FcOpConverter);
USE_OP(mul);
paddle/fluid/inference/tensorrt/convert/test_activation_op.cc
浏览文件 @ 018e2f3a
......@@ -37,7 +37,7 @@ TEST(ReluOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(10);
validator.Execute(1);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
浏览文件 @ 018e2f3a
......@@ -23,11 +23,11 @@ namespace tensorrt {
TEST(fc_op, test) {
std::unordered_set<std::string> parameters({"mul-Y"});
framework::Scope scope;
TRTConvertValidation validator(20, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims4(8, 3, 1, 1));
validator.DeclParamVar("mul-Y", nvinfer1::Dims2(3, 2));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(8, 2));
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims4(1, 10, 1, 1));
validator.DeclParamVar("mul-Y", nvinfer1::Dims2(10, 2));
// validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(1, 2));
// Prepare Op description
framework::OpDesc desc;
......@@ -38,9 +38,10 @@ TEST(fc_op, test) {
validator.SetOp(*desc.Proto());
validator.Execute(10);
validator.Execute(1);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(mul);
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
浏览文件 @ 018e2f3a
......@@ -39,7 +39,7 @@ TEST(MulOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(10);
validator.Execute(1);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 018e2f3a
......@@ -39,7 +39,7 @@ namespace tensorrt {
float random(float low, float high) {
static std::random_device rd;
static std::mt19937 mt(rd());
std::uniform_real_distribution<double> dist(1.0, 10.0);
std::uniform_real_distribution<double> dist(low, high);
return dist(mt);
}
......@@ -49,6 +49,7 @@ void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place,
size_t num_elements = analysis::AccuDims(dims, dims.size());
PADDLE_ENFORCE_GT(num_elements, 0);
auto* data = tensor->mutable_data<float>(place);
for (size_t i = 0; i < num_elements; i++) {
*(data + i) = random(0., 1.);
}
......@@ -68,7 +69,7 @@ class TRTConvertValidation {
int workspace_size = 1 << 10)
: parameters_(parameters), scope_(scope) {
// create engine.
engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
engine_.reset(new TensorRTEngine(batch_size, workspace_size, &stream_));
engine_->InitNetwork();
PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
......@@ -138,12 +139,11 @@ class TRTConvertValidation {
cudaStreamSynchronize(*engine_->stream());
ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
const size_t output_space_size = 200;
const size_t output_space_size = 2000;
for (const auto& output : op_desc_->OutputArgumentNames()) {
std::vector<float> fluid_out;
std::vector<float> trt_out(output_space_size);
engine_->GetOutputInCPU(output, &trt_out[0],
output_space_size * sizeof(float));
engine_->GetOutputInCPU(output, &trt_out[0], output_space_size);
cudaStreamSynchronize(*engine_->stream());
auto* var = scope_.FindVar(output);
......
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @ 018e2f3a
/* 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.
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
......@@ -26,6 +26,8 @@ namespace paddle {
namespace inference {
namespace tensorrt {
int TensorRTEngine::runtime_batch_ = 1;
void TensorRTEngine::Build(const DescType &paddle_model) {
PADDLE_ENFORCE(false, "not implemented");
}
......@@ -42,6 +44,7 @@ void TensorRTEngine::Execute(int batch_size) {
PADDLE_ENFORCE_NOT_NULL(stream_);
infer_context_->enqueue(batch_size, buffers.data(), *stream_, nullptr);
cudaStreamSynchronize(*stream_);
SetRuntimeBatch(batch_size);
}
TensorRTEngine::~TensorRTEngine() {
......@@ -80,17 +83,17 @@ void TensorRTEngine::FreezeNetwork() {
auto dims = infer_engine_->getBindingDimensions(slot_offset);
item.second = kDataTypeSize[static_cast<int>(
infer_engine_->getBindingDataType(slot_offset))] *
analysis::AccuDims(dims.d, dims.nbDims);
analysis::AccuDims(dims.d, dims.nbDims) * max_batch_;
PADDLE_ENFORCE_GT(item.second, 0);
}
auto &buf = buffer(item.first);
buf.max_size = item.second * max_batch_;
CHECK(buf.buffer == nullptr); // buffer should be allocated only once.
PADDLE_ENFORCE_EQ(0, cudaMalloc(&buf.buffer, buf.max_size));
PADDLE_ENFORCE_LE(buf.max_size, 1 << 30); // 10G
// buf.size will changed in the runtime.
PADDLE_ENFORCE_EQ(0, cudaMalloc(&buf.buffer, item.second * max_batch_));
buf.size = 0;
PADDLE_ENFORCE_LE(buf.max_size, 1 << 30); // 10G
buf.device = DeviceType::GPU;
}
}
......@@ -105,7 +108,7 @@ nvinfer1::ITensor *TensorRTEngine::DeclareInput(const std::string &name,
auto *input = infer_network_->addInput(name.c_str(), dtype, dims);
PADDLE_ENFORCE(input, "infer network add input %s failed", name);
buffer_sizes_[name] = kDataTypeSize[static_cast<int>(dtype)] *
analysis::AccuDims(dims.d, dims.nbDims);
analysis::AccuDims(dims.d, dims.nbDims) * max_batch_;
PADDLE_ENFORCE(input->isNetworkInput());
TensorRTEngine::SetITensor(name, input);
return input;
......@@ -149,35 +152,42 @@ void *TensorRTEngine::GetOutputInGPU(const std::string &name) {
void TensorRTEngine::GetOutputInGPU(const std::string &name, void *dst,
size_t max_size) {
// determine data size
auto *output = TensorRTEngine::GetITensor(name);
nvinfer1::Dims dims = output->getDimensions();
auto dim_size = analysis::AccuDims(dims.d, dims.nbDims);
size_t dst_size = dim_size * runtime_batch_ *
kDataTypeSize[static_cast<int>(output->getType())];
auto it = buffer_sizes_.find(name);
PADDLE_ENFORCE(it != buffer_sizes_.end());
PADDLE_ENFORCE_GT(it->second, 0);
PADDLE_ENFORCE_GE(max_size, it->second);
PADDLE_ENFORCE_LE(dst_size, it->second);
PADDLE_ENFORCE_GE(max_size, dst_size);
auto &buf = buffer(name);
PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated before");
PADDLE_ENFORCE_EQ(cudaMemcpyAsync(dst, buf.buffer, it->second,
PADDLE_ENFORCE_EQ(cudaMemcpyAsync(dst, buf.buffer, dst_size,
cudaMemcpyDeviceToDevice, *stream_),
0);
}
void TensorRTEngine::GetOutputInCPU(const std::string &name, void *dst,
size_t max_size) {
VLOG(4) << "get output in cpu";
auto &buf = buffer(name);
// Update needed buffer size.
auto slot_offset = infer_engine_->getBindingIndex(name.c_str());
auto dims = infer_engine_->getBindingDimensions(slot_offset);
buf.size = kDataTypeSize[static_cast<int>(
infer_engine_->getBindingDataType(slot_offset))] *
analysis::AccuDims(dims.d, dims.nbDims);
PADDLE_ENFORCE_LE(buf.size, buf.max_size);
// determine data size
auto *output = TensorRTEngine::GetITensor(name);
nvinfer1::Dims dims = output->getDimensions();
auto dim_size = analysis::AccuDims(dims.d, dims.nbDims);
size_t dst_size = dim_size * runtime_batch_ *
kDataTypeSize[static_cast<int>(output->getType())];
auto it = buffer_sizes_.find(name);
PADDLE_ENFORCE(it != buffer_sizes_.end());
PADDLE_ENFORCE_GT(it->second, 0);
PADDLE_ENFORCE_LE(dst_size, it->second);
PADDLE_ENFORCE_GE(max_size, dst_size);
auto &buf = buffer(name);
PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated before");
// DEBUG
memset(dst, 0, buf.size);
PADDLE_ENFORCE_EQ(
0, cudaMemcpy(dst, buf.buffer, buf.size, cudaMemcpyDeviceToHost));
PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(dst, buf.buffer, dst_size,
cudaMemcpyDeviceToHost, *stream_));
}
Buffer &TensorRTEngine::buffer(const std::string &name) {
......@@ -225,6 +235,12 @@ nvinfer1::ITensor *TensorRTEngine::GetITensor(const std::string &name) {
return itensor_map_[name];
}
void TensorRTEngine::SetRuntimeBatch(size_t batch_size) {
runtime_batch_ = batch_size;
}
int TensorRTEngine::GetRuntimeBatch() { return runtime_batch_; }
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ 018e2f3a
......@@ -117,10 +117,14 @@ class TensorRTEngine : public EngineBase {
nvinfer1::ICudaEngine* engine() { return infer_engine_.get(); }
nvinfer1::INetworkDefinition* network() { return infer_network_.get(); }
void SetRuntimeBatch(size_t batch_size);
int GetRuntimeBatch();
private:
// the max batch size
int max_batch_;
// the runtime batch size
static int runtime_batch_;
// the max memory size the engine uses
int max_workspace_;
......
paddle/fluid/inference/tensorrt/test_engine.cc
浏览文件 @ 018e2f3a
......@@ -28,7 +28,7 @@ class TensorRTEngineTest : public ::testing::Test {
protected:
void SetUp() override {
ASSERT_EQ(0, cudaStreamCreate(&stream_));
engine_ = new TensorRTEngine(1, 1 << 10, &stream_);
engine_ = new TensorRTEngine(10, 1 << 10, &stream_);
engine_->InitNetwork();
}
......@@ -71,7 +71,7 @@ TEST_F(TensorRTEngineTest, add_layer) {
LOG(INFO) << "to get output";
float y_cpu;
engine_->GetOutputInCPU("y", &y_cpu, sizeof(float));
engine_->GetOutputInCPU("y", &y_cpu, 1 * sizeof(float));
LOG(INFO) << "to checkout output";
ASSERT_EQ(y_cpu, x_v * 2 + 3);
......@@ -103,15 +103,49 @@ TEST_F(TensorRTEngineTest, add_layer_multi_dim) {
LOG(INFO) << "to get output";
float y_cpu[2] = {-1., -1.};
auto dims = engine_->GetITensor("y")->getDimensions();
ASSERT_EQ(dims.nbDims, 3);
ASSERT_EQ(dims.d[0], 2);
ASSERT_EQ(dims.d[1], 1);
engine_->GetOutputInCPU("y", &y_cpu[0], sizeof(float) * 2);
engine_->GetOutputInCPU("y", &y_cpu[0], 2 * sizeof(float));
ASSERT_EQ(y_cpu[0], 4.5);
ASSERT_EQ(y_cpu[1], 14.5);
}
TEST_F(TensorRTEngineTest, test_conv2d_temp) {
// Weight in CPU memory.
float raw_weight[9] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
float raw_bias[1] = {0};
TensorRTEngine::Weight weight(nvinfer1::DataType::kFLOAT, raw_weight, 9);
TensorRTEngine::Weight bias(nvinfer1::DataType::kFLOAT, raw_bias, 1);
auto* x = engine_->DeclareInput("x", nvinfer1::DataType::kFLOAT,
nvinfer1::Dims3{1, 3, 3});
auto* conv_layer =
TRT_ENGINE_ADD_LAYER(engine_, Convolution, *x, 1, nvinfer1::DimsHW{3, 3},
weight.get(), bias.get());
PADDLE_ENFORCE(conv_layer != nullptr);
conv_layer->setStride(nvinfer1::DimsHW{1, 1});
conv_layer->setPadding(nvinfer1::DimsHW{1, 1});
engine_->DeclareOutput(conv_layer, 0, "y");
engine_->FreezeNetwork();
ASSERT_EQ(engine_->engine()->getNbBindings(), 2);
float x_v[18] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
engine_->SetInputFromCPU("x", reinterpret_cast<void*>(&x_v),
18 * sizeof(float));
engine_->Execute(2);
LOG(INFO) << "to get output";
float* y_cpu = new float[18];
engine_->GetOutputInCPU("y", &y_cpu[0], 18 * sizeof(float));
ASSERT_EQ(y_cpu[0], 4.0);
ASSERT_EQ(y_cpu[1], 6.0);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ 018e2f3a
......@@ -210,13 +210,14 @@ void TestInference(const std::string& dirname,
// Ignore the profiling results of the first run
std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
bool CreateLocalScope = CreateVars;
if (PrepareContext) {
ctx = executor.Prepare(*inference_program, 0);
executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
&fetch_targets, true, CreateVars);
&fetch_targets, CreateLocalScope, CreateVars);
} else {
executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
true, CreateVars);
CreateLocalScope, CreateVars);
}
// Enable the profiler
......@@ -232,10 +233,11 @@ void TestInference(const std::string& dirname,
// Note: if you change the inference_program, you need to call
// executor.Prepare() again to get a new ExecutorPrepareContext.
executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
&fetch_targets, CreateVars);
&fetch_targets, CreateLocalScope,
CreateVars);
} else {
executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
CreateVars);
CreateLocalScope, CreateVars);
}
}
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 018e2f3a
......@@ -18,7 +18,7 @@ if(WITH_GRPC)
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(grpc_serde_test SRCS grpc_serde_test.cc
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(grpc_server_test SRCS rpc_server_test.cc
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op SERIAL)
return()
endif()
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ 018e2f3a
......@@ -36,20 +36,16 @@ void GRPCClient::InitEventLoop() {
client_thread_.reset(new std::thread(std::bind(&GRPCClient::Proceed, this)));
}
void GRPCClient::SendBeginPass() {
void GRPCClient::SendComplete() {
std::unique_lock<std::mutex> lk(completed_mutex_);
if (!completed_) {
for (auto& it : channels_) {
VLOG(3) << "send begin pass to: " << it.first;
this->AsyncSendBeginPass(it.first);
VLOG(3) << "send complete message to " << it.first;
this->AsyncSendComplete(it.first);
}
this->Wait();
}
void GRPCClient::SendEndPass() {
for (auto& it : channels_) {
VLOG(3) << "send end pass to " << it.first;
this->AsyncSendEndPass(it.first);
PADDLE_ENFORCE(this->Wait(), "internal grpc error");
completed_ = true;
}
this->Wait();
}
GRPCClient::~GRPCClient() {
......@@ -239,32 +235,19 @@ void GRPCClient::AsyncSendFetchBarrier(const std::string& ep,
req_count_++;
}
void GRPCClient::AsyncSendBeginPass(const std::string& ep, int64_t time_out) {
void GRPCClient::AsyncSendComplete(const std::string& ep, int64_t time_out) {
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
s->Prepare(time_out);
sendrecv::VariableMessage req;
req.set_varname(BEGIN_PASS_MESSAGE);
req.set_varname(COMPLETE_MESSAGE);
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
}
void GRPCClient::AsyncSendEndPass(const std::string& ep, int64_t time_out) {
const auto ch = GetChannel(ep);
FetchBarrierProcessor* s = new FetchBarrierProcessor(ch);
s->Prepare(time_out);
sendrecv::VariableMessage req;
req.set_varname(END_PASS_MESSAGE);
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
}
void GRPCClient::AsyncCheckpointNotify(const std::string& ep,
const std::string& dir,
int64_t time_out) {
......
paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ 018e2f3a
......@@ -174,7 +174,7 @@ class CheckpointNotifyProcessor : public BaseProcessor {
class GRPCClient : public RPCClient {
public:
GRPCClient() : ok_(true) {}
GRPCClient() : ok_(true), completed_(false) {}
virtual ~GRPCClient();
bool AsyncSendVar(const std::string& ep, const platform::DeviceContext& ctx,
......@@ -201,17 +201,12 @@ class GRPCClient : public RPCClient {
void AsyncCheckpointNotify(const std::string& ep, const std::string& dir,
int64_t time_out = FLAGS_rpc_deadline) override;
void AsyncSendBeginPass(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) override;
void AsyncSendEndPass(const std::string& ep,
void AsyncSendComplete(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) override;
bool Wait() override;
void SendBeginPass() override;
void SendEndPass() override;
void SendComplete() override;
protected:
void InitImpl() override;
......@@ -238,6 +233,10 @@ class GRPCClient : public RPCClient {
// mutex for GetChannel thread safety
std::mutex chan_mutex_;
DISABLE_COPY_AND_ASSIGN(GRPCClient);
// mutex for sending complete message only once
std::mutex completed_mutex_;
bool completed_;
};
} // namespace distributed
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ 018e2f3a
......@@ -43,8 +43,6 @@ constexpr char kRequestPassBarrier[] = "RequestPassBarrier";
#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
#define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
#define COMPLETE_MESSAGE "COMPLETE@RECV"
#define BEGIN_PASS_MESSAGE "BEGIN_PASS@RECV"
#define END_PASS_MESSAGE "END_PASS@RECV"
#define CHECKPOINT_SAVE_MESSAGE "SAVE@CHECKPOINTNOTIFY"
#define CHECKPOINT_LOAD_MESSAGE "LOAD@CHECKPOINTNOTIFY"
......
paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ 018e2f3a
......@@ -55,10 +55,9 @@ bool RequestSendHandler::Handle(const std::string& varname,
if (varname == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv BATCH_BARRIER_MESSAGE";
rpc_server_->IncreaseBatchBarrier(kRequestSend);
} else if (varname == BEGIN_PASS_MESSAGE) {
VLOG(3) << "sync: recv begin pass message";
rpc_server_->WaitCond(kRequestSend);
rpc_server_->BeginPass();
} else if (varname == COMPLETE_MESSAGE) {
VLOG(3) << "sync: recv complete message";
rpc_server_->Complete();
} else {
VLOG(3) << "sync: received var_name: " << varname;
rpc_server_->WaitCond(kRequestSend);
......@@ -94,14 +93,12 @@ bool RequestGetHandler::Handle(const std::string& varname,
if (varname == FETCH_BARRIER_MESSAGE) {
VLOG(3) << "sync: recv fetch barrier message";
rpc_server_->IncreaseBatchBarrier(kRequestGet);
} else if (varname == END_PASS_MESSAGE) {
rpc_server_->EndPass();
} else {
rpc_server_->WaitCond(kRequestGet);
*outvar = scope_->FindVar(varname);
}
} else {
if (varname != FETCH_BARRIER_MESSAGE && varname != END_PASS_MESSAGE) {
if (varname != FETCH_BARRIER_MESSAGE && varname != COMPLETE_MESSAGE) {
*outvar = scope_->FindVar(varname);
}
}
......
paddle/fluid/operators/distributed/rpc_client.h
浏览文件 @ 018e2f3a
......@@ -60,17 +60,13 @@ class RPCClient {
const std::string& dir,
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual void AsyncSendBeginPass(const std::string& ep,
virtual void AsyncSendComplete(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual void AsyncSendEndPass(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) = 0;
// BeginePass/EndPass tells all the pserver that start/end a pass, so that
// the pserver can increase/reduce it's barrier count, and continue to train
// Complete tells all the pserver instances that finishe the training,
// the pserver can reduce it's barrier count, and continue to train
// with other trainers.
virtual void SendBeginPass() = 0;
virtual void SendEndPass() = 0;
virtual void SendComplete() = 0;
virtual bool Wait() = 0;
......
paddle/fluid/operators/distributed/rpc_server.cc
浏览文件 @ 018e2f3a
......@@ -64,18 +64,7 @@ void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
}
}
void RPCServer::BeginPass() {
VLOG(4) << "RPCServer begin increase pass barrier";
{
std::unique_lock<std::mutex> lock(mutex_);
client_num_++;
VLOG(4) << "increase client_num to: " << client_num_;
}
barrier_cond_.notify_all();
}
void RPCServer::EndPass() {
VLOG(4) << "RPCServer begin increase pass barrier";
void RPCServer::Complete() {
{
std::unique_lock<std::mutex> lock(mutex_);
client_num_--;
......@@ -87,6 +76,11 @@ void RPCServer::EndPass() {
barrier_cond_.notify_all();
}
int RPCServer::GetClientNum() {
std::unique_lock<std::mutex> lock(mutex_);
return client_num_;
}
void RPCServer::ResetBarrierCounter() {
VLOG(3) << "RPCServer ResetBarrierCounter ";
std::unique_lock<std::mutex> lock(mutex_);
......
paddle/fluid/operators/distributed/rpc_server.h
浏览文件 @ 018e2f3a
......@@ -44,7 +44,7 @@ class RPCServer {
int GetSelectedPort() const { return selected_port_; }
int GetClientNum() const;
int GetClientNum();
void SavePort() const;
......@@ -64,8 +64,7 @@ class RPCServer {
void WaitCond(const std::string& rpc_name);
void IncreaseBatchBarrier(const std::string rpc_name);
void BeginPass();
void EndPass();
void Complete();
void ResetBarrierCounter();
......
paddle/fluid/operators/distributed/rpc_server_test.cc
浏览文件 @ 018e2f3a
......@@ -91,7 +91,7 @@ void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
}
}
void StartServer() {
void StartServer(const std::string& rpc_name) {
framework::ProgramDesc program;
framework::Scope scope;
platform::CPUPlace place;
......@@ -107,14 +107,14 @@ void StartServer() {
std::shared_ptr<framework::ExecutorPrepareContext>>
prefetch_var_name_to_prepared;
prefetch_var_name_to_prepared[in_var_name] = prepared[0];
g_req_handler->SetProgram(&program);
g_req_handler->SetPrefetchPreparedCtx(&prefetch_var_name_to_prepared);
g_req_handler->SetDevCtx(&ctx);
g_req_handler->SetScope(&scope);
g_req_handler->SetExecutor(&exe);
g_rpc_service->RegisterRPC(distributed::kRequestPrefetch,
g_req_handler.get());
g_rpc_service->RegisterRPC(rpc_name, g_req_handler.get());
g_req_handler->SetRPCServer(g_rpc_service.get());
std::thread server_thread(
......@@ -129,7 +129,7 @@ TEST(PREFETCH, CPU) {
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
std::thread server_thread(StartServer);
std::thread server_thread(StartServer, distributed::kRequestPrefetch);
g_rpc_service->WaitServerReady();
int port = g_rpc_service->GetSelectedPort();
......@@ -162,3 +162,24 @@ TEST(PREFETCH, CPU) {
g_rpc_service.reset(nullptr);
g_req_handler.reset(nullptr);
}
TEST(COMPLETE, CPU) {
g_req_handler.reset(new distributed::RequestSendHandler(true));
g_rpc_service.reset(new RPCSERVER_T("127.0.0.1:0", 2));
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
PADDLE_ENFORCE(client != nullptr);
std::thread server_thread(StartServer, distributed::kRequestSend);
g_rpc_service->WaitServerReady();
int port = g_rpc_service->GetSelectedPort();
std::string ep = paddle::string::Sprintf("127.0.0.1:%d", port);
client->AsyncSendComplete(ep);
client->Wait();
EXPECT_EQ(g_rpc_service->GetClientNum(), 1);
g_rpc_service->ShutDown();
server_thread.join();
g_rpc_service.reset(nullptr);
g_req_handler.reset(nullptr);
}
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
浏览文件 @ 018e2f3a
......@@ -38,12 +38,10 @@ class LoDTensorBlockingQueue {
public:
bool Push(const std::vector<framework::LoDTensor>& lod_tensor_vec) {
CheckDims(lod_tensor_vec);
return queue_.Send(lod_tensor_vec);
}
bool Push(std::vector<framework::LoDTensor>&& lod_tensor_vec) {
CheckDims(lod_tensor_vec);
return queue_.Send(std::move(lod_tensor_vec));
}
......@@ -65,21 +63,6 @@ class LoDTensorBlockingQueue {
inline bool IsClosed() const { return queue_.IsClosed(); }
private:
void CheckDims(
const std::vector<framework::LoDTensor>& lod_tensor_vec) const {
PADDLE_ENFORCE(dims_.size() == lod_tensor_vec.size(),
"Expect input size is %d but found %s", dims_.size(),
lod_tensor_vec.size());
for (size_t i = 0; i < dims_.size(); ++i) {
const auto& in_dims = framework::slice_ddim(
lod_tensor_vec[i].dims(), 1, lod_tensor_vec[i].dims().size());
const auto& expect_dims =
framework::slice_ddim(dims_[i], 1, dims_[i].size());
PADDLE_ENFORCE(in_dims == expect_dims,
"Dims of the %d-th input tensor do not match", i);
}
}
BlockingQueue<std::vector<framework::LoDTensor>> queue_;
std::vector<framework::DDim> dims_;
};
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 018e2f3a
......@@ -216,7 +216,7 @@ class ReshapeKernel {
if (shape_tensor) {
auto *shape_data = shape_tensor->data<int>();
framework::Tensor cpu_shape_tensor;
if (platform::is_gpu_place(ctx.GetPlace())) {
if (platform::is_gpu_place(shape_tensor->place())) {
TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor);
shape_data = cpu_shape_tensor.data<int>();
}
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 018e2f3a
......@@ -55,13 +55,14 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
"TensorRT' tensor input requires at least 2 dimensions");
PADDLE_ENFORCE_LE(shape.size(), 4UL,
"TensorRT' tensor input requires at most 4 dimensions");
switch (shape.size()) {
case 2:
return nvinfer1::Dims2(shape[0], shape[1]);
return nvinfer1::Dims2(1, shape[1]);
case 3:
return nvinfer1::Dims3(shape[0], shape[1], shape[2]);
return nvinfer1::Dims3(1, shape[1], shape[2]);
case 4:
return nvinfer1::Dims4(shape[0], shape[1], shape[2], shape[3]);
return nvinfer1::Dims4(1, shape[1], shape[2], shape[3]);
default:
return nvinfer1::Dims();
}
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ 018e2f3a
......@@ -93,13 +93,15 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
auto* fluid_v = context.scope().FindVar(y);
PADDLE_ENFORCE_NOT_NULL(fluid_v, "no output variable called %s", y);
auto* fluid_t = fluid_v->GetMutable<framework::LoDTensor>();
auto size = inference::analysis::AccuDims(dims.d, dims.nbDims);
fluid_t->Resize(framework::make_ddim(ddim));
// TODO(Superjomn) find some way to determine which device to output the
// tensor.
// if (platform::is_cpu_place(fluid_t->place())) {
// TODO(Superjomn) change this float to dtype size.
auto size = inference::analysis::AccuDims(dims.d, dims.nbDims) *
FLAGS_tensorrt_engine_batch_size;
engine->GetOutputInCPU(y,
fluid_t->mutable_data<float>(platform::CPUPlace()),
size * sizeof(float));
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ 018e2f3a
......@@ -64,36 +64,37 @@ TEST(TensorRTEngineOp, manual) {
LOG(INFO) << "create block desc";
framework::BlockDesc block_desc(&program, block_);
LOG(INFO) << "create mul op";
auto* mul = block_desc.AppendOp();
mul->SetType("mul");
mul->SetInput("X", std::vector<std::string>({"x"})); // 2 x 4
mul->SetInput("Y", std::vector<std::string>({"y"})); // 4 x 6
mul->SetOutput("Out", std::vector<std::string>({"z"})); // 2 x 6
LOG(INFO) << "create fc op";
auto* fc0 = block_desc.AppendOp();
fc0->SetType("fc");
fc0->SetInput("X", std::vector<std::string>({"x"})); // 4 x 1 x 1
fc0->SetInput("Y", std::vector<std::string>({"y"})); // 4 x 6
fc0->SetOutput("Out", std::vector<std::string>({"z"})); // 6 x 1 x 1
LOG(INFO) << "create fc op";
auto* fc = block_desc.AppendOp();
fc->SetType("mul");
fc->SetInput("X", std::vector<std::string>({"z"}));
fc->SetInput("Y", std::vector<std::string>({"y0"})); // 6 x 8
fc->SetOutput("Out", std::vector<std::string>({"z0"})); // 2 x 8
auto* fc1 = block_desc.AppendOp();
fc1->SetType("fc");
fc1->SetInput("X", std::vector<std::string>({"z"}));
fc1->SetInput("Y", std::vector<std::string>({"y0"})); // 6 x 8
fc1->SetOutput("Out", std::vector<std::string>({"z0"})); // 8 x 1 x 1
// Set inputs' variable shape in BlockDesc
AddTensorToBlockDesc(block_, "x", std::vector<int64_t>({2, 4}));
// the batch size is 2, so the dims of 'x' is {2, 4, 1, 1}
AddTensorToBlockDesc(block_, "x", std::vector<int64_t>({2, 4, 1, 1}));
AddTensorToBlockDesc(block_, "y", std::vector<int64_t>({4, 6}));
AddTensorToBlockDesc(block_, "y0", std::vector<int64_t>({6, 8}));
AddTensorToBlockDesc(block_, "z", std::vector<int64_t>({2, 6}));
// It is wired, need to copy manually.
*block_->add_ops() = *mul->Proto();
*block_->add_ops() = *fc->Proto();
*block_->add_ops() = *fc0->Proto();
*block_->add_ops() = *fc1->Proto();
ASSERT_EQ(block_->ops_size(), 2);
LOG(INFO) << "create tensorrt desc";
framework::OpDesc engine_op_desc(nullptr);
engine_op_desc.SetType("tensorrt_engine");
engine_op_desc.SetInput("Xs", std::vector<std::string>({"x", "y", "y0"}));
engine_op_desc.SetInput("Xs", std::vector<std::string>({"x"}));
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
......@@ -207,5 +208,4 @@ TEST(TensorRTEngineOp, fc) { Execute(40, 28, 28); }
} // namespace operators
} // namespace paddle
USE_TRT_CONVERTER(mul)
USE_TRT_CONVERTER(fc)
paddle/fluid/pybind/pybind.cc
浏览文件 @ 018e2f3a
......@@ -498,10 +498,7 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::Executor>(m, "Executor")
.def(py::init<const platform::Place &>())
#ifdef PADDLE_WITH_DISTRIBUTE
.def("begin_pass", &Executor::BeginPass)
.def("end_pass", &Executor::EndPass)
#endif
.def("close", &Executor::Close)
.def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope,
int block_id, bool create_local_scope, bool create_vars) {
pybind11::gil_scoped_release release;
......
python/paddle/fluid/executor.py
浏览文件 @ 018e2f3a
......@@ -247,6 +247,7 @@ class Executor(object):
p.set_place(place)
self.executor = core.Executor(p)
self.program_caches = dict()
self._closed = False
def as_lodtensor(self, data):
"""
......@@ -348,11 +349,23 @@ class Executor(object):
]
return outs
def begin_pass(self):
self.executor.begin_pass()
def close(self):
"""
Close this executor.
You can no long use this executor after calling this method.
For the distributed training, this method would free the resource on PServers related to
the current Trainer.
def end_pass(self):
self.executor.end_pass()
Example:
>>> cpu = core.CPUPlace()
>>> exe = Executor(cpu)
>>> ...
>>> exe.close()
"""
if not self._closed:
self.executor.close()
self._closed = True
def run(self,
program=None,
......@@ -405,6 +418,10 @@ class Executor(object):
>>> feed={'X': x},
>>> fetch_list=[loss.name])
"""
if self._closed:
raise RuntimeError("Attempted to use a closed Executor")
if feed is None:
feed = {}
if not isinstance(feed, dict):
......
python/paddle/fluid/framework.py
浏览文件 @ 018e2f3a
......@@ -32,7 +32,6 @@ except Exception, e:
import unique_name
__all__ = [
'Variable',
'Program',
'Operator',
'Parameter',
......@@ -302,7 +301,7 @@ class Variable(object):
__repr__ = __str__
def set_desc(self, input):
def _set_desc(self, input):
"""
Set the variable description.
......@@ -347,7 +346,7 @@ class Variable(object):
def type(self):
return self.desc.type()
def set_error_clip(self, error_clip):
def _set_error_clip(self, error_clip):
"""
Set the error_clip.
......
python/paddle/fluid/io.py
浏览文件 @ 018e2f3a
......@@ -796,104 +796,6 @@ def get_parameter_value_by_name(name, executor, program=None):
return get_parameter_value(var, executor)
def get_test_program(filelist, program=None, startup_program=None):
"""
Transpile current train program to a program to read test dataset
if the program is using reader ops like "open_files_op".
"""
def _copy_reader_var_(block, var, new_name=None):
if new_name == None:
new_name = var.name
new_var = block.create_var(
name=str(new_name), type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.persistable = True
return new_var
def _get_test_reader_name(train_reader_name):
return train_reader_name + "_test"
def _is_reader_op(op):
block = op.block
if "Out" in op.output_names:
reader_out = block.vars[op.output("Out")[0]]
if reader_out.type == core.VarDesc.VarType.READER:
return True
return False
if program == None:
program = default_main_program()
if startup_program == None:
startup_program = default_startup_program()
startup_block = startup_program.global_block()
# 1. find out the orignal reader var name
startup_reader_op_list = []
for op in startup_block.ops:
if _is_reader_op(op):
startup_reader_op_list.append(op)
if len(startup_reader_op_list) == 0:
return program
root_reader_op = startup_reader_op_list[0]
train_test_reader_map = {}
# 2. add operators to startup to read open and read test data files
for op in startup_reader_op_list:
assert (len(op.output("Out")) == 1)
train_reader_name = op.output("Out")[0]
train_reader = startup_block.vars[train_reader_name]
test_reader = _copy_reader_var_(
startup_block,
train_reader,
new_name=_get_test_reader_name(train_reader_name))
train_test_reader_map[train_reader.name] = test_reader
test_op_inputs = {}
for name in op.input_names:
train_arg_names = op.input(name)
test_arg_vars = []
for arg_name in train_arg_names:
arg_var = train_test_reader_map[
arg_name] if name == "UnderlyingReader" else startup_block.vars[
arg_name]
test_arg_vars.append(arg_var)
test_op_inputs[name] = test_arg_vars
test_op = startup_block.append_op(
type=op.type,
inputs=test_op_inputs,
outputs={'Out': [test_reader]},
attrs=op.attrs)
# root reader op's filelist attr for read test files
if op.type == root_reader_op.type:
test_op.set_attr("file_names", filelist)
if op.type == "create_multi_pass_reader":
test_op.set_attr("pass_num", 1)
# 3. rename reader vars in inference program to different name
# to avoid read from train data.
main_block = program.global_block()
for var in main_block.vars.values():
if var.type == core.VarDesc.VarType.READER:
main_block._rename_var(
str(var.name), str(_get_test_reader_name(var.name)))
for op in main_block.ops:
if op.type == root_reader_op.type:
test_op.set_attr("file_names", filelist)
if op.type == "create_multi_pass_reader":
test_op.set_attr("pass_num", 1)
startup_program._sync_with_cpp()
program._sync_with_cpp()
return program
def _load_slice_up_vars(executor, dirname, slice_vars_and_atts):
if slice_vars_and_atts == None or len(slice_vars_and_atts) == 0:
return
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 018e2f3a
......@@ -23,25 +23,17 @@ from ops import logical_and, logical_not, logical_or
import numpy
__all__ = [
'split_lod_tensor',
'merge_lod_tensor',
'While',
'Switch',
'lod_rank_table',
'max_sequence_len',
'lod_tensor_to_array',
'array_to_lod_tensor',
'increment',
'array_write',
'create_array',
'less_than',
'equal',
'array_read',
'shrink_memory',
'array_length',
'IfElse',
'DynamicRNN',
'ConditionalBlock',
'StaticRNN',
'reorder_lod_tensor_by_rank',
'ParallelDo',
......@@ -1457,7 +1449,7 @@ class IfElse(object):
if self.status == IfElse.OUT_IF_ELSE_BLOCKS:
raise ValueError("input must in true/false blocks")
if id(x) not in self.input_table:
parent_block = self.parent_block()
parent_block = self._parent_block()
out_true = parent_block.create_var(
name=unique_name.generate('ifelse_input' + self.helper.name),
dtype=x.dtype)
......@@ -1483,7 +1475,7 @@ class IfElse(object):
else:
return out_false
def parent_block(self):
def _parent_block(self):
current_block = self.helper.main_program.current_block()
return self.helper.main_program.block(current_block.parent_idx)
......@@ -1499,7 +1491,7 @@ class IfElse(object):
out_table = self.output_table[1 if self.status ==
self.IN_IF_ELSE_TRUE_BLOCKS else 0]
parent_block = self.parent_block()
parent_block = self._parent_block()
for each_out in outs:
if not isinstance(each_out, Variable):
raise TypeError("Each output should be a variable")
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 018e2f3a
......@@ -62,7 +62,7 @@ def noam_decay(d_model, warmup_steps):
The decayed learning rate.
"""
global_step = _decay_step_counter(1)
with init_on_cpu():
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * ops.elementwise_min(a, b)
......@@ -108,8 +108,6 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
"""
global_step = _decay_step_counter()
with init_on_cpu():
# update learning_rate
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
......@@ -138,7 +136,6 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
"""
global_step = _decay_step_counter()
with init_on_cpu():
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
......@@ -184,7 +181,6 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
"""
global_step = _decay_step_counter()
with init_on_cpu():
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
......@@ -224,13 +220,10 @@ def polynomial_decay(learning_rate,
"""
global_step = _decay_step_counter()
with init_on_cpu():
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(
shape=[1], dtype='float32', value=1.0)
zero_var = tensor.fill_constant(shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(shape=[1], dtype='float32', value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
......@@ -277,7 +270,6 @@ def piecewise_decay(boundaries, values):
global_step = _decay_step_counter()
with init_on_cpu():
lr = tensor.create_global_var(
shape=[1],
value=0.0,
......@@ -288,15 +280,16 @@ def piecewise_decay(boundaries, values):
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
shape=[1], dtype='float32', value=float(boundaries[i]))
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(values[len(values) - 1]))
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
......
python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.py → python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
浏览文件 @ 018e2f3a
......@@ -35,7 +35,7 @@ if len(sys.argv) == 1:
word_dict = paddle.dataset.imdb.word_dict()
else:
word_dict = load_vocab(sys.argv[1])
word_dict["<unk>"] = len(word_dict)
word_dict["<unk>"] = len(word_dict)
print "Dict dim = ", len(word_dict)
# input text data
......@@ -50,7 +50,7 @@ feeder = fluid.DataFeeder(feed_list=[data, label], place=fluid.CPUPlace())
BATCH_SIZE = 128
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=10000),
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
......
python/paddle/fluid/tests/demo/text_classification/train.py → python/paddle/fluid/tests/demo/file_reader/train.py
浏览文件 @ 018e2f3a
......@@ -19,7 +19,7 @@ import sys
TRAIN_FILES = ['train.recordio']
TEST_FILES = ['test.recordio']
DICT_DIM = 89528
DICT_DIM = 5147
# embedding dim
emb_dim = 128
......@@ -27,58 +27,46 @@ emb_dim = 128
# hidden dim
hid_dim = 128
# hidden dim2
hid_dim2 = 96
# class num
class_dim = 2
# epoch num
epoch_num = 10
def network_cfg(is_train, pass_num=100):
with fluid.unique_name.guard():
train_file_obj = fluid.layers.open_files(
filenames=TRAIN_FILES,
pass_num=pass_num,
shapes=[[-1, 1], [-1, 1]],
lod_levels=[1, 0],
dtypes=['int64', 'int64'])
test_file_obj = fluid.layers.open_files(
filenames=TEST_FILES,
pass_num=1,
def build_program(is_train):
file_obj_handle = fluid.layers.io.open_files(
filenames=TRAIN_FILES if is_train else TEST_FILES,
shapes=[[-1, 1], [-1, 1]],
lod_levels=[1, 0],
dtypes=['int64', 'int64'])
if is_train:
file_obj = fluid.layers.shuffle(train_file_obj, buffer_size=1000)
else:
file_obj = test_file_obj
file_obj = fluid.layers.io.double_buffer(file_obj_handle)
file_obj = fluid.layers.double_buffer(
file_obj,
name="train_double_buffer" if is_train else 'test_double_buffer')
with fluid.unique_name.guard():
data, label = fluid.layers.read_file(file_obj)
emb = fluid.layers.embedding(input=data, size=[DICT_DIM, emb_dim])
# sequence conv with window size = 3
win_size = 3
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=win_size,
filter_size=3,
act="tanh",
pool_type="max")
pool_type="sqrt")
# fc layer after conv
fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
# probability of each class
prediction = fluid.layers.fc(input=[fc_1],
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
# cross entropy loss
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......@@ -88,58 +76,62 @@ def network_cfg(is_train, pass_num=100):
if is_train:
# SGD optimizer
sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.01)
sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
return {
'loss': avg_cost,
'log': [avg_cost, acc],
'file': train_file_obj if is_train else test_file_obj
}
return {'loss': avg_cost, 'log': [avg_cost, acc], 'file': file_obj_handle}
def main():
train = fluid.Program()
startup = fluid.Program()
test = fluid.Program()
with fluid.program_guard(train, startup):
train_args = network_cfg(is_train=True)
test = fluid.Program()
train_args = build_program(is_train=True)
with fluid.program_guard(test, fluid.Program()):
test_args = network_cfg(is_train=False)
with fluid.program_guard(test, startup):
test_args = build_program(is_train=False)
use_cuda = fluid.core.is_compiled_with_cuda()
# startup
place = fluid.CUDAPlace(0)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place=place)
exe.run(startup)
train_exe = fluid.ParallelExecutor(
use_cuda=True, loss_name=train_args['loss'].name, main_program=train)
use_cuda=use_cuda,
loss_name=train_args['loss'].name,
main_program=train)
test_exe = fluid.ParallelExecutor(
use_cuda=use_cuda, main_program=test, share_vars_from=train_exe)
fetch_var_list = [var.name for var in train_args['log']]
for i in xrange(sys.maxint):
result = map(numpy.array,
train_exe.run(fetch_list=fetch_var_list
if i % 1000 == 0 else []))
if len(result) != 0:
print 'Train: ', result
if i % 1000 == 0:
test_exe = fluid.ParallelExecutor(
use_cuda=True, main_program=test, share_vars_from=train_exe)
for epoch_id in range(epoch_num):
# train
try:
batch_id = 0
while True:
loss, acc = map(numpy.array,
train_exe.run(fetch_list=fetch_var_list))
print 'Train epoch', epoch_id, 'batch', batch_id, 'loss:', loss, 'acc:', acc
batch_id += 1
except fluid.core.EOFException:
print 'End of epoch', epoch_id
train_args['file'].reset()
# test
loss = []
acc = []
try:
while True:
loss_np, acc_np = map(
numpy.array, test_exe.run(fetch_list=fetch_var_list))
loss_np, acc_np = map(numpy.array,
test_exe.run(fetch_list=fetch_var_list))
loss.append(loss_np[0])
acc.append(acc_np[0])
except:
test_args['file'].reset()
print 'TEST: ', numpy.mean(loss), numpy.mean(acc)
print 'Test loss:', numpy.mean(loss), 'acc:', numpy.mean(acc)
if __name__ == '__main__':
......
python/paddle/fluid/tests/test_error_clip.py
浏览文件 @ 018e2f3a
......@@ -36,7 +36,7 @@ with fluid.program_guard(main_program=prog):
avg_cost = fluid.layers.mean(cost)
prog_clip = prog.clone()
prog_clip.block(0).var(hidden1.name).set_error_clip(
prog_clip.block(0).var(hidden1.name)._set_error_clip(
fluid.clip.ErrorClipByValue(
max=CLIP_MAX, min=CLIP_MIN))
......
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @ 018e2f3a
......@@ -19,6 +19,10 @@ from paddle.fluid.executor import Executor
from paddle.fluid.optimizer import MomentumOptimizer
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.layers.control_flow import split_lod_tensor
from paddle.fluid.layers.control_flow import merge_lod_tensor
from paddle.fluid.layers.control_flow import ConditionalBlock
import unittest
import numpy as np
......@@ -34,11 +38,10 @@ class TestMNISTIfElseOp(unittest.TestCase):
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_image, false_image = split_lod_tensor(input=image, mask=cond)
true_out = layers.create_tensor(dtype='float32')
true_cond = layers.ConditionalBlock([cond])
true_cond = ConditionalBlock([cond])
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
......@@ -46,14 +49,14 @@ class TestMNISTIfElseOp(unittest.TestCase):
layers.assign(input=prob, output=true_out)
false_out = layers.create_tensor(dtype='float32')
false_cond = layers.ConditionalBlock([cond])
false_cond = ConditionalBlock([cond])
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
prob = layers.merge_lod_tensor(
prob = merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(loss)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 018e2f3a
......@@ -251,7 +251,7 @@ class OpTest(unittest.TestCase):
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
fetch_list.append(str(out_name))
# fetch_list = map(block.var, fetch_list)
if not isinstance(fetch_list[0], Variable):
if not isinstance(fetch_list[0], fluid.framework.Variable):
fetch_list = map(block.var, fetch_list)
outs = executor.run(program,
feed=feed_map,
......
python/paddle/fluid/tests/unittests/test_conditional_block.py
浏览文件 @ 018e2f3a
......@@ -18,14 +18,15 @@ import paddle.fluid.core as core
from paddle.fluid.framework import default_startup_program, default_main_program
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
from paddle.fluid.layers.control_flow import ConditionalBlock
import numpy
class ConditionalBlock(unittest.TestCase):
class ConditionalBlockTest(unittest.TestCase):
def test_forward(self):
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = layers.ConditionalBlock(inputs=[data])
cond = ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
with cond.block():
hidden = layers.fc(input=data, size=10)
......
python/paddle/fluid/tests/unittests/test_const_value.py
浏览文件 @ 018e2f3a
......@@ -16,7 +16,7 @@ import unittest
import paddle.fluid.framework as framework
class ConditionalBlock(unittest.TestCase):
class ConstantTest(unittest.TestCase):
def test_const_value(self):
self.assertEqual(framework.GRAD_VAR_SUFFIX, "@GRAD")
self.assertEqual(framework.TEMP_VAR_NAME, "@TEMP@")
......
python/paddle/fluid/tests/unittests/test_dyn_rnn.py
浏览文件 @ 018e2f3a
......@@ -17,6 +17,12 @@ import paddle
import unittest
import numpy
from paddle.fluid.layers.control_flow import lod_rank_table
from paddle.fluid.layers.control_flow import max_sequence_len
from paddle.fluid.layers.control_flow import lod_tensor_to_array
from paddle.fluid.layers.control_flow import array_to_lod_tensor
from paddle.fluid.layers.control_flow import shrink_memory
class TestDynRNN(unittest.TestCase):
def setUp(self):
......@@ -38,12 +44,11 @@ class TestDynRNN(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[1], dtype='float32')
rank_table = fluid.layers.lod_rank_table(x=sent_emb)
rank_table = lod_rank_table(x=sent_emb)
sent_emb_array = fluid.layers.lod_tensor_to_array(
x=sent_emb, table=rank_table)
sent_emb_array = lod_tensor_to_array(x=sent_emb, table=rank_table)
seq_len = fluid.layers.max_sequence_len(rank_table=rank_table)
seq_len = max_sequence_len(rank_table=rank_table)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
i.stop_gradient = False
......@@ -66,7 +71,7 @@ class TestDynRNN(unittest.TestCase):
mem = fluid.layers.array_read(array=mem_array, i=i)
ipt = fluid.layers.array_read(array=sent_emb_array, i=i)
mem = fluid.layers.shrink_memory(x=mem, i=i, table=rank_table)
mem = shrink_memory(x=mem, i=i, table=rank_table)
hidden = fluid.layers.fc(input=[mem, ipt], size=100, act='tanh')
......@@ -75,8 +80,7 @@ class TestDynRNN(unittest.TestCase):
fluid.layers.array_write(x=hidden, i=i, array=mem_array)
fluid.layers.less_than(x=i, y=seq_len, cond=cond)
all_timesteps = fluid.layers.array_to_lod_tensor(
x=out, table=rank_table)
all_timesteps = array_to_lod_tensor(x=out, table=rank_table)
last = fluid.layers.sequence_last_step(input=all_timesteps)
logits = fluid.layers.fc(input=last, size=1, act=None)
loss = fluid.layers.sigmoid_cross_entropy_with_logits(
......
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
浏览文件 @ 018e2f3a
......@@ -91,20 +91,21 @@ class TestLearningRateDecay(unittest.TestCase):
def check_decay_with_place(self, place, python_decay_fn, fluid_decay_fn,
kwargs):
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
decayed_lr = fluid_decay_fn(**kwargs)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe.run(startup_prog)
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(main_prog)
for step in range(10):
lr_val, = exe.run(fluid.default_main_program(),
feed={},
fetch_list=[decayed_lr])
lr_val, = exe.run(main_prog, feed={}, fetch_list=[decayed_lr])
python_decayed_lr = python_decay_fn(
global_step=float(step), **kwargs)
self.assertAlmostEqual(
......
python/paddle/fluid/tests/unittests/test_lod_rank_table.py
浏览文件 @ 018e2f3a
......@@ -12,7 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.fluid.layers import lod_rank_table, data
from paddle.fluid.layers import data
from paddle.fluid.layers.control_flow import lod_rank_table
from paddle.fluid.executor import Executor
import paddle.fluid.core as core
import numpy
......
python/paddle/fluid/tests/unittests/test_lod_tensor_array_ops.py
浏览文件 @ 018e2f3a
......@@ -20,6 +20,11 @@ from paddle.fluid.framework import Program, program_guard
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
from paddle.fluid.layers.control_flow import lod_rank_table
from paddle.fluid.layers.control_flow import max_sequence_len
from paddle.fluid.layers.control_flow import lod_tensor_to_array
from paddle.fluid.layers.control_flow import array_to_lod_tensor
class TestCPULoDTensorArrayOps(unittest.TestCase):
def place(self):
......@@ -137,13 +142,13 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
with program_guard(program):
x = layers.data(name='x', shape=[10])
x.persistable = True
table = layers.lod_rank_table(x, level=level)
max_len = layers.max_sequence_len(table)
table = lod_rank_table(x, level=level)
max_len = max_sequence_len(table)
max_len.persistable = True
array = layers.lod_tensor_to_array(x, table)
array = lod_tensor_to_array(x, table)
array.persistable = True
result = layers.array_to_lod_tensor(array, table)
result = array_to_lod_tensor(array, table)
result.persistable = True
exe = Executor(place)
scope = core.Scope()
......@@ -181,9 +186,9 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
with program_guard(program):
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False)
table = layers.lod_rank_table(x, level=0)
array = layers.lod_tensor_to_array(x, table)
result = layers.array_to_lod_tensor(array, table)
table = lod_rank_table(x, level=0)
array = lod_tensor_to_array(x, table)
result = array_to_lod_tensor(array, table)
mean = layers.mean(result)
......
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @ 018e2f3a
......@@ -107,44 +107,24 @@ class TestMNIST(TestParallelExecutorBase):
label = np.ones(shape=[32, 1], dtype='int64')
return img, label
# simple_fc
def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
def _compare_reduce_and_allreduce(self, model, use_cuda, random_data=True):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
img, label = self._init_data()
model, use_cuda=use_cuda, use_reduce=True)
self.check_network_convergence(
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=use_reduce)
model, use_cuda=use_cuda, allow_op_delay=True, use_reduce=True)
def check_simple_fc_convergence_with_Reduce(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, use_reduce=True)
self.check_network_convergence(
simple_fc_net,
use_cuda=use_cuda,
allow_op_delay=True,
use_reduce=True)
img, label = self._init_data()
img, label = self._init_data(random_data)
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
simple_fc_net,
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=False)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
simple_fc_net,
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
......@@ -153,7 +133,24 @@ class TestMNIST(TestParallelExecutorBase):
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
# simple_fc
def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
self.check_network_convergence(
simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
img, label = self._init_data()
self.check_network_convergence(
simple_fc_net,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=use_reduce)
def test_simple_fc(self):
# use_cuda
......@@ -162,8 +159,8 @@ class TestMNIST(TestParallelExecutorBase):
def test_simple_fc_with_new_strategy(self):
# use_cuda, use_reduce
self.check_simple_fc_convergence_with_Reduce(True)
self.check_simple_fc_convergence_with_Reduce(False)
self._compare_reduce_and_allreduce(simple_fc_net, True)
self._compare_reduce_and_allreduce(simple_fc_net, False)
def check_simple_fc_parallel_accuracy(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
......@@ -209,39 +206,13 @@ class TestMNIST(TestParallelExecutorBase):
"label": label},
use_cuda=use_cuda)
def check_batchnorm_fc_convergence_use_reduce(self, use_cuda):
if use_cuda and not core.is_compiled_with_cuda():
return
self.check_network_convergence(
fc_with_batchnorm, use_cuda=use_cuda, use_reduce=True)
img, label = self._init_data()
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=False)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
fc_with_batchnorm,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
use_reduce=True)
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
def test_batchnorm_fc(self):
self.check_batchnorm_fc_convergence(True)
self.check_batchnorm_fc_convergence(False)
def test_batchnorm_fc_with_new_strategy(self):
self.check_batchnorm_fc_convergence_use_reduce(True)
self.check_batchnorm_fc_convergence_use_reduce(False)
self._compare_reduce_and_allreduce(fc_with_batchnorm, True)
self._compare_reduce_and_allreduce(fc_with_batchnorm, False)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_op.py
浏览文件 @ 018e2f3a
......@@ -120,7 +120,7 @@ class BaseParallelForTest(unittest.TestCase):
pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl)
data = next(generator)
if isinstance(data, fluid.Variable):
if isinstance(data, fluid.framework.Variable):
data = [data]
with pd.do():
......
python/paddle/fluid/tests/unittests/test_reorder_lod_tensor.py
浏览文件 @ 018e2f3a
......@@ -15,6 +15,7 @@
import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.layers.control_flow import lod_rank_table
import numpy
......@@ -34,7 +35,7 @@ class TestReorderLoDTensor(unittest.TestCase):
dat.stop_gradient = False
rank_dat = fluid.layers.data(
name=cls.data_desc[1][0], shape=cls.data_desc[1][1])
table = fluid.layers.lod_rank_table(rank_dat)
table = lod_rank_table(rank_dat)
new_dat = fluid.layers.reorder_lod_tensor_by_rank(
x=dat, rank_table=table)
loss = fluid.layers.reduce_sum(new_dat)
......
python/paddle/fluid/tests/unittests/test_shrink_rnn_memory.py
浏览文件 @ 018e2f3a
......@@ -21,6 +21,9 @@ from paddle.fluid.framework import default_main_program, switch_main_program
from paddle.fluid.framework import Program
import numpy as np
from paddle.fluid.layers.control_flow import shrink_memory
from paddle.fluid.layers.control_flow import lod_rank_table
class TestShrinkRNNMemoryBase(unittest.TestCase):
def setUp(self):
......@@ -30,15 +33,15 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
x.stop_gradient = False
rank_table_tensor = layers.data(
'rank_table_tensor', shape=[1], dtype='float32', lod_level=1)
table = layers.lod_rank_table(x=rank_table_tensor)
table = lod_rank_table(x=rank_table_tensor)
i = layers.zeros(dtype='int64', shape=[1])
self.mem1 = layers.shrink_memory(x=x, i=i, table=table)
self.mem1 = shrink_memory(x=x, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
self.mem2 = layers.shrink_memory(x=self.mem1, i=i, table=table)
self.mem2 = shrink_memory(x=self.mem1, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
self.mem3 = layers.shrink_memory(x=self.mem2, i=i, table=table)
self.mem3 = shrink_memory(x=self.mem2, i=i, table=table)
mem3_mean = layers.mean(self.mem3)
append_backward(loss=mem3_mean)
self.x_grad = self.main_program.global_block().var('x@GRAD')
......
python/paddle/fluid/tests/unittests/test_split_and_merge_lod_tensor_op.py
浏览文件 @ 018e2f3a
......@@ -19,6 +19,8 @@ import paddle.fluid.layers as layers
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
from paddle.fluid.layers.control_flow import split_lod_tensor
from paddle.fluid.layers.control_flow import merge_lod_tensor
class TestCPULoDTensorArrayOps(unittest.TestCase):
......@@ -96,12 +98,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
y = layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out_true, out_false = split_lod_tensor(input=x, mask=y, level=level)
out_true.persistable = True
out_false.persistable = True
out = layers.merge_lod_tensor(
out = merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
out.persistable = True
......@@ -142,9 +143,8 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out = layers.merge_lod_tensor(
out_true, out_false = split_lod_tensor(input=x, mask=y, level=level)
out = merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
mean = layers.mean(out)
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 018e2f3a
......@@ -38,7 +38,7 @@ from ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, Block, \
Variable, Parameter, grad_var_name
Parameter, grad_var_name
from details import *
LOOKUP_TABLE_TYPE = "lookup_table"
......@@ -918,7 +918,8 @@ class DistributeTranspiler(object):
# create table optimize block in pserver program
table_opt_op = [
op for op in self.optimize_ops
if op.input("Param")[0] == self.table_name
if 'Param' in op.input_names and op.input("Param")[0] ==
self.table_name
][0]
table_opt_block = pserver_program.create_block(pre_block_idx)
# only support sgd now
......@@ -1075,7 +1076,6 @@ class DistributeTranspiler(object):
]
def _clone_var(self, block, var, persistable=True):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
shape=var.shape,
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 018e2f3a
......@@ -14,7 +14,7 @@
from collections import defaultdict
from .. import core
from ..framework import Program, default_main_program, Parameter, Variable
from ..framework import Program, default_main_program, Parameter
from ..backward import _rename_arg_
dtype_to_size = {
......
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