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Paddle
提交 265302ed 编写于 作者: Y yuyang18
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/fast_executor

上级 05cadf1b bd87f67f
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Showing with 898 addition and 164 deletion
CMakeLists.txt
浏览文件 @ 265302ed
......@@ -204,12 +204,11 @@ include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
set(WITH_ANAKIN OFF CACHE STRING "Disable Anakin first, will add it later." FORCE)
if(WITH_GPU)
include(cuda)
include(tensorrt)
include(external/anakin)
else()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
endif()
include(cudnn) # set cudnn libraries, must before configure
......
paddle/fluid/API.spec
浏览文件 @ 265302ed
......@@ -6,7 +6,7 @@ paddle.fluid.Program.create_block ArgSpec(args=['self', 'parent_idx'], varargs=N
paddle.fluid.Program.current_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.get_desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.inference_optimize ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.inference_optimize ArgSpec(args=['self', 'export_for_deployment'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.optimized_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
......@@ -18,6 +18,9 @@ paddle.fluid.Operator.all_attrs ArgSpec(args=['self'], varargs=None, keywords=No
paddle.fluid.Operator.attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr_type ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr_id ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr_ids ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_kernel ArgSpec(args=['self', 'op_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.input ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
......@@ -52,7 +55,7 @@ paddle.fluid.Inferencer.__init__ ArgSpec(args=['self', 'infer_func', 'param_path
paddle.fluid.Inferencer.infer ArgSpec(args=['self', 'inputs', 'return_numpy'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
paddle.fluid.InferenceTranspiler.__init__
......@@ -74,7 +77,7 @@ paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_pro
paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
......@@ -156,6 +159,7 @@ paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaul
paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
......@@ -324,7 +328,7 @@ paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
paddle.fluid.transpiler.InferenceTranspiler.__init__
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 265302ed
......@@ -28,6 +28,38 @@ namespace paddle {
namespace framework {
namespace ir {
/*
* The graph is a Directed Acyclic Single Static Assignment Graph.
*
* In more detail, the following properties must hold:
*
* The graph shouldn't contain cycle. Each node is a black-box to the graph
* so the node itself could be a loop operator.
*
* Each Variable-type node has only one input (thus single static assignment).
*
* The output/input of operator is variable and the output/input of variable
* is operator.
*
* The following data harzards in Program are addressed in the Graph:
*
* Write-After-Read
* a = op1(x)
* x = op2(b)
* A control-dependency connection is created bettwen op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Write-After-Write
* x = op1(a)
* x = op2(b)
* A control-dependency connection is created between op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Other properties currently hold, but is not enforced yet:
*
* Variable-type node (not control dep) with the same variable name share
* the same underlying VarDesc.
*/
class Graph {
public:
explicit Graph(const ProgramDesc &program);
......
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 265302ed
......@@ -36,7 +36,7 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
......@@ -59,11 +59,27 @@ class SumOpVarTypeInference : public VarTypeInference {
block->Var(out_var_name)->SetType(default_var_type);
}
};
class DummyOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
class DummyOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDesc &op_desc, BlockDesc *block) const override {}
};
} // namespace framework
} // namespace paddle
REGISTER_OPERATOR(sum, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(dummy, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP,
paddle::framework::SumOpMaker);
......@@ -110,5 +126,83 @@ TEST(GraphTest, Basic) {
}
ASSERT_EQ(nodes.size(), 5);
}
TEST(GraphTest, WriteAfterRead) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"a"});
op->SetAttr("op_role", 1);
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
control_dep1 = n->outputs[1];
ASSERT_EQ(n->outputs.size(), 2);
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
}
}
ASSERT_EQ(control_dep1, control_dep2);
}
TEST(GraphTest, WriteAfterWrite) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
ASSERT_EQ(n->outputs.size(), 2);
control_dep1 = n->outputs[1];
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
ASSERT_EQ(control_dep1, control_dep2);
}
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/op_desc.cc
浏览文件 @ 265302ed
......@@ -238,7 +238,20 @@ Attribute OpDesc::GetNullableAttr(const std::string &name) const {
}
}
int OpDesc::GetBlockAttr(const std::string &name) const {
std::vector<int> OpDesc::GetBlocksAttrIds(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
auto blocks = boost::get<std::vector<BlockDesc *>>(it->second);
std::vector<int> ids;
for (auto n : blocks) {
ids.push_back(n->ID());
}
return ids;
}
int OpDesc::GetBlockAttrId(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return boost::get<BlockDesc *>(it->second)->ID();
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 265302ed
......@@ -83,7 +83,9 @@ class OpDesc {
Attribute GetNullableAttr(const std::string &name) const;
int GetBlockAttr(const std::string &name) const;
int GetBlockAttrId(const std::string &name) const;
std::vector<int> GetBlocksAttrIds(const std::string &name) const;
void Rename(const std::string &old_name, const std::string &new_name);
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ 265302ed
......@@ -58,7 +58,7 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
for (const std::string &attr_name : op->AttrNames()) {
if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
int sub_block_id =
o.Block(block_id).Op(op_id)->GetBlockAttr(attr_name);
o.Block(block_id).Op(op_id)->GetBlockAttrId(attr_name);
op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
}
}
......
paddle/fluid/framework/tensor.cc
浏览文件 @ 265302ed
......@@ -112,5 +112,6 @@ Tensor& Tensor::Resize(const DDim& dims) {
const DDim& Tensor::dims() const { return dims_; }
int64_t Tensor::numel() const { return product(dims_); }
} // namespace framework
} // namespace paddle
paddle/fluid/framework/tensor_impl.h
浏览文件 @ 265302ed
......@@ -59,6 +59,14 @@ inline T* Tensor::mutable_data(platform::Place place) {
}
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
int rank = src.dims().size();
PADDLE_ENFORCE_GE(
rank, 2,
"'ReshapeToMatrix()' is only used for flatten high rank "
"tensors to matrixs. Can not be used in reshaping vectors.");
if (rank == 2) {
return src;
}
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 265302ed
......@@ -22,6 +22,9 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
namespace paddle {
namespace {
......@@ -58,6 +61,15 @@ bool NativePaddlePredictor::Init(
std::shared_ptr<framework::Scope> parent_scope) {
VLOG(3) << "Predictor::init()";
if (FLAGS_profile) {
LOG(WARNING) << "Profiler is actived, might affect the performance";
LOG(INFO) << "You can turn off by set gflags '-profile false'";
auto tracking_device = config_.use_gpu ? platform::ProfilerState::kAll
: platform::ProfilerState::kCPU;
platform::EnableProfiler(tracking_device);
}
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......@@ -102,6 +114,10 @@ bool NativePaddlePredictor::Init(
}
NativePaddlePredictor::~NativePaddlePredictor() {
if (FLAGS_profile) {
platform::DisableProfiler(platform::EventSortingKey::kTotal,
"./profile.log");
}
if (sub_scope_) {
scope_->DeleteScope(sub_scope_);
}
......
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @ 265302ed
......@@ -28,23 +28,26 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
int rank = x_dims.size();
PADDLE_ENFORCE_EQ(rank, label_dims.size(),
"Input(X) and Input(Label) shall have the same rank.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
framework::slice_ddim(label_dims, 0, rank - 1),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension.");
if (ctx->Attrs().Get<bool>("soft_label")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"If Attr(soft_label) == true, the 2nd dimension of "
PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
"If Attr(soft_label) == true, the last dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
"If Attr(softLabel) == false, the 2nd dimension of "
PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1UL,
"If Attr(softLabel) == false, the last dimension of "
"Input(Label) should be 1.");
}
ctx->SetOutputDim("Y", {x_dims[0], 1});
auto y_dims = x_dims;
y_dims[rank - 1] = 1;
ctx->SetOutputDim("Y", y_dims);
ctx->ShareLoD("X", /*->*/ "Y");
}
......@@ -74,24 +77,28 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal.");
PADDLE_ENFORCE_EQ(dy_dims[1], 1,
"The 2nd dimension of Input(Y@Grad) should be 1.");
int rank = x_dims.size();
PADDLE_ENFORCE_EQ(dy_dims.size(), rank,
"Input(Y@Grad) and Input(X) should have the same rank.");
PADDLE_ENFORCE_EQ(label_dims.size(), rank,
"Input(Label) and Input(X) should have the same rank.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
framework::slice_ddim(label_dims, 0, rank - 1),
"The Input(X) and Input(Label) should have the same "
"shape except the last dimension.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
framework::slice_ddim(dy_dims, 0, rank - 1),
"The Input(X) and Input(Y@Grad) should have the same "
"shape except the last dimension.");
PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
"The last dimension of Input(Y@Grad) should be 1.");
if (ctx->Attrs().Get<bool>("soft_label")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"When Attr(soft_label) == true, the 2nd dimension of "
PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
"When Attr(soft_label) == true, the last dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"When Attr(soft_label) == false, the 2nd dimension of "
PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1,
"When Attr(soft_label) == false, the last dimension of "
"Input(Label) should be 1.");
}
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
......@@ -113,18 +120,20 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x D],"
" where N is the batch size and D is the number of classes. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator.");
AddInput("Label",
"(Tensor), the ground truth which is a 2-D tensor. When "
"soft_label is set to false, Label is a Tensor<int64> with shape "
"[N x 1]. When soft_label is set to true, Label is a "
"Tensor<float/double> with shape [N x D].");
"(Tensor, default Tensor<float>), a tensor whose last dimension "
"size is equal to the number of classes. This input is a "
"probability computed by the previous operator, which is almost "
"always the result of a softmax operator.");
AddInput(
"Label",
"(Tensor), the tensor which represents the ground truth. It has the "
"same shape with 'X' except the last dimension. When soft_label is set "
"to false, the last dimension size is 1; when soft_label is set to "
"true, the last dimension size is equal to the number of classes.");
AddOutput("Y",
"(Tensor, default Tensor<float>), a 2-D tensor with shape "
"[N x 1]. The cross entropy loss.");
"(Tensor, default Tensor<float>), a tensor whose shape is same "
"with 'X' except that the last dimension size is 1. It "
"represents the cross entropy loss.");
AddAttr<bool>("soft_label",
"(bool, default false), a flag indicating whether to "
"interpretate the given labels as soft labels.")
......@@ -132,6 +141,12 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
CrossEntropy Operator.
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
of flattened matrixs.
It supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
......
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @ 265302ed
......@@ -33,8 +33,13 @@ class CrossEntropyOpKernel : public framework::OpKernel<T> {
auto* y = ctx.Output<Tensor>("Y");
y->mutable_data<T>(ctx.GetPlace());
int rank = x->dims().size();
Tensor x_2d = framework::ReshapeToMatrix(*x, rank - 1);
Tensor labels_2d = framework::ReshapeToMatrix(*labels, rank - 1);
Tensor y_2d = framework::ReshapeToMatrix(*y, rank - 1);
math::CrossEntropyFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), y, x, labels,
ctx.template device_context<DeviceContext>(), &y_2d, &x_2d, &labels_2d,
ctx.Attr<bool>("soft_label"));
}
};
......@@ -98,9 +103,12 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto* label = ctx.Input<Tensor>("Label");
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
int64_t class_num = x->dims()[1];
// Following computation only depends on the last dimension size. So it's
// unnecessary to convert tensors to 2-D views.
int rank = x->dims().size();
int64_t class_num = x->dims()[rank - 1];
if (ctx.Attr<bool>("soft_label")) {
XeSoftlabelGradFunctor<T> functor(dx_data, dy->data<T>(), x->data<T>(),
label->data<T>(),
......
paddle/fluid/operators/shape_op.cc
浏览文件 @ 265302ed
......@@ -38,7 +38,7 @@ class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Input", "(Tensor), The input tensor.");
AddOutput("Out",
"(Tensor), The shape of input tensor, the data type of the shape"
" is int64_t, will be on the same device with the input Tensor.");
" is int32_t, will be on the same device with the input Tensor.");
AddComment(R"DOC(
Shape Operator
......@@ -53,5 +53,5 @@ Get the shape of input tensor. Only support CPU input Tensor now.
namespace ops = paddle::operators;
REGISTER_OPERATOR(shape, ops::ShapeOp, ops::ShapeOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int64_t>,
REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int32_t>,
ops::ShapeKernel<float>, ops::ShapeKernel<double>);
paddle/fluid/operators/shape_op.cu
浏览文件 @ 265302ed
......@@ -15,6 +15,6 @@ limitations under the License. */
#include "paddle/fluid/operators/shape_op.h"
REGISTER_OP_CUDA_KERNEL(shape, paddle::operators::ShapeKernel<int>,
paddle::operators::ShapeKernel<int64_t>,
paddle::operators::ShapeKernel<int32_t>,
paddle::operators::ShapeKernel<float>,
paddle::operators::ShapeKernel<double>);
paddle/fluid/operators/shape_op.h
浏览文件 @ 265302ed
......@@ -27,7 +27,7 @@ class ShapeKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in_t = ctx.Input<Tensor>("Input");
auto* out_t = ctx.Output<Tensor>("Out");
auto out_data = out_t->mutable_data<int64_t>(platform::CPUPlace());
auto out_data = out_t->mutable_data<int32_t>(platform::CPUPlace());
auto in_dims = in_t->dims();
for (int i = 0; i < in_dims.size(); ++i) {
out_data[i] = in_dims[i];
......
paddle/fluid/operators/softmax_op.h
浏览文件 @ 265302ed
......@@ -31,16 +31,12 @@ class SoftmaxKernel : public framework::OpKernel<T> {
// allocate memory on device.
Out->mutable_data<T>(context.GetPlace());
auto dims = X->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_x;
framework::LoDTensor flattened_out;
flattened_x.ShareDataWith(*X).Resize(flattened_dims);
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
int rank = X->dims().size();
Tensor X_2d = framework::ReshapeToMatrix(*X, rank - 1);
Tensor Out_2d = framework::ReshapeToMatrix(*Out, rank - 1);
math::SoftmaxFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), &flattened_x,
&flattened_out);
context.template device_context<DeviceContext>(), &X_2d, &Out_2d);
}
};
......@@ -55,18 +51,14 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
// allocate memory on device.
dX->mutable_data<T>(context.GetPlace());
auto dims = Out->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_out;
framework::LoDTensor flattened_d_out;
framework::LoDTensor flattened_d_x;
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
flattened_d_out.ShareDataWith(*dOut).Resize(flattened_dims);
flattened_d_x.ShareDataWith(*dX).Resize(flattened_dims);
int rank = Out->dims().size();
Tensor Out_2d = framework::ReshapeToMatrix(*Out, rank - 1);
Tensor dOut_2d = framework::ReshapeToMatrix(*dOut, rank - 1);
Tensor dX_2d = framework::ReshapeToMatrix(*dX, rank - 1);
math::SoftmaxGradFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), &flattened_out,
&flattened_d_out, &flattened_d_x);
context.template device_context<DeviceContext>(), &Out_2d, &dOut_2d,
&dX_2d);
}
};
......
paddle/fluid/platform/profiler.cc
浏览文件 @ 265302ed
......@@ -270,12 +270,13 @@ struct EventItem {
double min_time;
double max_time;
double ave_time;
float ratio;
};
// Print results
void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
const std::string& sorted_domain, const size_t name_width,
const size_t data_width) {
const size_t data_width, double total) {
// Output header information
std::cout << "\n------------------------->"
<< " Profiling Report "
......@@ -300,7 +301,8 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
std::cout << std::setw(name_width) << "Event" << std::setw(data_width)
<< "Calls" << std::setw(data_width) << "Total"
<< std::setw(data_width) << "Min." << std::setw(data_width)
<< "Max." << std::setw(data_width) << "Ave." << std::endl;
<< "Max." << std::setw(data_width) << "Ave."
<< std::setw(data_width) << "Ratio." << std::endl;
for (size_t i = 0; i < events_table.size(); ++i) {
for (size_t j = 0; j < events_table[i].size(); ++j) {
const EventItem& event_item = events_table[i][j];
......@@ -309,7 +311,9 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
<< std::setw(data_width) << event_item.total_time
<< std::setw(data_width) << event_item.min_time
<< std::setw(data_width) << event_item.max_time
<< std::setw(data_width) << event_item.ave_time << std::endl;
<< std::setw(data_width) << event_item.ave_time
<< std::setw(data_width) << event_item.total_time / total
<< std::endl;
}
}
std::cout << std::endl;
......@@ -359,6 +363,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
std::vector<std::vector<EventItem>> events_table;
size_t max_name_width = 0;
double total = 0.; // the total time
for (size_t i = 0; i < events.size(); i++) {
std::list<Event> pushed_events;
std::vector<EventItem> event_items;
......@@ -379,6 +384,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
g_state == ProfilerState::kAll)
? rit->CudaElapsedMs(events[i][j])
: rit->CpuElapsedMs(events[i][j]);
total += event_time;
std::string event_name =
"thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
......@@ -387,7 +393,8 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
if (event_idx.find(event_name) == event_idx.end()) {
event_idx[event_name] = event_items.size();
EventItem event_item = {event_name, 1, event_time,
event_time, event_time, event_time};
event_time, event_time, event_time,
0.};
event_items.push_back(event_item);
} else {
int index = event_idx[event_name];
......@@ -431,7 +438,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
}
// Print report
PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12);
PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12, total);
}
void DisableProfiler(EventSortingKey sorted_key,
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 265302ed
......@@ -301,7 +301,8 @@ void BindOpDesc(pybind11::module *m) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr", &pd::OpDesc::GetBlockAttr)
.def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
......
python/paddle/fluid/backward.py
浏览文件 @ 265302ed
......@@ -344,7 +344,7 @@ def _append_backward_ops_(block,
grad_sub_block_list = []
# If the op has its own sub-block, deal with the sub-block first
if op.has_attr("sub_block"):
sub_block = program.block(op.block_attr("sub_block"))
sub_block = program.block(op.block_attr_id("sub_block"))
grad_sub_block = program.create_block()
grad_sub_block._set_forward_block_idx(sub_block.idx)
cb = _callback_lookup_(op)
......@@ -406,7 +406,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
for op_idx in range(start_op_idx, block.desc.op_size()):
op_desc = block.desc.op(op_idx)
if op_desc.has_attr("sub_block"):
sub_block = block.program.block(op_desc.block_attr("sub_block"))
sub_block = block.program.block(op_desc.block_attr_id("sub_block"))
_append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map)
new_vars = set()
# create new gradient variables
......
python/paddle/fluid/framework.py
浏览文件 @ 265302ed
......@@ -476,23 +476,25 @@ class Operator(object):
attrs=None):
self.block = block
self.desc = desc
self.attrs = attrs
if self.attrs is None:
self.attrs = dict()
# note: not add self.attrs here:
# https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
op_attrs = attrs
if op_attrs is None:
op_attrs = dict()
del attrs
op_maker = core.op_proto_and_checker_maker
if op_maker.kOpRoleAttrName() not in self.attrs:
self.attrs[op_maker.kOpRoleAttrName()] = self.block.program.op_role
if op_maker.kOpRoleAttrName() not in op_attrs:
op_attrs[op_maker.kOpRoleAttrName()] = self.block.program.op_role
role_var_name = op_maker.kOpRoleVarAttrName()
if len(self.block.program.
op_role_var) != 0 and role_var_name not in self.attrs:
self.attrs[role_var_name] = self.block.program.op_role_var
op_role_var) != 0 and role_var_name not in op_attrs:
op_attrs[role_var_name] = self.block.program.op_role_var
if role_var_name in self.attrs and len(self.attrs[role_var_name]) == 0:
del self.attrs[role_var_name]
if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
del op_attrs[role_var_name]
if len(self.desc.type()) != 0:
return
......@@ -576,15 +578,14 @@ class Operator(object):
arg.op = self
self.desc.set_output(out_proto.name, out_arg_names)
if self.attrs is not None:
if not isinstance(self.attrs, dict):
if op_attrs is not None:
if not isinstance(op_attrs, dict):
raise TypeError("'attrs' should be a dict.")
for attr in proto.attrs:
attr_name = attr.name
if (attr_name not in self.attrs) or (
self.attrs[attr_name] is None):
if (attr_name not in op_attrs) or (op_attrs[attr_name] is None):
continue
attr_val = self.attrs[attr_name]
attr_val = op_attrs[attr_name]
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
......@@ -732,7 +733,6 @@ class Operator(object):
Raises:
ValueError: If the type of value doesn't match with desc.attr_type(name).
"""
self.attrs[name] = val
self._update_desc_attr(name, val)
def _update_desc_attr(self, name, val):
......@@ -774,6 +774,18 @@ class Operator(object):
"""
return self.desc.attr(name)
def block_attr_id(self, name):
"""
Get the block attribute's id by name.
Args:
name(str): the attribute name.
Returns:
int: the block index.
"""
return self.desc.block_attr_id(name)
def block_attr(self, name):
"""
Get the block attribute by name.
......@@ -782,24 +794,64 @@ class Operator(object):
name(str): the attribute name.
Returns:
int: the block index.
block: the block attribute.
"""
return self.desc.block_attr(name)
id = self.block_attr_id(name)
assert (id >= 0 and id < len(self.block.program.blocks))
return self.block.program.blocks[id]
def blocks_attr(self, name):
"""
Get the blocks attribute by name.
Args:
name(str): the attribute name.
Returns:
list: list of the blocks attribute.
"""
attrs = []
for i in self.blocks_attr_ids(name):
assert (i >= 0 and i < len(self.block.program.blocks))
attrs.append(self.block.program.blocks[i])
return attrs
def blocks_attr_ids(self, name):
"""
Get the blocks attribute's ids by name.
Args:
name(str): the attribute name.
Returns:
list: list of the blocks ids.
"""
return self.desc.blocks_attr_ids(name)
def all_attrs(self):
"""
Get the attribute dict.
Returns:
dict: The Operator's attribute dict.
dict: The Operator's attribute dict, name->attr.
"""
attr_names = self.attr_names
attr_map = {}
for n in attr_names:
if n == 'sub_block':
attr_type = self.desc.attr_type(n)
if attr_type == core.AttrType.BLOCK:
attr_map[n] = self.block_attr(n)
else:
continue
if attr_type == core.AttrType.BLOCKS:
attr_map[n] = self.blocks_attr(n)
continue
attr_map[n] = self.attr(n)
return attr_map
......@@ -1518,11 +1570,17 @@ class Program(object):
The two code snippets above will generate same programs.
"""
if for_test:
p = self.inference_optimize()
p = self.inference_optimize(export_for_deployment=False)
else:
p = Program()
p.current_block_idx = self.current_block_idx
p._seed = self._seed
p.desc = core.ProgramDesc(self.desc)
p.blocks = [Block(p, i) for i in range(self.desc.num_blocks())]
p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())]
p._current_role = self._current_role
p._op_role_var = self._op_role_var
p._sync_with_cpp()
p._copy_param_info_from(self)
......@@ -1578,7 +1636,7 @@ class Program(object):
res._sync_with_cpp()
return res
def inference_optimize(self):
def inference_optimize(self, export_for_deployment=True):
"""
This method will create a new program and do following adjustments on it:
1. Remove all reader variables and their creator ops if exist.
......@@ -1589,6 +1647,10 @@ class Program(object):
attribute of operators to :code:`True`. All the :code:`Parameter`
information will be lost.
Args:
export_for_deployment(bool): remove the read ops that are added by py_reader
for cpp inference library
Notes: This API is a very low level API. Use
:code:`Program.clone(for_test=True)` instead.
......@@ -1603,6 +1665,7 @@ class Program(object):
# remove all readers and the read_op if exist
read_op_idx = 0
root_block = res.desc.block(0)
if export_for_deployment:
while True:
if read_op_idx >= root_block.op_size() or root_block.op(
read_op_idx).type() == 'read':
......
python/paddle/fluid/initializer.py
浏览文件 @ 265302ed
......@@ -264,7 +264,8 @@ class NormalInitializer(Initializer):
"dtype": int(var.dtype),
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed
"seed": self._seed,
"use_mkldnn": False
})
var.op = op
return op
......
python/paddle/fluid/io.py
浏览文件 @ 265302ed
......@@ -555,7 +555,8 @@ def save_inference_model(dirname,
executor,
main_program=None,
model_filename=None,
params_filename=None):
params_filename=None,
export_for_deployment=True):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` by the `executor`.
......@@ -577,6 +578,8 @@ def save_inference_model(dirname,
params_filename(str|None): The name of file to save all related parameters.
If it is setted None, parameters will be saved
in separate files .
export_for_deployment(bool): remove the read ops that are added by py_reader
for cpp inference lib. Default True
Returns:
None
......@@ -643,7 +646,8 @@ def save_inference_model(dirname,
copy_program.desc.flush()
pruned_program = copy_program.prune(targets=target_vars)
inference_program = pruned_program.inference_optimize()
inference_program = pruned_program.inference_optimize(
export_for_deployment=export_for_deployment)
fetch_var_names = [v.name for v in target_vars]
prepend_feed_ops(inference_program, feeded_var_names)
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 265302ed
......@@ -20,7 +20,9 @@ from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
from . import tensor
from . import nn
from . import ops
import math
import numpy
from functools import reduce
__all__ = [
......@@ -264,10 +266,11 @@ def detection_output(loc,
prior_box_var=prior_box_var,
target_box=loc,
code_type='decode_center_size')
old_shape = scores.shape
scores = nn.reshape(x=scores, shape=(-1, old_shape[-1]))
compile_shape = scores.shape
run_shape = ops.shape(scores)
scores = nn.flatten(x=scores, axis=2)
scores = nn.softmax(input=scores)
scores = nn.reshape(x=scores, shape=old_shape)
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
......@@ -677,9 +680,10 @@ def ssd_loss(location,
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
conf_shape = ops.shape(confidence)
def __reshape_to_2d(var):
return nn.reshape(x=var, shape=[-1, var.shape[-1]])
return nn.flatten(x=var, axis=2)
# 1. Find matched boundding box by prior box.
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
......@@ -690,7 +694,8 @@ def ssd_loss(location,
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
gt_label = nn.reshape(x=gt_label, shape=gt_label.shape + (1, ))
gt_label = nn.reshape(
x=gt_label, shape=(len(gt_label.shape) - 1) * (0, ) + (-1, 1))
gt_label.stop_gradient = True
target_label, _ = target_assign(
gt_label, matched_indices, mismatch_value=background_label)
......@@ -701,9 +706,12 @@ def ssd_loss(location,
target_label = __reshape_to_2d(target_label)
target_label.stop_gradient = True
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
conf_loss = nn.reshape(x=conf_loss, shape=(num, num_prior))
conf_loss = nn.reshape(
x=conf_loss,
shape=(num, num_prior),
actual_shape=ops.slice(
conf_shape, axes=[0], starts=[0], ends=[2]))
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
dtype = matched_indices.dtype
......@@ -772,7 +780,11 @@ 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 = nn.reshape(x=loss, shape=[-1, num_prior])
loss = nn.reshape(
x=loss,
shape=(num, num_prior),
actual_shape=ops.slice(
conf_shape, axes=[0], starts=[0], ends=[2]))
loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
if normalize:
normalizer = nn.reduce_sum(target_loc_weight)
......@@ -1005,13 +1017,7 @@ def multi_box_head(inputs,
"""
def _reshape_with_axis_(input, axis=1):
if not (axis > 0 and axis < len(input.shape)):
raise ValueError("The axis should be smaller than "
"the arity of input and bigger than 0.")
new_shape = [
-1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
]
out = nn.reshape(x=input, shape=new_shape)
out = nn.flatten(x=input, axis=axis)
return out
def _is_list_or_tuple_(data):
......@@ -1101,11 +1107,13 @@ def multi_box_head(inputs,
stride=stride)
mbox_loc = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
new_shape = [
compile_shape = [
mbox_loc.shape[0],
mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3] / 4, 4
]
mbox_loc_flatten = nn.reshape(mbox_loc, shape=new_shape)
run_shape = tensor.assign(numpy.array([0, -1, 4]).astype("int32"))
mbox_loc_flatten = nn.reshape(
mbox_loc, shape=compile_shape, actual_shape=run_shape)
mbox_locs.append(mbox_loc_flatten)
# get conf
......@@ -1117,11 +1125,15 @@ def multi_box_head(inputs,
padding=pad,
stride=stride)
conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
new_shape = [
new_shape = [0, -1, num_classes]
compile_shape = [
conf_loc.shape[0], conf_loc.shape[1] * conf_loc.shape[2] *
conf_loc.shape[3] / num_classes, num_classes
]
conf_loc_flatten = nn.reshape(conf_loc, shape=new_shape)
run_shape = tensor.assign(
numpy.array([0, -1, num_classes]).astype("int32"))
conf_loc_flatten = nn.reshape(
conf_loc, shape=compile_shape, actual_shape=run_shape)
mbox_confs.append(conf_loc_flatten)
if len(box_results) == 1:
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 265302ed
......@@ -112,6 +112,7 @@ __all__ = [
'log',
'crop',
'rank_loss',
'flatten',
]
......@@ -5361,3 +5362,70 @@ def rank_loss(label, left, right, name=None):
"Right": right},
outputs={'Out': out})
return out
def flatten(x, axis=1, name=None):
"""
**Flatten layer**
Flattens the input tensor into a 2D matrix.
Examples:
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
axis = 2
We get:
Out.shape = (3 * 100, 4 * 100)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
axis = 0
We get:
Out.shape = (1, 3 * 100 * 100 * 4)
Args:
x (Variable): A tensor of rank >= axis.
axis (int): Indicate up to which input dimensions (exclusive) should
be flattened to the outer dimension of the output.
The value for axis must be in the range [0, R], where R
is the rank of the input tensor. When axis = 0, the shape
of the output tensor is (1, (d_0 X d_1 ... d_n), where the
shape of the input tensor is (d_0, d_1, ... d_n).
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: A 2D tensor with the contents of the input tensor, with input
dimensions up to axis flattened to the outer dimension of
the output and remaining input dimensions flattened into the
inner dimension of the output.
Raises:
ValueError: If x is not a variable.
ValueError: If axis is not in range [0, rank(x)].
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32")
out = fluid.layers.flatten(x=x, axis=2)
"""
helper = LayerHelper('flatten', **locals())
if not (isinstance(x, Variable)):
raise ValueError("The input x should be a Variable")
if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='flatten',
inputs={"X": x},
outputs={'Out': out},
attrs={"axis": axis})
return out
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
浏览文件 @ 265302ed
......@@ -105,5 +105,107 @@ class TestCrossEntropyOp3(OpTest):
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp4(OpTest):
"""Test high rank tensor cross-entropy with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [10, 2, 4]
ins_num = np.prod(np.array(shape))
class_num = 10
X_2d = randomize_probability(ins_num, class_num, dtype='float64')
label_2d = np.random.randint(0, class_num, (ins_num, 1), dtype="int64")
cross_entropy_2d = np.asmatrix(
[[-np.log(X_2d[i][label_2d[i][0]])] for i in range(X_2d.shape[0])],
dtype="float64")
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [1])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": False}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
class TestCrossEntropyOp5(OpTest):
"""Test high rank tensor cross-entropy with vectorized soft labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [4, 3]
ins_num = np.prod(np.array(shape))
class_num = 37
X_2d = randomize_probability(ins_num, class_num)
label_2d = np.random.uniform(0.1, 1.0,
[ins_num, class_num]).astype("float32")
label_2d /= label_2d.sum(axis=1, keepdims=True)
cross_entropy_2d = (-label_2d * np.log(X_2d)).sum(
axis=1, keepdims=True).astype("float32")
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [class_num])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp6(OpTest):
"""Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [4, 3, 2]
ins_num = np.prod(np.array(shape))
class_num = 17
X_2d = randomize_probability(ins_num, class_num)
label_index_2d = np.random.randint(
0, class_num, (ins_num), dtype="int32")
label_2d = np.zeros(X_2d.shape)
label_2d[np.arange(ins_num), label_index_2d] = 1
cross_entropy_2d = np.asmatrix(
[[-np.log(X_2d[i][label_index_2d[i]])]
for i in range(X_2d.shape[0])],
dtype="float32")
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [class_num])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
self.inputs = {"X": X, "Label": label.astype(np.float32)}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_desc_clone.py 0 → 100644
浏览文件 @ 265302ed
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
import collections
SEED = 1
DTYPE = "float32"
paddle.dataset.mnist.fetch()
# random seed must set before configuring the network.
# fluid.default_startup_program().random_seed = SEED
def cnn_model(data):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=data,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
# TODO(dzhwinter) : refine the initializer and random seed settting
SIZE = 10
input_shape = conv_pool_2.shape
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
predict = fluid.layers.fc(
input=conv_pool_2,
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)))
return predict
def get_model(batch_size):
# Input data
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = cnn_model(images)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
# Evaluator
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
# Optimization
opt = fluid.optimizer.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
# Reader
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
opt.minimize(avg_cost)
return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id=trainer_id,
program=main_program,
pservers=pserver_endpoints,
trainers=trainers)
return t
def operator_equal(a, b):
for k, v in a.__dict__.iteritems():
if isinstance(v, fluid.framework.Program) or \
isinstance(v, fluid.framework.Block):
continue
elif isinstance(v, core.OpDesc):
if v.serialize_to_string() != b.__dict__[k].serialize_to_string():
raise ValueError("In operator_equal not equal:{0}\n".format(k))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(v.iteritems(), key=lambda x: x[0])
v1 = sorted(b.__dict__[k].iteritems(), key=lambda x: x[0])
if v0 != v1:
raise ValueError("In operator_equal not equal:{0}\n".format(k))
elif (v != b.__dict__[k]):
raise ValueError("In operator_equal not equal:{0}\n".format(k))
return True
def block_equal(a, b):
for k, v in a.__dict__.iteritems():
if isinstance(v, core.ProgramDesc) or isinstance(
v, fluid.framework.Program) or isinstance(v, core.BlockDesc):
continue
elif k == "ops":
for i in range(0, len(a.ops)):
if not operator_equal(a.ops[i], b.ops[i]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
assert (len(a.ops) == len(b.ops))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(v.iteritems(), key=lambda x: x[0])
v1 = sorted(b.__dict__[k].iteritems(), key=lambda x: x[0])
if v0 != v1:
raise ValueError("In block_equal not equal:{0}\n".format(k))
elif (v != b.__dict__[k]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
return True
def program_equal(a, b):
for k, v in a.__dict__.iteritems():
if isinstance(v, core.ProgramDesc):
continue
elif k == 'blocks':
for i in range(0, len(a.blocks)):
if not block_equal(a.blocks[i], b.blocks[i]):
raise ValueError("In operator_equal not equal:{0}\n".format(
k))
return False
assert (len(a.blocks) == len(b.blocks))
elif (v != b.__dict__[k]):
raise ValueError("In program_equal not equal:{0}\n".format(k))
return True
class TestDistMnist(unittest.TestCase):
def test_desc_clone(self):
get_model(batch_size=20)
pserver_endpoints = "127.0.0.1:9123"
trainers = 1
current_endpoint = "127.0.0.1:9123"
t = get_transpiler(0,
fluid.default_main_program(), pserver_endpoints,
trainers)
pserver_prog = t.get_pserver_program(current_endpoint)
startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
main = pserver_prog.clone()
startup = startup_prog.clone()
self.assertTrue(program_equal(main, pserver_prog))
self.assertTrue(program_equal(startup, startup_prog))
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 265302ed
......@@ -130,7 +130,7 @@ class TestDistBase(unittest.TestCase):
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._python_interp = "python"
def start_pserver(self, model_file):
def start_pserver(self, model_file, check_error_log):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps0_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
......@@ -139,11 +139,23 @@ class TestDistBase(unittest.TestCase):
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
ps0_pipe = subprocess.PIPE
ps1_pipe = subprocess.PIPE
if check_error_log:
print("ps0_cmd:", ps0_cmd)
print("ps1_cmd:", ps1_cmd)
ps0_pipe = open("/tmp/ps0_err.log", "wb")
ps1_pipe = open("/tmp/ps1_err.log", "wb")
ps0_proc = subprocess.Popen(
ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe)
ps1_proc = subprocess.Popen(
ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return ps0_proc, ps1_proc
ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe)
if not check_error_log:
return ps0_proc, ps1_proc, None, None
else:
return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
def _wait_ps_ready(self, pid):
retry_times = 50
......@@ -160,7 +172,7 @@ class TestDistBase(unittest.TestCase):
(e, retry_times))
retry_times -= 1
def check_with_place(self, model_file, delta=1e-3):
def check_with_place(self, model_file, delta=1e-3, check_error_log=False):
# *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
required_envs = {
"PATH": os.getenv("PATH"),
......@@ -169,17 +181,32 @@ class TestDistBase(unittest.TestCase):
"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
"FLAGS_cudnn_deterministic": "1"
}
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s %s trainer %s 0 %s %d FLASE" % \
(self._python_interp, model_file,
"127.0.0.1:1234", "127.0.0.1:1234", 1)
if not check_error_log:
local_proc = subprocess.Popen(
local_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
else:
print("trainer cmd:", local_cmd)
err_log = open("/tmp/trainer.err.log", "wb")
local_proc = subprocess.Popen(
local_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=err_log,
env=env_local)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = out
......@@ -187,7 +214,8 @@ class TestDistBase(unittest.TestCase):
sys.stderr.write('local_stderr: %s\n' % err)
# Run dist train to compare with local results
ps0, ps1 = self.start_pserver(model_file)
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model_file,
check_error_log)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
......@@ -205,15 +233,23 @@ class TestDistBase(unittest.TestCase):
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_pipe = subprocess.PIPE
tr1_pipe = subprocess.PIPE
if check_error_log:
print("tr0_cmd:", tr0_cmd)
print("tr1_cmd:", tr1_cmd)
tr0_pipe = open("/tmp/tr0_err.log", "wb")
tr1_pipe = open("/tmp/tr1_err.log", "wb")
tr0_proc = subprocess.Popen(
tr0_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stderr=tr0_pipe,
env=env0)
tr1_proc = subprocess.Popen(
tr1_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stderr=tr1_pipe,
env=env1)
tr0_proc.wait()
......@@ -230,6 +266,13 @@ class TestDistBase(unittest.TestCase):
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
# close trainer file
if check_error_log:
tr0_pipe.close()
tr1_pipe.close()
ps0_pipe.close()
ps1_pipe.close()
# FIXME: use terminate() instead of sigkill.
os.kill(ps0.pid, signal.SIGKILL)
os.kill(ps1.pid, signal.SIGKILL)
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 265302ed
......@@ -259,7 +259,7 @@ class TestLRDecayConditional(TranspilerTest):
serv_op = pserver.blocks[0].ops[0]
sub_blocks = []
optimize_blocks = []
for b in serv_op.attrs["optimize_blocks"]:
for b in serv_op.all_attrs()["optimize_blocks"]:
optimize_blocks.append(b.idx)
for b in pserver.blocks:
if b.idx not in optimize_blocks:
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 265302ed
......@@ -465,6 +465,17 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_flatten(self):
program = Program()
with program_guard(program):
x = layers.data(
name='x',
append_batch_size=False,
shape=[4, 4, 3],
dtype="float32")
out = layers.flatten(x, axis=1, name="flatten")
self.assertIsNotNone(out)
def test_shape(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_program.py
浏览文件 @ 265302ed
......@@ -17,6 +17,7 @@ import unittest
from paddle.fluid.framework import Program, default_main_program, program_guard, grad_var_name
import paddle.fluid.layers as layers
import paddle.fluid as fluid
main_program = default_main_program()
......@@ -98,6 +99,39 @@ class TestProgram(unittest.TestCase):
new_program = main_program.clone()
self.assertNotEqual(0, len(new_program.blocks[0].all_parameters()))
def test_program_inference_optimize(self):
def net():
reader = fluid.layers.py_reader(
capacity=10,
shapes=[[-1, 10], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'],
use_double_buffer=True)
in_data, label = fluid.layers.read_file(reader)
predict_label = fluid.layers.fc(in_data, size=2, act='softmax')
loss = fluid.layers.mean(
fluid.layers.cross_entropy(
input=predict_label, label=label))
optimizer = fluid.optimizer.Adam()
optimizer.minimize(loss)
startup_program = fluid.Program()
main_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
net()
no_read_program = main_program.inference_optimize()
keep_read_program = main_program.inference_optimize(
export_for_deployment=False)
no_read_ops = no_read_program.global_block().ops
keep_read_ops = keep_read_program.global_block().ops
self.assertEqual(len(keep_read_ops) - len(no_read_ops), 2)
self.assertEqual(keep_read_ops[0].type, 'create_double_buffer_reader')
self.assertEqual(keep_read_ops[1].type, 'read')
for i in range(len(no_read_ops)):
self.assertEqual(no_read_ops[i].type, keep_read_ops[i + 2].type)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ 265302ed
......@@ -68,7 +68,7 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr("block_attr"))
self.assertEqual(0, op.block_attr_id("block_attr"))
mul_op = block.append_op()
mul_op.set_type("mul")
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 265302ed
......@@ -530,7 +530,10 @@ class DistributeTranspiler(object):
pserver_program._sync_with_cpp()
return pserver_program
def get_startup_program(self, endpoint, pserver_program):
def get_startup_program(self,
endpoint,
pserver_program,
startup_program=None):
"""
Get startup program for current parameter server.
Modify operator input variables if there are variables that
......@@ -540,12 +543,17 @@ class DistributeTranspiler(object):
endpoint (str): current pserver endpoint.
pserver_program (Program): call get_pserver_program first and
pass the result here.
startup_program (Program): if pass None, will use
default_startup_program
Returns:
Program: parameter server side startup program.
"""
s_prog = Program()
if not startup_program:
orig_s_prog = default_startup_program()
else:
orig_s_prog = startup_program
s_prog.random_seed = orig_s_prog.random_seed
params = self.param_grad_ep_mapping[endpoint]["params"]
......@@ -584,12 +592,12 @@ class DistributeTranspiler(object):
if op.type in [
"gaussian_random", "fill_constant", "uniform_random"
]:
op.attrs["shape"] = new_outputs["Out"].shape
op.set_attr("shape", list(new_outputs["Out"].shape))
s_prog.global_block().append_op(
type=op.type,
inputs=new_inputs,
outputs=new_outputs,
attrs=op.attrs)
attrs=op.all_attrs())
return s_prog
# ====================== private transpiler functions =====================
......@@ -603,7 +611,7 @@ class DistributeTranspiler(object):
self.table_name = None
for op in self.origin_program.global_block().ops:
if op.type == LOOKUP_TABLE_TYPE:
if op.attrs['is_distributed'] is True:
if op.attr('is_distributed') is True:
if self.table_name is None:
self.table_name = op.input("W")[0]
if self.table_name != op.input("W")[0]:
......@@ -1263,7 +1271,7 @@ class DistributeTranspiler(object):
type=opt_op.type,
inputs=new_inputs,
outputs=outputs,
attrs=opt_op.attrs)
attrs=opt_op.all_attrs())
def _is_splited_grad_var(self, var, var_dict):
grad_block = None
......@@ -1296,7 +1304,7 @@ class DistributeTranspiler(object):
block._clone_variable(var)
return block.append_op(
type=op.type, inputs=inputs, outputs=outputs, attrs=op.attrs)
type=op.type, inputs=inputs, outputs=outputs, attrs=op.all_attrs())
def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
program = optimize_block.program
......@@ -1337,7 +1345,7 @@ class DistributeTranspiler(object):
type=opt_op.type,
inputs=inputs,
outputs=outputs,
attrs=opt_op.attrs)
attrs=opt_op.all_attrs())
def _is_op_connected(self, op1, op2):
# If one op's input is another op's output or
......@@ -1442,8 +1450,8 @@ class DistributeTranspiler(object):
# optimize
op_maker = core.op_proto_and_checker_maker
optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
if op_maker.kOpRoleAttrName() in op.attrs and \
int(op.attrs[op_maker.kOpRoleAttrName()]) == int(optimize_role):
if op_maker.kOpRoleAttrName() in op.attr_names and \
int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role):
return True
return False
......@@ -1466,8 +1474,8 @@ class DistributeTranspiler(object):
# and op_role_var to get the pair.
for input_name in op.input_arg_names:
if input_name.find("@GRAD") != -1 and \
op.attrs[RPC_OP_ROLE_ATTR_NAME]:
param_name = op.attrs[OP_ROLE_VAR_ATTR_NAME][0]
op.attr(RPC_OP_ROLE_ATTR_NAME):
param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
params_grads.append([
origin_var_dict[param_name],
origin_var_dict[input_name]
......
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