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PaddleDetection
提交 bed0ecf3 编写于 作者: L lujun
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checkpoint pr be moved here, test=develop

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paddle/fluid/operators/load_combine_op.cc
浏览文件 @ bed0ecf3
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <fstream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -19,21 +20,71 @@ limitations under the License. */
namespace paddle {
namespace operators {
class LoadCombineOp : public framework::OperatorBase {
class LoadCombineOp : public framework::OperatorWithKernel {
public:
LoadCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto model_from_memory = Attr<bool>("model_from_memory");
auto out_var_names = Outputs("Out");
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, platform::CPUPlace());
return kt;
}
};
class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput(
"Out",
"(vector) The output LoDTensors that will be read from the input file.")
.AsDuplicable();
AddAttr<bool>(
"load_as_fp16",
"(boolean, default false)"
"If true, the tensor will be first loaded and then "
"converted to float16 data type. Otherwise, the tensor will be "
"directly loaded without data type conversion.")
.SetDefault(false);
AddAttr<std::string>("file_path",
"(string) "
"LoDTensors will be loaded from \"file_path\".")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<bool>("model_from_memory",
"(boolean, default false)"
"If true, file_path is in memory, and LoDTensors will be "
"loaded directly from memory")
.SetDefault(false);
AddComment(R"DOC(
LoadCombine Operator.
LoadCombine operator loads LoDTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
serialized using the SaveCombine operator. The
LoadCombine operator applies a deserialization strategy to appropriately load
the LodTensors, and this strategy complements the serialization strategy used
in the SaveCombine operator. Hence, the LoadCombine operator is tightly coupled
with the SaveCombine operator, and can only deserialize one or more LoDTensors
that were saved using the SaveCombine operator.
)DOC");
}
};
template <typename DeviceContext, typename T>
class LoadCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto model_from_memory = ctx.Attr<bool>("model_from_memory");
auto &out_var_names = ctx.Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
......@@ -41,27 +92,27 @@ class LoadCombineOp : public framework::OperatorBase {
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename, std::ios::in | std::ios::binary);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::Scope &scope, const platform::Place &place,
const framework::ExecutionContext &context, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto out_vars = context.MultiOutputVar("Out");
for (size_t i = 0; i < out_var_names.size(); i++) {
auto *out_var = scope.FindVar(out_var_names[i]);
PADDLE_ENFORCE(out_vars[i] != nullptr,
"Output variable %s cannot be found", out_var_names[i]);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_names[i]);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
auto *tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
......@@ -84,8 +135,8 @@ class LoadCombineOp : public framework::OperatorBase {
&fp16_tensor);
// reset output tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
out_vars[i]->Clear();
tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
......@@ -97,48 +148,17 @@ class LoadCombineOp : public framework::OperatorBase {
}
};
class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput(
"Out",
"(vector) The output LoDTensors that will be read from the input file.")
.AsDuplicable();
AddAttr<bool>(
"load_as_fp16",
"(boolean, default false)"
"If true, the tensor will be first loaded and then "
"converted to float16 data type. Otherwise, the tensor will be "
"directly loaded without data type conversion.")
.SetDefault(false);
AddAttr<std::string>("file_path",
"(string) "
"LoDTensors will be loaded from \"file_path\".")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<bool>("model_from_memory",
"(boolean, default false)"
"If true, file_path is in memory, and LoDTensors will be "
"loaded directly from memory")
.SetDefault(false);
AddComment(R"DOC(
LoadCombine Operator.
LoadCombine operator loads LoDTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
serialized using the SaveCombine operator. The
LoadCombine operator applies a deserialization strategy to appropriately load
the LodTensors, and this strategy complements the serialization strategy used
in the SaveCombine operator. Hence, the LoadCombine operator is tightly coupled
with the SaveCombine operator, and can only deserialize one or more LoDTensors
that were saved using the SaveCombine operator.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load_combine, ops::LoadCombineOp,
ops::LoadCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load_combine,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/load_op.cc
浏览文件 @ bed0ecf3
......@@ -21,31 +21,63 @@ limitations under the License. */
namespace paddle {
namespace operators {
class LoadOp : public framework::OperatorBase {
class LoadOp : public framework::OperatorWithKernel {
public:
LoadOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, platform::CPUPlace());
return kt;
}
};
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput("Out", "The LoDTensor / SelectedRows need to be loaded");
AddAttr<bool>(
"load_as_fp16",
"If true, the tensor will be first loaded and then "
"converted to float16 data type. Otherwise, the tensor will be "
"directly loaded without data type conversion. Default is false.")
.SetDefault(false);
AddAttr<std::string>("file_path",
R"(Variable will be loaded from "file_path")")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddComment(
"Load operator will load a LoDTensor / SelectedRows variable from disk "
"file.");
}
};
template <typename DeviceContext, typename T>
class LoadOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
auto filename = ctx.Attr<std::string>("file_path");
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
auto out_var_name = Output("Out");
auto *out_var = scope.FindVar(out_var_name);
PADDLE_ENFORCE(out_var != nullptr,
"Output variable %s cannot be found in scope %p",
out_var_name, &scope);
auto out_var_name = ctx.Outputs("Out").data();
auto *out_var = ctx.OutputVar("Out");
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found ",
out_var_name);
PADDLE_ENFORCE(out_var != nullptr, "Output variable cannot be found ");
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var);
LoadLodTensor(fin, place, out_var, ctx);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
......@@ -57,14 +89,15 @@ class LoadOp : public framework::OperatorBase {
}
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
framework::Variable *var,
const framework::ExecutionContext &ctx) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto in_dtype = tensor->type();
auto out_dtype = load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
......@@ -97,27 +130,14 @@ class LoadOp : public framework::OperatorBase {
}
};
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput("Out", "The LoDTensor / SelectedRows need to be loaded");
AddAttr<bool>(
"load_as_fp16",
"If true, the tensor will be first loaded and then "
"converted to float16 data type. Otherwise, the tensor will be "
"directly loaded without data type conversion. Default is false.")
.SetDefault(false);
AddAttr<std::string>("file_path",
R"(Variable will be loaded from "file_path")")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddComment(
"Load operator will load a LoDTensor / SelectedRows variable from disk "
"file.");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load, ops::LoadOp, ops::LoadOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load, ops::LoadOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ bed0ecf3
......@@ -27,20 +27,53 @@ limitations under the License. */
namespace paddle {
namespace operators {
class SaveCombineOp : public framework::OperatorBase {
class SaveCombineOp : public framework::OperatorWithKernel {
public:
SaveCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto save_as_fp16 = Attr<bool>("save_as_fp16");
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
};
class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"X",
"(vector) Input LoDTensors that need to be saved together in a file.")
.AsDuplicable();
AddComment(R"DOC(
SaveCombine operator
This operator will serialize and write a list of input LoDTensor variables
to a file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
"Overwrite the output file if it exists.")
.SetDefault(true);
AddAttr<bool>("save_as_fp16",
"(boolean, default false)"
"If true, the tensor will be converted to float16 data "
"type and then saved. Otherwise, the tensor will be "
"directly saved without data type conversion.")
.SetDefault(false);
AddAttr<std::string>(
"file_path",
"(string)"
"The \"file_path\" where the LoDTensor variables will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
}
};
template <typename DeviceContext, typename T>
class SaveCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
......@@ -53,7 +86,8 @@ class SaveCombineOp : public framework::OperatorBase {
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto inp_var_names = Inputs("X");
auto &inp_var_names = ctx.Inputs("X");
auto &inp_vars = ctx.MultiInputVar("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
"The number of input variables should be greater than 0");
......@@ -62,16 +96,14 @@ class SaveCombineOp : public framework::OperatorBase {
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < inp_var_names.size(); i++) {
auto *var = scope.FindVar(inp_var_names[i]);
PADDLE_ENFORCE(var != nullptr,
PADDLE_ENFORCE(inp_vars[i] != nullptr,
"Cannot find variable %s for save_combine_op",
inp_var_names[i]);
PADDLE_ENFORCE(var->IsType<framework::LoDTensor>(),
PADDLE_ENFORCE(inp_vars[i]->IsType<framework::LoDTensor>(),
"SaveCombineOp only supports LoDTensor, %s has wrong type",
inp_var_names[i]);
auto &tensor = var->Get<framework::LoDTensor>();
auto &tensor = inp_vars[i]->Get<framework::LoDTensor>();
// Serialize tensors one by one
// Check types to see if a fp16 transformation is required
......@@ -95,38 +127,6 @@ class SaveCombineOp : public framework::OperatorBase {
}
};
class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"X",
"(vector) Input LoDTensors that need to be saved together in a file.")
.AsDuplicable();
AddComment(R"DOC(
SaveCombine operator
This operator will serialize and write a list of input LoDTensor variables
to a file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
"Overwrite the output file if it exists.")
.SetDefault(true);
AddAttr<bool>("save_as_fp16",
"(boolean, default false)"
"If true, the tensor will be converted to float16 data "
"type and then saved. Otherwise, the tensor will be "
"directly saved without data type conversion.")
.SetDefault(false);
AddAttr<std::string>(
"file_path",
"(string)"
"The \"file_path\" where the LoDTensor variables will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
}
};
} // namespace operators
} // namespace paddle
......@@ -134,3 +134,10 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
ops::SaveCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
save_combine,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_load_combine_op_test.cc
浏览文件 @ bed0ecf3
......@@ -19,8 +19,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save_combine);
USE_NO_KERNEL_OP(load_combine);
USE_CPU_ONLY_OP(save_combine);
USE_CPU_ONLY_OP(load_combine);
template <typename T, typename U>
T* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
......
paddle/fluid/operators/save_load_op_test.cc
浏览文件 @ bed0ecf3
......@@ -16,8 +16,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save);
USE_NO_KERNEL_OP(load);
USE_CPU_ONLY_OP(save);
USE_CPU_ONLY_OP(load);
TEST(SaveLoadOp, CPU) {
paddle::framework::Scope scope;
......
paddle/fluid/operators/save_op.cc
浏览文件 @ bed0ecf3
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
......@@ -29,29 +30,88 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
class SaveOp : public framework::OperatorBase {
class SaveOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.GetPlace());
}
};
class SaveOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor ) Input LoDTensor and SelectedRows to be saved");
AddComment(R"DOC(
Save operator
This operator will serialize and write LoDTensor / SelectedRows variable to file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
"Overwrite the output file if exist")
.SetDefault(true);
AddAttr<bool>("save_as_fp16",
"(boolean, default false)"
"If true, the tensor will be converted to float16 data "
"type and then saved. Otherwise, the tensor will be "
"directly saved without data type conversion.")
.SetDefault(false);
AddAttr<std::string>("file_path",
"(string)"
"The \"file_path\" where the variable will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddOutput(LOOKUP_TABLE_PATH,
"(string)"
"for pserver: The \"kLookupTablePath\" where checkpoint notify "
"to save lookup table variables"
" to directory specified.")
.AsDispensable();
}
};
class SaveOpVarTypeInference : public framework::VarTypeInference {
public:
SaveOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto iname = Input("X");
auto *var = scope.FindVar(iname);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s for save_op",
void operator()(framework::InferVarTypeContext *ctx) const override {
auto var_type = framework::proto::VarType::RAW;
ctx->SetType(LOOKUP_TABLE_PATH, var_type);
}
};
class SaveOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
template <typename DeviceContext, typename T>
class SaveOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto *input_var = ctx.InputVar("X");
auto iname = ctx.Inputs("X").data();
PADDLE_ENFORCE(input_var != nullptr, "Cannot find variable %s for save_op",
iname);
if (var->IsType<framework::LoDTensor>()) {
SaveLodTensor(place, var);
} else if (var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(scope, place, var);
if (input_var->IsType<framework::LoDTensor>()) {
SaveLodTensor(ctx, place, input_var);
} else if (input_var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(ctx, place, input_var);
} else {
PADDLE_ENFORCE(
false,
......@@ -60,10 +120,11 @@ class SaveOp : public framework::OperatorBase {
}
}
void SaveLodTensor(const platform::Place &place,
framework::Variable *var) const {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
void SaveLodTensor(const framework::ExecutionContext &ctx,
const platform::Place &place,
const framework::Variable *var) const {
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
......@@ -84,7 +145,7 @@ class SaveOp : public framework::OperatorBase {
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = Attr<bool>("save_as_fp16");
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
auto in_dtype = tensor.type();
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
......@@ -102,13 +163,15 @@ class SaveOp : public framework::OperatorBase {
fout.close();
}
void SaveSelectedRows(const framework::Scope &scope,
void SaveSelectedRows(const framework::ExecutionContext &ctx,
const platform::Place &place,
framework::Variable *var) const {
auto *lt_var = scope.FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
const framework::Variable *var) const {
framework::Variable *out_put_var = ctx.OutputVar(LOOKUP_TABLE_PATH);
PADDLE_ENFORCE(
lt_var != nullptr,
out_put_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
auto *lt_var = out_put_var->GetMutable<std::string>();
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
......@@ -130,50 +193,17 @@ class SaveOp : public framework::OperatorBase {
}
};
class SaveOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor ) Input LoDTensor and SelectedRows to be saved");
AddComment(R"DOC(
Save operator
This operator will serialize and write LoDTensor / SelectedRows variable to file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
"Overwrite the output file if exist")
.SetDefault(true);
AddAttr<bool>("save_as_fp16",
"(boolean, default false)"
"If true, the tensor will be converted to float16 data "
"type and then saved. Otherwise, the tensor will be "
"directly saved without data type conversion.")
.SetDefault(false);
AddAttr<std::string>("file_path",
"(string)"
"The \"file_path\" where the variable will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
}
};
class SaveOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext *ctx) const override {
auto out_var_name = ctx->Output(LOOKUP_TABLE_PATH).front();
ctx->SetType(out_var_name, framework::proto::VarType::RAW);
}
};
class SaveOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(save, ops::SaveOp, paddle::framework::EmptyGradOpMaker,
ops::SaveOpProtoMaker, ops::SaveOpVarTypeInference,
ops::SaveOpShapeInference);
REGISTER_OPERATOR(save, ops::SaveOp, ops::SaveOpProtoMaker,
ops::SaveOpVarTypeInference, ops::SaveOpShapeInference);
REGISTER_OP_CPU_KERNEL(
save, ops::SaveOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int8_t>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
python/paddle/fluid/framework.py
浏览文件 @ bed0ecf3
......@@ -644,10 +644,9 @@ class Operator(object):
outputs={"Out": [var1]})
"""
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
'checkpoint_notify', 'gen_nccl_id'
'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......
python/paddle/fluid/imperative/__init__.py
浏览文件 @ bed0ecf3
......@@ -29,9 +29,13 @@ from .tracer import *
from . import profiler
from .profiler import *
from . import checkpoint
from .checkpoint import *
__all__ = []
__all__ += layers.__all__
__all__ += base.__all__
__all__ += nn.__all__
__all__ += tracer.__all__
__all__ += profiler.__all__
__all__ += checkpoint.__all__
python/paddle/fluid/imperative/checkpoint.py 0 → 100644
浏览文件 @ bed0ecf3
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import collections
from .. import core
from ..framework import Variable, Parameter, default_main_program
from .layers import Layer
__all__ = ['save_persistables', 'load_persistables']
def save_persistables(obj, dirname, filename=None):
"""
This function filters out all variables in layer.parameters from the
give `layer` and then trys to load these variables from the folder
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
var_list(dict of Parameters|Layer): The parameters will
be saved. If it is None, nothing
will be deal.
dirname(str): The directory path.
filename(str|None): The file which saved all variables. If variables were
saved in differnet files, set it to None.
Default: None
Returns:
Examples:
.. code-block:: python
ptb_model = PtbModel(
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
x_data = np.arange(12).reshape(4, 3).astype('int64')
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
param_path = "./my_paddle_model"
fluid.imperative.checkpoint.save_persistables(ptb_model.parameters(), dirname=param_path,
layer=ptb_model)
"""
if isinstance(obj, collections.OrderedDict):
_save_var_to_file(obj, dirname, filename)
elif isinstance(obj, Layer):
_save_var_to_file(
obj.state_dict(include_sublayers=True), dirname, filename)
def load_persistables(obj, dirname, filename=None):
"""
This function trys to load persistable variables from the folder
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
obj(dict of Parameters|Layer): The parameters will be loaded.
dirname(str): The directory path.
filename(str|None): The file which saved all variables, this file path should be end with '.npz'. If variables were
saved in differnet files, set it to None.
Default: None
Returns:
dict: The parameter-dict resumed from file
Examples:
.. code-block:: python
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.parameters(), param_path)
param_1 = param_dict['PtbModel_0.w_1']
or:
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
filename = "model.file"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer, var_list, param_path,
filename=filename)
param_1 = param_dict['PtbModel_0.w_1']
"""
if isinstance(obj, collections.OrderedDict):
return _load_var_from_file(obj, dirname, filename)
elif isinstance(obj, Layer):
return _load_var_from_file(
obj.state_dict(include_sublayers=True), dirname, filename)
return {}
def _save_var_to_file(stat_dict, file_dir, file_name):
save_block = default_main_program().global_block()
save_var_map = {}
for each_var in stat_dict.items():
save_var_map[each_var.name] = each_var
if file_name is None:
save_block.append_op(
type='save',
inputs={'X': [each_var]},
outputs={},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
if file_name is not None:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
save_block.append_op(
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(file_dir, file_name)})
def _load_var_from_file(stat_dict, file_dir, file_name):
load_block = default_main_program().global_block()
load_var_map = {}
for each_var in stat_dict.items():
assert isinstance(each_var, Variable)
if each_var.type == core.VarDesc.VarType.RAW:
continue
new_var = _clone_var_in_block_(load_block, each_var)
if file_name is None:
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [new_var]},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
load_var_map[new_var.name] = new_var
if file_name is not None:
load_var_list = []
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
load_block.append_op(
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(file_dir, file_name)})
for res_var in load_var_list:
load_var_map[res_var.name] = res_var
return load_var_map
def _clone_var_in_block_(block, var):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
python/paddle/fluid/imperative/layers.py
浏览文件 @ bed0ecf3
......@@ -212,6 +212,34 @@ class Layer(core.Layer):
else:
object.__delattr__(self, name)
def state_dict(self, destination=None, prefix='', include_sublayers=True):
if destination is None:
destination = collections.OrderedDict()
for name, data in self._parameters.items():
if data is not None:
destination[prefix + name] = data
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
destination_temp = destination.copy()
destination_temp.update(
layer_item.state_dict(destination_temp, prefix +
layer_name + ".",
include_sublayers))
destination = destination_temp
return destination
def load_dict(self, stat_dict, include_sublayers=True):
for name, item in self.__dict__.get('_parameters', None).items():
if item.name in stat_dict:
self.__setattr__(name, stat_dict[item.name])
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
layer_item.load_dict(stat_dict)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py 0 → 100644
浏览文件 @ bed0ecf3
# 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 unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
from paddle.fluid.imperative.base import to_variable
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
pool_stride,
pool_padding=0,
pool_type='max',
global_pooling=False,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
act=None,
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
param_attr=None,
bias_attr=None,
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
pool_padding=pool_padding,
global_pooling=global_pooling,
use_cudnn=use_cudnn)
def forward(self, inputs):
x = self._conv2d(inputs)
x = self._pool2d(x)
return x
class MNIST(fluid.imperative.Layer):
def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
act="softmax")
def forward(self, inputs):
x = self._simple_img_conv_pool_1(inputs)
x = self._simple_img_conv_pool_2(x)
x = self._fc(x)
return x
class TestImperativeCheckpoint(unittest.TestCase):
def save_load_persistables(self):
seed = 90
epoch_num = 1
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
dy_param_init_value = {}
step = 0
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape(128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
avg_loss._backward()
sgd.minimize(avg_loss)
fluid.imperative.save_persistables(mnist, "save_dir")
mnist.clear_gradients()
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
mnist.load_dict(
fluid.imperative.load_persistables(mnist, "save_dir"))
restore = mnist.parameters()
self.assertEqual(len(dy_param_init_value), len(restore))
for value in restore:
self.assertTrue(
np.allclose(value, dy_param_init_value[value.name]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
step += 1
if step > 20:
break
if __name__ == '__main__':
unittest.main()
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