提交 352af966 编写于 作者: Q qiaolongfei

add python unit test

上级 5917e09c
......@@ -26,10 +26,8 @@ cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(shape_inference_map SRCS shape_inference_map.cc DEPS op_info operator)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator proto_desc)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info shape_inference_map)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry sum_op)
......
......@@ -26,7 +26,6 @@ limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/shape_inference_map.h"
namespace paddle {
namespace framework {
......@@ -55,16 +54,10 @@ class OpRegistry {
const std::string& grad_op_type) {
OperatorRegistrar<OpType, ProtoMakerType> reg(op_type.c_str());
reg.info.grad_op_type_ = grad_op_type;
auto proto = reg.info.Proto();
std::cout << "====== " << op_type << " =======" << std::endl;
std::cout << proto.SerializeAsString() << std::endl;
std::cout << "=============" << std::endl;
ShapeInferenceMap::Instance().CreateOpWithKernel(reg.info, op_type);
// register gradient op
if (!grad_op_type.empty()) {
OperatorRegistrar<GradOpType> grad_reg(grad_op_type.c_str());
ShapeInferenceMap::Instance().CreateOpWithKernel(grad_reg.info,
grad_op_type);
}
}
......
......@@ -20,6 +20,10 @@ limitations under the License. */
namespace paddle {
namespace framework {
class InferShapeContextBase;
typedef std::function<void(InferShapeContextBase *)> InferShapeFn;
class InferShapeContextBase {
public:
virtual ~InferShapeContextBase() {}
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/shape_inference_map.h"
namespace paddle {
namespace framework {
static VariableNameMap ConvertOpProtoVarsToVarNameMap(
const google::protobuf::RepeatedPtrField<OpProto::Var>& op_proto_vars) {
VariableNameMap ret_val;
for (auto& var : op_proto_vars) {
ret_val[var.name()] = {};
}
return ret_val;
}
static ShapeInferenceMap* g_shape_inference_map = nullptr;
ShapeInferenceMap& ShapeInferenceMap::Instance() {
if (g_shape_inference_map == nullptr) {
g_shape_inference_map = new ShapeInferenceMap();
}
return *g_shape_inference_map;
}
void ShapeInferenceMap::CreateOpWithKernel(const OpInfo& op_info,
const std::string& op_type) {
auto proto = op_info.Proto();
std::cout << "========= " << op_type << " in======" << std::endl;
std::cout << proto.SerializeAsString() << std::endl;
std::cout << "========= " << op_type << " out======" << std::endl;
const VariableNameMap inputs = ConvertOpProtoVarsToVarNameMap(proto.inputs());
const VariableNameMap outputs =
ConvertOpProtoVarsToVarNameMap(proto.outputs());
auto* op = op_info.Creator()(op_type, inputs, outputs, {});
auto* op_with_kernel = dynamic_cast<OperatorWithKernel*>(op);
auto it = op_shape_inference_map_.find(op_type);
if (it != op_shape_inference_map_.end()) {
PADDLE_THROW("OpWithKernel(%s) is already registered for infer_shape",
op_type);
}
if (op_with_kernel != nullptr) {
op_shape_inference_map_[op_type] = op_with_kernel;
}
}
} // namespace framework
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <unordered_map>
#include "paddle/framework/op_info.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/shape_inference.h"
namespace paddle {
namespace framework {
class ShapeInferenceMap {
public:
static ShapeInferenceMap& Instance();
void CreateOpWithKernel(const OpInfo& op_info, const std::string& op_type);
OperatorWithKernel* GetOpWithKernel(const std::string& op_type) {
auto it = op_shape_inference_map_.find(op_type);
if (it == op_shape_inference_map_.end()) {
return nullptr;
}
return it->second;
}
private:
ShapeInferenceMap() = default;
DISABLE_COPY_AND_ASSIGN(ShapeInferenceMap);
std::unordered_map<std::string, OperatorWithKernel*> op_shape_inference_map_;
};
} // namespace framework
} // namespace paddle
......@@ -223,13 +223,19 @@ All parameter, weight, gradient are variables in Paddle.
desc.InitializationErrorString());
return OpRegistry::CreateOp(desc);
})
.def("infer_shape",
[](const OpDescBind &op_desc, BlockDescBind &block) {
auto &shape_inference_map = ShapeInferenceMap::Instance();
auto *op = shape_inference_map.GetOpWithKernel(op_desc.Type());
if (op != nullptr) {
.def_static("infer_shape",
[](OpDescBind &op_desc, BlockDescBind &block) {
auto op = OpRegistry::CreateOp(*op_desc.Proto());
auto *op_with_kernel =
dynamic_cast<OperatorWithKernel *>(op.get());
if (op_with_kernel != nullptr) {
auto ctx = CompileTimeInferShapeContext(op_desc, block);
op->InferShape(&ctx);
op_with_kernel->InferShape(&ctx);
} else {
PADDLE_THROW(
"OP(%s) is not type of OperatorWithKernel, "
"should not call this function",
op_desc.Type());
}
})
.def("backward",
......
......@@ -10,11 +10,13 @@ class TestInferShape(unittest.TestCase):
block = prog.block(0)
self.assertIsNotNone(block)
shape = [10, 20]
# prepare input/output
x1 = block.new_var("x1")
x1.set_shape([10, 20])
x1.set_shape(shape)
x2 = block.new_var("x2")
x2.set_shape([10, 20])
x2.set_shape(shape)
out = block.new_var("out")
......@@ -24,6 +26,40 @@ class TestInferShape(unittest.TestCase):
sum_op_desc.set_input("X", ["x1", "x2"])
sum_op_desc.set_output("Out", ["out"])
sum_op = Operator("sum", X=["x1", "x2"], Out="out")
sum_op.infer_shape(sum_op_desc, block)
print(out.shape())
print(type(sum_op_desc))
print(type(block))
core.Operator.infer_shape(sum_op_desc, block)
self.assertEqual(out.shape(), shape)
def test_mul_op(self):
prog = core.ProgramDesc.__create_program_desc__()
self.assertIsNotNone(prog)
block = prog.block(0)
self.assertIsNotNone(block)
x_shape = [10, 20]
y_shape = [20, 30]
# prepare input/output
x1 = block.new_var("x")
x1.set_shape(x_shape)
x2 = block.new_var("y")
x2.set_shape(y_shape)
out = block.new_var("out")
# prepare the operator
mul_op_desc = block.append_op()
mul_op_desc.set_type("mul")
mul_op_desc.set_input("X", ["x"])
mul_op_desc.set_input("Y", ["y"])
mul_op_desc.set_output("Out", ["out"])
mul_op_desc.set_attr("x_num_col_dims", 1)
mul_op_desc.set_attr("y_num_col_dims", 1)
core.Operator.infer_shape(mul_op_desc, block)
self.assertEqual(out.shape(), [x_shape[0], y_shape[1]])
if __name__ == '__main__':
unittest.main()
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