未验证 提交 d954becb 编写于 作者: E emailweixu 提交者: GitHub

Merge pull request #7371 from emailweixu/assign_value_op

assign_value operator
...@@ -116,8 +116,8 @@ inline void Copy(const Tensor& src, const platform::Place& dst_place, ...@@ -116,8 +116,8 @@ inline void Copy(const Tensor& src, const platform::Place& dst_place,
* @param[in] src The external tensor. * @param[in] src The external tensor.
* @param[in] ctx The device context contains device resources. * @param[in] ctx The device context contains device resources.
* *
* * @note CopyFromVector assumes that the tensor has been resized * * @note CopyFromVector will resize dst to an 1D tensor with the same
* before invoking. * size as src.
*/ */
template <typename T> template <typename T>
inline void CopyFromVector(const std::vector<T>& src, inline void CopyFromVector(const std::vector<T>& src,
......
/* Copyright (c) 2018 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/operators/assign_value_op.h"
namespace paddle {
namespace operators {
class AssignValueOp : public framework::OperatorWithKernel {
public:
AssignValueOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AssignValueOp should not be null.");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
ctx->SetOutputDim("Out", framework::make_ddim(shape));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::proto::DataType(ctx.Attr<int>("dtype")), ctx.GetPlace());
}
};
class AssignValueOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AssignValueOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Out", "(Tensor) Output tensor of assign_value operator.");
AddAttr<std::vector<int>>("shape",
"(vector<int>) "
"Shape of values.");
AddAttr<int>("dtype", "data type of values")
.InEnum({framework::proto::DataType::INT32,
framework::proto::DataType::FP32});
AddAttr<std::vector<float>>("fp32_values", "store the float values")
.SetDefault({});
AddAttr<std::vector<int>>("int32_values", "store the int values")
.SetDefault({});
AddComment(R"DOC(
AssignValue operator
$$Out = values$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(assign_value, ops::AssignValueOp, ops::AssignValueOpMaker);
REGISTER_OP_CPU_KERNEL(assign_value, ops::AssignValueKernel<int>,
ops::AssignValueKernel<float>);
/* 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.
Indicesou 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/operators/assign_value_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(assign_value, ops::AssignValueKernel<int>,
ops::AssignValueKernel<float>);
/* Copyright (c) 2018 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace operators {
template <typename T>
class AssignValueKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& ctx) const {
auto shape = ctx.Attr<std::vector<int>>("shape");
auto* out = ctx.Output<framework::Tensor>("Out");
int dtype = ctx.Attr<int>("dtype");
const char* value_name = nullptr;
switch (dtype) {
case framework::proto::DataType::INT32:
value_name = "int32_values";
break;
case framework::proto::DataType::FP32:
value_name = "fp32_values";
break;
default:
PADDLE_THROW("Unsupported dtype for assign_value_op: %d", dtype);
break;
}
auto values = ctx.Attr<std::vector<T>>(value_name);
framework::CopyFromVector(values, ctx.device_context(), out);
out->Resize(framework::make_ddim(shape));
}
};
} // namespace operators
} // namespace paddle
from ..layer_helper import LayerHelper from ..layer_helper import LayerHelper
from ..param_attr import ParamAttr from ..param_attr import ParamAttr
from ..framework import convert_np_dtype_to_dtype_
from ..framework import Variable
from ..core import DataType
import numpy
__all__ = [ __all__ = [
'create_tensor', 'create_parameter', 'cast', 'concat', 'sums', 'assign', 'create_tensor', 'create_parameter', 'cast', 'concat', 'sums', 'assign',
...@@ -121,7 +125,7 @@ def assign(input, output): ...@@ -121,7 +125,7 @@ def assign(input, output):
This function copies the *input* Variable to the *output* Variable. This function copies the *input* Variable to the *output* Variable.
Args: Args:
input(Variable): The source variable input(Variable|numpy.ndarray): The source variable
output(Variable): The destination variable output(Variable): The destination variable
Returns: Returns:
...@@ -134,11 +138,37 @@ def assign(input, output): ...@@ -134,11 +138,37 @@ def assign(input, output):
fluid.layers.assign(hidden, out) fluid.layers.assign(hidden, out)
""" """
helper = LayerHelper('assign', **locals()) helper = LayerHelper('assign', **locals())
helper.append_op( if isinstance(input, Variable):
type='scale', helper.append_op(
inputs={'X': [input]}, type='scale',
outputs={'Out': [output]}, inputs={'X': [input]},
attrs={'scale': 1.0}) outputs={'Out': [output]},
attrs={'scale': 1.0})
elif isinstance(input, numpy.ndarray):
dtype = convert_np_dtype_to_dtype_(input.dtype)
if dtype == DataType.FP32:
value_name = "fp32_values"
values = [float(v) for v in input.flat]
elif dtype == DataType.INT32:
value_name = "int32_values"
values = [int(v) for v in input.flat]
else:
raise ValueError("Unsupported dtype %s", input.dtype)
if input.size > 1024 * 1024:
raise ValueError("The size of input is too big. Please consider "
"saving it to file and 'load_op' to load it")
helper.append_op(
type='assign_value',
outputs={'Out': [output]},
attrs={
'dtype': dtype,
'shape': list(input.shape),
value_name: values
})
else:
raise ValueError("Wrong type for assign input: %s" % type(input))
return output return output
......
import paddle.v2.fluid as fluid
import paddle.v2.fluid.layers as layers
import op_test
import numpy
import unittest
import paddle.v2.fluid.framework as framework
class TestAssignValueOp(op_test.OpTest):
def setUp(self):
self.op_type = "assign_value"
x = numpy.random.random(size=(2, 5)).astype(numpy.float32)
self.inputs = {}
self.outputs = {'Out': x}
self.attrs = {
'shape': x.shape,
'dtype': framework.convert_np_dtype_to_dtype_(x.dtype),
'fp32_values': [float(v) for v in x.flat]
}
def test_forward(self):
self.check_output()
def test_assign(self):
val = (
-100 + 200 * numpy.random.random(size=(2, 5))).astype(numpy.int32)
x = layers.create_tensor(dtype="float32")
layers.assign(input=val, output=x)
exe = fluid.Executor(fluid.CPUPlace())
fetched_x = exe.run(fluid.default_main_program(),
feed={},
fetch_list=[x])[0]
self.assertTrue(
numpy.array_equal(fetched_x, val),
"fetch_x=%s val=%s" % (fetched_x, val))
self.assertEqual(fetched_x.dtype, val.dtype)
if __name__ == '__main__':
unittest.main()
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