未验证 提交 d24d8c20 编写于 作者: Y Yang Yu

Add `lod_array_length` operator

上级 2a76b42e
/* Copyright (c) 2016 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. */
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
class LoDArrayLengthOp : public framework::OperatorBase {
public:
LoDArrayLengthOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
auto &out =
*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
out.Resize({1});
auto cpu = platform::CPUPlace();
*out.mutable_data<int64_t>(cpu) = static_cast<int64_t>(x.size());
}
};
class LoDArrayLengthProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDArrayLengthProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensorArray) The input tensor array.");
AddOutput("Out", "(Tensor) 1x1 CPU Tensor of length, int64_t");
AddComment(R"DOC(Get the length of lod tensor array
Out = len(X)
NOTE: The output is a CPU Tensor since the control variable should be only in
CPU and the length of LoDTensorArray should be used as control variables.
)DOC");
}
};
class LoDArrayLengthInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"));
PADDLE_ENFORCE(context->HasOutput("Out"));
context->SetOutputDim("Out", {1});
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lod_array_length, ops::LoDArrayLengthOp,
ops::LoDArrayLengthInferShape, ops::LoDArrayLengthProtoMaker,
paddle::framework::EmptyGradOpMaker);
...@@ -941,3 +941,12 @@ def shrink_memory(x, i, table, main_program=None): ...@@ -941,3 +941,12 @@ def shrink_memory(x, i, table, main_program=None):
outputs={'Out': [out]}, outputs={'Out': [out]},
attrs={}) attrs={})
return out return out
def array_length(array, main_program=None):
helper = LayerHelper('array_length', **locals())
tmp = helper.create_tmp_variable(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
return tmp
import unittest
import paddle.v2.framework.layers as layers
from paddle.v2.framework.executor import Executor
import paddle.v2.framework.core as core
import numpy
class TestLoDArrayLength(unittest.TestCase):
def test_array_length(self):
tmp = layers.zeros(shape=[10], dtype='int32')
i = layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = layers.array_write(tmp, i=i)
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = numpy.array(exe.run(fetch_list=[arr_len])[0])
self.assertEqual(11, result[0])
if __name__ == '__main__':
unittest.main()
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