diff --git a/paddle/operators/lod_array_length_op.cc b/paddle/operators/lod_array_length_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..80445eb575703be3354595672a4c064b30e0f18c --- /dev/null +++ b/paddle/operators/lod_array_length_op.cc @@ -0,0 +1,71 @@ +/* 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(); + auto &out = + *scope.FindVar(Output("Out"))->GetMutable(); + out.Resize({1}); + auto cpu = platform::CPUPlace(); + *out.mutable_data(cpu) = static_cast(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); diff --git a/python/paddle/v2/framework/layers.py b/python/paddle/v2/framework/layers.py index 22540b2b9731e95cbc5ca903c9b2fa4de1a59502..dc5827115d01ac3ce2db8eb13f1f9a55d9ce19f5 100644 --- a/python/paddle/v2/framework/layers.py +++ b/python/paddle/v2/framework/layers.py @@ -941,3 +941,12 @@ def shrink_memory(x, i, table, main_program=None): outputs={'Out': [out]}, attrs={}) 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 diff --git a/python/paddle/v2/framework/tests/test_lod_array_length_op.py b/python/paddle/v2/framework/tests/test_lod_array_length_op.py new file mode 100644 index 0000000000000000000000000000000000000000..af2b4d705e7ec121bd5f1350f0a642ae8c44bf1e --- /dev/null +++ b/python/paddle/v2/framework/tests/test_lod_array_length_op.py @@ -0,0 +1,21 @@ +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()