diff --git a/paddle/framework/grad_op_desc_maker.h b/paddle/framework/grad_op_desc_maker.h index 2082f8bb76fb62bc36f033fecbd4eaa76d12d949..f51753453bee255a0592e9c5b0fb2d9aa380e109 100644 --- a/paddle/framework/grad_op_desc_maker.h +++ b/paddle/framework/grad_op_desc_maker.h @@ -122,6 +122,11 @@ class GradOpDescMakerBase { return it->second; } + template + inline const T& Attr(const std::string& name) const { + return boost::get(GetAttr(name)); + } + std::string ForwardOpType() const { return this->fwd_op_.Type(); } private: diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 5de9ae559c435439f30931c7840e54e0d2bb744c..6fb8532b2a807e287de7fab03402f8a290eabcf2 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -143,7 +143,7 @@ class OpKernelRegistrar : public Registrar { /** * Macro to register Operator. When the input is duplicable, you should - * use REGISTER_OP_EX with deop_empty_grad=false instead. + * use REGISTER_OP_EX with drop_empty_grad=false instead. */ #define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \ grad_op_class) \ diff --git a/paddle/operators/cum_op.h b/paddle/operators/cum_op.h new file mode 100644 index 0000000000000000000000000000000000000000..e3813ac9036169345bd69d1d42db4618c6c45ebf --- /dev/null +++ b/paddle/operators/cum_op.h @@ -0,0 +1,111 @@ +/* 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/framework/operator.h" +#include "paddle/operators/detail/safe_ref.h" + +namespace paddle { +namespace operators { + +template +class CumKernel : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + + void Compute(const framework::ExecutionContext& context) const override { + auto& X = detail::Ref(context.Input("X"), + "Cannot get input tensor X, variable name = %s", + context.op().Input("X")); + + auto& Out = detail::Ref(context.Output("Out"), + "Cannot get output tensor Out, variable name = %s", + context.op().Output("Out")); + int axis = context.Attr("axis"); + bool exclusive = context.Attr("exclusive"); + bool reverse = context.Attr("reverse"); + auto x_dims = X.dims(); + if (axis == -1) { + axis = x_dims.size() - 1; + } + PADDLE_ENFORCE_LT( + axis, x_dims.size(), + "axis should be less than the dimensiotn of the input tensor"); + Out.mutable_data(context.GetPlace()); + + int pre = 1; + int post = 1; + int mid = x_dims[axis]; + for (int i = 0; i < axis; ++i) { + pre *= x_dims[i]; + } + for (int i = axis + 1; i < x_dims.size(); ++i) { + post *= x_dims[i]; + } + + auto x = framework::EigenVector::Flatten(X); + auto out = framework::EigenVector::Flatten(Out); + auto* place = + context.template device_context().eigen_device(); + + using IndexT = Eigen::DenseIndex; + if (pre == 1) { + if (post == 1) { + ComputeImp(*place, Eigen::DSizes(mid), x, out, + /* axis= */ 0, reverse, exclusive); + } else { + ComputeImp(*place, Eigen::DSizes(mid, post), x, out, + /* axis= */ 0, reverse, exclusive); + } + } else { + if (post == 1) { + ComputeImp(*place, Eigen::DSizes(pre, mid), x, out, + /* axis= */ 1, reverse, exclusive); + } else { + ComputeImp(*place, Eigen::DSizes(pre, mid, post), x, out, + /* axis= */ 1, reverse, exclusive); + } + } + } + + private: + template + void ComputeImp(Device d, const Dim& dims, X x, Out out, int axis, + bool reverse, bool exclusive) const { + if (!reverse) { + out.reshape(dims).device(d) = Functor()(x.reshape(dims), axis, exclusive); + } else { + std::array rev; + rev.fill(false); + rev[axis] = reverse; + out.reshape(dims).device(d) = + Functor()(x.reshape(dims).reverse(rev), axis, exclusive).reverse(rev); + } + } +}; + +template +struct CumsumFunctor { + using ELEMENT_TYPE = T; + template + const typename X::TensorScanSumOp operator()(X x, int axis, + bool exclusive) const { + return x.cumsum(axis, exclusive); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/cumsum_op.cc b/paddle/operators/cumsum_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..4933cc923d46ad053e16e060d94a39083709bbe1 --- /dev/null +++ b/paddle/operators/cumsum_op.cc @@ -0,0 +1,82 @@ +/* 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/cum_op.h" + +namespace paddle { +namespace operators { + +class CumOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext *ctx) const override { + ctx->SetOutputDim("Out", ctx->GetInputDim("X")); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; + +class CumsumOpMaker : public framework::OpProtoAndCheckerMaker { + public: + CumsumOpMaker(OpProto *proto, OpAttrChecker *op_checker) + : framework::OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Cumsum operator"); + AddOutput("Out", "Output of Cumsum operator"); + AddAttr("axis", + "(int, default -1). The dimenstion to accumulate along. " + "-1 means the last dimenstion") + .SetDefault(-1) + .EqualGreaterThan(-1); + AddAttr("exclusive", + "bool, default false). Whether to perform exclusive cumsum") + .SetDefault(false); + AddAttr("reverse", + "bool, default false). If true, the cumsum is performed in " + "the reversed direction") + .SetDefault(false); + AddComment(R"DOC( +The cumulative sum of the elements along a given axis. +By default, the first element of the result is the same of the first element of +the input. If exlusive is true, the first element of the result is 0. +)DOC"); + } +}; + +class CumsumGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto *grad_op = new framework::OpDesc(); + grad_op->SetType("cumsum"); + grad_op->SetInput("X", OutputGrad("Out")); + grad_op->SetOutput("Out", InputGrad("X")); + grad_op->SetAttr("axis", Attr("axis")); + grad_op->SetAttr("reverse", !Attr("reverse")); + grad_op->SetAttr("exclusive", Attr("exclusive")); + return std::unique_ptr(grad_op); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +using CPU = paddle::platform::CPUDeviceContext; + +REGISTER_OPERATOR(cumsum, ops::CumOp, ops::CumsumOpMaker, ops::CumsumGradMaker); +REGISTER_OP_CPU_KERNEL(cumsum, ops::CumKernel>, + ops::CumKernel>, + ops::CumKernel>) diff --git a/paddle/operators/cumsum_op.cu b/paddle/operators/cumsum_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..90661c4269a9fadaba1446e04b3f661f1227a978 --- /dev/null +++ b/paddle/operators/cumsum_op.cu @@ -0,0 +1,22 @@ +/* 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/cum_op.h" + +namespace ops = paddle::operators; +using CUDA = paddle::platform::CUDADeviceContext; + +REGISTER_OP_CUDA_KERNEL(cumsum, ops::CumKernel>, + ops::CumKernel>, + ops::CumKernel>) diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py index 38dea2892fc18a9c493878d816a246522e9b9886..bb3f71abbb00793a32b8c288ee3ea255cc9c584d 100644 --- a/python/paddle/v2/fluid/layers/ops.py +++ b/python/paddle/v2/fluid/layers/ops.py @@ -65,6 +65,8 @@ __all__ = [ 'logical_or', 'logical_xor', 'logical_not', + 'uniform_random', + 'cumsum', ] + __activations__ for _OP in set(__all__): diff --git a/python/paddle/v2/fluid/tests/op_test.py b/python/paddle/v2/fluid/tests/op_test.py index 3f6d7070c2987d0557c60db84a2c679cd2cfe36b..f8475813c0cb7b8c8da8c9c7e0a4626615e609ac 100644 --- a/python/paddle/v2/fluid/tests/op_test.py +++ b/python/paddle/v2/fluid/tests/op_test.py @@ -326,7 +326,8 @@ class OpTest(unittest.TestCase): self.assertTrue( np.allclose( actual_t, expect_t, atol=atol), - "Output (" + out_name + ") has diff at " + str(place)) + "Output (" + out_name + ") has diff at " + str(place) + + str(actual_t) + str(expect_t)) if isinstance(expect, tuple): self.assertListEqual(actual.lod(), expect[1], "Output (" + out_name + diff --git a/python/paddle/v2/fluid/tests/test_cumsum_op.py b/python/paddle/v2/fluid/tests/test_cumsum_op.py new file mode 100644 index 0000000000000000000000000000000000000000..e45ef457306bbdd33508e560cb8d339e801bb5e4 --- /dev/null +++ b/python/paddle/v2/fluid/tests/test_cumsum_op.py @@ -0,0 +1,127 @@ +# 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. + +import unittest +import numpy as np +from op_test import OpTest + + +class TestSumOp1(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.attrs = {'axis': 2} + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")} + self.outputs = {'Out': self.inputs['X'].cumsum(axis=2)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp2(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.attrs = {'axis': -1, 'reverse': True} + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")} + self.outputs = { + 'Out': np.flip( + np.flip( + self.inputs['X'], axis=2).cumsum(axis=2), axis=2) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp3(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.attrs = {'axis': 1} + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")} + self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp4(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.attrs = {'axis': 0} + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")} + self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp5(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.inputs = {'X': np.random.random((5, 6)).astype("float64")} + self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp7(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.inputs = {'X': np.random.random((6)).astype("float64")} + self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSumOp8(OpTest): + def setUp(self): + self.op_type = "cumsum" + self.attrs = {'axis': 2, "exclusive": True} + a = np.random.random((5, 6, 3)).astype("float64") + self.inputs = {'X': a} + self.outputs = { + 'Out': np.concatenate( + (np.zeros( + (5, 6, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)), + axis=2) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +if __name__ == '__main__': + unittest.main()