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tensorflow/tensorflow/core/kernels/sparse_tensor_dense_add_op.cc
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| /* Copyright 2016 The TensorFlow 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. | |
| ==============================================================================*/ | |
| #define EIGEN_USE_THREADS | |
| #include "tensorflow/core/kernels/sparse_tensor_dense_add_op.h" | |
| #include "tensorflow/core/framework/op_kernel.h" | |
| #include "tensorflow/core/framework/register_types.h" | |
| #include "tensorflow/core/framework/tensor.h" | |
| #include "tensorflow/core/framework/tensor_util.h" | |
| #include "tensorflow/core/framework/types.h" | |
| #include "tensorflow/core/util/sparse/sparse_tensor.h" | |
| namespace tensorflow { | |
| typedef Eigen::ThreadPoolDevice CPUDevice; | |
| // NOTE: does not support GPU yet. | |
| namespace { | |
| template <typename Index> | |
| Status ValidateInputs(const Tensor *a_indices, const Tensor *a_values, | |
| const Tensor *a_shape, const Tensor *b) { | |
| if (!TensorShapeUtils::IsMatrix(a_indices->shape())) { | |
| return errors::InvalidArgument( | |
| "Input a_indices should be a matrix but received shape: ", | |
| a_indices->shape().DebugString()); | |
| } | |
| if (!TensorShapeUtils::IsVector(a_values->shape()) || | |
| !TensorShapeUtils::IsVector(a_shape->shape())) { | |
| return errors::InvalidArgument( | |
| "Inputs a_values and a_shape should be vectors " | |
| "but received shapes: ", | |
| a_values->shape().DebugString(), " and ", | |
| a_shape->shape().DebugString()); | |
| } | |
| if (a_shape->NumElements() != b->dims()) { | |
| return errors::InvalidArgument( | |
| "Two operands have different ranks; received: ", a_shape->NumElements(), | |
| " and ", b->dims()); | |
| } | |
| const auto a_shape_flat = a_shape->flat<Index>(); | |
| for (int i = 0; i < b->dims(); ++i) { | |
| if (a_shape_flat(i) != b->dim_size(i)) { | |
| return errors::InvalidArgument( | |
| "Dimension ", i, | |
| " does not equal (no broadcasting is supported): sparse side ", | |
| a_shape_flat(i), " vs dense side ", b->dim_size(i)); | |
| } | |
| } | |
| return Status::OK(); | |
| } | |
| } // namespace | |
| template <typename Device, typename T, typename Index> | |
| class SparseTensorDenseAddOp : public OpKernel { | |
| public: | |
| explicit SparseTensorDenseAddOp(OpKernelConstruction *ctx) : OpKernel(ctx) {} | |
| void Compute(OpKernelContext *ctx) override { | |
| const Tensor *a_indices_t, *a_values_t, *a_shape_t, *b; | |
| OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices_t)); | |
| OP_REQUIRES_OK(ctx, ctx->input("a_values", &a_values_t)); | |
| OP_REQUIRES_OK(ctx, ctx->input("a_shape", &a_shape_t)); | |
| OP_REQUIRES_OK(ctx, ctx->input("b", &b)); | |
| OP_REQUIRES_OK( | |
| ctx, ValidateInputs<Index>(a_indices_t, a_values_t, a_shape_t, b)); | |
| Tensor *out_t; | |
| OP_REQUIRES_OK(ctx, ctx->allocate_output(0, b->shape(), &out_t)); | |
| const int ndims = static_cast<int>(a_indices_t->dim_size(1)); | |
| const auto a_indices_mat = a_indices_t->flat_inner_dims<Index>(); | |
| const auto a_values_flat = a_values_t->flat<T>(); | |
| switch (ndims) { | |
| #define NDIMS_CASE(N) \ | |
| case N: { \ | |
| auto out_tensor = out_t->tensor<T, N>(); \ | |
| out_tensor.device(ctx->eigen_device<Device>()) = b->tensor<T, N>(); \ | |
| const Index result = \ | |
| functor::ScatterNdFunctor<Device, T, Index, N, \ | |
| scatter_op::UpdateOp::ADD>()( \ | |
| ctx->eigen_device<Device>(), a_indices_mat, a_values_flat, \ | |
| out_tensor); \ | |
| OP_REQUIRES( \ | |
| ctx, result == -1, \ | |
| errors::InvalidArgument( \ | |
| "Sparse tensor has some invalid index on dimension ", result, \ | |
| "; dense tensor shape: ", b->shape().DebugString())); \ | |
| } break; | |
| NDIMS_CASE(1); | |
| NDIMS_CASE(2); | |
| NDIMS_CASE(3); | |
| NDIMS_CASE(4); | |
| NDIMS_CASE(5); | |
| default: | |
| OP_REQUIRES( | |
| ctx, false, | |
| errors::InvalidArgument("Only tensors with ranks between 1 and 5 " | |
| "are currently supported. Tensor rank: ", | |
| ndims)); | |
| #undef NDIMS_CASE | |
| } | |
| } | |
| }; | |
| namespace functor { | |
| template <typename T, typename Index, int NDIMS> | |
| struct ScatterNdFunctor<CPUDevice, T, Index, NDIMS, scatter_op::UpdateOp::ADD> { | |
| Index operator()(const CPUDevice &d, | |
| typename TTypes<Index>::ConstMatrix indices, | |
| typename TTypes<T>::ConstFlat updates, | |
| typename TTypes<T, NDIMS>::Tensor out) { | |
| Eigen::array<Eigen::DenseIndex, NDIMS> idx; | |
| const int num_nnz = static_cast<int>(indices.dimension(0)); | |
| for (int i = 0; i < num_nnz; ++i) { | |
| for (int d = 0; d < NDIMS; ++d) { | |
| idx[d] = internal::SubtleMustCopy(indices(i, d)); | |
| if (!FastBoundsCheck(idx[d], out.dimension(d))) { | |
| return d; // on failure: d nonnegative | |
| } | |
| } | |
| out(idx) += updates(i); | |
| } | |
| return -1; // on success | |
| } | |
| }; | |
| } // namespace functor | |
| #define REGISTER_KERNELS_CPU(TypeT, TypeIndex) \ | |
| REGISTER_KERNEL_BUILDER(Name("SparseTensorDenseAdd") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<TypeT>("T") \ | |
| .TypeConstraint<TypeIndex>("Tindices"), \ | |
| SparseTensorDenseAddOp<CPUDevice, TypeT, TypeIndex>) | |
| #define REGISTER_KERNELS(T) \ | |
| REGISTER_KERNELS_CPU(T, int64_t); \ | |
| REGISTER_KERNELS_CPU(T, int32) | |
| TF_CALL_NUMBER_TYPES(REGISTER_KERNELS); | |
| #undef REGISTER_KERNELS | |
| #undef REGISTER_KERNELS_CPU | |
| } // namespace tensorflow |