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tensorflow/tensorflow/core/kernels/ragged_range_op.cc
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| /* Copyright 2018 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. | |
| ==============================================================================*/ | |
| #include <limits> | |
| #include <memory> | |
| #include <string> | |
| #include <vector> | |
| #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_shape.h" | |
| namespace tensorflow { | |
| using errors::InvalidArgument; | |
| template <typename T, typename SPLITS_TYPE> | |
| class RaggedRangeOp : public OpKernel { | |
| public: | |
| using OpKernel::OpKernel; | |
| void Compute(OpKernelContext* context) override { | |
| const Tensor& starts_in = context->input(0); | |
| const Tensor& limits_in = context->input(1); | |
| const Tensor& deltas_in = context->input(2); | |
| // Check input tensor shapes. | |
| OP_REQUIRES(context, starts_in.shape().dims() <= 1, | |
| InvalidArgument("starts must be a scalar or vector")); | |
| OP_REQUIRES(context, limits_in.shape().dims() <= 1, | |
| InvalidArgument("limits must be a scalar or vector")); | |
| OP_REQUIRES(context, deltas_in.shape().dims() <= 1, | |
| InvalidArgument("deltas must be a scalar or vector")); | |
| // Determine which tensors we need to broadcast. | |
| bool broadcast_starts = starts_in.shape().dims() == 0; | |
| bool broadcast_limits = limits_in.shape().dims() == 0; | |
| bool broadcast_deltas = deltas_in.shape().dims() == 0; | |
| // nrows (number of output rows) is the size of the non-broadcast inputs, | |
| // or 1 if all inputs are scalars. | |
| std::vector<int> in_sizes; | |
| if (!broadcast_starts) in_sizes.push_back(starts_in.shape().dim_size(0)); | |
| if (!broadcast_limits) in_sizes.push_back(limits_in.shape().dim_size(0)); | |
| if (!broadcast_deltas) in_sizes.push_back(deltas_in.shape().dim_size(0)); | |
| for (int i = 1; i < in_sizes.size(); ++i) { | |
| OP_REQUIRES(context, in_sizes[i] == in_sizes[i - 1], | |
| InvalidArgument("starts, limits, and deltas must have the " | |
| "same shape")); | |
| } | |
| SPLITS_TYPE nrows = in_sizes.empty() ? 1 : in_sizes[0]; | |
| const auto& starts = starts_in.flat<T>(); | |
| const auto& limits = limits_in.flat<T>(); | |
| const auto& deltas = deltas_in.flat<T>(); | |
| // Construct the rt_nested_splits tensor. | |
| Tensor* rt_nested_splits_out = nullptr; | |
| OP_REQUIRES_OK(context, | |
| context->allocate_output(0, TensorShape({nrows + 1}), | |
| &rt_nested_splits_out)); | |
| auto rt_nested_splits = rt_nested_splits_out->flat<SPLITS_TYPE>(); | |
| rt_nested_splits(0) = 0; | |
| for (int row = 0; row < nrows; ++row) { | |
| T start = broadcast_starts ? starts(0) : starts(row); | |
| T limit = broadcast_limits ? limits(0) : limits(row); | |
| T delta = broadcast_deltas ? deltas(0) : deltas(row); | |
| OP_REQUIRES(context, delta != 0, InvalidArgument("Requires delta != 0")); | |
| rt_nested_splits(row + 1) = | |
| rt_nested_splits(row) + RangeSize(start, limit, delta); | |
| } | |
| SPLITS_TYPE nvals = rt_nested_splits(nrows); | |
| // Construct the rt_dense_values tensor. | |
| Tensor* rt_dense_values_out = nullptr; | |
| OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape({nvals}), | |
| &rt_dense_values_out)); | |
| auto rt_dense_values = rt_dense_values_out->flat<T>(); | |
| int value_index = 0; | |
| for (int row = 0; row < nrows; ++row) { | |
| SPLITS_TYPE row_size = rt_nested_splits(row + 1) - rt_nested_splits(row); | |
| T value = broadcast_starts ? starts(0) : starts(row); | |
| T delta = broadcast_deltas ? deltas(0) : deltas(row); | |
| for (SPLITS_TYPE i = 0; i < row_size; ++i) { | |
| rt_dense_values(value_index++) = T(value); | |
| value += delta; | |
| } | |
| } | |
| } | |
| private: | |
| // Returns the number of elements in the specified range. | |
| SPLITS_TYPE RangeSize(T start, T limit, T delta) { | |
| if (((delta > 0) && (limit < start)) || ((delta < 0) && (limit > start))) { | |
| return 0; | |
| } | |
| // The following is copied from tensorflow::RangeOp::Compute(). | |
| return (std::is_integral<T>::value | |
| ? ((std::abs(limit - start) + std::abs(delta) - 1) / | |
| std::abs(delta)) | |
| : std::ceil(std::abs((limit - start) / delta))); | |
| } | |
| }; | |
| #define REGISTER_CPU_KERNEL(TYPE) \ | |
| REGISTER_KERNEL_BUILDER(Name("RaggedRange") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<TYPE>("T") \ | |
| .TypeConstraint<int32>("Tsplits"), \ | |
| RaggedRangeOp<TYPE, int32>); \ | |
| REGISTER_KERNEL_BUILDER(Name("RaggedRange") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<TYPE>("T") \ | |
| .TypeConstraint<int64_t>("Tsplits"), \ | |
| RaggedRangeOp<TYPE, int64>); | |
| TF_CALL_float(REGISTER_CPU_KERNEL); | |
| TF_CALL_double(REGISTER_CPU_KERNEL); | |
| TF_CALL_int32(REGISTER_CPU_KERNEL); | |
| TF_CALL_int64(REGISTER_CPU_KERNEL); | |
| #undef REGISTER_CPU_KERNEL | |
| } // namespace tensorflow |