/* Copyright 2017 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. ==============================================================================*/ // XLA-specific Ops for FFT. #include #include #include #include "absl/container/inlined_vector.h" #include "absl/status/status.h" #include "absl/strings/str_cat.h" #include "tensorflow/compiler/tf2xla/mlir_xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "xla/hlo/builder/xla_builder.h" #include "xla/literal_util.h" #include "xla/util.h" #include "xla/xla_data.pb.h" #include "tensorflow/core/framework/bounds_check.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/ops_util.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/tensor_slice.h" #include "tensorflow/core/framework/types.pb.h" #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { namespace { using xla::FftType; class GenericFftOp : public XlaOpKernel { public: explicit GenericFftOp(OpKernelConstruction* ctx, FftType fft_type, int fft_rank) : XlaOpKernel(ctx), fft_type_(fft_type), fft_rank_(fft_rank) {} void Compile(XlaOpKernelContext* ctx) override { const TensorShape input_shape = ctx->InputShape(0); OP_REQUIRES( ctx, TensorShapeUtils::IsVectorOrHigher(input_shape), absl::InvalidArgumentError("input must be at least 1 dimensional")); std::vector fft_length; xla::XlaOp input = ctx->Input(0); if (fft_type_ == FftType::RFFT || fft_type_ == FftType::IRFFT) { OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &fft_length)); OP_REQUIRES(ctx, fft_length.size() == fft_rank_, absl::InvalidArgumentError(absl::StrCat( "fft_length must be length ", fft_rank_, " vector"))); // Zero pad or truncate the axes we're doing FFT on. absl::InlinedVector slice_sizes = input_shape.dim_sizes(); std::vector> padding_sizes( slice_sizes.size()); std::vector expected_sizes = fft_length; // IRFFT wants the innermost axis to be n / 2 + 1. if (fft_type_ == FftType::IRFFT) { expected_sizes[fft_rank_ - 1] = fft_length[fft_rank_ - 1] / 2 + 1; } for (int i = 0; i < fft_rank_; i++) { int index = input_shape.dims() - fft_rank_ + i; OP_REQUIRES( ctx, input_shape.dim_size(index) == 0 || input_shape.dim_size(index) >= expected_sizes[i], absl::InvalidArgumentError(absl::StrCat( "Input dimension ", index, " must have length of at least ", expected_sizes[i], " but got: ", input_shape.dim_size(index)))); if (input_shape.dim_size(index) > expected_sizes[i]) { slice_sizes[index] = expected_sizes[i]; } else { padding_sizes[index].second = expected_sizes[i] - input_shape.dim_size(index); } } std::vector start_indices(input_shape.dims(), 0); std::vector strides(input_shape.dims(), 1); input = xla::Pad(xla::Slice(input, start_indices, slice_sizes, strides), XlaHelpers::Zero(ctx->builder(), ctx->input_type(0)), xla::MakeEdgePaddingConfig(padding_sizes)); } else { // Innermost axis provides the FFT length. for (int i = 0; i < fft_rank_; i++) { fft_length.push_back( input_shape.dim_size(input_shape.dims() - fft_rank_ + i)); } } xla::XlaOp fft = xla::Fft(input, fft_type_, fft_length); ctx->SetOutput(0, fft); } protected: const FftType fft_type_; const int fft_rank_; private: GenericFftOp(const GenericFftOp&) = delete; void operator=(const GenericFftOp&) = delete; }; template class FFTOp : public GenericFftOp { public: explicit FFTOp(OpKernelConstruction* ctx) : GenericFftOp(ctx, /*fft_type=*/FftType::FFT, /*fft_rank=*/FFTRank) {} }; REGISTER_XLA_OP(Name("FFT").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), FFTOp<1>); REGISTER_XLA_OP(Name("FFT2D").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), FFTOp<2>); REGISTER_XLA_OP(Name("FFT3D").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), FFTOp<3>); template class IFFTOp : public GenericFftOp { public: explicit IFFTOp(OpKernelConstruction* ctx) : GenericFftOp(ctx, /*fft_type=*/FftType::IFFT, /*fft_rank=*/FFTRank) {} }; REGISTER_XLA_OP(Name("IFFT").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), MlirXlaOpKernel); REGISTER_XLA_OP(Name("IFFT2D").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), IFFTOp<2>); REGISTER_XLA_OP(Name("IFFT3D").TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}), IFFTOp<3>); template class RFFTOp : public GenericFftOp { public: explicit RFFTOp(OpKernelConstruction* ctx) : GenericFftOp(ctx, /*fft_type=*/FftType::RFFT, /*fft_rank=*/FFTRank) {} }; REGISTER_XLA_OP(Name("RFFT") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), RFFTOp<1>); REGISTER_XLA_OP(Name("RFFT2D") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), RFFTOp<2>); REGISTER_XLA_OP(Name("RFFT3D") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), RFFTOp<3>); template class IRFFTOp : public GenericFftOp { public: explicit IRFFTOp(OpKernelConstruction* ctx) : GenericFftOp(ctx, /*fft_type=*/FftType::IRFFT, /*fft_rank=*/FFTRank) {} }; REGISTER_XLA_OP(Name("IRFFT") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), IRFFTOp<1>); REGISTER_XLA_OP(Name("IRFFT2D") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), IRFFTOp<2>); REGISTER_XLA_OP(Name("IRFFT3D") .TypeConstraint("Treal", {DT_FLOAT, DT_DOUBLE}) .TypeConstraint("Tcomplex", {DT_COMPLEX64, DT_COMPLEX128}) .CompileTimeConstantInput("fft_length"), IRFFTOp<3>); } // namespace } // namespace tensorflow