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Remove unused operator at::_fft_with_size (#48905)
Summary: Pull Request resolved: #48905 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D25480385 Pulled By: mruberry fbshipit-source-id: 192d04a1b7e33b4e408cda8a82679c3ae3490a7d
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‎aten/src/ATen/native/SpectralOps.cpp‎

Lines changed: 0 additions & 169 deletions
Original file line numberDiff line numberDiff line change
@@ -19,12 +19,6 @@
1919

2020
namespace at { namespace native {
2121

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// Common code for all FFT functions
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static inline Tensor _fft(
24-
const Tensor &self, int64_t signal_ndim, bool complex_input,
25-
const bool complex_output, bool inverse, IntArrayRef signal_sizes,
26-
fft_norm_mode normalization, bool onesided);
27-
2822
namespace {
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// Promote inputs to FFT functions
@@ -416,139 +410,6 @@ Tensor fft_ifftshift(const Tensor& x, c10::optional<IntArrayRef> dim_opt) {
416410
}
417411

418412

419-
// This is a pass-through wrapper function that does the size check and
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// inferences. The actual forward implementation function is called
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// at::_fft_with_size which dispatches to _fft_cufft (CUDA) or _fft_mkl (CPU).
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static inline Tensor _fft(const Tensor &self, const int64_t signal_ndim,
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const bool complex_input, const bool complex_output,
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const bool inverse, IntArrayRef signal_sizes,
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const fft_norm_mode normalization, const bool onesided) {
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TORCH_CHECK(signal_ndim >= 1 && signal_ndim <= 3,
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"Expected signal_ndim to be 1, 2, or 3, but got signal_ndim=",
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signal_ndim);
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TORCH_CHECK(at::isFloatingType(self.scalar_type()),
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"Expected an input tensor of floating types, but got input=",
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self.toString(), self.sizes());
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auto signal_tensor_ndim = signal_ndim + static_cast<int64_t>(complex_input); // add complex dim
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if (self.dim() < signal_tensor_ndim) {
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std::ostringstream ss;
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ss << "Given signal_ndim=" << signal_ndim << ", expected an input tensor "
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<< "of at least " << signal_tensor_ndim << "D";
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if (complex_input) {
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ss << " (complex input adds an extra dimension)";
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}
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ss << ", but got input=" << self.toString() << self.sizes();
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AT_ERROR(ss.str());
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}
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auto self_shape = self.sizes();
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auto batch_ndim = self.dim() - signal_tensor_ndim;
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Tensor input = self;
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// flatten the batch dims
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if (batch_ndim == 0) {
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// slightly faster path for non-batch mode
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input = input.unsqueeze(0);
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} else if (batch_ndim > 1) {
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std::vector<int64_t> flatten_input_shape(signal_tensor_ndim + 1);
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std::copy(self_shape.begin() + batch_ndim, self_shape.end(), flatten_input_shape.begin() + 1);
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flatten_input_shape[0] = -1;
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input = input.reshape(flatten_input_shape);
459-
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}
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// now we assume that input is batched as [ B x signal_dims... ]
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if (complex_input) {
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TORCH_CHECK(input.size(signal_ndim + 1) == 2,
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"Expected an input tensor with a last dimension of size 2 "
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"representing real + imaginary components, but got input ",
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self.toString(), self.sizes());
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}
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// build signal_sizes and output_size
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TORCH_CHECK(signal_sizes.size() == 0 || static_cast<int64_t>(signal_sizes.size()) == signal_ndim,
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"Expected signal_sizes to be empty (default) or of signal_ndim=",
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signal_ndim, "D, but got signal_sizes=", signal_sizes);
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std::vector<int64_t> output_sizes(signal_ndim + 1 + static_cast<int64_t>(complex_output));
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output_sizes[0] = input.size(0); // batch size
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std::vector<int64_t> checked_signal_sizes(signal_ndim);
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for (int64_t i = 0; i < signal_ndim; i++) {
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int64_t input_size = input.size(i + 1);
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if (i == signal_ndim - 1 && onesided && complex_input && !complex_output) {
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// If last dim and complex-to-real onesided, input is only half of
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// signal, and we need to infer basing on signal_sizes, if given
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// See native/SpectralOpsUtils.h for detailed description.
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int64_t inferred_size;
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if (signal_sizes.size() > 0) {
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inferred_size = infer_ft_complex_to_real_onesided_size(input_size, signal_sizes[i]);
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} else {
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inferred_size = infer_ft_complex_to_real_onesided_size(input_size);
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}
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checked_signal_sizes[i] = inferred_size;
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output_sizes[i + 1] = inferred_size;
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} else {
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if (i == signal_ndim - 1 && onesided && !complex_input && complex_output) {
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// if last dim and real-to-complex onesided, output should be only
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// half of the signal, and we need to infer using input_size
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output_sizes[i + 1] = infer_ft_real_to_complex_onesided_size(input_size);
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} else {
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output_sizes[i + 1] = input_size;
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}
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checked_signal_sizes[i] = input_size;
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TORCH_CHECK(signal_sizes.size() == 0 || signal_sizes[i] == checked_signal_sizes[i],
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"Expected given signal_sizes=", signal_sizes," to have same "
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"shape with input at signal dimension ", i, ", but got "
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"signal_sizes=", signal_sizes, " and input=", self.toString(),
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self.sizes());
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}
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}
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if (complex_output) {
509-
output_sizes[signal_ndim + 1] = 2;
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}
511-
512-
Tensor output = at::_fft_with_size(input, signal_ndim, complex_input,
513-
complex_output, inverse,
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checked_signal_sizes,
515-
static_cast<int64_t>(normalization),
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onesided,
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output_sizes);
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519-
// unflatten the batch dims
520-
if (batch_ndim == 0) {
521-
// slightly faster path for non-batch mode
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output = output.squeeze(0);
523-
} else if (batch_ndim > 1) {
524-
auto output_ndim = self.dim() + static_cast<int64_t>(complex_output) - static_cast<int64_t>(complex_input);
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std::vector<int64_t> unflatten_output_shape(output_ndim);
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std::copy(self_shape.begin(), self_shape.begin() + batch_ndim, unflatten_output_shape.begin());
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std::copy(output_sizes.begin() + 1, output_sizes.end(), unflatten_output_shape.begin() + batch_ndim);
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output = output.reshape(unflatten_output_shape);
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}
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return output;
531-
}
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533-
// Wrapper to preserve the historic signature of _fft_with_size
534-
// NOTE: This is only used for torchscript backwards compatibility and the new
535-
// signature with normalization modes should be used in all other cases
536-
Tensor _fft_with_size(const Tensor& input, int64_t signal_ndim,
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bool complex_input, bool complex_output,
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bool inverse, IntArrayRef checked_signal_sizes,
539-
bool normalized, bool onesided,
540-
IntArrayRef output_sizes) {
541-
fft_norm_mode norm;
542-
if (normalized) {
543-
norm = fft_norm_mode::by_root_n;
544-
} else {
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norm = inverse ? fft_norm_mode::by_n : fft_norm_mode::none;
546-
}
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return at::_fft_with_size(
548-
input, signal_ndim, complex_input, complex_output, inverse,
549-
checked_signal_sizes, static_cast<int64_t>(norm), onesided, output_sizes);
550-
}
551-
552413
// We call the following methods via CUDA hooks because they are really only
553414
// valid when CUDA is available. See native/cuda/CuFFTPlanCache.h for more details.
554415
int64_t _cufft_get_plan_cache_max_size(int64_t device_index) {
@@ -567,36 +428,6 @@ void _cufft_clear_plan_cache(int64_t device_index) {
567428
detail::getCUDAHooks().cuFFTClearPlanCache(device_index);
568429
}
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570-
static Tensor fft(const Tensor& self, const int64_t signal_ndim, const bool normalized) {
571-
return _fft(self, signal_ndim, /* complex_input */ true,
572-
/* complex_output */ true, /* inverse */ false, {},
573-
normalized ? fft_norm_mode::by_root_n : fft_norm_mode::none,
574-
/* onesided */ false);
575-
}
576-
577-
static Tensor ifft(const Tensor& self, const int64_t signal_ndim, const bool normalized) {
578-
return _fft(self, signal_ndim, /* complex_input */ true,
579-
/* complex_output */ true, /* inverse */ true, {},
580-
normalized ? fft_norm_mode::by_root_n : fft_norm_mode::by_n,
581-
/* onesided */ false);
582-
}
583-
584-
static Tensor rfft(const Tensor& self, const int64_t signal_ndim, const bool normalized,
585-
const bool onesided) {
586-
return _fft(self, signal_ndim, /* complex_input */ false,
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/* complex_output */ true, /* inverse */ false, {},
588-
normalized ? fft_norm_mode::by_root_n : fft_norm_mode::none,
589-
onesided);
590-
}
591-
592-
static Tensor irfft(const Tensor& self, const int64_t signal_ndim, const bool normalized,
593-
const bool onesided, IntArrayRef signal_sizes) {
594-
return _fft(self, signal_ndim, /* complex_input */ true,
595-
/* complex_output */ false, /* inverse */ true, signal_sizes,
596-
normalized ? fft_norm_mode::by_root_n : fft_norm_mode::by_n,
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onesided);
598-
}
599-
600431
template <typename Stream, typename T>
601432
static Stream& write_opt(Stream& SS, const optional<T>& value) {
602433
if (value) {

‎aten/src/ATen/native/cuda/SpectralOps.cu‎

Lines changed: 0 additions & 107 deletions
Original file line numberDiff line numberDiff line change
@@ -589,112 +589,5 @@ Tensor _fft_c2c_cufft(const Tensor& self, IntArrayRef dim, int64_t normalization
589589
return output;
590590
}
591591
592-
// cuFFT
593-
// Currently not utilizing multi GPUs so this can be potentially sped up.
594-
Tensor _fft_cufft(const Tensor& self, int64_t signal_ndim,
595-
bool complex_input, bool complex_output, bool inverse,
596-
IntArrayRef checked_signal_sizes, int64_t normalization, bool onesided,
597-
IntArrayRef output_sizes) {
598-
599-
CuFFTParamsLRUCache& plan_cache = cufft_get_plan_cache(self.device().index());
600-
601-
Tensor input = self;
602-
const auto fft_type = GetCuFFTTransformType(complex_input, complex_output);
603-
604-
if (complex_input) {
605-
TORCH_CHECK(input.size(-1) == 2, "Expected a complex (size 2) last dimension");
606-
}
607-
608-
609-
// Slice when twosided complex-to-real. This is not always needed because we
610-
// calculate the inembed. But it will benefit us in certain cases where we
611-
// clone the input tensor.
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//
613-
// See NOTE [ cuFFT Embedded Strides ].
614-
// See NOTE [ Fourier Transform Conjugate Symmetry ] in native/SpectralOpsUtils.h.
615-
if (fft_type == CuFFTTransformType::C2R && !onesided) {
616-
auto onesided_size = infer_ft_real_to_complex_onesided_size(checked_signal_sizes[signal_ndim - 1]);
617-
input = input.narrow(signal_ndim, 0, onesided_size);
618-
}
619-
620-
// cuFFT requires input and output data pointers to complex type aligned.
621-
// Our newly allocated output tensor is always 512 bytes aligned so it is fine
622-
// (see kRoundSmall and kRoundLarge in THCCachingAllocator.cpp), but we do
623-
// need to check input tensor to make sure that it is not unaligned, e.g.,
624-
// from a slicing.
625-
bool must_clone = false;
626-
auto complex_size_bytes = 2 * input.element_size();
627-
if (reinterpret_cast<std::uintptr_t>(input.data_ptr()) % complex_size_bytes != 0) {
628-
must_clone = true;
629-
}
630-
631-
if (complex_input) {
632-
auto strides = input.strides();
633-
// Real/imag dimension must be like complex type.
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must_clone |= strides.back() != 1;
635-
// Strides of other dimensions needs to be aligned when viewed as complex
636-
// type, i.e., multiples of 2.
637-
must_clone |= std::any_of(strides.begin(), strides.end() - 1,
638-
[&](int64_t stride) { return stride % 2 != 0; });
639-
640-
// Complex to real FFTs may overwrite the input buffer (gh-34551)
641-
must_clone |= !complex_output;
642-
}
643-
644-
if (must_clone) {
645-
input = input.clone(MemoryFormat::Contiguous);
646-
}
647-
648-
// Now that we have done error check and data_ptr checks, we delegate all
649-
// further cuFFT parameter computation and plan creation to the helper class
650-
// CuFFTConfig in CuFFTPlanCache.h.
651-
652-
// If plan caching is enabled, we check the cache. Note that this accesses
653-
// plan_cache.max_size() and thus makes this function less functional.
654-
// However, integrating additional arguments into the "public" level c++ APIs,
655-
// e.g., irfft, is difficult as we have a long call sequence looking like
656-
// irfft --> _fft --> _fft_with_size --dispatching-to-> _fft_cufft
657-
658-
DimVector in_strides(signal_ndim + 1);
659-
auto input_strides = input.strides();
660-
for (int64_t i = signal_ndim; i >= 0; --i) {
661-
in_strides[i] = complex_input ? input_strides[i] / 2 : input_strides[i];
662-
}
663-
664-
DimVector out_strides(signal_ndim + 1);
665-
out_strides[signal_ndim] = 1;
666-
if (fft_type == CuFFTTransformType::R2C && onesided) {
667-
out_strides[signal_ndim - 1] = checked_signal_sizes[signal_ndim - 1] / 2 + 1;
668-
} else {
669-
out_strides[signal_ndim - 1] = checked_signal_sizes[signal_ndim - 1];
670-
}
671-
for (int64_t i = signal_ndim - 2; i >= 0; --i) {
672-
out_strides[i] = out_strides[i + 1] * checked_signal_sizes[i];
673-
}
674-
675-
DimVector full_sizes(signal_ndim + 1);
676-
full_sizes[0] = self.size(0);
677-
std::copy(checked_signal_sizes.begin(), checked_signal_sizes.end(), full_sizes.begin() + 1);
678-
CuFFTParams Params(in_strides, out_strides, full_sizes, fft_type,
679-
c10::toValueType(input.scalar_type()));
680-
681-
// This read is not locked for perf reason. Shouldn't matter too much because
682-
// we check again after acquiring the lock.
683-
if (plan_cache.max_size() > 0) {
684-
std::lock_guard<std::mutex> guard(plan_cache.mutex);
685-
if (plan_cache.max_size() > 0) { // check again after acquiring the lock
686-
const CuFFTConfig &config = plan_cache.lookup(Params);
687-
return _run_cufft(config, input, signal_ndim, complex_input,
688-
complex_output, inverse, checked_signal_sizes,
689-
static_cast<fft_norm_mode>(normalization),
690-
onesided, output_sizes, must_clone);
691-
}
692-
}
693-
CuFFTConfig config(Params);
694-
return _run_cufft(config, input, signal_ndim, complex_input,
695-
complex_output, inverse, checked_signal_sizes,
696-
static_cast<fft_norm_mode>(normalization),
697-
onesided, output_sizes, must_clone);
698-
}
699592
700593
}} // at::native

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