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未验证 提交 319f95d0 编写于 作者: K KP 提交者： GitHub
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Add complex type compatibility for stft api and stft op. (#40113) 

* Add stft_op.

* Add stft_grad_op.

* Add stft_op unittest.

* [DLTP-45176] Add complex compatibility in static mode for stft api.

* [DLTP-45176] Add complex compatibility in static mode for stft api.

* Add doc.

* Update unitests of stft op.

* Update spectral helper.

* fix coding style.
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paddle/fluid/operators/frame_op.cc
浏览文件 @ 319f95d0
......@@ -64,18 +64,26 @@ class FrameOp : public framework::OperatorWithKernel {
end_axis = x_rank - 2;
}
bool contain_unknown_dim = phi::contain_unknown_dim(x_dims);
bool check = ctx->IsRuntime() || !contain_unknown_dim;
if (check) {
PADDLE_ENFORCE_LE(frame_length, seq_length,
platform::errors::InvalidArgument(
"Attribute(frame_length) of FrameOp should be less "
"equal than sequence length, but got (%s) > (%s).",
frame_length, seq_length));
}
// It won't go into for loop when x_rank == 1U.
for (int i = start_axis; i <= end_axis; i++) {
output_shape.push_back(x_dims[i]);
}
if (seq_length == -1) {
n_frames = -1;
} else {
n_frames = 1 + (seq_length - frame_length) / hop_length;
}
if (axis == 0) {
// (n_frames, frame_length, ...)
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 319f95d0
......@@ -98,9 +98,17 @@ REGISTER_OP_CPU_KERNEL(
mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,
ops::MeanKernel<paddle::platform::CPUDeviceContext, double>,
ops::MeanKernel<paddle::platform::CPUDeviceContext,
paddle::platform::bfloat16>);
paddle::platform::bfloat16>,
ops::MeanKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::MeanKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CPU_KERNEL(
mean_grad, ops::MeanGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::MeanGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::MeanGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::bfloat16>);
paddle::platform::bfloat16>,
ops::MeanGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::MeanGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/mean_op.cu
浏览文件 @ 319f95d0
......@@ -102,10 +102,17 @@ namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
mean, ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, float>,
ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, double>,
ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, plat::float16>);
ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, plat::float16>,
ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<float>>,
ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CUDA_KERNEL(
mean_grad,
ops::MeanCUDAGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::MeanCUDAGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::MeanCUDAGradKernel<paddle::platform::CUDADeviceContext, plat::float16>,
ops::MeanCUDAGradKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
paddle::platform::complex<float>>,
ops::MeanCUDAGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/overlap_add_op.cc
浏览文件 @ 319f95d0
......@@ -54,6 +54,7 @@ class OverlapAddOp : public framework::OperatorWithKernel {
std::vector<int64_t> output_shape;
int n_frames;
int frame_length;
int seq_length;
int start_axis;
int end_axis;
......@@ -69,14 +70,22 @@ class OverlapAddOp : public framework::OperatorWithKernel {
end_axis = x_rank - 3;
}
bool contain_unknown_dim = phi::contain_unknown_dim(x_dims);
bool check = ctx->IsRuntime() || !contain_unknown_dim;
if (check) {
PADDLE_ENFORCE_LE(
hop_length, frame_length,
platform::errors::InvalidArgument(
"Attribute(hop_length) of OverlapAddOp should be less or equal "
"than frame_length, but got hop_length(%s) > frame_length(%s).",
hop_length, frame_length));
}
const int seq_length = (n_frames - 1) * hop_length + frame_length;
if (n_frames == -1) {
seq_length = -1;
} else {
seq_length = (n_frames - 1) * hop_length + frame_length;
}
// It won't go into for loop when x_rank == 2U.
for (int i = start_axis; i <= end_axis; i++) {
......
paddle/fluid/operators/spectral_op.cc
浏览文件 @ 319f95d0
......@@ -13,28 +13,7 @@
// limitations under the License.
#include "paddle/fluid/operators/spectral_op.h"
#include <algorithm>
#include <functional>
#include <memory>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/transpose_op.h"
#include "paddle/fluid/platform/complex.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
#if defined(PADDLE_WITH_ONEMKL)
#include "paddle/phi/backends/dynload/mklrt.h"
#elif defined(PADDLE_WITH_POCKETFFT)
#include "extern_pocketfft/pocketfft_hdronly.h"
#endif
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/fluid/operators/spectral_helper.h"
namespace paddle {
namespace operators {
......@@ -355,465 +334,6 @@ FFTNormMode get_norm_from_string(const std::string& norm, bool forward) {
norm));
}
// FFT Functors
#if defined(PADDLE_WITH_ONEMKL)
#define MKL_DFTI_CHECK(expr) \
do { \
MKL_LONG status = (expr); \
if (!phi::dynload::DftiErrorClass(status, DFTI_NO_ERROR)) \
PADDLE_THROW( \
platform::errors::External(phi::dynload::DftiErrorMessage(status))); \
} while (0);
namespace {
struct DftiDescriptorDeleter {
void operator()(DFTI_DESCRIPTOR_HANDLE handle) {
if (handle != nullptr) {
MKL_DFTI_CHECK(phi::dynload::DftiFreeDescriptor(&handle));
}
}
};
// A RAII wrapper for MKL_DESCRIPTOR*
class DftiDescriptor {
public:
void init(DFTI_CONFIG_VALUE precision, DFTI_CONFIG_VALUE signal_type,
MKL_LONG signal_ndim, MKL_LONG* sizes) {
PADDLE_ENFORCE_EQ(desc_.get(), nullptr,
platform::errors::AlreadyExists(
"DftiDescriptor has already been initialized."));
DFTI_DESCRIPTOR* raw_desc;
MKL_DFTI_CHECK(phi::dynload::DftiCreateDescriptorX(
&raw_desc, precision, signal_type, signal_ndim, sizes));
desc_.reset(raw_desc);
}
DFTI_DESCRIPTOR* get() const {
DFTI_DESCRIPTOR* raw_desc = desc_.get();
PADDLE_ENFORCE_NOT_NULL(raw_desc,
platform::errors::PreconditionNotMet(
"DFTI DESCRIPTOR has not been initialized."));
return raw_desc;
}
private:
std::unique_ptr<DFTI_DESCRIPTOR, DftiDescriptorDeleter> desc_;
};
DftiDescriptor _plan_mkl_fft(const framework::proto::VarType::Type& in_dtype,
const framework::proto::VarType::Type& out_dtype,
const framework::DDim& in_strides,
const framework::DDim& out_strides,
const std::vector<int>& signal_sizes,
FFTNormMode normalization, bool forward) {
const DFTI_CONFIG_VALUE precision = [&] {
switch (in_dtype) {
case framework::proto::VarType::FP32:
return DFTI_SINGLE;
case framework::proto::VarType::COMPLEX64:
return DFTI_SINGLE;
case framework::proto::VarType::FP64:
return DFTI_DOUBLE;
case framework::proto::VarType::COMPLEX128:
return DFTI_DOUBLE;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Invalid input datatype (%s), input data type should be FP32, "
"FP64, COMPLEX64 or COMPLEX128.",
framework::DataTypeToString(in_dtype)));
}
}();
// C2C, R2C, C2R
const FFTTransformType fft_type = GetFFTTransformType(in_dtype, out_dtype);
const DFTI_CONFIG_VALUE domain =
(fft_type == FFTTransformType::C2C) ? DFTI_COMPLEX : DFTI_REAL;
DftiDescriptor descriptor;
std::vector<MKL_LONG> fft_sizes(signal_sizes.cbegin(), signal_sizes.cend());
const MKL_LONG signal_ndim = fft_sizes.size() - 1;
descriptor.init(precision, domain, signal_ndim, fft_sizes.data() + 1);
// placement inplace or not inplace
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(descriptor.get(), DFTI_PLACEMENT,
DFTI_NOT_INPLACE));
// number of transformations
const MKL_LONG batch_size = fft_sizes[0];
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(
descriptor.get(), DFTI_NUMBER_OF_TRANSFORMS, batch_size));
// input & output distance
const MKL_LONG idist = in_strides[0];
const MKL_LONG odist = out_strides[0];
MKL_DFTI_CHECK(
phi::dynload::DftiSetValue(descriptor.get(), DFTI_INPUT_DISTANCE, idist));
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(descriptor.get(),
DFTI_OUTPUT_DISTANCE, odist));
// input & output stride
std::vector<MKL_LONG> mkl_in_stride(1 + signal_ndim, 0);
std::vector<MKL_LONG> mkl_out_stride(1 + signal_ndim, 0);
for (MKL_LONG i = 1; i <= signal_ndim; i++) {
mkl_in_stride[i] = in_strides[i];
mkl_out_stride[i] = out_strides[i];
}
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(
descriptor.get(), DFTI_INPUT_STRIDES, mkl_in_stride.data()));
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(
descriptor.get(), DFTI_OUTPUT_STRIDES, mkl_out_stride.data()));
// conjugate even storage
if (!(fft_type == FFTTransformType::C2C)) {
MKL_DFTI_CHECK(phi::dynload::DftiSetValue(
descriptor.get(), DFTI_CONJUGATE_EVEN_STORAGE, DFTI_COMPLEX_COMPLEX));
}
MKL_LONG signal_numel =
std::accumulate(fft_sizes.cbegin() + 1, fft_sizes.cend(), 1UL,
std::multiplies<MKL_LONG>());
if (normalization != FFTNormMode::none) {
const double scale =
((normalization == FFTNormMode::by_sqrt_n)
? 1.0 / std::sqrt(static_cast<double>(signal_numel))
: 1.0 / static_cast<double>(signal_numel));
const auto scale_direction = [&]() {
if (fft_type == FFTTransformType::R2C ||
(fft_type == FFTTransformType::C2C && forward)) {
return DFTI_FORWARD_SCALE;
} else {
// (fft_type == FFTTransformType::C2R ||
// (fft_type == FFTTransformType::C2C && !forward))
return DFTI_BACKWARD_SCALE;
}
}();
MKL_DFTI_CHECK(
phi::dynload::DftiSetValue(descriptor.get(), scale_direction, scale));
}
// commit the descriptor
MKL_DFTI_CHECK(phi::dynload::DftiCommitDescriptor(descriptor.get()));
return descriptor;
}
// Execute a general fft operation (can be c2c, onesided r2c or onesided c2r)
template <typename DeviceContext, typename Ti, typename To>
void exec_fft(const DeviceContext& ctx, const Tensor* x, Tensor* out,
const std::vector<int64_t>& axes, FFTNormMode normalization,
bool forward) {
const framework::DDim& in_sizes = x->dims();
const int ndim = in_sizes.size();
const int signal_ndim = axes.size();
const int batch_ndim = ndim - signal_ndim;
const framework::DDim& out_sizes = out->dims();
// make a dim permutation
std::vector<int> dim_permute(ndim);
std::iota(dim_permute.begin(), dim_permute.end(), 0);
std::vector<bool> is_transformed_dim(ndim, false);
for (const auto& d : axes) {
is_transformed_dim[d] = true;
}
const auto batch_end =
std::partition(dim_permute.begin(), dim_permute.end(),
[&](size_t axis) { return !is_transformed_dim[axis]; });
std::copy(axes.cbegin(), axes.cend(), batch_end);
// transpose input according to that permutation
framework::DDim transposed_input_shape = in_sizes.transpose(dim_permute);
std::vector<int64_t> transposed_input_shape_ =
phi::vectorize(transposed_input_shape);
framework::Tensor transposed_input;
transposed_input.Resize(transposed_input_shape);
const auto place = ctx.GetPlace();
transposed_input.mutable_data<Ti>(place);
TransCompute<platform::CPUDeviceContext, Ti>(ndim, ctx, *x, &transposed_input,
dim_permute);
// make an collapsed input: collapse batch axes for input
const int batch_size = std::accumulate(
transposed_input_shape.Get(), transposed_input_shape.Get() + batch_ndim,
1L, std::multiplies<int64_t>());
std::vector<int> collapsed_input_shape_(1 + signal_ndim);
collapsed_input_shape_[0] = batch_size;
std::copy(transposed_input_shape_.begin() + batch_ndim,
transposed_input_shape_.end(), collapsed_input_shape_.begin() + 1);
const framework::DDim collapsed_input_shape =
phi::make_ddim(collapsed_input_shape_);
transposed_input.Resize(collapsed_input_shape);
framework::Tensor& collapsed_input = transposed_input;
// make a collapsed output
std::vector<int> collapsed_output_shape_(1 + signal_ndim);
collapsed_output_shape_[0] = batch_size;
for (int i = 0; i < signal_ndim; i++) {
collapsed_output_shape_[1 + i] = out_sizes[axes[i]];
}
const framework::DDim collapsed_output_shape =
phi::make_ddim(collapsed_output_shape_);
framework::Tensor collapsed_output;
collapsed_output.Resize(collapsed_output_shape);
collapsed_output.mutable_data(place, out->type());
// signal sizes
std::vector<int> signal_sizes(1 + signal_ndim);
signal_sizes[0] = batch_size;
for (int i = 0; i < signal_ndim; i++) {
signal_sizes[1 + i] =
std::max(collapsed_input_shape[1 + i], collapsed_output_shape[1 + i]);
}
// input & output stride
const framework::DDim input_stride = phi::stride(collapsed_input_shape);
const framework::DDim output_stride = phi::stride(collapsed_output_shape);
// make a DFTI_DESCRIPTOR
DftiDescriptor desc =
_plan_mkl_fft(framework::TransToProtoVarType(x->dtype()),
framework::TransToProtoVarType(out->dtype()), input_stride,
output_stride, signal_sizes, normalization, forward);
const FFTTransformType fft_type =
GetFFTTransformType(framework::TransToProtoVarType(x->dtype()),
framework::TransToProtoVarType(out->type()));
if (fft_type == FFTTransformType::C2R && forward) {
framework::Tensor collapsed_input_conj(collapsed_input.dtype());
collapsed_input_conj.mutable_data<Ti>(collapsed_input.dims(),
ctx.GetPlace());
// conjugate the input
platform::ForRange<DeviceContext> for_range(ctx, collapsed_input.numel());
phi::funcs::ConjFunctor<Ti> functor(collapsed_input.data<Ti>(),
collapsed_input.numel(),
collapsed_input_conj.data<Ti>());
for_range(functor);
MKL_DFTI_CHECK(phi::dynload::DftiComputeBackward(
desc.get(), collapsed_input_conj.data(), collapsed_output.data()));
} else if (fft_type == FFTTransformType::R2C && !forward) {
framework::Tensor collapsed_output_conj(collapsed_output.dtype());
collapsed_output_conj.mutable_data<To>(collapsed_output.dims(),
ctx.GetPlace());
MKL_DFTI_CHECK(phi::dynload::DftiComputeForward(
desc.get(), collapsed_input.data(), collapsed_output_conj.data()));
// conjugate the output
platform::ForRange<DeviceContext> for_range(ctx, collapsed_output.numel());
phi::funcs::ConjFunctor<To> functor(collapsed_output_conj.data<To>(),
collapsed_output.numel(),
collapsed_output.data<To>());
for_range(functor);
} else {
if (forward) {
MKL_DFTI_CHECK(phi::dynload::DftiComputeForward(
desc.get(), collapsed_input.data(), collapsed_output.data()));
} else {
MKL_DFTI_CHECK(phi::dynload::DftiComputeBackward(
desc.get(), collapsed_input.data(), collapsed_output.data()));
}
}
// resize for the collapsed output
framework::DDim transposed_output_shape = out_sizes.transpose(dim_permute);
collapsed_output.Resize(transposed_output_shape);
framework::Tensor& transposed_output = collapsed_output;
// reverse the transposition
std::vector<int> reverse_dim_permute(ndim);
for (int i = 0; i < ndim; i++) {
reverse_dim_permute[dim_permute[i]] = i;
}
TransCompute<platform::CPUDeviceContext, To>(ndim, ctx, transposed_output,
out, reverse_dim_permute);
}
} // anonymous namespace
template <typename Ti, typename To>
struct FFTC2CFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
exec_fft<platform::CPUDeviceContext, Ti, To>(ctx, x, out, axes,
normalization, forward);
}
};
template <typename Ti, typename To>
struct FFTR2CFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
exec_fft<platform::CPUDeviceContext, Ti, To>(ctx, x, out, axes,
normalization, forward);
}
};
template <typename Ti, typename To>
struct FFTC2RFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
if (axes.size() > 1) {
const std::vector<int64_t> c2c_dims(axes.begin(), axes.end() - 1);
Tensor temp;
temp.mutable_data<Ti>(x->dims(), ctx.GetPlace());
FFTC2CFunctor<platform::CPUDeviceContext, Ti, Ti> c2c_functor;
c2c_functor(ctx, x, &temp, c2c_dims, normalization, forward);
const std::vector<int64_t> new_axes{axes.back()};
exec_fft<platform::CPUDeviceContext, Ti, To>(ctx, &temp, out, new_axes,
normalization, forward);
} else {
exec_fft<platform::CPUDeviceContext, Ti, To>(ctx, x, out, axes,
normalization, forward);
}
}
};
#elif defined(PADDLE_WITH_POCKETFFT)
namespace {
template <typename T>
T compute_factor(int64_t size, FFTNormMode normalization) {
constexpr auto one = static_cast<T>(1);
switch (normalization) {
case FFTNormMode::none:
return one;
case FFTNormMode::by_n:
return one / static_cast<T>(size);
case FFTNormMode::by_sqrt_n:
return one / std::sqrt(static_cast<T>(size));
}
PADDLE_THROW(
platform::errors::InvalidArgument("Unsupported normalization type"));
}
} // anonymous namespace
template <typename Ti, typename To>
struct FFTC2CFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
using R = typename Ti::value_type;
using C = std::complex<R>;
const auto& input_dim = x->dims();
const std::vector<size_t> in_sizes = phi::vectorize<size_t>(input_dim);
std::vector<std::ptrdiff_t> in_strides =
phi::vectorize<std::ptrdiff_t>(phi::stride(input_dim));
const int64_t data_size = sizeof(C);
std::transform(in_strides.begin(), in_strides.end(), in_strides.begin(),
[&](std::ptrdiff_t s) { return s * data_size; });
const auto* in_data = reinterpret_cast<const C*>(x->data<Ti>());
auto* out_data = reinterpret_cast<C*>(out->data<To>());
// pocketfft requires std::vector<size_t>
std::vector<size_t> axes_(axes.size());
std::copy(axes.begin(), axes.end(), axes_.begin());
// compuet factor
int64_t signal_numel = 1;
for (auto i : axes) {
signal_numel *= in_sizes[i];
}
R factor = compute_factor<R>(signal_numel, normalization);
pocketfft::c2c(in_sizes, in_strides, in_strides, axes_, forward, in_data,
out_data, factor);
}
};
template <typename Ti, typename To>
struct FFTR2CFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
using R = Ti;
using C = std::complex<R>;
const auto& input_dim = x->dims();
const std::vector<size_t> in_sizes = phi::vectorize<size_t>(input_dim);
std::vector<std::ptrdiff_t> in_strides =
phi::vectorize<std::ptrdiff_t>(phi::stride(input_dim));
{
const int64_t data_size = sizeof(R);
std::transform(in_strides.begin(), in_strides.end(), in_strides.begin(),
[&](std::ptrdiff_t s) { return s * data_size; });
}
const auto& output_dim = out->dims();
const std::vector<size_t> out_sizes = phi::vectorize<size_t>(output_dim);
std::vector<std::ptrdiff_t> out_strides =
phi::vectorize<std::ptrdiff_t>(phi::stride(output_dim));
{
const int64_t data_size = sizeof(C);
std::transform(out_strides.begin(), out_strides.end(),
out_strides.begin(),
[&](std::ptrdiff_t s) { return s * data_size; });
}
const auto* in_data = x->data<R>();
auto* out_data = reinterpret_cast<C*>(out->data<To>());
// pocketfft requires std::vector<size_t>
std::vector<size_t> axes_(axes.size());
std::copy(axes.begin(), axes.end(), axes_.begin());
// compuet normalization factor
int64_t signal_numel = 1;
for (auto i : axes) {
signal_numel *= in_sizes[i];
}
R factor = compute_factor<R>(signal_numel, normalization);
pocketfft::r2c(in_sizes, in_strides, out_strides, axes_, forward, in_data,
out_data, factor);
}
};
template <typename Ti, typename To>
struct FFTC2RFunctor<platform::CPUDeviceContext, Ti, To> {
void operator()(const platform::CPUDeviceContext& ctx, const Tensor* x,
Tensor* out, const std::vector<int64_t>& axes,
FFTNormMode normalization, bool forward) {
using R = To;
using C = std::complex<R>;
const auto& input_dim = x->dims();
const std::vector<size_t> in_sizes = phi::vectorize<size_t>(input_dim);
std::vector<std::ptrdiff_t> in_strides =
phi::vectorize<std::ptrdiff_t>(phi::stride(input_dim));
{
const int64_t data_size = sizeof(C);
std::transform(in_strides.begin(), in_strides.end(), in_strides.begin(),
[&](std::ptrdiff_t s) { return s * data_size; });
}
const auto& output_dim = out->dims();
const std::vector<size_t> out_sizes = phi::vectorize<size_t>(output_dim);
std::vector<std::ptrdiff_t> out_strides =
phi::vectorize<std::ptrdiff_t>(phi::stride(output_dim));
{
const int64_t data_size = sizeof(R);
std::transform(out_strides.begin(), out_strides.end(),
out_strides.begin(),
[&](std::ptrdiff_t s) { return s * data_size; });
}
const auto* in_data = reinterpret_cast<const C*>(x->data<Ti>());
auto* out_data = out->data<R>();
// pocketfft requires std::vector<size_t>
std::vector<size_t> axes_(axes.size());
std::copy(axes.begin(), axes.end(), axes_.begin());
// compuet normalization factor
int64_t signal_numel = 1;
for (auto i : axes) {
signal_numel *= out_sizes[i];
}
R factor = compute_factor<R>(signal_numel, normalization);
pocketfft::c2r(out_sizes, in_strides, out_strides, axes_, forward, in_data,
out_data, factor);
}
};
#endif
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/spectral_op.cu.h 0 → 100644
浏览文件 @ 319f95d0
此差异已折叠。
paddle/fluid/operators/spectral_op.h
浏览文件 @ 319f95d0
......@@ -11,8 +11,11 @@
#pragma once
#define NOMINMAX // to use std::min std::max correctly on windows
#include <algorithm>
#include <functional>
#include <iostream>
#include <memory>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/convert_utils.h"
......@@ -23,8 +26,10 @@
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/conj_op.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/transpose_op.h"
#include "paddle/fluid/platform/complex.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
#include "paddle/phi/kernels/funcs/padding.h"
#if defined(__NVCC__) || defined(__HIPCC__)
#include "thrust/device_vector.h"
......
paddle/fluid/operators/stft_op.cc 0 → 100644
浏览文件 @ 319f95d0
// Copyright (c) 2021 PaddlePaddle 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 "paddle/fluid/operators/stft_op.h"
#include "paddle/fluid/operators/spectral_helper.h"
namespace paddle {
namespace operators {
class StftOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "frame");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "frame");
const int n_fft = ctx->Attrs().Get<int>("n_fft");
const int hop_length = ctx->Attrs().Get<int>("hop_length");
const auto x_dims = ctx->GetInputDim("X");
const int x_rank = x_dims.size();
const bool onesided = ctx->Attrs().Get<bool>("onesided");
PADDLE_ENFORCE_EQ(
x_rank, 2,
platform::errors::InvalidArgument(
"Input(X) of StftOp should be a tensor with shape [N, T], "
"but got rank %s.",
x_rank));
PADDLE_ENFORCE_GT(
hop_length, 0,
platform::errors::InvalidArgument(
"Attribute(hop_length) should be greater than 0, but got %s.",
hop_length));
int seq_length = x_dims[x_rank - 1];
int n_frames = 1 + (seq_length - n_fft) / hop_length;
PADDLE_ENFORCE_LE(n_fft, seq_length,
platform::errors::InvalidArgument(
"Attribute(frame_length) should be less equal than "
"sequence length, but got (%s) > (%s).",
n_fft, seq_length));
std::vector<int64_t> output_shape;
output_shape.push_back(x_dims[0]);
if (onesided) {
output_shape.push_back(n_fft / 2 + 1);
} else {
output_shape.push_back(n_fft);
}
output_shape.push_back(n_frames);
ctx->SetOutputDim("Out", phi::make_ddim(output_shape));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
const auto in_dtype = OperatorWithKernel::IndicateVarDataType(ctx, "X");
return framework::OpKernelType(in_dtype, ctx.GetPlace());
}
};
class StftOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input waveforms with shape (N, T)");
AddOutput("Out",
"The complex STFT output tensor with shape (N, n_fft, "
"num_frames) or (N, n_fft/2 + 1, num_frames)");
AddAttr<int>("n_fft", "The number of input samples to perform FFT");
AddAttr<int>("hop_length", "Number of samples between adjacent frames");
AddAttr<bool>("normalized",
"Control whether to scale the output by 1/sqrt(n_fft)");
AddAttr<bool>("onesided",
"Control whether to return half of the FFT output");
AddComment(R"DOC(
Short-time Fourier transform (STFT).
)DOC");
}
};
template <typename T>
class StftGradOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("stft_grad");
grad_op->SetInput("X", this->Input("X"));
grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
grad_op->SetAttrMap(this->Attrs());
}
};
class StftGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
const auto out_grad_name = framework::GradVarName("Out");
OP_INOUT_CHECK(ctx->HasInput(out_grad_name), "Input", out_grad_name,
"stft_grad");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "stft_grad");
const auto x_grad_name = framework::GradVarName("X");
OP_INOUT_CHECK(ctx->HasOutput(x_grad_name), "Output", x_grad_name,
"stft_grad");
ctx->ShareDim("X", /*->*/ x_grad_name);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
const auto in_dtype = OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out"));
const auto kernel_dtype = framework::ToRealType(in_dtype);
return framework::OpKernelType(kernel_dtype, ctx.GetPlace());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(stft, ops::StftOp, ops::StftOpMaker,
ops::StftGradOpMaker<paddle::framework::OpDesc>,
ops::StftGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(stft_grad, ops::StftGradOp);
REGISTER_OP_CPU_KERNEL(
stft, ops::StftKernel<paddle::platform::CPUDeviceContext, float>,
ops::StftKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
stft_grad, ops::StftGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::StftGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/stft_op.cu 0 → 100644
浏览文件 @ 319f95d0
// Copyright (c) 2022 PaddlePaddle 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 "paddle/fluid/operators/spectral_op.cu.h"
#include "paddle/fluid/operators/stft_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
stft, ops::StftKernel<paddle::platform::CUDADeviceContext, float>,
ops::StftKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
stft_grad, ops::StftGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::StftGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/stft_op.h 0 → 100644
浏览文件 @ 319f95d0
// Copyright (c) 2021 PaddlePaddle 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.
#pragma once
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/frame_op.h"
#include "paddle/fluid/operators/spectral_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class StftKernel : public framework::OpKernel<T> {
public:
/*
Batch Signals (N, T) -> Frames (N, n_fft, num_frames) -> FFTR2C -> (N,
n_fft/2 + 1, num_frames) or (N, n_fft, num_frames)
*/
void Compute(const framework::ExecutionContext& ctx) const override {
using C = paddle::platform::complex<T>;
const Tensor* x = ctx.Input<Tensor>("X");
Tensor* out = ctx.Output<Tensor>("Out");
out->mutable_data<C>(ctx.GetPlace());
const size_t x_rank = x->dims().size();
const size_t out_rank = out->dims().size();
const int n_fft = ctx.Attr<int>("n_fft");
const int hop_length = ctx.Attr<int>("hop_length");
const bool normalized = ctx.Attr<bool>("normalized");
const bool onesided = ctx.Attr<bool>("onesided");
const int n_frames = out->dims()[out_rank - 1];
const int seq_length = x->dims()[x_rank - 1];
auto& dev_ctx = ctx.device_context<DeviceContext>();
std::vector<int64_t> axes = {1};
// Frame
Tensor frames;
framework::DDim frames_dims(out->dims());
frames_dims.at(axes.back()) = n_fft;
frames.mutable_data<T>(frames_dims, ctx.GetPlace());
FrameFunctor<DeviceContext, T>()(dev_ctx, x, &frames, seq_length, n_fft,
n_frames, hop_length, /*is_grad*/ false);
// FFTR2C
FFTNormMode normalization;
if (normalized) {
normalization = get_norm_from_string("ortho", true);
} else {
normalization = get_norm_from_string("backward", true);
}
FFTR2CFunctor<DeviceContext, T, C> fft_r2c_func;
if (onesided) {
fft_r2c_func(dev_ctx, &frames, out, axes, normalization, true);
} else {
framework::DDim onesided_dims(out->dims());
const int64_t onesided_axis_size = out->dims().at(axes.back()) / 2 + 1;
onesided_dims.at(axes.back()) = onesided_axis_size;
Tensor onesided_out;
onesided_out.mutable_data<C>(onesided_dims, ctx.GetPlace());
fft_r2c_func(dev_ctx, &frames, &onesided_out, axes, normalization, true);
fill_conj<DeviceContext, C>(dev_ctx, &onesided_out, out, axes);
}
}
};
template <typename DeviceContext, typename T>
class StftGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using C = paddle::platform::complex<T>;
auto& dev_ctx = ctx.device_context<DeviceContext>();
const auto* dy = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
dx->mutable_data<T>(ctx.GetPlace());
const size_t dy_rank = dy->dims().size();
const size_t dx_rank = dx->dims().size();
const int n_fft = ctx.Attr<int>("n_fft");
const int hop_length = ctx.Attr<int>("hop_length");
const bool normalized = ctx.Attr<bool>("normalized");
const bool onesided = ctx.Attr<bool>("onesided");
const int n_frames = dy->dims()[dy_rank - 1];
const int seq_length = dx->dims()[dx_rank - 1];
std::vector<int64_t> axes = {1};
Tensor d_frames;
framework::DDim d_frames_dims(dy->dims());
d_frames_dims.at(axes.back()) = n_fft;
d_frames.mutable_data<T>(d_frames_dims, ctx.GetPlace());
Tensor complex_d_frames;
complex_d_frames.mutable_data<C>(d_frames_dims, ctx.GetPlace());
// dy -> d_frames
FFTNormMode normalization;
if (normalized) {
normalization = get_norm_from_string("ortho", true);
} else {
normalization = get_norm_from_string("backward", true);
}
FFTC2CFunctor<DeviceContext, C, C> fft_c2c_func;
if (!onesided) {
fft_c2c_func(dev_ctx, dy, &complex_d_frames, axes, normalization, false);
} else {
Tensor full_dy;
full_dy.mutable_data<C>(d_frames_dims, ctx.GetPlace());
auto zero_length = static_cast<int>(full_dy.dims().at(axes.back()) -
dy->dims().at(axes.back()));
auto rank = dy->dims().size();
std::vector<int> pads(rank * 2, 0);
pads[axes.back() * 2 + 1] = zero_length;
phi::funcs::PaddingFunctor<DeviceContext, C>(
rank, ctx.template device_context<DeviceContext>(), pads,
static_cast<C>(0), *dy, &full_dy);
fft_c2c_func(dev_ctx, &full_dy, &complex_d_frames, axes, normalization,
false);
}
framework::TransComplexToReal(
framework::TransToProtoVarType(d_frames.dtype()),
framework::TransToProtoVarType(complex_d_frames.dtype()),
complex_d_frames, &d_frames);
// d_frames -> dx
FrameFunctor<DeviceContext, T>()(dev_ctx, &d_frames, dx, seq_length, n_fft,
n_frames, hop_length, /*is_grad*/ true);
}
};
} // namespace operators
} // namespace paddle
paddle/phi/kernels/cpu/pad3d_kernel.cc
浏览文件 @ 319f95d0
......@@ -15,6 +15,7 @@
#include "paddle/phi/kernels/pad3d_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/complex.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
......@@ -574,5 +575,13 @@ void Pad3dKernel(const Context& dev_ctx,
} // namespace phi
PD_REGISTER_KERNEL(
pad3d, CPU, ALL_LAYOUT, phi::Pad3dKernel, float, double, int, int64_t) {}
PD_REGISTER_KERNEL(pad3d,
CPU,
ALL_LAYOUT,
phi::Pad3dKernel,
float,
double,
int,
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
paddle/phi/kernels/gpu/pad3d_kernel.cu
浏览文件 @ 319f95d0
......@@ -19,6 +19,7 @@
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/common/complex.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
......@@ -585,4 +586,6 @@ PD_REGISTER_KERNEL(pad3d,
float,
double,
int,
int64_t) {}
int64_t,
phi::dtype::complex<float>,
phi::dtype::complex<double>) {}
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @ 319f95d0
......@@ -266,9 +266,10 @@ def generate_activation_fn(op_type):
op_type)
else:
# abs exp square ops support dtype(int32, int64, float16, float32, float64)
check_variable_and_dtype(
x, 'x', ['int32', 'int64', 'float16', 'float32', 'float64'],
op_type)
check_variable_and_dtype(x, 'x', [
'int32', 'int64', 'float16', 'float32', 'float64', 'complex64',
'complex128'
], op_type)
helper = LayerHelper(op_type, **locals())
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 319f95d0
......@@ -5616,9 +5616,10 @@ def transpose(x, perm, name=None):
out, _ = _C_ops.transpose2(x, 'axis', perm)
return out
check_variable_and_dtype(
x, 'x', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'transpose')
check_variable_and_dtype(x, 'x', [
'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64',
'complex128'
], 'transpose')
check_type(perm, 'perm', (list, tuple), 'transpose')
if isinstance(perm, tuple):
perm = list(perm)
......@@ -6410,10 +6411,10 @@ def squeeze(input, axes, name=None):
return out
helper = LayerHelper("squeeze", **locals())
check_variable_and_dtype(
input, 'input',
['float16', 'float32', 'float64', 'bool', 'int8', 'int32', 'int64'],
'squeeze')
check_variable_and_dtype(input, 'input', [
'float16', 'float32', 'float64', 'bool', 'int8', 'int32', 'int64',
'complex64', 'complex128'
], 'squeeze')
check_type(axes, 'axis/axes', (list, tuple), 'squeeze')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
......@@ -6471,8 +6472,16 @@ def unsqueeze(input, axes, name=None):
check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'unsqueeze')
check_variable_and_dtype(input, 'input', [
'float16', 'float32', 'float64', 'bool', 'int8', 'int16', 'int32',
'int64'
'float16',
'float32',
'float64',
'bool',
'int8',
'int16',
'int32',
'int64',
'complex64',
'complex128',
], 'unsqueeze')
helper = LayerHelper("unsqueeze2", **locals())
inputs = {"X": input}
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 319f95d0
......@@ -756,7 +756,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
check_shape(shape)
check_dtype(dtype, 'dtype', [
'bool', 'float16', 'float32', 'float64', 'uint8', 'int16', 'int32',
'int64'
'int64', 'complex64', 'complex128'
], 'fill_constant')
check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
......
python/paddle/fluid/tests/unittests/test_stft_op.py 0 → 100644
浏览文件 @ 319f95d0
# Copyright (c) 2021 PaddlePaddle 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.
import numpy as np
from numpy.lib.stride_tricks import as_strided
import paddle
import unittest
from op_test import OpTest
def frame_from_librosa(x, frame_length, hop_length, axis=-1):
if axis == -1 and not x.flags["C_CONTIGUOUS"]:
x = np.ascontiguousarray(x)
elif axis == 0 and not x.flags["F_CONTIGUOUS"]:
x = np.asfortranarray(x)
n_frames = 1 + (x.shape[axis] - frame_length) // hop_length
strides = np.asarray(x.strides)
if axis == -1:
shape = list(x.shape)[:-1] + [frame_length, n_frames]
strides = list(strides) + [hop_length * x.itemsize]
elif axis == 0:
shape = [n_frames, frame_length] + list(x.shape)[1:]
strides = [hop_length * x.itemsize] + list(strides)
else:
raise ValueError("Frame axis={} must be either 0 or -1".format(axis))
return as_strided(x, shape=shape, strides=strides)
def stft_np(x, n_fft, hop_length, **kwargs):
frames = frame_from_librosa(x, n_fft, hop_length)
res = np.fft.rfft(frames, axis=1)
return res
class TestStftOp(OpTest):
def setUp(self):
self.op_type = "stft"
self.shape, self.type, self.attrs = self.initTestCase()
self.inputs = {
'X': np.random.random(size=self.shape).astype(self.type),
}
self.outputs = {'Out': stft_np(x=self.inputs['X'], **self.attrs)}
def initTestCase(self):
input_shape = (2, 100)
input_type = 'float64'
attrs = {
'n_fft': 50,
'hop_length': 15,
'normalized': False,
'onesided': True,
}
return input_shape, input_type, attrs
def test_check_output(self):
paddle.enable_static()
self.check_output()
paddle.disable_static()
def test_check_grad_normal(self):
paddle.enable_static()
self.check_grad(['X'], 'Out')
paddle.disable_static()
if __name__ == '__main__':
unittest.main()
python/paddle/signal.py
浏览文件 @ 319f95d0
......@@ -119,6 +119,7 @@ def frame(x, frame_length, hop_length, axis=-1, name=None):
f'Unexpected hop_length: {hop_length}. It should be an positive integer.'
)
if in_dygraph_mode():
if frame_length > x.shape[axis]:
raise ValueError(
f'Attribute frame_length should be less equal than sequence length, '
......@@ -306,8 +307,7 @@ def stft(x,
y1 = stft(x, n_fft=512, center=False, onesided=False) # [8, 512, 372]
"""
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'complex64', 'complex128'],
'stft')
x, 'x', ['float32', 'float64', 'complex64', 'complex128'], 'stft')
x_rank = len(x.shape)
assert x_rank in [1, 2], \
......@@ -325,6 +325,7 @@ def stft(x,
if win_length is None:
win_length = n_fft
if in_dygraph_mode():
assert 0 < n_fft <= x.shape[-1], \
f'n_fft should be in (0, seq_length({x.shape[-1]})], but got {n_fft}.'
......@@ -359,7 +360,7 @@ def stft(x,
x_frames = x_frames.transpose(
perm=[0, 2,
1]) # switch n_fft to last dim, egs: (batch, num_frames, n_fft)
x_frames = x_frames * window
x_frames = paddle.multiply(x_frames, window)
norm = 'ortho' if normalized else 'backward'
if is_complex(x_frames):
......@@ -495,6 +496,7 @@ def istft(x,
n_frames = x.shape[-1]
fft_size = x.shape[-2]
if in_dygraph_mode():
if onesided:
assert (fft_size == n_fft // 2 + 1), \
'fft_size should be equal to n_fft // 2 + 1({}) when onesided is True, but got {}.'.format(n_fft // 2 + 1, fft_size)
......@@ -506,7 +508,10 @@ def istft(x,
assert len(window.shape) == 1 and len(window) == win_length, \
'expected a 1D window tensor of size equal to win_length({}), but got window with shape {}.'.format(win_length, window.shape)
else:
window = paddle.ones(shape=(win_length, ))
window_dtype = paddle.float32 if x.dtype in [
paddle.float32, paddle.complex64
] else paddle.float64
window = paddle.ones(shape=(win_length, ), dtype=window_dtype)
if win_length < n_fft:
pad_left = (n_fft - win_length) // 2
......@@ -534,15 +539,15 @@ def istft(x,
x = x[:, :, :n_fft // 2 + 1]
out = fft_c2r(x=x, n=None, axis=-1, norm=norm, forward=False, name=None)
out = paddle.multiply(out, window).transpose(
perm=[0, 2, 1]) # (batch, n_fft, num_frames)
out = overlap_add(
x=(out * window).transpose(
perm=[0, 2, 1]), # (batch, n_fft, num_frames)
hop_length=hop_length,
axis=-1) # (batch, seq_length)
x=out, hop_length=hop_length, axis=-1) # (batch, seq_length)
window_envelop = overlap_add(
x=paddle.tile(
x=window * window, repeat_times=[n_frames, 1]).transpose(
x=paddle.multiply(window, window).unsqueeze(0),
repeat_times=[n_frames, 1]).transpose(
perm=[1, 0]), # (n_fft, num_frames)
hop_length=hop_length,
axis=-1) # (seq_length, )
......@@ -561,7 +566,7 @@ def istft(x,
window_envelop = window_envelop[start:start + length]
# Check whether the Nonzero Overlap Add (NOLA) constraint is met.
if window_envelop.abs().min().item() < 1e-11:
if in_dygraph_mode() and window_envelop.abs().min().item() < 1e-11:
raise ValueError(
'Abort istft because Nonzero Overlap Add (NOLA) condition failed. For more information about NOLA constraint please see `scipy.signal.check_NOLA`(https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.check_NOLA.html).'
)
......
python/paddle/tensor/linalg.py
浏览文件 @ 319f95d0
......@@ -147,7 +147,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
var_names = {'x': x, 'y': y}
for name, val in var_names.items():
check_variable_and_dtype(
val, name, ['float16', 'float32', 'float64'], 'matmul')
val, name,
['float16', 'float32', 'float64', 'complex64', 'complex128'],
'matmul')
__check_input(x, y)
......
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