mirror of
https://github.com/ROCm/jax.git
synced 2025-04-24 19:16:06 +00:00
136 lines
4.6 KiB
Python
136 lines
4.6 KiB
Python
# Copyright 2019 Google LLC
|
|
#
|
|
# 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
|
|
#
|
|
# https://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.
|
|
|
|
|
|
from functools import partial
|
|
|
|
import numpy as onp
|
|
|
|
from jax.abstract_arrays import ShapedArray
|
|
from jax.api import jit, vjp
|
|
from jax.core import Primitive
|
|
from jax.interpreters import xla
|
|
from jax.util import prod
|
|
from . import dtypes, lax
|
|
from ..lib.xla_bridge import xla_client
|
|
from ..interpreters import ad
|
|
from ..interpreters import batching
|
|
|
|
|
|
def _promote_to_complex(arg):
|
|
dtype = onp.result_type(arg, onp.complex64)
|
|
# XLA's FFT op only supports C64.
|
|
if dtype == onp.complex128:
|
|
dtype = onp.complex64
|
|
return lax.convert_element_type(arg, dtype)
|
|
|
|
def _promote_to_real(arg):
|
|
dtype = onp.result_type(arg, onp.float64)
|
|
# XLA's FFT op only supports F32.
|
|
if dtype == onp.float64:
|
|
dtype = onp.float32
|
|
return lax.convert_element_type(arg, dtype)
|
|
|
|
def fft(x, fft_type, fft_lengths):
|
|
if fft_type == xla_client.FftType.RFFT:
|
|
if onp.iscomplexobj(x):
|
|
raise ValueError("only real valued inputs supported for rfft")
|
|
x = _promote_to_real(x)
|
|
else:
|
|
x = _promote_to_complex(x)
|
|
if len(fft_lengths) == 0:
|
|
# XLA FFT doesn't support 0-rank.
|
|
return x
|
|
fft_lengths = tuple(fft_lengths)
|
|
return fft_p.bind(x, fft_type=fft_type, fft_lengths=fft_lengths)
|
|
|
|
def fft_impl(x, fft_type, fft_lengths):
|
|
return xla.apply_primitive(fft_p, x, fft_type=fft_type, fft_lengths=fft_lengths)
|
|
|
|
_complex_dtype = lambda dtype: (onp.zeros((), dtype) + onp.zeros((), onp.complex64)).dtype
|
|
_real_dtype = lambda dtype: onp.zeros((), dtype).real.dtype
|
|
_is_even = lambda x: x % 2 == 0
|
|
|
|
def fft_abstract_eval(x, fft_type, fft_lengths):
|
|
if fft_type == xla_client.FftType.RFFT:
|
|
shape = (x.shape[:-len(fft_lengths)] + fft_lengths[:-1]
|
|
+ (fft_lengths[-1] // 2 + 1,))
|
|
dtype = _complex_dtype(x.dtype)
|
|
elif fft_type == xla_client.FftType.IRFFT:
|
|
shape = x.shape[:-len(fft_lengths)] + fft_lengths
|
|
dtype = _real_dtype(x.dtype)
|
|
else:
|
|
shape = x.shape
|
|
dtype = x.dtype
|
|
return ShapedArray(shape, dtype)
|
|
|
|
def fft_translation_rule(c, x, fft_type, fft_lengths):
|
|
return c.Fft(x, fft_type, fft_lengths)
|
|
|
|
def _naive_rfft(x, fft_lengths):
|
|
y = fft(x, xla_client.FftType.FFT, fft_lengths)
|
|
n = fft_lengths[-1]
|
|
return y[..., : n//2 + 1]
|
|
|
|
@partial(jit, static_argnums=1)
|
|
def _rfft_transpose(t, fft_lengths):
|
|
# The transpose of RFFT can't be expressed only in terms of irfft. Instead of
|
|
# manually building up larger twiddle matrices (which would increase the
|
|
# asymptotic complexity and is also rather complicated), we rely JAX to
|
|
# transpose a naive RFFT implementation.
|
|
dummy_shape = t.shape[:-len(fft_lengths)] + fft_lengths
|
|
dummy_primals = lax.full_like(t, 0.0, onp.float64, dummy_shape)
|
|
_, jvpfun = vjp(partial(_naive_rfft, fft_lengths=fft_lengths), dummy_primals)
|
|
result, = jvpfun(t)
|
|
return result
|
|
|
|
def _irfft_transpose(t, fft_lengths):
|
|
# The transpose of IRFFT is the RFFT of the cotangent times a scaling
|
|
# factor and a mask. The mask scales the cotangent for the Hermitian
|
|
# symmetric components of the RFFT by a factor of two, since these components
|
|
# are de-duplicated in the RFFT.
|
|
x = fft(t, xla_client.FftType.RFFT, fft_lengths)
|
|
n = x.shape[-1]
|
|
is_odd = fft_lengths[-1] % 2
|
|
full = partial(lax.full_like, t, dtype=onp.float64)
|
|
mask = lax.concatenate(
|
|
[full(1.0, shape=(1,)),
|
|
full(2.0, shape=(n - 2 + is_odd,)),
|
|
full(1.0, shape=(1 - is_odd,))],
|
|
dimension=0)
|
|
scale = 1 / prod(fft_lengths)
|
|
return scale * mask * x
|
|
|
|
def fft_transpose_rule(t, fft_type, fft_lengths):
|
|
if fft_type == xla_client.FftType.RFFT:
|
|
result = _rfft_transpose(t, fft_lengths)
|
|
elif fft_type == xla_client.FftType.IRFFT:
|
|
result = _irfft_transpose(t, fft_lengths)
|
|
else:
|
|
result = fft(t, fft_type, fft_lengths)
|
|
return result,
|
|
|
|
def fft_batching_rule(batched_args, batch_dims, fft_type, fft_lengths):
|
|
x, = batched_args
|
|
bd, = batch_dims
|
|
x = batching.moveaxis(x, bd, 0)
|
|
return fft(x, fft_type, fft_lengths), 0
|
|
|
|
fft_p = Primitive('fft')
|
|
fft_p.def_impl(fft_impl)
|
|
fft_p.def_abstract_eval(fft_abstract_eval)
|
|
xla.translations[fft_p] = fft_translation_rule
|
|
ad.deflinear(fft_p, fft_transpose_rule)
|
|
batching.primitive_batchers[fft_p] = fft_batching_rule
|