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[API compatibility] support windows api (#76237)
* align_window_func_with_torch * adapt the parameters * add test * fix the cpu_only_paddle * add comment * fix the operate logic * add device process * optimize code
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‎python/paddle/__init__.py‎

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@@ -228,6 +228,13 @@ def new_init(self, *args, **kwargs):
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is_autocast_enabled,
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)
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from .amp.auto_cast import autocast
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from .audio.functional.window import ( # noqa: F401
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bartlett_window,
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blackman_window,
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hamming_window,
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hann_window,
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kaiser_window,
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)
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from .autograd import (
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enable_grad,
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grad,

‎python/paddle/audio/functional/window.py‎

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@@ -13,6 +13,7 @@
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from __future__ import annotations
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import math
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import warnings
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from typing import TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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from paddle import Tensor
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from paddle._typing import PlaceLike
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from ..features.layers import _WindowLiteral
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from paddle.base.framework import (
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_current_expected_place,
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_get_paddle_place,
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core,
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in_dynamic_or_pir_mode,
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)
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class WindowFunctionRegister:
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def __init__(self):
@@ -445,3 +454,287 @@ def get_window(
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params = (win_length, *args)
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kwargs = {'sym': sym}
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return winfunc(*params, dtype=dtype, **kwargs)
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def _apply_window_postprocess(
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w: Tensor,
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*,
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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) -> Tensor:
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if layout is not None:
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warnings.warn("layout only supports 'strided' in Paddle; ignored")
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if in_dynamic_or_pir_mode():
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device = (
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_get_paddle_place(device)
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if device is not None
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else _current_expected_place()
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)
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if (
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pin_memory
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and paddle.in_dynamic_mode()
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and device is not None
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and not isinstance(
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device, (core.CUDAPinnedPlace, core.XPUPinnedPlace)
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)
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):
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if isinstance(device, core.CUDAPlace) or (
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isinstance(device, core.Place) and device.is_gpu_place()
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):
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device = core.CUDAPinnedPlace()
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elif isinstance(device, core.XPUPlace) or (
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isinstance(device, core.Place) and device.is_xpu_place()
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):
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device = core.XPUPinnedPlace()
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else:
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raise RuntimeError(
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f"Pinning memory is not supported for {device}"
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)
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w = w.to(device=device)
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if pin_memory and paddle.in_dynamic_mode():
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w = w.pin_memory()
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if requires_grad is True:
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w.stop_gradient = False
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return w
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def hamming_window(
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window_length: int,
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periodic: bool = True,
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alpha: float = 0.54,
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beta: float = 0.46,
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*,
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dtype: str = 'float64',
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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):
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"""
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Compute a generalized Hamming window.
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Args:
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window_length (int): The size of the returned window. Must be positive.
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periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True.
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alpha (float, optional): The coefficient α in the equation above. Defaults to 0.54.
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beta (float, optional): The coefficient β in the equation above. Defaults to 0.46.
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dtype (str, optional): The data type of the returned tensor. Defaults to 'float64'.
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layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None.
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device(PlaceLike|None, optional): The desired device of returned tensor.
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if None, uses the current device for the default tensor type (see paddle.device.set_device()).
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device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
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pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False
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requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False.
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Returns:
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Tensor: A 1-D tensor of shape `(window_length,)` containing the Hamming window.
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> win = paddle.hamming_window(400, requires_grad=True)
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>>> win = paddle.hamming_window(256, alpha=0.5, beta=0.5)
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"""
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w0 = get_window('hamming', window_length, fftbins=periodic, dtype=dtype)
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alpha0, beta0 = 0.54, 0.46
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B = beta / beta0
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A = alpha - B * alpha0
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w = A + B * w0
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return _apply_window_postprocess(
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w,
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layout=layout,
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device=device,
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pin_memory=pin_memory,
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requires_grad=requires_grad,
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)
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def hann_window(
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window_length: int,
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periodic: bool = True,
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*,
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dtype: str = 'float64',
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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):
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"""
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Compute a Hann window.
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Args:
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window_length (int): The size of the returned window. Must be positive.
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periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True.
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dtype (str, optional): The data type of the returned tensor. Defaults to 'float64'.
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layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None.
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device(PlaceLike|None, optional): The desired device of returned tensor.
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if None, uses the current device for the default tensor type (see paddle.device.set_device()).
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device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
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pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False
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requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False.
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Returns:
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Tensor: A 1-D tensor of shape `(window_length,)` containing the Hann window.
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> win = paddle.hann_window(512)
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>>> win = paddle.hann_window(512, requires_grad=True)
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"""
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w = get_window('hann', window_length, fftbins=periodic, dtype=dtype)
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return _apply_window_postprocess(
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w,
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layout=layout,
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device=device,
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pin_memory=pin_memory,
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requires_grad=requires_grad,
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)
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def kaiser_window(
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window_length: int,
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periodic: bool = True,
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beta: float = 12.0,
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*,
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dtype: str = 'float64',
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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):
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"""
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Compute a Kaiser window.
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Args:
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window_length (int): The size of the returned window. Must be positive.
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periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True.
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beta (float, optional): Shape parameter for the window. Defaults to 12.0.
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dtype (str, optional): The data type of the returned tensor. Defaults to 'float64'.
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layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None.
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device(PlaceLike|None, optional): The desired device of returned tensor.
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if None, uses the current device for the default tensor type (see paddle.device.set_device()).
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device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
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pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False
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requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False.
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Returns:
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Tensor: A 1-D tensor of shape `(window_length,)` containing the Kaiser window.
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> win = paddle.kaiser_window(400, beta=8.6)
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>>> win = paddle.kaiser_window(400, requires_grad=True)
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"""
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w = get_window(
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('kaiser', beta), window_length, fftbins=periodic, dtype=dtype
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)
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return _apply_window_postprocess(
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w,
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layout=layout,
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device=device,
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pin_memory=pin_memory,
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requires_grad=requires_grad,
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)
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def blackman_window(
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window_length: int,
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periodic: bool = True,
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*,
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dtype: str = 'float64',
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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):
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"""
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Compute a Blackman window.
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Args:
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window_length (int): The size of the returned window. Must be positive.
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periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True.
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dtype (str, optional): The data type of the returned tensor. Defaults to 'float64'.
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layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None.
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device(PlaceLike|None, optional): The desired device of returned tensor.
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if None, uses the current device for the default tensor type (see paddle.device.set_device()).
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device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
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pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False
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requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False.
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Returns:
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Tensor: A 1-D tensor of shape `(window_length,)` containing the Blackman window.
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> win = paddle.blackman_window(256)
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>>> win = paddle.blackman_window(256, requires_grad=True)
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"""
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w = get_window('blackman', window_length, fftbins=periodic, dtype=dtype)
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return _apply_window_postprocess(
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w,
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layout=layout,
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device=device,
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pin_memory=pin_memory,
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requires_grad=requires_grad,
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)
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def bartlett_window(
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window_length: int,
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periodic: bool = True,
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*,
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dtype: str = 'float64',
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layout: str | None = None,
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device: PlaceLike | None = None,
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pin_memory: bool = False,
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requires_grad: bool = False,
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):
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"""
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Compute a Bartlett window.
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Args:
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window_length (int): The size of the returned window. Must be positive.
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periodic (bool, optional): If True, returns a window for use as a periodic function; if False, returns a symmetric window. Defaults to True.
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dtype (str, optional): The data type of the returned tensor. Defaults to 'float64'.
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layout (str, optional): Only included for API consistency with PyTorch; ignored in Paddle. Defaults to None.
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device(PlaceLike|None, optional): The desired device of returned tensor.
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if None, uses the current device for the default tensor type (see paddle.device.set_device()).
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device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.
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pin_memory(bool, optional): If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False
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requires_grad(bool, optional): If autograd should record operations on the returned tensor. Default: False.
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Returns:
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Tensor: A 1-D tensor of shape `(window_length,)` containing the Bartlett window.
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Examples:
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.. code-block:: python
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>>> import paddle
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>>> n_fft = 512
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>>> win = paddle.bartlett_window(n_fft)
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>>> win = paddle.bartlett_window(n_fft, requires_grad=True)
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"""
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w = get_window('bartlett', window_length, fftbins=periodic, dtype=dtype)
734+
return _apply_window_postprocess(
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w,
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layout=layout,
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device=device,
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pin_memory=pin_memory,
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requires_grad=requires_grad,
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)

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