wehub-resource-sync
264 lines
9.1 KiB
Python
264 lines
9.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import paddle
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from paddle import _C_ops
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from paddle.utils.decorator_utils import param_one_alias
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from ...base.data_feeder import check_variable_and_dtype
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from ...base.layer_helper import LayerHelper
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from ...framework import in_dynamic_or_pir_mode
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if TYPE_CHECKING:
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from typing_extensions import Never
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from paddle import Tensor
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from paddle.nn import Layer
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__all__ = []
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def l2_norm(
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x: Tensor, axis: int, epsilon: float = 1e-12, name: str | None = None
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) -> Tensor:
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if len(x.shape) == 1:
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axis = 0
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if in_dynamic_or_pir_mode():
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out, norm = _C_ops.norm(x, 1 if axis is None else axis, epsilon, False)
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return paddle.squeeze(norm, axis=[axis])
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check_variable_and_dtype(x, "X", ("float32", "float64"), "norm")
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helper = LayerHelper("l2_normalize", **locals())
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out = helper.create_variable_for_type_inference(dtype=x.dtype)
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norm = helper.create_variable_for_type_inference(dtype=x.dtype)
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helper.append_op(
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type="norm",
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inputs={"X": x},
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outputs={"Out": out, "Norm": norm},
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attrs={
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"axis": 1 if axis is None else axis,
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"epsilon": epsilon,
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},
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)
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return paddle.squeeze(norm, axis=[axis])
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def norm_except_dim(p: Tensor, dim: int) -> Tensor:
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shape = p.shape
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ndims = len(shape)
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if dim == -1:
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return paddle.sqrt(paddle.sum(paddle.square(p)) + 1e-12)
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elif dim == 0:
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p_matrix = paddle.reshape(p, (shape[0], -1))
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return l2_norm(p_matrix, axis=1)
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elif dim == ndims - 1:
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p_matrix = paddle.reshape(p, (-1, shape[-1]))
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return l2_norm(p_matrix, axis=0)
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else:
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perm = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = paddle.transpose(p, perm)
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return norm_except_dim(p_transposed, 0)
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def _weight_norm(v: Tensor, g: Tensor, dim: int) -> Tensor:
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shape = v.shape
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ndims = len(shape)
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if dim == -1:
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v_normalized = v / (paddle.sqrt(paddle.sum(paddle.square(v))) + 1e-12)
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elif dim == 0:
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p_matrix = paddle.reshape(v, (shape[0], -1))
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v_normalized = paddle.nn.functional.normalize(p_matrix, axis=1)
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v_normalized = paddle.reshape(v_normalized, shape)
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elif dim == ndims - 1:
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p_matrix = paddle.reshape(v, (-1, shape[-1]))
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v_normalized = paddle.nn.functional.normalize(p_matrix, axis=0)
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v_normalized = paddle.reshape(v_normalized, shape)
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else:
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perm = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = paddle.transpose(v, perm)
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transposed_shape = p_transposed.shape
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p_matrix = paddle.reshape(p_transposed, (p_transposed.shape[0], -1))
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v_normalized = paddle.nn.functional.normalize(p_matrix, axis=1)
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v_normalized = paddle.reshape(v_normalized, transposed_shape)
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v_normalized = paddle.transpose(v_normalized, perm)
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weight = paddle.tensor.math._multiply_with_axis(
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v_normalized, g, axis=dim if dim is not None else -1
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)
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return weight
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class WeightNorm:
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name: str
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dim: int
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def __init__(self, name: str, dim: int) -> None:
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if dim is None:
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dim = -1
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self.name = name
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self.dim = dim
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def compute_weight(self, layer: Layer) -> Tensor:
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g = getattr(layer, self.name + '_g')
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v = getattr(layer, self.name + '_v')
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return _weight_norm(v, g, self.dim)
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@staticmethod
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def apply(layer: Layer, name: str, dim: int) -> WeightNorm:
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for k, hook in layer._forward_pre_hooks.items():
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if isinstance(hook, WeightNorm) and hook.name == name:
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raise RuntimeError(
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"Cannot register two weight_norm hooks on "
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f"the same parameter {name}"
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)
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if dim is None:
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dim = -1
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# support dim is negative number, (dim = -1) == (dim = None)
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weight_dim = len(layer._parameters[name].shape)
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assert dim < weight_dim and dim >= -1 * weight_dim, (
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"dim must set between [-R, R), R means the dimension of weight."
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)
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if dim != -1:
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dim = (dim + weight_dim) % weight_dim
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fn = WeightNorm(name, dim)
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w = getattr(layer, name)
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del layer._parameters[name]
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g_var = norm_except_dim(w, dim)
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v = layer.create_parameter(w.shape, dtype=w.dtype)
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layer.add_parameter(name + "_v", v)
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g = layer.create_parameter(g_var.shape, dtype=g_var.dtype)
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layer.add_parameter(name + '_g', g)
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with paddle.no_grad():
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paddle.assign(w, v)
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paddle.assign(g_var, g)
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setattr(layer, name, fn.compute_weight(layer))
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layer.register_forward_pre_hook(fn)
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return fn
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def remove(self, layer: Layer) -> None:
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w_var = self.compute_weight(layer)
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delattr(layer, self.name)
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del layer._parameters[self.name + '_g']
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del layer._parameters[self.name + '_v']
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w = layer.create_parameter(w_var.shape, dtype=w_var.dtype)
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layer.add_parameter(self.name, w)
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with paddle.no_grad():
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paddle.assign(w_var, w)
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def __call__(self, layer: Layer, inputs: Never) -> None:
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setattr(layer, self.name, self.compute_weight(layer))
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@param_one_alias(["layer", "module"])
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def weight_norm(layer: Layer, name: str = 'weight', dim: int = 0) -> Layer:
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r"""
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Applies weight normalization to a parameter according to the
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following formula:
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.. math::
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\mathbf{w} = g \dfrac{v}{\|v\|}
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Weight normalization is a reparameterization of the weight vectors in a neural network that
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decouples the magnitude of those weight vectors from their direction. Weight normalization
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replaces the parameter specified by ``name`` (eg: 'weight') with two parameters: one parameter
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specifying the magnitude (eg: 'weight_g') and one parameter specifying the direction
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(eg: 'weight_v'). Weight normalization has been implemented as discussed in this paper:
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`Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
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<https://arxiv.org/pdf/1602.07868.pdf>`_.
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Parameters:
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layer(Layer): Layer of paddle, which has weight.
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Alias: ``module``.
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name(str, optional): Name of the weight parameter. Default: 'weight'.
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dim(int, optional): Dimension over which to compute the norm. Dim is a non-negative number
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which is less than the rank of weight Tensor. For Example, dim can be chosen from 0,
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1, 2, 3 for convolution whose weight shape is [cout, cin, kh, kw] and rank is 4.
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If dim is set to None, meaning that all elements will be normalized. Default: 0.
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Returns:
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Origin layer with weight norm hook.
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Examples:
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.. code-block:: pycon
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>>> from paddle.nn import Conv2D
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>>> from paddle.nn.utils import weight_norm
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>>> conv = Conv2D(3, 5, 3)
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>>> wn = weight_norm(conv)
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>>> print(conv.weight_g.shape)
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paddle.Size([5])
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>>> print(conv.weight_v.shape)
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paddle.Size([5, 3, 3, 3])
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"""
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WeightNorm.apply(layer, name, dim)
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return layer
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def remove_weight_norm(layer: Layer, name: str = 'weight') -> Layer:
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"""
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remove weight normalization from layer.
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Parameters:
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layer(Layer): Layer of paddle, which has weight.
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name(str, optional): Name of the weight parameter. Default: 'weight'.
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Returns:
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Layer, the origin layer without weight norm
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.nn import Conv2D
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>>> from paddle.nn.utils import weight_norm, remove_weight_norm
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>>> paddle.seed(2023)
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>>> conv = Conv2D(3, 5, 3)
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>>> wn = weight_norm(conv)
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>>> print(conv.weight_g)
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Parameter containing:
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Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=False,
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[1.35883713, 1.32126212, 1.56303072, 1.20874095, 1.22893476])
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>>> remove_weight_norm(conv)
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>>> # The following is the effect after removing the weight norm:
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>>> # print(conv.weight_g)
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>>> # AttributeError: 'Conv2D' object has no attribute 'weight_g'
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"""
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for k, hook in layer._forward_pre_hooks.items():
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if isinstance(hook, WeightNorm) and hook.name == name:
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hook.remove(layer)
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del layer._forward_pre_hooks[k]
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return layer
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raise ValueError(f"weight_norm of '{name}' not found in {layer}")
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