129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
import math
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import warnings
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import torch
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from torch.nn.init import _calculate_fan_in_and_fan_out
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
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"The distribution of values may be incorrect.",
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stacklevel=2,
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)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.0))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(
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tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
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) -> torch.Tensor:
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
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applied while sampling the normal with mean/std applied, therefore a, b args
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should be adjusted to match the range of mean, std args.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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def trunc_normal_tf_(
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tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
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) -> torch.Tensor:
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
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bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
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and the result is subsquently scaled and shifted by the mean and std args.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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_no_grad_trunc_normal_(tensor, 0, 1.0, a, b)
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with torch.no_grad():
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tensor.mul_(std).add_(mean)
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return tensor
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def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
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fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
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if mode == "fan_in":
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denom = fan_in
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elif mode == "fan_out":
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denom = fan_out
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elif mode == "fan_avg":
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denom = (fan_in + fan_out) / 2
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variance = scale / denom # type: ignore
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if distribution == "truncated_normal":
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# constant is stddev of standard normal truncated to (-2, 2)
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trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
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elif distribution == "normal":
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tensor.normal_(std=math.sqrt(variance))
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elif distribution == "uniform":
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bound = math.sqrt(3 * variance)
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# pylint: disable=invalid-unary-operand-type
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tensor.uniform_(-bound, bound)
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else:
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raise ValueError(f"invalid distribution {distribution}")
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def lecun_normal_(tensor):
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variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
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