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| import torch | |
| import math | |
| import warnings | |
| from torch.nn.init import _calculate_fan_in_and_fan_out | |
| def _no_grad_trunc_normal_(tensor, mean, std, a, b): | |
| # Cut & paste from PyTorch official master until it's in a few official releases - RW | |
| # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf | |
| def norm_cdf(x): | |
| # Computes standard normal cumulative distribution function | |
| return (1. + math.erf(x / math.sqrt(2.))) / 2. | |
| if (mean < a - 2 * std) or (mean > b + 2 * std): | |
| warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " | |
| "The distribution of values may be incorrect.", | |
| stacklevel=2) | |
| with torch.no_grad(): | |
| # Values are generated by using a truncated uniform distribution and | |
| # then using the inverse CDF for the normal distribution. | |
| # Get upper and lower cdf values | |
| l = norm_cdf((a - mean) / std) | |
| u = norm_cdf((b - mean) / std) | |
| # Uniformly fill tensor with values from [l, u], then translate to | |
| # [2l-1, 2u-1]. | |
| tensor.uniform_(2 * l - 1, 2 * u - 1) | |
| # Use inverse cdf transform for normal distribution to get truncated | |
| # standard normal | |
| tensor.erfinv_() | |
| # Transform to proper mean, std | |
| tensor.mul_(std * math.sqrt(2.)) | |
| tensor.add_(mean) | |
| # Clamp to ensure it's in the proper range | |
| tensor.clamp_(min=a, max=b) | |
| return tensor | |
| def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): | |
| # type: (Tensor, float, float, float, float) -> Tensor | |
| r"""Fills the input Tensor with values drawn from a truncated | |
| normal distribution. The values are effectively drawn from the | |
| normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` | |
| with values outside :math:`[a, b]` redrawn until they are within | |
| the bounds. The method used for generating the random values works | |
| best when :math:`a \leq \text{mean} \leq b`. | |
| Args: | |
| tensor: an n-dimensional `torch.Tensor` | |
| mean: the mean of the normal distribution | |
| std: the standard deviation of the normal distribution | |
| a: the minimum cutoff value | |
| b: the maximum cutoff value | |
| Examples: | |
| >>> w = torch.empty(3, 5) | |
| >>> nn.init.trunc_normal_(w) | |
| """ | |
| return _no_grad_trunc_normal_(tensor, mean, std, a, b) | |
| def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): | |
| fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) | |
| if mode == 'fan_in': | |
| denom = fan_in | |
| elif mode == 'fan_out': | |
| denom = fan_out | |
| elif mode == 'fan_avg': | |
| denom = (fan_in + fan_out) / 2 | |
| variance = scale / denom | |
| if distribution == "truncated_normal": | |
| # constant is stddev of standard normal truncated to (-2, 2) | |
| trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) | |
| elif distribution == "normal": | |
| tensor.normal_(std=math.sqrt(variance)) | |
| elif distribution == "uniform": | |
| bound = math.sqrt(3 * variance) | |
| tensor.uniform_(-bound, bound) | |
| else: | |
| raise ValueError(f"invalid distribution {distribution}") | |
| def lecun_normal_(tensor): | |
| variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') | |