107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
# coding=utf-8
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# Copyright 2020-present, the HuggingFace Inc. team.
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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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"""
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Masked Linear module: A fully connected layer that computes an adaptive binary mask on the fly.
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The mask (binary or not) is computed at each forward pass and multiplied against
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the weight matrix to prune a portion of the weights.
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The pruned weight matrix is then multiplied against the inputs (and if necessary, the bias is added).
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"""
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import math
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import torch
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from torch import nn
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from torch.nn import init
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from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
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class MaskedLinear(nn.Linear):
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"""
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Fully Connected layer with on the fly adaptive mask.
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If needed, a score matrix is created to store the importance of each associated weight.
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"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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mask_init: str = "constant",
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mask_scale: float = 0.0,
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pruning_method: str = "topK",
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):
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"""
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Args:
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in_features (`int`)
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Size of each input sample
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out_features (`int`)
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Size of each output sample
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bias (`bool`)
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If set to ``False``, the layer will not learn an additive bias.
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Default: ``True``
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mask_init (`str`)
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The initialization method for the score matrix if a score matrix is needed.
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Choices: ["constant", "uniform", "kaiming"]
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Default: ``constant``
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mask_scale (`float`)
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The initialization parameter for the chosen initialization method `mask_init`.
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Default: ``0.``
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pruning_method (`str`)
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Method to compute the mask.
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Choices: ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
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Default: ``topK``
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"""
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super(MaskedLinear, self).__init__(in_features=in_features, out_features=out_features, bias=bias)
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assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
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self.pruning_method = pruning_method
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if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
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self.mask_scale = mask_scale
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self.mask_init = mask_init
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self.mask_scores = nn.Parameter(torch.empty(self.weight.size()))
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self.init_mask()
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def init_mask(self):
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if self.mask_init == "constant":
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init.constant_(self.mask_scores, val=self.mask_scale)
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elif self.mask_init == "uniform":
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init.uniform_(self.mask_scores, a=-self.mask_scale, b=self.mask_scale)
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elif self.mask_init == "kaiming":
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init.kaiming_uniform_(self.mask_scores, a=math.sqrt(5))
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def forward(self, input: torch.tensor, threshold: float):
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# Get the mask
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if self.pruning_method == "topK":
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mask = TopKBinarizer.apply(self.mask_scores, threshold)
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elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
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sig = "sigmoied" in self.pruning_method
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mask = ThresholdBinarizer.apply(self.mask_scores, threshold, sig)
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elif self.pruning_method == "magnitude":
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mask = MagnitudeBinarizer.apply(self.weight, threshold)
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elif self.pruning_method == "l0":
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l, r, b = -0.1, 1.1, 2 / 3
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if self.training:
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u = torch.zeros_like(self.mask_scores).uniform_().clamp(0.0001, 0.9999)
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s = torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
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else:
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s = torch.sigmoid(self.mask_scores)
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s_bar = s * (r - l) + l
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mask = s_bar.clamp(min=0.0, max=1.0)
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# Mask weights with computed mask
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weight_thresholded = mask * self.weight
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# Compute output (linear layer) with masked weights
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return nn.functional.linear(input, weight_thresholded, self.bias)
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