wehub-resource-sync
128 lines
4.1 KiB
Python
128 lines
4.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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from fairseq.modules import Fp32GroupNorm
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class KmeansVectorQuantizer(nn.Module):
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def __init__(
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self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25
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):
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"""Vector quantization using straight pass-through estimator (i.e. kmeans)
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Args:
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dim: input dimension (channels)
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num_vars: number of quantized vectors per group
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groups: number of groups for vector quantization
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combine_groups: whether to use the vectors for all groups
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vq_dim: dimensionality of the resulting quantized vector
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time_first: if true, expect input in BxTxC format, otherwise in BxCxT
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gamma: commitment loss coefficient
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"""
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super().__init__()
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self.groups = groups
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self.combine_groups = combine_groups
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self.input_dim = dim
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self.num_vars = num_vars
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self.vq_dim = vq_dim
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self.time_first = time_first
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assert (
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vq_dim % groups == 0
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), f"dim {vq_dim} must be divisible by groups {groups} for concatenation"
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self.var_dim = vq_dim // groups
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num_groups = groups if not combine_groups else 1
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self.embedding = nn.Parameter(
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0.01 * torch.randn(num_vars, num_groups, self.var_dim)
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)
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self.projection = nn.Sequential(
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nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False),
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Fp32GroupNorm(groups, dim),
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)
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self.gamma = gamma
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self.mse_mean = nn.MSELoss(reduction="mean")
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def _pass_grad(self, x, y):
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"""Manually set gradient for backward pass.
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for y = f(x), ensure that during the backward pass,
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dL/dy = dL/dx regardless of f(x).
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Returns:
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y, with the gradient forced to be dL/dy = dL/dx.
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"""
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return y.detach() + (x - x.detach())
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@property
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def expand_embedding(self):
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if self.combine_groups:
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return self.embedding.expand(self.num_vars, self.groups, self.var_dim)
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return self.embedding
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def forward_idx(self, x):
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res = self.forward(x, produce_targets=True)
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return res["x"], res["targets"]
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def forward(self, x, produce_targets=False):
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result = {"num_vars": self.num_vars}
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if self.time_first:
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x = x.transpose(1, 2)
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bsz, fsz, tsz = x.shape
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ze = self.projection(x)
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ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2)
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d = (
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(ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1))
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.view(self.num_vars, bsz, tsz, self.groups, -1)
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.norm(dim=-1, p=2)
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)
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idx = d.argmin(dim=0)
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zq = (
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torch.stack(
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[
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self.expand_embedding[idx[..., group], group]
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for group in range(self.groups)
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],
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dim=-2,
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)
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.view(bsz, tsz, self.groups * self.var_dim)
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.permute(0, 2, 1)
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)
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assert ze.shape == zq.shape, (ze.shape, zq.shape)
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x = self._pass_grad(ze, zq)
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hard_x = (
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idx.new_zeros(bsz * tsz * self.groups, self.num_vars)
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.scatter_(-1, idx.view(-1, 1), 1.0)
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.view(bsz * tsz, self.groups, -1)
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)
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hard_probs = torch.mean(hard_x.float(), dim=0)
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result["code_perplexity"] = torch.exp(
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-torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
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).sum()
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if produce_targets:
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result["targets"] = idx
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if self.time_first:
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x = x.transpose(1, 2) # BCT -> BTC
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result["x"] = x
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ze = ze.float()
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zq = zq.float()
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latent_loss = self.mse_mean(zq, ze.detach())
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commitment_loss = self.mse_mean(ze, zq.detach())
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result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss
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return result
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