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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn as nn | |
| from vec2wav2.models.fairseq_modules.fp32_group_norm import Fp32GroupNorm | |
| class KmeansVectorQuantizer(nn.Module): | |
| def __init__( | |
| self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25 | |
| ): | |
| """Vector quantization using straight pass-through estimator (i.e. kmeans) | |
| Args: | |
| dim: input dimension (channels) | |
| num_vars: number of quantized vectors per group | |
| groups: number of groups for vector quantization | |
| combine_groups: whether to use the vectors for all groups | |
| vq_dim: dimensionality of the resulting quantized vector | |
| time_first: if true, expect input in BxTxC format, otherwise in BxCxT | |
| gamma: commitment loss coefficient | |
| """ | |
| super().__init__() | |
| self.groups = groups | |
| self.combine_groups = combine_groups | |
| self.input_dim = dim | |
| self.num_vars = num_vars | |
| self.vq_dim = vq_dim | |
| self.time_first = time_first | |
| assert ( | |
| vq_dim % groups == 0 | |
| ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation" | |
| self.var_dim = vq_dim // groups | |
| num_groups = groups if not combine_groups else 1 | |
| self.embedding = nn.Parameter( | |
| 0.01 * torch.randn(num_vars, num_groups, self.var_dim) | |
| ) | |
| self.projection = nn.Sequential( | |
| nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False), | |
| Fp32GroupNorm(groups, dim), | |
| ) | |
| self.gamma = gamma | |
| self.mse_mean = nn.MSELoss(reduction="mean") | |
| def _pass_grad(self, x, y): | |
| """Manually set gradient for backward pass. | |
| for y = f(x), ensure that during the backward pass, | |
| dL/dy = dL/dx regardless of f(x). | |
| Returns: | |
| y, with the gradient forced to be dL/dy = dL/dx. | |
| """ | |
| return y.detach() + (x - x.detach()) | |
| def expand_embedding(self): | |
| if self.combine_groups: | |
| return self.embedding.expand(self.num_vars, self.groups, self.var_dim) | |
| return self.embedding | |
| def forward_idx(self, x): | |
| res = self.forward(x, produce_targets=True) | |
| return res["x"], res["targets"] | |
| def forward_idx_limited(self, x, valid_label2vqidx_mat): | |
| # mask_mat = convert_valid_label2vqidx_to_mask_mat(valid_label2vqidx) | |
| res = self.forward_group2(x, mask_mat=valid_label2vqidx_mat, produce_targets=True) | |
| return res['x'], res['targets'] | |
| def forward(self, x, produce_targets=False): | |
| result = {"num_vars": self.num_vars} | |
| if self.time_first: | |
| x = x.transpose(1, 2) | |
| bsz, fsz, tsz = x.shape | |
| ze = self.projection(x) | |
| ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2) | |
| d = ( | |
| (ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1)) | |
| .view(self.num_vars, bsz, tsz, self.groups, -1) | |
| .norm(dim=-1, p=2) | |
| ) | |
| idx = d.argmin(dim=0) | |
| zq = ( | |
| torch.stack( | |
| [ | |
| self.expand_embedding[idx[..., group], group] | |
| for group in range(self.groups) | |
| ], | |
| dim=-2, | |
| ) | |
| .view(bsz, tsz, self.groups * self.var_dim) | |
| .permute(0, 2, 1) | |
| ) | |
| assert ze.shape == zq.shape, (ze.shape, zq.shape) | |
| x = self._pass_grad(ze, zq) | |
| with torch.no_grad(): | |
| hard_x = ( | |
| idx.new_zeros(bsz * tsz * self.groups, self.num_vars) | |
| .scatter_(-1, idx.view(-1, 1), 1.0) | |
| .view(bsz * tsz, self.groups, -1) | |
| ) | |
| hard_probs = torch.mean(hard_x.float(), dim=0) | |
| result["code_perplexity"] = torch.exp( | |
| -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) | |
| ).sum() | |
| if produce_targets: | |
| result["targets"] = idx | |
| if self.time_first: | |
| x = x.transpose(1, 2) # BCT -> BTC | |
| result["x"] = x | |
| ze = ze.float() | |
| zq = zq.float() | |
| latent_loss = self.mse_mean(zq, ze.detach()) | |
| commitment_loss = self.mse_mean(ze, zq.detach()) | |
| result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss | |
| return result | |
| def forward_group2(self, x, mask_mat=None, produce_targets=False, inf=999999): | |
| assert mask_mat is not None | |
| result = {"num_vars": self.num_vars} | |
| if self.time_first: | |
| x = x.transpose(1, 2) | |
| bsz, fsz, tsz = x.shape | |
| ze = self.projection(x) | |
| ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2) | |
| ze_0 = ze_[:, :, 0, None, :] | |
| ze_1 = ze_[:, :, 1, None, :] # 4 * 100 * 320 * 128 | |
| cb0_expand = self.expand_embedding[:, 0, :] | |
| cb1_expand = self.expand_embedding[:, 1, :] # 320 * 128 | |
| dist_0 = ((ze_0 - cb0_expand) ** 2).sum(dim=-1)[:, :, :, None] | |
| dist_1 = ((ze_1 - cb1_expand) ** 2).sum(dim=-1)[:, :, None, :] | |
| res_0, res_1 = torch.broadcast_tensors(dist_0, dist_1) | |
| mask_mat = (1 - mask_mat[None, None, :, :].to(res_0.device) * torch.ones_like(res_0)) * inf | |
| # mask_mat = mask_mat.to(x.device) | |
| d_flt = (res_0 + res_1 + mask_mat).view(bsz, tsz, -1) | |
| idx_flt = torch.argmin(d_flt, dim=-1) | |
| idx = torch.stack((idx_flt // self.num_vars, idx_flt % self.num_vars), dim=-1) | |
| zq = ( | |
| torch.stack( | |
| [ | |
| self.expand_embedding[idx[..., group], group] | |
| for group in range(self.groups) | |
| ], | |
| dim=-2, | |
| ) | |
| .view(bsz, tsz, self.groups * self.var_dim) | |
| .permute(0, 2, 1) | |
| ) | |
| assert ze.shape == zq.shape, (ze.shape, zq.shape) | |
| x = self._pass_grad(ze, zq) | |
| with torch.no_grad(): | |
| hard_x = ( | |
| idx.new_zeros(bsz * tsz * self.groups, self.num_vars) | |
| .scatter_(-1, idx.view(-1, 1), 1.0) | |
| .view(bsz * tsz, self.groups, -1) | |
| ) | |
| hard_probs = torch.mean(hard_x.float(), dim=0) | |
| result["code_perplexity"] = torch.exp( | |
| -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) | |
| ).sum() | |
| if produce_targets: | |
| result["targets"] = idx | |
| if self.time_first: | |
| x = x.transpose(1, 2) # BCT -> BTC | |
| result["x"] = x | |
| ze = ze.float() | |
| zq = zq.float() | |
| latent_loss = self.mse_mean(zq, ze.detach()) | |
| commitment_loss = self.mse_mean(ze, zq.detach()) | |
| result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss | |
| return result | |
| if __name__ == "__main__": | |
| quantizer = KmeansVectorQuantizer(dim=256, num_vars=320, groups=2, combine_groups=False, vq_dim=256, time_first=True) | |
| x = torch.ones(4, 100, 256) | |
| result = quantizer.forward_group2(x, mask_mat=torch.randint(0, 2, (320, 320))) | |
| print(result) | |