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Running on Zero
Running on Zero
| import math | |
| import torch | |
| import torch.nn as nn | |
| from indextts.utils.xtransformers import RelativePositionBias | |
| def zero_module(module): | |
| """ | |
| Zero out the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().zero_() | |
| return module | |
| class GroupNorm32(nn.GroupNorm): | |
| def forward(self, x): | |
| return super().forward(x.float()).type(x.dtype) | |
| def normalization(channels): | |
| """ | |
| Make a standard normalization layer. | |
| :param channels: number of input channels. | |
| :return: an nn.Module for normalization. | |
| """ | |
| groups = 32 | |
| if channels <= 16: | |
| groups = 8 | |
| elif channels <= 64: | |
| groups = 16 | |
| while channels % groups != 0: | |
| groups = int(groups / 2) | |
| assert groups > 2 | |
| return GroupNorm32(groups, channels) | |
| class QKVAttentionLegacy(nn.Module): | |
| """ | |
| A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping | |
| """ | |
| def __init__(self, n_heads): | |
| super().__init__() | |
| self.n_heads = n_heads | |
| def forward(self, qkv, mask=None, rel_pos=None): | |
| """ | |
| Apply QKV attention. | |
| :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. | |
| :return: an [N x (H * C) x T] tensor after attention. | |
| """ | |
| bs, width, length = qkv.shape | |
| assert width % (3 * self.n_heads) == 0 | |
| ch = width // (3 * self.n_heads) | |
| q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) | |
| scale = 1 / math.sqrt(math.sqrt(ch)) | |
| weight = torch.einsum( | |
| "bct,bcs->bts", q * scale, k * scale | |
| ) # More stable with f16 than dividing afterwards | |
| if rel_pos is not None: | |
| weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1]) | |
| weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) | |
| if mask is not None: | |
| # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs. | |
| mask = mask.repeat(self.n_heads, 1).unsqueeze(1) | |
| weight = weight * mask | |
| a = torch.einsum("bts,bcs->bct", weight, v) | |
| return a.reshape(bs, -1, length) | |
| class AttentionBlock(nn.Module): | |
| """ | |
| An attention block that allows spatial positions to attend to each other. | |
| Originally ported from here, but adapted to the N-d case. | |
| https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. | |
| """ | |
| def __init__( | |
| self, | |
| channels, | |
| num_heads=1, | |
| num_head_channels=-1, | |
| do_checkpoint=True, | |
| relative_pos_embeddings=False, | |
| ): | |
| super().__init__() | |
| self.channels = channels | |
| self.do_checkpoint = do_checkpoint | |
| if num_head_channels == -1: | |
| self.num_heads = num_heads | |
| else: | |
| assert ( | |
| channels % num_head_channels == 0 | |
| ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" | |
| self.num_heads = channels // num_head_channels | |
| self.norm = normalization(channels) | |
| self.qkv = nn.Conv1d(channels, channels * 3, 1) | |
| # split heads before split qkv | |
| self.attention = QKVAttentionLegacy(self.num_heads) | |
| self.proj_out = zero_module(nn.Conv1d(channels, channels, 1)) | |
| if relative_pos_embeddings: | |
| self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64) | |
| else: | |
| self.relative_pos_embeddings = None | |
| def forward(self, x, mask=None): | |
| b, c, *spatial = x.shape | |
| x = x.reshape(b, c, -1) | |
| qkv = self.qkv(self.norm(x)) | |
| h = self.attention(qkv, mask, self.relative_pos_embeddings) | |
| h = self.proj_out(h) | |
| return (x + h).reshape(b, c, *spatial) | |