| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torch.utils.checkpoint |
| import einops |
| from einops import rearrange, repeat |
| from inspect import isfunction |
| from .rotary import RotaryEmbedding |
|
|
|
|
| if hasattr(nn.functional, 'scaled_dot_product_attention'): |
| ATTENTION_MODE = 'flash' |
| else: |
| ATTENTION_MODE = 'math' |
| print(f'attention mode is {ATTENTION_MODE}') |
|
|
|
|
| def add_mask(sim, mask): |
| b, ndim = sim.shape[0], mask.ndim |
| if ndim == 3: |
| mask = rearrange(mask, "b n m -> b 1 n m") |
| if ndim == 2: |
| mask = repeat(mask, "n m -> b 1 n m", b=b) |
| max_neg_value = -torch.finfo(sim.dtype).max |
| sim = sim.masked_fill(~mask, max_neg_value) |
| return sim |
|
|
|
|
| def create_mask(q_shape, k_shape, device, q_mask=None, k_mask=None): |
| def default(val, d): |
| return val if val is not None else (d() if isfunction(d) else d) |
| b, i, j, device = q_shape[0], q_shape[-2], k_shape[-2], device |
| q_mask = default(q_mask, torch.ones((b, i), device=device, dtype=torch.bool)) |
| k_mask = default(k_mask, torch.ones((b, j), device=device, dtype=torch.bool)) |
| attn_mask = rearrange(q_mask, 'b i -> b 1 i 1') * rearrange(k_mask, 'b j -> b 1 1 j') |
| return attn_mask |
|
|
|
|
| class Attention(nn.Module): |
| def __init__(self, dim, context_dim=None, num_heads=8, |
| qkv_bias=False, qk_scale=None, qk_norm='layernorm', |
| attn_drop=0., proj_drop=0., rope_mode='shared'): |
| super().__init__() |
| self.num_heads = num_heads |
| head_dim = dim // num_heads |
| self.scale = qk_scale or head_dim ** -0.5 |
|
|
| if context_dim is None: |
| self.cross_attn = False |
| else: |
| self.cross_attn = True |
|
|
| context_dim = dim if context_dim is None else context_dim |
|
|
| self.to_q = nn.Linear(dim, dim, bias=qkv_bias) |
| self.to_k = nn.Linear(context_dim, dim, bias=qkv_bias) |
| self.to_v = nn.Linear(context_dim, dim, bias=qkv_bias) |
|
|
| if qk_norm is None: |
| self.norm_q = nn.Identity() |
| self.norm_k = nn.Identity() |
| elif qk_norm == 'layernorm': |
| self.norm_q = nn.LayerNorm(head_dim) |
| self.norm_k = nn.LayerNorm(head_dim) |
| else: |
| raise NotImplementedError |
|
|
| self.attn_drop_p = attn_drop |
| self.attn_drop = nn.Dropout(attn_drop) |
| self.proj = nn.Linear(dim, dim) |
| self.proj_drop = nn.Dropout(proj_drop) |
|
|
| if self.cross_attn: |
| assert rope_mode == 'none' |
| self.rope_mode = rope_mode |
| if self.rope_mode == 'shared' or self.rope_mode == 'x_only': |
| self.rotary = RotaryEmbedding(dim=head_dim) |
| elif self.rope_mode == 'dual': |
| self.rotary_x = RotaryEmbedding(dim=head_dim) |
| self.rotary_c = RotaryEmbedding(dim=head_dim) |
|
|
| def _rotary(self, q, k, extras): |
| if self.rope_mode == 'shared': |
| q, k = self.rotary(q=q, k=k) |
| elif self.rope_mode == 'x_only': |
| q_x, k_x = self.rotary(q=q[:, :, extras:, :], k=k[:, :, extras:, :]) |
| q_c, k_c = q[:, :, :extras, :], k[:, :, :extras, :] |
| q = torch.cat((q_c, q_x), dim=2) |
| k = torch.cat((k_c, k_x), dim=2) |
| elif self.rope_mode == 'dual': |
| q_x, k_x = self.rotary_x(q=q[:, :, extras:, :], k=k[:, :, extras:, :]) |
| q_c, k_c = self.rotary_c(q=q[:, :, :extras, :], k=k[:, :, :extras, :]) |
| q = torch.cat((q_c, q_x), dim=2) |
| k = torch.cat((k_c, k_x), dim=2) |
| elif self.rope_mode == 'none': |
| pass |
| else: |
| raise NotImplementedError |
| return q, k |
|
|
| def _attn(self, q, k, v, mask_binary): |
| if ATTENTION_MODE == 'flash': |
| x = F.scaled_dot_product_attention(q, k, v, |
| dropout_p=self.attn_drop_p, |
| attn_mask=mask_binary) |
| x = einops.rearrange(x, 'B H L D -> B L (H D)') |
| elif ATTENTION_MODE == 'math': |
| attn = (q @ k.transpose(-2, -1)) * self.scale |
| attn = add_mask(attn, mask_binary) if mask_binary is not None else attn |
| attn = attn.softmax(dim=-1) |
| attn = self.attn_drop(attn) |
| x = (attn @ v).transpose(1, 2) |
| x = einops.rearrange(x, 'B H L D -> B L (H D)') |
| else: |
| raise NotImplementedError |
| return x |
|
|
| def forward(self, x, context=None, context_mask=None, extras=0): |
| B, L, C = x.shape |
| if context is None: |
| context = x |
|
|
| q = self.to_q(x) |
| k = self.to_k(context) |
| v = self.to_v(context) |
|
|
| if context_mask is not None: |
| mask_binary = create_mask(x.shape, context.shape, |
| x.device, None, context_mask) |
| else: |
| mask_binary = None |
|
|
| q = einops.rearrange(q, 'B L (H D) -> B H L D', H=self.num_heads) |
| k = einops.rearrange(k, 'B L (H D) -> B H L D', H=self.num_heads) |
| v = einops.rearrange(v, 'B L (H D) -> B H L D', H=self.num_heads) |
|
|
| q = self.norm_q(q) |
| k = self.norm_k(k) |
|
|
| q, k = self._rotary(q, k, extras) |
|
|
| x = self._attn(q, k, v, mask_binary) |
|
|
| x = self.proj(x) |
| x = self.proj_drop(x) |
| return x |
