| import torch | |
| import torch.nn as nn | |
| 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, k, 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], q.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, | |
| attn_drop=0., proj_drop=0., use_rope=False): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| head_dim = dim // num_heads | |
| self.scale = qk_scale or head_dim ** -0.5 | |
| 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) | |
| 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) | |
| self.use_rope = use_rope | |
| if self.use_rope: | |
| self.rotary = RotaryEmbedding(dim=head_dim) | |
| def forward(self, x, context=None, context_mask=None): | |
| B, L, C = x.shape | |
| q = self.to_q(x) | |
| if context is None: | |
| context = x | |
| else: | |
| assert self.use_rope is False | |
| k = self.to_k(context) | |
| v = self.to_v(context) | |
| if context_mask is not None: | |
| mask_binary = create_mask(x, context, None, context_mask) | |
| else: | |
| mask_binary = None | |
| q = einops.rearrange(q, 'B L (H D) -> B H L D', H=self.num_heads).float() | |
| k = einops.rearrange(k, 'B L (H D) -> B H L D', H=self.num_heads).float() | |
| v = einops.rearrange(v, 'B L (H D) -> B H L D', H=self.num_heads).float() | |
| if self.use_rope: | |
| q, k = self.rotary(q=q, k=k) | |
| if ATTENTION_MODE == 'flash': | |
| x = torch.nn.functional.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).reshape(B, L, C) | |
| else: | |
| raise NotImplementedError | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x |