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Running on Zero
Running on Zero
| import torch | |
| from torch import Tensor | |
| from torch import nn | |
| import torch.nn.functional as F | |
| XFORMERS_AVAILABLE = False | |
| class Attention(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| num_heads: int = 8, | |
| qkv_bias: bool = True, | |
| proj_bias: bool = True, | |
| attn_drop: float = 0.0, | |
| proj_drop: float = 0.0, | |
| norm_layer: nn.Module = nn.LayerNorm, | |
| qk_norm: bool = False, | |
| fused_attn: bool = True, | |
| rope=None, | |
| ) -> None: | |
| super().__init__() | |
| assert dim % num_heads == 0, "dim should be divisible by num_heads" | |
| self.num_heads = num_heads | |
| self.head_dim = dim // num_heads | |
| self.scale = self.head_dim ** -0.5 | |
| self.fused_attn = fused_attn | |
| self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
| self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() | |
| self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity() | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.proj = nn.Linear(dim, dim, bias=proj_bias) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| self.rope = rope | |
| def forward(self, x: Tensor, pos=None, attn_mask=None, kv_cache=None) -> Tensor: | |
| B, N, C = x.shape | |
| qkv = self.qkv(x) | |
| qkv = qkv.reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) | |
| q, k, v = qkv.unbind(0) | |
| q, k = self.q_norm(q), self.k_norm(k) | |
| if self.rope is not None: | |
| q = self.rope(q, pos) | |
| k = self.rope(k, pos) | |
| if kv_cache is not None: | |
| k_cache, v_cache = kv_cache | |
| if k_cache is not None and v_cache is not None: | |
| k = torch.cat([k_cache, k], dim=2) | |
| v = torch.cat([v_cache, v], dim=2) | |
| kv_cache = [k, v] | |
| if self.fused_attn: | |
| q = q.contiguous() | |
| k = k.contiguous() | |
| v = v.contiguous() | |
| if attn_mask is not None: | |
| attn_mask = attn_mask.contiguous() | |
| x = F.scaled_dot_product_attention( | |
| q, | |
| k, | |
| v, | |
| dropout_p=self.attn_drop.p if self.training else 0.0, | |
| attn_mask=attn_mask, | |
| ) | |
| else: | |
| q = q * self.scale | |
| attn = q @ k.transpose(-2, -1) | |
| if attn_mask is not None: | |
| attn = attn + attn_mask | |
| attn = attn.softmax(dim=-1) | |
| attn = self.attn_drop(attn) | |
| x = attn @ v | |
| x = x.transpose(1, 2).reshape(B, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| if kv_cache is not None: | |
| return x, kv_cache | |
| return x | |
| class MemEffAttention(Attention): | |
| def forward(self, x: Tensor, attn_bias=None, pos=None) -> Tensor: | |
| assert pos is None | |
| if not XFORMERS_AVAILABLE: | |
| if attn_bias is not None: | |
| raise AssertionError("xFormers is required for using nested tensors") | |
| return super().forward(x) | |
| B, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) | |
| q, k, v = unbind(qkv, 2) | |
| x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) | |
| x = x.reshape([B, N, C]) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |