| from typing import * |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from .full_attn import scaled_dot_product_attention |
| from .rope import RotaryPositionEmbedder |
|
|
|
|
| class MultiHeadRMSNorm(nn.Module): |
| def __init__(self, dim: int, heads: int): |
| super().__init__() |
| self.scale = dim ** 0.5 |
| self.gamma = nn.Parameter(torch.ones(heads, dim)) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| return (F.normalize(x.float(), dim = -1) * self.gamma * self.scale).to(x.dtype) |
| |
|
|
| class MultiHeadAttention(nn.Module): |
| def __init__( |
| self, |
| channels: int, |
| num_heads: int, |
| ctx_channels: Optional[int]=None, |
| type: Literal["self", "cross"] = "self", |
| attn_mode: Literal["full", "windowed"] = "full", |
| window_size: Optional[int] = None, |
| shift_window: Optional[Tuple[int, int, int]] = None, |
| qkv_bias: bool = True, |
| use_rope: bool = False, |
| rope_freq: Tuple[float, float] = (1.0, 10000.0), |
| qk_rms_norm: bool = False, |
| ): |
| super().__init__() |
| assert channels % num_heads == 0 |
| assert type in ["self", "cross"], f"Invalid attention type: {type}" |
| assert attn_mode in ["full", "windowed"], f"Invalid attention mode: {attn_mode}" |
| assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" |
| |
| if attn_mode == "windowed": |
| raise NotImplementedError("Windowed attention is not yet implemented") |
| |
| self.channels = channels |
| self.head_dim = channels // num_heads |
| self.ctx_channels = ctx_channels if ctx_channels is not None else channels |
| self.num_heads = num_heads |
| self._type = type |
| self.attn_mode = attn_mode |
| self.window_size = window_size |
| self.shift_window = shift_window |
| self.use_rope = use_rope |
| self.qk_rms_norm = qk_rms_norm |
|
|
| if self._type == "self": |
| self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias) |
| else: |
| self.to_q = nn.Linear(channels, channels, bias=qkv_bias) |
| self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias) |
| |
| if self.qk_rms_norm: |
| self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) |
| self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads) |
| |
| self.to_out = nn.Linear(channels, channels) |
| |
| def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, phases: Optional[torch.Tensor] = None) -> torch.Tensor: |
| B, L, C = x.shape |
| if self._type == "self": |
| qkv = self.to_qkv(x) |
| qkv = qkv.reshape(B, L, 3, self.num_heads, -1) |
| |
| if self.attn_mode == "full": |
| if self.qk_rms_norm or self.use_rope: |
| q, k, v = qkv.unbind(dim=2) |
| if self.qk_rms_norm: |
| q = self.q_rms_norm(q) |
| k = self.k_rms_norm(k) |
| if self.use_rope: |
| assert phases is not None, "Phases must be provided for RoPE" |
| q = RotaryPositionEmbedder.apply_rotary_embedding(q, phases) |
| k = RotaryPositionEmbedder.apply_rotary_embedding(k, phases) |
| h = scaled_dot_product_attention(q, k, v) |
| else: |
| h = scaled_dot_product_attention(qkv) |
| elif self.attn_mode == "windowed": |
| raise NotImplementedError("Windowed attention is not yet implemented") |
| else: |
| Lkv = context.shape[1] |
| q = self.to_q(x) |
| kv = self.to_kv(context) |
| q = q.reshape(B, L, self.num_heads, -1) |
| kv = kv.reshape(B, Lkv, 2, self.num_heads, -1) |
| if self.qk_rms_norm: |
| q = self.q_rms_norm(q) |
| k, v = kv.unbind(dim=2) |
| k = self.k_rms_norm(k) |
| h = scaled_dot_product_attention(q, k, v) |
| else: |
| h = scaled_dot_product_attention(q, kv) |
| h = h.reshape(B, L, -1) |
| h = self.to_out(h) |
| return h |
|
|
