| from typing import * |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from .. import VarLenTensor, SparseTensor |
| from .full_attn import sparse_scaled_dot_product_attention |
| from .windowed_attn import sparse_windowed_scaled_dot_product_self_attention |
| from .rope import SparseRotaryPositionEmbedder |
|
|
|
|
| class SparseMultiHeadRMSNorm(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: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]: |
| x_type = x.dtype |
| x = x.float() |
| if isinstance(x, VarLenTensor): |
| x = x.replace(F.normalize(x.feats, dim=-1) * self.gamma * self.scale) |
| else: |
| x = F.normalize(x, dim=-1) * self.gamma * self.scale |
| return x.to(x_type) |
|
|
|
|
| class SparseMultiHeadAttention(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", "double_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[int, int] = (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", "double_windowed"], f"Invalid attention mode: {attn_mode}" |
| assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention" |
| assert type == "self" or use_rope is False, "Rotary position embeddings only supported for self-attention" |
| if attn_mode == 'double_windowed': |
| assert window_size % 2 == 0, "Window size must be even for double windowed attention" |
| assert num_heads % 2 == 0, "Number of heads must be even for double windowed attention" |
| 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 = SparseMultiHeadRMSNorm(self.head_dim, num_heads) |
| self.k_rms_norm = SparseMultiHeadRMSNorm(self.head_dim, num_heads) |
| |
| self.to_out = nn.Linear(channels, channels) |
|
|
| if use_rope: |
| self.rope = SparseRotaryPositionEmbedder(self.head_dim, rope_freq=rope_freq) |
|
|
| @staticmethod |
| def _linear(module: nn.Linear, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]: |
| if isinstance(x, VarLenTensor): |
| return x.replace(module(x.feats)) |
| else: |
| return module(x) |
|
|
| @staticmethod |
| def _reshape_chs(x: Union[VarLenTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[VarLenTensor, torch.Tensor]: |
| if isinstance(x, VarLenTensor): |
| return x.reshape(*shape) |
| else: |
| return x.reshape(*x.shape[:2], *shape) |
|
|
| def _fused_pre(self, x: Union[VarLenTensor, torch.Tensor], num_fused: int) -> Union[VarLenTensor, torch.Tensor]: |
| if isinstance(x, VarLenTensor): |
| x_feats = x.feats.unsqueeze(0) |
| else: |
| x_feats = x |
| x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1) |
| return x.replace(x_feats.squeeze(0)) if isinstance(x, VarLenTensor) else x_feats |
| |
| def forward(self, x: SparseTensor, context: Optional[Union[VarLenTensor, torch.Tensor]] = None) -> SparseTensor: |
| if self._type == "self": |
| qkv = self._linear(self.to_qkv, x) |
| qkv = self._fused_pre(qkv, num_fused=3) |
| if self.qk_rms_norm or self.use_rope: |
| q, k, v = qkv.unbind(dim=-3) |
| if self.qk_rms_norm: |
| q = self.q_rms_norm(q) |
| k = self.k_rms_norm(k) |
| if self.use_rope: |
| q, k = self.rope(q, k) |
| qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1)) |
| if self.attn_mode == "full": |
| h = sparse_scaled_dot_product_attention(qkv) |
| elif self.attn_mode == "windowed": |
| h = sparse_windowed_scaled_dot_product_self_attention( |
| qkv, self.window_size, shift_window=self.shift_window |
| ) |
| elif self.attn_mode == "double_windowed": |
| qkv0 = qkv.replace(qkv.feats[:, :, self.num_heads//2:]) |
| qkv1 = qkv.replace(qkv.feats[:, :, :self.num_heads//2]) |
| h0 = sparse_windowed_scaled_dot_product_self_attention( |
| qkv0, self.window_size, shift_window=(0, 0, 0) |
| ) |
| h1 = sparse_windowed_scaled_dot_product_self_attention( |
| qkv1, self.window_size, shift_window=tuple([self.window_size//2] * 3) |
| ) |
| h = qkv.replace(torch.cat([h0.feats, h1.feats], dim=1)) |
| else: |
| q = self._linear(self.to_q, x) |
| q = self._reshape_chs(q, (self.num_heads, -1)) |
| kv = self._linear(self.to_kv, context) |
| kv = self._fused_pre(kv, num_fused=2) |
| if self.qk_rms_norm: |
| q = self.q_rms_norm(q) |
| k, v = kv.unbind(dim=-3) |
| k = self.k_rms_norm(k) |
| h = sparse_scaled_dot_product_attention(q, k, v) |
| else: |
| h = sparse_scaled_dot_product_attention(q, kv) |
| h = self._reshape_chs(h, (-1,)) |
| h = self._linear(self.to_out, h) |
| return h |
|
|
