| from typing import * |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from .full_attn import scaled_dot_product_attention |
|
|
|
|
| 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 RotaryPositionEmbedder(nn.Module): |
| def __init__(self, hidden_size: int, in_channels: int = 3): |
| super().__init__() |
| assert hidden_size % 2 == 0, "Hidden size must be divisible by 2" |
| self.hidden_size = hidden_size |
| self.in_channels = in_channels |
| self.freq_dim = hidden_size // in_channels // 2 |
| self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim |
| self.freqs = 1.0 / (10000 ** self.freqs) |
| |
| def _get_phases(self, indices: torch.Tensor) -> torch.Tensor: |
| self.freqs = self.freqs.to(indices.device) |
| phases = torch.outer(indices, self.freqs) |
| phases = torch.polar(torch.ones_like(phases), phases) |
| return phases |
| |
| def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor: |
| x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) |
| x_rotated = x_complex * phases |
| x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype) |
| return x_embed |
| |
| def forward(self, q: torch.Tensor, k: torch.Tensor, indices: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: |
| """ |
| Args: |
| q (sp.SparseTensor): [..., N, D] tensor of queries |
| k (sp.SparseTensor): [..., N, D] tensor of keys |
| indices (torch.Tensor): [..., N, C] tensor of spatial positions |
| """ |
| if indices is None: |
| indices = torch.arange(q.shape[-2], device=q.device) |
| if len(q.shape) > 2: |
| indices = indices.unsqueeze(0).expand(q.shape[:-2] + (-1,)) |
| |
| phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1) |
| if phases.shape[1] < self.hidden_size // 2: |
| phases = torch.cat([phases, torch.polar( |
| torch.ones(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device), |
| torch.zeros(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device) |
| )], dim=-1) |
| q_embed = self._rotary_embedding(q, phases) |
| k_embed = self._rotary_embedding(k, phases) |
| return q_embed, k_embed |
| |
|
|
| 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, |
| 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) |
|
|
| if use_rope: |
| self.rope = RotaryPositionEmbedder(channels) |
| |
| def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, indices: 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.use_rope: |
| q, k, v = qkv.unbind(dim=2) |
| q, k = self.rope(q, k, indices) |
| qkv = torch.stack([q, k, v], dim=2) |
| if self.attn_mode == "full": |
| if self.qk_rms_norm: |
| q, k, v = qkv.unbind(dim=2) |
| q = self.q_rms_norm(q) |
| k = self.k_rms_norm(k) |
| 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 |
|
|
