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Zero
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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
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