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from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from .. import SparseTensor
from .full_attn import sparse_scaled_dot_product_attention
from .serialized_attn import SerializeMode, sparse_serialized_scaled_dot_product_self_attention
from .windowed_attn import sparse_windowed_scaled_dot_product_self_attention
from ...attention import RotaryPositionEmbedder
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[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]:
x_type = x.dtype
x = x.float()
if isinstance(x, SparseTensor):
x = x.replace(F.normalize(x.feats, dim=-1))
else:
x = F.normalize(x, dim=-1)
return (x * self.gamma * self.scale).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", "serialized", "windowed"] = "full",
window_size: Optional[int] = None,
shift_sequence: Optional[int] = None,
shift_window: Optional[Tuple[int, int, int]] = None,
serialize_mode: Optional[SerializeMode] = 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", "serialized", "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"
self.channels = channels
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_sequence = shift_sequence
self.shift_window = shift_window
self.serialize_mode = serialize_mode
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(channels // num_heads, num_heads)
self.k_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads)
self.to_out = nn.Linear(channels, channels)
if use_rope:
self.rope = RotaryPositionEmbedder(channels)
@staticmethod
def _linear(module: nn.Linear, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]:
if isinstance(x, SparseTensor):
return x.replace(module(x.feats))
else:
return module(x)
@staticmethod
def _reshape_chs(x: Union[SparseTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[SparseTensor, torch.Tensor]:
if isinstance(x, SparseTensor):
return x.reshape(*shape)
else:
return x.reshape(*x.shape[:2], *shape)
def _fused_pre(self, x: Union[SparseTensor, torch.Tensor], num_fused: int) -> Union[SparseTensor, torch.Tensor]:
if isinstance(x, SparseTensor):
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, SparseTensor) else x_feats
def _rope(self, qkv: SparseTensor) -> SparseTensor:
q, k, v = qkv.feats.unbind(dim=1) # [T, H, C]
q, k = self.rope(q, k, qkv.coords[:, 1:])
qkv = qkv.replace(torch.stack([q, k, v], dim=1))
return qkv
def forward(self, x: Union[SparseTensor, torch.Tensor], context: Optional[Union[SparseTensor, torch.Tensor]] = None) -> Union[SparseTensor, torch.Tensor]:
if self._type == "self":
qkv = self._linear(self.to_qkv, x)
qkv = self._fused_pre(qkv, num_fused=3)
if self.use_rope:
qkv = self._rope(qkv)
if self.qk_rms_norm:
q, k, v = qkv.unbind(dim=1)
q = self.q_rms_norm(q)
k = self.k_rms_norm(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 == "serialized":
h = sparse_serialized_scaled_dot_product_self_attention(
qkv, self.window_size, serialize_mode=self.serialize_mode, shift_sequence=self.shift_sequence, shift_window=self.shift_window
)
elif self.attn_mode == "windowed":
h = sparse_windowed_scaled_dot_product_self_attention(
qkv, self.window_size, shift_window=self.shift_window
)
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=1)
k = self.k_rms_norm(k)
kv = kv.replace(torch.stack([k.feats, v.feats], dim=1))
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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