wehub-resource-sync
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
import torch
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from torch import nn
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from typing import Optional, Tuple
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from ..kernel.rotary import apply_rotary_emb
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from flash_attn import flash_attn_func
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@torch.compile
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def diff_func(attn1: torch.Tensor, attn2: torch.Tensor, lambda_val: torch.Tensor) -> torch.Tensor:
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return attn1 - torch.sigmoid(lambda_val).unsqueeze(-1) * attn2
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class MultiheadFlashDiffV2(nn.Module):
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"""
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Differential Attention Version 2 (DiffAttnV2) implementation using Flash Attention.
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"""
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def __init__(
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self,
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use_diff_v2: bool, # If False, acts as a baseline Transformer attention
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d_model: int, # Model dimension
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num_heads: int, # Number of output heads
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num_kv_heads: Optional[int], # Number of KV heads for GQA
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head_dim: int, # Dimension per head
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):
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super().__init__()
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self.use_diff_v2 = use_diff_v2
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self.d_model = d_model
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
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self.head_dim = head_dim
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self.num_q_heads = 2 * self.num_heads if self.use_diff_v2 else self.num_heads
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self.q_proj = nn.Linear(self.d_model, self.num_q_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.d_model, self.num_kv_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.d_model, self.num_kv_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.d_model, bias=False)
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self.lambda_proj = nn.Linear(self.d_model, self.num_heads, bias=False) if self.use_diff_v2 else None
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def forward(
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self,
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x: torch.Tensor, # Input tensor [bsz, seq_len, d_model]
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rel_pos: Tuple[torch.Tensor, torch.Tensor], # Rotary embedding (cos, sin)
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) -> torch.Tensor:
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"""
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Forward pass for MultiheadFlashDiffV2.
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Args:
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x: Input hidden states of shape [batch, length, d_model]
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rel_pos: Tuple of (cos, sin) tensors for rotary positional embeddings
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Returns:
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Output tensor of shape [batch, length, d_model]
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"""
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bsz, tgt_len, _ = x.size()
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src_len = tgt_len
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q = self.q_proj(x)
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k = self.k_proj(x)
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v = self.v_proj(x)
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q = q.view(bsz, tgt_len, self.num_q_heads, self.head_dim)
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k = k.view(bsz, src_len, self.num_kv_heads, self.head_dim)
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v = v.view(bsz, src_len, self.num_kv_heads, self.head_dim)
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q = apply_rotary_emb(q, *rel_pos, interleaved=True)
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k = apply_rotary_emb(k, *rel_pos, interleaved=True)
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attn = flash_attn_func(q, k, v, causal=True)
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if self.use_diff_v2:
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lambda_val = self.lambda_proj(x)
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attn1, attn2 = attn[:, :, 0::2], attn[:, :, 1::2]
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attn = diff_func(attn1, attn2, lambda_val)
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attn = attn.reshape(bsz, tgt_len, self.num_heads * self.head_dim)
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output = self.o_proj(attn)
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return output
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