| |
| |
|
|
| from __future__ import annotations |
|
|
| from typing import TYPE_CHECKING, Optional, Tuple |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torch.utils.checkpoint |
| from einops import rearrange |
| from transformers.utils import logging |
|
|
| from fla.modules import GroupNorm |
| from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn |
|
|
| if TYPE_CHECKING: |
| from fla.models.utils import Cache |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class ForgettingAttention(nn.Module): |
|
|
| def __init__( |
| self, |
| hidden_size: int = 2048, |
| num_heads: int = 32, |
| num_kv_heads: Optional[int] = None, |
| qkv_bias: bool = False, |
| qk_norm: bool = False, |
| window_size: Optional[int] = None, |
| use_output_gate: bool = False, |
| layer_idx: int = None |
| ): |
| super().__init__() |
|
|
| self.hidden_size = hidden_size |
| self.num_heads = num_heads |
| if num_kv_heads is None: |
| self.num_kv_heads = self.num_heads |
| else: |
| self.num_kv_heads = num_kv_heads |
| self.num_kv_groups = num_heads // self.num_kv_heads |
| self.head_dim = self.hidden_size // self.num_heads |
| self.kv_dim = self.num_kv_heads * self.head_dim |
| self.qkv_bias = qkv_bias |
| self.qk_norm = qk_norm |
|
|
| self.window_size = window_size |
| self.use_output_gate = use_output_gate |
| self.layer_idx = layer_idx |
|
|
| self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.qkv_bias) |
| self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) |
| self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) |
| self.f_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True) |
|
|
| if use_output_gate: |
| self.g_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) |
| self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) |
|
|
| if qk_norm: |
| self.q_norm = GroupNorm( |
| num_groups=self.num_heads, |
| hidden_size=self.hidden_size, |
| is_rms_norm=True, |
| ) |
| self.k_norm = GroupNorm( |
| num_groups=self.num_kv_heads, |
| hidden_size=self.kv_dim, |
| is_rms_norm=True, |
| ) |
|
|
| def forward( |
| self, |
| hidden_states: torch.Tensor, |
| attention_mask: Optional[torch.LongTensor] = None, |
| past_key_values: Optional[Cache] = None, |
| output_attentions: bool = False, |
| use_cache: bool = False, |
| **kwargs, |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| if attention_mask is not None: |
| assert len(attention_mask.shape) == 2, ( |
| "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " |
| "for padding purposes (0 indicating padding). " |
| "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." |
| ) |
|
|
| cu_seqlens = kwargs.get('cu_seqlens', None) |
| q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states) |
| f = F.logsigmoid(self.f_proj(hidden_states).float()) |
| if self.qk_norm: |
| q, k = self.q_norm(q), self.k_norm(k) |
|
|
| q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim) |
| k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) |
| v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) |
|
|
| o = parallel_forgetting_attn(q, k, v, f, cu_seqlens=cu_seqlens) |
| o = rearrange(o, '... h d -> ... (h d)') |
| if self.use_output_gate: |
| o = self.g_proj(hidden_states).sigmoid() * o |
| o = self.o_proj(o) |
|
|
| return o, None, past_key_values |
|
|
