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