# -*- coding: utf-8 -*- # Copyright (c) 2024, Songlin Yang, Yu Zhang from __future__ import annotations import warnings from typing import TYPE_CHECKING, Optional, Tuple import torch import torch.nn as nn from einops import rearrange from fla.modules import (FusedRMSNormSwishGate, RMSNorm, RotaryEmbedding, ShortConvolution) from fla.modules.activations import swiglu, swish from fla.ops.abc.chunk import chunk_abc if TYPE_CHECKING: from fla.models.utils import Cache class ABCAttention(nn.Module): def __init__( self, hidden_size: int = 1024, expand_k: float = 0.5, expand_v: float = 1.0, num_heads: int = 4, use_short_conv: bool = False, conv_size: int = 4, conv_bias: bool = False, num_slots: Optional[int] = None, elementwise_affine: Optional[bool] = True, norm_eps: float = 1e-5, gate_low_rank_dim: int = 16, gate_logit_normalizer: int = 16, use_input_gate: bool = False, use_output_gate: bool = True, use_norm: bool = True, clamp_min: Optional[float] = -32, clamp_max: Optional[float] = 32, layer_idx: Optional[int] = None, **kwargs ) -> ABCAttention: super().__init__() self.hidden_size = hidden_size self.expand_k = expand_k self.expand_v = expand_v self.num_heads = num_heads self.key_dim = int(self.hidden_size * self.expand_k) self.value_dim = int(self.hidden_size * self.expand_v) self.head_k_dim = self.key_dim // self.num_heads self.head_v_dim = self.value_dim // self.num_heads self.use_short_conv = use_short_conv self.conv_size = conv_size self.conv_bias = conv_bias self.gate_low_rank_dim = gate_low_rank_dim self.gate_logit_normalizer = gate_logit_normalizer self.use_input_gate = use_input_gate self.use_output_gate = use_output_gate self.use_norm = use_norm if num_slots is None: num_slots = self.head_k_dim self.num_slots = num_slots self.norm_eps = norm_eps self.clamp_min = clamp_min self.clamp_max = clamp_max self.layer_idx = layer_idx if layer_idx is None: warnings.warn( f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) if use_output_gate: self.g_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) self.s_proj = nn.Linear(self.hidden_size, self.num_heads * self.num_slots, bias=False) self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) if use_short_conv: self.conv_size = conv_size self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu') self.k_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu') self.v_conv1d = ShortConvolution(self.value_dim, conv_size, activation='silu') if self.use_norm: if self.use_output_gate: self.g_norm = FusedRMSNormSwishGate(self.head_v_dim, elementwise_affine, norm_eps) else: self.g_norm = RMSNorm(hidden_size=self.head_v_dim, elementwise_affine=elementwise_affine, eps=norm_eps) if self.use_rope: self.rotary = RotaryEmbedding(self.head_k_dim) self.apply(self._initialize_weights) def _initialize_weights(self, module: nn.Module): if getattr(module, "_is_hf_initialized", False): return if isinstance(module, nn.Linear): nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5) if module.bias is not None: nn.init.zeros_(module.bias) module._is_hf_initialized = True def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, **kwargs ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: 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." ) last_state = None if past_key_values is not None and len(past_key_values) > self.layer_idx: last_state = past_key_values[self.layer_idx] if self.use_short_conv: conv_state_q, conv_state_k, conv_state_v = None, None, None if last_state is not None: conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None q, conv_state_q = self.q_conv1d(x=self.q_proj(hidden_states), mask=conv_mask, cache=conv_state_q, output_final_state=use_cache) k, conv_state_k = self.k_conv1d(x=self.k_proj(hidden_states), mask=conv_mask, cache=conv_state_k, output_final_state=use_cache) v, conv_state_v = self.v_conv1d(x=self.v_proj(hidden_states), mask=conv_mask, cache=conv_state_v, output_final_state=use_cache) else: q = self.q_proj(hidden_states) k = self.k_proj(hidden_states) v = self.v_proj(hidden_states) if self.use_input_gate: q, k, v = map(lambda x: swish(x), (q, k, v)) # dealing with left-padding if attention_mask is not None: v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) q, k, v = map(lambda x: rearrange(x, '... (h d) -> ... h d', h=self.num_heads), (q, k, v)) if self.use_rope: seqlen_offset = 0 if past_key_values is not None: seqlen_offset = past_key_values.get_seq_length(self.layer_idx) q, k = self.rotary(q, k, seqlen_offset) s = rearrange(self.s_proj(hidden_states), '... (h m) -> ... h m', h=self.num_heads) s = s.clamp_(self.clamp_min, self.clamp_max) recurrent_state = last_state['recurrent_state'] if last_state is not None else None o, recurrent_state = chunk_abc( q=q, k=k, v=v, s=s, initial_state=recurrent_state, output_final_state=use_cache, head_first=False ) if past_key_values is not None: past_key_values.update( recurrent_state=recurrent_state, conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, layer_idx=self.layer_idx, offset=q.shape[2] ) if self.use_norm and not self.use_output_gate: o = self.g_norm(o) elif self.use_output_gate: g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', h=self.num_heads) o = self.g_norm(o, g) if self.use_norm else swiglu(g, o) o = rearrange(o, '... h d -> ... (h d)') o = self.o_proj(o) return o, None, past_key_values def state_size(self, seq_len: int = 2048): return self.num_heads * self.key_dim * self.head_v_dim