# -*- coding: utf-8 -*- from __future__ import annotations import warnings from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from transformers.cache_utils import Cache from fla.modules import (FusedRMSNormSwishGateLinear, RMSNormLinear, RotaryEmbedding, ShortConvolution) from fla.modules.activations import ACT2FN, swiglu_linear, swish from fla.ops.abc.chunk_gate import chunk_gated_abc class GatedABCAttention(nn.Module): def __init__( self, hidden_size: int = 1024, expand_k: float = 1., expand_v: float = 1., num_heads: int = 4, num_kv_heads: Optional[int] = None, use_short_conv: bool = False, conv_size: int = 4, conv_bias: bool = False, share_conv_kernel: bool = True, num_slots: Optional[int] = None, elementwise_affine: Optional[bool] = True, norm_eps: float = 1e-5, gate_low_rank_dim: Optional[int] = None, gate_logit_normalizer: int = 16, feature_map: str = 'swish', use_rope: bool = False, use_output_gate: bool = False, use_norm: bool = True, layer_idx: Optional[int] = None, **kwargs ) -> GatedABCAttention: super().__init__() self.hidden_size = hidden_size self.expand_k = expand_k self.expand_v = expand_v self.num_heads = num_heads self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads self.num_kv_groups = self.num_heads // self.num_kv_heads self.key_dim = int(hidden_size * expand_k) self.value_dim = int(hidden_size * expand_v) self.key_dim_per_group = self.key_dim // self.num_kv_groups self.value_dim_per_group = self.value_dim // self.num_kv_groups 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.share_conv_kernel = share_conv_kernel if gate_low_rank_dim is None: gate_low_rank_dim = self.hidden_size // 16 self.gate_low_rank_dim = gate_low_rank_dim self.gate_logit_normalizer = gate_logit_normalizer self.feature_map = feature_map self.use_rope = use_rope 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.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_per_group, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.value_dim_per_group, bias=False) self.f_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.num_slots, bias=False) if use_output_gate: self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) if use_short_conv: self.conv_size = conv_size if share_conv_kernel: self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation='silu') else: self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu') self.k_conv1d = ShortConvolution(self.key_dim_per_group, conv_size, activation='silu') self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu') if self.use_norm: if self.use_output_gate: self.g_norm = FusedRMSNormSwishGateLinear(self.hidden_size, elementwise_affine, norm_eps) else: self.g_norm = RMSNormLinear(self.hidden_size, elementwise_affine, norm_eps) self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) 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, lower_bound: Optional[torch.Tensor] = None, **kwargs ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: last_state = past_key_values[self.layer_idx] if use_cache else None if self.use_short_conv: conv_state = last_state[0] if use_cache else None if self.share_conv_kernel: # conv state is updated inplace hidden_states = self.h_conv1d(hidden_states, attention_mask, conv_state) q = self.q_proj(hidden_states) k = self.k_proj(hidden_states) v = self.v_proj(hidden_states) else: conv_state_q = last_state[0] if use_cache else None conv_state_k = last_state[1] if use_cache else None conv_state_v = last_state[2] if use_cache else None q = self.q_proj(hidden_states) k = self.k_proj(hidden_states) v = self.v_proj(hidden_states) q = self.q_conv1d(q, attention_mask, conv_state_q) k = self.k_conv1d(k, attention_mask, conv_state_k) v = self.v_conv1d(v, attention_mask, conv_state_v) else: q = self.q_proj(hidden_states) k = self.k_proj(hidden_states) v = self.v_proj(hidden_states) f = self.f_proj(hidden_states) if self.use_rope: q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads) k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads) 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) q = rearrange(q, 'b n h d -> b h n d', h=self.num_heads) k = rearrange(k, 'b n h d -> b h n d', h=self.num_kv_heads) else: q = rearrange(q, 'b n (h d) -> b h n d', h=self.num_heads) if self.num_kv_groups > 1: k = repeat(k, 'b n (h d) -> b (h g) n d', h=self.num_kv_heads, g=self.num_kv_groups) else: k = rearrange(k, 'b n (h d) -> b h n d', h=self.num_kv_heads) if self.num_kv_groups > 1: v = repeat(v, 'b n (h d) -> b (h g) n d', h=self.num_kv_heads, g=self.num_kv_groups) f = repeat(f, 'b n (h m) -> b (h g) n m', h=self.num_kv_heads, g=self.num_kv_groups) else: v = rearrange(v, 'b n (h d) -> b h n d', h=self.num_kv_heads) f = rearrange(f, 'b n (h m) -> b h n m', h=self.num_kv_heads) if self.feature_map is not None: q, k, v = map(lambda x: ACT2FN[self.feature_map](x), (q, k, v)) f = F.logsigmoid(f) / self.gate_logit_normalizer s = (1 - f.exp()).to(f.dtype) # dealing with left-padding if attention_mask is not None: s = s.mul_(attention_mask.view(attention_mask.shape[0], 1, -1, 1)) v = v.mul_(attention_mask.view(attention_mask.shape[0], 1, -1, 1)) recurrent_state = last_state[-2:] if use_cache else None o, recurrent_state = chunk_gated_abc(q, k, v, s, f, initial_state=recurrent_state, output_final_state=use_cache) if past_key_values is not None: if self.use_short_conv: if self.share_conv_kernel: last_state = (conv_state,) + recurrent_state else: last_state = (conv_state_q, conv_state_k, conv_state_v) + recurrent_state else: last_state = recurrent_state past_key_values.update(last_state, self.layer_idx, q.shape[2]) o = rearrange(o, 'b h t d -> b t (h d)') if self.use_norm and not self.use_output_gate: o = swish(o) o = self.g_norm(o, self.o_proj.weight, self.o_proj.bias) elif self.use_output_gate and not self.use_norm: o = swiglu_linear(self.g_proj(hidden_states), o, self.o_proj.weight, self.o_proj.bias) elif self.use_output_gate and self.use_norm: o = self.g_norm(o, self.g_proj(hidden_states), self.o_proj.weight, self.o_proj.bias) else: o = self.o_proj(o) return o, None, past_key_values def init_state(self, batch_size: int) -> Tuple[torch.Tensor]: param = next(self.parameters()) state = tuple() if self.use_short_conv: if self.share_conv_kernel: state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),) else: state += (param.new_zeros(batch_size, self.key_dim, self.conv_size), param.new_zeros(batch_size, self.key_dim, self.conv_size), param.new_zeros(batch_size, self.value_dim, self.conv_size)) state += (param.new_zeros(batch_size, self.num_heads, self.head_k_dim, self.num_slots), param.new_zeros(batch_size, self.num_heads, self.num_slots, self.head_v_dim)) return state def state_size(self, sequence_length: int = 2048): return self.num_heads * self.key_dim * self.head_v_dim