| |
| |
|
|
| 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 FusedRMSNormGated, 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_rope: bool = True, |
| 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_rope = use_rope |
| 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 = FusedRMSNormGated( |
| hidden_size=self.head_v_dim, |
| elementwise_affine=elementwise_affine, |
| eps=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) |
|
|
| 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] |
|
|
| cu_seqlens = kwargs.get('cu_seqlens', None) |
| if cu_seqlens is not None: |
| raise NotImplementedError("Training with cu_seqlens is not supported yet for ABCAttention") |
| 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, |
| cu_seqlens=cu_seqlens |
| ) |
| 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, |
| cu_seqlens=cu_seqlens |
| ) |
| 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, |
| cu_seqlens=cu_seqlens |
| ) |
| 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)) |
| |
| if attention_mask is not None: |
| v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) |
|
|
| q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) |
| v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) |
| 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=seqlen_offset) |
|
|
| s = rearrange(self.s_proj(hidden_states), '... (h m) -> ... h m', m=self.num_slots) |
| 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, |
| ) |
| 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[1] |
| ) |
|
|
| 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', d=self.head_v_dim) |
| 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 2 * self.num_slots * self.hidden_size |
|
|
