| |
|
|
| from __future__ import annotations |
|
|
| import warnings |
| from typing import Optional, Tuple |
|
|
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from transformers.cache_utils import Cache |
|
|
| from fla.modules import (FusedRMSNormSwishGate, RMSNorm, RotaryEmbedding, |
| ShortConvolution) |
| from fla.modules.activations import swiglu, swish |
| from fla.modules.convolution import proj_then_conv1d |
| from fla.ops.abc.chunk import chunk_abc |
|
|
|
|
| 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, |
| share_conv_kernel: bool = True, |
| 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.share_conv_kernel = share_conv_kernel |
|
|
| 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 |
| 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, 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(self.head_v_dim, elementwise_affine, 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 self.use_short_conv: |
| if self.share_conv_kernel: |
| hidden_states = self.h_conv1d(hidden_states) |
| q = self.q_proj(hidden_states) |
| k = self.k_proj(hidden_states) |
| v = self.v_proj(hidden_states) |
| else: |
| q = proj_then_conv1d(hidden_states, self.q_proj.weight, self.q_conv1d.weight, self.q_conv1d.bias) |
| k = proj_then_conv1d(hidden_states, self.k_proj.weight, self.k_conv1d.weight, self.k_conv1d.bias) |
| v = proj_then_conv1d(hidden_states, self.v_proj.weight, self.v_conv1d.weight, self.v_conv1d.bias) |
| 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 self.use_rope: |
| q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads) |
| k = rearrange(k, '... (h d) -> ... h d', h=self.num_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_heads) |
| else: |
| 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_heads) |
| v = rearrange(v, 'b n (h d) -> b h n d', h=self.num_heads) |
|
|
| |
| s = rearrange(self.s_proj(hidden_states), 'b t (h m) -> b h t m', h=self.num_heads) |
| s = s.clamp_(self.clamp_min, self.clamp_max) |
|
|
| last_state = past_key_values[self.layer_idx] if use_cache else None |
| o, last_state = chunk_abc(q, k, v, s, initial_state=last_state, output_final_state=use_cache) |
| if past_key_values is not None and last_state is not None: |
| 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 = self.g_norm(o) |
| elif self.use_output_gate: |
| g = rearrange(self.g_proj(hidden_states), 'b t (h d) -> b t h d', h=self.num_heads) |
| o = self.g_norm(o, g) if self.use_norm else swiglu(g, o) |
| o = rearrange(o, 'b t h d -> b t (h d)') |
| 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: |
| state += (param.new_zeros(batch_size, self.hidden_size, 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 |
|
|
