# -*- 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 import torch.nn.functional as F from einops import rearrange from fla.modules import RMSNorm, ShortConvolution from fla.modules.activations import swish from fla.modules.feature_map import (ReLUFeatureMap, SwishFeatureMap, T2RFeatureMap) from fla.modules.layernorm import rms_norm_linear from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa if TYPE_CHECKING: from fla.models.utils import Cache class GatedSlotAttention(nn.Module): def __init__( self, mode: str = 'chunk', 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, num_slots: Optional[int] = None, elementwise_affine: Optional[bool] = True, norm_first: bool = True, norm_eps: float = 1e-5, gate_logit_normalizer: int = 8, feature_map: str = 'swish', use_output_gate: bool = False, use_norm: bool = True, layer_idx: Optional[int] = None, scale: Optional[float] = 1., **kwargs ) -> GatedSlotAttention: super().__init__() self.mode = mode 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.gate_logit_normalizer = gate_logit_normalizer self.use_output_gate = use_output_gate self.use_norm = use_norm self.scale = scale if num_slots is None: num_slots = self.head_k_dim self.num_slots = num_slots self.norm_first = norm_first 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." ) if norm_first: self.norm = RMSNorm(self.hidden_size, eps=norm_eps) self.register_module('feature_map', None) if feature_map == 'swish': self.feature_map = SwishFeatureMap() elif feature_map == 'relu': self.feature_map = ReLUFeatureMap() elif feature_map == 't2r': self.feature_map = T2RFeatureMap(self.head_k_dim, self.head_k_dim) else: raise NotImplementedError(f"Feature map `{feature_map}` is not supported now.") 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_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_per_group, conv_size, activation='silu') self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu') self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, eps=norm_eps) self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) 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." ) # launching the triton kernel for just one token will actually be slower mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode if self.norm_first: hidden_states = self.norm(hidden_states) 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) f = self.f_proj(hidden_states) q = rearrange(q, 'b t (h d) -> b t h d', h=self.num_heads) k = rearrange(k, 'b t (h d) -> b t h d', h=self.num_kv_heads) v = rearrange(v, 'b t (h d) -> b t h d', h=self.num_kv_heads) f = rearrange(f, 'b t (h m) -> b t h m', h=self.num_kv_heads) if self.feature_map is not None: q, k = map(lambda x: self.feature_map(x), (q, k)) v = swish(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[:, -s.shape[1]:, None, None]) v = v.mul_(attention_mask[:, -v.shape[1]:, None, None]) recurrent_state = last_state['recurrent_state'] if last_state is not None else None if mode == 'fused_recurrent': o, recurrent_state = fused_recurrent_gsa( q=q, k=k, v=v, s=s, g=f, initial_state=recurrent_state, output_final_state=use_cache, scale=self.scale, head_first=False ) elif mode == 'chunk': o, recurrent_state = chunk_gsa( q=q, k=k, v=v, s=s, g=f, initial_state=recurrent_state, output_final_state=use_cache, scale=self.scale, head_first=False ) else: raise NotImplementedError(f"Not supported mode `{mode}`.") 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] ) o = rearrange(o, 'b t h d -> b t (h d)') o = rms_norm_linear(swish(o), self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias) return o, None, past_key_values def state_size(self, *args, **kwargs) -> int: return 2 * self.num_slots * self.hidden_size