# -*- coding: utf-8 -*- # Copyright (c) 2024, Songlin Yang, Yu Zhang from typing import Optional import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from fla.modules import RMSNorm from fla.modules.feature_map import (DPFPFeatureMap, HadamardFeatureMap, HedgehogFeatureMap, T2RFeatureMap) from fla.ops.linear_attn import (chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn) class LinearAttention(nn.Module): def __init__( self, mode: str = 'chunk', hidden_size: str = 1024, expand_k: int = 1.0, expand_v: int = 1.0, num_heads: int = 8, num_kv_heads: Optional[int] = None, feature_map: str = 'elementwise_product', tie_feature_map_qk: bool = False, output_norm: str = 'rmsnorm', norm_q: bool = False, norm_k: bool = False, # standard linear attention normalization do_feature_map_norm: bool = False, elementwise_affine: bool = True, norm_eps: float = 1e-5, **kwargs ): super().__init__() self.hidden_size = hidden_size self.mode = mode self.num_heads = num_heads self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_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 assert mode in ['chunk', 'fused_chunk', 'fused_recurrent'], f"Not suppoerted mode `{mode}`." assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" self.head_qk_dim = self.key_dim // num_heads self.head_v_dim = self.value_dim // num_heads self.do_feature_map_norm = do_feature_map_norm if feature_map == 'hedgehog': if tie_feature_map_qk: self.feature_map_q = self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_qk_dim) else: self.feature_map_q = HedgehogFeatureMap(head_dim=self.head_qk_dim) self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_qk_dim) elif feature_map == 't2r': if tie_feature_map_qk: self.feature_map_q = self.feature_map_k = T2RFeatureMap(head_dim=self.head_qk_dim) else: self.feature_map_q = T2RFeatureMap(head_dim=self.head_qk_dim) self.feature_map_k = T2RFeatureMap(head_dim=self.head_qk_dim) elif feature_map == 'elementwise_product': if tie_feature_map_qk: self.feature_map_q = self.feature_map_k = HadamardFeatureMap(head_dim=self.head_qk_dim) else: self.feature_map_q = HadamardFeatureMap(head_dim=self.head_qk_dim) self.feature_map_k = HadamardFeatureMap(head_dim=self.head_qk_dim) elif feature_map == 'dpfp': self.feature_map_q = DPFPFeatureMap(head_dim=self.head_qk_dim) self.feature_map_k = DPFPFeatureMap(head_dim=self.head_qk_dim) elif feature_map == 'elu': def elu(x): return F.elu(x) + 1 self.feature_map_q = elu self.feature_map_k = elu elif feature_map == 'relu': self.feature_map_q = nn.ReLU() self.feature_map_k = nn.ReLU() elif feature_map == 'identity': self.feature_map_q = nn.Identity() self.feature_map_k = nn.Identity() else: raise NotImplementedError(f"Not supported feature map `{feature_map}`.") self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False) self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False) if output_norm == 'rmsnorm': self.norm = RMSNorm(hidden_size=self.head_v_dim, elementwise_affine=elementwise_affine, eps=norm_eps) elif output_norm == 'identity': self.norm = nn.Identity() else: raise NotImplementedError(f"Not supported output norm `{output_norm}`.") self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) self.norm_q = norm_q self.norm_k = norm_k 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, x): mode = self.mode q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads) if self.num_kv_groups > 1: k, v = (repeat(x, '... (h d) -> ... (h g) d', h=self.num_kv_heads, g=self.num_kv_groups) for x in (k, v)) else: k, v = (rearrange(x, '... (h d) -> ... h d', h=self.num_kv_heads) for x in (k, v)) q = self.feature_map_q(q) k = self.feature_map_k(k) if self.norm_q: q = q / (q.sum(-1, True) + 1e-4) if self.norm_k: k = k / (k.sum(-1, True) + 1e-4) if mode == 'chunk': o, final_state = chunk_linear_attn( q=q, k=k, v=v, normalize=self.do_feature_map_norm, head_first=False ) elif mode == 'fused_chunk': o, final_state = fused_chunk_linear_attn( q=q, k=k, v=v, normalize=self.do_feature_map_norm, ) elif mode == 'fused_recurrent': o, final_state = fused_recurrent_linear_attn( q=q, k=k, v=v, normalize=self.do_feature_map_norm, ) else: raise NotImplementedError o = self.norm(o) o = self.o_proj(o) return o