|
|
|
|
|
|
|
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, |
|
|
|
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 |
|
|
