# -*- coding: utf-8 -*- # Copyright (c) 2024, Songlin Yang, Yu Zhang # "HGRN2: Gated Linear RNNs with State Expansion"[https://arxiv.org/abs/2404.07904] from __future__ import annotations 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.layernorm import rms_norm_linear from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla if TYPE_CHECKING: from fla.models.utils import Cache class HGRN2Attention(nn.Module): def __init__( self, mode: str = 'chunk', hidden_size: int = 1024, num_heads: Optional[int] = None, expand_ratio: Optional[int] = 128, use_short_conv: bool = False, conv_size: int = 4, conv_bias: bool = False, elementwise_affine: Optional[bool] = True, norm_eps: float = 1e-5, layer_idx: int = None ) -> HGRN2Attention: super().__init__() self.mode = mode self.hidden_size = hidden_size if expand_ratio is None and num_heads is not None: expand_ratio = hidden_size // num_heads elif expand_ratio is not None and num_heads is None: num_heads = hidden_size // expand_ratio elif expand_ratio is None and num_heads is None: raise RuntimeError("One of `expand_ratio` or `num_heads` should be provided.") self.num_heads = num_heads self.expand_ratio = expand_ratio self.use_short_conv = use_short_conv self.conv_size = conv_size self.conv_bias = conv_bias self.forget_dim = int(self.num_heads * self.expand_ratio) self.input_dim = hidden_size self.layer_idx = layer_idx assert mode in ['chunk', 'fused_recurrent', 'fused_chunk'], f"Not suppoerted mode `{mode}`." assert self.forget_dim % num_heads == 0, f"forget dim must be divisible by num_heads of {num_heads}" assert self.input_dim % num_heads == 0, f"input dim must be divisible by num_heads of {num_heads}" self.head_f_dim = self.expand_ratio self.head_i_dim = self.hidden_size // num_heads self.q_proj = nn.Linear(hidden_size, self.forget_dim, bias=False) self.f_proj = nn.Linear(hidden_size, self.forget_dim, bias=False) self.i_proj = nn.Linear(hidden_size, self.input_dim, bias=False) if use_short_conv: self.conv_size = conv_size self.q_conv1d = ShortConvolution(self.forget_dim, conv_size, activation=None) self.f_conv1d = ShortConvolution(self.forget_dim, conv_size, activation=None) self.i_conv1d = ShortConvolution(self.input_dim, conv_size, activation=None) self.g_norm = RMSNorm(hidden_size=self.hidden_size, elementwise_affine=elementwise_affine, eps=norm_eps) self.o_proj = nn.Linear(self.input_dim, 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, lower_bound: Optional[torch.Tensor] = None, **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 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_f, conv_state_i = None, None, None if last_state is not None: conv_state_q, conv_state_f, conv_state_i = 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) f, conv_state_f = self.f_conv1d(x=self.f_proj(hidden_states), mask=conv_mask, cache=conv_state_f, output_final_state=use_cache) i, conv_state_i = self.i_conv1d(x=self.i_proj(hidden_states), mask=conv_mask, cache=conv_state_i, output_final_state=use_cache) else: q = self.q_proj(hidden_states) f = self.f_proj(hidden_states) i = self.i_proj(hidden_states) # dealing with left-padding if attention_mask is not None: i = i.mul_(attention_mask[:, -i.shape[-2]:, None]) q = swish(q) # improve precision f = f.float() # the lower bound for the first layer is zero if lower_bound is None or self.layer_idx == 0: k, g = 1 - f.sigmoid(), F.logsigmoid(f) else: g = lower_bound + (1 - lower_bound) * f.sigmoid() k, g = 1 - g, g.log() q, k, i, g = map(lambda x: rearrange(x, '... (h d) -> ... h d', h=self.num_heads), (q, k.to(i), i, g)) recurrent_state = last_state['recurrent_state'] if last_state is not None else None if mode == 'fused_recurrent': o, recurrent_state = fused_recurrent_gla( q=q, k=k, v=i, gk=g, initial_state=recurrent_state, output_final_state=use_cache, head_first=False ) elif mode == 'fused_chunk': o, recurrent_state = fused_chunk_gla( q=q, k=k, v=i, g=g, initial_state=recurrent_state, output_final_state=use_cache, head_first=False ) elif mode == 'chunk': o, recurrent_state = chunk_gla( q=q, k=k, v=i, g=g, initial_state=recurrent_state, output_final_state=use_cache, 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_f, conv_state_i) if self.use_short_conv else None, layer_idx=self.layer_idx, offset=q.shape[2] ) o = rearrange(o, '... h d -> ... (h d)') o = rms_norm_linear(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, **kwargs) -> int: state_size = self.forget_dim * self.head_i_dim for module in self.children(): if isinstance(module, ShortConvolution): state_size += module.state_size return state_size