# -*- coding: utf-8 -*- # "HGRN2: Gated Linear RNNs with State Expansion"[https://arxiv.org/abs/2404.07904] from __future__ import annotations from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from transformers.cache_utils import Cache from fla.modules import RMSNorm, ShortConvolution from fla.modules.activations import swish from fla.ops.gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla 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, share_conv_kernel: bool = True, 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 else: 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.share_conv_kernel = share_conv_kernel 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 if share_conv_kernel: self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation='silu') else: self.q_conv1d = ShortConvolution(self.forget_dim, conv_size, activation='silu') self.f_conv1d = ShortConvolution(self.forget_dim, conv_size, activation='silu') self.i_conv1d = ShortConvolution(self.input_dim, conv_size, activation='silu') self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, 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]]: # 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 = past_key_values[self.layer_idx] if use_cache else None if self.use_short_conv: conv_state = last_state[0] if use_cache else None if self.share_conv_kernel: # conv state is updated inplace hidden_states = self.h_conv1d(hidden_states, attention_mask, conv_state) q = self.q_proj(hidden_states) f = self.f_proj(hidden_states) i = self.i_proj(hidden_states) else: conv_state_q = last_state[0] if use_cache else None conv_state_f = last_state[1] if use_cache else None conv_state_i = last_state[2] if use_cache else None q = self.q_proj(hidden_states) f = self.f_proj(hidden_states) i = self.i_proj(hidden_states) q = self.q_conv1d(q, attention_mask, conv_state_q) f = self.f_conv1d(f, attention_mask, conv_state_f) i = self.i_conv1d(i, attention_mask, conv_state_i) 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.unsqueeze(-1)) q = swish(q) # 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, 'b l (h d) -> b h l d', h=self.num_heads), (q, k, i, g)) recurrent_state = last_state[-1] if use_cache else None if mode == 'fused_recurrent': o, recurrent_state = fused_recurrent_gla(q, k, i, g, initial_state=recurrent_state, output_final_state=use_cache) elif mode == 'fused_chunk': o, recurrent_state = fused_chunk_gla(q, k, i, g, initial_state=recurrent_state, output_final_state=use_cache) elif mode == 'chunk': o, recurrent_state = chunk_gla(q, k, i, g, initial_state=recurrent_state, output_final_state=use_cache) else: raise NotImplementedError(f"Not supported mode `{mode}`.") if past_key_values is not None: if self.use_short_conv: if self.share_conv_kernel: last_state = (conv_state, recurrent_state) else: last_state = (conv_state_q, conv_state_f, conv_state_i, recurrent_state) else: last_state = (recurrent_state,) past_key_values.update(last_state, self.layer_idx, q.shape[2]) o = self.g_norm(rearrange(o, 'b h l d -> b l (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: if self.share_conv_kernel: state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),) else: state += (param.new_zeros(batch_size, self.forget_dim, self.conv_size), param.new_zeros(batch_size, self.forget_dim, self.conv_size), param.new_zeros(batch_size, self.input_dim, self.conv_size)) state += (param.new_zeros(batch_size, self.num_heads, self.head_f_dim, self.head_i_dim),) return state 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