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# "HGRN2: Gated Linear RNNs with State Expansion"[https://arxiv.org/abs/2404.07904]
from __future__ import annotations
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
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 transformers.processing_utils import Unpack
from fla.models.utils import Cache
class HGRN2Attention(nn.Module):
def __init__(
self,
mode: str = 'chunk',
hidden_size: int = 1024,
num_heads: int | None = None,
expand_ratio: int | None = 128,
use_short_conv: bool = False,
conv_size: int = 4,
conv_bias: bool = False,
elementwise_affine: bool | None = 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 not None:
num_heads = hidden_size // expand_ratio
elif expand_ratio is None and num_heads is not None:
expand_ratio = hidden_size // num_heads
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 supported 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(
hidden_size=self.forget_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
self.f_conv1d = ShortConvolution(
hidden_size=self.forget_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
self.i_conv1d = ShortConvolution(
hidden_size=self.input_dim,
kernel_size=conv_size,
bias=conv_bias,
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)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor | None = None,
past_key_values: Cache | None = None,
use_cache: bool | None = False,
output_attentions: bool | None = False,
lower_bound: torch.Tensor | None = None,
**kwargs: Unpack[dict],
) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]:
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."
)
batch_size, q_len, _ = hidden_states.shape
mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 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]
cu_seqlens = kwargs.get('cu_seqlens')
if attention_mask is not None:
indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:])
hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0)
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']
q, conv_state_q = self.q_conv1d(
x=self.q_proj(hidden_states),
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
f, conv_state_f = self.f_conv1d(
x=self.f_proj(hidden_states),
cache=conv_state_f,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
i, conv_state_i = self.i_conv1d(
x=self.i_proj(hidden_states),
cache=conv_state_i,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
else:
q = self.q_proj(hidden_states)
f = self.f_proj(hidden_states)
i = self.i_proj(hidden_states)
q = swish(q)
g = F.logsigmoid(f)
# the lower bound for the first layer is zero
if lower_bound is not None and self.layer_idx > 0:
g = torch.logaddexp(lower_bound.log(), torch.log1p(-lower_bound) + g)
k = 1 - g.exp()
q, k, g = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_f_dim), (q, k.to(i), g))
i = rearrange(i, '... (h d) -> ... h d', d=self.head_i_dim)
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,
cu_seqlens=cu_seqlens,
)
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,
)
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,
cu_seqlens=cu_seqlens,
)
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_len,
)
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)
if attention_mask is not None:
o = pad_input(o.squeeze(0), indices, batch_size, q_len)
return o, None, past_key_values
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