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from __future__ import annotations
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from einops import rearrange
from torch.nn import functional as F
from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution
from fla.modules.l2norm import l2_norm
from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
class MesaNet(nn.Module):
"""
The layer implementaion for [MesaNet: Sequence Modeling by Locally Optimal Test-Time Training]. # noqa
Args:
hidden_size (int, Optional):
The hidden size of the input. Default: 2048.
expand_v (float, Optional):
The expansion ratio for the value dim. Default: 1.
num_heads (int, Optional):
The number of heads. Default: 16.
mode (str, Optional):
Which MesaNet kernel to use.
Currently available: `chunk`.
Default: `chunk`.
use_output_gate (bool, Optional):
Whether to use output gate. Default: `False`.
conv_size (int):
The kernel size of the short convolution. Default: 4.
layer_idx (int, Optional):
The index of the layer. Default: None.
norm_eps (float, Optional):
The epsilon value for the normalization layer. Default: 1e-5.
lambda_lower_bound (float):
The lower bound for the lambda parameter. Default: 0.25.
max_cg_step_training (int):
The maximum number of CG steps for training. Default: 30.
max_cg_step_decoding (int):
The maximum number of CG steps for decoding. Default: 30.
"""
def __init__(
self,
hidden_size: int = 2048,
num_heads: int = 16,
head_dim: int = 128,
mode: str = 'chunk',
use_output_gate: bool = False,
use_short_conv: bool = True,
conv_size: int = 4,
conv_bias: bool = False,
layer_idx: int = None,
norm_eps: float = 1e-5,
lambda_lower_bound: float = 0.25,
max_cg_step_training: int = 30,
max_cg_step_decoding: int = 30,
**kwargs,
) -> MesaNet:
super().__init__()
self.mode = mode
self.hidden_size = hidden_size
self.use_output_gate = use_output_gate
self.use_short_conv = use_short_conv
self.conv_size = conv_size
self.conv_bias = conv_bias
self.num_heads = num_heads
self.head_dim = head_dim
self.key_dim = self.num_heads * self.head_dim
self.value_dim = self.key_dim
self.head_k_dim = self.head_dim
self.head_v_dim = self.head_dim
self.layer_idx = layer_idx
self.lambda_lower_bound = lambda_lower_bound
self.max_cg_step_training = max_cg_step_training
self.max_cg_step_decoding = max_cg_step_decoding
self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
self.a_proj = nn.Linear(hidden_size, self.num_heads, bias=True)
self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=True)
lambda_initial_value = 1.0
init_lamb_value = torch.log(torch.exp(torch.tensor(lambda_initial_value - lambda_lower_bound)) - 1.0)
init_lamb_params = torch.empty(self.key_dim, dtype=torch.float32).fill_(init_lamb_value)
self.lambda_params = nn.Parameter(init_lamb_params)
self.lambda_params._no_weight_decay = True
self.conv_size = conv_size
self.q_conv1d = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=self.conv_bias,
activation='silu',
)
self.k_conv1d = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=self.conv_bias,
activation='silu',
)
if use_output_gate:
self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps)
else:
self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps)
self.o_proj = nn.Linear(self.value_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,
**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
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)
conv_state_q, conv_state_k = None, None
if last_state is not None:
conv_state_q, conv_state_k = 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,
)
k, conv_state_k = self.k_conv1d(
x=self.k_proj(hidden_states),
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
v = self.v_proj(hidden_states)
q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim)
beta = self.b_proj(hidden_states).float().sigmoid()
g = F.logsigmoid(self.a_proj(hidden_states).float())
lamb = F.softplus(self.lambda_params.float()) + self.lambda_lower_bound
lamb = lamb.reshape(self.num_heads, -1)
last_h_kk, last_h_kv = last_state['recurrent_state'] if last_state is not None else (None, None)
# prefilling or training
# Note that QK will be normalized inside the kernel to avoid saving the activations, thereby reducing the memory usage.
if last_state is None:
o, h_kk, h_kv = chunk_mesa_net(
q=q,
k=k,
v=v,
g=g,
beta=beta,
lamb=lamb,
output_final_state=use_cache,
max_CG_iteration=self.max_cg_step_training,
use_qk_l2norm_in_kernel=True,
cu_seqlens=cu_seqlens,
)
# decoding
else:
q = l2_norm(q)
k = l2_norm(k)
o, h_kk, h_kv = mesa_net_decoding_one_step(
q=q.squeeze(0),
k=k.squeeze(0),
v=v.squeeze(0),
g=g.squeeze(0),
beta=beta.squeeze(0),
lamb=lamb,
prev_h_kk=last_h_kk,
prev_h_kv=last_h_kv,
max_CG_iteration=self.max_cg_step_decoding,
)
o = o.unsqueeze(0).to(q)
if past_key_values is not None:
past_key_values.update(
recurrent_state=(h_kk, h_kv),
conv_state=(conv_state_q, conv_state_k),
layer_idx=self.layer_idx,
offset=q_len,
)
if self.use_output_gate:
g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim)
o = self.o_norm(o, g)
else:
o = self.o_norm(o)
o = rearrange(o, 'b t h d -> b t (h d)')
o = self.o_proj(o)
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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