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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 transformers.utils import logging
from fla.layers.utils import pad_input, unpad_input
from fla.modules import RMSNorm, ShortConvolution
from fla.modules.l2norm import l2_norm
from fla.ops.attn.decoding import attn_decoding_one_step
from fla.ops.path_attn.parallel import parallel_path_attn
if TYPE_CHECKING:
from fla.models.utils import Cache
logger = logging.get_logger(__name__)
class PaTHAttention(nn.Module):
def __init__(
self,
hidden_size: int = 2048,
num_heads: int = 32,
num_kv_heads: int | None = None,
use_forget_gate: bool = False,
use_qk_norm: bool = False,
layer_idx: int = None,
use_low_rank_w: bool = True,
use_w_shortconv: bool = True,
conv_size: int = 3,
conv_bias: bool = False,
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
if num_kv_heads is None:
self.num_kv_heads = self.num_heads
else:
self.num_kv_heads = num_kv_heads
self.head_dim = self.hidden_size // self.num_heads
self.kv_dim = self.num_kv_heads * self.head_dim
self.layer_idx = layer_idx
self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
# We use low-rank parameterization for the w_proj to reduce parameters in MHA settings.
if use_low_rank_w:
self.w_proj = nn.Sequential(
nn.Linear(self.hidden_size, 32, bias=False),
nn.Linear(32, self.kv_dim, bias=False),
)
# In MQA/GQA settings, key/value heads are shared, so we use a standard linear projection
# which doesn't introduce too many parameters
else:
self.w_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
# per head norm
if use_qk_norm:
self.maybe_q_norm = RMSNorm(self.head_dim)
self.maybe_k_norm = RMSNorm(self.head_dim)
else:
self.maybe_q_norm = nn.Identity()
self.maybe_k_norm = nn.Identity()
if use_w_shortconv:
self.w_conv1d = ShortConvolution(hidden_size=self.kv_dim, kernel_size=conv_size, bias=conv_bias, activation='silu')
self.use_w_shortconv = use_w_shortconv
self.bt_proj = nn.Linear(self.hidden_size, self.num_kv_heads, bias=True)
self.use_forget_gate = use_forget_gate
if use_forget_gate:
self.g_proj = nn.Linear(self.hidden_size, self.num_heads, bias=True)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
if use_cache:
assert past_key_values is not None, "past_key_values must be provided when use_cache is True"
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.size()
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
w = self.w_proj(hidden_states)
beta = self.bt_proj(hidden_states).float().sigmoid() * 2 # allowing negative eigenvalues
g = F.logsigmoid(self.g_proj(hidden_states).float()) if self.use_forget_gate else None
cu_seqlens = kwargs.get('cu_seqlens')
assert not (cu_seqlens is not None and attention_mask is not None), (
"cu_seqlens should not be provided when attention_mask is not None"
)
# Training
if attention_mask is None:
assert use_cache is False, "use_cache should be False in training"
if self.use_w_shortconv:
w, _ = self.w_conv1d(w, cache=None, output_final_state=False, cu_seqlens=cu_seqlens)
q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim)
k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim)
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim)
q, k = self.maybe_q_norm(q), self.maybe_k_norm(k)
w = l2_norm(w, output_dtype=torch.float32)
o, _ = parallel_path_attn(q=q, k=k, v=v, w=w, beta=beta, g=g, cu_seqlens=cu_seqlens)
# Prefilling or decoding
else:
assert self.training is False, "attention mask is not supported in training. Please use variable length input."
try:
last_state = past_key_values[self.layer_idx]
except KeyError:
last_state = None
# Decoding
if last_state is not None:
if g is not None:
past_k, past_v, past_g = last_state['attn_state']
else:
past_k, past_v = last_state['attn_state']
past_g = None
w_conv_state = last_state['conv_state']
past_k = rearrange(past_k, '... (h d) -> ... h d', d=self.head_dim)
if self.use_w_shortconv:
w, w_conv_state = self.w_conv1d(w, cache=w_conv_state, output_final_state=use_cache, cu_seqlens=cu_seqlens)
w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim)
w = l2_norm(w, output_dtype=torch.float32)
@torch.compile
def rank_one_update(k, w, beta):
original_dtype = k.dtype
k = k.float()
w = w.float()
beta = beta.float()
k = k - beta[..., None].float() * (k * w).sum(-1, keepdim=True) * w
return k.to(original_dtype)
past_k = rank_one_update(past_k, w, beta)
past_k = rearrange(past_k, '... h d -> ... (h d)')
k = torch.cat([past_k, k], dim=1)
v = torch.cat([past_v, v], dim=1)
g = torch.cat([past_g, g], dim=1) if g is not None else None
past_key_values[self.layer_idx]['attn_state'] = (k, v, g) if g is not None else (k, v)
past_key_values.update(
conv_state=w_conv_state,
layer_idx=self.layer_idx,
offset=q_len,
)
if g is not None:
q, (k, v, g), indices_q, cu_seqlens, max_seq_lens = unpad_input(
q, (k, v, g), attention_mask, q_len, keepdim=True)
max_seqlen_q, max_seqlen_k = max_seq_lens
else:
q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(
q, (k, v), attention_mask, q_len, keepdim=True)
max_seqlen_q, max_seqlen_k = max_seq_lens
_, cu_seqlens = cu_seqlens
q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim)
k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim)
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
assert max_seqlen_q == 1, "only support q_len == 1 for decoding"
o = attn_decoding_one_step(q, k, v, g, cu_seqlens=cu_seqlens, do_gate_scale=True) # reduced to fox's decoding
# Prefilling
else:
v_cache = v.clone()
g_cache = g.clone() if g is not None else None
if g is None:
q, (k, v, w, beta), indices_q, cu_seqlens, max_seq_lens = unpad_input(
q, (k, v, w, beta), attention_mask, q_len, keepdim=True)
else:
q, (k, v, w, beta, g), indices_q, cu_seqlens, max_seq_lens = unpad_input(
q, (k, v, w, beta, g), attention_mask, q_len, keepdim=True)
max_seqlen_q, max_seqlen_k = max_seq_lens
assert max_seqlen_q == max_seqlen_k, "max_seqlen_q should be equal to max_seqlen_k in prefilling"
_, cu_seqlens = cu_seqlens
if self.use_w_shortconv:
w, w_conv_state = self.w_conv1d(w, cache=None, output_final_state=use_cache, cu_seqlens=cu_seqlens)
else:
w_conv_state = None
q = rearrange(q, '... (h d) -> ... h d', d=self.head_dim)
k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim)
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
w = rearrange(w, '... (h d) -> ... h d', d=self.head_dim)
w = l2_norm(w, output_dtype=torch.float32)
o, k_cache = parallel_path_attn(q=q, k=k, v=v, w=w, beta=beta, g=g,
cu_seqlens=cu_seqlens, use_cache=use_cache)
if use_cache:
k_cache = pad_input(k_cache.squeeze(0), indices_q, batch_size, q_len)
k_cache = rearrange(k_cache, '... h d -> ... (h d)')
past_key_values.update(
attn_state=(k_cache, v_cache, g_cache) if g_cache is not None else (k_cache, v_cache),
conv_state=w_conv_state,
layer_idx=self.layer_idx,
offset=q_len,
)
o = pad_input(o.squeeze(0), indices_q, batch_size, q_len)
o = rearrange(o, '... h d -> ... (h d)')
o = self.o_proj(o)
return o, None, past_key_values
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