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from __future__ import annotations
import math
import warnings
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from einops import rearrange, repeat
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.ops.gated_delta_product import chunk_gated_delta_product
from fla.ops.gated_delta_rule import fused_recurrent_gated_delta_rule
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
class GatedDeltaProduct(nn.Module):
"""
Generalized version of GatedDoubleDeltaNet that supports arbitrary number of householder transformations.
"""
def __init__(
self,
hidden_size: int = 2048,
expand_v: float = 2,
head_dim: int = 256,
num_heads: int = 6,
num_v_heads: int = None,
mode: str = 'chunk',
use_output_gate: bool = True,
use_short_conv: bool = True,
conv_size: int = 4,
conv_bias: bool = False,
layer_idx: int = None,
norm_eps: float = 1e-5,
use_forget_gate: bool = True,
allow_neg_eigval: bool = True,
num_householder: int = 2,
**kwargs,
) -> GatedDeltaProduct:
super().__init__()
self.mode = mode
self.hidden_size = hidden_size
self.expand_v = expand_v
self.use_forget_gate = use_forget_gate
self.allow_neg_eigval = allow_neg_eigval
self.num_householder = num_householder
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.head_dim = head_dim
self.num_heads = num_heads
self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads
self.head_k_dim = head_dim
self.head_v_dim = int(self.head_dim * self.expand_v)
self.key_dim = int(self.num_heads * self.head_k_dim)
self.value_dim = int(self.num_v_heads * self.head_v_dim)
self.layer_idx = layer_idx
# Consistency check: Ensure expand_v produces integer values
if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5):
raise ValueError(
f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. "
f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.",
)
if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0:
raise ValueError(
f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.",
)
if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5):
raise ValueError(
f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. "
f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.",
)
assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`."
self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.k_proj = nn.Linear(hidden_size, self.key_dim * num_householder, bias=False)
self.v_proj = nn.Linear(hidden_size, self.value_dim * num_householder, bias=False)
self.b_proj = nn.Linear(hidden_size, self.num_v_heads * num_householder, bias=False)
if self.use_forget_gate:
self.a_proj = nn.Linear(hidden_size, self.num_v_heads, bias=False)
A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_(0, 16)
self.A_log = nn.Parameter(torch.log(A))
self.A_log._no_weight_decay = True
# hard coded for now
dt_min = 0.001
dt_max = 0.1
dt_init_floor = 1e-4
dt = torch.exp(
torch.rand(self.num_v_heads) * (math.log(dt_max) - math.log(dt_min))
+ math.log(dt_min),
)
dt = torch.clamp(dt, min=dt_init_floor)
# Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
inv_dt = dt + torch.log(-torch.expm1(-dt))
self.dt_bias = nn.Parameter(inv_dt)
# Just to be explicit. Without this we already don't put wd on dt_bias because of the check
# name.endswith("bias") in param_grouping.py
self.dt_bias._no_weight_decay = True
if use_short_conv:
self.conv_size = conv_size
self.q_conv1d = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
self.k_conv1d = ShortConvolution(
hidden_size=self.key_dim * num_householder,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
self.v_conv1d = ShortConvolution(
hidden_size=self.value_dim * num_householder,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
else:
warnings.warn(
"ShortConvolution is crucial to the performance. "
"Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing.",
)
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 _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: 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
# change to inference mode.
mode = 'fused_recurrent' if q_len <= 64 else self.mode
if self.training:
assert mode == 'chunk', "Only chunk mode is supported in training."
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_k, conv_state_v = None, None, None
if last_state is not None:
conv_state_q, conv_state_k, conv_state_v = 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, conv_state_v = self.v_conv1d(
x=self.v_proj(hidden_states),
cache=conv_state_v,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
else:
q = F.silu(self.q_proj(hidden_states))
k = F.silu(self.k_proj(hidden_states))
v = F.silu(self.v_proj(hidden_states))
q = rearrange(q, '... (h d) -> ... h d', d=self.head_k_dim)
k = rearrange(k, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_k_dim)
v = rearrange(v, '... t (n h d) -> ... (t n) h d', n=self.num_householder, d=self.head_v_dim)
if self.num_v_heads > self.num_heads:
q, k = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q, k))
beta = self.b_proj(hidden_states).sigmoid()
if self.allow_neg_eigval:
beta = beta * 2.
beta = rearrange(beta, '... t (n h) -> ... (t n) h', n=self.num_householder)
if self.use_forget_gate:
g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias)
else:
g = None
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
if mode == 'chunk':
o, recurrent_state = chunk_gated_delta_product(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
num_householder=self.num_householder,
use_qk_l2norm_in_kernel=True,
)
elif mode == 'fused_recurrent':
if self.use_forget_gate:
g_new = g.new_zeros(g.shape[0], g.shape[1], self.num_householder, g.shape[2])
g_new[:, :, 0] = g
g = rearrange(g_new, '... t n h -> ... (t n) h')
q_new = q.new_zeros(q.shape[0], q.shape[1], self.num_householder, q.shape[2], q.shape[3])
q_new[:, :, -1] = q
q = rearrange(q_new, '... t n h d-> ... (t n) h d')
o, recurrent_state = fused_recurrent_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens * self.num_householder if cu_seqlens is not None else None,
use_qk_l2norm_in_kernel=True,
)
o = rearrange(o, '... (t n) h d -> ... t n h d', n=self.num_householder)[..., -1, :, :].contiguous()
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None,
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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