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from __future__ import annotations
import math
from typing import TYPE_CHECKING, Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from fla.modules import FusedRMSNormSwishGate, RMSNorm, ShortConvolution
from fla.ops.delta_rule import chunk_delta_rule
from fla.ops.gated_delta_rule import chunk_gated_delta_rule
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
def elu_p1(x):
return (F.elu(x, 1.0, False) + 1.0).to(x)
def sum_norm(x):
return (x / x.sum(-1, keepdim=True)).to(x)
def interleave_multiple_sequences(*sequences):
"""
Interleave multiple sequences together.
For example, with sequences [A1, A2], [B1, B2], [C1, C2],
returns [A1, B1, C1, A2, B2, C2]
"""
if isinstance(sequences[0], (list, tuple)):
sequences = sequences[0]
if len(sequences) == 1:
return sequences[0]
# All sequences should have the same shape
assert all(s.shape == sequences[0].shape for s in sequences)
# Get the original shape
batch_size, seq_len, *rest = sequences[0].shape
# Stack sequences along a new dimension
stacked = torch.stack(sequences, dim=2)
# Reshape to interleave
reshaped = stacked.view(batch_size, seq_len * len(sequences), *rest)
return reshaped
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_householder: int = 2, # New parameter for number of householder transformations
mode: str = "chunk",
use_gate: bool = True,
use_forget_gate: bool = True, # when true Gated DeltaProduct, when false DeltaProduct
use_short_conv: bool = True,
conv_size: int = 4,
conv_bias: bool = False,
layer_idx: int | None = None,
norm_eps: float = 1e-5,
allow_neg_eigval: bool = False, # when true (Gated) DeltaProduct [-1, 1], when false (Gated) DeltaProduct [0, 1]
**kwargs,
) -> None:
super().__init__()
self.mode = mode
self.hidden_size = hidden_size
self.expand_v = expand_v
self.use_gate = use_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_householder = num_householder
self.allow_neg_eigval = allow_neg_eigval
self.use_forget_gate = use_forget_gate
self.key_dim = self.num_heads * self.head_dim
self.value_dim = int(self.key_dim * self.expand_v)
self.head_qk_dim = head_dim
self.head_v_dim = int(head_dim * self.expand_v)
self.layer_idx = layer_idx
self.silu = nn.SiLU()
assert mode in ["chunk", "fused_recurrent"], f"Not supported mode `{mode}`."
# Create multiple projection layers for each householder transformation
self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.k_projs = nn.ModuleList(
[
nn.Linear(hidden_size, self.key_dim, bias=False)
for _ in range(num_householder)
]
)
self.v_projs = nn.ModuleList(
[
nn.Linear(hidden_size, self.value_dim, bias=False)
for _ in range(num_householder)
]
)
self.b_projs = nn.ModuleList(
[
nn.Linear(hidden_size, self.num_heads, bias=False)
for _ in range(num_householder)
]
)
if use_short_conv:
self.q_conv1ds = nn.ModuleList(
[
ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
activation="silu",
)
for _ in range(num_householder)
]
)
self.k_conv1ds = nn.ModuleList(
[
ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
activation="silu",
)
for _ in range(num_householder)
]
)
self.v_conv1ds = nn.ModuleList(
[
ShortConvolution(
hidden_size=self.value_dim,
kernel_size=conv_size,
activation="silu",
)
for _ in range(num_householder)
]
)
if self.use_forget_gate:
self.a_proj = nn.Linear(hidden_size, self.num_heads, bias=False)
A = torch.empty(self.num_heads, dtype=torch.float32).uniform_(0, 16)
A_log = torch.log(A)
self.A_log = nn.Parameter(A_log)
self.A_log._no_weight_decay = True
# Initialize dt parameters
dt_min = 0.001
dt_max = 0.1
dt_init_floor = 1e-4
dt = torch.exp(
torch.rand(self.num_heads) * (math.log(dt_max) - math.log(dt_min))
+ math.log(dt_min)
)
dt = torch.clamp(dt, min=dt_init_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
self.dt_bias = nn.Parameter(inv_dt)
self.dt_bias._no_weight_decay = True
if use_gate:
self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
self.o_norm = FusedRMSNormSwishGate(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)
self.k_id = torch.nn.Identity()
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,
**kwargs: Unpack[Dict],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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)."
)
mode = (
"chunk" # 'fused_recurrent' if hidden_states.shape[1] <= 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]
# Process each householder transformation
ks, vs, betas = [], [], []
conv_states = []
for i in range(self.num_householder):
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"][
i
]
conv_mask = (
attention_mask[:, -hidden_states.shape[1]:]
if attention_mask is not None
else None
)
k, conv_state_k = self.k_conv1ds[i](
x=self.k_projs[i](hidden_states),
mask=conv_mask,
cache=conv_state_k,
output_final_state=use_cache,
)
v, conv_state_v = self.v_conv1ds[i](
x=self.v_projs[i](hidden_states),
mask=conv_mask,
cache=conv_state_v,
output_final_state=use_cache,
)
conv_states.append((conv_state_q, conv_state_k, conv_state_v))
else:
k = self.silu(self.k_projs[i](hidden_states))
v = self.silu(self.v_projs[i](hidden_states))
ks.append(k)
vs.append(v)
beta = self.b_projs[i](
hidden_states
).sigmoid() # bs, sequence_length, num_heads
if attention_mask is not None:
beta = beta.mul(attention_mask[:, -hidden_states.shape[1]:, None])
if self.allow_neg_eigval:
beta = beta * 2
betas.append(beta)
if self.use_short_conv:
q, conv_state_q = self.q_conv1ds[0](
x=self.q_proj(hidden_states),
mask=conv_mask,
cache=conv_state_q,
output_final_state=use_cache,
)
else:
q = self.silu(self.q_proj(hidden_states))
q = interleave_multiple_sequences(
[torch.zeros_like(q)] * (self.num_householder - 1) + [q]
)
# Interleave all sequences
k = interleave_multiple_sequences(ks)
v = interleave_multiple_sequences(vs)
beta = interleave_multiple_sequences(betas)
q, k, v = (
rearrange(x, "b t (h d) -> b t h d", h=self.num_heads) for x in (q, k, v)
)
recurrent_state = (
last_state["recurrent_state"] if last_state is not None else None
)
offsets = kwargs.get("offsets")
if mode == "chunk":
if self.use_forget_gate:
g = -self.A_log.float().exp() * F.softplus(
self.a_proj(hidden_states).float() + self.dt_bias
)
if attention_mask is not None:
g = g.mul(attention_mask[:, -g.shape[-2]:, None])
# Interleave g with zeros for non-first transformations
g = interleave_multiple_sequences(
[g] + [torch.zeros_like(g)] * (self.num_householder - 1)
)
o, recurrent_state = chunk_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=offsets,
head_first=False,
use_qk_l2norm_in_kernel=True
)
else:
o, recurrent_state = chunk_delta_rule(
q=q,
k=k,
v=v,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=offsets,
head_first=False,
use_qk_l2norm_in_kernel=True
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
# Take every nth element for n householder transformations
o = o[:, self.num_householder - 1:: self.num_householder, :]
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=conv_states if self.use_short_conv else None,
layer_idx=self.layer_idx,
offset=q.shape[2],
)
if self.use_gate:
g = rearrange(
self.g_proj(hidden_states),
"... (h d) -> ... h d",
h=self.num_heads,
)
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)
return o, None, past_key_values
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