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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 rotary_embedding_torch.rotary_embedding_torch import rotate_half
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.gla import chunk_gla, fused_recurrent_gla
from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
from fla.ops.sse import prepare_sample_relpos_global_index_flat, softmax_and_mask
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
def sort_along_l(q, k, v, gk, beta, e, cu_seqlens, K, emulq, emulk):
_, L, H, D = q.shape
N = e.size(-1)
S = len(cu_seqlens) - 1
e = F.softmax(e, dim=-1, dtype=torch.float)
topk_value, topk_expert = torch.topk(e, k=K, dim=2) # [1, L, K]
topk_value = topk_value.to(q.dtype)
mask_w = torch.zeros_like(e, dtype=torch.bool).scatter_(dim=-1, index=topk_expert, src=torch.ones_like(topk_expert, dtype=torch.bool))
experts_flat = topk_expert.reshape(L * K) # [L*K]
values_flat = topk_value.reshape(L * K) # [L*K]
sample_idx_flat, relpos_flat, global_idx_flat, lengths = prepare_sample_relpos_global_index_flat(cu_seqlens, K) # ([L*K] * 3, S)
assert sample_idx_flat.dtype == torch.long and relpos_flat.dtype == torch.long and global_idx_flat.dtype == torch.long
bits_pos = int(lengths.max().item()).bit_length()
bits_exp = int((N - 1)).bit_length()
shift_exp = bits_pos
shift_samp = bits_pos + bits_exp
## sort by (sample_idx <- expert_idx <- relpos_in_sample)
key = (sample_idx_flat << shift_samp) | (experts_flat << shift_exp) | relpos_flat
order = torch.argsort(key, stable=False)
experts_sorted = experts_flat.take(order)
sample_sorted = sample_idx_flat.take(order)
global_sorted = global_idx_flat.take(order) # gather index
values_sorted = values_flat.take(order) # sorted eta
# pos_sorted = relpos_flat.take(order)
## x: [1, L, H, D] -> y: [1, L*K, H, D]
index4gather = global_sorted[None, :, None, None].expand(1, L * K, H, D)
if beta is None:
q, k, v, gk = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v, gk)] # GLA
else:
q, k, v = [torch.gather(x, dim=1, index=index4gather) for x in (q, k, v)] # GDN
gk, beta = [torch.gather(x, dim=1, index=index4gather[..., 0]) for x in (gk, beta)]
if emulq:
q = q * values_sorted[None, :, None, None]
if emulk:
k = k * values_sorted[None, :, None, None]
## calculate offsets (new cu_seqlens)
pair_id = sample_sorted * N + experts_sorted # [L*K]
counts = torch.bincount(pair_id, minlength=S * N) # [S*N]
state_sizes = counts.view(S, N)
offsets = torch.zeros(1 + S * N, dtype=torch.long, device=q.device)
offsets[1:] = counts.cumsum(dim=0)
offsets = torch.unique(offsets)
return q, k, v, gk, beta, e, mask_w, offsets, state_sizes, global_sorted
class PoseRoPE(nn.Module):
"""Camera-pose-conditioned rotary embedding for linear attention.
Maps each token's camera pose to a per-dim-pair rotation angle and rotates
q/k by it. Because rotations compose to their difference, the bilinear form
q_i . k_j then depends on the RELATIVE pose (angle_j - angle_i), giving the
linear attention an explicit relative-camera signal it cannot recover from
additive absolute-pose injection alone. Zero-init -> identity at start.
"""
def __init__(self, pose_dim: int, head_dim: int, hidden: int = 64):
super().__init__()
assert head_dim % 2 == 0
self.net = nn.Sequential(
nn.Linear(pose_dim, hidden, bias=True),
nn.SiLU(),
nn.Linear(hidden, head_dim // 2, bias=True),
)
nn.init.zeros_(self.net[-1].weight)
nn.init.zeros_(self.net[-1].bias)
def forward(self, x: torch.Tensor, pose: torch.Tensor) -> torch.Tensor:
# x: [b, l, h, d]; pose: [b, l, pose_dim]
ang = self.net(pose)
self._angle_norm = ang.detach().abs().mean() # diagnostic: rotation magnitude
ang = repeat(ang, "b l n -> b l (n r)", r=2)
ang = ang.unsqueeze(2).float()
out = x.float() * ang.cos() + rotate_half(x).float() * ang.sin()
return out.to(x.dtype)
class SSEGLA(nn.Module):
"""
The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577).
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: 2.0.
head_dim (int, Optional):
The dimension of each head. Default: 256.
num_heads (int, Optional):
The number of heads. Default: 4.
num_v_heads (int, Optional):
The number of heads for the value projection, equal to `num_heads` if `None`.
GVA is applied if `num_v_heads` > `num_heads`. Default: `None`.
mode (str, Optional):
Which GLA kernel to use.
Currently available: `chunk` and `fused_recurrent`.
Default: `chunk`.
use_output_gate (bool, Optional):
Whether to use output gate. Default: `True`.
use_short_conv (bool, Optional):
Whether to use short convolutions. Default: `False`.
conv_size (int, Optional):
The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4.
conv_bias (bool, Optional):
Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`.
num_sparse_partition (int, optional):
Number of state partitions. Default: 4.
num_writer (int, optional):
Top-k write size (number of writers). Default: 1.
num_reader (int, optional):
Top-k read size (number of readers). Default: 1.
sse_implementation (str, optional):
SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`.
use_q_softmax (bool, optional):
Whether to apply softmax to the query. Default: `False`.
use_k_softmax (bool, optional):
Whether to apply softmax to the key. Default: `True`.
emulq (bool, optional):
Whether to use a read gate operating on the state output (Q). Default: `True`.
emulk (bool, optional):
Whether to use a write gate operating on the state input (KV). Default: `True`.
gate_logit_normalizer (int, Optional):
The normalizer for the gate logits, appied after `logsigmoid`. Default: 16.
gate_low_rank_dim (int, Optional):
The low rank dim for the gate projection. Default: 16.
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.
"""
def __init__(
self,
hidden_size: int = 2048,
expand_v: float = 1.,
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 = False,
conv_size: int = 4,
conv_bias: bool = False,
num_sparse_partition: int = 4,
num_writer: int = 1,
num_reader: int = 1,
sse_implementation: str = "varlen",
use_q_softmax: bool = False,
use_k_softmax: bool = True,
emulq: bool = True,
emulk: bool = True,
gate_logit_normalizer: int = 16,
gate_low_rank_dim: int = 16,
layer_idx: int = None,
norm_eps: float = 1e-5,
# ---- Camera-Guided SSE-GLA (CGLA) extension ----
# If pose_dim is None: behaves exactly like vanilla SSEGLA (no pose influence).
# If pose_dim is an int > 0: pose features (per-token) are injected into the
# sparse stream only (shared stream remains view-invariant):
# - routing eta : partition selection becomes viewpoint-aware
# - sparse q2/k2: pose-labeled read/write within partitions
# - sparse gate : pose-aware forgetting
# All pose-injection projections are zero-initialized so at step 0 the model
# is mathematically identical to vanilla SSEGLA; the pose signal is learned
# in from zero to avoid disturbing early optimization.
pose_dim: int = None,
pose_bottleneck: int = 64,
rope=None,
use_pose_rope: bool = False,
use_pose_gate_mod: bool = False,
**kwargs,
) -> SSEGLA:
super().__init__()
self.rope = rope
self.use_pose_rope = use_pose_rope
self.mode = mode
self.hidden_size = hidden_size
self.expand_v = expand_v
assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \
"num_reader and num_writer must be less than num_sparse_partition."
assert sse_implementation in ["mask", "varlen"], \
f"Unknown SSE implementation {sse_implementation}"
self.num_sparse_partition = num_sparse_partition
self.num_writer = num_writer
self.num_reader = num_reader
self.sse_implementation = {
"mask": self.sse_linear_attention_mask,
"varlen": self.sse_linear_attention_varlen,
}[sse_implementation]
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.use_q_softmax = use_q_softmax
self.use_k_softmax = use_k_softmax
self.emulq = emulq
self.emulk = emulk
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, bias=False)
self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.key_dim, bias=False))
self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.key_dim, bias=False))
self.gate_logit_normalizer = gate_logit_normalizer
self.gk_proj = nn.ModuleList([nn.Sequential(nn.Linear(hidden_size, gate_low_rank_dim, bias=False),
nn.Linear(gate_low_rank_dim, self.key_dim, bias=True))
for _ in range(2)])
self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False)
if use_short_conv:
self.conv_size = conv_size
self.q_conv1d_shared = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
self.k_conv1d_shared = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
if use_output_gate:
self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, 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)
# ---- CGLA: pose injection modules (only when pose_dim is provided) ----
# Design rationale:
# - pose_encoder: lifts per-token pose (e.g. 6-dim Plucker rays) into the
# model's hidden space so all four injection heads share a common
# pose representation per block.
# - pose_q_proj / pose_k_proj: low-rank projections (bottleneck = head_v_dim
# by default, matching the existing lora_q/k_proj shape) whose outputs
# are added to q2 and k2 BEFORE the activation. This makes the sparse
# stream's read/write pose-aware while leaving q1/k1 (shared stream)
# untouched -> shared stream stays view-invariant.
# - pose_gk_proj: low-rank (bottleneck = gate_low_rank_dim) projection
# added to the sparse gate pre-activation; lets the gate modulate
# forgetting based on viewpoint change.
# - pose_e_proj: direct projection added to the routing logits; this is
# the primary knob that turns sparse partitions into viewpoint buckets.
#
# Zero-init policy (LoRA-style): the "up"-Linear of each injection is
# zeroed so that at step 0 pose contributes exactly 0 to q2/k2/gk2/eta,
# giving behavior identical to vanilla SSEGLA. Gradients on the "up"
# weights are non-zero from step 1 onward, unlocking the "down" weights
# and the encoder in subsequent steps.
self.pose_dim = pose_dim
self.pose_bottleneck = pose_bottleneck
if pose_dim is not None and pose_dim > 0:
self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False)
# sparse Q/K injection (matches lora_q/k_proj shape style)
self.pose_q_proj = nn.Sequential(
nn.Linear(hidden_size, pose_bottleneck, bias=False),
nn.Linear(pose_bottleneck, self.key_dim, bias=False),
)
self.pose_k_proj = nn.Sequential(
nn.Linear(hidden_size, pose_bottleneck, bias=False),
nn.Linear(pose_bottleneck, self.key_dim, bias=False),
)
# sparse gate injection (matches gk_proj shape: low-rank = gate_low_rank_dim)
self.pose_gk_proj = nn.Sequential(
nn.Linear(hidden_size, gate_low_rank_dim, bias=False),
nn.Linear(gate_low_rank_dim, self.key_dim, bias=False),
)
# routing injection (direct)
self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False)
# Zero-init the "up" projection of each injection -> at step 0 the
# contribution of pose to q2/k2/gk2/eta is exactly 0.
nn.init.zeros_(self.pose_q_proj[1].weight)
nn.init.zeros_(self.pose_k_proj[1].weight)
nn.init.zeros_(self.pose_gk_proj[1].weight)
nn.init.zeros_(self.pose_e_proj.weight)
self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None
self.pose_gate_mod = nn.Linear(hidden_size, self.key_dim, bias=True) if use_pose_gate_mod else None
if self.pose_gate_mod is not None:
nn.init.zeros_(self.pose_gate_mod.weight)
nn.init.zeros_(self.pose_gate_mod.bias)
else:
self.pose_encoder = None
self.pose_rope = None
self.pose_gate_mod = None
def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None):
"""
q1: [bsz, qlen, nhead, head_dim]
q2: [bsz, qlen, nhead, head_dim]
k1: [bsz, klen, nhead, head_dim]
k2: [bsz, klen, nhead, head_dim]
v: [bsz, klen, nhead, head_dim]
gk1: [bsz, klen, nhead, head_dim]
gk2: [bsz, klen, nhead, head_dim]
eta: [bsz, klen, num_sparse_partition]
"""
assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader"
bsz, q_len, nhead, _ = q1.shape
if q_len <= 64:
mode = 'fused_recurrent'
else:
mode = self.mode
v1 = v
v2 = v
if cu_seqlens is None:
cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device)
q1, k1, gk1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, gk1, v]]
q2, k2, gk2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, gk2, v]]
S = len(cu_seqlens) - 1
if use_cache:
recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \
torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device)
recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \
torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device)
q2, k2, v2, gk2, _, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, gk2, None, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk)
aux_loss = torch.zeros(()).to(eta)
if self.training:
p = torch.mean(eta.float(), dim=(0, 1))
f = torch.mean(mask.float(), dim=(0, 1))
aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer
# print(f"layer {self.layer_idx}, aux_loss {aux_loss}")
q, k, gk, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, gk1, v1), (q2, k2, gk2, v2))]
offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]])
recurrent_state_rec = None
if use_cache:
state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu()
recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0)
if mode == 'fused_recurrent':
o, recurrent_state_rec = fused_recurrent_gla(
q=q,
k=k,
v=v,
gk=gk,
initial_state=recurrent_state_rec,
output_final_state=use_cache,
cu_seqlens=offsets,
)
elif mode == 'chunk':
o, recurrent_state_rec = chunk_gla(
q=q,
k=k,
v=v,
g=gk,
initial_state=recurrent_state_rec,
output_final_state=use_cache,
cu_seqlens=offsets,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
if recurrent_state_rec is not None:
recurrent_state1 = recurrent_state_rec[:S]
recurrent_state2[state_id] = recurrent_state_rec[S:]
recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0)
else:
recurrent_state = None
o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:]
o2_reduce = torch.zeros_like(o1)
o2_reduce.index_add_(dim=1, index=global_sorted, source=o2)
o = o1 + o2_reduce
if bsz > 1:
o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous()
return o, recurrent_state, aux_loss
def sse_linear_attention_mask(self, q1, q2, k1, k2, v, gk1, gk2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None):
"""
q1: [bsz, qlen, nhead, head_dim]
q2: [bsz, qlen, nhead, head_dim]
k1: [bsz, klen, nhead, head_dim]
k2: [bsz, klen, nhead, head_dim]
v: [bsz, klen, nhead, head_dim]
gk1: [bsz, klen, nhead, head_dim]
gk2: [bsz, klen, nhead, head_dim]
eta: [bsz, klen, num_sparse_partition]
"""
bsz, q_len, nhead, _ = q1.shape
if q_len <= 64:
mode = 'fused_recurrent'
else:
mode = self.mode
q2, k2, v2, gk2, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, gk2, eta, self.num_writer, self.num_reader)
# writer-only auxloss
aux_loss = torch.zeros(()).to(eta)
if self.training:
p = torch.mean(eta.float(), dim=(0, 1))
f = torch.mean(mask_w.float(), dim=(0, 1))
aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer
# print(f"layer {self.layer_idx}, aux_loss {aux_loss}")
q, k, gk, v = [torch.cat(pair, dim=-2) for pair in zip((q1, k1, gk1, v), (q2, k2, gk2, v2))]
if mode == 'fused_recurrent':
o, recurrent_state = fused_recurrent_gla(
q=q,
k=k,
v=v,
gk=gk,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
elif mode == 'chunk':
o, recurrent_state = chunk_gla(
q=q,
k=k,
v=v,
g=gk,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1)
o = o.sum(2)
return o, recurrent_state, aux_loss
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,
# CGLA: optional per-token pose features (B, T, pose_dim). If provided and
# self.pose_dim is set, pose is injected into the sparse stream only.
pose_emb: torch.Tensor | None = None,
write_gate: 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
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)
# ---- CGLA: encode pose once per block (shared across injection heads) ----
pose_feat = None
if self.pose_encoder is not None and pose_emb is not None:
# Cast pose to hidden dtype to stay on one compute dtype through the block.
pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype))
if attention_mask is not None:
# Match the unpadded hidden_states layout: (1, sum_seqlens, hidden_size).
pose_feat = index_first_axis(
rearrange(pose_feat, "b s ... -> (b s) ..."), indices
).unsqueeze(0)
if write_gate is not None and attention_mask is not None:
write_gate = index_first_axis(
rearrange(write_gate, "b s ... -> (b s) ..."), indices
).unsqueeze(0)
q1 = self.q_proj(hidden_states)
k1 = self.k_proj(hidden_states)
q2 = q1 + self.lora_q_proj(hidden_states)
k2 = k1 + self.lora_k_proj(hidden_states)
v = self.v_proj(hidden_states)
gk1 = self.gk_proj[0](hidden_states)
gk2 = self.gk_proj[1](hidden_states)
eta = self.e_proj(hidden_states)
# ---- CGLA: pose injection into the sparse stream (pre-activation) ----
# Shared stream (q1, k1, gk1) is intentionally left untouched: it stays
# a view-invariant global memory. Pose drives (a) which sparse partitions
# a token reads/writes (eta), (b) the pose-labeled address for that
# read/write (q2, k2), and (c) how much history to forget within that
# partition (gk2). Because the "up" weights are zero-initialized, at step
# 0 all four additions contribute exactly 0 and the module is numerically
# identical to vanilla SSEGLA.
if pose_feat is not None:
pose_q = self.pose_q_proj(pose_feat)
q2 = q2 + pose_q
k2 = k2 + self.pose_k_proj(pose_feat)
gk2 = gk2 + self.pose_gk_proj(pose_feat)
eta = eta + self.pose_e_proj(pose_feat)
# behavior-neutral diagnostic: ||pose injection|| / ||q2||
self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6)
if self.use_short_conv:
conv_state_q, conv_state_k = None, None
if last_state is not None:
conv_state_q, conv_state_k = last_state['conv_state']
q1, conv_state_q = self.q_conv1d_shared(
x=q1,
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
k1, conv_state_k = self.k_conv1d_shared(
x=k1,
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
q1, q2, k1, k2, gk1, gk2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2, gk1, gk2))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim)
if self.use_q_softmax:
q1 = F.softmax(q1.float(), dim=-1).to(v)
q2 = F.softmax(q2.float(), dim=-1).to(v)
else:
q1 = F.silu(q1)
q2 = F.silu(q2)
if self.use_k_softmax:
k1 = F.softmax(k1.float(), dim=-1).to(v)
k2 = F.softmax(k2.float(), dim=-1).to(v)
else:
k1 = F.silu(k1)
k2 = F.silu(k2)
v = F.silu(v)
if write_gate is not None:
v = v * write_gate.unsqueeze(-1)
gk1 = F.logsigmoid(gk1) / self.gate_logit_normalizer
gk2 = F.logsigmoid(gk2) / self.gate_logit_normalizer
# ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ----
# Multiplicative, single zero-init Linear: grad to it is propto gk (nonzero),
# so unlike the additive zero-up-proj injection it does not vanish at start.
if self.pose_gate_mod is not None and pose_feat is not None:
gm = rearrange(self.pose_gate_mod(pose_feat), '... (h d) -> ... h d', d=self.head_k_dim)
gm = torch.tanh(gm)
self._gatemod_norm = gm.detach().abs().mean()
scale = 1.0 + gm
gk1 = gk1 * scale
gk2 = gk2 * scale
# ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ----
if self.pose_rope is not None and pose_emb is not None and attention_mask is None:
q1 = self.pose_rope(q1, pose_emb)
q2 = self.pose_rope(q2, pose_emb)
k1 = self.pose_rope(k1, pose_emb)
k2 = self.pose_rope(k2, pose_emb)
# ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ----
# q/k are [b, l, h, d] with l = T*H*W in (t,h,w) order; rope wants
# the seq dim at -2, so transpose to [b, h, l, d] and back.
if self.rope is not None:
def _rope(x):
return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype)
q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2)
if self.num_v_heads > self.num_heads:
q1, q2, k1, k2, gk1, gk2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2, gk1, gk2))
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
# fp32 stability: plain CGLA (no pose_rope) leaves q/k unbounded, so the
# bf16 recurrence produces nan on a rising fraction of steps. Run the
# kernel in fp32 (same math, more precision); cast the output back.
_sse_dtype = v.dtype
with torch.autocast(device_type='cuda', enabled=False):
o, recurrent_state, aux_loss = self.sse_implementation(
q1.float(),
q2.float(),
k1.float(),
k2.float(),
v.float(),
gk1.float(),
gk2.float(),
eta.float(),
recurrent_state=recurrent_state,
use_cache=use_cache,
cu_seqlens=cu_seqlens,
)
o = o.to(_sse_dtype)
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=(conv_state_q, conv_state_k) 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, aux_loss), past_key_values
class SSEGDN(nn.Module):
"""
The layer implementaion for [SSE: Scaling Linear Attention with Sparse State Expansion](https://arxiv.org/pdf/2507.16577).
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: 2.0.
head_dim (int, Optional):
The dimension of each head. Default: 256.
num_heads (int, Optional):
The number of heads. Default: 4.
num_v_heads (int, Optional):
The number of heads for the value projection, equal to `num_heads` if `None`.
GVA is applied if `num_v_heads` > `num_heads`. Default: `None`.
mode (str, Optional):
Which Gated DeltaNet kernel to use.
Currently available: `chunk` and `fused_recurrent`.
Default: `chunk`.
use_output_gate (bool, Optional):
Whether to use output gate. Default: `True`.
use_short_conv (bool, Optional):
Whether to use short convolutions. Default: `False`.
allow_neg_eigval (bool, Optional):
Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2.
See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537)
conv_size (int, Optional):
The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4.
conv_bias (bool, Optional):
Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`.
num_sparse_partition (int, optional):
Number of state partitions. Default: 4.
num_writer (int, optional):
Top-k write size (number of writers). Default: 1.
num_reader (int, optional):
Top-k read size (number of readers). Default: 1.
sse_implementation (str, optional):
SSE implementation to use. One of `"varlen"` or `"mask"`. Default: `"varlen"`.
use_q_softmax (bool, optional):
Whether to apply softmax to the query. Default: `False`.
use_k_softmax (bool, optional):
Whether to apply softmax to the key. Default: `True`.
emulq (bool, optional):
Whether to use a read gate operating on the state output (Q). Default: `True`.
emulk (bool, optional):
Whether to use a write gate operating on the state input (KV). Default: `True`.
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.
"""
def __init__(
self,
hidden_size: int = 2048,
expand_v: float = 1.,
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 = False,
allow_neg_eigval: bool = False,
conv_size: int = 4,
conv_bias: bool = False,
num_sparse_partition: int = 4,
num_writer: int = 1,
num_reader: int = 1,
sse_implementation: str = "varlen",
use_q_softmax: bool = False,
use_k_softmax: bool = False,
emulq: bool = True,
emulk: bool = True,
layer_idx: int = None,
norm_eps: float = 1e-5,
# ---- Camera-Guided SSE-GDN (CGLA) extension (mirrors SSEGLA) ----
# If pose_dim is None: behaves exactly like vanilla SSEGDN (no pose
# influence). If pose_dim is an int > 0: per-token camera-pose features
# are injected into the sparse stream only (shared stream stays
# view-invariant): routing eta, sparse q2/k2, sparse gate g2. With
# use_pose_rope, q/k are additionally rotated by pose-derived angles
# (relative camera PE). All pose-injection "up" projections are
# zero-init so at step 0 the model is identical to vanilla SSEGDN.
pose_dim: int = None,
pose_bottleneck: int = 64,
rope=None,
use_pose_rope: bool = False,
use_pose_gate_mod: bool = False,
**kwargs,
) -> SSEGDN:
super().__init__()
self.mode = mode
self.allow_neg_eigval = allow_neg_eigval
self.hidden_size = hidden_size
self.expand_v = expand_v
self.rope = rope
self.use_pose_rope = use_pose_rope
assert num_reader < num_sparse_partition and num_writer < num_sparse_partition, \
"num_reader and num_writer must be less than num_sparse_partition."
assert sse_implementation in ["mask", "varlen"], \
f"Unknown SSE implementation {sse_implementation}"
self.num_sparse_partition = num_sparse_partition
self.num_writer = num_writer
self.num_reader = num_reader
self.sse_implementation = {
"mask": self.sse_linear_attention_mask,
"varlen": self.sse_linear_attention_varlen,
}[sse_implementation]
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.use_q_softmax = use_q_softmax
self.use_k_softmax = use_k_softmax
self.emulq = emulq
self.emulk = emulk
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, bias=False)
self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
self.lora_q_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.key_dim, bias=False))
self.lora_k_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.key_dim, bias=False))
self.a_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False)
self.b_proj = nn.Linear(hidden_size, self.num_v_heads*2, bias=False)
A = torch.empty(self.num_v_heads*2, 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*2) * (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
self.e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False)
if use_short_conv:
self.conv_size = conv_size
self.q_conv1d_shared = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
self.k_conv1d_shared = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation=None,
)
if use_output_gate:
self.g_proj = nn.Sequential(nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, 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)
# ---- CGLA: pose injection modules (only when pose_dim is provided) ----
# Mirrors SSEGLA's camera-guided extension. Differences vs SSEGLA:
# - the SSE-GDN sparse gate is the per-head scalar g2 (chunked from g,
# shape (B, L, num_v_heads)), NOT the per-head-dim gk2 of SSE-GLA.
# So pose_gk_proj outputs num_v_heads (added to g2 AFTER chunk =>
# sparse half only); pose_gate_mod outputs num_v_heads*2 (applied to
# g BEFORE chunk, modulating both g1 and g2, mirroring SSE-GLA's
# gk1/gk2 modulation).
# - q2/k2/eta injection is identical to SSE-GLA (key_dim /
# num_sparse_partition).
# Zero-init policy (LoRA-style): the "up"-Linear of each injection is
# zeroed so that at step 0 pose contributes exactly 0 to q2/k2/g2/eta.
self.pose_dim = pose_dim
self.pose_bottleneck = pose_bottleneck
if pose_dim is not None and pose_dim > 0:
self.pose_encoder = nn.Linear(pose_dim, hidden_size, bias=False)
# sparse Q/K injection (matches lora_q/k_proj shape style)
self.pose_q_proj = nn.Sequential(
nn.Linear(hidden_size, pose_bottleneck, bias=False),
nn.Linear(pose_bottleneck, self.key_dim, bias=False),
)
self.pose_k_proj = nn.Sequential(
nn.Linear(hidden_size, pose_bottleneck, bias=False),
nn.Linear(pose_bottleneck, self.key_dim, bias=False),
)
# sparse gate injection: SSE-GDN's gate is the per-head scalar g
# (num_v_heads*2 pre-chunk -> g1, g2 each (B, L, num_v_heads)). The
# additive injection is applied post-chunk on g2 (sparse half only),
# so the projection outputs num_v_heads.
self.pose_gk_proj = nn.Sequential(
nn.Linear(hidden_size, pose_bottleneck, bias=False),
nn.Linear(pose_bottleneck, self.num_v_heads, bias=False),
)
# routing injection (direct)
self.pose_e_proj = nn.Linear(hidden_size, self.num_sparse_partition, bias=False)
# Zero-init the "up" projection of each injection -> at step 0 the
# contribution of pose to q2/k2/g2/eta is exactly 0.
nn.init.zeros_(self.pose_q_proj[1].weight)
nn.init.zeros_(self.pose_k_proj[1].weight)
nn.init.zeros_(self.pose_gk_proj[1].weight)
nn.init.zeros_(self.pose_e_proj.weight)
self.pose_rope = PoseRoPE(pose_dim, self.head_k_dim) if use_pose_rope else None
self.pose_gate_mod = nn.Linear(hidden_size, self.num_v_heads * 2, bias=True) if use_pose_gate_mod else None
if self.pose_gate_mod is not None:
nn.init.zeros_(self.pose_gate_mod.weight)
nn.init.zeros_(self.pose_gate_mod.bias)
else:
self.pose_encoder = None
self.pose_rope = None
self.pose_gate_mod = None
def sse_linear_attention_varlen(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None):
"""
q1: [bsz, qlen, nhead, head_dim]
q2: [bsz, qlen, nhead, head_dim]
k1: [bsz, klen, nhead, head_dim]
k2: [bsz, klen, nhead, head_dim]
v: [bsz, klen, nhead, head_dim]
g1: [bsz, klen, nhead]
g2: [bsz, klen, nhead]
b1: [bsz, klen, nhead]
b2: [bsz, klen, nhead]
eta: [bsz, klen, num_sparse_partition]
"""
assert self.num_writer == self.num_reader, "varlen only support num_writer == num_reader"
bsz, q_len, nhead, _ = q1.shape
# change to inference mode.
mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode
if self.training:
assert mode == 'chunk', "Only chunk mode is supported in training."
v1 = v
v2 = v
if cu_seqlens is None:
cu_seqlens = torch.arange(0, (bsz + 1) * q_len, q_len, dtype=torch.int32, device=q1.device)
q1, k1, v1 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q1, k1, v]]
q2, k2, v2 = [rearrange(src, 'b l h d -> 1 (b l) h d').contiguous() for src in [q2, k2, v]]
g1, g2, b1, b2 = [rearrange(src, 'b l h -> 1 (b l) h').contiguous() for src in [g1, g2, b1, b2]]
S = len(cu_seqlens) - 1
if use_cache:
recurrent_state1 = recurrent_state[:S] if recurrent_state is not None else \
torch.zeros(S, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device)
recurrent_state2 = recurrent_state[S:] if recurrent_state is not None else \
torch.zeros(S*self.num_sparse_partition, self.num_heads, self.head_dim, self.head_dim).to(torch.float32).to(v.device)
q2, k2, v2, g2, b2, eta, mask, offsets, state_sizes, global_sorted = sort_along_l(q2, k2, v2, g2, b2, eta, cu_seqlens, self.num_writer, self.emulq, self.emulk)
aux_loss = torch.zeros(()).to(eta)
if self.training:
p = torch.mean(eta.float(), dim=(0, 1))
f = torch.mean(mask.float(), dim=(0, 1))
aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer
# print(f"layer {self.layer_idx}, aux_loss {aux_loss}")
q, k, g, b, v = [torch.cat(pair, dim=1) for pair in zip((q1, k1, g1, b1, v1), (q2, k2, g2, b2, v2))]
offsets = torch.cat([cu_seqlens.to(offsets), offsets[1:] + cu_seqlens[-1]])
recurrent_state_rec = None
if use_cache:
state_id = torch.nonzero(state_sizes.flatten(), as_tuple=True)[0].cpu()
recurrent_state_rec = torch.cat((recurrent_state1, recurrent_state2[state_id]), dim=0)
if mode == 'fused_recurrent':
o, recurrent_state_rec = fused_recurrent_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=b,
initial_state=recurrent_state_rec,
output_final_state=use_cache,
cu_seqlens=offsets,
use_qk_l2norm_in_kernel=True,
)
elif mode == 'chunk':
o, recurrent_state_rec = chunk_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=b,
initial_state=recurrent_state_rec,
output_final_state=use_cache,
cu_seqlens=offsets,
use_qk_l2norm_in_kernel=True,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
if recurrent_state_rec is not None:
recurrent_state1 = recurrent_state_rec[:S]
recurrent_state2[state_id] = recurrent_state_rec[S:]
recurrent_state = torch.cat((recurrent_state1, recurrent_state2), dim=0)
else:
recurrent_state = None
o1, o2 = o[:, :cu_seqlens[-1]], o[:, cu_seqlens[-1]:]
o2_reduce = torch.zeros_like(o1)
o2_reduce.index_add_(dim=1, index=global_sorted, source=o2)
o = o1 + o2_reduce
if bsz > 1:
o = rearrange(o, "1 (b l) h d -> b l h d", b=bsz).contiguous()
return o, recurrent_state, aux_loss
def sse_linear_attention_mask(self, q1, q2, k1, k2, v, g1, g2, b1, b2, eta, recurrent_state=None, use_cache=False, cu_seqlens=None):
"""
q1: [bsz, qlen, nhead, head_dim]
q2: [bsz, qlen, nhead, head_dim]
k1: [bsz, klen, nhead, head_dim]
k2: [bsz, klen, nhead, head_dim]
v: [bsz, klen, nhead, head_dim]
g1: [bsz, klen, nhead]
g2: [bsz, klen, nhead]
b1: [bsz, klen, nhead]
b2: [bsz, klen, nhead]
eta: [bsz, klen, num_sparse_partition]
"""
bsz, q_len, nhead, _ = q1.shape
# change to inference mode.
mode = 'fused_recurrent' if q_len // self.num_sparse_partition <= 64 else self.mode
if self.training:
assert mode == 'chunk', "Only chunk mode is supported in training."
q2, k2, v2, _, eta, mask_w, mask_r = softmax_and_mask(q2, k2, v, v, eta, self.num_writer, self.num_reader)
g2, b2 = [repeat(x, "b l h -> b l n h", n=self.num_sparse_partition) for x in (g2, b2)]
mask_r = mask_r[..., None]
g2, b2 = g2 * mask_r, b2 * mask_r
g2, b2 = [rearrange(x, "b l n h -> b l (n h)") for x in (g2, b2)]
# writer-only auxloss
aux_loss = torch.zeros(()).to(eta)
if self.training:
p = torch.mean(eta.float(), dim=(0, 1))
f = torch.mean(mask_w.float(), dim=(0, 1))
aux_loss = torch.sum(p * f) * self.num_sparse_partition / self.num_writer
# print(f"layer {self.layer_idx}, aux_loss {aux_loss}")
q, k, g, b, v = [torch.cat(pair, dim=2) for pair in zip((q1, k1, g1, b1, v), (q2, k2, g2, b2, v2))]
if mode == 'chunk':
o, recurrent_state = chunk_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=b,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
use_qk_l2norm_in_kernel=True,
)
elif mode == 'fused_recurrent':
o, recurrent_state = fused_recurrent_gated_delta_rule(
q=q,
k=k,
v=v,
g=g,
beta=b,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
use_qk_l2norm_in_kernel=True,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
o = rearrange(o, "b l (n h) d -> b l n h d", n=self.num_sparse_partition+1)
o = o.sum(2)
return o, recurrent_state, aux_loss
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,
# CGLA: optional per-token pose features (B, T, pose_dim). If provided and
# self.pose_dim is set, pose is injected into the sparse stream only.
pose_emb: torch.Tensor | None = None,
write_gate: 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
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)
# ---- CGLA: encode pose once per block (shared across injection heads) ----
pose_feat = None
if self.pose_encoder is not None and pose_emb is not None:
pose_feat = self.pose_encoder(pose_emb.to(hidden_states.dtype))
if attention_mask is not None:
pose_feat = index_first_axis(
rearrange(pose_feat, "b s ... -> (b s) ..."), indices
).unsqueeze(0)
if write_gate is not None and attention_mask is not None:
write_gate = index_first_axis(
rearrange(write_gate, "b s ... -> (b s) ..."), indices
).unsqueeze(0)
q1 = self.q_proj(hidden_states)
k1 = self.k_proj(hidden_states)
q2 = q1 + self.lora_q_proj(hidden_states)
k2 = k1 + self.lora_k_proj(hidden_states)
v = self.v_proj(hidden_states)
b = self.b_proj(hidden_states).sigmoid()
if self.allow_neg_eigval:
b = b * 2.
g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias)
# ---- CGLA: pose gate-rate modulation (camera motion -> forgetting) ----
# Multiplicative, single zero-init Linear on g (pre-chunk, num_v_heads*2).
if self.pose_gate_mod is not None and pose_feat is not None:
gm = torch.tanh(self.pose_gate_mod(pose_feat).float())
self._gatemod_norm = gm.detach().abs().mean()
g = g * (1.0 + gm.to(g.dtype))
b1, b2 = torch.chunk(b, 2, dim=-1)
g1, g2 = torch.chunk(g, 2, dim=-1)
eta = self.e_proj(hidden_states)
# ---- CGLA: pose injection into the sparse stream (pre-activation) ----
# Shared stream (q1, k1, g1, b1) is left untouched; pose drives the sparse
# read/write address (q2, k2), the sparse forgetting gate (g2), and the
# viewpoint routing (eta). Zero-init "up" weights => 0 at step 0.
if pose_feat is not None:
pose_q = self.pose_q_proj(pose_feat)
q2 = q2 + pose_q
k2 = k2 + self.pose_k_proj(pose_feat)
# g2 is fp32 (cast above); align the injection dtype to match.
g2 = g2 + self.pose_gk_proj(pose_feat).to(g2.dtype)
eta = eta + self.pose_e_proj(pose_feat)
self._pose_norm = pose_q.detach().norm() / (q2.detach().norm() + 1e-6)
if self.use_short_conv:
conv_state_q, conv_state_k = None, None
if last_state is not None:
conv_state_q, conv_state_k = last_state['conv_state']
q1, conv_state_q = self.q_conv1d_shared(
x=q1,
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
k1, conv_state_k = self.k_conv1d_shared(
x=k1,
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
q1, q2, k1, k2 = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q1, q2, k1, k2))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim)
if self.use_q_softmax:
q1 = F.softmax(q1.float(), dim=-1).to(v)
q2 = F.softmax(q2.float(), dim=-1).to(v)
else:
q1 = F.silu(q1)
q2 = F.silu(q2)
if self.use_k_softmax:
k1 = F.softmax(k1.float(), dim=-1).to(v)
k2 = F.softmax(k2.float(), dim=-1).to(v)
else:
k1 = F.silu(k1)
k2 = F.silu(k2)
v = F.silu(v)
# ---- CGLA: write gate on v (dfot noise_write_gate) ----
if write_gate is not None:
v = v * write_gate.unsqueeze(-1)
# ---- CGLA: Pose-RoPE (relative camera rotation into q.k bilinear) ----
if self.pose_rope is not None and pose_emb is not None and attention_mask is None:
q1 = self.pose_rope(q1, pose_emb)
q2 = self.pose_rope(q2, pose_emb)
k1 = self.pose_rope(k1, pose_emb)
k2 = self.pose_rope(k2, pose_emb)
# ---- CGLA: 3D RoPE on q/k (both shared and sparse streams) ----
if self.rope is not None:
def _rope(x):
return self.rope(x.transpose(1, 2)).transpose(1, 2).to(x.dtype)
q1, q2, k1, k2 = _rope(q1), _rope(q2), _rope(k1), _rope(k2)
if self.num_v_heads > self.num_heads:
q1, q2, k1, k2 = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads), (q1, q2, k1, k2))
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
import os as _dbgos
if _dbgos.environ.get('CGLA_NAN_DEBUG'):
import torch as _dt
for _nm, _tn in [('q1',q1),('q2',q2),('k1',k1),('k2',k2),('v',v),('g1',g1),('g2',g2),('b1',b1),('b2',b2),('eta',eta)]:
if _tn is not None and not bool(_dt.isfinite(_tn).all()):
with open('/data1/echo_cgla_runs/nan_debug.log','a') as _f:
_f.write('NONFINITE_INPUT layer=%s tensor=%s\n' % (self.layer_idx, _nm))
break
o, recurrent_state, aux_loss = self.sse_implementation(
q1,
q2,
k1,
k2,
v,
g1,
g2,
b1,
b2,
eta,
recurrent_state=recurrent_state,
use_cache=use_cache,
cu_seqlens=cu_seqlens,
)
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=(conv_state_q, conv_state_k) 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, aux_loss), past_key_values
|