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SeqCond model — self-contained HuggingFace implementation.
All model code is embedded here so that trust_remote_code=True works without
any dependency on the original seqcond package.
Architecture:
- Hybrid recurrent-transformer: every (seqcond_ratio+1)-th block (1-indexed)
is a standard Transformer decoder block; the rest are SeqCond blocks.
- SeqCond blocks use complex-exponential accumulators (den_acc, re_acc, im_acc)
for O(1) per-token autoregressive decoding.
- Transformer blocks use GQA with RoPE and KV-cache for autoregressive decoding.
"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_seqcond import SeqCondConfig
# ---------------------------------------------------------------------------
# Optional Triton kernels (accelerates SeqCond step, not required)
# ---------------------------------------------------------------------------
try:
from .triton_kernels import (
gated_rmsnorm_triton,
seqcond_step_triton,
TRITON_AVAILABLE,
)
except ImportError:
gated_rmsnorm_triton = None
TRITON_AVAILABLE = False
seqcond_step_triton = None
# ---------------------------------------------------------------------------
# Normalisation layers
# ---------------------------------------------------------------------------
class RMSNorm(nn.Module):
def __init__(self, hidden_size: int, epsilon: float = 1e-5):
super().__init__()
self.epsilon = epsilon
self.scale = nn.Parameter(torch.ones(hidden_size))
def forward(self, x: torch.Tensor) -> torch.Tensor:
orig = x.dtype
x = x.float()
x = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.epsilon)
return (x * self.scale.float()).to(orig)
class GatedRMSNorm(nn.Module):
"""RMSNorm with SiLU gating: rmsnorm(x * silu(residual))."""
def __init__(self, hidden_size: int, epsilon: float = 1e-6):
super().__init__()
self.epsilon = epsilon
self.weight = nn.Parameter(torch.ones(hidden_size))
def forward(self, x: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
orig = x.dtype
x = x.float() * F.silu(residual.float())
x = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.epsilon)
return (x * self.weight.float()).to(orig)
# ---------------------------------------------------------------------------
# Rotary Position Embedding
# ---------------------------------------------------------------------------
def precompute_freqs(maxlen: int, head_dim: int) -> Tuple[torch.Tensor, torch.Tensor]:
half_d = head_dim // 2
pos = np.arange(maxlen)[:, None]
dim = np.arange(half_d)[None, :]
angles = pos * (1.0 / (10000 ** (dim / half_d)))
cos = torch.from_numpy(np.cos(angles).astype(np.float32))
sin = torch.from_numpy(np.sin(angles).astype(np.float32))
return cos, sin
def apply_rope(tensor: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
dim = tensor.shape[-1] // 2
cos = cos[..., :dim]
sin = sin[..., :dim]
x1, x2 = tensor[..., :dim], tensor[..., dim:]
return torch.cat([x1 * cos - x2 * sin, x2 * cos + x1 * sin], dim=-1).view(tensor.shape)
# ---------------------------------------------------------------------------
# Transformer decoder block (GQA + RoPE)
# ---------------------------------------------------------------------------
class RotarySelfAttention(nn.Module):
def __init__(
self,
d_model: int,
num_heads: int,
num_kv_heads: Optional[int] = None,
dropout: float = 0.0,
qk_norm: bool = False,
qk_norm_eps: float = 1e-6,
):
super().__init__()
self.d_model = d_model
self.num_heads = num_heads
self._num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
self.num_groups = num_heads // self._num_kv_heads
self.head_dim = d_model // num_heads
self.dropout = dropout
self.qk_norm = qk_norm
self.qk_norm_eps = qk_norm_eps
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.k_proj = nn.Linear(d_model, self._num_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(d_model, self._num_kv_heads * self.head_dim, bias=False)
self.out_proj = nn.Linear(d_model, d_model, bias=False)
def _repeat_kv(self, x: torch.Tensor) -> torch.Tensor:
if self.num_groups == 1:
return x
b, l = x.shape[:2]
extra = x.shape[2:]
x = x.view(b, l, self._num_kv_heads, 1, *extra[1:])
x = x.expand(b, l, self._num_kv_heads, self.num_groups, *extra[1:])
return x.reshape(b, l, self.num_heads, *extra[1:])
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
mask: Optional[torch.Tensor] = None,
return_state: bool = False,
):
b, l = x.shape[0], x.shape[1]
q = self.q_proj(x).reshape(b, l, self.num_heads, self.head_dim)
k = self.k_proj(x).reshape(b, l, self._num_kv_heads, self.head_dim)
v = self.v_proj(x).reshape(b, l, self._num_kv_heads, self.head_dim)
q = apply_rope(q, cos, sin)
cos_kv = cos[:, :, : self._num_kv_heads, :] if self._num_kv_heads < self.num_heads else cos
sin_kv = sin[:, :, : self._num_kv_heads, :] if self._num_kv_heads < self.num_heads else sin
k = apply_rope(k, cos_kv, sin_kv)
if self.qk_norm:
q_f = q.float(); k_f = k.float()
q = (q_f * torch.rsqrt(q_f.pow(2).mean(-1, keepdim=True) + self.qk_norm_eps)).to(q.dtype)
k = (k_f * torch.rsqrt(k_f.pow(2).mean(-1, keepdim=True) + self.qk_norm_eps)).to(k.dtype)
k_cache = k; v_cache = v
k = self._repeat_kv(k); v = self._repeat_kv(v)
scale = 1.0 / math.sqrt(self.head_dim)
scores = torch.einsum("blhd,bmhd->bhlm", q, k) * scale
causal = torch.tril(torch.ones(l, l, dtype=torch.bool, device=x.device)).unsqueeze(0).unsqueeze(0)
scores = torch.where(causal, scores, torch.full_like(scores, -1e4))
attn = F.softmax(scores.float(), dim=-1).to(v.dtype)
if self.dropout > 0 and self.training:
attn = F.dropout(attn, p=self.dropout)
out = torch.einsum("bhql,blhd->bqhd", attn, v).reshape(b, l, self.d_model).to(x.dtype)
if return_state:
return self.out_proj(out), (k_cache, v_cache)
return self.out_proj(out)
def step(
self,
x_t: torch.Tensor,
kv_cache: Tuple[torch.Tensor, torch.Tensor],
pos: torch.Tensor,
cos_t: torch.Tensor,
sin_t: torch.Tensor,
seq_len: Optional[int] = None,
) -> Tuple[torch.Tensor, Tuple]:
b = x_t.shape[0]
q = self.q_proj(x_t).reshape(b, 1, self.num_heads, self.head_dim)
k_new = self.k_proj(x_t).reshape(b, 1, self._num_kv_heads, self.head_dim)
v_new = self.v_proj(x_t).reshape(b, 1, self._num_kv_heads, self.head_dim)
q = apply_rope(q, cos_t, sin_t)
cos_kv = cos_t[:, :, : self._num_kv_heads, :] if self._num_kv_heads < self.num_heads else cos_t
sin_kv = sin_t[:, :, : self._num_kv_heads, :] if self._num_kv_heads < self.num_heads else sin_t
k_new = apply_rope(k_new, cos_kv, sin_kv)
if self.qk_norm:
q_f = q.float(); k_f = k_new.float()
q = (q_f * torch.rsqrt(q_f.pow(2).mean(-1, keepdim=True) + self.qk_norm_eps)).to(q.dtype)
k_new = (k_f * torch.rsqrt(k_f.pow(2).mean(-1, keepdim=True) + self.qk_norm_eps)).to(k_new.dtype)
k_cache, v_cache = kv_cache
pos_idx = pos.long().view(b, 1, 1, 1).expand(-1, 1, k_new.size(2), k_new.size(3))
k_cache.scatter_(1, pos_idx, k_new.to(k_cache.dtype))
v_cache.scatter_(1, pos_idx, v_new.to(v_cache.dtype))
if seq_len is not None:
k_slice, v_slice = k_cache[:, :seq_len], v_cache[:, :seq_len]; L = seq_len
else:
k_slice, v_slice = k_cache, v_cache; L = k_cache.shape[1]
k_r = self._repeat_kv(k_slice); v_r = self._repeat_kv(v_slice)
mask = torch.arange(L, device=k_cache.device).view(1, 1, 1, L) > pos.long().view(b, 1, 1, 1)
scale = 1.0 / math.sqrt(self.head_dim)
scores = torch.einsum("bqhd,bkhd->bhqk", q, k_r) * scale
scores = scores.masked_fill(mask, float("-inf"))
attn = F.softmax(scores.float(), dim=-1).to(v_r.dtype)
out = torch.einsum("bhqk,bkhd->bqhd", attn, v_r).reshape(b, self.d_model).to(x_t.dtype)
return self.out_proj(out), (k_cache, v_cache)
class TransformerDecoderBlock(nn.Module):
def __init__(
self,
d_model: int,
num_heads: int,
d_ff: int,
num_kv_heads: Optional[int] = None,
dropout: float = 0.0,
norm_eps: float = 1e-6,
qk_norm: bool = False,
qk_norm_eps: float = 1e-6,
):
super().__init__()
self.norm1 = RMSNorm(d_model, epsilon=norm_eps)
self.attn = RotarySelfAttention(d_model, num_heads, num_kv_heads, dropout, qk_norm, qk_norm_eps)
self.norm2 = RMSNorm(d_model, epsilon=norm_eps)
self.ff_in = nn.Linear(d_model, 2 * d_ff, bias=True)
self.ff_out = nn.Linear(d_ff, d_model, bias=True)
self.dropout = dropout
def forward(self, x, cos, sin, mask=None, return_state=False):
y = self.norm1(x)
if return_state:
y, kv = self.attn(y, cos=cos, sin=sin, mask=mask, return_state=True)
else:
y = self.attn(y, cos=cos, sin=sin, mask=mask)
if self.dropout > 0 and self.training:
y = F.dropout(y, p=self.dropout)
x = x + y
y = self.norm2(x)
u, v = self.ff_in(y).chunk(2, dim=-1)
y = self.ff_out(F.silu(v) * u)
if self.dropout > 0 and self.training:
y = F.dropout(y, p=self.dropout)
out = x + y
return (out, kv) if return_state else out
def step(self, x_t, kv_cache, pos, cos_t, sin_t, seq_len=None):
y = self.norm1(x_t)
y, new_kv = self.attn.step(y, kv_cache, pos, cos_t, sin_t, seq_len=seq_len)
x_t = x_t + y
y = self.norm2(x_t)
u, v = self.ff_in(y).chunk(2, dim=-1)
return x_t + self.ff_out(F.silu(v) * u), new_kv
# ---------------------------------------------------------------------------
# SeqCond attention block
# ---------------------------------------------------------------------------
class SeqCondAttention(nn.Module):
def __init__(
self,
d_model: int,
num_heads: int = 12,
num_query_heads: int = 6,
num_anchor_heads: int = 0,
num_thetas: int = 1,
conv_kernel_size: int = 4,
expand_factor: int = 1,
out_expand_factor: int = 3,
dropout: float = 0.0,
maxlen: Optional[int] = None,
**kwargs,
):
super().__init__()
assert num_heads % num_query_heads == 0
self.d_model = d_model
self.K = num_heads
self.K_q = num_query_heads
self.n_rep = num_heads // num_query_heads
self.M = num_thetas
self.num_decay_heads = num_heads - num_anchor_heads
self.num_anchor_heads = num_anchor_heads
self.conv_kernel_size = conv_kernel_size
self.dropout_rate = dropout
self.maxlen = maxlen
d_inner = int(d_model * expand_factor)
self.H = max(1, d_inner // (self.K * self.M))
self.dim_memory = self.K * self.H
self.dim_query_head = self.H * self.M * 2
self.dim_query_total = self.K_q * self.dim_query_head
self.dim_expand = self.H * out_expand_factor
self.dim_swiglu_head = self.dim_expand * 2
self.dim_swiglu_total = self.K * self.dim_swiglu_head
self.dim_mem_total = self.dim_memory + self.K
self.dim_conv_total = self.dim_mem_total + self.dim_query_total
self.in_proj = nn.Linear(d_model, self.dim_conv_total, bias=False)
self.conv_weight = nn.Parameter(torch.empty(self.dim_conv_total, 1, conv_kernel_size))
nn.init.kaiming_normal_(self.conv_weight)
# Cached buffers (computed lazily)
self.register_buffer("_conv_kernel_t", None)
self.register_buffer("_theta_cached", None)
self.register_buffer("_w_int_cached", None)
self.register_buffer("_decay_slopes_cached", None)
self.register_buffer("_anchor_slopes_cached", None)
self.register_buffer("_phase_scale_b", None)
self.register_buffer("_score_scale_b", None)
self.register_buffer("_score_bias_b", None)
self._triton_out_re_buffer = None
self._triton_out_im_buffer = None
self._triton_norm_buffer = None
if self.M == 1:
init_theta = np.geomspace(0.001, 3.0, self.K).reshape(1, 1, self.K, 1, 1)
init_theta = np.tile(init_theta, (1, 1, 1, self.H, 1))
x = np.clip((init_theta - 0.001) / 2.999, 1e-4, 1 - 1e-4)
self.theta_raw = nn.Parameter(torch.from_numpy((np.log(x) - np.log(1 - x)).astype(np.float32)))
self.w_int_raw = nn.Parameter(torch.zeros(1, 1, self.K_q, self.n_rep, self.H, 1))
else:
init_vals = np.geomspace(0.001, 3.0, self.M).reshape(1, 1, 1, 1, self.M)
init_vals = np.tile(init_vals, (1, 1, self.K, self.H, 1))
self.theta_d_raw = nn.Parameter(torch.from_numpy(np.log(np.exp(init_vals) - 1.0 + 1e-4).astype(np.float32)))
self.w_int_raw = nn.Parameter(torch.zeros(1, 1, self.K_q, self.n_rep, self.H, self.M))
if self.num_decay_heads > 0:
self.decay_slopes = nn.Parameter(
torch.from_numpy(np.log(np.exp(np.geomspace(0.001, 0.1, self.num_decay_heads)) - 1).astype(np.float32))
)
if self.num_anchor_heads > 0:
self.anchor_slopes = nn.Parameter(
torch.from_numpy(np.log(np.exp(np.geomspace(0.01, 0.1, self.num_anchor_heads)) - 1).astype(np.float32))
)
self.score_scale = nn.Parameter(torch.ones(self.K))
self.score_bias = nn.Parameter(torch.zeros(self.K))
self.phase_scale = nn.Parameter(torch.ones(self.K))
self.gate_proj = nn.Linear(d_model, self.K * 2 * self.H, bias=False)
self.gated_norm = GatedRMSNorm(self.K * 2 * self.H)
self.W_readout = nn.Parameter(torch.empty(self.K, 2 * self.H, self.dim_swiglu_head))
nn.init.xavier_uniform_(self.W_readout)
self.out_proj = nn.Linear(self.dim_swiglu_total // 2, d_model, bias=False)
def forward(self, x: torch.Tensor, mask=None, return_state: bool = False):
B, L, D = x.shape
z_conv = self.in_proj(x)
z_conv_t = F.pad(z_conv.transpose(1, 2), (self.conv_kernel_size - 1, 0))
z_conv = F.silu(F.conv1d(z_conv_t, self.conv_weight, groups=self.dim_conv_total).transpose(1, 2))
z_mem = z_conv[..., : self.dim_mem_total]
q_raw = z_conv[..., self.dim_mem_total :]
k_val = z_mem[..., : self.dim_memory].reshape(B, L, self.K, self.H)
s_raw = z_mem[..., self.dim_memory :]
q_raw = q_raw.reshape(B, L, self.K_q, 1, self.H, self.M, 2)
q_re, q_im = q_raw[..., 0], q_raw[..., 1]
if self.M == 1:
theta = 0.001 + 2.999 * torch.sigmoid(self.theta_raw)
else:
theta_d = F.softplus(self.theta_d_raw) + 1e-4
theta_accum = torch.cumsum(theta_d, dim=-1)
theta = 0.001 + (theta_accum / theta_accum[..., -1:]) * 2.999
w_int = torch.exp(self.w_int_raw)
w_int = w_int / (w_int.sum(dim=-1, keepdim=True) + 1e-6)
pos = torch.arange(L, dtype=torch.float32, device=x.device)
log_w_list = []
if self.num_decay_heads > 0:
slopes = F.softplus(self.decay_slopes).view(1, 1, -1)
dist = torch.clamp((self.maxlen or L) - 1 - pos, min=0.0).view(1, L, 1)
log_w_list.append(-slopes * dist)
if self.num_anchor_heads > 0:
log_w_list.append(-F.softplus(self.anchor_slopes).view(1, 1, -1) * pos.view(1, L, 1))
log_tw = torch.cat(log_w_list, dim=2) if log_w_list else torch.zeros(1, L, self.K, device=x.device)
score_raw = self.score_scale.view(1, 1, -1) * s_raw.float() + self.score_bias.view(1, 1, -1)
p_w = (F.softplus(score_raw) * torch.exp(log_tw)).clamp(1e-4, 5000.0)
k_f32 = k_val.float().unsqueeze(-1)
p_w_b = p_w.unsqueeze(-1).unsqueeze(-1)
phase_scale_b = self.phase_scale.view(1, 1, self.K, 1, 1)
k_scaled = k_f32 * phase_scale_b
phi = (k_scaled / (1.0 + k_scaled.abs())) * theta
kvw = k_f32 * p_w_b
re = kvw * torch.cos(phi)
im = kvw * torch.sin(phi)
flat_size = self.K * self.H * self.M
stack = torch.cat([p_w.float(), re.reshape(B, L, -1), im.reshape(B, L, -1)], dim=-1)
cumsum = torch.cumsum(stack, dim=1)
den_acc = cumsum[..., : self.K]
re_acc = cumsum[..., self.K : self.K + flat_size].reshape(B, L, self.K, self.H, self.M)
im_acc = cumsum[..., self.K + flat_size :].reshape(B, L, self.K, self.H, self.M)
inv_den = (1.0 / torch.clamp(den_acc, min=1e-4)).unsqueeze(-1).unsqueeze(-1)
state_re_g = (re_acc * inv_den).reshape(B, L, self.K_q, self.n_rep, self.H, self.M)
state_im_g = (im_acc * inv_den).reshape(B, L, self.K_q, self.n_rep, self.H, self.M)
scale = 1.0 / (self.H ** 0.5)
match_re = ((state_re_g * q_re + state_im_g * q_im) * scale).float()
match_im = ((state_im_g * q_re - state_re_g * q_im) * scale).float()
out_re = ((match_re * w_int.float()).sum(dim=-1)).reshape(B, L, self.K, self.H).to(x.dtype)
out_im = ((match_im * w_int.float()).sum(dim=-1)).reshape(B, L, self.K, self.H).to(x.dtype)
out_complex = self.gated_norm(torch.cat([out_re, out_im], dim=-1).reshape(B, L, -1), self.gate_proj(x))
out_complex = out_complex.reshape(B, L, self.K, 2 * self.H)
y_raw = torch.einsum("blkf,kfn->blkn", out_complex, self.W_readout.to(out_complex.dtype))
y_val, y_gate = y_raw.chunk(2, dim=-1)
output = self.out_proj((y_val * torch.sigmoid(y_gate)).reshape(B, L, -1).to(x.dtype))
if return_state:
z_pre = self.in_proj(x)
buf_sz = self.conv_kernel_size - 1
conv_buf = z_pre[:, -buf_sz:] if L >= buf_sz else torch.cat([
torch.zeros(B, buf_sz - L, self.dim_conv_total, device=x.device, dtype=z_pre.dtype), z_pre], dim=1)
state = (
p_w.sum(dim=1),
re_acc[:, -1],
im_acc[:, -1],
torch.full((B,), L, dtype=torch.float32, device=x.device),
conv_buf,
)
return output, state
return output
def step(self, x_t: torch.Tensor, state: Tuple, use_triton: bool = False) -> Tuple:
B, D = x_t.shape
den_acc, re_acc, im_acc, pos, conv_buffer = state
z_conv = self.in_proj(x_t)
if self._conv_kernel_t is None or self._conv_kernel_t.device != z_conv.device:
self._conv_kernel_t = self.conv_weight[:, 0, :].t().contiguous()
conv_input = torch.cat([conv_buffer, z_conv.unsqueeze(1)], dim=1)
z_conv_act = F.silu((conv_input * self._conv_kernel_t).sum(dim=1))
z_mem = z_conv_act[..., : self.dim_mem_total]
q_raw = z_conv_act[..., self.dim_mem_total :]
k_val = z_mem[..., : self.dim_memory].reshape(B, self.K, self.H)
s_raw = z_mem[..., self.dim_memory :]
q_raw = q_raw.reshape(B, self.K_q, 1, self.H, self.M, 2)
q_re, q_im = q_raw[..., 0], q_raw[..., 1]
if self._theta_cached is None:
if self.M == 1:
self._theta_cached = (0.001 + 2.999 * torch.sigmoid(self.theta_raw))[0, 0]
else:
theta_d = F.softplus(self.theta_d_raw) + 1e-4
theta_accum = torch.cumsum(theta_d, dim=-1)
self._theta_cached = (0.001 + (theta_accum / theta_accum[..., -1:]) * 2.999)[0, 0]
w = torch.exp(self.w_int_raw)
self._w_int_cached = w / (w.sum(dim=-1, keepdim=True) + 1e-6)
self._w_int_cached = self._w_int_cached[0, 0]
theta = self._theta_cached
w_int = self._w_int_cached
if self._decay_slopes_cached is None and self.num_decay_heads > 0:
self._decay_slopes_cached = F.softplus(self.decay_slopes).view(1, -1)
if self._anchor_slopes_cached is None and self.num_anchor_heads > 0:
self._anchor_slopes_cached = F.softplus(self.anchor_slopes).view(1, -1)
if self._score_scale_b is None:
self._score_scale_b = self.score_scale.view(1, -1)
self._score_bias_b = self.score_bias.view(1, -1)
self._phase_scale_b = self.phase_scale.view(1, self.K, 1, 1)
log_w_list = []
if self.num_decay_heads > 0:
dist = (self.maxlen or 2048) - 1 - pos.unsqueeze(-1)
log_w_list.append(-self._decay_slopes_cached * dist.clamp(min=0.0))
if self.num_anchor_heads > 0:
log_w_list.append(-self._anchor_slopes_cached * pos.unsqueeze(-1))
log_tw = torch.cat(log_w_list, dim=1) if log_w_list else torch.zeros(B, self.K, device=x_t.device)
if (
use_triton
and x_t.is_cuda
and self.n_rep == 1
and TRITON_AVAILABLE
and seqcond_step_triton is not None
):
if (
self._triton_out_re_buffer is None
or self._triton_out_re_buffer.shape != (B, self.K, self.H)
or self._triton_out_re_buffer.device != x_t.device
):
self._triton_out_re_buffer = torch.empty(
B, self.K, self.H, device=x_t.device, dtype=torch.float32
)
self._triton_out_im_buffer = torch.empty_like(
self._triton_out_re_buffer
)
out_re, out_im = seqcond_step_triton(
k_val,
s_raw,
q_re.squeeze(2),
q_im.squeeze(2),
re_acc,
im_acc,
den_acc,
theta,
w_int,
self.phase_scale,
self.score_scale,
self.score_bias,
log_tw,
out_re_buffer=self._triton_out_re_buffer,
out_im_buffer=self._triton_out_im_buffer,
)
out_complex = torch.cat([out_re, out_im], dim=-1)
else:
score_raw = self._score_scale_b * s_raw.float() + self._score_bias_b
p_w = (F.softplus(score_raw) * torch.exp(log_tw)).clamp(1e-4, 5000.0)
k_f32 = k_val.float().unsqueeze(-1)
k_scaled = k_f32 * self._phase_scale_b
phi = (k_scaled / (1.0 + k_scaled.abs())) * theta
kvw = k_f32 * p_w.unsqueeze(-1).unsqueeze(-1)
re = kvw * torch.cos(phi)
im = kvw * torch.sin(phi)
den_acc.add_(p_w); re_acc.add_(re); im_acc.add_(im)
inv_den = (1.0 / torch.clamp(den_acc, min=1e-4)).unsqueeze(-1).unsqueeze(-1)
state_re_g = (re_acc * inv_den).reshape(B, self.K_q, self.n_rep, self.H, self.M)
state_im_g = (im_acc * inv_den).reshape(B, self.K_q, self.n_rep, self.H, self.M)
scale = 1.0 / (self.H ** 0.5)
match_re = ((state_re_g * q_re + state_im_g * q_im) * scale).float()
match_im = ((state_im_g * q_re - state_re_g * q_im) * scale).float()
out_re = ((match_re * w_int.float()).sum(-1)).reshape(B, self.K, self.H).to(x_t.dtype)
out_im = ((match_im * w_int.float()).sum(-1)).reshape(B, self.K, self.H).to(x_t.dtype)
out_complex = torch.cat([out_re, out_im], dim=-1)
out_complex = out_complex.reshape(B, self.K, 2 * self.H)
out_complex_flat = out_complex.reshape(B, -1)
gate_for_norm = self.gate_proj(x_t)
if use_triton and x_t.is_cuda and gated_rmsnorm_triton is not None:
if (
self._triton_norm_buffer is None
or self._triton_norm_buffer.shape != out_complex_flat.shape
or self._triton_norm_buffer.device != x_t.device
):
self._triton_norm_buffer = torch.empty(
out_complex_flat.shape,
device=x_t.device,
dtype=torch.float32,
)
out_flat = gated_rmsnorm_triton(
out_complex_flat,
gate_for_norm,
self.gated_norm.weight,
self.gated_norm.epsilon,
out_buffer=self._triton_norm_buffer,
)
else:
out_flat = self.gated_norm(out_complex_flat, gate_for_norm)
out_complex = out_flat.to(x_t.dtype).reshape(B, self.K, 2 * self.H)
y_raw = torch.einsum("bkf,kfn->bkn", out_complex, self.W_readout.to(out_complex.dtype))
y_val, y_gate = y_raw.chunk(2, dim=-1)
out = self.out_proj((y_val * torch.sigmoid(y_gate)).reshape(B, -1).to(x_t.dtype))
pos.add_(1).clamp_(max=(self.maxlen or 2048) - 1)
if self.conv_kernel_size > 1:
if self.conv_kernel_size > 2:
conv_buffer[:, :-1, :].copy_(conv_buffer[:, 1:, :].clone())
conv_buffer[:, -1, :].copy_(z_conv)
return out, (den_acc, re_acc, im_acc, pos, conv_buffer)
class SeqCondBlock(nn.Module):
def __init__(self, d_model: int, norm_eps: float = 1e-6, **kwargs):
super().__init__()
self.norm = RMSNorm(d_model, epsilon=norm_eps)
self.attn = SeqCondAttention(d_model=d_model, **kwargs)
def forward(self, x, mask=None, return_state=False):
if return_state:
out, state = self.attn(self.norm(x), mask=mask, return_state=True)
return x + out, state
return x + self.attn(self.norm(x), mask=mask)
def step(self, x_t, state, use_triton=False):
out, new_state = self.attn.step(self.norm(x_t), state, use_triton=use_triton)
return x_t + out, new_state
# ---------------------------------------------------------------------------
# Core SeqCond language model
# ---------------------------------------------------------------------------
class SeqCondModel(nn.Module):
"""Core SeqCond model (no HF wrapper). Used internally by SeqCondForCausalLM."""
def __init__(self, config: SeqCondConfig):
super().__init__()
self.d_model = config.d_model
self.d_ff = config.d_ff
self.num_layers = config.num_layers
self.vocab_size = config.vocab_size
self.maxlen = config.maxlen
self.num_heads = config.num_heads
self.num_kv_heads = config.num_kv_heads if config.num_kv_heads is not None else config.num_heads
self.seqcond_ratio = config.seqcond_ratio
self.embedding = nn.Embedding(config.vocab_size, config.d_model)
self.use_positional_embedding = config.use_positional_embedding
if config.use_positional_embedding:
self.position_embedding = nn.Embedding(config.maxlen, config.d_model)
head_dim = config.d_model // config.num_heads
cos, sin = precompute_freqs(config.maxlen, head_dim)
self.register_buffer("cos_emb", cos)
self.register_buffer("sin_emb", sin)
self.blocks = nn.ModuleList()
self.block_types = []
for i in range(config.num_layers):
if (i + 1) % (config.seqcond_ratio + 1) == 0:
block = TransformerDecoderBlock(
d_model=config.d_model,
num_heads=config.num_heads,
d_ff=config.d_ff,
num_kv_heads=self.num_kv_heads,
dropout=config.dropout,
qk_norm=config.qk_norm,
qk_norm_eps=config.qk_norm_eps,
)
self.block_types.append("transformer")
else:
block = SeqCondBlock(
d_model=config.d_model,
num_heads=config.seqcond_heads,
num_query_heads=config.num_query_heads,
num_anchor_heads=config.num_anchor_heads,
num_thetas=config.num_thetas,
conv_kernel_size=config.conv_kernel_size,
expand_factor=config.expand_factor,
out_expand_factor=config.out_expand_factor,
dropout=config.dropout,
maxlen=config.maxlen,
)
self.block_types.append("seqcond")
self.blocks.append(block)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
if config.tie_weights:
self.lm_head.weight = self.embedding.weight
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
B, L = input_ids.shape
x = self.embedding(input_ids)
if self.use_positional_embedding:
x = x + self.position_embedding(torch.arange(L, device=input_ids.device))
cos = self.cos_emb[:L].unsqueeze(0).unsqueeze(2).expand(B, L, self.num_heads, -1)
sin = self.sin_emb[:L].unsqueeze(0).unsqueeze(2).expand(B, L, self.num_heads, -1)
for block, bt in zip(self.blocks, self.block_types):
x = block(x, cos, sin) if bt == "transformer" else block(x)
return self.lm_head(x)
def prefill(self, input_ids: torch.Tensor, return_all_logits: bool = False):
B, L = input_ids.shape
device = input_ids.device
x = self.embedding(input_ids)
if self.use_positional_embedding:
x = x + self.position_embedding(torch.arange(L, device=device))
cos = self.cos_emb[:L].unsqueeze(0).unsqueeze(2).expand(B, L, self.num_heads, -1)
sin = self.sin_emb[:L].unsqueeze(0).unsqueeze(2).expand(B, L, self.num_heads, -1)
states = []
for block, bt in zip(self.blocks, self.block_types):
if bt == "transformer":
x, kv = block(x, cos, sin, return_state=True)
k, v = kv
k_cache = torch.zeros(B, self.maxlen, self.num_kv_heads, self.d_model // self.num_heads, device=device, dtype=k.dtype)
v_cache = torch.zeros_like(k_cache)
k_cache[:, :L] = k; v_cache[:, :L] = v
states.append((k_cache, v_cache))
else:
x, state = block(x, return_state=True)
states.append(state)
logits = self.lm_head(x)
if return_all_logits:
return logits, states
return logits[:, -1:, :], states
def init_state(self, batch_size: int, device: torch.device) -> List:
states = []
for block, bt in zip(self.blocks, self.block_types):
if bt == "transformer":
k = torch.zeros(batch_size, self.maxlen, self.num_kv_heads, self.d_model // self.num_heads, device=device)
states.append((k, torch.zeros_like(k)))
else:
a = block.attn
states.append((
torch.zeros(batch_size, a.K, device=device),
torch.zeros(batch_size, a.K, a.H, a.M, device=device),
torch.zeros(batch_size, a.K, a.H, a.M, device=device),
torch.zeros(batch_size, device=device),
torch.zeros(batch_size, a.conv_kernel_size - 1, a.dim_conv_total, device=device),
))
return states
def step(self, token_id: torch.Tensor, states: List, pos=None, seq_len=None, use_triton=False):
B = token_id.size(0)
if pos is None:
for state, bt in zip(states, self.block_types):
if bt == "seqcond":
pos = state[3]; break
if pos is None:
pos = torch.zeros(B, device=token_id.device, dtype=torch.long)
x = self.embedding(token_id).squeeze(1)
pos = pos.clamp(max=self.maxlen - 1)
if self.use_positional_embedding:
x = x + torch.index_select(self.position_embedding.weight, 0, pos.long())
pos_idx = pos.long()
cos_t = torch.index_select(self.cos_emb, 0, pos_idx).unsqueeze(1).unsqueeze(1).expand(B, 1, self.num_heads, -1)
sin_t = torch.index_select(self.sin_emb, 0, pos_idx).unsqueeze(1).unsqueeze(1).expand(B, 1, self.num_heads, -1)
new_states = []
for block, bt, state in zip(self.blocks, self.block_types, states):
if bt == "transformer":
x, ns = block.step(x, state, pos, cos_t, sin_t, seq_len=seq_len)
else:
x, ns = block.step(x, state, use_triton=use_triton)
new_states.append(ns)
return self.lm_head(x), new_states
# ---------------------------------------------------------------------------
# HuggingFace wrapper
# ---------------------------------------------------------------------------
class SeqCondPreTrainedModel(PreTrainedModel):
config_class = SeqCondConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=0.02)
class SeqCondForCausalLM(SeqCondPreTrainedModel):
"""
SeqCond causal language model, HuggingFace-compatible.
Supports:
- Standard HF forward() for training / perplexity evaluation.
- Custom generate() using state-based O(1) decoding.
- generate_batch() for batched generation with per-sample early stopping.
- precompute() / use_cuda_graph=True for CUDA-graph-accelerated decoding.
"""
_CUDA_GRAPH_SEQ_LENS = [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
def __init__(self, config: SeqCondConfig):
super().__init__(config)
self.model = SeqCondModel(config)
self.post_init()
# CUDA graph state
self._cg_graphs: dict = {}
self._cg_logits: dict = {}
self._cg_token: Optional[torch.Tensor] = None
self._cg_states: Optional[list] = None
self._cg_use_triton: bool = False
self._cg_ready: bool = False # True after precompute() has been called
# ------------------------------------------------------------------
# CUDA graph helpers
# ------------------------------------------------------------------
def _cg_get_seq_len(self, pos: int) -> int:
for s in self._CUDA_GRAPH_SEQ_LENS:
if s >= pos + 1:
return s
return self._CUDA_GRAPH_SEQ_LENS[-1]
def _cg_copy_states(self, src, dst):
for s, d in zip(src, dst):
for st, dt in zip(s, d):
dt.copy_(st)
def _cg_capture(self, seq_len: int):
saved = self.model.init_state(1, device=self._cg_token.device)
self._cg_copy_states(self._cg_states, saved)
stream = torch.cuda.Stream()
stream.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(stream):
for _ in range(3):
self.model.step(self._cg_token, self._cg_states,
seq_len=seq_len, use_triton=self._cg_use_triton)
torch.cuda.current_stream().wait_stream(stream)
self._cg_copy_states(saved, self._cg_states)
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
logits, _ = self.model.step(self._cg_token, self._cg_states,
seq_len=seq_len, use_triton=self._cg_use_triton)
self._cg_copy_states(saved, self._cg_states)
self._cg_graphs[seq_len] = graph
self._cg_logits[seq_len] = logits
@torch.no_grad()
def precompute(self, max_seq_len: int = 2048, use_triton: bool = False):
"""Pre-capture CUDA graphs up to max_seq_len. Call once after loading."""
if not torch.cuda.is_available():
return
if self._cg_use_triton != use_triton:
self._cg_graphs = {}
self._cg_logits = {}
self._cg_use_triton = use_triton
device = next(self.parameters()).device
self._cg_token = torch.zeros((1, 1), dtype=torch.long, device=device)
self._cg_states = self.model.init_state(1, device=device)
for s in self._CUDA_GRAPH_SEQ_LENS:
if s > max_seq_len:
break
self._cg_capture(s)
self._cg_ready = True
print(f"Pre-captured {len(self._cg_graphs)} CUDA graphs (triton={use_triton}).")
def get_input_embeddings(self):
return self.model.embedding
def set_input_embeddings(self, value):
self.model.embedding = value
def get_output_embeddings(self):
return self.model.lm_head
def set_output_embeddings(self, value):
self.model.lm_head = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
**kwargs,
) -> CausalLMOutputWithPast:
"""
Standard forward pass (used for training / perplexity).
Note: attention_mask is accepted for API compatibility but is not used
in the forward pass — SeqCond is always causal.
"""
logits = self.model(input_ids)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
)
return CausalLMOutputWithPast(loss=loss, logits=logits)
@staticmethod
def _detect_triton() -> bool:
try:
import triton # noqa: F401
return True
except ImportError:
return False
@torch.no_grad()
def generate(
self,
input_ids: torch.LongTensor,
max_new_tokens: int = 1024,
temperature: float = 0.15,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.1,
eos_token_id: Optional[int] = None,
acceleration: str = "auto",
use_triton: Optional[bool] = None,
use_cuda_graph: Optional[bool] = None,
**kwargs,
) -> torch.LongTensor:
"""
Autoregressive generation with state-based O(1) decoding.
Args:
acceleration: One of ``"auto"`` (default), ``"cuda_graph"``,
``"triton"`` (cuda_graph + triton), or ``"none"``.
``"auto"`` uses CUDA graphs when a GPU is available, and adds
Triton kernels automatically if the triton package is installed.
Explicit ``use_triton`` / ``use_cuda_graph`` kwargs override this.
Returns the full sequence (prompt + generated tokens) as a LongTensor.
"""
# ------------------------------------------------------------------
# Resolve acceleration mode
# ------------------------------------------------------------------
on_cuda = torch.cuda.is_available() and input_ids.device.type == "cuda"
if acceleration == "auto":
_use_cuda_graph = on_cuda
_use_triton = on_cuda and self._detect_triton()
elif acceleration == "triton":
_use_cuda_graph = on_cuda
_use_triton = on_cuda
elif acceleration == "cuda_graph":
_use_cuda_graph = on_cuda
_use_triton = False
else: # "none"
_use_cuda_graph = False
_use_triton = False
# Legacy kwargs override
if use_cuda_graph is not None:
_use_cuda_graph = use_cuda_graph and on_cuda
if use_triton is not None:
_use_triton = use_triton and on_cuda
# Lazy precompute on first generate() call
if _use_cuda_graph and not self._cg_ready:
self.precompute(max_seq_len=2048, use_triton=_use_triton)
elif _use_cuda_graph and self._cg_use_triton != _use_triton:
self.precompute(max_seq_len=2048, use_triton=_use_triton)
use_triton = _use_triton
use_cuda_graph = _use_cuda_graph
if eos_token_id is None:
eos_token_id = self.config.eos_token_id
device = input_ids.device
B = input_ids.size(0)
# Prefill
logits, states = self.model.prefill(input_ids)
logits = logits.squeeze(1) # (B, vocab)
generated = input_ids.tolist()
finished = [False] * B
token_buf = torch.zeros((B, 1), dtype=torch.long, device=device)
seq_len = input_ids.size(1)
# CUDA graph: sync prefill states into static buffer once before decode loop
if use_cuda_graph and torch.cuda.is_available() and B == 1:
if self._cg_token is None:
self._cg_use_triton = use_triton
self._cg_token = torch.zeros((1, 1), dtype=torch.long, device=device)
self._cg_states = self.model.init_state(1, device=device)
self._cg_copy_states(states, self._cg_states)
states = self._cg_states
for _ in range(max_new_tokens):
# Temperature scaling
if temperature > 0:
ls = logits / temperature
else:
ls = logits.clone()
# Repetition penalty
if repetition_penalty != 1.0:
for bi, toks in enumerate(generated):
for t in set(toks):
if 0 <= t < self.config.vocab_size:
ls[bi, t] /= repetition_penalty
# Sampling
if temperature == 0:
next_tokens = torch.argmax(ls, dim=-1)
else:
if top_k > 0:
kth = torch.topk(ls, top_k, dim=-1).values[:, -1:]
ls = ls.masked_fill(ls < kth, float("-inf"))
if top_p < 1.0:
sorted_ls, sorted_idx = torch.sort(ls, dim=-1, descending=True)
cum_probs = torch.cumsum(F.softmax(sorted_ls, dim=-1), dim=-1)
sorted_remove = cum_probs > top_p
sorted_remove[:, 1:] = sorted_remove[:, :-1].clone()
sorted_remove[:, 0] = False
remove = torch.zeros_like(sorted_remove)
remove.scatter_(1, sorted_idx, sorted_remove)
ls = ls.masked_fill(remove, float("-inf"))
probs = F.softmax(ls, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(-1)
for bi in range(B):
tok = next_tokens[bi].item()
generated[bi].append(tok)
if eos_token_id is not None and tok == eos_token_id:
finished[bi] = True
token_buf[bi, 0] = tok
if all(finished):
break
seq_len += 1
if use_cuda_graph and torch.cuda.is_available() and B == 1:
cg_sl = self._cg_get_seq_len(seq_len - 1)
if cg_sl not in self._cg_graphs:
self._cg_capture(cg_sl)
self._cg_token.copy_(token_buf)
self._cg_graphs[cg_sl].replay()
logits = self._cg_logits[cg_sl]
else:
logits, states = self.model.step(token_buf, states, seq_len=seq_len, use_triton=use_triton)
max_len = max(len(g) for g in generated)
pad_id = self.config.pad_token_id or 0
out = torch.full((B, max_len), pad_id, dtype=torch.long, device=device)
for bi, g in enumerate(generated):
out[bi, : len(g)] = torch.tensor(g, dtype=torch.long, device=device)
return out
@torch.no_grad()
def generate_batch(
self,
input_ids_list: List[torch.LongTensor],
max_new_tokens: int = 1024,
temperature: float = 0.7,
eos_token_id: Optional[int] = None,
use_triton: bool = False,
) -> List[List[int]]:
"""
Batched generation: each prompt is prefilled independently, then
decoded in lockstep with per-sample early stopping.
Args:
input_ids_list: List of 1D LongTensors, one per prompt.
Returns:
List of generated token id lists (completion only, EOS stripped).
"""
if eos_token_id is None:
eos_token_id = self.config.eos_token_id
device = input_ids_list[0].device
B = len(input_ids_list)
# Per-sample prefill
all_logits, all_states = [], []
for ids in input_ids_list:
lg, st = self.model.prefill(ids.unsqueeze(0))
all_logits.append(lg.squeeze(1))
all_states.append(st)
logits = torch.cat(all_logits, dim=0)
# Stack states
num_blocks = len(all_states[0])
states = [
tuple(torch.cat([s[i][j] for s in all_states], dim=0) for j in range(len(all_states[0][i])))
for i in range(num_blocks)
]
generated = [[] for _ in range(B)]
finished = [False] * B
active_map = list(range(B))
token_buf = torch.zeros((B, 1), dtype=torch.long, device=device)
seq_len = max(ids.size(0) for ids in input_ids_list)
for _ in range(max_new_tokens):
B_cur = len(active_map)
if B_cur == 0:
break
if temperature == 0:
next_tokens = torch.argmax(logits, dim=-1)
else:
probs = F.softmax(logits / temperature, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(-1)
newly_done = set()
for bi in range(B_cur):
oi = active_map[bi]
tok = next_tokens[bi].item()
generated[oi].append(tok)
if eos_token_id is not None and tok == eos_token_id:
finished[oi] = True
newly_done.add(bi)
else:
token_buf[bi, 0] = tok
if all(finished):
break
if newly_done:
keep = [bi for bi in range(B_cur) if bi not in newly_done]
if not keep:
break
keep_idx = torch.tensor(keep, device=device)
token_buf = token_buf[keep_idx].contiguous()
states = [tuple(s[keep_idx].contiguous() for s in st) for st in states]
active_map = [active_map[bi] for bi in keep]
seq_len += 1
logits, states = self.model.step(token_buf, states, seq_len=seq_len, use_triton=use_triton)
results = []
for toks in generated:
if toks and toks[-1] == eos_token_id:
toks = toks[:-1]
results.append(toks)
return results
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