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from dataclasses import dataclass
from typing import Tuple, Optional, Literal
from functools import lru_cache
from contextlib import contextmanager
import torch
from torch import nn
import torch.nn.functional as F
import torch.distributed as dist
from kernel import act_quant, fp4_act_quant, fp8_gemm, fp4_gemm, sparse_attn, hc_split_sinkhorn
world_size = 1
rank = 0
block_size = 128
fp4_block_size = 32
default_dtype = torch.bfloat16
scale_fmt = None
scale_dtype = torch.float32
@contextmanager
def set_dtype(dtype):
"""Temporarily override torch default dtype, restoring it on exit (even if an exception occurs)."""
prev = torch.get_default_dtype()
torch.set_default_dtype(dtype)
try:
yield
finally:
torch.set_default_dtype(prev)
@dataclass
class ModelArgs:
"""Model hyperparameters. Field names match the config JSON keys."""
max_batch_size: int = 4
max_seq_len: int = 4096
dtype: Literal["bf16", "fp8"] = "fp8"
scale_fmt: Literal[None, "ue8m0"] = "ue8m0"
expert_dtype: Literal[None, "fp4"] = None
scale_dtype: Literal["fp32", "fp8"] = "fp8"
vocab_size: int = 129280
dim: int = 4096
moe_inter_dim: int = 4096
n_layers: int = 7
n_hash_layers: int = 0
n_mtp_layers: int = 1
n_heads: int = 64
# moe
n_routed_experts: int = 8
n_shared_experts: int = 1
n_activated_experts: int = 2
score_func: Literal["softmax", "sigmoid", "sqrtsoftplus"] = "sqrtsoftplus"
route_scale: float = 1.
swiglu_limit: float = 0.
# mqa
q_lora_rank: int = 1024
head_dim: int = 512
rope_head_dim: int = 64
norm_eps: float = 1e-6
o_groups: int = 8
o_lora_rank: int = 1024
window_size: int = 128
compress_ratios: Tuple[int] = (0, 0, 4, 128, 4, 128, 4, 0)
# yarn
compress_rope_theta: float = 40000.0
original_seq_len: int = 0
rope_theta: float = 10000.0
rope_factor: float = 40
beta_fast: int = 32
beta_slow: int = 1
# index
index_n_heads: int = 64
index_head_dim: int = 128
index_topk: int = 512
# hc
hc_mult: int = 4
hc_sinkhorn_iters: int = 20
hc_eps: float = 1e-6
class ParallelEmbedding(nn.Module):
"""Embedding sharded along the vocab dimension. Each rank holds vocab_size // world_size rows.
Out-of-range indices are zero-masked before all_reduce to combine partial embeddings."""
def __init__(self, vocab_size: int, dim: int):
super().__init__()
self.vocab_size = vocab_size
self.dim = dim
assert vocab_size % world_size == 0, f"Vocabulary size must be divisible by world size (world_size={world_size})"
self.part_vocab_size = (vocab_size // world_size)
self.vocab_start_idx = rank * self.part_vocab_size
self.vocab_end_idx = self.vocab_start_idx + self.part_vocab_size
self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
if world_size > 1:
mask = (x < self.vocab_start_idx) | (x >= self.vocab_end_idx)
x = x - self.vocab_start_idx
x[mask] = 0
y = F.embedding(x, self.weight)
if world_size > 1:
y[mask] = 0
dist.all_reduce(y)
return y
def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
"""Dispatches to fp4_gemm / fp8_gemm / F.linear based on weight dtype.
For quantized weights, x is first quantized to FP8 via act_quant."""
assert bias is None
if weight.dtype == torch.float4_e2m1fn_x2:
x, s = act_quant(x, block_size, scale_fmt, scale_dtype)
return fp4_gemm(x, s, weight, weight.scale, scale_dtype)
elif weight.dtype == torch.float8_e4m3fn:
x, s = act_quant(x, block_size, scale_fmt, scale_dtype)
return fp8_gemm(x, s, weight, weight.scale, scale_dtype)
else:
return F.linear(x, weight)
class Linear(nn.Module):
"""Linear layer supporting BF16, FP8, and FP4 weight formats with per-block scaling."""
def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
super().__init__()
self.in_features = in_features
self.out_features = out_features
dtype = dtype or default_dtype
if dtype == torch.float4_e2m1fn_x2:
# FP4: weight is [out, in//2] in float4_e2m1fn_x2, logically [out, in] in fp4
# Scale is [out, in//32] in float8_e8m0fnu (1 scale per 32 fp4 elements along K)
self.weight = nn.Parameter(torch.empty(out_features, in_features // 2, dtype=torch.float4_e2m1fn_x2))
scale_out_features = out_features
scale_in_features = in_features // fp4_block_size
self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float8_e8m0fnu))
elif dtype == torch.float8_e4m3fn:
self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
scale_out_features = (out_features + block_size - 1) // block_size
scale_in_features = (in_features + block_size - 1) // block_size
self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float8_e8m0fnu))
else:
self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
self.register_parameter("scale", None)
if bias:
self.bias = nn.Parameter(torch.empty(out_features))
else:
self.register_parameter("bias", None)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return linear(x, self.weight, self.bias)
class ColumnParallelLinear(Linear):
"""Shards output dim across TP ranks. No all-reduce needed on output."""
def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
assert out_features % world_size == 0, f"Output features must be divisible by world size (world_size={world_size})"
self.part_out_features = out_features // world_size
super().__init__(in_features, self.part_out_features, bias, dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return linear(x, self.weight, self.bias)
class RowParallelLinear(Linear):
"""Shards input dim across TP ranks. All-reduce on output to sum partial results."""
def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
assert in_features % world_size == 0, f"Input features must be divisible by world size (world_size={world_size})"
self.part_in_features = in_features // world_size
super().__init__(self.part_in_features, out_features, bias, dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
y = linear(x, self.weight, None)
if world_size > 1:
y = y.float()
dist.all_reduce(y)
if self.bias is not None:
y += self.bias
return y.type_as(x)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.dim = dim
self.eps = eps
# rmsnorm in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for convenient.
self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
def forward(self, x: torch.Tensor):
dtype = x.dtype
x = x.float()
var = x.square().mean(-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype)
@lru_cache(2)
def precompute_freqs_cis(dim, seqlen, original_seq_len, base, factor, beta_fast, beta_slow) -> torch.Tensor:
"""Precomputes complex exponentials for rotary embeddings with YaRN scaling.
When original_seq_len > 0, applies frequency interpolation with a smooth
linear ramp between beta_fast and beta_slow correction ranges."""
def find_correction_dim(num_rotations, dim, base, max_seq_len):
return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))
def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
return max(low, 0), min(high, dim-1)
def linear_ramp_factor(min, max, dim):
if min == max:
max += 0.001
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
ramp_func = torch.clamp(linear_func, 0, 1)
return ramp_func
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
if original_seq_len > 0:
low, high = find_correction_range(beta_fast, beta_slow, dim, base, original_seq_len)
smooth = 1 - linear_ramp_factor(low, high, dim // 2)
freqs = freqs / factor * (1 - smooth) + freqs * smooth
t = torch.arange(seqlen)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
return freqs_cis
def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor, inverse: bool = False) -> torch.Tensor:
"""Applies rotary positional embeddings in-place. Uses conjugate for inverse (de-rotation)."""
y = x
x = torch.view_as_complex(x.float().unflatten(-1, (-1, 2)))
if inverse:
freqs_cis = freqs_cis.conj()
if x.ndim == 3:
freqs_cis = freqs_cis.view(1, x.size(1), x.size(-1))
else:
freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
x = torch.view_as_real(x * freqs_cis).flatten(-2)
y.copy_(x)
return y
def rotate_activation(x: torch.Tensor) -> torch.Tensor:
"""Applies randomized Hadamard rotation to spread information across dims before FP8 quant."""
assert x.dtype == torch.bfloat16
from fast_hadamard_transform import hadamard_transform
return hadamard_transform(x, scale=x.size(-1) ** -0.5)
@lru_cache(1)
def get_window_topk_idxs(window_size: int, bsz: int, seqlen: int, start_pos: int):
if start_pos >= window_size - 1:
start_pos %= window_size
matrix = torch.cat([torch.arange(start_pos + 1, window_size), torch.arange(0, start_pos + 1)], dim=0)
elif start_pos > 0:
matrix = F.pad(torch.arange(start_pos + 1), (0, window_size - start_pos - 1), value=-1)
else:
base = torch.arange(seqlen).unsqueeze(1)
matrix = (base - window_size + 1).clamp(0) + torch.arange(min(seqlen, window_size))
matrix = torch.where(matrix > base, -1, matrix)
return matrix.unsqueeze(0).expand(bsz, -1, -1)
@lru_cache(2)
def get_compress_topk_idxs(ratio: int, bsz: int, seqlen: int, start_pos: int, offset: int):
if start_pos > 0:
matrix = torch.arange(0, (start_pos + 1) // ratio) + offset
else:
matrix = torch.arange(seqlen // ratio).repeat(seqlen, 1)
mask = matrix >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
matrix = torch.where(mask, -1, matrix + offset)
return matrix.unsqueeze(0).expand(bsz, -1, -1)
class Compressor(nn.Module):
"""Compresses KV cache via learned gated pooling over `compress_ratio` consecutive tokens.
When overlap=True (ratio==4), uses overlapping windows for smoother compression boundaries."""
def __init__(self, args: ModelArgs, compress_ratio: int = 4, head_dim: int = 512, rotate: bool = False):
super().__init__()
self.dim = args.dim
self.head_dim = head_dim
self.rope_head_dim = args.rope_head_dim
self.nope_head_dim = head_dim - args.rope_head_dim
self.compress_ratio = compress_ratio
self.overlap = compress_ratio == 4
self.rotate = rotate
coff = 1 + self.overlap
self.ape = nn.Parameter(torch.empty(compress_ratio, coff * self.head_dim, dtype=torch.float32))
# wkv and wgate in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for convenient.
# When overlap, the first half of dims is for overlapping compression, second half for normal.
self.wkv = Linear(self.dim, coff * self.head_dim, dtype=torch.float32)
self.wgate = Linear(self.dim, coff * self.head_dim, dtype=torch.float32)
self.norm = RMSNorm(self.head_dim, args.norm_eps)
self.kv_cache: torch.Tensor = None # assigned lazily from Attention.kv_cache
# State buffers for decode-phase incremental compression.
# With overlap: state[:, :ratio] = overlapping window, state[:, ratio:] = current window.
self.register_buffer("kv_state", torch.zeros(args.max_batch_size, coff * compress_ratio, coff * self.head_dim, dtype=torch.float32), persistent=False)
self.register_buffer("score_state", torch.full((args.max_batch_size, coff * compress_ratio, coff * self.head_dim), float("-inf"), dtype=torch.float32), persistent=False)
self.freqs_cis: torch.Tensor = None
def overlap_transform(self, tensor: torch.Tensor, value=0):
# tensor: [b,s,r,2d]
b, s, _, _ = tensor.size()
ratio, d = self.compress_ratio, self.head_dim
new_tensor = tensor.new_full((b, s, 2 * ratio, d), value)
new_tensor[:, :, ratio:] = tensor[:, :, :, d:]
new_tensor[:, 1:, :ratio] = tensor[:, :-1, :, :d]
return new_tensor
def forward(self, x: torch.Tensor, start_pos: int):
assert self.kv_cache is not None
bsz, seqlen, _ = x.size()
ratio, overlap, d, rd = self.compress_ratio, self.overlap, self.head_dim, self.rope_head_dim
dtype = x.dtype
# compression need fp32
x = x.float()
kv = self.wkv(x)
score = self.wgate(x)
if start_pos == 0:
should_compress = seqlen >= ratio
remainder = seqlen % ratio
cutoff = seqlen - remainder
offset = ratio if overlap else 0
if overlap and cutoff >= ratio:
self.kv_state[:bsz, :ratio] = kv[:, cutoff-ratio : cutoff]
self.score_state[:bsz, :ratio] = score[:, cutoff-ratio : cutoff] + self.ape
if remainder > 0:
kv, self.kv_state[:bsz, offset : offset+remainder] = kv.split([cutoff, remainder], dim=1)
self.score_state[:bsz, offset : offset+remainder] = score[:, cutoff:] + self.ape[:remainder]
score = score[:, :cutoff]
kv = kv.unflatten(1, (-1, ratio))
score = score.unflatten(1, (-1, ratio)) + self.ape
if overlap:
kv = self.overlap_transform(kv, 0)
score = self.overlap_transform(score, float("-inf"))
kv = (kv * score.softmax(dim=2)).sum(dim=2)
else:
should_compress = (start_pos + 1) % self.compress_ratio == 0
score += self.ape[start_pos % ratio]
if overlap:
self.kv_state[:bsz, ratio + start_pos % ratio] = kv.squeeze(1)
self.score_state[:bsz, ratio + start_pos % ratio] = score.squeeze(1)
if should_compress:
kv_state = torch.cat([self.kv_state[:bsz, :ratio, :d], self.kv_state[:bsz, ratio:, d:]], dim=1)
score_state = torch.cat([self.score_state[:bsz, :ratio, :d], self.score_state[:bsz, ratio:, d:]], dim=1)
kv = (kv_state * score_state.softmax(dim=1)).sum(dim=1, keepdim=True)
self.kv_state[:bsz, :ratio] = self.kv_state[:bsz, ratio:]
self.score_state[:bsz, :ratio] = self.score_state[:bsz, ratio:]
else:
self.kv_state[:bsz, start_pos % ratio] = kv.squeeze(1)
self.score_state[:bsz, start_pos % ratio] = score.squeeze(1)
if should_compress:
kv = (self.kv_state[:bsz] * self.score_state[:bsz].softmax(dim=1)).sum(dim=1, keepdim=True)
if not should_compress:
return
kv = self.norm(kv.to(dtype))
if start_pos == 0:
freqs_cis = self.freqs_cis[:cutoff:ratio]
else:
freqs_cis = self.freqs_cis[start_pos + 1 - self.compress_ratio].unsqueeze(0)
apply_rotary_emb(kv[..., -rd:], freqs_cis)
if self.rotate:
kv = rotate_activation(kv)
fp4_act_quant(kv, fp4_block_size, True)
else:
act_quant(kv[..., :-rd], 64, scale_fmt, scale_dtype, True)
if start_pos == 0:
self.kv_cache[:bsz, :seqlen // ratio] = kv
else:
self.kv_cache[:bsz, start_pos // ratio] = kv.squeeze(1)
return kv
class Indexer(torch.nn.Module):
"""Selects top-k compressed KV positions for sparse attention via learned scoring.
Has its own Compressor (with Hadamard rotation) to build compressed KV for scoring."""
def __init__(self, args: ModelArgs, compress_ratio: int = 4):
super().__init__()
self.dim = args.dim
self.n_heads = args.index_n_heads
self.n_local_heads = args.index_n_heads // world_size
self.head_dim = args.index_head_dim
self.rope_head_dim = args.rope_head_dim
self.index_topk = args.index_topk
self.q_lora_rank = args.q_lora_rank
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.head_dim)
self.weights_proj = ColumnParallelLinear(self.dim, self.n_heads, dtype=torch.bfloat16)
self.softmax_scale = self.head_dim ** -0.5
self.compress_ratio = compress_ratio
self.compressor = Compressor(args, compress_ratio, self.head_dim, True)
self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len // compress_ratio, self.head_dim), persistent=False)
self.freqs_cis = None
def forward(self, x: torch.Tensor, qr: torch.Tensor, start_pos: int, offset: int):
bsz, seqlen, _ = x.size()
freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
ratio = self.compress_ratio
rd = self.rope_head_dim
end_pos = start_pos + seqlen
if self.compressor.kv_cache is None:
self.compressor.kv_cache = self.kv_cache
self.compressor.freqs_cis = self.freqs_cis
q = self.wq_b(qr)
q = q.unflatten(-1, (self.n_local_heads, self.head_dim))
apply_rotary_emb(q[..., -rd:], freqs_cis)
q = rotate_activation(q)
# use fp4 simulation for q and kv in indexer
fp4_act_quant(q, fp4_block_size, True)
self.compressor(x, start_pos)
weights = self.weights_proj(x) * (self.softmax_scale * self.n_heads ** -0.5)
# We performed QAT here, kv could also use fp8 format, though current implementation uses bf16
index_score = torch.einsum("bshd,btd->bsht", q, self.kv_cache[:bsz, :end_pos // ratio])
index_score = (index_score.relu_() * weights.unsqueeze(-1)).sum(dim=2)
if world_size > 1:
dist.all_reduce(index_score)
if start_pos == 0:
mask = torch.arange(seqlen // ratio).repeat(seqlen, 1) >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
index_score += torch.where(mask, float("-inf"), 0)
topk_idxs = index_score.topk(min(self.index_topk, end_pos // ratio), dim=-1)[1]
if start_pos == 0:
mask = topk_idxs >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
topk_idxs = torch.where(mask, -1, topk_idxs + offset)
else:
topk_idxs += offset
return topk_idxs
class Attention(nn.Module):
"""Multi-head Latent Attention (MLA) with sliding window + optional KV compression.
Uses low-rank Q projection (wq_a -> q_norm -> wq_b) and grouped low-rank O projection."""
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.layer_id = layer_id
self.dim = args.dim
self.n_heads = args.n_heads
self.n_local_heads = args.n_heads // world_size
self.q_lora_rank = args.q_lora_rank
self.o_lora_rank = args.o_lora_rank
self.head_dim = args.head_dim
self.rope_head_dim = args.rope_head_dim
self.nope_head_dim = args.head_dim - args.rope_head_dim
self.n_groups = args.o_groups
self.n_local_groups = self.n_groups // world_size
self.window_size = args.window_size
self.compress_ratio = args.compress_ratios[layer_id]
self.eps = args.norm_eps
self.attn_sink = nn.Parameter(torch.empty(self.n_local_heads, dtype=torch.float32))
self.wq_a = Linear(self.dim, self.q_lora_rank)
self.q_norm = RMSNorm(self.q_lora_rank, self.eps)
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.head_dim)
self.wkv = Linear(self.dim, self.head_dim)
self.kv_norm = RMSNorm(self.head_dim, self.eps)
self.wo_a = ColumnParallelLinear(self.n_heads * self.head_dim // self.n_groups, self.n_groups * args.o_lora_rank, dtype=torch.bfloat16)
self.wo_b = RowParallelLinear(self.n_groups * args.o_lora_rank, self.dim)
self.softmax_scale = self.head_dim ** -0.5
if self.compress_ratio:
self.compressor = Compressor(args, self.compress_ratio, self.head_dim)
if self.compress_ratio == 4:
self.indexer = Indexer(args, self.compress_ratio)
else:
self.indexer = None
kv_cache_size = args.window_size + (args.max_seq_len // self.compress_ratio if self.compress_ratio else 0)
self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, kv_cache_size, self.head_dim), persistent=False)
if self.compress_ratio:
original_seq_len, rope_theta = args.original_seq_len, args.compress_rope_theta
else:
# disable YaRN and use base rope_theta in pure sliding-window attention
original_seq_len, rope_theta = 0, args.rope_theta
freqs_cis = precompute_freqs_cis(self.rope_head_dim, args.max_seq_len, original_seq_len,
rope_theta, args.rope_factor, args.beta_fast, args.beta_slow)
self.register_buffer("freqs_cis", freqs_cis, persistent=False)
def forward(self, x: torch.Tensor, start_pos: int):
bsz, seqlen, _ = x.size()
freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
win = self.window_size
ratio = self.compress_ratio
rd = self.rope_head_dim
if self.compress_ratio and self.compressor.kv_cache is None:
self.compressor.kv_cache = self.kv_cache[:, win:]
self.compressor.freqs_cis = self.freqs_cis
if self.indexer is not None:
self.indexer.freqs_cis = self.freqs_cis
# q
qr = q = self.q_norm(self.wq_a(x))
q = self.wq_b(q).unflatten(-1, (self.n_local_heads, self.head_dim))
q *= torch.rsqrt(q.square().mean(-1, keepdim=True) + self.eps)
apply_rotary_emb(q[..., -rd:], freqs_cis)
# win kv & topk_idxs
kv = self.wkv(x)
kv = self.kv_norm(kv)
apply_rotary_emb(kv[..., -rd:], freqs_cis)
# FP8-simulate non-rope dims to match QAT; rope dims stay bf16 for positional precision
act_quant(kv[..., :-rd], 64, scale_fmt, scale_dtype, True)
topk_idxs = get_window_topk_idxs(win, bsz, seqlen, start_pos)
if self.compress_ratio:
offset = kv.size(1) if start_pos == 0 else win
if self.indexer is not None:
compress_topk_idxs = self.indexer(x, qr, start_pos, offset)
else:
compress_topk_idxs = get_compress_topk_idxs(ratio, bsz, seqlen, start_pos, offset)
topk_idxs = torch.cat([topk_idxs, compress_topk_idxs], dim=-1)
topk_idxs = topk_idxs.int()
# compress kv & attn
if start_pos == 0:
if seqlen <= win:
self.kv_cache[:bsz, :seqlen] = kv
else:
cutoff = seqlen % win
self.kv_cache[:bsz, cutoff: win], self.kv_cache[:bsz, :cutoff] = kv[:, -win:].split([win - cutoff, cutoff], dim=1)
if self.compress_ratio:
if (kv_compress := self.compressor(x, start_pos)) is not None:
kv = torch.cat([kv, kv_compress], dim=1)
# We performed QAT here, kv could also use fp8 format, though current implementation uses bf16
o = sparse_attn(q, kv, self.attn_sink, topk_idxs, self.softmax_scale)
else:
self.kv_cache[:bsz, start_pos % win] = kv.squeeze(1)
if self.compress_ratio:
self.compressor(x, start_pos)
o = sparse_attn(q, self.kv_cache[:bsz], self.attn_sink, topk_idxs, self.softmax_scale)
apply_rotary_emb(o[..., -rd:], freqs_cis, True)
# o
o = o.view(bsz, seqlen, self.n_local_groups, -1)
wo_a = self.wo_a.weight.view(self.n_local_groups, self.o_lora_rank, -1)
# NOTE: wo_a is FP8 in checkpoint; could do FP8 einsum here for better perf,
# but using BF16 for simplicity.
o = torch.einsum("bsgd,grd->bsgr", o, wo_a)
x = self.wo_b(o.flatten(2))
return x
class Gate(nn.Module):
"""MoE gating: computes expert routing scores and selects top-k experts.
Supports hash-based routing (first n_hash_layers) where expert indices are
predetermined per token ID, and score-based routing (remaining layers)."""
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.dim = args.dim
self.topk = args.n_activated_experts
self.score_func = args.score_func
self.route_scale = args.route_scale
self.hash = layer_id < args.n_hash_layers
self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
if self.hash:
self.tid2eid = nn.Parameter(torch.empty(args.vocab_size, args.n_activated_experts, dtype=torch.int32), requires_grad=False)
self.bias = None
else:
self.bias = nn.Parameter(torch.empty(args.n_routed_experts, dtype=torch.float32))
def forward(self, x: torch.Tensor, input_ids: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
scores = linear(x.float(), self.weight.float())
if self.score_func == "softmax":
scores = scores.softmax(dim=-1)
elif self.score_func == "sigmoid":
scores = scores.sigmoid()
else:
scores = F.softplus(scores).sqrt()
original_scores = scores
# Bias shifts scores for expert selection (topk) but does not affect routing weights.
if self.bias is not None:
scores = scores + self.bias
if self.hash:
indices = self.tid2eid[input_ids]
else:
indices = scores.topk(self.topk, dim=-1)[1]
weights = original_scores.gather(1, indices)
if self.score_func != "softmax":
weights /= weights.sum(dim=-1, keepdim=True)
weights *= self.route_scale
return weights, indices
class Expert(nn.Module):
"""Single MoE expert: SwiGLU FFN (w1, w2, w3). Computation in float32 for stability."""
def __init__(self, dim: int, inter_dim: int, dtype=None, swiglu_limit=0):
super().__init__()
self.w1 = Linear(dim, inter_dim, dtype=dtype)
self.w2 = Linear(inter_dim, dim, dtype=dtype)
self.w3 = Linear(dim, inter_dim, dtype=dtype)
self.swiglu_limit = swiglu_limit
def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor] = None) -> torch.Tensor:
dtype = x.dtype
gate = self.w1(x).float()
up = self.w3(x).float()
if self.swiglu_limit > 0:
up = torch.clamp(up, min=-self.swiglu_limit, max=self.swiglu_limit)
gate = torch.clamp(gate, max=self.swiglu_limit)
x = F.silu(gate) * up
if weights is not None:
x = weights * x
return self.w2(x.to(dtype))
class MoE(nn.Module):
"""Mixture-of-Experts: gate routes each token to top-k routed experts + 1 shared expert.
Experts are sharded across TP ranks; each rank handles n_routed_experts // world_size experts."""
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.layer_id = layer_id
self.dim = args.dim
assert args.n_routed_experts % world_size == 0, f"Number of experts must be divisible by world size (world_size={world_size})"
self.n_routed_experts = args.n_routed_experts
self.n_local_experts = args.n_routed_experts // world_size
self.n_activated_experts = args.n_activated_experts
self.experts_start_idx = rank * self.n_local_experts
self.experts_end_idx = self.experts_start_idx + self.n_local_experts
self.gate = Gate(layer_id, args)
expert_dtype = torch.float4_e2m1fn_x2 if args.expert_dtype == "fp4" else None
self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim, dtype=expert_dtype, swiglu_limit=args.swiglu_limit) if self.experts_start_idx <= i < self.experts_end_idx else None
for i in range(self.n_routed_experts)])
assert args.n_shared_experts == 1
# no swiglu_limit
self.shared_experts = Expert(args.dim, args.moe_inter_dim)
def forward(self, x: torch.Tensor, input_ids: torch.Tensor) -> torch.Tensor:
shape = x.size()
x = x.view(-1, self.dim)
weights, indices = self.gate(x, input_ids.flatten())
y = torch.zeros_like(x, dtype=torch.float32)
counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
for i in range(self.experts_start_idx, self.experts_end_idx):
if counts[i] == 0:
continue
expert = self.experts[i]
idx, top = torch.where(indices == i)
y[idx] += expert(x[idx], weights[idx, top, None])
if world_size > 1:
dist.all_reduce(y)
y += self.shared_experts(x)
return y.type_as(x).view(shape)
class Block(nn.Module):
"""Transformer block with Hyper-Connections (HC) mixing.
Instead of a simple residual, HC maintains `hc_mult` copies of the hidden state.
hc_pre: reduces hc copies -> 1 via learned weighted sum (pre-weights from Sinkhorn).
hc_post: expands 1 -> hc copies via learned post-weights + combination matrix."""
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.layer_id = layer_id
self.norm_eps = args.norm_eps
self.attn = Attention(layer_id, args)
self.ffn = MoE(layer_id, args)
self.attn_norm = RMSNorm(args.dim, self.norm_eps)
self.ffn_norm = RMSNorm(args.dim, self.norm_eps)
self.hc_mult = hc_mult = args.hc_mult
self.hc_sinkhorn_iters = args.hc_sinkhorn_iters
self.hc_eps = args.hc_eps
mix_hc = (2 + hc_mult) * hc_mult
hc_dim = hc_mult * args.dim
with set_dtype(torch.float32):
self.hc_attn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
self.hc_ffn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
self.hc_attn_base = nn.Parameter(torch.empty(mix_hc))
self.hc_ffn_base = nn.Parameter(torch.empty(mix_hc))
self.hc_attn_scale = nn.Parameter(torch.empty(3))
self.hc_ffn_scale = nn.Parameter(torch.empty(3))
def hc_pre(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor):
# x: [b,s,hc,d], hc_fn: [mix_hc,hc*d], hc_scale: [3], hc_base: [mix_hc], y: [b,s,hc,d]
shape, dtype = x.size(), x.dtype
x = x.flatten(2).float()
rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + self.norm_eps)
mixes = F.linear(x, hc_fn) * rsqrt
pre, post, comb = hc_split_sinkhorn(mixes, hc_scale, hc_base, self.hc_mult, self.hc_sinkhorn_iters, self.hc_eps)
y = torch.sum(pre.unsqueeze(-1) * x.view(shape), dim=2)
return y.to(dtype), post, comb
def hc_post(self, x: torch.Tensor, residual: torch.Tensor, post: torch.Tensor, comb: torch.Tensor):
# x: [b,s,d], residual: [b,s,hc,d], post: [b,s,hc], comb: [b,s,hc,hc], y: [b,s,hc,d]
y = post.unsqueeze(-1) * x.unsqueeze(-2) + torch.sum(comb.unsqueeze(-1) * residual.unsqueeze(-2), dim=2)
return y.type_as(x)
def forward(self, x: torch.Tensor, start_pos: int, input_ids: Optional[torch.Tensor]) -> torch.Tensor:
residual = x
x, post, comb = self.hc_pre(x, self.hc_attn_fn, self.hc_attn_scale, self.hc_attn_base)
x = self.attn_norm(x)
x = self.attn(x, start_pos)
x = self.hc_post(x, residual, post, comb)
residual = x
x, post, comb = self.hc_pre(x, self.hc_ffn_fn, self.hc_ffn_scale, self.hc_ffn_base)
x = self.ffn_norm(x)
x = self.ffn(x, input_ids)
x = self.hc_post(x, residual, post, comb)
return x
class ParallelHead(nn.Module):
def __init__(self, vocab_size: int, dim: int, norm_eps: float = 1e-6, hc_eps: float = 1e-6):
super().__init__()
self.vocab_size = vocab_size
self.dim = dim
self.norm_eps = norm_eps
self.hc_eps = hc_eps
self.part_vocab_size = (vocab_size // world_size)
# lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim, dtype=torch.float32))
def get_logits(self, x):
return F.linear(x[:, -1].float(), self.weight)
def forward(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor, norm: RMSNorm):
# x: [b,s,hc,d]
x = self.hc_head(x, hc_fn, hc_scale, hc_base)
logits = self.get_logits(norm(x))
if world_size > 1:
all_logits = [torch.empty_like(logits) for _ in range(world_size)]
dist.all_gather(all_logits, logits)
logits = torch.cat(all_logits, dim=-1)
return logits
def hc_head(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor):
shape, dtype = x.size(), x.dtype
x = x.flatten(2).float()
rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + self.norm_eps)
mixes = F.linear(x, hc_fn) * rsqrt
pre = torch.sigmoid(mixes * hc_scale + hc_base) + self.hc_eps
y = torch.sum(pre.unsqueeze(-1) * x.view(shape), dim=2)
return y.to(dtype)
class MTPBlock(Block):
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__(layer_id, args)
self.e_proj = Linear(args.dim, args.dim)
self.h_proj = Linear(args.dim, args.dim)
self.enorm = RMSNorm(args.dim, args.norm_eps)
self.hnorm = RMSNorm(args.dim, args.norm_eps)
self.norm = RMSNorm(args.dim, args.norm_eps)
self.hc_mult = hc_mult = args.hc_mult
hc_dim = hc_mult * args.dim
with set_dtype(torch.float32):
self.hc_head_fn = nn.Parameter(torch.empty(hc_mult, hc_dim))
self.hc_head_base = nn.Parameter(torch.empty(hc_mult))
self.hc_head_scale = nn.Parameter(torch.empty(1))
self.embed: ParallelEmbedding = None
self.head: ParallelHead = None
@torch.inference_mode()
def forward(self, x: torch.Tensor, start_pos: int, input_ids: torch.Tensor) -> torch.Tensor:
# x: [b,s,hc,d]
assert self.embed is not None and self.head is not None
e = self.embed(input_ids)
e = self.enorm(e)
x = self.hnorm(x)
x = self.e_proj(e).unsqueeze(2) + self.h_proj(x)
x = super().forward(x, start_pos, input_ids)
logits = self.head(x, self.hc_head_fn, self.hc_head_scale, self.hc_head_base, self.norm)
return logits
class Transformer(nn.Module):
"""Full DeepSeek-V4 model: embed -> HC-expand -> N blocks -> HC-head -> logits.
Sets global state (world_size, rank, default_dtype, scale_fmt, scale_dtype) in __init__."""
def __init__(self, args: ModelArgs):
global world_size, rank, default_dtype, scale_fmt, scale_dtype
world_size = dist.get_world_size() if dist.is_initialized() else 1
rank = dist.get_rank() if dist.is_initialized() else 0
default_dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
scale_fmt = "ue8m0" if args.scale_dtype == "fp8" else args.scale_fmt
scale_dtype = torch.float8_e8m0fnu if args.scale_dtype == "fp8" else torch.float32
super().__init__()
self.max_seq_len = args.max_seq_len
self.norm_eps = args.norm_eps
self.hc_eps = args.hc_eps
self.embed = ParallelEmbedding(args.vocab_size, args.dim)
self.layers = torch.nn.ModuleList()
for layer_id in range(args.n_layers):
self.layers.append(Block(layer_id, args))
self.norm = RMSNorm(args.dim, self.norm_eps)
self.head = ParallelHead(args.vocab_size, args.dim, self.norm_eps, self.hc_eps)
self.mtp = torch.nn.ModuleList()
for layer_id in range(args.n_mtp_layers):
self.mtp.append(MTPBlock(args.n_layers + layer_id, args))
self.mtp[-1].embed = self.embed
self.mtp[-1].head = self.head
self.hc_mult = hc_mult = args.hc_mult
hc_dim = hc_mult * args.dim
with set_dtype(torch.float32):
self.hc_head_fn = nn.Parameter(torch.empty(hc_mult, hc_dim))
self.hc_head_base = nn.Parameter(torch.empty(hc_mult))
self.hc_head_scale = nn.Parameter(torch.empty(1))
@torch.inference_mode()
def forward(self, input_ids: torch.Tensor, start_pos: int = 0):
h = self.embed(input_ids)
# Expand to hc_mult copies for Hyper-Connections
h = h.unsqueeze(2).repeat(1, 1, self.hc_mult, 1)
for layer in self.layers:
h = layer(h, start_pos, input_ids)
logits = self.head(h, self.hc_head_fn, self.hc_head_scale, self.hc_head_base, self.norm)
return logits
if __name__ == "__main__":
torch.set_default_dtype(torch.bfloat16)
torch.set_default_device("cuda")
torch.manual_seed(0)
args = ModelArgs(n_hash_layers=0)
x = torch.randint(0, args.vocab_size, (2, 128))
model = Transformer(args)
print(model(x).size())
for i in range(128, 150):
print(i, model(x[:, 0:1], i).size())
h = torch.randn(2, 128, args.hc_mult, args.dim)
mtp = model.mtp[0]
print(mtp(h, 0, x).size())
print(mtp(h[:, 0:1], 1, x[:, 0:1]).size())
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