Metadata-Version: 2.2
Name: fusedtok
Version: 1.8.0
Summary: Fused CUDA kernels for LLM inference: attention decode (contiguous + paged kv-cache), RMSNorm, RoPE, SwiGLU, fused sampling, INT8 quantized GEMM - zero-copy torch support
Keywords: cuda,llm,inference,kernels,deep-learning,pytorch,attention,int8,sampling
Author: Hai-Wenxiang
License: MIT License
         
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Requires-Dist: pytest>=7; extra == "test"
Description-Content-Type: text/markdown

# fusedtok

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**Fused CUDA kernels for LLM inference** - RMSNorm / RoPE / SwiGLU / attention
decode and friends, with **zero-copy torch tensor support**: up to
**8.8x faster than PyTorch SDPA** (attention decode, RTX 3060, see
[Benchmarks](#benchmarks)).

**中文文档请看 [README_zh.md](https://github.com/Hai-Wenxiang/fusedtok/blob/main/README_zh.md)** | English below.

## Why

LLM inference frameworks launch many small, memory-bound operators per token. Each launch
round-trips through global memory. `fusedtok` fuses them into single kernels to cut memory
traffic and launch overhead.

## Operators

49 operators + helpers (`fusedtok.__all__`; 34 were frozen at 1.0, the
rest arrived in minor releases - see "API stability" below). `axpy` is
the hello-world demo op kept from
the v0.x skeleton - functional, but not a performance feature.

| Status | Kernel | Notes |
|---|---|---|
| ✅ | RMSNorm (+residual) | LLaMA/Qwen style, fused residual add |
| ✅ | LayerNorm | with affine |
| ✅ | RoPE | interleaved **and** NeoX layouts, kv-cache `pos_offset` |
| ✅ | SwiGLU | fused MLP activation |
| ✅ | Softmax (row-wise) | numerically stable |
| ✅ | SiLU / GeLU / GeLU-tanh / ReLU / Tanh / Sigmoid | elementwise |
| ✅ | add / mul | elementwise binary (fused add+residual pattern) |
| ✅ | top-k / top-p (nucleus) | arrival-ticket radix + early-exit compaction, replayed from a cached CUDA graph; deterministic ties (parity-to-winning across the whole k range on both test GPUs) |
| ✅ | argmax / temperature | greedy decoding helpers |
| ✅ | sample_topp | fused nucleus sampling: softmax -> top-p -> seeded draw, global-mass threshold |
| ✅ | sample_topk | fused top-k sampling: softmax -> top-k -> renormalize within the window -> seeded draw (1.8-2.1x vs the topk+multinomial composite @131k) |
| ✅ | sample_minp | fused min-p sampling (v1.3): keep every token with p >= min_p * p_max -> renormalize -> seeded draw - a value threshold, so no global-mass reduction; adaptive nucleus by construction |
| ✅ | sample_eta / sample_eta_batched | fused eta-cutoff sampling (v1.6, Hewitt 2022): keep every token with p >= eta * min(1, exp(-H)) - the threshold derives from the distribution's own entropy; adaptive by construction |
| ✅ | sample_typical / sample_typical_batched | fused locally typical sampling (v1.6, Meister 2022): keep the smallest set whose mass reaches `typical`, ordered by surprise-vs-entropy closeness - a contiguous band of the value-sorted window |
| ✅ | sample_topa / sample_topa_batched | fused top-a sampling (v1.8): keep every token with p >= top_a * p_max^2 - a value threshold like min-p with the squared peak driving the bar (flat rows keep almost everything); min-p's widening bound with the cutoff derived from the existing total |
| ✅ | sample_nsigma / sample_nsigma_batched | fused top-n-sigma sampling (v1.8, Shi et al. 2024): keep every token whose scaled logit stays at or above `mean - nsigma * sigma` - the row's own spread sets the bar; moments from one extra pass, accumulated in doubles so arrival-order drift cannot survive the variance's cancellation |
| ✅ | argmax_batched | row-wise greedy argmax for a whole `[rows, vocab]` batch in one launch (v1.8): no readback on the zero-copy path, CUDA-graph capturable; ~28x the per-row loop in wall time at B=8 on a submission-bound host |
| ✅ | sample_topp/minp/topk_batched | batched sampling (v1.4): one call, `[rows, vocab]` logits in, one seeded token per row out - every row runs the single-row pipeline verbatim; the batched call is 4-6x faster in wall time than looping per row on submission-bound hosts, and sits at native batched-multinomial level on peaked decode logits |
| ✅ | repetition penalty | CTRL-style, applied to sampled token ids |
| ✅ | logit_penalties | the HF penalty trio in one call (v1.6.1): CTRL repetition scale, then presence, then count-weighted frequency shifts - once per distinct id, duplicates never stack; bit-exact across CPU and GPU |
| ✅ | logit_penalties_batched | the same trio for a whole `[rows, vocab]` batch (v1.7): ragged per-row histories (decode_step_batched's id layout), each row bit-identical to the single-row op |
| ✅ | decode_step | the whole decode step fused: penalty -> temperature -> nucleus sample, one call, one readback |
| ✅ | decode_step_batched | the fused decode step for a whole batch (v1.5): per-row ragged histories through per-row penalty bitmaps, one seeded token per row - every row runs the single-row `decode_step` pipeline up to the documented ulp boundary |
| ✅ | quantize_int8 / dequantize_int8 / qadd_int8 | symmetric per-tensor INT8, fused dequant-add-requant |
| ✅ | qgemm | INT8 matmul, int32-exact: cp.async double-buffered pipelined IMMA GEMM with runtime tile tuning (64x64 / 128x128) + warp-per-row GEMV (M=1 decode; 2x vs fp16 projection) |
| ✅ | qgemm_perchannel | the W8A8 layout real INT8 inference uses: per-output-channel weight scales fused into the same kernel's epilogue at zero cost |
| ✅ | attention_decode | single-token causal attention with GQA over a contiguous kv-cache: online softmax, flash-decoding split over long caches, per-sequence lengths; **float32 / bfloat16 / float16 storage** (half-precision cache = half the decode bytes, softmax stays float32) |
| ✅ | kv_append | the cache-write side of the contiguous decode loop (v1.3): one fresh token's k/v rows per sequence scattered in place into the cache at row `lens[b]` (one tiny kernel, f32/bf16/fp16) |
| ✅ | attention_decode_paged | the v1.2 headline: the same decode attention over a **vLLM-style block-pool kv-cache** `[Nb, Hkv, P, D]` walked through a per-sequence block table - fragmentation-free cache memory; any valid table honored, f32/bf16/fp16 storage, ~1.09-1.14x the contiguous op (7.82x / 4.28x vs SDPA on the pre-expanded-heads reference) |
| ✅ | kv_append_paged | the cache-write side of the paged loop: one fresh token's k/v rows per sequence scattered in place into the pool at position `lens[b]` (one tiny kernel, f32/bf16/fp16) |
| ✅ | attention_prefill | fresh-sequence attention over S query rows (causal / bidirectional), float32 / bf16 / fp16 storage; convenience path - heavyweight prefill stays SDPA/flash territory (honest ~0.45x f32) |
| ✅ | axpy | `a*x + b` - the v0.x hello-world demo op, kept for API compatibility |

## Install

```bash
pip install fusedtok
```

Prebuilt wheels on PyPI (built with CUDA 12.4): **Linux x86_64**
(manylinux, cp310-cp313) and **Windows x86_64** (cp311-cp313). On other
platforms or Python versions pip builds from source automatically:

```bash
git clone https://github.com/Hai-Wenxiang/fusedtok.git
cd fusedtok
pip install .
```

**Requirements:**

- an NVIDIA GPU of the **RTX 30 series (Ampere) or newer** - e.g. RTX 3060/3090, RTX 4080, RTX 5090, A100, H100
- CUDA Toolkit >= 12.0
- A C++17 compiler (MSVC on Windows, GCC/Clang on Linux); Python 3.10+

<details>
<summary>What is "compute capability"? (click to expand)</summary>

Compute capability is NVIDIA's version number for a GPU architecture generation - not a
performance score. CUDA code must be compiled for a specific architecture to run on it.
The wheel builds native cubins for compute capability 8.0 (A100) and 8.6 (RTX 30) plus a
compute_86 PTX fallback, so Ampere runs natively and newer architectures (RTX 40/50, ...)
JIT the PTX with their driver.

| Compute capability | Architecture | Example GPUs |
|---|---|---|
| 7.5 | Turing | GTX 16xx, RTX 20xx (not supported) |
| 8.0 / 8.6 | Ampere | A100, RTX 30xx |
| 8.9 | Ada | RTX 40xx (via PTX) |
| 9.0 | Hopper | H100 (via PTX) |
| 12.0 | Blackwell | RTX 50xx (via PTX) |

Check yours: run `nvidia-smi` to see your GPU model, then look it up at
https://developer.nvidia.com/cuda-gpus

</details>

## Usage

numpy in / numpy out, or torch in / torch out - including **zero-copy CUDA**:
kernels read and write torch device buffers directly via `data_ptr()`, with
no staging copies and no host synchronization.

```python
import numpy as np
import torch
import fusedtok

x = np.random.randn(4, 1024).astype(np.float32)
w = np.random.rand(1024).astype(np.float32)

# CPU reference implementation (ground truth, runs anywhere)
y = fusedtok.rmsnorm(x, w, eps=1e-6)

# staged CUDA: copies to GPU, runs kernel, copies back
y = fusedtok.rmsnorm(x, w, cuda=True)

# zero-copy CUDA with torch tensors: kernels run in torch's own buffers,
# stream-ordered with other torch operations
xt, wt = torch.from_numpy(x).cuda(), torch.from_numpy(w).cuda()
yt = fusedtok.rmsnorm(xt, wt)          # -> CUDA torch tensor

# RoPE with kv-cache position offset, NeoX (LLaMA-HF) layout
q = torch.randn(1, 4096, device="cuda")          # new token only
q_rot, k_rot = fusedtok.rope(q, k=None, pos_offset=1023, neox=True)

# attention over a GQA kv-cache: one call per decode step, no score
# materialization, variable-length batches share one cache tensor
out = fusedtok.attention_decode(
    q_heads,                                    # [B, Hq, D] new token
    k_cache, v_cache,                           # [B, Hkv, T, D]
    lens=torch.tensor([1023, 512], dtype=torch.int32, device="cuda"))
# grow a contiguous cache: append each new token's rows, then decode
fusedtok.kv_append(k_cache, v_cache, k_new, v_new, lens)
out = fusedtok.attention_decode(q_heads, k_cache, v_cache, lens + 1)
# ...or over a paged (vLLM-style block-pool) cache: pools [Nb, Hkv, P, D]
# + a per-sequence block table; append each new token, then decode
fusedtok.kv_append_paged(k_pool, v_pool, block_table, k_new, v_new, lens)
out = fusedtok.attention_decode_paged(q_heads, k_pool, v_pool,
                                      block_table, lens + 1)
# fresh-sequence prefill (causal by default; convenience path)
ctx = fusedtok.attention_prefill(q_all, k_all, v_all, causal=True)

# sampling side: the whole decode step in one fused call
token = fusedtok.decode_step(logits, sampled_ids, penalty=1.1,
                             p=0.9, temperature=0.8, seed=step)
# or step by step:
logits = fusedtok.repetition_penalty(logits, sampled_ids, penalty=1.1)
token = fusedtok.sample_topp(logits, p=0.9, temperature=0.8, seed=step)
# top-k sampling variant (renormalizes within the k survivors)
token = fusedtok.sample_topk(logits, k=50, temperature=0.8, seed=step)
# concurrent decode: [rows, vocab] in, one token per row out (default
# seeds are 0..B-1, so identical rows still draw independently)
tokens = fusedtok.sample_topp_batched(batch_logits, p=0.9,
                                      temperature=0.8)
# ...with per-row repetition penalties: ragged histories, one flat
# array + offsets is the serving-fast form
tokens = fusedtok.decode_step_batched(batch_logits, flat_ids,
                                      penalty=1.1, p=0.9,
                                      ids_offsets=offsets)
```

A minimal per-token sampling loop:

```python
import torch, fusedtok as ft

h = torch.zeros(1, 4096, device="cuda")            # decoder state
w = torch.load("rms_weight.pt").cuda()             # float32 weights
generated = []
for step in range(256):
    h = ft.rmsnorm(h, w, residual=h)               # fused add + norm
    q = ft.rope(q, k=None, pos_offset=step, neox=True)
    logits = model_output(h)                       # your model
    tok = ft.decode_step(logits, generated, penalty=1.1,
                         p=0.9, temperature=0.8, seed=step)
    generated.append(int(tok))
```

Every function accepts float32 numpy arrays or torch tensors (other dtypes
are converted with a copy) and returns float32 outputs of the same family.
CUDA torch tensors may be **bfloat16** on every operator that moves tensor
data (elementwise / norms / RoPE / attention), and the attention operators
additionally take **float16** - kernels compute in float32 and convert at
the load/store boundary (norm weights are upcast to float32 automatically;
sampling/selection ops stay float32).
CUDA torch tensors select the zero-copy path automatically.

See `examples/demo.py` for a runnable tour of every operator, and the
[usage guide](https://github.com/Hai-Wenxiang/fusedtok/blob/main/docs/en/usage.md) for the topic-structured manual - one page per theme:
quickstart, the execution model (paths / dtypes / streams / CUDA
graphs), attention, the sampling contract, the INT8 workflow, how to
read the benchmarks, and an FAQ with a glossary. Also available in
[中文](https://github.com/Hai-Wenxiang/fusedtok/blob/main/docs/zh/usage.md).

## Correctness

Every kernel ships with a CPU reference implementation and element-wise parity tests
(pytest). Tests run on machines without a GPU (CUDA cases skip automatically).

## API stability

1.0 froze the public surface at 34 names; new operators arrive in
minor releases (49 as of v1.8), and every name in `fusedtok.__all__`
keeps its signature across the 1.x series.
Type stubs (`__init__.pyi`, PEP 561 `py.typed`) ship with the package.
Breaking changes require a new major version and a deprecation window.
Determinism promises: selection ties resolve to the earliest index;
sampling is deterministic per seed.

## Benchmarks

RTX 3060 (sm_86), float32, zero-copy torch tensors, CUDA-event timing over
3 independent rounds (means below; per-round values in the JSON), vs
the equivalent PyTorch reference (composite eager expressions; attention
references use **pre-expanded** heads - `repeat_interleave` outside the
timed region). Largest shape per op; full data:
`docs/benchmarks/benchmark_rtx3060.json`, reproduce with `python benchmarks/bench.py`:

| Op | Shape | fusedtok | PyTorch reference | Speedup |
|---|---|---:|---:|---:|
| attention_decode (GQA) | T=16384, D=128 | 855 µs | 7540 µs (SDPA) | **8.82x** |
| attention_decode_paged (GQA) | T=16384, D=128, P=16 | 965 µs | 7543 µs (SDPA) | **7.82x** |
| RoPE NeoX (q+k) | [8192×4096] | 1617 µs | 9913 µs | **6.13x** |
| kv_append (contiguous cache write) | B=8, T=4096 | 13 µs | 49 µs (advanced indexing) | **3.72x** |
| sample_topp p=0.9 (peaked) | [131072] | 140 µs | 517 µs (sort+mask+multinomial) | **3.69x** |
| RMSNorm (+residual) | [4096×4096] | 604 µs | 2034 µs | **3.37x** |
| attention_decode bf16 | T=16384, D=128 | 845 µs | 1776 µs (SDPA bf16) | **2.10x** |
| sample_topk k=50 | [131072] | 143 µs | 255 µs (topk+multinomial) | **1.79x** |
| SwiGLU | [4096×4096] | 601 µs | 1008 µs | **1.68x** |
| top-k (k=50) | [131072] | 75 µs | 119 µs (CUB) | **1.58x** |
| LayerNorm | [4096×4096] | 440 µs | 606 µs | **1.38x** |
| sample_minp p=0.05 (peaked) | [131072] | 143 µs | 194 µs (mask+multinomial) | **1.35x** |
| sample_topa a=0.2 (peaked) | [131072] | 167 µs | 183 µs (mask+multinomial) | **1.10x** |
| top-k (k=4096, mid-k) | [131072] | 106 µs | 113 µs | 1.07x (honest) |
| Softmax | [4096×4096] | 405 µs | 426 µs | **1.05x** |
| SiLU / GeLU / add | [4096×4096] | ~405-601 µs | ~406-601 µs | ~1.0x |
| argmax | [131072] | 48 µs | 40 µs | 0.82x (event-timed, noisy on WDDM; across-run spread 0.67-1.21x this release - see below) |
| int8 qgemm pc (W8A8) | [4096×4096×4096] | 3497 µs (39.3 TOPS) | 2014 µs (cuBLASLt + broadcast) | 0.58x (honest) |
| int8 qgemm (IMMA) | [4096×11008×4096] | 9372 µs (39.4 TOPS) | 4423 µs (cuBLASLt) | 0.47x (honest) |
| attention_prefill (causal) | S=1024, D=128 | 5662 µs | 2540 µs (SDPA flash) | 0.45x (honest) |
| sample_minp p=0.05 (wide nucleus) | [131072] | 501 µs | 194 µs | 0.39x (honest: one widening retry plus a 32-64k sort; the torch boolean-mask composite never sorts - see the peaked min-p row for min-p's win scenario) |
| sample_nsigma 1.5 (peaked) | [131072] | 1914 µs | 206 µs (mask+multinomial) | 0.11x (honest: the sigma cutoff keeps the top ~94% of a spiked row - nsigma trims the LOW tail of the distribution, so a spiked row samples near the full vocabulary through the widening ladder; the paper's case for nsigma is output quality, not speed) |
| sample_topp p=0.9 (flat worst case) | [131072] | 1333 µs | 341 µs | 0.26x (honest, see below) |

Batched samplers (v1.4) and batched decode steps (v1.5) - one call for
the whole `[8, 131072]` batch, referenced against torch's native
batched draw (softmax + multinomial over the 2-D tensor; the decode
row adds the gather-penalty; per-round values in the JSON):

| Op | Shape | fusedtok | PyTorch reference | Speedup |
|---|---|---:|---:|---:|
| sample_topk_batched k=50 | [8×131072] | 201 µs | 297 µs (topk+multinomial) | **1.48x** |
| sample_minp_batched p=0.05 | [8×131072] | 230 µs | 293 µs (mask+multinomial) | **1.27x** |
| sample_topa_batched a=0.2 | [8×131072] | 268 µs | 295 µs (mask+multinomial) | **1.10x** |
| sample_topp_batched p=0.9 | [8×131072] | 297 µs | 215 µs (multinomial) | 0.72x (reference-side WDDM swing; see below) |
| decode_step_batched (penalty 1.3, ~64-token histories) | [8×131072] | 322 µs | 265 µs (penalize+softmax+multinomial) | 0.82x (but **5.2x** vs looping the single row, wall time - see below) |
| argmax_batched | [8×131072] | 33 µs | 24 µs (argmax) | 0.74x (event-timed vs torch's one-kernel row argmax), but **28x** vs looping the single row on wall time (8 submissions -> 1) |
| sample_nsigma_batched 1.5 | [8×131072] | 3719 µs | 318 µs (mask+multinomial) | 0.09x (honest, same wide-nucleus caveat as the single row) |
| sample_topp_batched (flat worst case) | [8×131072] | 3540 µs | 213 µs | 0.06x (honest, same caveat as the single row) |

Row-wise kernels (norms, softmax) autotune their thread-block size per
shape at first call (v0.4.1); the table reflects the tuned choices.

![fusedtok vs PyTorch reference](https://raw.githubusercontent.com/Hai-Wenxiang/fusedtok/main/docs/benchmarks/benchmark_rtx3060.png)

**RTX 5060 Ti (Blackwell, sm_120)** - same suite, largest shape per op
(full data: `docs/benchmarks/benchmark_rtx5060ti.json`):

| Op | Shape | fusedtok | PyTorch reference | Speedup |
|---|---|---:|---:|---:|
| RoPE NeoX (q+k) | [8192×4096] | 1384 µs | 8371 µs | **6.05x** |
| attention_decode (GQA) | T=16384, D=128 | 574 µs | 2681 µs (SDPA) | **4.67x** |
| attention_decode_paged (GQA) | T=16384, D=128, P=16 | 627 µs | 2681 µs (SDPA) | **4.28x** |
| RMSNorm (+residual) | [4096×4096] | 505 µs | 1658 µs | **3.28x** |
| sample_topp p=0.9 (peaked) | [131072] | 62 µs | 155 µs (sort+mask+multinomial) | **2.48x** |
| sample_topk k=50 | [131072] | 45 µs | 94 µs (topk+multinomial) | **2.06x** |
| kv_append (contiguous cache write) | B=8, T=4096 | 9 µs | 21 µs (advanced indexing) | **2.23x** |
| SwiGLU | [4096×4096] | 504 µs | 858 µs | **1.70x** |
| top-k (k=50) | [131072] | 27 µs | 42 µs (CUB) | **1.53x** |
| attention_decode bf16 | T=16384, D=128 | 547 µs | 641 µs (SDPA bf16) | **1.17x** |
| sample_topa a=0.2 (peaked) | [131072] | 65 µs | 78 µs (mask+multinomial) | **1.20x** |
| sample_minp p=0.05 (peaked) | [131072] | 62 µs | 73 µs (mask+multinomial) | **1.18x** |
| top-k (k=4096, mid-k) | [131072] | 50 µs | 54 µs (CUB) | 1.09x |
| LayerNorm / Softmax | [4096×4096] | ~346 µs | ~344-351 µs | ~1.0x |
| argmax | [131072] | 17 µs | 14 µs | 0.82x (event-timed, noisy) |
| int8 qgemm pc (W8A8) | [4096×4096×4096] | 2078 µs (66.1 TOPS) | 1144 µs (cuBLASLt + broadcast) | 0.55x (honest) |
| attention_prefill (causal) | S=1024, D=128 | 3297 µs | 1421 µs (SDPA flash) | 0.43x (honest) |
| int8 qgemm (IMMA) | [4096×11008×4096] | 5482 µs (67.3 TOPS) | 2193 µs (cuBLASLt) | 0.40x (honest) |
| sample_minp p=0.05 (wide nucleus) | [131072] | 219 µs | 74 µs | 0.34x (honest: same wide-nucleus caveat as the 3060 row) |
| sample_nsigma 1.5 (peaked) | [131072] | 1048 µs | 85 µs (mask+multinomial) | 0.08x (honest: same long-tail-filter caveat as the 3060 row - a spiked row samples near the full vocabulary) |
| sample_topp p=0.9 (flat worst case) | [131072] | 1054 µs | 166 µs | 0.16x (honest, see below) |

Batched samplers (v1.4) and batched decode steps (v1.5) on the same
`[8, 131072]` shapes:

| Op | Shape | fusedtok | PyTorch reference | Speedup |
|---|---|---:|---:|---:|
| sample_topk_batched k=50 | [8×131072] | 98 µs | 115 µs (topk+multinomial) | **1.18x** |
| sample_minp_batched p=0.05 | [8×131072] | 120 µs | 112 µs (mask+multinomial) | 0.93x (parity) |
| sample_topa_batched a=0.2 | [8×131072] | 133 µs | 113 µs (mask+multinomial) | 0.85x |
| decode_step_batched (penalty 1.3, ~64-token histories) | [8×131072] | 147 µs | 99 µs (penalize+softmax+multinomial) | 0.68x (but **4.5x** vs looping the single row, wall time - see below) |
| sample_topp_batched p=0.9 | [8×131072] | 133 µs | 83 µs (multinomial) | 0.62x |
| argmax_batched | [8×131072] | 20 µs | 11 µs (argmax) | 0.54x (event-timed vs torch's one-kernel row argmax; the per-row loop's submission cost is what the batched call removes) |
| sample_nsigma_batched 1.5 | [8×131072] | 1726 µs | 122 µs (mask+multinomial) | 0.07x (honest, same long-tail caveat as the single row) |
| sample_topp_batched (flat worst case) | [8×131072] | 1747 µs | 83 µs | 0.05x (honest) |

On smaller shapes the Blackwell card shows bigger wins (softmax 1.71x,
RMSNorm 3.11x at 256 rows, attention decode 3.77x at T=4096 running
~187 GB/s) - the launch-overhead share shrinks as shapes grow; full
sweep in the JSON.

![fusedtok vs PyTorch reference (RTX 5060 Ti)](https://raw.githubusercontent.com/Hai-Wenxiang/fusedtok/main/docs/benchmarks/benchmark_rtx5060ti.png)

The PyPI wheel ships sm_80/sm_86 cubins plus a compute_86 PTX fallback -
verified to JIT and run correctly on Blackwell (sm_120) drivers.

Fusions win big (RoPE / RMSNorm / SwiGLU) because eager mode round-trips
intermediate tensors through global memory. The v0.4 selection pipeline
(arrival-ticket radix rounds + early-exit compaction, replayed from a
cached CUDA graph) beats torch's CUB radix select at small k on both
GPUs; the v1.0 retune (in-block-sort threshold and sort chunk both
dropped 2048 -> 1024 - a single block bitonic-sorting 2048 keys was the
whole mid-k regression) brings the mid-k window to parity-or-winning
as well. The fused samplers win against the
eager composites when the logits look like real decode output
(sample_topp peaked: 3.69x / 2.48x; sample_topk: 1.79x / 2.06x; the
composites themselves swing run-to-run on WDDM - per-round values in
the JSON). On a flat distribution sample_topp is honestly 0.16x on a
5060 Ti and 0.26x on a 3060 (the fusedtok side is stable; the
reference's rounds carry WDDM noise) - the nucleus then
spans ~90% of the vocabulary and the pipeline must effectively order
the whole thing. v1.2 cut that worst case ~8.5x (18.2ms -> 2.2ms at
n=131072 on a 3060) with three token-preserving changes - an adaptive
window jump driven by a p*total mass bound, a full-vocabulary fast
path that skips the selection stages, and a batched-load serial sampling
walk (the strictly sequential float adds are the CPU-parity determinism
contract; only the loads got pipelined); v1.3 cut it another ~1.6x
(2.2ms -> 1.4ms on a 3060, 1.6ms -> 1.0ms on a 5060 Ti) by having the
first walk record prefix-sum checkpoints that the inverse-CDF walk
binary-searches - still bit-identical tokens - but torch's fully parallel
sort still owns that regime. sample_minp (v1.3) wins on peaked logits
and honestly loses the wide-nucleus row (0.27-0.39x: one widening
retry plus a 32-64k sort; the torch boolean-mask composite never
sorts) - v1.4 gave min-p the same adaptive jump top-p has had since
v1.2 (a sufficient bound from the one-time global total:
wide-nucleus rows skip the ladder's intermediate stops, 28-30% faster on
that row, tokens bit-identical).

The v1.4 batched samplers put the whole `[rows, vocab]` batch through
one call: every row runs the single-row pipeline verbatim (per-row
parity, including the widening loop with per-row finish tracking), so
the speedup over the per-row loop is pure batching - 4-6x in wall
time at B=8 on submission-bound hosts (3060: topp 1340 -> 274 µs,
minp 1399 -> 237 µs; 5060 Ti: 3.9x / 4.3x). On peaked logits the
batched calls sit at torch's native batched-multinomial level
(the batched min-p, top-k and top-a calls win on this
benchmark: 1.27x/1.18x, 1.48x/1.18x and 1.10x/0.85x on the two GPUs), and the flat worst case
keeps the singles' honest caveat one tier lower (0.05-0.06x). v1.5
extends batching to the whole decode step: `decode_step_batched` runs
per-row repetition penalties through per-row vocab bitmaps inside the
same pipeline - on wall-time probes at B=8 it is 5.2x over looping
`decode_step` on peaked rows on a 3060 (1676 -> 321 µs; torch's
native penalize+softmax+multinomial composite: 266 µs) and 4.5x on a
5060 Ti (646 -> 145 µs), and 3.1x on mid-tail rows (3060).

attention_decode wins big at decode (one launch streams the GQA cache
once, while SDPA pays head expansion or small-query inefficiency);
attention_decode_paged (v1.2) pays only ~1.09-1.14x for the
block-table indirection of the fragmentation-free vLLM-style cache
layout, with bit-identical output on matching slice schedules;
attention_prefill is the honest convenience path at ~0.45x of SDPA's
flash backend - no tensor cores by design, so heavyweight prefill stays
with SDPA/FlashAttention. kv_append (v1.3) writes one token's k/v rows
into the contiguous cache in a single launch (a multi-x win over the
advanced-indexing scatter whose exact factor tracks that reference's
WDDM swing; a tiny launch-bound op - parity-level cost per decode
step). The INT8 decode GEMV moves half the bytes of
an fp16 projection and runs at full memory bandwidth (2x); the pipelined
IMMA GEMM (v1.0 rework: cp.async double-buffered slabs, runtime-tuned
64x64 / 128x128 tiles) reaches ~39 TOPS on a 3060 and ~67 TOPS on a
5060 Ti - 2x-4x the v0.4 kernel - but cuBLASLt (`torch._int_mm`) still
holds a ~2.1-2.5x lead on the per-tensor rows (its tiles pipeline
deeper and its epilogue is tuned per-arch); the W8A8 rows' gap is only
~1.7-1.8x. For now qgemm is the exact / graph-capturable /
zero-copy INT8 path, not the fastest one; honest numbers, a
CUTLASS-class schedule stays future work. The per-channel variant
(`qgemm_perchannel`, the W8A8 layout INT8 inference actually uses)
fuses the per-output-channel scale multiply into the same epilogue at
zero kernel cost - the composite torch reference pays for that
broadcast separately, which is where its 0.55-0.58x comes from.

All sampling rows above have measured fixed logits since 1.1.1 (the bench
seeds torch's RNG); since v1.2 the peaked row spikes +20 and the flat
row uses near-uniform logits - both regimes are now deterministically
what their labels say (the v1.1 peaked row sat on the coverage boundary
at n=131072 and flipped regimes per seed). The argmax rows are
event-timed over a host-synchronized call and swing on WDDM
(0.73-1.24x across runs); wall-clock probes with the sync excluded
from the timed loop measure 1.12x (3060) / 0.96x (5060 Ti) - v1.2
removed one CUDA submission and one allocation per call.

## Development

See [CONTRIBUTING.md](https://github.com/Hai-Wenxiang/fusedtok/blob/main/CONTRIBUTING.md) for the full guide (test rules,
error contract, determinism invariants). Quick start:

```bash
# Windows: run inside a VS developer prompt (vcvars64)
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
cmake --build build
# from repo root: PYTHONPATH picks up the built module, conftest.py adds python/
$env:PYTHONPATH = "$PWD/build"        # Windows
PYTHONPATH=$PWD/build                 # Linux
python -m pytest tests -q
python benchmarks/bench.py            # GPU benchmark + chart
```

Both Windows and Linux are supported: Windows builds through MSVC via
nvcc, and CI builds and runs the CPU test suite on every push.

## Roadmap

- v0.2 (released): bf16 zero-copy, radix-select top-k/top-p, fused nucleus
  sampling, single-read softmax, CUDA-graph verified
- v0.3 (released): chunk-merge selection sort + parallel nucleus count,
  bf16x4/x8 vectorized elementwise, INT8 quantize/dequantize utilities
- v0.4 (released): arrival-ticket selection pipeline (no cooperative
  launch, early-exit compaction, cached CUDA graphs), stream-aware
  launchers everywhere (real CUDA-graph capture), INT8 compute path
  (IMMA qgemm + decode GEMV), fused decode_step sampling
- v0.4.1 (released): runtime block-size autotuning for the row-wise kernels
  (norms/softmax pick 128..1024 threads per shape at first call)
- v0.5 (released): attention - GQA decode attention over a contiguous
  kv-cache (flash-decoding split over long caches, per-sequence lengths)
  and a tiled prefill path (honest ~0.45x of SDPA flash - the
  convenience path); single-chart-per-GPU benchmarks; Windows wheels in
  the PyPI publish pipeline
- 1.0 (released): pipelined tensor-core INT8 GEMM (cp.async
  double-buffering, runtime tile tuning; 17 -> 39 TOPS on a 3060) with
  per-channel weight scales (W8A8), fused top-k sampling (2.1x vs the
  topk+multinomial composite), top-k mid-range-k parity, text hygiene
  gate, wheel matrix expansion (Linux cp310-313, Windows cp311-313),
  API freeze
- 1.1 (released): half-precision attention - `attention_decode` /
  `attention_prefill` accept bfloat16 and float16 caches (float32
  compute, half the decode bytes); parallel exp precompute halves the
  flat-distribution sampling worst case with bit-identical tokens
- 1.2 (released): paged kv-cache attention - `attention_decode_paged`
  over a vLLM-style block pool `[Nb, Hkv, P, D]` + per-sequence block
  tables (~1.09-1.14x the contiguous op, any valid table honored) and
  `kv_append_paged` (the in-place cache-write side); flat-distribution
  sampling worst case cut ~8.5x (adaptive widening jump + full-vocab
  fast path + batched-load serial walk, tokens bit-identical); argmax
  launch diet (one submission and one allocation less per call)
- 1.2.1 (released): audit-driven hardening - selection workspace
  overflow past 131072-token vocabularies fixed (Qwen-scale vocabs),
  host-side lens/table/id validation with a documented device-tensor
  trust boundary (CUDA-graph capture works with `lens` now), empty-
  input and dtype/contiguity guards on the zero-copy paths, honest
  benchmark bandwidth (four rows were over-billed 1.5x), warning-clean
  builds (MSVC /W3 + GCC -Wall -Wextra), and the documentation
  restructured into topic pages with a natural-sounding Chinese
  rewrite
- 1.3 (released): `sample_minp` (min-p sampling - value-threshold
  nucleus relative to p_max, no global-mass reduction, adaptive by
  construction) and `kv_append` (the contiguous cache-write side); the
  sampling serial walk gains checkpoint bisection (walk 1 records
  prefix sums, walk 2 binary-searches them - flat worst case cut
  another ~1.6x, tokens bit-identical); zero-copy helpers reject CPU
  operands (a host pointer in a kernel poisons the CUDA context)
- 1.3.1 (released): audit-driven hardening - staged-path lens/block-table
  value validation closed (bad values used to become silent GPU
  out-of-bounds writes) plus integer-input hardening; two latent
  sampling-walk bugs fixed (stride>=2 checkpoint double-count, adaptive
  widening reading the wrong workspace word - mid-tail ~28% faster with
  bit-identical tokens); softmax tuner capped to keep sanitizer gates
  clean; kernel/launcher cleanup; benchmark tables fully regenerated
- 1.4 (released): batched sampling - `sample_topp/minp/topk_batched`
  sample a whole `[rows, vocab]` batch in one call (per-row parity with
  the singles, one seed per row, rows finish at their own window sizes);
  min-p gains the adaptive widening jump (a sufficient bound from the
  one-time global total - wide-nucleus rows skip the ladder's
  intermediate stops, tokens bit-identical)
- 1.4.1 (released): audit-driven hardening - staged-path batched shape
  validation closed (rows/n used to be trusted against the buffer),
  shared widen-bound helpers and a batched-sequencer cleanup, lazy
  per-row totals for both widen modes, stream-scoped syncs; benchmark
  tables regenerated with the shipping version stamp and the
  documentation overhauled (stale numbers, stiff phrasing, glossary
  and FAQ additions)
- 1.5 (released): a batched `decode_step` - per-row ragged histories
  through per-row penalty bitmaps, the whole penalize -> scale ->
  sample chain in one call. The other 1.5 candidate (per-row window
  sizes inside one batched attempt, so a wide row no longer lifts the
  uniform window) was implemented three ways, measured as a net loss
  or parity at B=8/B=32 on an RTX 3060 (the serial inverse-CDF walk
  dominates each attempt round and the merge ladder carries a
  per-launch floor that window-bucketing multiplies), and was dropped
  with the numbers in the changelog - revisit if the walk goes
  parallel
- 1.5.1 (released): audit-driven hardening - the int8 GEMV silently
  dropped the head of every row its alignment gate rejected
  (k % 4 != 0); the kv_append staged bindings and the rope /
  temperature raw launchers validate what their siblings already
  did; scalar-fallback kernels index in 64-bit; both docs languages
  resynced to the 1.5.0 JSONs with the stiff phrasing rewrites
- 1.5.2 (released): audit-driven hardening, round three - the
  batched radix rounds now apply the row's repetition penalty (the
  unpenalized selection prefix mis-composed decode_step_batched's
  window under penalty != 1); the kv_append_paged span is computed
  in 64-bit; duplicate penalty ids can no longer diverge between
  the CPU and GPU paths; rope grids, launcher guards and the batched
  CPU references got the same checks their siblings have
- 1.8 (released): two more truncation rules and batched greedy -
  `sample_topa` (top-a: keep every token with `p_i >= top_a * p_max^2`,
  min-p's prefix machinery with the cutoff derived from the existing
  total - `top_a / total` in exp units - and min-p's sufficient
  widening bound; `top_a = 1.0` keeps both leaders of a two-horse
  distribution), `sample_nsigma` (keep every token whose scaled logit
  stays at or above `mean - nsigma * sigma` - the row's first two
  moments from one new pass, accumulated in doubles so atomic
  arrival-order drift cannot survive the variance's cancellation), and
  `argmax_batched` (one launch for a whole `[rows, vocab]` greedy
  step, no readback, CUDA-graph capturable); the entropy-family mass
  passes fused into one kernel (eta/typical/nsigma run two
  full-vocabulary passes per attempt instead of three, tokens
  bit-identical) (44 -> 49 public names)
- 1.7 (released): `logit_penalties_batched` - the penalty trio for a
  whole `[rows, vocab]` batch with ragged per-row histories, each row
  bit-identical to the single-row op; the batched histograms ride a
  capture-safe cached workspace with a workspace-free borrowed-output
  fallback, and both apply kernels force separate roundings so the
  FFMA fusion cannot split CPU and GPU by an ulp (43 -> 44 public
  names)
- 1.6 (released): entropy-adaptive sampling - `sample_eta` (eta
  cutoff, Hewitt 2022: keep every token with
  `p_i >= eta * min(1, exp(-H))`, a value-threshold prefix like min-p,
  reusing min-p's adaptive widening bound) and `sample_typical`
  (locally typical, Meister 2022: the smallest set whose mass reaches
  `typical`, ordered by `|surprise - H|` - a contiguous band of the
  value-sorted window, no analytic widening bound so the honest x8
  ladder), each single-row and batched (38 -> 42 public names)
- 1.6.1 (released): `logit_penalties` - the HF penalty trio (CTRL
  repetition scale, presence shift, count-weighted frequency shift)
  in one call, once per distinct id, duplicates never stack,
  bit-exact across CPU and GPU - plus audit-driven hardening: the
  spurious "typical nucleus not covered" throw at `typical = 1.0`,
  the batched typical trust-boundary gap, and the eta widening
  bound's unit mismatch (42 -> 43 public names)

- future candidates (unscheduled): bf16/fp16 tensor-core prefill
  (rewrite-level), a CUTLASS-class INT8 GEMM schedule (the current
  qgemm is the exact/graph-capturable/zero-copy path, not the fastest
  one), 16-bit radix keys (changes the determinism contract)

## Community

- [Contributing guide](https://github.com/Hai-Wenxiang/fusedtok/blob/main/CONTRIBUTING.md) - setup, rules of the road, PR process
- [Code of conduct](https://github.com/Hai-Wenxiang/fusedtok/blob/main/CODE_OF_CONDUCT.md)
- [Security policy](https://github.com/Hai-Wenxiang/fusedtok/blob/main/SECURITY.md)
- [Changelog](https://github.com/Hai-Wenxiang/fusedtok/blob/main/CHANGELOG.md)

## License

MIT - see [LICENSE](https://github.com/Hai-Wenxiang/fusedtok/blob/main/LICENSE). Third-party notices: [NOTICES.md](https://github.com/Hai-Wenxiang/fusedtok/blob/main/NOTICES.md).
