Record: Fused MLP (Triton+CUTLASS EVT) + MLP 3.5× + Mixed int5/int6 + SLOT + Brotli — 1.1088 BPB (3-seed mean)

[abaybektursun]

Mechanistic Interpretability: For a deep-dive analysis of this model — including SVD utilization, quantization sensitivity, logit lens, and calibration — see Mechanistic Interpretability of PR 1105.

Custom CUTLASS 3.x EVT + Triton TMA kernels recover the 3.3ms/step throughput regression from full Hessian GPTQ (our merged PR 1019). Mechanistic analysis of that model revealed MLP at 94.4% SVD rank utilization (fully packed) while attention Q sat at 72.6% — the model was parameter-starved in MLP, not attention. MLP 3.5×, enabled by Hessian-based mixed int5/int6 quantization, acts on that finding. Brotli-11 saves 581KB.

Results: val_bpb 1.1088 (3-seed mean) | 1.8722 nats | 8×H100 SXM | 600s | ~14.52 MB

Seed	Steps	ms/step	Post-EMA BPB	Sliding BPB	val_loss (nats)	Artifact
314	6,812	87.7	1.1256	1.1086	1.87174	14,519,020
999	6,833	87.7	1.1259	1.1086	1.87187	14,522,798
1337	6,811	87.7	1.1265	1.1093	1.87306	14,526,779
Mean	6,819	87.7		1.1088	1.8722

Mixed quantization: 10 layers int6, 56 layers int5, no pruning needed. SLOT eval adds ~54s (well within 10-min budget).

Our merged PR 1019 (current SOTA): 1.88059 nats (1.1138 BPB). Delta: −0.00836 nats (−0.0050 BPB). Welch's t = −9.98, df ≈ 3.20, p < 0.01. Clears 0.005-nat threshold.

What does −0.005 BPB look like? Side-by-side generation (temp=0.8)

Prompt (50 tokens): "Insurance Company Declares Living Man Dead George Johannesen is very much alive. Which is why it was so surpr"

PR 1019 model (1.1138 BPB)

This model (1.1088 BPB)

ising to be insurance company. Living Man? Maybe. All Living Man? It was a miracle. However, right now, Living Man is still dead. Well, why is he not living? Living man Rachel Drobles is a person whose life is tested by the techniques of the car industry. Like all techniques, the life of Rachel is tested by the astounding things she has become. Therefore, whether it is a mistake, or in-residence, or a mistake happening, is perpetuated. Therefore, Lyon Man is dead. Can Living Man H

ising to be insurance company. Living Man is the only insurance company that makes property insurance. It is a company that makes vacation insurance, but it still has the same degree of property insurance. So how does a living man die? So say I think there are ways to teach people how to be insured. The first step is to measure the health of the living man and the stress of his situation. To measure the health of the living man, it is important to measure his or her weight. What is the hazard to the living man? Living Man is the only insurance company that specializes in repairs

The old model drifts into incoherence ("Rachel Drobles... techniques of the car industry... Lyon Man is dead"). The new model stays on topic — insurance, health measurement, living man — and maintains grammatical coherence throughout. Both are wrong (the real text is about a cancelled driver's license), but the new model's errors are at least topically plausible. Remarkable that a 0.005 BPB difference — just 0.4% relative — produces a visible jump in coherence. Attention head analysis shows why: the model shifted from pattern-matching (induction heads: 2→1) to stronger sequence continuation (previous-token heads: 22→28), consistent with staying on topic instead of drifting.

Prior results: full stack without SLOT (val_bpb 1.1125, 3-seed)

Seed	Steps	ms/step	Post-EMA BPB	Sliding BPB	val_loss (nats)	Artifact
314	6,844	87.7	1.1253	1.1123	1.87802	14,519,698
999	6,846	87.7	1.1256	1.1124	1.87821	14,517,302
1337	6,828	87.7	1.1261	1.1129	1.87910	14,525,480
Mean	6,839	87.7		1.1125	1.8784

Delta vs PR 1019: −0.00215 nats (below 0.005 threshold). Delta vs PR 549: −0.01158 nats, t = −17.63, p < 0.01.

Prior results: fused kernels + Brotli only (val_bpb 1.1138, 3-seed)

Seed	Steps	ms/step	Pre-quant BPB	Sliding BPB	val_loss (nats)	Artifact
314	7,176	83.4	1.1325	1.1133	1.8798	15,507,095
42	7,173	83.7	1.1325	1.1134	1.8800	15,498,065
999	7,176	83.5	1.1339	1.1146	1.8820	15,512,666
Mean	7,175	83.5		1.1138	1.8806

Delta vs PR 549: −0.00943 nats. Welch's t = −10.26, df ≈ 3.78, p < 0.01.

Throughput recovery

Submission	ms/step	BPB	What changed
Our PR 549	83.4	1.1194	Leaderboard SOTA baseline
Our PR 1019 (merged SOTA)	86.7	1.1147	+Full GPTQ, +XSA-all, +BigramHash 3072. +3.3ms regression.
This PR (kernels only)	83.5	1.1138	+Fused MLP kernels, +Brotli. Regression erased.
This PR (full stack)	87.7	1.1125	+MLP 3.5×, +mixed int5/int6, +LR floor.
This PR (+ SLOT)	87.7	1.1088	+SLOT eval. −0.0037 BPB from SLOT alone. Clears 0.005 nats vs PR 1019.

Our PR 1019 (now merged as SOTA) traded throughput for quality — full Hessian GPTQ and BigramHash 3072×112 added 3.3ms/step. Fused MLP kernels recover that regression. Mechanistic analysis of that model identified MLP as the capacity bottleneck, leading to MLP 3.5× (enabled by mixed quantization + Brotli headroom).

Changes vs our PR 1019

1. Fused MLP Kernels: Triton TMA Forward + CUTLASS EVT Backward

Forward (Triton TMA): Fuses F.linear(x, up_w) → LeakyReLU(0.5) → square into a single kernel. The 302MB intermediate never touches HBM.

Backward (CUTLASS EVT): Fuses (go @ down_w.T) * act_grad into a single CUTLASS 3.x kernel via Epilogue Visitor Tree. The elementwise multiply runs in the GEMM epilogue while tiles are still in registers — eliminating one 302MB write + read per layer.

Key design insight — pre-computed activation gradient: We store the activation gradient in the forward pass instead of the pre-activation:

Standard:  store pre, recompute grad in backward with branch
Ours:      act_grad = (pre > 0) ? 2·pre : 0.5·pre    ← one branch, forward only
           post    = 0.5 · act_grad · pre              ← branch-free recovery
           dpre    = (go @ W_down.T) * act_grad         ← branch-free backward

The identity post = 0.5 · act_grad · pre holds for both signs because:

• pre > 0: act_grad = 2·pre → 0.5 · 2pre · pre = pre² = LeakyReLU(0.5)(pre)² ✓
• pre ≤ 0: act_grad = 0.5·pre → 0.5 · 0.5pre · pre = 0.25·pre² = (0.5·pre)² ✓

This eliminates all branching from the backward, reducing the CUTLASS EVT epilogue to a trivial 3-node tree: Sm90EVT<multiplies, AccFetch, AuxLoad>. No conditionals in the kernel.

CUTLASS EVT is a hard dependency — no silent fallback.

Kernel benchmarks + incremental deltas (2×H100)

Per-layer kernel timing:

Variant	dpre time	Δ per layer	Δ per step (×11)
cuBLAS unfused	1.221 ms	baseline	baseline
Triton precomp	1.105 ms	−0.116 ms	−1.275 ms
CUTLASS Pingpong	1.073 ms	−0.148 ms	−1.623 ms

CUTLASS vs Triton: +0.032 ms/layer, +0.347 ms/step kernel-level.

End-to-end training (35 steps, seed=42):

Config	Step avg	Δ
Triton fwd + Triton bwd	313.90 ms	baseline
Triton fwd + CUTLASS EVT bwd	313.47 ms	−0.43 ms

Kernel-level 0.347ms translates to 0.43ms end-to-end (cache/scheduling interactions).

8×H100: 86.7ms (our PR 1019, unfused) → 83.5ms (this PR) = −3.2ms/step (−3.7%).

Step-time profile — where all 313ms goes (2×H100, Nsight)

Component	Share
Flash Attention 3 (fwd+bwd)	20.1%	FA3 forward alone is 17.5% — single largest kernel
Fused MLP (Triton+CUTLASS)	13.5%	Our optimization target
cuBLAS GEMMs (MLP bwd dW/dx, attn proj)	19.1%	Remaining unfused matmuls
torch.compile fusions (cross-layer)	21.6%	Preserved — see below
Unfused elementwise (LN, residuals)	21.0%
Communication + other	4.7%	NCCL, copies, softmax

Why surgical fusion, not full-MLP autograd.Function: The 21.6% from torch.compile's cross-layer fusions (RMSNorm backward, residual adds, RoPE backward) only exists because these ops are visible to the compiler. Wrapping the full MLP backward in autograd.Function makes it opaque to Inductor — all backward GEMMs plus cross-layer fusion run in eager mode, 2.7× slower net (identified in our PR 670). We fuse only forward and one backward GEMM+pointwise, preserving the compiler's scope.

Top individual kernels:

Kernel	Share
FA3 forward (device_kernel)	17.3%
Fused MLP (_fused_leaky...)	13.4%
cuBLAS GEMM (nvjet 256×128)	12.2%
elementwise_kernel	7.8%
vectorized_elementwise_kernel	5.6%
vectorized_layer_norm_kernel	4.3%

Wall-clock breakdown: forward+backward compute ~94%, NCCL ~1.6%, CPU overhead ~4.1%.

2. Brotli-11 Compression (replaces LZMA-9)

−581 KB (−5.9%) vs LZMA-9. Independently discovered; PR 1089 (mikeapedia) also uses Brotli.

3. Memmap Multi-Shard Data Pipeline + GPU Prefetch

Coprime-stride sampling, daemon thread, CUDA stream prefetch. Credit: DeepReinforce (PR 726).

4. MLP 3.5× (1536 → 1792 hidden dim)

Motivated by mechanistic analysis: SVD analysis of our PR 1019 model showed MLP at 94.4% rank utilization (fully packed) while attention Q sat at 72.6% (spare capacity). The model was parameter-starved in MLP, not attention — so we made MLP wider.

Increases hidden dim from 3.0 × 512 = 1536 to 3.5 × 512 = 1792. Model goes from 27.07M to 29.95M params (+2.88M). At uniform int6, the 29.95M model compresses to 17.36 MB — 1.36 MB over the 16 MB limit. This is what makes mixed quantization (change 5) necessary.

Impact: −0.003 BPB from capacity, +13ms/step on 2×H100 (bigger GEMMs). Credit: PR 185 (dttdrv), PR 344 (aryanbhosale).

5. Mixed int5/int6 Quantization (Hessian-based)

Motivated by mechanistic analysis: Per-matrix quantization sensitivity showed MLP accounts for 80% of int6 quantization damage (MLP_down: +0.0039 BPB total, all Q matrices: +0.0003 BPB total — a 13× gap). Giving more bits to MLP is the optimal allocation.

Instead of uniform int6 for all layers, use int5 as default and promote the top 10 most sensitive layers to int6 based on Hessian trace ranking. Sensitivity = trace(H) where H = X^TX collected during GPTQ calibration. MLP projection layers in early blocks are most sensitive — they get int6; the remaining 56 layers get int5.

Uniform int5 loses ~0.019 BPB (catastrophic). Targeted Hessian-based allocation keeps quality loss under ~0.003 BPB while saving ~1.5 MB — exactly the headroom MLP 3.5× needs to fit under 16 MB. The wider MLP also made the model 3.6× less sensitive to quantization overall — information distributed across more dimensions means no single weight is load-bearing.

Credit: mixed quant concept PR 76 (Will DePue), gradient-guided PR 332 (saml212), Hessian-based PR 1089 (mikeapedia).

6. LR Floor (0.05)

During warmdown, learning rate normally decays to 0. With lr_floor=0.05, it stops at 5% of peak instead. Prevents the optimizer from stalling, which helps with quantization-sensitive weight distributions still being refined at end of training.

Impact: ~0.001 BPB. Credit: PR 130 (mohosy).

7. SLOT Eval (Selective Logit Offset Tuning)

Eval-time adaptation: tunes a 512-dim delta vector at the last hidden layer using AdamW (lr=0.003, 5 steps) on validation tokens already scored. −0.0037 BPB on top of the full stack (1.1123 → 1.1086). Adds 54s eval time (148s total vs 94s without), well within the 10-minute eval budget.

This is what pushes us past the 0.005-nat threshold vs our merged PR 1019.

Credit: PR 609 (saml212).

Negative Results

• Turbo-Muon (AOL + Polar Express NS4): +0.0018 BPB worse on 8×H100 AND artifact over 16MB. Early convergence advantage at step 500 doesn't hold at 7000+ steps. Reverted to standard NS5.
• 2:4 Structured Sparsity: +0.672 BPB. Dead.
• Calibration regression: ECE increased from 0.24% (PR 1019 model) to 1.26% (this model) — the mixed int5/int6 quantization introduces slight overconfidence. Model entropy dropped from 1.899 to 1.847 nats (more confident) while accuracy dropped from 54.99% to 54.46%. Under investigation.

Architecture

Component	Setting	Source
Layers	11 (512d, 8 GQA / 4 KV heads)	Baseline
MLP	3.5× (1792), LeakyReLU(0.5)²	This work (concept: PR 185 (dttdrv), PR 344 (aryanbhosale))
MLP Forward	Fused Triton TMA kernel	This work (profiling: our PR 670)
MLP Backward	CUTLASS EVT Pingpong + pre-computed act_grad	This work
Attention	XSA on all 11 layers	PR 478 (gowtham0992)
BigramHash	3072 × 112	Our PR 1019 (concept: PR 162 (raahilshah))
RoPE	Partial (16/64 dims)	PR 315 (jfprincz)
LN Scale	1/√(layer+1)	PR 315 (jfprincz)
VE128	Layers 9-10	PR 374 (unnir)
SmearGate	Position-mixing gate	PR 65 (aquariouseworkman)
U-Net skips	Encoder-decoder connections	PR 289
Weight avg	EMA(0.997) + SWA(every 50)	PR 401 (newjordan)
Quantization	Full Hessian GPTQ mixed int5/int6 (Hessian-based, AR self-gen)	This work (GPTQ: PR 535 (raahilshah), mixed: PR 1089 (mikeapedia))
Compression	Brotli quality=11	This work (independently: PR 1089 (mikeapedia))
Data Pipeline	Memmap multi-shard + GPU prefetch	PR 726 (DeepReinforce)
Warmdown	4000 iterations, LR floor 0.05	PR 364 (shikhar1729), LR floor: PR 130 (mohosy)
Optimizer	Parallel Muon (NS5)	Our PR 399
Late QAT	STE at LR scale < 0.15	PR 286 (chris-buckley)
Selective pruning	±1 by reconstruction error	PR 609 (saml212)
Eval	SLOT (Selective Logit Offset Tuning)	PR 609 (saml212)
Flash Attention 3	Hopper kernels	PR 122 (mtybadger)

Calibration legality: AR self-generated (64 seqs × 2048 tokens, temp=0.8). No val data, no train data accessed during quantization. Same method as our PR 1019.

Setup & Reproduction

# 1. Python dependencies
pip install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128
pip install flash_attn_3 --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291
pip install sentencepiece brotli

# 2. CUTLASS headers (header-only, no build needed for CUTLASS itself)
cd /opt && git clone --depth 1 --branch v3.7.0 https://github.com/NVIDIA/cutlass

# 3. Build CUTLASS EVT extension
cd cutlass_evt_fusion
CUTLASS_PATH=/opt/cutlass python3 setup.py build_ext --inplace
cd ..

# 4. Set library paths (auto-detect from Python packages)
export LD_LIBRARY_PATH=$(python3 -c "import torch; print(torch.__path__[0] + '/lib')"):$(python3 -c "import nvidia.cuda_runtime; print(nvidia.cuda_runtime.__path__[0] + '/lib')"):${LD_LIBRARY_PATH:-}

# 5. Download data
python3 data/cached_challenge_fineweb.py --variant sp1024

# 6. Train (3 seeds)
for SEED in 314 42 999; do
  BIGRAM_VOCAB_SIZE=3072 BIGRAM_DIM=112 WARMDOWN_ITERS=4000 SEED=$SEED \
  torchrun --standalone --nproc_per_node=8 train_gpt.py 2>&1 | tee train_seed${SEED}.log
done

🤖 Generated with Claude Code

[mikeapedia]

@abaybektursun - this is a fantastic write-up! Congrats on the SLOT improvement. If you need to free up even more room, you should check out the shrink.py script I used in PR 1089. I was able to shrink the train_gpt.py file by ~100KB. That might let you reduce pruning and/or promote one more group to int6.

https://github.com/openai/parameter-golf/pull/1089/changes#diff-c9824523a27aeee853cd71187ce5f1950a89929d0ea00053ac0fc5f85eeb4c6a

[abaybektursun]

Ohhh I think with newer Pytroch performance and speed will be even better! I will try it when I can get my hands around 8xH100s