<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm</path>
<type>directory</type>
<entries>
benchmark.py
check.py
problem.yaml
PROMPT.txt
reference.py
shapes.py
sota.py

(7 entries)
</entries>

<path>/home/infatoshi/cuda/KernelBench-Hard/CLAUDE.md</path>
<type>file</type>
<content>
1: # KernelBench-Hard — Developer Instructions
2: 
3: Last updated: 2026-04-27.
4: 
5: This file is for **coding agents editing the repo** (you, via Claude Code). Do not confuse with `problems/<X>/PROMPT.txt` — those are the human-voice queries fed to agents _under test_.
6: 
7: For the journey behind the current design, read [DEVLOG.md](./DEVLOG.md).
8: 
9: ## What this repo is
10: 
11: Small kernel benchmark. Frontier coding agents are given URLs to SOTA implementations (sonic-moe, flashinfer, marlin) and asked to write a competitive kernel on RTX PRO 6000 Blackwell (SM120) in 45 minutes. Roofline-graded. Published artifact is the best kernel per (problem × model × harness), plus the agent trace.
12: 
13: See [SPEC.md](./SPEC.md) for methodology. See [README.md](./README.md) for the model matrix and quick start.
14: 
15: ## Non-negotiable rules
16: 
17: - **uv only.** No bare `python`, no `pip`. Use `uv run ...`, `uv add ...`, `uv pip install ...`.
18: - **Before committing:** `uv run ruff check . --fix && uv run pytest`.
19: - **Never edit `problems/*/solution.py`**. Those files are agent output; they're gitignored for a reason. If you need to inspect one, read it from `outputs/runs/<run>/<problem>/solution.py`.
20: - **Never modify `problems/*/reference.py`, `check.py`, `benchmark.py`, `problem.yaml`, `shapes.py`, or `PROMPT.txt`** once a sweep has been published. Those define the ben

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<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/reference.py</path>
<type>file</type>
<content>
1: """Naive W4A16 weight-only quantized GEMM reference (correctness only).
2: 
3: AWQ/GPTQ-style scheme:
4:   x:      (M, K)               bf16
5:   w_q:    (K // 2, N)          uint8   -- two int4 weights packed per byte (low nibble = even-K, high = odd-K)
6:   scales: (K // group, N)      bf16
7:   zeros:  (K // group, N)      bf16    -- asymmetric (stored already as float zero-point)
8:   out:    (M, N)                bf16
9: 
10: Dequant (per group along K):
11:   w_bf[k, n] = (w_q[k, n] - zeros[k // group, n]) * scales[k // group, n]
12: where w_q[k, n] is the unpacked 4-bit value (0..15).
13: 
14: This reference unpacks to a full bf16 matrix and then runs torch.matmul. Slow and
15: memory-heavy on the dequant; the agent's solution must fuse unpack+GEMM.
16: """
17: from __future__ import annotations
18: 
19: import torch
20: import torch.nn as nn
21: 
22: OP_TYPE = "gemm_w4a16"
23: SUPPORTED_PRECISIONS = ["int4_bf16"]
24: HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
25: 
26: GROUP_SIZE = 128
27: 
28: 
29: def _pack_int4(w_q: torch.Tensor) -> torch.Tensor:
30:     """Pack (K, N) uint8 in [0,15] into (K//2, N) uint8.
31: 
32:     Even rows go in the low nibble, odd rows in the high nibble.
33:     """
34:     K, N = w_q.shape
35:     assert K % 2 == 0
36:     lo = w_q[0::2].to(torch.uint8) & 0xF
37:     hi = w_q[1::2].to(torch.uint8) & 0xF
38:     ret

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<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/check.py</path>
<type>file</type>
<content>
1: """Correctness runner for FP8 GEMM.
2: 
3: Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
4: each, with per-dtype atol/rtol. Also rejects forbidden ops by grep.
5: """
6: import re
7: import sys
8: from pathlib import Path
9: 
10: import torch
11: import yaml
12: 
13: # Make the repo's src/ importable
14: REPO_ROOT = Path(__file__).resolve().parents[2]
15: sys.path.insert(0, str(REPO_ROOT))
16: 
17: from src.eval.correctness import check_correctness  # noqa: E402
18: 
19: 
20: def main():
21:     try:
22:         import reference
23:         import shapes
24:         import solution
25:     except Exception as e:
26:         print(f"FAIL: import error: {e}")
27:         sys.exit(1)
28: 
29:     problem_yaml = Path("problem.yaml")
30:     meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
31: 
32:     # --- Forbidden-op check ------------------------------------------------
33:     sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
34:     for forbidden in meta.get("forbidden", []):
35:         pat = re.escape(forbidden)
36:         if re.search(pat, sol_src):
37:             print(f"FAIL: forbidden op used: {forbidden}")
38:             sys.exit(1)
39: 
40:     device = torch.device("cuda:0")
41:     tol_override = meta.get("tolerance") or None
42: 
43:     # --- Per-shape

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<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/shapes.py</path>
<type>file</type>
<content>
1: """Shape sweep for W4A16 GEMM.
2: 
3: Llama-style up_proj / qkv_proj shapes. Decode (M=1) is the bandwidth-bound
4: case every inference engine optimizes -- it's the bar to beat.
5: """
6: 
7: SHAPES = [
8:     {"M": 1,   "N": 12288, "K": 4096},   # decode: memory-bound on int4 weight read
9:     {"M": 32,  "N": 12288, "K": 4096},   # small prefill: mixed regime
10:     {"M": 256, "N": 12288, "K": 4096},   # larger prefill: approaching compute
11:     {"M": 1,   "N": 4096,  "K": 4096},   # decode: square shape
12:     {"M": 16,  "N": 14336, "K": 4096},   # speculative-decode-ish
13: ]

(End of file - total 13 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/problem.yaml</path>
<type>file</type>
<content>
1: name: 07_w4a16_gemm
2: display_name: "W4A16 Weight-only Quantized GEMM"
3: precision: int4_bf16
4: regime: memory  # decode-dominant; M=1 is bandwidth-bound on the int4 weight stream
5: 
6: # Dense-equivalent FLOPs (matmul work, ignoring dequant arithmetic).
7: flops_formula: "2 * M * N * K"
8: 
9: # Bytes moved per call (memory roofline):
10: #   x:      M*K*2          (bf16 activations, streamed in once)
11: #   w_q:    (K/2)*N        (packed int4, 0.5 B/elem)
12: #   scales: (K/128)*N*2    (bf16 scales)
13: #   zeros:  (K/128)*N*2    (bf16 zero-points)
14: #   out:    M*N*2          (bf16 store)
15: bytes_formula: "M*K*2 + (K/2)*N + (K/128)*N*2 + (K/128)*N*2 + M*N*2"
16: 
17: hardware: [RTX_PRO_6000]
18: peak_tflops_key: bf16
19: peak_bandwidth_key: dram
20: 
21: tolerance:
22:   bfloat16: 0.10  # group-quant adds noise on top of bf16 accumulator slop
23: 
24: # Forbidden ops -- agent must write the unpack + GEMM themselves, not call a
25: # vendor library that does both.
26: forbidden:
27:   - "bitsandbytes.functional.dequantize_4bit"
28:   - "bitsandbytes.functional.gemv_4bit"
29:   - "marlin_kernel.gemm"
30:   - "torch.nn.functional.linear"
31: 
32: sota:
33:   name: "bitsandbytes NF4 (gemv_4bit / dequantize_4bit + matmul)"
34:   url: "https://github.com/TimDettmers/bitsandbytes"
35:   function: "bitsandbytes.functional.gemv_4bit"
36:   notes: |
37:     

... (757 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/benchmark.py</path>
<type>file</type>
<content>
1: """Roofline benchmark for FP8 GEMM.
2: 
3: For each shape: times eager reference, compiled reference, SOTA (if available),
4: and the agent's solution. Reports achieved TFLOPS, GB/s, and peak_fraction.
5: 
6: Output lines the harness picks up:
7:   shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
8:   peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
9: """
10: import sys
11: from math import exp, log
12: from pathlib import Path
13: 
14: import torch
15: import yaml
16: 
17: REPO_ROOT = Path(__file__).resolve().parents[2]
18: sys.path.insert(0, str(REPO_ROOT))
19: 
20: from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
21: from src.eval.timing import time_fn  # noqa: E402
22: from src.hardware import get as get_hw  # noqa: E402
23: 
24: 
25: def _eval_formula(expr: str, vars: dict) -> float:
26:     # Very small eval: only names from `vars` are valid.
27:     return float(eval(expr, {"__builtins__": {}}, vars))
28: 
29: 
30: def main():
31:     import reference
32:     import shapes
33:     import solution
34: 
35:     meta = yaml.safe_load(Path("problem.yaml").read_text())
36:     hw = get_hw(meta["hardware"][0])
37:     peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
38:     peak_gbps = hw.peak_bandwidth_gb_s
39:     regime = meta.get("regime", "compute")
40:     flops_formul

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<path>/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/sota.py</path>
<type>file</type>
<content>
1: """SOTA reference for W4A16 GEMM.
2: 
3: Library survey on RTX PRO 6000 Blackwell (SM120, CC 12.0):
4: 
5:   - Marlin (IST-DASLab):         no SM120 kernels (Ampere/Hopper only). Skip.
6:   - GPTQ-Triton (fpgaminer):     unmaintained; pure Triton path works on SM120
7:                                  but is not faster than Marlin on its target HW
8:                                  and has no Blackwell tuning. Skip as primary.
9:   - AWQ (mit-han-lab/llm-awq):   CUDA kernels not built for SM120 in the wheel.
10:                                  Skip.
11:   - bitsandbytes >= 0.49.2:      CUDA kernels compile and run on SM120 (verified
12:                                  on this machine). Different quant scheme (NF4,
13:                                  symmetric, blocksize 64) than our reference's
14:                                  AWQ-style asymmetric INT4 with group_size 128,
15:                                  but it occupies the same memory regime and is
16:                                  the only tuned W4A16-class kernel that runs on
17:                                  SM120 today. Used here as an *informational*
18:                                  SOTA line, not as a numerical reference.
19: 
20: The benchmark calls `sota_forward(x, ref_model)` and times it; correctness is
21: NOT checked against this path (the quant scheme differs).
22: """
23: from 

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