codex / ? session: ? cwd: /workspace/problems/07_w4a16_gemm
harness
codex
model
?
turns
102
tools called
60
events
168
input toks
5,060,764
output toks
34,821
cache hit
4,842,368
solution.py
final answer
benchmark.log
check.log
result.json
view raw solution.py
from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl


GROUP_SIZE = 128


@triton.jit
def _w4a16_gemm_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    out_ptr,
    M: tl.constexpr,
    N: tl.constexpr,
    K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    if BLOCK_K == 256:
        offs_k128 = tl.arange(0, 128)
        for k0 in range(0, K, 256):
            for part in tl.static_range(0, 2):
                k = k0 + part * 128 + offs_k128
                a = tl.load(
                    x_ptr + offs_m[:, None] * K + k[None, :],
                    mask=offs_m[:, None] < M,
                    other=0.0,
                )

                packed = tl.load(
                    wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
                    mask=offs_n[None, :] < N,
                    other=0,
                )
                q_lo = packed & 0x0F
                q_hi = (packed >> 4) & 0x0F
                q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)

                group = k0 // 128 + part
                s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
                z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
                b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)

                acc += tl.dot(a, b, out_dtype=tl.float32)
    else:
        offs_k = tl.arange(0, BLOCK_K)
        for k0 in range(0, K, BLOCK_K):
            k = k0 + offs_k
            a = tl.load(
                x_ptr + offs_m[:, None] * K + k[None, :],
                mask=offs_m[:, None] < M,
                other=0.0,
            )

            packed = tl.load(
                wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
                mask=offs_n[None, :] < N,
                other=0,
            )
            q_lo = packed & 0x0F
            q_hi = (packed >> 4) & 0x0F
            q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)

            group = k0 // 128
            s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
            z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
            b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)

            acc += tl.dot(a, b, out_dtype=tl.float32)

    tl.store(
        out_ptr + offs_m[:, None] * N + offs_n[None, :],
        acc.to(tl.bfloat16),
        mask=(offs_m[:, None] < M) & (offs_n[None, :] < N),
    )


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
        super().__init__()
        assert group_size == GROUP_SIZE
        assert K % GROUP_SIZE == 0
        assert K % 2 == 0
        self.M, self.N, self.K = M, N, K
        self.group_size = group_size
        self.register_buffer("w_q", torch.empty((K // 2, N), dtype=torch.uint8))
        self.register_buffer("scales", torch.empty((K // group_size, N), dtype=torch.bfloat16))
        self.register_buffer("zeros", torch.empty((K // group_size, N), dtype=torch.bfloat16))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        M, N, K = self.M, self.N, self.K
        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)

        if M == 1:
            if N <= 4096:
                bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
            else:
                bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
        elif M <= 16:
            bm, bn, bk, warps, stages = 16, 128, 128, 8, 4
        elif M <= 32:
            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
        else:
            bm, bn, bk, warps, stages = 128, 64, 32, 4, 3

        grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
        _w4a16_gemm_kernel[grid](
            x,
            self.w_q,
            self.scales,
            self.zeros,
            out,
            M,
            N,
            K,
            BLOCK_M=bm,
            BLOCK_N=bn,
            BLOCK_K=bk,
            num_warps=warps,
            num_stages=stages,
        )
        return out


M = 1
N = 12288
K = 4096


def get_inputs():
    x = torch.randn(M, K, dtype=torch.bfloat16)
    return [x]


def get_init_inputs():
    return [M, N, K]
Implemented `solution.py` with a fused Triton W4A16 GEMM kernel. It unpacks int4 weights, applies per-group bf16 zero/scale dequantization, and feeds the tile directly into `tl.dot`. Verification: - `uv run python check.py` -> `PASS` - `uv run python benchmark.py` -> `RESULT: OK` - Final benchmark geomean `peak_fraction: 0.2011` Final per-shape sample: - shape 0: `0.061 ms`, `436.9 GB/s` - shape 1: `0.061 ms`, `458.0 GB/s` - shape 2: `0.159 ms`, `221.0 GB/s` - shape 3: `0.037 ms`, `244.1 GB/s` - shape 4: `0.055 ms`, `575.0 GB/s`
warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo/.venv` and will be ignored; use `--active` to target the active environment instead
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:58:13.975733+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:58:14.189646+00:00 elapsed_s=0.214 ms=0.061344
shape=0 variant=solution tflops=1.641 gbps=436.415 ms=0.061
shape=0 solution_peak_fraction=0.2425
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:58:14.522226+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:58:14.530434+00:00 elapsed_s=0.008 ms=0.060064
shape=1 variant=solution tflops=53.630 gbps=462.628 ms=0.060
shape=1 solution_peak_fraction=0.2570
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:58:14.870976+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:58:14.885181+00:00 elapsed_s=0.014 ms=0.158304
shape=2 variant=solution tflops=162.787 gbps=221.898 ms=0.158
shape=2 solution_peak_fraction=0.1233
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:58:14.991505+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:58:15.029744+00:00 elapsed_s=0.038 ms=0.036064
shape=3 variant=solution tflops=0.930 gbps=247.595 ms=0.036
shape=3 solution_peak_fraction=0.1376
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:58:15.418000+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-13T11:58:15.425939+00:00 elapsed_s=0.008 ms=0.054848
shape=4 variant=solution tflops=34.259 gbps=579.510 ms=0.055
shape=4 solution_peak_fraction=0.3219
peak_fraction: 0.2025
RESULT: OK

warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo/.venv` and will be ignored; use `--active` to target the active environment instead
warning: Ignoring existing virtual environment linked to non-existent Python interpreter: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo/.venv/bin/python3 -> python
Using CPython 3.11.15
Removed virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo/.venv
Creating virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo/.venv
   Building kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo
      Built kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_053815_codex_gpt-5.5_07_w4a16_gemm/repo
Installed 56 packages in 85ms
PASS

{
    "run_id": "20260613_053815_codex_gpt-5.5_07_w4a16_gemm",
    "run_group": "",
    "problem": "07_w4a16_gemm",
    "harness": "codex",
    "model": "gpt-5.5",
    "reasoning_effort": "xhigh",
    "started_at": "2026-06-13T05:38:15-06:00",
    "harness_finished_at": "2026-06-13T05:58:07-06:00",
    "finished_at": "2026-06-13T05:58:15-06:00",
    "start_epoch": 1781350695,
    "harness_end_epoch": 1781351887,
    "end_epoch": 1781351895,
    "has_solution": true,
    "correct": true,
    "failure_reason": "pass",
    "retryable_infra_failure": false,
    "minimum_useful_output_tokens": 5000,
    "peak_fraction": 0.2025,
    "template_mutated": false,
    "elapsed_seconds": 1192,
    "total_elapsed_seconds": 1200,
    "check_elapsed_seconds": 5,
    "benchmark_elapsed_seconds": 3,
    "check_timeout_seconds": 180,
    "benchmark_timeout_seconds": 1800,
    "check_exit_code": 0,
    "benchmark_exit_code": 0,
    "harness_exit_code": 0,
    "session_complete": true,
    "agent_cuda_disabled": false,
    "agent_container": true,
    "agent_container_image": "nvcr.io/nvidia/tensorrt-llm/release:latest",
    "agent_container_network": "bridge",
    "gpu_queue_mode": "agent_container_native_profiling_path_wrapper_gpu_lock",
    "output_tokens_per_second": 29.21224832214765,
    "usage": {"input_tokens": 5060764, "output_tokens": 34821, "cache_read_tokens": 4842368, "cache_creation_tokens": null, "reasoning_tokens": 15680, "total_cost_usd": null}
}

timeline (168 events)

system
session start model=None ctx=?
system
task_started turn=019ec0c6
system
<permissions instructions> Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. </permissions instructions> <apps_instructions> ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. </apps_instructions> <skills_instructions> ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /home/agent/.codex/skills/.system/imagegen/SKILL.md) - openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains. (file: /home/agent/.codex/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, and baseline placeholders you can edit before publishing or testing. Use when Codex needs to create a new local plugin, add optional plugin structure, or generate or update repo-root `.agents/plugins/marketplace.json` entries for plugin ordering and availability metadata. (file: /home/agent/.codex/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/agent/.codex/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/agent/.codex/skills/.system/skill-installer/SKILL.md) ### How to use skills - Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths. - Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback. - How to use a skill (progressive disclosure): 1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow. 2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed. 3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything. 4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks. 5) If `assets/` or templates exist, reuse them instead of recreating from scratch. - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why. - Context hygiene: - Keep context small: summarize long sections instead of pasting them; only load extra files when needed. - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked. - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice. - Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue. </skills_instructions>
user
<environment_context> <cwd>/workspace/problems/07_w4a16_gemm</cwd> <shell>bash</shell> <current_date>2026-06-13</current_date> <timezone>Etc/UTC</timezone> </environment_context>
user
I need you to write a weight-only int4 quantized GEMM (W4A16) for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py. The scheme is AWQ/GPTQ-style asymmetric int4 with explicit zero-points and per-group bf16 scales. Inputs are x of shape (M, K) bf16, w_q of shape (K // 2, N) uint8 (two int4 weights packed per byte, low nibble = even-K row, high nibble = odd-K row), scales of shape (K // 128, N) bf16, and zeros of shape (K // 128, N) bf16. Group size is 128 along K. Dequant per group is w_bf[k, n] = (unpack(w_q)[k, n] - zeros[k // 128, n]) * scales[k // 128, n], and the output is (M, N) bf16. Correctness tolerance is 0.10 abs/rel — group-quant adds noise on top of bf16 accumulator slop. The shapes you have to handle are M=1 N=12288 K=4096 (decode, memory-bound on the int4 weight read), M=32 N=12288 K=4096 (small prefill, mixed regime), M=256 N=12288 K=4096 (larger prefill, approaching compute-bound), M=1 N=4096 K=4096 (decode, square), and M=16 N=14336 K=4096 (speculative-decode-ish). This needs to be a real custom kernel that fuses unpack and GEMM in the same pass — a separate dequant-then-matmul wastes the entire bandwidth advantage of int4. Don't import or call bitsandbytes.functional.dequantize_4bit, bitsandbytes.functional.gemv_4bit, marlin_kernel.gemm, or torch.nn.functional.linear. Try CUTLASS mixed-input GEMM (the intended path), Triton with a fused dequant epilogue, CUDA C++ via load_inline, or inline PTX — your call. Anything you're uncertain about, look up PTX docs, clone CUTLASS or Marlin or bitsandbytes or other reference repos, read library headers, and investigate. Your flywheel is implement, profile (ncu, nsys, torch.profiler — whatever's useful) and time it with benchmark.py, verify correctness by running `python check.py` and reading the output, then iterate. Don't substitute your own one-off correctness snippets for check.py — it iterates over every shape, your spot-check almost certainly won't. If `python check.py` hasn't printed PASS, you're not done. Take as long as you need to actually push the number up. Workspace isolation note: you are already running inside the archive-local problem workspace, /workspace/problems/07_w4a16_gemm. Write the final answer to solution.py in the current directory only. The source repository's problems/ tree is not mounted. Container note: inside this run, the visible workspace path is /workspace/problems/07_w4a16_gemm. The source repository, old runs, leaderboards, and host harness memory are not mounted. Container network mode is bridge. Run all Python through `uv run ...` so you use the workspace uv environment; it is built from the same uv.lock as the official scoring environment. The container image's system python has a different torch build and is NOT the scoring environment.
assistant
I’ll inspect the harness and reference first so the kernel matches the exact callable interface and data layout, then I’ll iterate against `uv run python check.py` and the benchmark.
assistant
exec_command
args
{
  "cmd": "pwd && rg --files",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 12000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,240p' reference.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 12000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,260p' check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,260p' benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
tool
stdout (214 chars)
Chunk ID: a27107
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 28
Output:
/workspace/problems/07_w4a16_gemm
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml
tool
stdout (4307 chars) — TRUNCATED
Chunk ID: e81fab
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1051
Output:
"""Naive W4A16 weight-only quantized GEMM reference (correctness only).

AWQ/GPTQ-style scheme:
  x:      (M, K)               bf16
  w_q:    (K // 2, N)          uint8   -- two int4 weights packed per byte (low nibble = even-K, high = odd-K)
  scales: (K // group, N)      bf16
  zeros:  (K // group, N)      bf16    -- asymmetric (stored already as float zero-point)
  out:    (M, N)                bf16

Dequant (per group along K):
  w_bf[k, n] = (w_q[k, n] - zeros[k // group, n]) * scales[k // group, n]
where w_q[k, n] is the unpacked 4-bit value (0..15).

This reference unpacks to a full bf16 matrix and then runs torch.matmul. Slow and
memory-heavy on the dequant; the agent's solution must fuse unpack+GEMM.
"""
from __future__ import annotations

import torch
import torch.nn as nn

OP_TYPE = "gemm_w4a16"
SUPPORTED_PRECISIONS = ["int4_bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]

GROUP_SIZE = 128


def _pack_int4(w_q: torch.Tensor) -> torch.Tensor:
    """Pack (K, N) uint8 in [0,15] into (K//2, N) uint8.

    Even rows go in the low nibble, odd rows in the high nibble.
    """
    K, N = w_q.shape
    assert K % 2 == 0
    lo = w_q[0::2].to(torch.uint8) & 0xF
    hi = w_q[1::2].to(torch.uint8) & 0xF
    return (lo | (hi << 4)).contiguous()


def _unpack_int4(w_packed: torch.Tensor, K: int) -> torch.Tensor:
    """Unpack (K//2, N) uint8 -> (K, N) uint8 in [0

... (2807 more chars)
tool
stdout (4308 chars) — TRUNCATED
Chunk ID: dd91a2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1052
Output:
"""Correctness runner for FP8 GEMM.

Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
each, with per-dtype atol/rtol. Also rejects forbidden ops by grep.
"""
import re
import sys
from pathlib import Path

import torch
import yaml

# Make the repo's src/ importable
REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.correctness import check_correctness  # noqa: E402
from src.eval.numeric_stress import (  # noqa: E402
    numeric_stress_cases,
    numeric_stress_context,
    tolerance_for_case,
)


def main():
    try:
        import reference
        import shapes
        import solution
    except Exception as e:
        print(f"FAIL: import error: {e}")
        sys.exit(1)

    problem_yaml = Path("problem.yaml")
    meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}

    # --- Forbidden-op check ------------------------------------------------
    sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
    for forbidden in meta.get("forbidden", []):
        pat = re.escape(forbidden)
        if re.search(pat, sol_src):
            print(f"FAIL: forbidden op used: {forbidden}")
            sys.exit(1)

    device = torch.device("cuda:0")
    tol_override = meta.get("tolerance") or None

    # --- Per-shape correctness ------------------------

... (2808 more chars)
tool
stdout (5251 chars) — TRUNCATED
Chunk ID: e1057d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1287
Output:
"""Roofline benchmark for FP8 GEMM.

For each shape: times the agent's solution first. Optional eager, compiled,
and SOTA diagnostics are enabled with KBH_BENCHMARK_BASELINES=1. Reports achieved TFLOPS, GB/s, and peak_fraction.

Output lines the harness picks up:
  shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
  peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
"""
import sys
from math import exp, log
from pathlib import Path

import torch
import yaml

REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
from src.eval.timing import benchmark_baselines_enabled, time_variant  # noqa: E402
from src.hardware import get as get_hw  # noqa: E402


def _eval_formula(expr: str, vars: dict) -> float:
    # Very small eval: only names from `vars` are valid.
    return float(eval(expr, {"__builtins__": {}}, vars))


def main():
    import reference
    import shapes
    import solution

    meta = yaml.safe_load(Path("problem.yaml").read_text())
    hw = get_hw(meta["hardware"][0])
    peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
    peak_gbps = hw.peak_bandwidth_gb_s
    regime = meta.get("regime", "compute")
    flops_formula = meta["flops_formula"]
    bytes_formula = meta["bytes_formula"]
    num_perf_trials = in

... (3751 more chars)
assistant
The harness loads the solution with the same buffers as the reference and checks all five shapes plus numeric stress cases. I’m reading the shape metadata and any provided baseline now so I can optimize the right regimes instead of guessing.
assistant
exec_command
args
{
  "cmd": "sed -n '1,200p' shapes.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,220p' problem.yaml",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,260p' sota.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
assistant
exec_command
args
{
  "cmd": "ls -la",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "test -f solution.py && sed -n '1,260p' solution.py || true",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
tool
stdout (656 chars)
Chunk ID: 170f34
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 138
Output:
"""Shape sweep for W4A16 GEMM.

Llama-style up_proj / qkv_proj shapes. Decode (M=1) is the bandwidth-bound
case every inference engine optimizes -- it's the bar to beat.
"""

SHAPES = [
    {"M": 1,   "N": 12288, "K": 4096},   # decode: memory-bound on int4 weight read
    {"M": 32,  "N": 12288, "K": 4096},   # small prefill: mixed regime
    {"M": 256, "N": 12288, "K": 4096},   # larger prefill: approaching compute
    {"M": 1,   "N": 4096,  "K": 4096},   # decode: square shape
    {"M": 16,  "N": 14336, "K": 4096},   # speculative-decode-ish
]
tool
stdout (2017 chars) — TRUNCATED
Chunk ID: 785d72
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 479
Output:
name: 07_w4a16_gemm
display_name: "W4A16 Weight-only Quantized GEMM"
precision: int4_bf16
regime: memory  # decode-dominant; M=1 is bandwidth-bound on the int4 weight stream

# Dense-equivalent FLOPs (matmul work, ignoring dequant arithmetic).
flops_formula: "2 * M * N * K"

# Bytes moved per call (memory roofline):
#   x:      M*K*2          (bf16 activations, streamed in once)
#   w_q:    (K/2)*N        (packed int4, 0.5 B/elem)
#   scales: (K/128)*N*2    (bf16 scales)
#   zeros:  (K/128)*N*2    (bf16 zero-points)
#   out:    M*N*2          (bf16 store)
bytes_formula: "M*K*2 + (K/2)*N + (K/128)*N*2 + (K/128)*N*2 + M*N*2"

hardware: [RTX_PRO_6000]
peak_tflops_key: bf16
peak_bandwidth_key: dram

tolerance:
  bfloat16: 0.10  # group-quant adds noise on top of bf16 accumulator slop

# Forbidden ops -- agent must write the unpack + GEMM themselves, not call a
# vendor library that does both.
forbidden:
  - "bitsandbytes.functional.dequantize_4bit"
  - "bitsandbytes.functional.gemv_4bit"
  - "marlin_kernel.gemm"
  - "torch.nn.functional.linear"

sota:
  name: "bitsandbytes NF4 (gemv_4bit / dequantize_4bit + matmul)"
  url: "https://github.com/TimDettmers/bitsandbytes"
  function: "bitsandbytes.functional.gemv_4bit"
  notes: |
    Marlin (IST-DASLab) is the W4A16 SOTA on Ampere/Hopper but does not have
    SM120 (Blackwell consumer) kernels yet. GPTQ-Triton is unmaintained and
 

... (517 more chars)
tool
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Chunk ID: 47be32
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 934
Output:
"""SOTA reference for W4A16 GEMM.

Library survey on RTX PRO 6000 Blackwell (SM120, CC 12.0):

  - Marlin (IST-DASLab):         no SM120 kernels (Ampere/Hopper only). Skip.
  - GPTQ-Triton (fpgaminer):     unmaintained; pure Triton path works on SM120
                                 but is not faster than Marlin on its target HW
                                 and has no Blackwell tuning. Skip as primary.
  - AWQ (mit-han-lab/llm-awq):   CUDA kernels not built for SM120 in the wheel.
                                 Skip.
  - bitsandbytes >= 0.49.2:      CUDA kernels compile and run on SM120 (verified
                                 on this machine). Different quant scheme (NF4,
                                 symmetric, blocksize 64) than our reference's
                                 AWQ-style asymmetric INT4 with group_size 128,
                                 but it occupies the same memory regime and is
                                 the only tuned W4A16-class kernel that runs on
                                 SM120 today. Used here as an *informational*
                                 SOTA line, not as a numerical reference.

The benchmark calls `sota_forward(x, ref_model)` and times it; correctness is
NOT checked against this path (the quant scheme differs).
"""
from __future__ import annotations

import torch

_BNB_OK: bool | None = None


def is_availab

... (2338 more chars)
tool
stdout (564 chars)
Chunk ID: 67398f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 115
Output:
total 48
drwxrwxr-x 2 1002 1002 4096 Jun 13 11:38 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 11:38 ..
-rw-rw-r-- 1 1002 1002 2421 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5146 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 4205 Jun  2 01:08 check.py
-rw-rw-r-- 1 1002 1002 1913 Apr 26 04:22 problem.yaml
-rw-rw-r-- 1 1002 1002 4202 Apr 26 04:25 reference.py
-rw-rw-r-- 1 1002 1002  552 Apr 26 04:22 shapes.py
-rw-rw-r-- 1 1002 1002 3734 Apr 26 04:24 sota.py
tool
stdout (102 chars)
Chunk ID: 0390d3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
There’s no existing `solution.py`, so I’m starting from a clean implementation. The scoring regime is explicitly memory roofline, but the M=32/256 cases still need a GEMM-shaped kernel rather than only a decode-specialized GEMV.
assistant
exec_command
args
{
  "cmd": "<290 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 12000
}
cmd (290 chars)
uv run python - <<'PY'
import torch, triton, sys
print('torch', torch.__version__)
print('cuda available', torch.cuda.is_available())
if torch.cuda.is_available():
    print('device', torch.cuda.get_device_name(0), torch.cuda.get_device_capability(0))
print('triton', triton.__version__)
PY
assistant
exec_command
args
{
  "cmd": "nvidia-smi --query-gpu=name,compute_cap,memory.total,memory.used --format=csv,noheader",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,260p' /workspace/src/eval/timing.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,220p' /workspace/src/eval/correctness.py && sed -n '1,220p' /workspace/src/eval/numeric_stress.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 20000
}
tool
stdout (113 chars)
Chunk ID: db0980
Wall time: 1.0013 seconds
Process running with session ID 98420
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 97968d
Wall time: 1.0014 seconds
Process running with session ID 65436
Original token count: 0
Output:
tool
stdout (4419 chars) — TRUNCATED
Chunk ID: 205a01
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1079
Output:
"""Centralized GPU timing for benchmark.py files.

All problems' benchmark.py should call `time_fn` from here so we have one
implementation of warmup, L2 flush, and CUDA event capture to maintain.

Methodology:
  - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
    torch.compile reduce-overhead CUDA-graph capture.
  - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
    (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
    we measure HBM-load bandwidth, not L2-cached bandwidth).
  - GPU timing via cuda.Event with synchronize() AFTER record() but
    BEFORE elapsed_time() — the canonical NVIDIA pattern.
  - Reported value is the median of `iters` trials, robust to outliers.

Notes / known biases not addressed here:
  - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
    launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
    On small shapes where launch overhead matters, this gives compile an
    artificial advantage. We accept this as the cost of using the agreed
    "compiled" baseline policy.
  - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
  - Median over a fairly small number of trials (default 30) is fine for
    headline numbers but won't catch bimodal distributions. Use --extra
    instrumentation if you ever care.
"""
from __future__ import annotations


... (2919 more chars)
tool
stdout (10249 chars) — TRUNCATED
Chunk ID: 817563
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2536
Output:
"""Per-dtype correctness tolerance.

Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
We extend to fp8 (0.1, permissive) and int (exact).
"""
from __future__ import annotations

import torch

DEFAULT_TOLERANCE = {
    torch.float32: {"atol": 1e-4, "rtol": 1e-4},
    torch.float16: {"atol": 1e-2, "rtol": 1e-2},
    torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
    torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
    torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
    torch.int8: {"atol": 0, "rtol": 0},
    torch.int32: {"atol": 0, "rtol": 0},
    torch.int64: {"atol": 0, "rtol": 0},
}


def _coerce_float(x):
    """Accept int/float/str scientific-notation as float; pass through dicts."""
    if isinstance(x, (int, float)):
        return float(x)
    if isinstance(x, str):
        try:
            return float(x)
        except ValueError:
            return x
    return x


def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
    """Lookup atol/rtol for a given dtype, with optional per-problem override.

    The override dict is keyed by str(dtype). Values may be:
      - a single number (or a string parseable as float, e.g. "5e-2") -> used
        as both atol and rtol
      - a dict {"atol": ..., "rtol": ...} -> used directly, with values coerced
    """
    # Accept several spellings of the same dtype key (PyYA

... (8749 more chars)
assistant
write_stdin
args
{
  "session_id": 98420,
  "chars": "",
  "yield_time_ms": 1000,
  "max_output_tokens": 12000
}
assistant
write_stdin
args
{
  "session_id": 65436,
  "chars": "",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
tool
stdout (113 chars)
Chunk ID: 6c0d03
Wall time: 5.0012 seconds
Process running with session ID 98420
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 00f665
Wall time: 5.0011 seconds
Process running with session ID 65436
Original token count: 0
Output:
assistant
The correctness checker also scales activations by `1e-3` and `64.0`, so accumulation accuracy matters; I’m going to keep accumulation in fp32 for Triton prototypes unless profiling shows the cost is unacceptable.
assistant
write_stdin
args
{
  "session_id": 98420,
  "chars": "",
  "yield_time_ms": 30000,
  "max_output_tokens": 12000
}
assistant
write_stdin
args
{
  "session_id": 65436,
  "chars": "",
  "yield_time_ms": 30000,
  "max_output_tokens": 8000
}
tool
stdout (114 chars)
Chunk ID: 43b92d
Wall time: 30.0009 seconds
Process running with session ID 98420
Original token count: 0
Output:
tool
stdout (182 chars)
Chunk ID: b6757d
Wall time: 24.9674 seconds
Process exited with code 0
Original token count: 20
Output:
NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 12.0, 97887 MiB, 40719 MiB
assistant
write_stdin
args
{
  "session_id": 98420,
  "chars": "",
  "yield_time_ms": 30000,
  "max_output_tokens": 12000
}
tool
stdout (748 chars)
Chunk ID: b8d1e5
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 161
Output:
Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
   Building kernelbench-hard @ file:///workspace
      Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
         If the cache and target directories are on different filesystems, hardlinking may not be supported.
         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 56 packages in 3.17s
torch 2.11.0+cu130
cuda available True
device NVIDIA RTX PRO 6000 Blackwell Workstation Edition (12, 0)
triton 3.6.0
assistant
I’m going to put in a first Triton fused kernel now: it unpacks each int4 byte inside the K loop, applies the per-group scale and zero-point, and feeds the dequantized tile directly into `tl.dot`. I’ll specialize launch tile sizes by M so decode and prefill do not use the same shape blindly.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (124 lines)
--- /dev/null
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+
+GROUP_SIZE = 128
+
+
+@triton.jit
+def _w4a16_gemm_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    M: tl.constexpr,
+    N: tl.constexpr,
+    K: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_k = tl.arange(0, BLOCK_K)
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
+
+    for k0 in range(0, K, BLOCK_K):
+        k = k0 + offs_k
+        a = tl.load(
+            x_ptr + offs_m[:, None] * K + k[None, :],
+            mask=offs_m[:, None] < M,
+            other=0.0,
+        )
+
+        packed = tl.load(
+            wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+            mask=offs_n[None, :] < N,
+            other=0,
+        )
+        q_lo = packed & 0x0F
+        q_hi = (packed >> 4) & 0x0F
+        q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+        group = k0 // GROUP_SIZE
+        s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+        z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+        b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+        acc += tl.dot(a, b, out_dtype=tl.float32)
+
+    tl.store(
+        out_ptr + offs_m[:, None] * N + offs_n[None, :],
+        acc.to(tl.bfloat16),
+        mask=(offs_m[:, None] < M) & (offs_n[None, :] < N),
+    )
+
+
+class Model(nn.Module):
+    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
+        super().__init__()
+        assert group_size == GROUP_SIZE
+        assert K % GROUP_SIZE == 0
+        assert K % 2 == 0
+        self.M, self.N, self.K = M, N, K
+        self.group_size = group_size
+        self.register_buffer("w_q", torch.empty((K // 2, N), dtype=torch.uint8))
+        self.register_buffer("scales", torch.empty((K // group_size, N), dtype=torch.bfloat16))
+        self.register_buffer("zeros", torch.empty((K // group_size, N), dtype=torch.bfloat16))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        M, N, K = self.M, self.N, self.K
+        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
+
+        if M == 1:
+            bm, bn, bk, warps = 1, 64, 128, 4
+        elif M <= 16:
+            bm, bn, bk, warps = 16, 64, 128, 4
+        elif M <= 32:
+            bm, bn, bk, warps = 32, 64, 128, 4
+        else:
+            bm, bn, bk, warps = 32, 64, 128, 4
+
+        grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
+        _w4a16_gemm_kernel[grid](
+            x,
+            self.w_q,
+            self.scales,
+            self.zeros,
+            out,
+            M,
+            N,
+            K,
+            BLOCK_M=bm,
+            BLOCK_N=bn,
+            BLOCK_K=bk,
+            num_warps=warps,
+            num_stages=3,
+        )
+        return out
+
+
+M = 1
+N = 12288
+K = 4096
+
+
+def get_inputs():
+    x = torch.randn(M, K, dtype=torch.bfloat16)
+    return [x]
+
+
+def get_init_inputs():
+    return [M, N, K]
args
{
  "raw": "<3421 chars \u2014 see below>"
}
raw (3421 chars)
*** Begin Patch
*** Add File: /workspace/problems/07_w4a16_gemm/solution.py
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+
+GROUP_SIZE = 128
+
+
+@triton.jit
+def _w4a16_gemm_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    M: tl.constexpr,
+    N: tl.constexpr,
+    K: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_k = tl.arange(0, BLOCK_K)
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
+
+    for k0 in range(0, K, BLOCK_K):
+        k = k0 + offs_k
+        a = tl.load(
+            x_ptr + offs_m[:, None] * K + k[None, :],
+            mask=offs_m[:, None] < M,
+            other=0.0,
+        )
+
+        packed = tl.load(
+            wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+            mask=offs_n[None, :] < N,
+            other=0,
+        )
+        q_lo = packed & 0x0F
+        q_hi = (packed >> 4) & 0x0F
+        q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+        group = k0 // GROUP_SIZE
+        s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+        z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+        b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+        acc += tl.dot(a, b, out_dtype=tl.float32)
+
+    tl.store(
+        out_ptr + offs_m[:, None] * N + offs_n[None, :],
+        acc.to(tl.bfloat16),
+        mask=(offs_m[:, None] < M) & (offs_n[None, :] < N),
+    )
+
+
+class Model(nn.Module):
+    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
+        super().__init__()
+        assert group_size == GROUP_SIZE
+        assert K % GROUP_SIZE == 0
+        assert K % 2 == 0
+        self.M, self.N, self.K = M, N, K
+        self.group_size = group_size
+        self.register_buffer("w_q", torch.empty((K // 2, N), dtype=torch.uint8))
+        self.register_buffer("scales", torch.empty((K // group_size, N), dtype=torch.bfloat16))
+        self.register_buffer("zeros", torch.empty((K // group_size, N), dtype=torch.bfloat16))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        M, N, K = self.M, self.N, self.K
+        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
+
+        if M == 1:
+            bm, bn, bk, warps = 1, 64, 128, 4
+        elif M <= 16:
+            bm, bn, bk, warps = 16, 64, 128, 4
+        elif M <= 32:
+            bm, bn, bk, warps = 32, 64, 128, 4
+        else:
+            bm, bn, bk, warps = 32, 64, 128, 4
+
+        grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
+        _w4a16_gemm_kernel[grid](
+            x,
+            self.w_q,
+            self.scales,
+            self.zeros,
+            out,
+            M,
+            N,
+            K,
+            BLOCK_M=bm,
+            BLOCK_N=bn,
+            BLOCK_K=bk,
+            num_warps=warps,
+            num_stages=3,
+        )
+        return out
+
+
+M = 1
+N = 12288
+K = 4096
+
+
+def get_inputs():
+    x = torch.randn(M, K, dtype=torch.bfloat16)
+    return [x]
+
+
+def get_init_inputs():
+    return [M, N, K]
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  A /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The first implementation is in place. I’m running the official checker now because small details like bf16 rounding, mask shape, or stress scaling can fail even when a quick spot check looks fine.
assistant
exec_command
args
{
  "cmd": "uv run python check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (114 chars)
Chunk ID: 8efd75
Wall time: 30.0013 seconds
Process running with session ID 93810
Original token count: 0
Output:
assistant
write_stdin
args
{
  "session_id": 93810,
  "chars": "",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (114 chars)
Chunk ID: e70b56
Wall time: 30.0010 seconds
Process running with session ID 93810
Original token count: 0
Output:
assistant
The checker is still running through the shape sweep; initial Triton compilation on SM120 is taking most of this first pass.
assistant
write_stdin
args
{
  "session_id": 93810,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 20000
}
tool
stdout (3078 chars) — TRUNCATED
Chunk ID: 1249c5
Wall time: 48.4448 seconds
Process exited with code 1
Original token count: 744
Output:
Traceback (most recent call last):
  File "/workspace/problems/07_w4a16_gemm/check.py", line 119, in <module>
    main()
  File "/workspace/problems/07_w4a16_gemm/check.py", line 80, in main
    sol_out = sol_model(*inputs)
              ^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/07_w4a16_gemm/solution.py", line 92, in forward
    _w4a16_gemm_kernel[grid](
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 720, in run
    kernel = self._do_compile(key, signature, device, constexprs, options, attrs, warmup)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 849, in 

... (1578 more chars)
assistant
The first failure is a Triton compile-time constant issue, not a math issue. I’m patching that so the group size is a kernel constexpr/literal and then rerunning the checker.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (125 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,121 +1 @@
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import triton
-import triton.language as tl
-
-
-GROUP_SIZE = 128
-
-
-@triton.jit
-def _w4a16_gemm_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    out_ptr,
-    M: tl.constexpr,
-    N: tl.constexpr,
-    K: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-):
-    pid_m = tl.program_id(0)
-    pid_n = tl.program_id(1)
-
-    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
-    offs_k = tl.arange(0, BLOCK_K)
-
-    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
-
-    for k0 in range(0, K, BLOCK_K):
-        k = k0 + offs_k
-        a = tl.load(
-            x_ptr + offs_m[:, None] * K + k[None, :],
-            mask=offs_m[:, None] < M,
-            other=0.0,
-        )
-
-        packed = tl.load(
-            wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
-            mask=offs_n[None, :] < N,
-            other=0,
-        )
-        q_lo = packed & 0x0F
-        q_hi = (packed >> 4) & 0x0F
-        q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
-
-        group = k0 // GROUP_SIZE
-        s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-        z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-        b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
-
-        acc += tl.dot(a, b, out_dtype=tl.float32)
-
-    tl.store(
-        out_ptr + offs_m[:, None] * N + offs_n[None, :],
-        acc.to(tl.bfloat16),
-        mask=(offs_m[:, None] < M) & (offs_n[None, :] < N),
-    )
-
-
-class Model(nn.Module):
-    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
-        super().__init__()
-        assert group_size == GROUP_SIZE
-        assert K % GROUP_SIZE == 0
-        assert K % 2 == 0
-        self.M, self.N, self.K = M, N, K
-        self.group_size = group_size
-        self.register_buffer("w_q", torch.empty((K // 2, N), dtype=torch.uint8))
-        self.register_buffer("scales", torch.empty((K // group_size, N), dtype=torch.bfloat16))
-        self.register_buffer("zeros", torch.empty((K // group_size, N), dtype=torch.bfloat16))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        M, N, K = self.M, self.N, self.K
-        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
-
-        if M == 1:
-            bm, bn, bk, warps = 1, 64, 128, 4
-        elif M <= 16:
-            bm, bn, bk, warps = 16, 64, 128, 4
-        elif M <= 32:
-            bm, bn, bk, warps = 32, 64, 128, 4
-        else:
-            bm, bn, bk, warps = 32, 64, 128, 4
-
-        grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
-        _w4a16_gemm_kernel[grid](
-            x,
-            self.w_q,
-            self.scales,
-            self.zeros,
-            out,
-            M,
-            N,
-            K,
-            BLOCK_M=bm,
-            BLOCK_N=bn,
-            BLOCK_K=bk,
-            num_warps=warps,
-            num_stages=3,
-        )
-        return out
-
-
-M = 1
-N = 12288
-K = 4096
-
-
-def get_inputs():
-    x = torch.randn(M, K, dtype=torch.bfloat16)
-    return [x]
-
-
-def get_init_inputs():
-    return [M, N, K]
+        group = k0 // 128
args
{
  "raw": "<157 chars \u2014 see below>"
}
raw (157 chars)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
-        group = k0 // GROUP_SIZE
+        group = k0 // 128
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
exec_command
args
{
  "cmd": "uv run python check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (108 chars)
Chunk ID: 2b3149
Wall time: 13.3440 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
Correctness passes across the official shape and stress sweep. Now I’m timing it with the official benchmark to see where the first kernel is leaving performance on the table.
assistant
exec_command
args
{
  "cmd": "uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 20000
}
tool
stdout (114 chars)
Chunk ID: 178982
Wall time: 30.0011 seconds
Process running with session ID 42120
Original token count: 0
Output:
assistant
The benchmark is running the median timing loop with L2 flushes. I’ll use the per-shape times to decide whether to split out a true decode kernel or tune the tensor-core GEMM tiles first.
assistant
write_stdin
args
{
  "session_id": 42120,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 20000
}
tool
stdout (1732 chars) — TRUNCATED
Chunk ID: 6811c2
Wall time: 53.9284 seconds
Process exited with code 0
Original token count: 407
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:45:01.941313+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:45:02.156918+00:00 elapsed_s=0.216 ms=0.063872
shape=0 variant=solution tflops=1.576 gbps=419.142 ms=0.064
shape=0 solution_peak_fraction=0.2329
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:45:02.479124+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:45:02.487377+00:00 elapsed_s=0.008 ms=0.060544
shape=1 variant=solution tflops=53.205 gbps=458.960 ms=0.061
shape=1 solution_peak_fraction=0.2550
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:45:02.816287+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:45:02.834532+00:00 elapsed_s=0.018 ms=0.226976
shape=2 variant=solution tflops=113.535 gbps=154.762 ms=0.227
shape=2 solution_peak_fraction=0.0860
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:45:02.937066+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:45:02.990121+00:00 elapsed_s=0.053 ms=0.056848
shape=3 variant=solution tflops=0.590 gbps=157.073 ms=0.057
shape=3 solution_peak_fraction=0.0873
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:45:03.362778+00:00
benchmark_event event=v

... (232 more chars)
assistant
The first fused kernel already clears the benchmark threshold, but two shapes are weak: `M=256,N=12288` is tile-choice sensitive, and `M=1,N=4096` is launch/occupancy sensitive. I’m going to tune the Triton block sizes against the official timing helper rather than changing the math.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/tune_tiles.py
diff (72 lines)
--- /dev/null
+++ b//workspace/problems/07_w4a16_gemm/tune_tiles.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+import itertools
+import sys
+from pathlib import Path
+
+import torch
+
+ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(ROOT))
+
+from src.eval.timing import time_fn  # noqa: E402
+
+import reference  # noqa: E402
+import solution  # noqa: E402
+
+
+def run_shape(shape, configs):
+    device = torch.device("cuda:0")
+    reference.M, reference.N, reference.K = shape
+    ref = reference.Model(*reference.get_init_inputs()).to(device).eval()
+    x = reference.get_inputs()[0].to(device)
+
+    print(f"\nshape M={shape[0]} N={shape[1]} K={shape[2]}")
+    for bm, bn, bk, warps, stages in configs:
+        out = torch.empty((shape[0], shape[1]), device=device, dtype=torch.bfloat16)
+
+        def fn(x):
+            grid = (triton_cdiv(shape[0], bm), triton_cdiv(shape[1], bn))
+            solution._w4a16_gemm_kernel[grid](
+                x,
+                ref.w_q,
+                ref.scales,
+                ref.zeros,
+                out,
+                shape[0],
+                shape[1],
+                shape[2],
+                BLOCK_M=bm,
+                BLOCK_N=bn,
+                BLOCK_K=bk,
+                num_warps=warps,
+                num_stages=stages,
+            )
+            return out
+
+        try:
+            ms = time_fn(fn, [x], iters=20, warmup=5)
+            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {ms:.6f} ms")
+        except Exception as e:
+            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {type(e).__name__}: {e}")
+
+
+def triton_cdiv(a, b):
+    return (a + b - 1) // b
+
+
+if __name__ == "__main__":
+    # Keep this scratch tuner small enough that compile time is still useful.
+    decode_cfgs = []
+    for bn, warps in itertools.product([32, 64, 128, 256], [4, 8]):
+        decode_cfgs.append((1, bn, 128, warps, 3))
+    run_shape((1, 4096, 4096), decode_cfgs)
+    run_shape((1, 12288, 4096), decode_cfgs)
+
+    prefill_cfgs = []
+    for bm, bn, bk, warps in itertools.product([32, 64, 128], [32, 64, 128], [64, 128], [4, 8]):
+        prefill_cfgs.append((bm, bn, bk, warps, 3))
+    run_shape((256, 12288, 4096), prefill_cfgs)
args
{
  "raw": "<2326 chars \u2014 see below>"
}
raw (2326 chars)
*** Begin Patch
*** Add File: /workspace/problems/07_w4a16_gemm/tune_tiles.py
+from __future__ import annotations
+
+import itertools
+import sys
+from pathlib import Path
+
+import torch
+
+ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(ROOT))
+
+from src.eval.timing import time_fn  # noqa: E402
+
+import reference  # noqa: E402
+import solution  # noqa: E402
+
+
+def run_shape(shape, configs):
+    device = torch.device("cuda:0")
+    reference.M, reference.N, reference.K = shape
+    ref = reference.Model(*reference.get_init_inputs()).to(device).eval()
+    x = reference.get_inputs()[0].to(device)
+
+    print(f"\nshape M={shape[0]} N={shape[1]} K={shape[2]}")
+    for bm, bn, bk, warps, stages in configs:
+        out = torch.empty((shape[0], shape[1]), device=device, dtype=torch.bfloat16)
+
+        def fn(x):
+            grid = (triton_cdiv(shape[0], bm), triton_cdiv(shape[1], bn))
+            solution._w4a16_gemm_kernel[grid](
+                x,
+                ref.w_q,
+                ref.scales,
+                ref.zeros,
+                out,
+                shape[0],
+                shape[1],
+                shape[2],
+                BLOCK_M=bm,
+                BLOCK_N=bn,
+                BLOCK_K=bk,
+                num_warps=warps,
+                num_stages=stages,
+            )
+            return out
+
+        try:
+            ms = time_fn(fn, [x], iters=20, warmup=5)
+            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {ms:.6f} ms")
+        except Exception as e:
+            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {type(e).__name__}: {e}")
+
+
+def triton_cdiv(a, b):
+    return (a + b - 1) // b
+
+
+if __name__ == "__main__":
+    # Keep this scratch tuner small enough that compile time is still useful.
+    decode_cfgs = []
+    for bn, warps in itertools.product([32, 64, 128, 256], [4, 8]):
+        decode_cfgs.append((1, bn, 128, warps, 3))
+    run_shape((1, 4096, 4096), decode_cfgs)
+    run_shape((1, 12288, 4096), decode_cfgs)
+
+    prefill_cfgs = []
+    for bm, bn, bk, warps in itertools.product([32, 64, 128], [32, 64, 128], [64, 128], [4, 8]):
+        prefill_cfgs.append((bm, bn, bk, warps, 3))
+    run_shape((256, 12288, 4096), prefill_cfgs)
*** End Patch
tool
stdout (148 chars)
output:
  Success. Updated the following files:
  A /workspace/problems/07_w4a16_gemm/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The tuner only calls the same fused kernel with different constexpr tile sizes; it is not replacing the official checker, just measuring launch configurations.
assistant
exec_command
args
{
  "cmd": "uv run python tune_tiles.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (114 chars)
Chunk ID: 603666
Wall time: 30.0016 seconds
Process running with session ID 10128
Original token count: 0
Output:
assistant
This tuning pass compiles several Triton specializations. I’m keeping it constrained to the known weak shapes so compile time stays bounded.
assistant
write_stdin
args
{
  "session_id": 10128,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (3586 chars) — TRUNCATED
Chunk ID: caaa70
Wall time: 32.5483 seconds
Process exited with code 0
Original token count: 871
Output:

shape M=1 N=4096 K=4096
bm=  1 bn= 32 bk=128 warps=4 stages=3: 0.047280 ms
bm=  1 bn= 32 bk=128 warps=8 stages=3: 0.047600 ms
bm=  1 bn= 64 bk=128 warps=4 stages=3: 0.052912 ms
bm=  1 bn= 64 bk=128 warps=8 stages=3: 0.052432 ms
bm=  1 bn=128 bk=128 warps=4 stages=3: 0.082992 ms
bm=  1 bn=128 bk=128 warps=8 stages=3: 0.068912 ms
bm=  1 bn=256 bk=128 warps=4 stages=3: OutOfResources: out of resource: shared memory, Required: 133376, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm=  1 bn=256 bk=128 warps=8 stages=3: OutOfResources: out of resource: shared memory, Required: 131328, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.

shape M=1 N=12288 K=4096
bm=  1 bn= 32 bk=128 warps=4 stages=3: 0.058880 ms
bm=  1 bn= 32 bk=128 warps=8 stages=3: 0.060096 ms
bm=  1 bn= 64 bk=128 warps=4 stages=3: 0.059248 ms
bm=  1 bn= 64 bk=128 warps=8 stages=3: 0.059056 ms
bm=  1 bn=128 bk=128 warps=4 stages=3: 0.091088 ms
bm=  1 bn=128 bk=128 warps=8 stages=3: 0.071968 ms
bm=  1 bn=256 bk=128 warps=4 stages=3: OutOfResources: out of resource: shared memory, Required: 133376, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm=  1 bn=256 bk=128 warps=8 stages=3: OutOfResources: out of resource: shared memory, Required: 131328, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.

shape M=256 N=12288 K=4096


... (2086 more chars)
assistant
Tile tuning found clear wins: decode prefers `BLOCK_N=32`, while the M=256 case improves with a taller `BLOCK_M=128` and `BLOCK_K=64`. I’m checking the M=16 and M=32 shapes before patching the final parameters.
assistant
exec_command
args
{
  "cmd": "<286 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
cmd (286 chars)
uv run python - <<'PY'
from tune_tiles import run_shape
import itertools
cfgs=[]
for bm in [16,32,64]:
  for bn in [32,64,128]:
    for bk in [64,128]:
      for warps in [4,8]:
        cfgs.append((bm,bn,bk,warps,3))
run_shape((16,14336,4096), cfgs)
run_shape((32,12288,4096), cfgs)
PY
tool
stdout (3831 chars) — TRUNCATED
Chunk ID: 017f19
Wall time: 11.2085 seconds
Process exited with code 0
Original token count: 932
Output:

shape M=16 N=14336 K=4096
bm= 16 bn= 32 bk= 64 warps=4 stages=3: 0.064096 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=3: 0.068128 ms
bm= 16 bn= 32 bk=128 warps=4 stages=3: 0.061984 ms
bm= 16 bn= 32 bk=128 warps=8 stages=3: 0.066160 ms
bm= 16 bn= 64 bk= 64 warps=4 stages=3: 0.064048 ms
bm= 16 bn= 64 bk= 64 warps=8 stages=3: 0.065744 ms
bm= 16 bn= 64 bk=128 warps=4 stages=3: 0.053040 ms
bm= 16 bn= 64 bk=128 warps=8 stages=3: 0.055792 ms
bm= 16 bn=128 bk= 64 warps=4 stages=3: 0.087136 ms
bm= 16 bn=128 bk= 64 warps=8 stages=3: 0.058736 ms
bm= 16 bn=128 bk=128 warps=4 stages=3: 0.094272 ms
bm= 16 bn=128 bk=128 warps=8 stages=3: 0.053584 ms
bm= 32 bn= 32 bk= 64 warps=4 stages=3: 0.064560 ms
bm= 32 bn= 32 bk= 64 warps=8 stages=3: 0.068160 ms
bm= 32 bn= 32 bk=128 warps=4 stages=3: 0.062576 ms
bm= 32 bn= 32 bk=128 warps=8 stages=3: 0.067104 ms
bm= 32 bn= 64 bk= 64 warps=4 stages=3: 0.067136 ms
bm= 32 bn= 64 bk= 64 warps=8 stages=3: 0.068784 ms
bm= 32 bn= 64 bk=128 warps=4 stages=3: 0.057184 ms
bm= 32 bn= 64 bk=128 warps=8 stages=3: 0.060944 ms
bm= 32 bn=128 bk= 64 warps=4 stages=3: 0.094032 ms
bm= 32 bn=128 bk= 64 warps=8 stages=3: 0.064784 ms
bm= 32 bn=128 bk=128 warps=4 stages=3: 0.087536 ms
bm= 32 bn=128 bk=128 warps=8 stages=3: 0.062944 ms
bm= 64 bn= 32 bk= 64 warps=4 stages=3: 0.071264 ms
bm= 64 bn= 32 bk= 64 warps=8 stages=3: 0.076128 ms
bm= 64 bn= 32 bk=128 warps=4 stages=3: 0.0

... (2331 more chars)
assistant
The current M=16 and M=32 choices are already near-best in the tuner. I’m patching only the measured wins: narrower decode N tiles and a taller M=256 tile with smaller K chunks.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (14 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1 +1,10 @@
-        group = k0 // 128
+        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
+
+        if M == 1:
+            bm, bn, bk, warps = 1, 32, 128, 4
+        elif M <= 16:
+            bm, bn, bk, warps = 16, 64, 128, 4
+        elif M <= 32:
+            bm, bn, bk, warps = 32, 64, 128, 4
+        else:
+            bm, bn, bk, warps = 128, 64, 64, 4
args
{
  "raw": "<539 chars \u2014 see below>"
}
raw (539 chars)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
         out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
 
         if M == 1:
-            bm, bn, bk, warps = 1, 64, 128, 4
+            bm, bn, bk, warps = 1, 32, 128, 4
         elif M <= 16:
             bm, bn, bk, warps = 16, 64, 128, 4
         elif M <= 32:
             bm, bn, bk, warps = 32, 64, 128, 4
         else:
-            bm, bn, bk, warps = 32, 64, 128, 4
+            bm, bn, bk, warps = 128, 64, 64, 4
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The tuned launch parameters are in `solution.py`. I’m rerunning the official correctness check after the tile changes, then I’ll take a fresh benchmark.
assistant
exec_command
args
{
  "cmd": "uv run python check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (107 chars)
Chunk ID: ae66c0
Wall time: 2.6115 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
Correctness still passes. I’m running the benchmark again to quantify the tuned version.
assistant
exec_command
args
{
  "cmd": "uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 20000
}
tool
stdout (1731 chars) — TRUNCATED
Chunk ID: f4e64b
Wall time: 2.3970 seconds
Process exited with code 0
Original token count: 407
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:47:26.475114+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:47:26.686103+00:00 elapsed_s=0.211 ms=0.062192
shape=0 variant=solution tflops=1.619 gbps=430.465 ms=0.062
shape=0 solution_peak_fraction=0.2391
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:47:26.998044+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:47:27.006399+00:00 elapsed_s=0.008 ms=0.060512
shape=1 variant=solution tflops=53.233 gbps=459.203 ms=0.061
shape=1 solution_peak_fraction=0.2551
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:47:27.326664+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:47:27.342085+00:00 elapsed_s=0.015 ms=0.173520
shape=2 variant=solution tflops=148.512 gbps=202.439 ms=0.174
shape=2 solution_peak_fraction=0.1125
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:47:27.444233+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:47:27.485404+00:00 elapsed_s=0.041 ms=0.050000
shape=3 variant=solution tflops=0.671 gbps=178.586 ms=0.050
shape=3 solution_peak_fraction=0.0992
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:47:27.843038+00:00
benchmark_event event=va

... (231 more chars)
assistant
The tuned version improved the weak M=256 shape substantially and brought the square decode case almost to the 0.1 memory fraction line. I’m doing one narrower decode sweep now, including smaller K tiles and stage counts, because that case is mostly launch plus occupancy overhead.
assistant
exec_command
args
{
  "cmd": "<380 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
cmd (380 chars)
uv run python - <<'PY'
from tune_tiles import run_shape
cfgs=[]
for bn in [16,32,48,64,96,128]:
  for bk in [32,64,128,256]:
    for warps in [1,2,4,8]:
      for stages in [2,3,4]:
        # Triton arange block sizes need powers of two for bk, bn is okay if power of 2? keep non-pow to observe errors.
        cfgs.append((1,bn,bk,warps,stages))
run_shape((1,4096,4096), cfgs)
PY
tool
stdout (40167 chars) — TRUNCATED
Chunk ID: 2f6081
Wall time: 30.0015 seconds
Process running with session ID 34161
Original token count: 12633
Output:
Total output lines: 1589


shape M=1 N=4096 K=4096
bm=  1 bn= 16 bk= 32 warps=1 stages=2: 0.095792 ms
bm=  1 bn= 16 bk= 32 warps=1 stages=3: 0.096832 ms
bm=  1 bn= 16 bk= 32 warps=1 stages=4: 0.097984 ms
bm=  1 bn= 16 bk= 32 warps=2 stages=2: 0.093072 ms
bm=  1 bn= 16 bk= 32 warps=2 stages=3: 0.088992 ms
bm=  1 bn= 16 bk= 32 warps=2 stages=4: 0.089408 ms
bm=  1 bn= 16 bk= 32 warps=4 stages=2: 0.088896 ms
bm=  1 bn= 16 bk= 32 warps=4 stages=3: 0.088208 ms
bm=  1 bn= 16 bk= 32 warps=4 stages=4: 0.088432 ms
bm=  1 bn= 16 bk= 32 warps=8 stages=2: 0.090640 ms
bm=  1 bn= 16 bk= 32 warps=8 stages=3: 0.093312 ms
bm=  1 bn= 16 bk= 32 warps=8 stages=4: 0.092672 ms
bm=  1 bn= 16 bk= 64 warps=1 stages=2: 0.070912 ms
bm=  1 bn= 16 bk= 64 warps=1 stages=3: 0.044960 ms
bm=  1 bn= 16 bk= 64 warps=1 stages=4: 0.040224 ms
bm=  1 bn= 16 bk= 64 warps=2 stages=2: 0.062400 ms
bm=  1 bn= 16 bk= 64 warps=2 stages=3: 0.060992 ms
bm=  1 bn= 16 bk= 64 warps=2 stages=4: 0.060864 ms
bm=  1 bn= 16 bk= 64 warps=4 stages=2: 0.058656 ms
bm=  1 bn= 16 bk= 64 warps=4 stages=3: 0.058080 ms
bm=  1 bn= 16 bk= 64 warps=4 stages=4: 0.058512 ms
bm=  1 bn= 16 bk= 64 warps=8 stages=2: 0.059808 ms
bm=  1 bn= 16 bk= 64 warps=8 stages=3: 0.060032 ms
bm=  1 bn= 16 bk= 64 warps=8 stages=4: 0.059008 ms
bm=  1 bn= 16 bk=128 warps=1 stages=2: 0.052784 ms
bm=  1 bn= 16 bk=128 warps=1 stages=3: 0.039456 ms
bm=  

... (38667 more chars)
assistant
write_stdin
args
{
  "session_id": 34161,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
tool
stdout (114 chars)
Chunk ID: 43cf4a
Wall time: 60.0009 seconds
Process running with session ID 34161
Original token count: 0
Output:
assistant
The decode sweep has already identified a much faster shape: `BLOCK_N=32`, `BLOCK_K=256`, four stages. The rest of that broad sweep is mostly redundant compile work, so I’m stopping it and applying the useful result.
assistant
write_stdin
args
{
  "session_id": 34161,
  "chars": "\u0003",
  "yield_time_ms": 1000,
  "max_output_tokens": 20000
}
tool
stdout (105 chars)
write_stdin failed: stdin is closed for this session; rerun exec_command with tty=true to keep stdin open
assistant
The process does not accept an interrupt through this session, so I’ll let it drain while continuing with file edits in a separate step.
assistant
One caveat from the decode sweep: the fastest `BLOCK_K=256` measurement used the current single-group scale load, so I need to split that tile into two 128-wide dequant groups for correctness. I’m checking for the leftover tuner process and then patching a correct 256-wide decode path.
assistant
exec_command
args
{
  "cmd": "ps -eo pid,ppid,stat,cmd | rg 'tune_tiles|uv run python|python -' || true",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,180p' solution.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 12000
}
tool
stdout (273 chars)
Chunk ID: 6b6d96
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 43
Output:
   1228      73 Ss   /usr/bin/bash -c ps -eo pid,ppid,stat,cmd | rg 'tune_tiles|uv run python|python -' || true
   1230    1228 R    rg tune_tiles|uv run python|python -
tool
stdout (3307 chars) — TRUNCATED
Chunk ID: eeddd8
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 801
Output:
from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl


GROUP_SIZE = 128


@triton.jit
def _w4a16_gemm_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    out_ptr,
    M: tl.constexpr,
    N: tl.constexpr,
    K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    for k0 in range(0, K, BLOCK_K):
        k = k0 + offs_k
        a = tl.load(
            x_ptr + offs_m[:, None] * K + k[None, :],
            mask=offs_m[:, None] < M,
            other=0.0,
        )

        packed = tl.load(
            wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
            mask=offs_n[None, :] < N,
            other=0,
        )
        q_lo = packed & 0x0F
        q_hi = (packed >> 4) & 0x0F
        q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)

        group = k0 // 128
        s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
        z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
        b = ((q - z[None, :]) * s[None, 

... (1807 more chars)
assistant
I’m adding a correct `BLOCK_K=256` specialization that still applies scales and zero-points per 128-wide group. That preserves the fused path while reducing outer-loop overhead for decode.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (80 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,10 +1,73 @@
+    pid_n = tl.program_id(1)
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
+
+    if BLOCK_K == 256:
+        offs_k128 = tl.arange(0, 128)
+        for k0 in range(0, K, 256):
+            for part in tl.static_range(0, 2):
+                k = k0 + part * 128 + offs_k128
+                a = tl.load(
+                    x_ptr + offs_m[:, None] * K + k[None, :],
+                    mask=offs_m[:, None] < M,
+                    other=0.0,
+                )
+
+                packed = tl.load(
+                    wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+                    mask=offs_n[None, :] < N,
+                    other=0,
+                )
+                q_lo = packed & 0x0F
+                q_hi = (packed >> 4) & 0x0F
+                q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+                group = k0 // 128 + part
+                s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+                z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+                b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+                acc += tl.dot(a, b, out_dtype=tl.float32)
+    else:
+        offs_k = tl.arange(0, BLOCK_K)
+        for k0 in range(0, K, BLOCK_K):
+            k = k0 + offs_k
+            a = tl.load(
+                x_ptr + offs_m[:, None] * K + k[None, :],
+                mask=offs_m[:, None] < M,
+                other=0.0,
+            )
+
+            packed = tl.load(
+                wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+                mask=offs_n[None, :] < N,
+                other=0,
+            )
+            q_lo = packed & 0x0F
+            q_hi = (packed >> 4) & 0x0F
+            q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+            group = k0 // 128
+            s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+            z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+            b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+            acc += tl.dot(a, b, out_dtype=tl.float32)
         out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
 
         if M == 1:
-            bm, bn, bk, warps = 1, 32, 128, 4
+            bm, bn, bk, warps, stages = 1, 32, 256, 4, 4
         elif M <= 16:
-            bm, bn, bk, warps = 16, 64, 128, 4
+            bm, bn, bk, warps, stages = 16, 64, 128, 4, 3
         elif M <= 32:
-            bm, bn, bk, warps = 32, 64, 128, 4
+            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
         else:
-            bm, bn, bk, warps = 128, 64, 64, 4
+            bm, bn, bk, warps, stages = 128, 64, 64, 4, 3
+            BLOCK_N=bn,
+            BLOCK_K=bk,
+            num_warps=warps,
+            num_stages=stages,
+        )
args
{
  "raw": "<4040 chars \u2014 see below>"
}
raw (4040 chars — TRUNCATED)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
     pid_n = tl.program_id(1)
 
     offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
     offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
-    offs_k = tl.arange(0, BLOCK_K)
 
     acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
 
-    for k0 in range(0, K, BLOCK_K):
-        k = k0 + offs_k
-        a = tl.load(
-            x_ptr + offs_m[:, None] * K + k[None, :],
-            mask=offs_m[:, None] < M,
-            other=0.0,
-        )
-
-        packed = tl.load(
-            wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
-            mask=offs_n[None, :] < N,
-            other=0,
-        )
-        q_lo = packed & 0x0F
-        q_hi = (packed >> 4) & 0x0F
-        q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
-
-        group = k0 // 128
-        s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-        z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-        b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
-
-        acc += tl.dot(a, b, out_dtype=tl.float32)
+    if BLOCK_K == 256:
+        offs_k128 = tl.arange(0, 128)
+        for k0 in range(0, K, 256):
+            for part in tl.static_range(0, 2):
+                k = k0 + part * 128 + offs_k128
+                a = tl.load(
+                    x_ptr + offs_m[:, None] * K + k[None, :],
+                    mask=offs_m[:, None] < M,
+                    other=0.0,
+                )
+
+                packed = tl.load(
+                    wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+                    mask=offs_n[None, :] < N,
+                    other=0,
+                )
+                q_lo = packed & 0x0F
+                q_hi = (packed >> 4) & 0x0F
+                q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+                group = k0 // 128 + part
+                s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+                z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+                b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+                acc += tl.dot(a, b, out_dtype=tl.float32)
+    else:
+        offs_k = tl.arange(0, BLOCK_K)
+        for k0 in range(0, K, BLOCK_K):
+            k = k0 + offs_k
+            a = tl.load(
+                x_ptr + offs_m[:, None] * K + k[None, :],
+                mask=offs_m[:, None] < M,
+                other=0.0,
+            )
+
+            packed = tl.load(
+                wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
+                mask=offs_n[None, :] < N,
+                other=0,
+            )
+            q_lo = packed & 0x0F
+            q_hi = (packed >> 4) & 0x0F
+            q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
+
+            group = k0 // 128
+            s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+            z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
+            b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
+
+            acc += tl.dot(a, b, out_dtype=tl.float32)
@@
         out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
 
         if M == 1:
-            bm, bn, bk, warps = 1, 32, 128, 4
+            bm, bn, bk, warps, stages = 1, 32, 256, 4, 4
         elif M <= 16:
-            bm, bn, bk, warps = 16, 64, 128, 4
+            bm, bn, bk, warps, stages = 16, 64, 128, 4, 3
         elif M <= 32:
-            bm, bn, bk, warps = 32, 64, 128, 4
+            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
         else:
-            bm, bn, bk, warps = 128, 64, 64, 4
+            bm, bn, bk, warps, stages = 128, 64, 64, 4, 3
@@
             BLOCK_N=bn,
             BLOCK_K=bk,
             num_warps=warps,
-            num_stages=3,
+            num_

... (40 more chars)
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The decode specialization is patched. I’m running the full checker again because this is exactly the kind of change where performance tuning can silently cross a group boundary.
assistant
exec_command
args
{
  "cmd": "uv run python check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (108 chars)
Chunk ID: 7f5f61
Wall time: 22.5503 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
Correctness passes with the `BLOCK_K=256` decode path. I’m benchmarking again to see whether the correct split-group variant keeps the speedup.
assistant
exec_command
args
{
  "cmd": "uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 20000
}
tool
stdout (1731 chars) — TRUNCATED
Chunk ID: 250ecd
Wall time: 2.5395 seconds
Process exited with code 0
Original token count: 407
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:50:46.263158+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:50:46.477116+00:00 elapsed_s=0.214 ms=0.076304
shape=0 variant=solution tflops=1.319 gbps=350.853 ms=0.076
shape=0 solution_peak_fraction=0.1949
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:50:46.822008+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:50:46.830282+00:00 elapsed_s=0.008 ms=0.060880
shape=1 variant=solution tflops=52.911 gbps=456.427 ms=0.061
shape=1 solution_peak_fraction=0.2536
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:50:47.185959+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:50:47.201276+00:00 elapsed_s=0.015 ms=0.173712
shape=2 variant=solution tflops=148.348 gbps=202.216 ms=0.174
shape=2 solution_peak_fraction=0.1123
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:50:47.310812+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:50:47.353724+00:00 elapsed_s=0.043 ms=0.047536
shape=3 variant=solution tflops=0.706 gbps=187.842 ms=0.048
shape=3 solution_peak_fraction=0.1044
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:50:47.742742+00:00
benchmark_event event=va

... (231 more chars)
assistant
The correct 256-wide decode path helps the smaller decode shape but hurts the larger one. I’m going to split decode by N, and I’m doing a focused retune with only correct group handling before applying the final decode choices.
assistant
exec_command
args
{
  "cmd": "<277 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
cmd (277 chars)
uv run python - <<'PY'
from tune_tiles import run_shape
cfgs=[]
for bn in [8,16,32,64]:
  for bk in [128,256]:
    for warps in [1,2,4,8]:
      for stages in [2,3,4]:
        cfgs.append((1,bn,bk,warps,stages))
run_shape((1,12288,4096), cfgs)
run_shape((1,4096,4096), cfgs)
PY
tool
stdout (8326 chars) — TRUNCATED
Chunk ID: 913dc0
Wall time: 30.0013 seconds
Process running with session ID 5169
Original token count: 2053
Output:

shape M=1 N=12288 K=4096
bm=  1 bn=  8 bk=128 warps=1 stages=2: 0.075872 ms
bm=  1 bn=  8 bk=128 warps=1 stages=3: 0.071152 ms
bm=  1 bn=  8 bk=128 warps=1 stages=4: 0.073376 ms
bm=  1 bn=  8 bk=128 warps=2 stages=2: 0.074512 ms
bm=  1 bn=  8 bk=128 warps=2 stages=3: 0.074608 ms
bm=  1 bn=  8 bk=128 warps=2 stages=4: 0.074880 ms
bm=  1 bn=  8 bk=128 warps=4 stages=2: 0.085696 ms
bm=  1 bn=  8 bk=128 warps=4 stages=3: 0.086752 ms
bm=  1 bn=  8 bk=128 warps=4 stages=4: 0.087648 ms
bm=  1 bn=  8 bk=128 warps=8 stages=2: 0.105568 ms
bm=  1 bn=  8 bk=128 warps=8 stages=3: 0.098080 ms
bm=  1 bn=  8 bk=128 warps=8 stages=4: 0.098944 ms
bm=  1 bn=  8 bk=256 warps=1 stages=2: 0.074704 ms
bm=  1 bn=  8 bk=256 warps=1 stages=3: 0.075936 ms
bm=  1 bn=  8 bk=256 warps=1 stages=4: 0.077040 ms
bm=  1 bn=  8 bk=256 warps=2 stages=2: 0.075152 ms
bm=  1 bn=  8 bk=256 warps=2 stages=3: 0.076224 ms
bm=  1 bn=  8 bk=256 warps=2 stages=4: 0.077408 ms
bm=  1 bn=  8 bk=256 warps=4 stages=2: 0.085488 ms
bm=  1 bn=  8 bk=256 warps=4 stages=3: 0.086432 ms
bm=  1 bn=  8 bk=256 warps=4 stages=4: 0.088144 ms
bm=  1 bn=  8 bk=256 warps=8 stages=2: 0.098048 ms
bm=  1 bn=  8 bk=256 warps=8 stages=3: 0.099280 ms
bm=  1 bn=  8 bk=256 warps=8 stages=4: 0.106432 ms
bm=  1 bn= 16 bk=128 warps=1 stages=2: 0.067856 ms
bm=  1 bn= 16 bk=128 warps=1 stages=3: 0.065696 ms
bm=  1 bn= 16 bk=128 warps=1 sta

... (6826 more chars)
assistant
write_stdin
args
{
  "session_id": 5169,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (1737 chars) — TRUNCATED
Chunk ID: 3900c7
Wall time: 8.7303 seconds
Process exited with code 0
Original token count: 409
Output:

bm=  1 bn= 32 bk=256 warps=2 stages=3: 0.032976 ms
bm=  1 bn= 32 bk=256 warps=2 stages=4: 0.032464 ms
bm=  1 bn= 32 bk=256 warps=4 stages=2: 0.045408 ms
bm=  1 bn= 32 bk=256 warps=4 stages=3: 0.047728 ms
bm=  1 bn= 32 bk=256 warps=4 stages=4: 0.045056 ms
bm=  1 bn= 32 bk=256 warps=8 stages=2: 0.046176 ms
bm=  1 bn= 32 bk=256 warps=8 stages=3: 0.047648 ms
bm=  1 bn= 32 bk=256 warps=8 stages=4: 0.047600 ms
bm=  1 bn= 64 bk=128 warps=1 stages=2: 0.103248 ms
bm=  1 bn= 64 bk=128 warps=1 stages=3: 0.093760 ms
bm=  1 bn= 64 bk=128 warps=1 stages=4: 0.093488 ms
bm=  1 bn= 64 bk=128 warps=2 stages=2: 0.081456 ms
bm=  1 bn= 64 bk=128 warps=2 stages=3: 0.075488 ms
bm=  1 bn= 64 bk=128 warps=2 stages=4: 0.074816 ms
bm=  1 bn= 64 bk=128 warps=4 stages=2: 0.052496 ms
bm=  1 bn= 64 bk=128 warps=4 stages=3: 0.052656 ms
bm=  1 bn= 64 bk=128 warps=4 stages=4: 0.052592 ms
bm=  1 bn= 64 bk=128 warps=8 stages=2: 0.053440 ms
bm=  1 bn= 64 bk=128 warps=8 stages=3: 0.051888 ms
bm=  1 bn= 64 bk=128 warps=8 stages=4: 0.051568 ms
bm=  1 bn= 64 bk=256 warps=1 stages=2: 0.112464 ms
bm=  1 bn= 64 bk=256 warps=1 stages=3: 0.107344 ms
bm=  1 bn= 64 bk=256 warps=1 stages=4: 0.107424 ms
bm=  1 bn= 64 bk=256 warps=2 stages=2: 0.067168 ms
bm=  1 bn= 64 bk=256 warps=2 stages=3: 0.076624 ms
bm=  1 bn= 64 bk=256 warps=2 stages=4: 0.062480 ms
bm=  1 bn= 64 bk=256 warps=4 stages=2: 0.048560 ms
bm=  1 bn= 64 bk=2

... (237 more chars)
assistant
The focused decode retune gives two different winners: the large decode shape likes `N=64,K=256,8 warps,2 stages`, while the square decode shape likes `N=32,K=256,2 warps,4 stages`. I’m applying that N-specific split.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (80 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,73 +1,7 @@
-    pid_n = tl.program_id(1)
-
-    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
-
-    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
-
-    if BLOCK_K == 256:
-        offs_k128 = tl.arange(0, 128)
-        for k0 in range(0, K, 256):
-            for part in tl.static_range(0, 2):
-                k = k0 + part * 128 + offs_k128
-                a = tl.load(
-                    x_ptr + offs_m[:, None] * K + k[None, :],
-                    mask=offs_m[:, None] < M,
-                    other=0.0,
-                )
-
-                packed = tl.load(
-                    wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
-                    mask=offs_n[None, :] < N,
-                    other=0,
-                )
-                q_lo = packed & 0x0F
-                q_hi = (packed >> 4) & 0x0F
-                q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
-
-                group = k0 // 128 + part
-                s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-                z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-                b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
-
-                acc += tl.dot(a, b, out_dtype=tl.float32)
-    else:
-        offs_k = tl.arange(0, BLOCK_K)
-        for k0 in range(0, K, BLOCK_K):
-            k = k0 + offs_k
-            a = tl.load(
-                x_ptr + offs_m[:, None] * K + k[None, :],
-                mask=offs_m[:, None] < M,
-                other=0.0,
-            )
-
-            packed = tl.load(
-                wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
-                mask=offs_n[None, :] < N,
-                other=0,
-            )
-            q_lo = packed & 0x0F
-            q_hi = (packed >> 4) & 0x0F
-            q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)
-
-            group = k0 // 128
-            s = tl.load(scales_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-            z = tl.load(zeros_ptr + group * N + offs_n, mask=offs_n < N, other=0.0).to(tl.float32)
-            b = ((q - z[None, :]) * s[None, :]).to(tl.bfloat16)
-
-            acc += tl.dot(a, b, out_dtype=tl.float32)
         out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
 
         if M == 1:
-            bm, bn, bk, warps, stages = 1, 32, 256, 4, 4
-        elif M <= 16:
-            bm, bn, bk, warps, stages = 16, 64, 128, 4, 3
-        elif M <= 32:
-            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
-        else:
-            bm, bn, bk, warps, stages = 128, 64, 64, 4, 3
-            BLOCK_N=bn,
-            BLOCK_K=bk,
-            num_warps=warps,
-            num_stages=stages,
-        )
+            if N <= 4096:
+                bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
+            else:
+                bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
args
{
  "raw": "<420 chars \u2014 see below>"
}
raw (420 chars)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
         out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
 
         if M == 1:
-            bm, bn, bk, warps, stages = 1, 32, 256, 4, 4
+            if N <= 4096:
+                bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
+            else:
+                bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The N-specific decode parameters are patched. I’m running the official checker and benchmark again.
assistant
exec_command
args
{
  "cmd": "uv run python check.py && uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (1736 chars) — TRUNCATED
Chunk ID: 364eeb
Wall time: 5.3515 seconds
Process exited with code 0
Original token count: 408
Output:
PASS
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:51:56.786017+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:51:56.993060+00:00 elapsed_s=0.207 ms=0.062080
shape=0 variant=solution tflops=1.622 gbps=431.241 ms=0.062
shape=0 solution_peak_fraction=0.2396
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:51:57.331450+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:51:57.339775+00:00 elapsed_s=0.008 ms=0.061040
shape=1 variant=solution tflops=52.772 gbps=455.230 ms=0.061
shape=1 solution_peak_fraction=0.2529
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:51:57.693081+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:51:57.708528+00:00 elapsed_s=0.015 ms=0.172976
shape=2 variant=solution tflops=148.979 gbps=203.076 ms=0.173
shape=2 solution_peak_fraction=0.1128
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:51:57.821372+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:51:57.860876+00:00 elapsed_s=0.039 ms=0.036288
shape=3 variant=solution tflops=0.925 gbps=246.067 ms=0.036
shape=3 solution_peak_fraction=0.1367
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:51:58.251222+00:00
benchmark_event eve

... (236 more chars)
assistant
The decode split raised the geomean again, with the square decode case now comfortably above the 0.1 memory fraction. I’m doing a small prefill sweep around the M=256 winner to see if stage count or a full-height M tile helps.
assistant
exec_command
args
{
  "cmd": "<285 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
cmd (285 chars)
uv run python - <<'PY'
from tune_tiles import run_shape
cfgs=[]
for bm in [64,128,256]:
  for bn in [16,32,64,128]:
    for bk in [32,64,128]:
      for warps in [2,4,8]:
        for stages in [2,3,4]:
          cfgs.append((bm,bn,bk,warps,stages))
run_shape((256,12288,4096), cfgs)
PY
tool
stdout (8340 chars) — TRUNCATED
Chunk ID: 72a33d
Wall time: 30.0014 seconds
Process running with session ID 76129
Original token count: 2056
Output:

shape M=256 N=12288 K=4096
bm= 64 bn= 16 bk= 32 warps=2 stages=2: 0.312864 ms
bm= 64 bn= 16 bk= 32 warps=2 stages=3: 0.269120 ms
bm= 64 bn= 16 bk= 32 warps=2 stages=4: 0.263328 ms
bm= 64 bn= 16 bk= 32 warps=4 stages=2: 0.305984 ms
bm= 64 bn= 16 bk= 32 warps=4 stages=3: 0.286736 ms
bm= 64 bn= 16 bk= 32 warps=4 stages=4: 0.284544 ms
bm= 64 bn= 16 bk= 32 warps=8 stages=2: 0.431312 ms
bm= 64 bn= 16 bk= 32 warps=8 stages=3: 0.354832 ms
bm= 64 bn= 16 bk= 32 warps=8 stages=4: 0.356912 ms
bm= 64 bn= 16 bk= 64 warps=2 stages=2: 0.294256 ms
bm= 64 bn= 16 bk= 64 warps=2 stages=3: 0.280000 ms
bm= 64 bn= 16 bk= 64 warps=2 stages=4: 0.273040 ms
bm= 64 bn= 16 bk= 64 warps=4 stages=2: 0.268992 ms
bm= 64 bn= 16 bk= 64 warps=4 stages=3: 0.255040 ms
bm= 64 bn= 16 bk= 64 warps=4 stages=4: 0.253248 ms
bm= 64 bn= 16 bk= 64 warps=8 stages=2: 0.303360 ms
bm= 64 bn= 16 bk= 64 warps=8 stages=3: 0.299424 ms
bm= 64 bn= 16 bk= 64 warps=8 stages=4: 0.300976 ms
bm= 64 bn= 16 bk=128 warps=2 stages=2: 0.286320 ms
bm= 64 bn= 16 bk=128 warps=2 stages=3: 0.275312 ms
bm= 64 bn= 16 bk=128 warps=2 stages=4: 0.353376 ms
bm= 64 bn= 16 bk=128 warps=4 stages=2: 0.282208 ms
bm= 64 bn= 16 bk=128 warps=4 stages=3: 0.271488 ms
bm= 64 bn= 16 bk=128 warps=4 stages=4: 0.291488 ms
bm= 64 bn= 16 bk=128 warps=8 stages=2: 0.274048 ms
bm= 64 bn= 16 bk=128 warps=8 stages=3: 0.275072 ms
bm= 64 bn= 16 bk=128 warps=8 

... (6840 more chars)
assistant
The M=256 sweep is still running. So far the existing `128x64x64` region is competitive; I’m waiting for the full-height M tiles before deciding whether to change it.
assistant
write_stdin
args
{
  "session_id": 76129,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
tool
stdout (15560 chars) — TRUNCATED
Chunk ID: 0ccde4
Wall time: 53.8973 seconds
Process exited with code 0
Original token count: 3864
Output:

bm=128 bn= 32 bk= 64 warps=2 stages=4: 0.283184 ms
bm=128 bn= 32 bk= 64 warps=4 stages=2: 0.190368 ms
bm=128 bn= 32 bk= 64 warps=4 stages=3: 0.174752 ms
bm=128 bn= 32 bk= 64 warps=4 stages=4: 0.200736 ms
bm=128 bn= 32 bk= 64 warps=8 stages=2: 0.213296 ms
bm=128 bn= 32 bk= 64 warps=8 stages=3: 0.181024 ms
bm=128 bn= 32 bk= 64 warps=8 stages=4: 0.200432 ms
bm=128 bn= 32 bk=128 warps=2 stages=2: 0.252960 ms
bm=128 bn= 32 bk=128 warps=2 stages=3: 0.260112 ms
bm=128 bn= 32 bk=128 warps=2 stages=4: OutOfResources: out of resource: shared memory, Required: 119168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm=128 bn= 32 bk=128 warps=4 stages=2: 0.202512 ms
bm=128 bn= 32 bk=128 warps=4 stages=3: 0.181376 ms
bm=128 bn= 32 bk=128 warps=4 stages=4: OutOfResources: out of resource: shared memory, Required: 119168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm=128 bn= 32 bk=128 warps=8 stages=2: 0.193520 ms
bm=128 bn= 32 bk=128 warps=8 stages=3: 0.190848 ms
bm=128 bn= 32 bk=128 warps=8 stages=4: OutOfResources: out of resource: shared memory, Required: 119168, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm=128 bn= 64 bk= 32 warps=2 stages=2: 0.198240 ms
bm=128 bn= 64 bk= 32 warps=2 stages=3: 0.174944 ms
bm=128 bn= 64 bk= 32 warps=2 stages=4: 0.257856 ms
bm=128 bn= 64 bk= 32 warps=4 stages=2: 0.167504 m

... (14060 more chars)
assistant
The M=256 sweep found a better prefill tile: `128x64x32` with four warps and three stages. I’m applying that, then I’ll revalidate and retime.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (14 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,7 +1,4 @@
-        out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
-
-        if M == 1:
-            if N <= 4096:
-                bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
-            else:
-                bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
+        elif M <= 32:
+            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
+        else:
+            bm, bn, bk, warps, stages = 128, 64, 32, 4, 3
args
{
  "raw": "<311 chars \u2014 see below>"
}
raw (311 chars)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
         elif M <= 32:
             bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
         else:
-            bm, bn, bk, warps, stages = 128, 64, 64, 4, 3
+            bm, bn, bk, warps, stages = 128, 64, 32, 4, 3
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The M=256 tile change is in. I’m running the official check plus benchmark again.
assistant
exec_command
args
{
  "cmd": "uv run python check.py && uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (114 chars)
Chunk ID: 58afed
Wall time: 30.0036 seconds
Process running with session ID 94508
Original token count: 0
Output:
assistant
This check includes a new `BLOCK_K=32` specialization, so it is compiling one more kernel variant before timing.
assistant
write_stdin
args
{
  "session_id": 94508,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (114 chars)
Chunk ID: 23d564
Wall time: 60.0006 seconds
Process running with session ID 94508
Original token count: 0
Output:
assistant
It is still in the combined check/benchmark command. I’m waiting rather than launching overlapping GPU work so the timing result stays meaningful.
assistant
write_stdin
args
{
  "session_id": 94508,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (1737 chars) — TRUNCATED
Chunk ID: 199e5e
Wall time: 36.3432 seconds
Process exited with code 0
Original token count: 408
Output:
PASS
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:55:54.434356+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:55:54.658001+00:00 elapsed_s=0.224 ms=0.061408
shape=0 variant=solution tflops=1.639 gbps=435.960 ms=0.061
shape=0 solution_peak_fraction=0.2422
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:55:54.993110+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:55:55.001371+00:00 elapsed_s=0.008 ms=0.061072
shape=1 variant=solution tflops=52.745 gbps=454.992 ms=0.061
shape=1 solution_peak_fraction=0.2528
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:55:55.363192+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:55:55.377418+00:00 elapsed_s=0.014 ms=0.159088
shape=2 variant=solution tflops=161.985 gbps=220.804 ms=0.159
shape=2 solution_peak_fraction=0.1227
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:55:55.490187+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:55:55.529933+00:00 elapsed_s=0.040 ms=0.035904
shape=3 variant=solution tflops=0.935 gbps=248.699 ms=0.036
shape=3 solution_peak_fraction=0.1382
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:55:55.920274+00:00
benchmark_event ev

... (237 more chars)
assistant
The M=256 change lifted the geomean to about 0.20. The remaining M=16/M=32 shapes are already fast, but I’m checking a small stage-count sweep around their current winners for a possible cheap gain.
assistant
exec_command
args
{
  "cmd": "<422 chars \u2014 see below>",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
cmd (422 chars)
uv run python - <<'PY'
from tune_tiles import run_shape
cfgs=[]
for shape_m in [16,32]:
  cfgs=[]
  for bm in ([16,32] if shape_m==16 else [16,32,64]):
    for bn in [32,64,128]:
      for bk in [32,64,128]:
        for warps in [2,4,8]:
          for stages in [2,3,4]:
            cfgs.append((bm,bn,bk,warps,stages))
  if shape_m==16:
    run_shape((16,14336,4096), cfgs)
  else:
    run_shape((32,12288,4096), cfgs)
PY
tool
stdout (8326 chars) — TRUNCATED
Chunk ID: 79d66f
Wall time: 30.0009 seconds
Process running with session ID 48927
Original token count: 2053
Output:

shape M=16 N=14336 K=4096
bm= 16 bn= 32 bk= 32 warps=2 stages=2: 0.100608 ms
bm= 16 bn= 32 bk= 32 warps=2 stages=3: 0.076432 ms
bm= 16 bn= 32 bk= 32 warps=2 stages=4: 0.071136 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=2: 0.095760 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=3: 0.072432 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=4: 0.067920 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=2: 0.104096 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=3: 0.087408 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=4: 0.083152 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=2: 0.078096 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=3: 0.065664 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=4: 0.065072 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=2: 0.075488 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=3: 0.062848 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=4: 0.061792 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=2: 0.082896 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=3: 0.068624 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=4: 0.069168 ms
bm= 16 bn= 32 bk=128 warps=2 stages=2: 0.067008 ms
bm= 16 bn= 32 bk=128 warps=2 stages=3: 0.063712 ms
bm= 16 bn= 32 bk=128 warps=2 stages=4: 0.076080 ms
bm= 16 bn= 32 bk=128 warps=4 stages=2: 0.063792 ms
bm= 16 bn= 32 bk=128 warps=4 stages=3: 0.060064 ms
bm= 16 bn= 32 bk=128 warps=4 stages=4: 0.073632 ms
bm= 16 bn= 32 bk=128 warps=8 stages=2: 0.068304 ms
bm= 16 bn= 32 bk=128 warps=8 stages=3: 0.065792 ms
bm= 16 bn= 32 bk=128 warps=8 s

... (6826 more chars)
assistant
For M=16 the sweep suggests a small improvement with a wider N tile (`16x128x128`, eight warps, four stages). I’m waiting for the M=32 half of the sweep to finish before changing both.
assistant
write_stdin
args
{
  "session_id": 48927,
  "chars": "",
  "yield_time_ms": 60000,
  "max_output_tokens": 50000
}
tool
stdout (13731 chars) — TRUNCATED
Chunk ID: 31d2cf
Wall time: 33.6839 seconds
Process exited with code 0
Original token count: 3407
Output:

bm= 32 bn=128 bk=128 warps=4 stages=4: OutOfResources: out of resource: shared memory, Required: 106496, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
bm= 32 bn=128 bk=128 warps=8 stages=2: 0.081040 ms
bm= 32 bn=128 bk=128 warps=8 stages=3: 0.062864 ms
bm= 32 bn=128 bk=128 warps=8 stages=4: OutOfResources: out of resource: shared memory, Required: 108032, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.

shape M=32 N=12288 K=4096
bm= 16 bn= 32 bk= 32 warps=2 stages=2: 0.105920 ms
bm= 16 bn= 32 bk= 32 warps=2 stages=3: 0.085584 ms
bm= 16 bn= 32 bk= 32 warps=2 stages=4: 0.084688 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=2: 0.099440 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=3: 0.087008 ms
bm= 16 bn= 32 bk= 32 warps=4 stages=4: 0.088480 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=2: 0.132976 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=3: 0.122272 ms
bm= 16 bn= 32 bk= 32 warps=8 stages=4: 0.120880 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=2: 0.084928 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=3: 0.078944 ms
bm= 16 bn= 32 bk= 64 warps=2 stages=4: 0.079776 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=2: 0.083616 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=3: 0.076352 ms
bm= 16 bn= 32 bk= 64 warps=4 stages=4: 0.078464 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=2: 0.121312 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=3: 0.099888 ms
bm= 16 bn= 32 bk= 64 warps=8 stages=4: 0.0996

... (12231 more chars)
assistant
For M=16, the best measured tile is `16x128x128` with eight warps and four stages; M=32 should stay on the existing `32x64x128` tile. I’m applying only the M=16 change.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/solution.py
diff (14 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,4 +1,7 @@
-        elif M <= 32:
-            bm, bn, bk, warps, stages = 32, 64, 128, 4, 3
-        else:
-            bm, bn, bk, warps, stages = 128, 64, 32, 4, 3
+        if M == 1:
+            if N <= 4096:
+                bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
+            else:
+                bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
+        elif M <= 16:
+            bm, bn, bk, warps, stages = 16, 128, 128, 8, 4
args
{
  "raw": "<428 chars \u2014 see below>"
}
raw (428 chars)
*** Begin Patch
*** Update File: /workspace/problems/07_w4a16_gemm/solution.py
@@
         if M == 1:
             if N <= 4096:
                 bm, bn, bk, warps, stages = 1, 32, 256, 2, 4
             else:
                 bm, bn, bk, warps, stages = 1, 64, 256, 8, 2
         elif M <= 16:
-            bm, bn, bk, warps, stages = 16, 64, 128, 4, 3
+            bm, bn, bk, warps, stages = 16, 128, 128, 8, 4
*** End Patch
tool
stdout (146 chars)
output:
  Success. Updated the following files:
  M /workspace/problems/07_w4a16_gemm/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
The M=16 tile is patched. I’m running the official checker and benchmark one more time.
assistant
exec_command
args
{
  "cmd": "uv run python check.py && uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 60000,
  "max_output_tokens": 30000
}
tool
stdout (1737 chars) — TRUNCATED
Chunk ID: c14c31
Wall time: 11.7101 seconds
Process exited with code 0
Original token count: 408
Output:
PASS
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:57:34.182123+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:57:34.388233+00:00 elapsed_s=0.206 ms=0.061728
shape=0 variant=solution tflops=1.631 gbps=433.700 ms=0.062
shape=0 solution_peak_fraction=0.2409
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:57:34.733931+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:57:34.742205+00:00 elapsed_s=0.008 ms=0.060496
shape=1 variant=solution tflops=53.247 gbps=459.324 ms=0.060
shape=1 solution_peak_fraction=0.2552
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:57:35.090161+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:57:35.104443+00:00 elapsed_s=0.014 ms=0.159328
shape=2 variant=solution tflops=161.741 gbps=220.472 ms=0.159
shape=2 solution_peak_fraction=0.1225
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:57:35.215030+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:57:35.253700+00:00 elapsed_s=0.039 ms=0.035984
shape=3 variant=solution tflops=0.932 gbps=248.146 ms=0.036
shape=3 solution_peak_fraction=0.1379
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:57:35.638326+00:00
benchmark_event ev

... (237 more chars)
assistant
The latest tuned version passes and benchmarks at a geomean memory fraction around 0.20. I’m cleaning up the scratch tuner so the deliverable is just the solution file and harness-generated metadata.
assistant
exec_command
args
{
  "cmd": "ls -la",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 8000
}
assistant
exec_command
args
{
  "cmd": "sed -n '1,220p' solution.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 1000,
  "max_output_tokens": 16000
}
tool
stdout (780 chars)
Chunk ID: adeeb1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 169
Output:
total 68
drwxrwxr-x 3 1002 1002 4096 Jun 13 11:45 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 11:38 ..
-rw-rw-r-- 1 1002 1002 2421 Apr 27 09:21 PROMPT.txt
drwxr-xr-x 2 1002 1002 4096 Jun 13 11:57 __pycache__
-rw-rw-r-- 1 1002 1002 5146 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 4205 Jun  2 01:08 check.py
-rw-r--r-- 1 1002 1002    7 Jun 13 11:57 framework.txt
-rw-rw-r-- 1 1002 1002 1913 Apr 26 04:22 problem.yaml
-rw-rw-r-- 1 1002 1002 4202 Apr 26 04:25 reference.py
-rw-rw-r-- 1 1002 1002  552 Apr 26 04:22 shapes.py
-rw-r--r-- 1 1002 1002 4568 Jun 13 11:57 solution.py
-rw-rw-r-- 1 1002 1002 3734 Apr 26 04:24 sota.py
-rw-r--r-- 1 1002 1002 2165 Jun 13 11:45 tune_tiles.py
tool
stdout (4673 chars) — TRUNCATED
Chunk ID: d0b60c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1142
Output:
from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl


GROUP_SIZE = 128


@triton.jit
def _w4a16_gemm_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    out_ptr,
    M: tl.constexpr,
    N: tl.constexpr,
    K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    acc = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    if BLOCK_K == 256:
        offs_k128 = tl.arange(0, 128)
        for k0 in range(0, K, 256):
            for part in tl.static_range(0, 2):
                k = k0 + part * 128 + offs_k128
                a = tl.load(
                    x_ptr + offs_m[:, None] * K + k[None, :],
                    mask=offs_m[:, None] < M,
                    other=0.0,
                )

                packed = tl.load(
                    wq_ptr + (k[:, None] // 2) * N + offs_n[None, :],
                    mask=offs_n[None, :] < N,
                    other=0,
                )
                q_lo = packed & 0x0F
                q_hi = (packed >> 4) & 0x0F
                q = tl.where((k[:, None] & 1) == 0, q_lo, q_hi).to(tl.float32)

                group = k0 // 128 + part
              

... (3173 more chars)
assistant
The scratch tuner is no longer needed; I’m removing it so the workspace deliverable is centered on `solution.py`.
assistant
apply_patch /workspace/problems/07_w4a16_gemm/tune_tiles.py
diff (72 lines)
--- a//workspace/problems/07_w4a16_gemm/tune_tiles.py
+++ b//workspace/problems/07_w4a16_gemm/tune_tiles.py
@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-import itertools
-import sys
-from pathlib import Path
-
-import torch
-
-ROOT = Path(__file__).resolve().parents[2]
-sys.path.insert(0, str(ROOT))
-
-from src.eval.timing import time_fn  # noqa: E402
-
-import reference  # noqa: E402
-import solution  # noqa: E402
-
-
-def run_shape(shape, configs):
-    device = torch.device("cuda:0")
-    reference.M, reference.N, reference.K = shape
-    ref = reference.Model(*reference.get_init_inputs()).to(device).eval()
-    x = reference.get_inputs()[0].to(device)
-
-    print(f"\nshape M={shape[0]} N={shape[1]} K={shape[2]}")
-    for bm, bn, bk, warps, stages in configs:
-        out = torch.empty((shape[0], shape[1]), device=device, dtype=torch.bfloat16)
-
-        def fn(x):
-            grid = (triton_cdiv(shape[0], bm), triton_cdiv(shape[1], bn))
-            solution._w4a16_gemm_kernel[grid](
-                x,
-                ref.w_q,
-                ref.scales,
-                ref.zeros,
-                out,
-                shape[0],
-                shape[1],
-                shape[2],
-                BLOCK_M=bm,
-                BLOCK_N=bn,
-                BLOCK_K=bk,
-                num_warps=warps,
-                num_stages=stages,
-            )
-            return out
-
-        try:
-            ms = time_fn(fn, [x], iters=20, warmup=5)
-            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {ms:.6f} ms")
-        except Exception as e:
-            print(f"bm={bm:3d} bn={bn:3d} bk={bk:3d} warps={warps} stages={stages}: {type(e).__name__}: {e}")
-
-
-def triton_cdiv(a, b):
-    return (a + b - 1) // b
-
-
-if __name__ == "__main__":
-    # Keep this scratch tuner small enough that compile time is still useful.
-    decode_cfgs = []
-    for bn, warps in itertools.product([32, 64, 128, 256], [4, 8]):
-        decode_cfgs.append((1, bn, 128, warps, 3))
-    run_shape((1, 4096, 4096), decode_cfgs)
-    run_shape((1, 12288, 4096), decode_cfgs)
-
-    prefill_cfgs = []
-    for bm, bn, bk, warps in itertools.product([32, 64, 128], [32, 64, 128], [64, 128], [4, 8]):
-        prefill_cfgs.append((bm, bn, bk, warps, 3))
-    run_shape((256, 12288, 4096), prefill_cfgs)
args
{
  "raw": "<95 chars \u2014 see below>"
}
raw (95 chars)
*** Begin Patch
*** Delete File: /workspace/problems/07_w4a16_gemm/tune_tiles.py
*** End Patch
tool
stdout (148 chars)
output:
  Success. Updated the following files:
  D /workspace/problems/07_w4a16_gemm/tune_tiles.py
metadata:
  exit_code: 0
  duration_seconds: 0.0
assistant
I’m doing a final official correctness run after removing the scratch file.
assistant
exec_command
args
{
  "cmd": "uv run python check.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 12000
}
tool
stdout (107 chars)
Chunk ID: 4bedb3
Wall time: 2.6454 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
Correctness still passes. I’m taking one final benchmark sample for the numbers I’ll report back.
assistant
exec_command
args
{
  "cmd": "uv run python benchmark.py",
  "workdir": "/workspace/problems/07_w4a16_gemm",
  "yield_time_ms": 30000,
  "max_output_tokens": 20000
}
tool
stdout (1731 chars) — TRUNCATED
Chunk ID: 9e7717
Wall time: 2.5293 seconds
Process exited with code 0
Original token count: 407
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:57:58.828990+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:57:59.035618+00:00 elapsed_s=0.207 ms=0.061280
shape=0 variant=solution tflops=1.643 gbps=436.871 ms=0.061
shape=0 solution_peak_fraction=0.2427
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:57:59.377997+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:57:59.386251+00:00 elapsed_s=0.008 ms=0.060672
shape=1 variant=solution tflops=53.092 gbps=457.992 ms=0.061
shape=1 solution_peak_fraction=0.2544
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:57:59.735061+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:57:59.749311+00:00 elapsed_s=0.014 ms=0.158912
shape=2 variant=solution tflops=162.164 gbps=221.049 ms=0.159
shape=2 solution_peak_fraction=0.1228
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T11:57:59.859593+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T11:57:59.901446+00:00 elapsed_s=0.042 ms=0.036576
shape=3 variant=solution tflops=0.917 gbps=244.129 ms=0.037
shape=3 solution_peak_fraction=0.1356
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-13T11:58:00.285395+00:00
benchmark_event event=va

... (231 more chars)
assistant
Implemented `solution.py` with a fused Triton W4A16 GEMM kernel. It unpacks int4 weights, applies per-group bf16 zero/scale dequantization, and feeds the tile directly into `tl.dot`. Verification: - `uv run python check.py` -> `PASS` - `uv run python benchmark.py` -> `RESULT: OK` - Final benchmark geomean `peak_fraction: 0.2011` Final per-shape sample: - shape 0: `0.061 ms`, `436.9 GB/s` - shape 1: `0.061 ms`, `458.0 GB/s` - shape 2: `0.159 ms`, `221.0 GB/s` - shape 3: `0.037 ms`, `244.1 GB/s` - shape 4: `0.055 ms`, `575.0 GB/s`
system
task_complete