cursor / Composer 2.5 Fast session: 44887826-9ede-48b3-b876-5e37f6c2c03d cwd: /workspace/problems/07_w4a16_gemm
harness
cursor
model
Composer 2.5 Fast
turns
64
tools called
50
events
116
input toks
58,000
output toks
26,569
cache hit
1,082,987
duration
1398.8s
solution.py
final answer
benchmark.log
check.log
result.json
view raw solution.py
"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
from __future__ import annotations

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

GROUP_SIZE = 128


# ---------------------------------------------------------------------------
# Decode path: M == 1 split-K GEMV — parallelize over (N-tile, K-group).
# ---------------------------------------------------------------------------
@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 256}, num_warps=4),
        triton.Config({"BLOCK_N": 512}, num_warps=8),
        triton.Config({"BLOCK_N": 128}, num_warps=4),
        triton.Config({"BLOCK_N": 1024}, num_warps=8),
    ],
    key=["N", "K"],
)
@triton.jit
def _w4a16_gemv_splitk_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    partial_ptr,
    N,
    K,
    stride_wqn,
    stride_sg,
    stride_sn,
    stride_pg,
    BLOCK_N: tl.constexpr,
    GROUP_SIZE: tl.constexpr,
    PACKED_PER_GROUP: tl.constexpr,
):
    pid_n = tl.program_id(0)
    pid_g = tl.program_id(1)

    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    n_mask = offs_n < N

    s = tl.load(
        scales_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)
    z = tl.load(
        zeros_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)

    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
    base_k = pid_g * GROUP_SIZE
    base_packed = pid_g * PACKED_PER_GROUP

    for pi in tl.static_range(PACKED_PER_GROUP):
        packed_row = base_packed + pi
        k0 = base_k + 2 * pi

        w_packed = tl.load(
            wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
        ).to(tl.int32)
        w_lo = (w_packed & 0xF).to(tl.float32)
        w_hi = ((w_packed >> 4) & 0xF).to(tl.float32)

        x0 = tl.load(x_ptr + k0).to(tl.float32)
        x1 = tl.load(x_ptr + k0 + 1).to(tl.float32)

        acc += x0 * (w_lo - z) * s + x1 * (w_hi - z) * s

    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)


@triton.jit
def _reduce_partial_kernel(
    partial_ptr,
    out_ptr,
    N,
    n_groups,
    stride_pg,
    BLOCK_N: tl.constexpr,
):
    pid = tl.program_id(0)
    offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)
    n_mask = offs_n < N
    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
    for g in tl.range(0, n_groups):
        acc += tl.load(partial_ptr + g * stride_pg + offs_n, mask=n_mask, other=0.0)
    tl.store(out_ptr + offs_n, acc.to(tl.bfloat16), mask=n_mask)


# ---------------------------------------------------------------------------
# General GEMM path: M > 1 — one tl.dot per quant group (128 K).
# ---------------------------------------------------------------------------
@triton.autotune(
    configs=[
        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_warps=8, num_stages=2),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256}, num_warps=8, num_stages=2),
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 256}, num_warps=8, num_stages=2),
        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
    ],
    key=["M", "N", "K"],
)
@triton.jit
def _w4a16_gemm_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    out_ptr,
    M,
    N,
    K,
    stride_xm,
    stride_xk,
    stride_wqn,
    stride_sg,
    stride_sn,
    stride_om,
    stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    GROUP_SIZE: tl.constexpr,
    PACKED_PER_GROUP: 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)
    m_mask = offs_m < M
    n_mask = offs_n < N

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    n_groups = K // GROUP_SIZE

    for g in tl.range(0, n_groups):
        s = tl.load(
            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
        ).to(tl.bfloat16)
        z = tl.load(
            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
        ).to(tl.bfloat16)

        base_k = g * GROUP_SIZE
        base_packed = g * PACKED_PER_GROUP
        packed_rows = base_packed + tl.arange(0, PACKED_PER_GROUP)
        wq_ptrs = wq_ptr + packed_rows[:, None] * stride_wqn + offs_n[None, :]
        wq_mask = n_mask[None, :]
        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)

        w_lo = (wq_packed & 0xF).to(tl.bfloat16)
        w_hi = ((wq_packed >> 4) & 0xF).to(tl.bfloat16)
        w_deq_lo = (w_lo - z[None, :]) * s[None, :]
        w_deq_hi = (w_hi - z[None, :]) * s[None, :]

        offs_k_even = base_k + 2 * tl.arange(0, PACKED_PER_GROUP)
        offs_k_odd = offs_k_even + 1
        x_even_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_even[None, :] * stride_xk
        x_odd_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_odd[None, :] * stride_xk
        k_even_mask = offs_k_even[None, :] < K
        k_odd_mask = offs_k_odd[None, :] < K
        x_even = tl.load(x_even_ptrs, mask=m_mask[:, None] & k_even_mask, other=0.0).to(
            tl.bfloat16
        )
        x_odd = tl.load(x_odd_ptrs, mask=m_mask[:, None] & k_odd_mask, other=0.0).to(
            tl.bfloat16
        )

        acc += tl.dot(x_even, w_deq_lo, out_dtype=tl.float32)
        acc += tl.dot(x_odd, w_deq_hi, out_dtype=tl.float32)

    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
    out_mask = m_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


def w4a16_gemm(
    x: torch.Tensor,
    w_q: torch.Tensor,
    scales: torch.Tensor,
    zeros: torch.Tensor,
    group_size: int = GROUP_SIZE,
) -> torch.Tensor:
    """x: (M,K) bf16; w_q: (K//2,N) uint8; scales/zeros: (K//group,N) bf16 -> (M,N) bf16."""
    assert x.is_contiguous()
    assert w_q.is_contiguous()
    assert scales.is_contiguous()
    assert zeros.is_contiguous()
    M, K = x.shape
    Kh, N = w_q.shape
    assert Kh * 2 == K
    assert scales.shape == zeros.shape
    assert scales.shape[0] == K // group_size
    assert scales.shape[1] == N

    if M == 1:
        n_groups = K // group_size
        packed_per_group = group_size // 2
        partial = torch.empty((n_groups, N), dtype=torch.float32, device=x.device)
        grid = lambda meta: (
            triton.cdiv(N, meta["BLOCK_N"]),
            n_groups,
        )
        _w4a16_gemv_splitk_kernel[grid](
            x,
            w_q,
            scales,
            zeros,
            partial,
            N,
            K,
            w_q.stride(0),
            scales.stride(0),
            scales.stride(1),
            partial.stride(0),
            GROUP_SIZE=group_size,
            PACKED_PER_GROUP=packed_per_group,
        )
        out = torch.empty((1, N), dtype=torch.bfloat16, device=x.device)
        reduce_grid = lambda meta: (triton.cdiv(N, meta["BLOCK_N"]),)
        _reduce_partial_kernel[reduce_grid](
            partial,
            out,
            N,
            n_groups,
            partial.stride(0),
            BLOCK_N=256,
        )
        return out
    else:
        out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
        packed_per_group = group_size // 2
        grid = lambda meta: (
            triton.cdiv(M, meta["BLOCK_M"]),
            triton.cdiv(N, meta["BLOCK_N"]),
        )
        _w4a16_gemm_kernel[grid](
            x,
            w_q,
            scales,
            zeros,
            out,
            M,
            N,
            K,
            x.stride(0),
            x.stride(1),
            w_q.stride(0),
            scales.stride(0),
            scales.stride(1),
            out.stride(0),
            out.stride(1),
            GROUP_SIZE=group_size,
            PACKED_PER_GROUP=packed_per_group,
        )
        return out


class Model(nn.Module):
    """W4A16 GEMM: y = x @ dequant(w_q, scales, zeros)."""

    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
        super().__init__()
        assert K % group_size == 0
        assert K % 2 == 0
        self.M, self.N, self.K = M, N, K
        self.group_size = group_size
        n_groups = K // group_size

        torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
        w_full = torch.randn(K, N, dtype=torch.float32) * 0.02

        w_g = w_full.view(n_groups, group_size, N)
        w_min = w_g.min(dim=1, keepdim=True).values
        w_max = w_g.max(dim=1, keepdim=True).values
        scales = (w_max - w_min).clamp_min(1e-8) / 15.0
        zeros = (-w_min / scales).round().clamp(0, 15)
        w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8).view(K, N)

        lo = w_q[0::2] & 0xF
        hi = w_q[1::2] & 0xF
        w_packed = (lo | (hi << 4)).contiguous()

        self.register_buffer("w_q", w_packed)
        self.register_buffer("scales", scales.squeeze(1).to(torch.bfloat16))
        self.register_buffer("zeros", zeros.squeeze(1).to(torch.bfloat16))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return w4a16_gemm(x, self.w_q, self.scales, self.zeros, self.group_size)


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]
I'll start by reading the reference implementation and workspace layout, then build a fused W4A16 GEMM kernel and iterate with `check.py` and `benchmark.py`. Implementing a fused Triton W4A16 GEMM kernel and validating correctness first. Autotune configs exceeded shared memory — reducing block sizes and pipeline stages. Correctness passes. Running the benchmark to measure performance. Optimizing with a specialized M=1 GEMV kernel and group-aware K tiling so scales/zeros load once per 128-element group. The specialized GEMV regressed decode performance. Switching to a split-K GEMV for M=1 to parallelize the K reduction. Atomic adds are producing wrong results. Switching to a partial buffer and a final reduction. Geomean is 0.1243 (passes). Pushing M>1 performance and profiling further. Implementing a group-aware GEMM that uses `tl.dot` per 128-element group to improve M>1 performance. Fixing the group GEMM to use even/odd K splits instead of expensive `tl.where` scatter. Vectorizing packed-weight loads across the full group. Geomean is 0.1449. Tuning large-M configs to improve shape 2. Adding a fused reduction kernel for the M=1 path. Implemented a fused W4A16 GEMM in `solution.py` using Triton. `python check.py` prints **PASS**, and benchmark geomean peak_fraction is **0.1497** (above the 0.1 bar). ## Approach Two kernels, both fusing int4 unpack + asymmetric dequant + matmul in one pass: ### 1. Decode path (`M == 1`) — split-K GEMV - Grid: `(N-tile, K-group)` — 32 groups × ~48 N-tiles for the main decode shape - Each block handles one quant group (128 K) for a slice of N - Unpacks two int4 values per packed byte (low nibble = even K, high = odd K) - Loads scales/zeros **once per group** (not per K index) - Writes per-group partial sums, then a small Triton reduction kernel sums over groups ### 2. Prefill path (`M > 1`) — group-aware GEMM - Iterates over quant groups; each group does two `tl.dot` calls (even/odd K lanes) - Vectorized packed-weight load: all 64 packed rows per group in one shot - Uses bf16 tensor cores via `tl.dot`, fp32 accumulation - Autotuned over `BLOCK_M` / `BLOCK_N` tile sizes ## Performance (vs naive reference) | Shape | M | N | GB/s | Peak fraction | |-------|---|---|------|---------------| | decode | 1 | 12288 | **620** | 34.5% | | small prefill | 32 | 12288 | 255 | 14.2% | | large prefill | 256 | 12288 | 115 | 6.4% | | decode square | 1 | 4096 | 267 | 14.8% | | spec-decode | 16 | 14336 | 293 | 16.3% | Decode hits ~620 GB/s (~34% of 1.8 TB/s peak) — roughly **18× faster** than the naive unpack-then-matmul reference on the primary decode shape. The M=256 case is the weak point under the memory roofline metric (same byte count regardless of M), but the geomean clears the threshold comfortably.
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/20260615_123336_cursor_composer-2.5-fast_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=2026-06-15T18:57:14.630788+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-15T18:57:15.212475+00:00 elapsed_s=0.582 ms=0.042656
shape=0 variant=solution tflops=2.360 gbps=627.613 ms=0.043
shape=0 solution_peak_fraction=0.3487
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-15T18:57:15.850310+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-15T18:57:16.890642+00:00 elapsed_s=1.040 ms=0.108672
shape=1 variant=solution tflops=29.642 gbps=255.698 ms=0.109
shape=1 solution_peak_fraction=0.1421
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-15T18:57:17.531937+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-15T18:57:18.650366+00:00 elapsed_s=1.118 ms=0.304448
shape=2 variant=solution tflops=84.644 gbps=115.380 ms=0.304
shape=2 solution_peak_fraction=0.0641
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-15T18:57:18.866714+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-15T18:57:19.285467+00:00 elapsed_s=0.419 ms=0.032896
shape=3 variant=solution tflops=1.020 gbps=271.440 ms=0.033
shape=3 solution_peak_fraction=0.1508
benchmark_event event=variant_start shape=4 variant=solution ts=2026-06-15T18:57:20.047627+00:00
benchmark_event event=variant_end shape=4 variant=solution ts=2026-06-15T18:57:21.072748+00:00 elapsed_s=1.025 ms=0.107936
shape=4 variant=solution tflops=17.409 gbps=294.480 ms=0.108
shape=4 solution_peak_fraction=0.1636
peak_fraction: 0.1509
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/20260615_123336_cursor_composer-2.5-fast_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/20260615_123336_cursor_composer-2.5-fast_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/20260615_123336_cursor_composer-2.5-fast_07_w4a16_gemm/repo/.venv
Creating virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260615_123336_cursor_composer-2.5-fast_07_w4a16_gemm/repo/.venv
   Building kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260615_123336_cursor_composer-2.5-fast_07_w4a16_gemm/repo
      Built kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260615_123336_cursor_composer-2.5-fast_07_w4a16_gemm/repo
Installed 56 packages in 66ms
PASS

{
    "run_id": "20260615_123336_cursor_composer-2.5-fast_07_w4a16_gemm",
    "run_group": "",
    "problem": "07_w4a16_gemm",
    "harness": "cursor",
    "model": "composer-2.5-fast",
    "reasoning_effort": "",
    "started_at": "2026-06-15T12:33:36-06:00",
    "harness_finished_at": "2026-06-15T12:56:58-06:00",
    "finished_at": "2026-06-15T12:57:21-06:00",
    "start_epoch": 1781548416,
    "harness_end_epoch": 1781549818,
    "end_epoch": 1781549841,
    "has_solution": true,
    "correct": true,
    "failure_reason": "pass",
    "retryable_infra_failure": false,
    "minimum_useful_output_tokens": 5000,
    "peak_fraction": 0.1509,
    "template_mutated": false,
    "elapsed_seconds": 1402,
    "total_elapsed_seconds": 1425,
    "check_elapsed_seconds": 15,
    "benchmark_elapsed_seconds": 8,
    "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": 18.950784593437945,
    "usage": {"input_tokens": 58000, "output_tokens": 26569, "cache_read_tokens": 1082987, "cache_creation_tokens": 0, "reasoning_tokens": null, "total_cost_usd": null}
}

timeline (116 events)

system — init
session start cwd=/workspace/problems/07_w4a16_gemm model=Composer 2.5 Fast permissions=default
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 start by reading the reference implementation and workspace layout, then build a fused W4A16 GEMM kernel and iterate with `check.py` and `benchmark.py`.
assistant
glob
args
{
  "targetDirectory": "/workspace/problems/07_w4a16_gemm",
  "globPattern": "**/*"
}
assistant
read /workspace/problems/07_w4a16_gemm/reference.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/reference.py"
}
assistant
read /workspace/problems/07_w4a16_gemm/check.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/check.py"
}
assistant
read /workspace/problems/07_w4a16_gemm/benchmark.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/benchmark.py"
}
tool
stdout (4202 chars) — TRUNCATED
"""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 [0,15]."""
    Kh, N = w_packed.shape
    assert Kh * 2 == K
    out = torch.empty((K, N), dtype=torch.uint

... (2702 more chars)
tool
stdout (4203 chars) — TRUNCATED
"""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 --------------------------------------------
    all_shapes = shapes.SHAPES
    for shape_idx, shape in enumerate(all_shapes):
  

... (2703 more chars)
tool
stdout (5146 chars) — TRUNCATED
"""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 = int(meta.get("num_perf_trials", 30))

    device = torch.device("cuda:0")
    include_baselines = benchmark

... (3646 more chars)
tool
stdout (4 chars)
null
assistant
read /workspace/problems/07_w4a16_gemm/shapes.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/shapes.py"
}
assistant
read /workspace/problems/07_w4a16_gemm/problem.yaml
args
{
  "path": "/workspace/problems/07_w4a16_gemm/problem.yaml"
}
assistant
read /workspace/problems/07_w4a16_gemm/sota.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/sota.py"
}
assistant
glob
args
{
  "targetDirectory": "/workspace/problems/07_w4a16_gemm",
  "globPattern": "solution.py"
}
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python -c \"import torch; print(torch.__version__); print(torch.cuda.get_device_name(0)); print(torch.cuda.get_device_capability(0)); print(torch.cuda.get_device_properties(0))\"",
  "workingDirectory": "",
  "timeout": 30000,
  "toolCallId": "tool_41ce07eb-c154-433e-a97a-27bf78f5264",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": false,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
        

... (1273 more chars)
tool
stdout (552 chars)
"""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 (1913 chars) — TRUNCATED
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
    does not target SM120. bitsandbytes 0.49.2 *does* run on SM120 -- it
    autotunes its CUDA kernels f

... (413 more chars)
tool
stdout (3734 chars) — TRUNCATED
"""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_available() -> bool:
    global _BNB_OK
    if _BNB_OK is not None:
        return _BNB_OK
    try:
        imp

... (2234 more chars)
tool
stdout (4 chars)
null
tool
stdout (4 chars)
null
assistant
await
args
{
  "taskId": "68350",
  "blockUntilMs": 60000
}
assistant
grep /workspace
args
{
  "pattern": "cutlass|triton",
  "path": "/workspace",
  "glob": "**/pyproject.toml",
  "caseInsensitive": false,
  "multiline": false,
  "toolCallId": "tool_2f163c5e-9350-4e09-974d-3cab9759160",
  "offset": 0
}
assistant
glob
args
{
  "targetDirectory": "/workspace",
  "globPattern": "**/*"
}
tool
stdout (4 chars)
null
tool
stdout (4 chars)
null
tool
stdout (4 chars)
null
assistant
read /home/agent/.cursor/projects/workspace-problems-07-w4a16-gemm/terminals/68350.txt
args
{
  "path": "/home/agent/.cursor/projects/workspace-problems-07-w4a16-gemm/terminals/68350.txt"
}
assistant
read /workspace/pyproject.toml
args
{
  "path": "/workspace/pyproject.toml"
}
assistant
read /workspace/src/hardware/rtx_pro_6000.py
args
{
  "path": "/workspace/src/hardware/rtx_pro_6000.py"
}
tool
stdout (358 chars)
---
pid: 367
cwd: "/workspace/problems/07_w4a16_gemm"
command: "cd /workspace/problems/07_w4a16_gemm && uv run python -c \"import torch; print(torch.__version__); print(torch.cuda.get_device_name(0)); print(torch.cuda.get_device_capability(0)); print(torch.cuda.get_device_properties(0))\""
started_at: 2026-06-15T18:33:44.517Z
running_for_ms: 90005    
---
tool
stdout (756 chars)
[project]
name = "kernelbench-hard"
version = "0.1.0"
description = "Surgical GPU kernel benchmark. Small deck, frontier agents, roofline metric, link-don't-spoil problem briefs."
requires-python = ">=3.11"
dependencies = [
    "torch>=2.11",
    "numpy>=1.26",
    "pyyaml>=6.0",
    "rich>=13.7",
    "matplotlib>=3.9",
    "pytest>=8.0",
    "ruff>=0.6",
    "einops>=0.8.2",
    "requests<2.33",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project.scripts]
kbh = "src.kbh.cli:main"

[tool.hatch.build.targets.wheel]
packages = ["src"]

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "W", "I", "B", "UP"]
ignore = ["E501"]

[tool.pytest.ini_options]
testpaths = ["tests"]
tool
stdout (1420 chars)
"""RTX PRO 6000 Blackwell Workstation — SM120, consumer-lineage Blackwell.

Peak tensor-core throughputs are dense-matrix advertised peaks. Actual kernels
will see 60-85% of peak on well-tuned code.
"""
from dataclasses import dataclass


@dataclass(frozen=True)
class HardwareTarget:
    name: str
    sm: str
    vram_gb: int
    peak_bandwidth_gb_s: float  # DRAM
    peak_tflops_dense: dict[str, float]  # dtype -> TFLOPS


RTX_PRO_6000 = HardwareTarget(
    name="RTX PRO 6000 Blackwell Workstation",
    sm="sm_120a",
    vram_gb=96,
    peak_bandwidth_gb_s=1800.0,
    peak_tflops_dense={
        # Blackwell GB202 dense tensor peaks. Derived from NVIDIA's headline
        # 4000 fp4-sparse AI TOPS -> fp4 dense 2000 -> fp8 1000 -> bf16 500
        # (each precision step = 2x; sparse = 2x dense). Verified empirically:
        # cuBLAS hits fp8 773 / bf16 412 TFLOPS on 4096^3 (~77-82% of these,
        # normal cuBLAS efficiency). The prior table was ~2.5x too low, which
        # produced peak_fraction > 1.0 for real fp8 kernels. fp32 is the 125
        # TFLOPS SIMT figure from the spec sheet.
        "fp4": 2000.0,
        "nvfp4": 2000.0,
        "mxfp4": 2000.0,
        "fp6": 1000.0,
        "fp8": 1000.0,
        "bf16": 500.0,
        "fp16": 500.0,
        "tf32": 250.0,
        "fp32": 125.0,  # non-tensor-core SIMT fp32 (spec sheet)
        "int8": 1000.0,
        "int4": 2000.0,
    },
)
assistant
await
args
{
  "taskId": "68350",
  "blockUntilMs": 120000
}
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python -c \"import triton; print('triton', triton.__version__)\" 2>&1 | head -5",
  "workingDirectory": "",
  "timeout": 120000,
  "toolCallId": "tool_af5c3a14-ca25-45e7-bce0-15d04b9d0c2",
  "simpleCommands": [
    "cd",
    "uv",
    "head"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          

... (1327 more chars)
tool
stdout (4 chars)
null
tool
stdout (4 chars)
null
assistant
read /home/agent/.cursor/projects/workspace-problems-07-w4a16-gemm/terminals/68350.txt
args
{
  "path": "/home/agent/.cursor/projects/workspace-problems-07-w4a16-gemm/terminals/68350.txt"
}
assistant
shell
args
{
  "command": "<239 chars \u2014 see below>",
  "workingDirectory": "",
  "timeout": 120000,
  "toolCallId": "tool_45605db8-a7b2-459e-8881-57f06df523f",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": false,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /workspace/problems/07_w4a16_gemm"
      },
      {
        "name": "uv"

... (1114 more chars)
command (239 chars)
cd /workspace/problems/07_w4a16_gemm && uv run python -c "
import torch
print('cuda', torch.cuda.is_available())
if torch.cuda.is_available():
    print(torch.cuda.get_device_name(0))
    print('cap', torch.cuda.get_device_capability(0))
"
tool
stdout (1292 chars)
---
pid: 367
cwd: "/workspace/problems/07_w4a16_gemm"
command: "cd /workspace/problems/07_w4a16_gemm && uv run python -c \"import torch; print(torch.__version__); print(torch.cuda.get_device_name(0)); print(torch.cuda.get_device_capability(0)); print(torch.cuda.get_device_properties(0))\""
started_at: 2026-06-15T18:33:44.517Z
running_for_ms: 120006   
---
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 883ms
2.11.0+cu130
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
(12, 0)
_CudaDeviceProperties(name='NVIDIA RTX PRO 6000 Blackwell Workstation Edition', major=12, minor=0, total_memory=97288MB, multi_processor_count=188, uuid=d594fb7a-abcb-bad5-1fc4-675b478a47df, pci_bus_id=1, pci_device_id=0, pci_domain_id=0, L2_cache_size=128MB)

---
exit_code: 0
elapsed_ms: 121703
ended_at: 2026-06-15T18:35:46.220Z
---
tool
stdout (4 chars)
null
assistant
read /workspace/src/eval/numeric_stress.py
args
{
  "path": "/workspace/src/eval/numeric_stress.py"
}
assistant
read /workspace/src/eval/correctness.py
args
{
  "path": "/workspace/src/eval/correctness.py"
}
tool
stdout (6468 chars) — TRUNCATED
"""Numeric distribution stress cases for correctness checks.

These cases are not hidden shapes. They rerun the same shape/seed validation
under a few scale regimes that catch kernels that only work for the nominal
N(0, 1)-ish inputs.
"""
from __future__ import annotations

import os
from collections.abc import Iterator, Mapping, Sequence
from contextlib import contextmanager
from dataclasses import dataclass, field

import torch

ToleranceOverride = Mapping[str, float | Mapping[str, float]]


@dataclass(frozen=True)
class NumericStressCase:
    name: str
    input_scales: Mapping[int, float] = field(default_factory=dict)
    state_scales: Mapping[str, float] = field(default_factory=dict)
    tolerance: ToleranceOverride | None = None


NOMINAL_CASE = NumericStressCase("nominal")

_SMALL_BF16 = {"bfloat16": {"atol": 1e-4, "rtol": 5e-2}}
_MED_BF16 = {"bfloat16": {"atol": 5e-4, "rtol": 5e-2}}
_LARGE_BF16 = {"bfloat16": {"atol": 2e-1, "rtol": 5e-2}}
_TINY_FP32 = {"float32": {"atol": 1e-7, "rtol": 1e-4}}
_LARGE_FP32 = {"float32": {"atol": 1e-1, "rtol": 1e-4}}

_CASES: dict[str, tuple[NumericStressCase, ...]] = {
    "01_fp8_gemm": (
        # fp8 x fp8 has accumulation-order noise vs the bf16-matmul reference that
        # scales with input magnitude; atols are calibrated to absorb it (measured
        # fp8-MMA residual x ~1.5 margin) while rtol=5e-2 still catches gross error.
        NumericStressCase("small_input", input_scales={0: 1e-3},
                          tolerance={

... (4968 more chars)
tool
stdout (4072 chars) — TRUNCATED
"""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 (PyYAML quirks):
    # "torch.bfloat16", "bfloat16", torch.bfloat16
    if override is not None:
        type_

... (2572 more chars)
assistant
Implementing a fused Triton W4A16 GEMM kernel and validating correctness first.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (182 lines)
--- /dev/null
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -0,0 +1,179 @@
+"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+GROUP_SIZE = 128
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 128}, num_warps=4, num_stages=3),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=3),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 512, "BLOCK_K": 64}, num_warps=8, num_stages=3),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=3),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=4),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=4),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=4),
+        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=4),
+    ],
+    key=["M", "N", "K"],
+)
+@triton.jit
+def _w4a16_gemm_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    M,
+    N,
+    K,
+    stride_xm,
+    stride_xk,
+    stride_wq_kh,
+    stride_wqn,
+    stride_sg,
+    stride_sn,
+    stride_om,
+    stride_on,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    GROUP_SIZE: 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), dtype=tl.float32)
+
+    for k0 in range(0, K, BLOCK_K):
+        offs_k = k0 + tl.arange(0, BLOCK_K)
+        k_mask = offs_k < K
+
+        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
+        x_mask = (offs_m[:, None] < M) & k_mask[None, :]
+        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
+
+        packed_k = offs_k // 2
+        shift = (offs_k % 2) * 4
+
+        wq_ptrs = wq_ptr + packed_k[:, None] * stride_wq_kh + offs_n[None, :] * stride_wqn
+        wq_mask = k_mask[:, None] & (offs_n[None, :] < N)
+        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
+
+        w_int = (wq_packed >> shift[:, None]) & 0xF
+        w_bf = w_int.to(tl.bfloat16)
+
+        g_idx = offs_k // GROUP_SIZE
+        s_ptrs = scales_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
+        z_ptrs = zeros_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
+        s = tl.load(s_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
+        z = tl.load(z_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
+
+        w_deq = (w_bf - z) * s
+        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
+
+    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
+    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)
+
+
+def w4a16_gemm(
+    x: torch.Tensor,
+    w_q: torch.Tensor,
+    scales: torch.Tensor,
+    zeros: torch.Tensor,
+    group_size: int = GROUP_SIZE,
+) -> torch.Tensor:
+    """x: (M,K) bf16; w_q: (K//2,N) uint8; scales/zeros: (K//group,N) bf16 -> (M,N) bf16."""
+    assert x.is_contiguous()
+    assert w_q.is_contiguous()
+    assert scales.is_contiguous()
+    assert zeros.is_contiguous()
+    M, K = x.shape
+    Kh, N = w_q.shape
+    assert Kh * 2 == K
+    assert scales.shape == zeros.shape
+    assert scales.shape[0] == K // group_size
+    assert scales.shape[1] == N
+
+    out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
+
+    grid = lambda meta: (
+        triton.cdiv(M, meta["BLOCK_M"]),
+        triton.cdiv(N, meta["BLOCK_N"]),
+    )
+    _w4a16_gemm_kernel[grid](
+        x,
+        w_q,
+        scales,
+        zeros,
+        out,
+        M,
+        N,
+        K,
+        x.stride(0),
+        x.stride(1),
+        w_q.stride(0),
+        w_q.stride(1),
+        scales.stride(0),
+        scales.stride(1),
+        out.stride(0),
+        out.stride(1),
+        GROUP_SIZE=group_size,
+    )
+    return out
+
+
+class Model(nn.Module):
+    """W4A16 GEMM: y = x @ dequant(w_q, scales, zeros)."""
+
+    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
+        super().__init__()
+        assert K % group_size == 0
+        assert K % 2 == 0
+        self.M, self.N, self.K = M, N, K
+        self.group_size = group_size
+        n_groups = K // group_size
+
+        torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
+        w_full = torch.randn(K, N, dtype=torch.float32) * 0.02
+
+        w_g = w_full.view(n_groups, group_size, N)
+        w_min = w_g.min(dim=1, keepdim=True).values
+        w_max = w_g.max(dim=1, keepdim=True).values
+        scales = (w_max - w_min).clamp_min(1e-8) / 15.0
+        zeros = (-w_min / scales).round().clamp(0, 15)
+        w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8).view(K, N)
+
+        lo = w_q[0::2] & 0xF
+        hi = w_q[1::2] & 0xF
+        w_packed = (lo | (hi << 4)).contiguous()
+
+        self.register_buffer("w_q", w_packed)
+        self.register_buffer("scales", scales.squeeze(1).to(torch.bfloat16))
+        self.register_buffer("zeros", zeros.squeeze(1).to(torch.bfloat16))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return w4a16_gemm(x, self.w_q, self.scales, self.zeros, self.group_size)
+
+
+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
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<5570 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_f3f5b42c-8822-44b7-8e50-17f9d893745",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /workspace/problems/07_w4a16_gemm"

... (905 more chars)
tool
stdout (5081 chars) — TRUNCATED
failure:
  command: 'cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1'
  workingDirectory: ''
  exitCode: 1
  signal: ''
  stdout:
    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 165, in forward
        return w4a16_gemm(x, self.w_q, self.scales, self.zeros, self.group_size)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/workspace/problems/07_w4a16_gemm/solution.py", line 113, in w4a16_gemm
        _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)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      

... (3581 more chars)
assistant
Autotune configs exceeded shared memory — reducing block sizes and pipeline stages.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (190 lines — TRUNCATED)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,179 +1,13 @@
-"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import triton
-import triton.language as tl
-
-GROUP_SIZE = 128
-
-
 @triton.autotune(
     configs=[
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 128}, num_warps=4, num_stages=3),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=3),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 512, "BLOCK_K": 64}, num_warps=8, num_stages=3),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=3),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=4),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=4),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=4),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=4),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 64}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 32}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32}, num_warps=8, num_stages=2),
     ],
     key=["M", "N", "K"],
-)
-@triton.jit
-def _w4a16_gemm_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    out_ptr,
-    M,
-    N,
-    K,
-    stride_xm,
-    stride_xk,
-    stride_wq_kh,
-    stride_wqn,
-    stride_sg,
-    stride_sn,
-    stride_om,
-    stride_on,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    GROUP_SIZE: 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), dtype=tl.float32)
-
-    for k0 in range(0, K, BLOCK_K):
-        offs_k = k0 + tl.arange(0, BLOCK_K)
-        k_mask = offs_k < K
-
-        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
-        x_mask = (offs_m[:, None] < M) & k_mask[None, :]
-        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
-
-        packed_k = offs_k // 2
-        shift = (offs_k % 2) * 4
-
-        wq_ptrs = wq_ptr + packed_k[:, None] * stride_wq_kh + offs_n[None, :] * stride_wqn
-        wq_mask = k_mask[:, None] & (offs_n[None, :] < N)
-        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
-
-        w_int = (wq_packed >> shift[:, None]) & 0xF
-        w_bf = w_int.to(tl.bfloat16)
-
-        g_idx = offs_k // GROUP_SIZE
-        s_ptrs = scales_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
-        z_ptrs = zeros_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
-        s = tl.load(s_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
-        z = tl.load(z_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
-
-        w_deq = (w_bf - z) * s
-        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
-
-    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
-    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
-    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)
-
-
-def w4a16_gemm(
-    x: torch.Tensor,
-    w_q: torch.Tensor,
-    scales: torch.Tensor,
-    zeros: torch.Tensor,
-    group_size: int = GROUP_SIZE,
-) -> torch.Tensor:
-    """x: (M,K) bf16; w_q: (K//2,N) uint8; scales/zeros: (K//group,N) bf16 -> (M,N) bf16."""
-    assert x.is_contiguous()
-    assert w_q.is_contiguous()
-    assert scales.is_contiguous()
-    assert zeros.is_contiguous()
-    M, K = x.shape
-    Kh, N = w_q.shape
-    assert Kh * 2 == K
-    assert scales.shape == zeros.shape
-    assert scales.shape[0] == K // group_size
-    assert scales.shape[1] == N
-
-    out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
-
-    grid = lambda meta: (
-        triton.cdiv(M, meta["BLOCK_M"]),
-        triton.cdiv(N, meta["BLOCK_N"]),
-    )
-    _w4a16_gemm_kernel[grid](
-        x,
-        w_q,
-        scales,
-        zeros,
-        out,
-        M,
-        N,
-        K,
-        x.stride(0),
-        x.stride(1),
-        w_q.stride(0),
-        w_q.stride(1),
-        scales.stride(0),
-        scales.stride(1),
-        out.stride(0),
-        out.stride(1),
-        GROUP_SIZE=group_size,
-    )
-    return out
-
-
-class Model(nn.Module):
-    """W4A16 GEMM: y = x @ dequant(w_q, scales, zeros)."""
-
-    def __init__(self, M: int, N: int, K: int, group_size: int = GROUP_SIZE):
-        super().__init__()
-        assert K % group_size == 0
-        assert K % 2 == 0
-        self.M, self.N, self.K = M, N, K
-        self.group_size = group_size
-        n_groups = K // group_size
-
-        torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
-        w_full = torch.randn(K, N, dtype=torch.float32) * 0.02
-
-        w_g = w_full.view(n_groups, group_size, N)
-        w_min = w_g.min(dim=1, keepdim=True).values
-        w_max = w_g.max(dim=1, keepdim=True).values
-        scales = (w_max - w_min).clamp_min(1e-8) / 15.0
-        zeros = (-w_min / scales).round().clamp(0, 15)
-        w_q = ((w_g / scales) + zeros).round().clamp(0,

... (660 more chars)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<843 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_4ad90896-ffb3-48e8-ab42-abd8832e2f6",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /workspace/problems/07_w4a16_gemm"

... (899 more chars)
tool
stdout (4 chars)
null
assistant
Correctness passes. Running the benchmark to measure performance.
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_a8d611f9-08c6-440d-9973-aaaa42e07df",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /workspace/problems/07_w4a16_g

... (909 more chars)
tool
stdout (4 chars)
null
assistant
Optimizing with a specialized M=1 GEMV kernel and group-aware K tiling so scales/zeros load once per 128-element group.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (276 lines — TRUNCATED)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,13 +1,265 @@
+"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+GROUP_SIZE = 128
+
+
+# ---------------------------------------------------------------------------
+# Decode path: M == 1 GEMV — one program per N-tile, K-reduction fused with dequant.
+# ---------------------------------------------------------------------------
 @triton.autotune(
     configs=[
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 64}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 32}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_N": 256}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_N": 512}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_N": 1024}, num_warps=8, num_stages=1),
+    ],
+    key=["N", "K"],
+)
+@triton.jit
+def _w4a16_gemv_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    N,
+    K,
+    stride_wqn,
+    stride_sg,
+    stride_sn,
+    stride_on,
+    BLOCK_N: tl.constexpr,
+    GROUP_SIZE: tl.constexpr,
+    PACKED_PER_GROUP: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)
+    n_mask = offs_n < N
+
+    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
+    n_groups = K // GROUP_SIZE
+
+    for g in tl.range(0, n_groups):
+        s = tl.load(
+            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.float32)
+        z = tl.load(
+            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.float32)
+
+        base_k = g * GROUP_SIZE
+        base_packed = g * PACKED_PER_GROUP
+
+        for pi in tl.static_range(PACKED_PER_GROUP):
+            packed_row = base_packed + pi
+            k0 = base_k + 2 * pi
+
+            w_packed = tl.load(
+                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
+            ).to(tl.int32)
+            w_lo = (w_packed & 0xF).to(tl.float32)
+            w_hi = ((w_packed >> 4) & 0xF).to(tl.float32)
+
+            x0 = tl.load(x_ptr + k0).to(tl.float32)
+            x1 = tl.load(x_ptr + k0 + 1).to(tl.float32)
+
+            acc += x0 * (w_lo - z) * s + x1 * (w_hi - z) * s
+
+    tl.store(out_ptr + offs_n, acc.to(tl.bfloat16), mask=n_mask)
+
+
+# ---------------------------------------------------------------------------
+# General GEMM path: M > 1
+# ---------------------------------------------------------------------------
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=8, num_stages=2),
     ],
     key=["M", "N", "K"],
-)+)
+@triton.jit
+def _w4a16_gemm_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    M,
+    N,
+    K,
+    stride_xm,
+    stride_xk,
+    stride_wq_kh,
+    stride_wqn,
+    stride_sg,
+    stride_sn,
+    stride_om,
+    stride_on,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    GROUP_SIZE: 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), dtype=tl.float32)
+
+    for k0 in range(0, K, BLOCK_K):
+        offs_k = k0 + tl.arange(0, BLOCK_K)
+        k_mask = offs_k < K
+
+        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
+        x_mask = (offs_m[:, None] < M) & k_mask[None, :]
+        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
+
+        packed_k = offs_k // 2
+        shift = (offs_k % 2) * 4
+
+        wq_ptrs = wq_ptr + packed_k[:, None] * stride_wq_kh + offs_n[None, :] * stride_wqn
+        wq_mask = k_mask[:, None] & (offs_n[None, :] < N)
+        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
+
+        w_int = (wq_packed >> shift[:, None]) & 0xF
+        w_bf = w_int.to(tl.bfloat16)
+
+        g_idx = offs_k // GROUP_SIZE
+        s_ptrs = scales_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
+        z_ptrs = zeros_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
+        s = tl.load(s_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
+        z = tl.load(z_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
+
+        w_deq = (w_bf - z) * s
+        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
+
+    out_ptrs = out_ptr + offs_m[:, None] * str

... (3432 more chars)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<8256 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_9dbf0708-e038-405c-89cb-16c59ac9aef",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1490 more chars)
tool
stdout (4 chars)
null
assistant
The specialized GEMV regressed decode performance. Switching to a split-K GEMV for M=1 to parallelize the K reduction.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (163 lines — TRUNCATED)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -10,19 +10,18 @@
 
 
 # ---------------------------------------------------------------------------
-# Decode path: M == 1 GEMV — one program per N-tile, K-reduction fused with dequant.
+# Decode path: M == 1 split-K GEMV — parallelize over (N-tile, K-group).
 # ---------------------------------------------------------------------------
 @triton.autotune(
     configs=[
-        triton.Config({"BLOCK_N": 256}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_N": 512}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_N": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_N": 1024}, num_warps=8, num_stages=1),
+        triton.Config({"BLOCK_N": 256}, num_warps=4),
+        triton.Config({"BLOCK_N": 128}, num_warps=4),
+        triton.Config({"BLOCK_N": 512}, num_warps=8),
     ],
     key=["N", "K"],
 )
 @triton.jit
-def _w4a16_gemv_kernel(
+def _w4a16_gemv_splitk_kernel(
     x_ptr,
     wq_ptr,
     scales_ptr,
@@ -33,45 +32,43 @@
     stride_wqn,
     stride_sg,
     stride_sn,
-    stride_on,
     BLOCK_N: tl.constexpr,
     GROUP_SIZE: tl.constexpr,
     PACKED_PER_GROUP: tl.constexpr,
 ):
-    pid = tl.program_id(0)
-    offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)
+    pid_n = tl.program_id(0)
+    pid_g = tl.program_id(1)
+
+    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
     n_mask = offs_n < N
 
+    s = tl.load(
+        scales_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+    ).to(tl.float32)
+    z = tl.load(
+        zeros_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+    ).to(tl.float32)
+
     acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
-    n_groups = K // GROUP_SIZE
-
-    for g in tl.range(0, n_groups):
-        s = tl.load(
-            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.float32)
-        z = tl.load(
-            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.float32)
-
-        base_k = g * GROUP_SIZE
-        base_packed = g * PACKED_PER_GROUP
-
-        for pi in tl.static_range(PACKED_PER_GROUP):
-            packed_row = base_packed + pi
-            k0 = base_k + 2 * pi
-
-            w_packed = tl.load(
-                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
-            ).to(tl.int32)
-            w_lo = (w_packed & 0xF).to(tl.float32)
-            w_hi = ((w_packed >> 4) & 0xF).to(tl.float32)
-
-            x0 = tl.load(x_ptr + k0).to(tl.float32)
-            x1 = tl.load(x_ptr + k0 + 1).to(tl.float32)
-
-            acc += x0 * (w_lo - z) * s + x1 * (w_hi - z) * s
-
-    tl.store(out_ptr + offs_n, acc.to(tl.bfloat16), mask=n_mask)
+    base_k = pid_g * GROUP_SIZE
+    base_packed = pid_g * PACKED_PER_GROUP
+
+    for pi in tl.static_range(PACKED_PER_GROUP):
+        packed_row = base_packed + pi
+        k0 = base_k + 2 * pi
+
+        w_packed = tl.load(
+            wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
+        ).to(tl.int32)
+        w_lo = (w_packed & 0xF).to(tl.float32)
+        w_hi = ((w_packed >> 4) & 0xF).to(tl.float32)
+
+        x0 = tl.load(x_ptr + k0).to(tl.float32)
+        x1 = tl.load(x_ptr + k0 + 1).to(tl.float32)
+
+        acc += x0 * (w_lo - z) * s + x1 * (w_hi - z) * s
+
+    tl.atomic_add(out_ptr + offs_n, acc, mask=n_mask)
 
 
 # ---------------------------------------------------------------------------
@@ -79,13 +76,15 @@
 # ---------------------------------------------------------------------------
 @triton.autotune(
     configs=[
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=4, num_stages=2),
         triton.Config({"BLOCK_M": 16, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
         triton.Config({"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
         triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
         triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
         triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
         triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=8, num_stages=2),
     ],
     key=["M", "N", "K"],
 )
@@ -171,12 +170,15 @@
     assert scales.shape[0] == K // group_size
     assert scales.shape[1] == N
 
-    out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
-
     if M == 1:
+        out = torch.zeros((M, N), dtype=torch.float32, device=x.device)
+        n_groups = K // group_size
         packed_per_group = group_size // 2
-        grid = lambda meta: (triton.cdiv(N, meta["BLOCK_N"]),)
-        _w4a16_gemv_kernel[grid](
+        grid = lambda meta: (
+            triton.cdiv(N, meta["BLOCK_N"]),
+            n_groups,
+        )
+        _w4a16_gemv_splitk_kernel[grid](
             x,
             w_q,
             scales,
@@ -187,11 +189,12 @@
             w_q.stride(0),
             scales.stride(0),
             scales.stride(1),
-            out.stride(1),
             GROUP_SIZE=group_size,
             PACKED_PER_GROUP=packed_per_group,
         )
+        return out.to(torch.bfloat16)
     else:
+        out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
         grid = lambda meta: (
             triton.cdiv(M, meta["BLOCK_M"]),
             triton.cdiv(N, meta["BLOCK_N"]),
@@ -215,7 +218,7 @@
             out.stride(1),
             GROUP_SIZE=group_size,
         )
-    return out
+        return out
 
 
 class Model(

... (12 more chars)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<8389 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_cbfb669e-19e3-44cb-87c2-1655f1f9078",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1485 more chars)
tool
stdout (686 chars)
failure:
  command: 'cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1'
  workingDirectory: ''
  exitCode: 1
  signal: ''
  stdout:
    FAIL: shape 0 {'M': 1, 'N': 12288, 'K': 4096} seed 42 case nominal: tolerance exceeded: max_abs_diff=9659.19 max_rel_diff=10753 bad=12288/12288 worst_idx=(0, 3980) (atol=0.1, rtol=0.1)
  stderr: ''
  executionTime: 37427
  interleavedOutput:
    FAIL: shape 0 {'M': 1, 'N': 12288, 'K': 4096} seed 42 case nominal: tolerance exceeded: max_abs_diff=9659.19 max_rel_diff=10753 bad=12288/12288 worst_idx=(0, 3980) (atol=0.1, rtol=0.1)
  aborted: False
  localExecutionTimeMs: 37359
isBackground: False
assistant
Atomic adds are producing wrong results. Switching to a partial buffer and a final reduction.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (272 lines — TRUNCATED)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,179 +1,7 @@
-"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
-from __future__ import annotations
-
-import torch
-import torch.nn as nn
-import triton
-import triton.language as tl
-
-GROUP_SIZE = 128
-
-
-# ---------------------------------------------------------------------------
-# Decode path: M == 1 split-K GEMV — parallelize over (N-tile, K-group).
-# ---------------------------------------------------------------------------
-@triton.autotune(
-    configs=[
-        triton.Config({"BLOCK_N": 256}, num_warps=4),
-        triton.Config({"BLOCK_N": 128}, num_warps=4),
-        triton.Config({"BLOCK_N": 512}, num_warps=8),
-    ],
-    key=["N", "K"],
-)
-@triton.jit
-def _w4a16_gemv_splitk_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    out_ptr,
-    N,
-    K,
-    stride_wqn,
-    stride_sg,
-    stride_sn,
-    BLOCK_N: tl.constexpr,
-    GROUP_SIZE: tl.constexpr,
-    PACKED_PER_GROUP: tl.constexpr,
-):
-    pid_n = tl.program_id(0)
-    pid_g = tl.program_id(1)
-
-    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
-    n_mask = offs_n < N
-
-    s = tl.load(
-        scales_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-    ).to(tl.float32)
-    z = tl.load(
-        zeros_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-    ).to(tl.float32)
-
-    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
-    base_k = pid_g * GROUP_SIZE
-    base_packed = pid_g * PACKED_PER_GROUP
-
-    for pi in tl.static_range(PACKED_PER_GROUP):
-        packed_row = base_packed + pi
-        k0 = base_k + 2 * pi
-
-        w_packed = tl.load(
-            wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
-        ).to(tl.int32)
-        w_lo = (w_packed & 0xF).to(tl.float32)
-        w_hi = ((w_packed >> 4) & 0xF).to(tl.float32)
-
-        x0 = tl.load(x_ptr + k0).to(tl.float32)
-        x1 = tl.load(x_ptr + k0 + 1).to(tl.float32)
-
-        acc += x0 * (w_lo - z) * s + x1 * (w_hi - z) * s
-
-    tl.atomic_add(out_ptr + offs_n, acc, mask=n_mask)
-
-
-# ---------------------------------------------------------------------------
-# General GEMM path: M > 1
-# ---------------------------------------------------------------------------
-@triton.autotune(
-    configs=[
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=8, num_stages=2),
-    ],
-    key=["M", "N", "K"],
-)
-@triton.jit
-def _w4a16_gemm_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    out_ptr,
-    M,
-    N,
-    K,
-    stride_xm,
-    stride_xk,
-    stride_wq_kh,
-    stride_wqn,
-    stride_sg,
-    stride_sn,
-    stride_om,
-    stride_on,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    GROUP_SIZE: 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), dtype=tl.float32)
-
-    for k0 in range(0, K, BLOCK_K):
-        offs_k = k0 + tl.arange(0, BLOCK_K)
-        k_mask = offs_k < K
-
-        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
-        x_mask = (offs_m[:, None] < M) & k_mask[None, :]
-        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
-
-        packed_k = offs_k // 2
-        shift = (offs_k % 2) * 4
-
-        wq_ptrs = wq_ptr + packed_k[:, None] * stride_wq_kh + offs_n[None, :] * stride_wqn
-        wq_mask = k_mask[:, None] & (offs_n[None, :] < N)
-        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
-
-        w_int = (wq_packed >> shift[:, None]) & 0xF
-        w_bf = w_int.to(tl.bfloat16)
-
-        g_idx = offs_k // GROUP_SIZE
-        s_ptrs = scales_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
-        z_ptrs = zeros_ptr + g_idx[:, None] * stride_sg + offs_n[None, :] * stride_sn
-        s = tl.load(s_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
-        z = tl.load(z_ptrs, mask=wq_mask, other=0.0).to(tl.bfloat16)
-
-        w_deq = (w_bf - z) * s
-        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
-
-    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
-    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
-    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)
-
-
-def w4a16_gemm(
-    x: torch.Tensor,
-    w_q: torch.Tensor,
-    scales: torch.Tensor,
-    zeros: torch.Tensor,
-    group_size: int = GROUP_SIZE,
-) -> torch.Tensor:
-    """x: (M,K) bf16; w_q: (K//2,N) uint8; scales/zeros: (K//group,N) bf16 -> (M,N) bf16."""
-    assert x.is_contiguous()
-    assert w_q.is_contiguous()
-    assert scales.is_contiguous()
-    assert zeros.is_contiguous()
-    M, K = x.shape
-    Kh, N = w_q.shape
-    assert Kh * 2 == K
-    assert scales.shape == zeros.shape
-    assert scales.shape[0] == K // group_size
-    assert scales.shape[1] == N
-
     if M == 1:
-        out = torch.zeros((M, N), dtype=torch.float32, device=x.devi

... (2938 more chars)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<724 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (41 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,24 +1,16 @@
-    if M == 1:
-        n_groups = K // group_size
-        packed_per_group = group_size // 2
-        partial = torch.empty((n_groups, N), dtype=torch.float32, device=x.device)
-        grid = lambda meta: (
-            triton.cdiv(N, meta["BLOCK_N"]),
-            n_groups,
-        )
-        _w4a16_gemv_splitk_kernel[grid](
-            x,
-            w_q,
-            scales,
-            zeros,
-            partial,
-            N,
-            K,
-            w_q.stride(0),
-            scales.stride(0),
-            scales.stride(1),
-            partial.stride(0),
-            GROUP_SIZE=group_size,
-            PACKED_PER_GROUP=packed_per_group,
-        )
-        return partial.sum(dim=0, dtype=torch.bfloat16).unsqueeze(0)+def _w4a16_gemv_splitk_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    partial_ptr,
+    N,
+    K,
+    stride_wqn,
+    stride_sg,
+    stride_sn,
+    stride_pg,
+    BLOCK_N: tl.constexpr,
+    GROUP_SIZE: tl.constexpr,
+    PACKED_PER_GROUP: tl.constexpr,
+):
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<272 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (18 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,16 +1 @@
-def _w4a16_gemv_splitk_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    partial_ptr,
-    N,
-    K,
-    stride_wqn,
-    stride_sg,
-    stride_sn,
-    stride_pg,
-    BLOCK_N: tl.constexpr,
-    GROUP_SIZE: tl.constexpr,
-    PACKED_PER_GROUP: tl.constexpr,
-):+    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_0537af84-e520-4caa-a30a-9938e1d6711",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1481 more chars)
tool
stdout (4 chars)
null
assistant
Geomean is 0.1243 (passes). Pushing M>1 performance and profiling further.
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && KBH_BENCHMARK_BASELINES=1 uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_3864b180-e178-4a39-9c01-50d1553a8f0",
  "simpleCommands": [
    "cd",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /wor

... (949 more chars)
assistant
read /workspace/problems/07_w4a16_gemm/solution.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py"
}
tool
stdout (8504 chars) — TRUNCATED
"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
from __future__ import annotations

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

GROUP_SIZE = 128


# ---------------------------------------------------------------------------
# Decode path: M == 1 split-K GEMV — parallelize over (N-tile, K-group).
# ---------------------------------------------------------------------------
@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 256}, num_warps=4),
        triton.Config({"BLOCK_N": 128}, num_warps=4),
        triton.Config({"BLOCK_N": 512}, num_warps=8),
    ],
    key=["N", "K"],
)
@triton.jit
def _w4a16_gemv_splitk_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    partial_ptr,
    N,
    K,
    stride_wqn,
    stride_sg,
    stride_sn,
    stride_pg,
    BLOCK_N: tl.constexpr,
    GROUP_SIZE: tl.constexpr,
    PACKED_PER_GROUP: tl.constexpr,
):
    pid_n = tl.program_id(0)
    pid_g = tl.program_id(1)

    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    n_mask = offs_n < N

    s = tl.load(
        scales_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)
    z = tl.load(
        zeros_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)

    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
    base_k = pid_g * GROUP_SIZE
    base_packed = pid_g * PACKED_PER_GROUP

    for pi in tl.static_range(PACKED_P

... (7004 more chars)
tool
stdout (4 chars)
null
assistant
Implementing a group-aware GEMM that uses `tl.dot` per 128-element group to improve M>1 performance.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (93 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1 +1,91 @@
-    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)+# ---------------------------------------------------------------------------
+# General GEMM path: M > 1 — one tl.dot per quant group (128 K).
+# ---------------------------------------------------------------------------
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+    ],
+    key=["M", "N", "K"],
+)
+@triton.jit
+def _w4a16_gemm_kernel(
+    x_ptr,
+    wq_ptr,
+    scales_ptr,
+    zeros_ptr,
+    out_ptr,
+    M,
+    N,
+    K,
+    stride_xm,
+    stride_xk,
+    stride_wqn,
+    stride_sg,
+    stride_sn,
+    stride_om,
+    stride_on,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    GROUP_SIZE: tl.constexpr,
+    PACKED_PER_GROUP: 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)
+    m_mask = offs_m < M
+    n_mask = offs_n < N
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
+    n_groups = K // GROUP_SIZE
+
+    for g in tl.range(0, n_groups):
+        s = tl.load(
+            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.bfloat16)
+        z = tl.load(
+            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.bfloat16)
+
+        base_k = g * GROUP_SIZE
+        base_packed = g * PACKED_PER_GROUP
+
+        w_deq = tl.zeros((GROUP_SIZE, BLOCK_N), dtype=tl.bfloat16)
+        for pi in tl.static_range(PACKED_PER_GROUP):
+            packed_row = base_packed + pi
+            k0 = base_k + 2 * pi
+            k1 = k0 + 1
+
+            w_packed = tl.load(
+                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
+            ).to(tl.int32)
+            w_lo = (w_packed & 0xF).to(tl.bfloat16)
+            w_hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
+            w_deq = tl.where(
+                tl.arange(0, GROUP_SIZE)[:, None] == k0,
+                (w_lo - z) * s,
+                w_deq,
+            )
+            w_deq = tl.where(
+                tl.arange(0, GROUP_SIZE)[:, None] == k1,
+                (w_hi - z) * s,
+                w_deq,
+            )
+
+        offs_k = base_k + tl.arange(0, GROUP_SIZE)
+        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
+        x_mask = m_mask[:, None] & (offs_k[None, :] < K)
+        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
+
+        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
+
+    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
+    out_mask = m_mask[:, None] & n_mask[None, :]
+    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<3184 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (119 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,91 +1,27 @@
-# ---------------------------------------------------------------------------
-# General GEMM path: M > 1 — one tl.dot per quant group (128 K).
-# ---------------------------------------------------------------------------
-@triton.autotune(
-    configs=[
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
-    ],
-    key=["M", "N", "K"],
-)
-@triton.jit
-def _w4a16_gemm_kernel(
-    x_ptr,
-    wq_ptr,
-    scales_ptr,
-    zeros_ptr,
-    out_ptr,
-    M,
-    N,
-    K,
-    stride_xm,
-    stride_xk,
-    stride_wqn,
-    stride_sg,
-    stride_sn,
-    stride_om,
-    stride_on,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    GROUP_SIZE: tl.constexpr,
-    PACKED_PER_GROUP: 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)
-    m_mask = offs_m < M
-    n_mask = offs_n < N
-
-    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
-    n_groups = K // GROUP_SIZE
-
-    for g in tl.range(0, n_groups):
-        s = tl.load(
-            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.bfloat16)
-        z = tl.load(
-            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.bfloat16)
-
-        base_k = g * GROUP_SIZE
-        base_packed = g * PACKED_PER_GROUP
-
-        w_deq = tl.zeros((GROUP_SIZE, BLOCK_N), dtype=tl.bfloat16)
-        for pi in tl.static_range(PACKED_PER_GROUP):
-            packed_row = base_packed + pi
-            k0 = base_k + 2 * pi
-            k1 = k0 + 1
-
-            w_packed = tl.load(
-                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
-            ).to(tl.int32)
-            w_lo = (w_packed & 0xF).to(tl.bfloat16)
-            w_hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
-            w_deq = tl.where(
-                tl.arange(0, GROUP_SIZE)[:, None] == k0,
-                (w_lo - z) * s,
-                w_deq,
-            )
-            w_deq = tl.where(
-                tl.arange(0, GROUP_SIZE)[:, None] == k1,
-                (w_hi - z) * s,
-                w_deq,
-            )
-
-        offs_k = base_k + tl.arange(0, GROUP_SIZE)
-        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
-        x_mask = m_mask[:, None] & (offs_k[None, :] < K)
-        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
-
-        acc += tl.dot(x, w_deq, out_dtype=tl.float32)
-
-    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
-    out_mask = m_mask[:, None] & n_mask[None, :]
-    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)+    else:
+        out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
+        packed_per_group = group_size // 2
+        grid = lambda meta: (
+            triton.cdiv(M, meta["BLOCK_M"]),
+            triton.cdiv(N, meta["BLOCK_N"]),
+        )
+        _w4a16_gemm_kernel[grid](
+            x,
+            w_q,
+            scales,
+            zeros,
+            out,
+            M,
+            N,
+            K,
+            x.stride(0),
+            x.stride(1),
+            w_q.stride(0),
+            scales.stride(0),
+            scales.stride(1),
+            out.stride(0),
+            out.stride(1),
+            GROUP_SIZE=group_size,
+            PACKED_PER_GROUP=packed_per_group,
+        )
+        return out
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<724 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
Fixing the group GEMM to use even/odd K splits instead of expensive `tl.where` scatter.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (62 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,27 +1,34 @@
-    else:
-        out = torch.empty((M, N), dtype=torch.bfloat16, device=x.device)
-        packed_per_group = group_size // 2
-        grid = lambda meta: (
-            triton.cdiv(M, meta["BLOCK_M"]),
-            triton.cdiv(N, meta["BLOCK_N"]),
-        )
-        _w4a16_gemm_kernel[grid](
-            x,
-            w_q,
-            scales,
-            zeros,
-            out,
-            M,
-            N,
-            K,
-            x.stride(0),
-            x.stride(1),
-            w_q.stride(0),
-            scales.stride(0),
-            scales.stride(1),
-            out.stride(0),
-            out.stride(1),
-            GROUP_SIZE=group_size,
-            PACKED_PER_GROUP=packed_per_group,
-        )
-        return out+    for g in tl.range(0, n_groups):
+        s = tl.load(
+            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.bfloat16)
+        z = tl.load(
+            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
+        ).to(tl.bfloat16)
+
+        base_k = g * GROUP_SIZE
+        base_packed = g * PACKED_PER_GROUP
+
+        w_deq_lo = tl.zeros((PACKED_PER_GROUP, BLOCK_N), dtype=tl.bfloat16)
+        w_deq_hi = tl.zeros((PACKED_PER_GROUP, BLOCK_N), dtype=tl.bfloat16)
+        for pi in tl.static_range(PACKED_PER_GROUP):
+            packed_row = base_packed + pi
+            w_packed = tl.load(
+                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
+            ).to(tl.int32)
+            w_lo = (w_packed & 0xF).to(tl.bfloat16)
+            w_hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
+            row = tl.full([1, 1], pi, tl.int32)
+            w_deq_lo = tl.where(row == pi, (w_lo - z) * s, w_deq_lo)
+            w_deq_hi = tl.where(row == pi, (w_hi - z) * s, w_deq_hi)
+
+        offs_k = base_k + tl.arange(0, GROUP_SIZE)
+        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
+        x_mask = m_mask[:, None] & (offs_k[None, :] < K)
+        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
+        x_grp = tl.reshape(x, (BLOCK_M, PACKED_PER_GROUP, 2))
+        x_even = x_grp[:, :, 0]
+        x_odd = x_grp[:, :, 1]
+
+        acc += tl.dot(x_even, w_deq_lo, out_dtype=tl.float32)
+        acc += tl.dot(x_odd, w_deq_hi, out_dtype=tl.float32)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<1565 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
Vectorizing packed-weight loads across the full group.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (44 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,34 +1,10 @@
-    for g in tl.range(0, n_groups):
-        s = tl.load(
-            scales_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.bfloat16)
-        z = tl.load(
-            zeros_ptr + g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
-        ).to(tl.bfloat16)
+        base_packed = g * PACKED_PER_GROUP
+        packed_rows = base_packed + tl.arange(0, PACKED_PER_GROUP)
+        wq_ptrs = wq_ptr + packed_rows[:, None] * stride_wqn + offs_n[None, :]
+        wq_mask = n_mask[None, :]
+        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
 
-        base_k = g * GROUP_SIZE
-        base_packed = g * PACKED_PER_GROUP
-
-        w_deq_lo = tl.zeros((PACKED_PER_GROUP, BLOCK_N), dtype=tl.bfloat16)
-        w_deq_hi = tl.zeros((PACKED_PER_GROUP, BLOCK_N), dtype=tl.bfloat16)
-        for pi in tl.static_range(PACKED_PER_GROUP):
-            packed_row = base_packed + pi
-            w_packed = tl.load(
-                wq_ptr + packed_row * stride_wqn + offs_n, mask=n_mask, other=0
-            ).to(tl.int32)
-            w_lo = (w_packed & 0xF).to(tl.bfloat16)
-            w_hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
-            row = tl.full([1, 1], pi, tl.int32)
-            w_deq_lo = tl.where(row == pi, (w_lo - z) * s, w_deq_lo)
-            w_deq_hi = tl.where(row == pi, (w_hi - z) * s, w_deq_hi)
-
-        offs_k = base_k + tl.arange(0, GROUP_SIZE)
-        x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk
-        x_mask = m_mask[:, None] & (offs_k[None, :] < K)
-        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.bfloat16)
-        x_grp = tl.reshape(x, (BLOCK_M, PACKED_PER_GROUP, 2))
-        x_even = x_grp[:, :, 0]
-        x_odd = x_grp[:, :, 1]
-
-        acc += tl.dot(x_even, w_deq_lo, out_dtype=tl.float32)
-        acc += tl.dot(x_odd, w_deq_hi, out_dtype=tl.float32)+        w_lo = (wq_packed & 0xF).to(tl.bfloat16)
+        w_hi = ((wq_packed >> 4) & 0xF).to(tl.bfloat16)
+        w_deq_lo = (w_lo - z[None, :]) * s[None, :]
+        w_deq_hi = (w_hi - z[None, :]) * s[None, :]
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<505 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_a45c9944-0f0f-44a9-90b3-2f056642f4e",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1478 more chars)
tool
stdout (10186 chars) — TRUNCATED
failure:
  command: 'cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1'
  workingDirectory: ''
  exitCode: 1
  signal: ''
  stdout:
    Traceback (most recent call last):
      File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 43, in wrapper
        return fn(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^
      File "/workspace/.venv/lib/python3.11/site-packages/triton/language/core.py", line 1101, in __getitem__
        raise ValueError(f"unsupported tensor index: {sl}")
    ValueError: unsupported tensor index: constexpr[0]
    
    The above exception was the direct cause of the following exception:
    
    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 261, in forward
        return

... (8686 more chars)
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (23 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,10 +1,12 @@
-        base_packed = g * PACKED_PER_GROUP
-        packed_rows = base_packed + tl.arange(0, PACKED_PER_GROUP)
-        wq_ptrs = wq_ptr + packed_rows[:, None] * stride_wqn + offs_n[None, :]
-        wq_mask = n_mask[None, :]
-        wq_packed = tl.load(wq_ptrs, mask=wq_mask, other=0).to(tl.int32)
-
-        w_lo = (wq_packed & 0xF).to(tl.bfloat16)
-        w_hi = ((wq_packed >> 4) & 0xF).to(tl.bfloat16)
-        w_deq_lo = (w_lo - z[None, :]) * s[None, :]
-        w_deq_hi = (w_hi - z[None, :]) * s[None, :]+        offs_k_even = base_k + 2 * tl.arange(0, PACKED_PER_GROUP)
+        offs_k_odd = offs_k_even + 1
+        x_even_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_even[None, :] * stride_xk
+        x_odd_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_odd[None, :] * stride_xk
+        k_even_mask = offs_k_even[None, :] < K
+        k_odd_mask = offs_k_odd[None, :] < K
+        x_even = tl.load(x_even_ptrs, mask=m_mask[:, None] & k_even_mask, other=0.0).to(
+            tl.bfloat16
+        )
+        x_odd = tl.load(x_odd_ptrs, mask=m_mask[:, None] & k_odd_mask, other=0.0).to(
+            tl.bfloat16
+        )
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<621 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_dcd645cb-a94b-4873-98dc-32a8c73edea",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1478 more chars)
tool
stdout (4 chars)
null
assistant
Geomean is 0.1449. Tuning large-M configs to improve shape 2.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (27 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,12 +1,14 @@
-        offs_k_even = base_k + 2 * tl.arange(0, PACKED_PER_GROUP)
-        offs_k_odd = offs_k_even + 1
-        x_even_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_even[None, :] * stride_xk
-        x_odd_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_k_odd[None, :] * stride_xk
-        k_even_mask = offs_k_even[None, :] < K
-        k_odd_mask = offs_k_odd[None, :] < K
-        x_even = tl.load(x_even_ptrs, mask=m_mask[:, None] & k_even_mask, other=0.0).to(
-            tl.bfloat16
-        )
-        x_odd = tl.load(x_odd_ptrs, mask=m_mask[:, None] & k_odd_mask, other=0.0).to(
-            tl.bfloat16
-        )+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 128, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+    ],
+    key=["M", "N", "K"],
+)
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<812 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (21 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,14 +1,9 @@
 @triton.autotune(
     configs=[
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 256}, num_warps=8, num_stages=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
-        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_N": 256}, num_warps=4),
+        triton.Config({"BLOCK_N": 512}, num_warps=8),
+        triton.Config({"BLOCK_N": 128}, num_warps=4),
+        triton.Config({"BLOCK_N": 1024}, num_warps=8),
     ],
-    key=["M", "N", "K"],
+    key=["N", "K"],
 )
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<277 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
Adding a fused reduction kernel for the M=1 path.
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (32 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,9 +1,22 @@
-@triton.autotune(
-    configs=[
-        triton.Config({"BLOCK_N": 256}, num_warps=4),
-        triton.Config({"BLOCK_N": 512}, num_warps=8),
-        triton.Config({"BLOCK_N": 128}, num_warps=4),
-        triton.Config({"BLOCK_N": 1024}, num_warps=8),
-    ],
-    key=["N", "K"],
-)+    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)
+
+
+@triton.jit
+def _reduce_partial_kernel(
+    partial_ptr,
+    out_ptr,
+    N,
+    n_groups,
+    stride_pg,
+    BLOCK_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)
+    n_mask = offs_n < N
+    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
+    for g in tl.range(0, n_groups):
+        acc += tl.load(partial_ptr + g * stride_pg + offs_n, mask=n_mask, other=0.0)
+    tl.store(out_ptr + offs_n, acc.to(tl.bfloat16), mask=n_mask)
+
+
+# ---------------------------------------------------------------------------
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<627 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
edit /workspace/problems/07_w4a16_gemm/solution.py
diff (49 lines)
--- a//workspace/problems/07_w4a16_gemm/solution.py
+++ b//workspace/problems/07_w4a16_gemm/solution.py
@@ -1,22 +1,26 @@
-    tl.store(partial_ptr + pid_g * stride_pg + offs_n, acc, mask=n_mask)
-
-
-@triton.jit
-def _reduce_partial_kernel(
-    partial_ptr,
-    out_ptr,
-    N,
-    n_groups,
-    stride_pg,
-    BLOCK_N: tl.constexpr,
-):
-    pid = tl.program_id(0)
-    offs_n = pid * BLOCK_N + tl.arange(0, BLOCK_N)
-    n_mask = offs_n < N
-    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
-    for g in tl.range(0, n_groups):
-        acc += tl.load(partial_ptr + g * stride_pg + offs_n, mask=n_mask, other=0.0)
-    tl.store(out_ptr + offs_n, acc.to(tl.bfloat16), mask=n_mask)
-
-
-# ---------------------------------------------------------------------------+        _w4a16_gemv_splitk_kernel[grid](
+            x,
+            w_q,
+            scales,
+            zeros,
+            partial,
+            N,
+            K,
+            w_q.stride(0),
+            scales.stride(0),
+            scales.stride(1),
+            partial.stride(0),
+            GROUP_SIZE=group_size,
+            PACKED_PER_GROUP=packed_per_group,
+        )
+        out = torch.empty((1, N), dtype=torch.bfloat16, device=x.device)
+        reduce_grid = lambda meta: (triton.cdiv(N, meta["BLOCK_N"]),)
+        _reduce_partial_kernel[reduce_grid](
+            partial,
+            out,
+            N,
+            n_groups,
+            partial.stride(0),
+            BLOCK_N=256,
+        )
+        return out
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "streamContent": "<720 chars \u2014 see diff>"
}
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "workingDirectory": "",
  "timeout": 300000,
  "toolCallId": "tool_ce468aad-6900-400a-96cc-c9af010e4ba",
  "simpleCommands": [
    "cd",
    "uv",
    "uv"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "full

... (1476 more chars)
tool
stdout (4 chars)
null
assistant
shell
args
{
  "command": "cd /workspace/problems/07_w4a16_gemm && uv run python check.py 2>&1 && cat framework.txt",
  "workingDirectory": "",
  "timeout": 30000,
  "toolCallId": "tool_b2ef9d50-7d36-4ab7-ad52-bfa7adb96a4",
  "simpleCommands": [
    "cd",
    "uv",
    "cat"
  ],
  "hasInputRedirect": false,
  "hasOutputRedirect": true,
  "parsingResult": {
    "parsingFailed": false,
    "executableCommands": [
      {
        "name": "cd",
        "args": [
          {
            "type": "word",
            "value": "/workspace/problems/07_w4a16_gemm"
          }
        ],
        "fullText": "cd /wo

... (1133 more chars)
assistant
read /workspace/problems/07_w4a16_gemm/solution.py
args
{
  "path": "/workspace/problems/07_w4a16_gemm/solution.py"
}
tool
stdout (9792 chars) — TRUNCATED
"""Fused W4A16 GEMM: unpack int4 weights + dequant + matmul in one Triton kernel."""
from __future__ import annotations

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

GROUP_SIZE = 128


# ---------------------------------------------------------------------------
# Decode path: M == 1 split-K GEMV — parallelize over (N-tile, K-group).
# ---------------------------------------------------------------------------
@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 256}, num_warps=4),
        triton.Config({"BLOCK_N": 512}, num_warps=8),
        triton.Config({"BLOCK_N": 128}, num_warps=4),
        triton.Config({"BLOCK_N": 1024}, num_warps=8),
    ],
    key=["N", "K"],
)
@triton.jit
def _w4a16_gemv_splitk_kernel(
    x_ptr,
    wq_ptr,
    scales_ptr,
    zeros_ptr,
    partial_ptr,
    N,
    K,
    stride_wqn,
    stride_sg,
    stride_sn,
    stride_pg,
    BLOCK_N: tl.constexpr,
    GROUP_SIZE: tl.constexpr,
    PACKED_PER_GROUP: tl.constexpr,
):
    pid_n = tl.program_id(0)
    pid_g = tl.program_id(1)

    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    n_mask = offs_n < N

    s = tl.load(
        scales_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)
    z = tl.load(
        zeros_ptr + pid_g * stride_sg + offs_n * stride_sn, mask=n_mask, other=0.0
    ).to(tl.float32)

    acc = tl.zeros((BLOCK_N,), dtype=tl.float32)
    base_k = pid_g * GROUP_SIZE
    base_packed = pid_g * P

... (8292 more chars)
tool
stdout (4 chars)
null
assistant
Implemented a fused W4A16 GEMM in `solution.py` using Triton. `python check.py` prints **PASS**, and benchmark geomean peak_fraction is **0.1497** (above the 0.1 bar). ## Approach Two kernels, both fusing int4 unpack + asymmetric dequant + matmul in one pass: ### 1. Decode path (`M == 1`) — split-K GEMV - Grid: `(N-tile, K-group)` — 32 groups × ~48 N-tiles for the main decode shape - Each block handles one quant group (128 K) for a slice of N - Unpacks two int4 values per packed byte (low nibble = even K, high = odd K) - Loads scales/zeros **once per group** (not per K index) - Writes per-group partial sums, then a small Triton reduction kernel sums over groups ### 2. Prefill path (`M > 1`) — group-aware GEMM - Iterates over quant groups; each group does two `tl.dot` calls (even/odd K lanes) - Vectorized packed-weight load: all 64 packed rows per group in one shot - Uses bf16 tensor cores via `tl.dot`, fp32 accumulation - Autotuned over `BLOCK_M` / `BLOCK_N` tile sizes ## Performance (vs naive reference) | Shape | M | N | GB/s | Peak fraction | |-------|---|---|------|---------------| | decode | 1 | 12288 | **620** | 34.5% | | small prefill | 32 | 12288 | 255 | 14.2% | | large prefill | 256 | 12288 | 115 | 6.4% | | decode square | 1 | 4096 | 267 | 14.8% | | spec-decode | 16 | 14336 | 293 | 16.3% | Decode hits ~620 GB/s (~34% of 1.8 TB/s peak) — roughly **18× faster** than the naive unpack-then-matmul reference on the primary decode shape. The M=256 case is the weak point under the memory roofline metric (same byte count regardless of M), but the geomean clears the threshold comfortably.