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/workspace/problems/06_sonic_moe_swiglu
PROMPT.txt
shapes.py
sota.py
reference.py
check.py
benchmark.py
problem.yaml

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"""Naive grouped GEMM + fused SwiGLU reference (correctness only, NOT the SOTA).

This is the up-projection of an MoE FFN. Each token i is assigned to K experts;
expert_indices[i*K + j] tells you which expert. Tokens are dispatched to experts
according to routing metadata; we compute, per expert e:

    h_e = silu(x_e @ W_gate[e])  *  (x_e @ W_up[e])

where x_e is the slice of permuted hidden states routed to expert e, with
expert_offsets[e]:expert_offsets[e+1] giving its row range in the permuted layout.

The reference loops over experts in Python. Slow, but pedagogically clear and
correct. Forbidden ops (torch.matmul, torch.bmm, F.linear, sonic_moe imports)
are NOT used here, but the reference is exempt — only solution.py is checked.
"""
from __future__ import annotations

import torch
import torch.nn as nn
import torch.nn.functional as F

OP_TYPE = "grouped_gemm_swiglu"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]


class Model(nn.Module):
    """Up-projection of a top-K MoE FFN with fused SwiGLU.

    Inputs at call time:
      hidden_states:    (T_perm, H)  bf16, already permuted to expert order
      expert_offsets:   (E+1,)       int32, prefix sums of token counts per expert
                                     so expert e owns rows [offsets[e]:offsets[e+1]]
                                     T_perm = T_total * K (each token 

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"""Correctness runner for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

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

    # --- P

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"""Roofline benchmark for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

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:
    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("

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"""Shape sweep for Sonic-MoE up-projection (grouped GEMM + fused SwiGLU).

Defaults match the sonic-moe paper's headline configuration. We add:
  - a smaller shape for fast iteration during agent development
  - a wider intermediate (different aspect ratio) to stress N-tile selection
"""

SHAPES = [
    # Headline sonic-moe shape: 32K tokens, 128 experts, top-8.
    {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8},

    # Fast-iteration shape (~16x cheaper). Same expert count to keep the
    # variable-length grouped layout meaningful, but smaller token / hidden dims.
    {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4},

    # Different aspect ratio: smaller H, wider I (intermediate-heavy FFN).
    # Forces tiles to handle larger N relative to K.
    {"T_total": 16384, "H": 2048, "I": 4096, "E": 64, "K": 8},
]

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name: 06_sonic_moe_swiglu
display_name: "Sonic-MoE up-projection (Grouped GEMM + SwiGLU)"
precision: bf16
regime: compute
hardware: [RTX_PRO_6000]
peak_tflops_key: bf16
peak_bandwidth_key: dram

# Dense-equivalent FLOPs: gate GEMM + up GEMM + SwiGLU (negligible elementwise).
# Per token: 2*H*I FMAs for gate, 2*H*I for up => 2 * T_total * H * (2*I).
# (Each of T_total tokens visits K experts, but total work = T_total * K * (2*H*I*2)
# only if you count routing. The standard MoE FLOPs convention counts only the
# active per-token compute: T_total tokens * 2 * (2*I) * H. We follow that.)
flops_formula: "2 * T_total * H * (2 * I)"

# Bytes moved (approximate, lower bound):
#   read hidden (T_perm = T_total*K rows of H bf16) + read 2 weight matrices per
#   expert (E * H * 2*I bf16) + write output (T_perm rows of I bf16).
bytes_formula: "T_total*K*H*2 + E*H*(2*I)*2 + T_total*K*I*2"

tolerance:
  bfloat16: 0.02

# Forbidden ops -- agent must write the grouped GEMM + fused SwiGLU themselves.
# - torch.matmul / torch.bmm / F.linear: cuBLAS dispatch, defeats the point.
# - sonic_moe imports: vendor-call cheating; the SOTA is graded separately.
forbidden:
  - "torch.matmul"
  - "torch.bmm"
  - "torch.nn.functional.linear"
  - "F.linear"
  - "from sonic_moe"
  - "import sonic_moe"

sota:
  name: "Sonic-MoE up-projection (Tri Dao)"
  url: "https://github.com/Dao-AILab/sonic-moe"
  func

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"""SOTA reference for Sonic-MoE up-projection: Tri Dao's sonic-moe.

Status (2026-04): sonic-moe ships on PyPI as `sonic-moe` (>=0.1.2.post1) and
requires Python>=3.12. It dispatches to QuACK CuTeDSL grouped GEMM kernels.
SM120 (RTX PRO 6000 Blackwell Workstation) support is in-progress upstream --
the package installs cleanly but kernels may fail at launch on SM120 (the
QuACK grouped-GEMM path targets Sm90/Sm100 in the public release).

If the live call fails, `is_available()` returns False and the benchmark scores
the agent against PyTorch eager + the documented H100 paper ceiling (see
problem.yaml.sota.reference_throughput_tflops_h100). Agents are FORBIDDEN from
importing sonic_moe in solution.py (see problem.yaml.forbidden).
"""
from __future__ import annotations

import torch


def _try_sonic_moe(
    hidden_states: torch.Tensor,
    W_gate: torch.Tensor,
    W_up: torch.Tensor,
    expert_offsets: torch.Tensor,
) -> torch.Tensor | None:
    try:
        import sonic_moe  # type: ignore  # noqa: F401
    except Exception:
        return None
    try:
        # Public sonic-moe API surface is still stabilizing. The expected entry
        # point bundles gate+up weights as a single (E, H, 2*I) tensor and fuses
        # SwiGLU. Adapt to the actual signature once SM120 lands.
        W = torch.cat([W_gate, W_up], dim=-1).contiguous()  # (E, H, 2*I)
        from sonic_moe 

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total 40
drwxrwxr-x 2 1002 1002 4096 Jun 13 11:12 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 11:12 ..
-rw-rw-r-- 1 1002 1002 2409 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5442 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 4091 Jun  2 01:08 check.py
-rw-rw-r-- 1 1002 1002 1839 Apr 26 04:20 problem.yaml
-rw-rw-r-- 1 1002 1002 3802 Apr 26 04:25 reference.py
-rw-rw-r-- 1 1002 1002  841 Apr 26 04:25 shapes.py
-rw-rw-r-- 1 1002 1002 2822 Apr 26 04:20 sota.py

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"""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": (
        NumericStressCase("small_input", input_scales={0: 1e-3}, tolerance=_SMALL_BF16),
        NumericStressCase("large_input", input_scales={0: 64.0}, tolerance=_LARGE_BF16),
        NumericStressCase(
            "small_weight",
            sta

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"""Per-dtype correctness tolerance.

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

import torch

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


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


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

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

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"""Centralized GPU timing for benchmark.py files.

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

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

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


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Output:
Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
   Building kernelbench-hard @ file:///workspace
      Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
         If the cache and target directories are on different filesystems, hardlinking may not be supported.
         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 56 packages in 2.30s
torch 2.11.0+cu130
cuda available True
device NVIDIA RTX PRO 6000 Blackwell Workstation Edition
capability (12, 0)
triton 3.6.0

output:
  Success. Updated the following files:
  A /workspace/problems/06_sonic_moe_swiglu/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

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72682 MiB, 97887 MiB, 0 %

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Output:
      1       0 Ssl        04:05  0.0  0.0 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/06_sonic_moe_swiglu I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU 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 op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 128 ex

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Output:
      1       0 Ssl        04:08  0.0  0.0 44928 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/06_sonic_moe_swiglu I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU 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 op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 

... (5955 more chars)

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Output:
PASS

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Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:19:01.246308+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:19:02.714433+00:00 elapsed_s=1.468 ms=33.259537
shape=0 variant=solution tflops=24.794 gbps=185.631 ms=33.260
shape=0 solution_peak_fraction=0.1240
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:19:09.435595+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:19:09.459220+00:00 elapsed_s=0.024 ms=0.712640
shape=1 variant=solution tflops=48.215 gbps=894.609 ms=0.713
shape=1 solution_peak_fraction=0.2411
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:19:37.945315+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:19:38.607073+00:00 elapsed_s=0.662 ms=22.341552
shape=2 variant=solution tflops=24.607 gbps=168.211 ms=22.342
shape=2 solution_peak_fraction=0.1230
peak_fraction: 0.1543
RESULT: OK

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Output:
(16, 64, 64, 4, 4) 1.148592
(32, 64, 64, 4, 4) 0.710864
(64, 64, 64, 4, 4) 0.525040
(64, 64, 64, 8, 4) 0.824368
(16, 128, 64, 4, 4) OutOfResources out of resource: shared memory, Required: 104448, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(16, 128, 64, 8, 4) OutOfResources out of resource: shared memory, Required: 104448, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 128, 64, 4, 4) OutOfResources out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 128, 64, 8, 4) OutOfResources out of resource: shared memory, Required: 110592, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(32, 64, 32, 4, 4) 0.694128
(32, 64, 128, 4, 3) 0.746976

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Output:
shape 0

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Output:
(32, 64, 64, 4, 4) 32.331024
(64, 64, 64, 4, 4) 24.347408
(128, 64, 64, 4, 4) 21.453232
(64, 64, 32, 4, 4) 24.232304
(64, 64, 128, 4, 3) 25.231600
(32, 128, 64, 4, 3) 31.175648
(64, 128, 64, 4, 3) 21.288624
shape 2
(32, 64, 64, 4, 4) 21.872720
(64, 64, 64, 4, 4) 15.303888
(128, 64, 64, 4, 4) 13.663552
(64, 64, 32, 4, 4) 14.978912
(64, 64, 128, 4, 3) 15.613792
(32, 128, 64, 4, 3) 20.269441
(64, 128, 64, 4, 3) 13.200576

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Output:

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Output:
(64, 64, 64, 4, 4) 0.526944
(128, 64, 64, 4, 4) 0.489088
(64, 128, 64, 4, 3) 0.464480
(128, 128, 64, 4, 3) 18.359680
(128, 64, 32, 4, 4) 0.448640
(128, 64, 128, 4, 3) OutOfResources out of resource: shared memory, Required: 131072, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
(128, 64, 64, 8, 4) 0.469760

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Output:

Chunk ID: b94c91
Wall time: 30.0019 seconds
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Output:

Chunk ID: 656142
Wall time: 73.5294 seconds
Process exited with code 0
Original token count: 217
Output:
shape 0
(64, 128, 64, 4, 3) 20.759104
(64, 128, 32, 4, 4) 20.825521
(64, 128, 32, 4, 3) 19.912448
(64, 128, 64, 8, 3) 21.800048
(128, 64, 64, 4, 4) 22.193664
(128, 64, 32, 4, 4) 19.644048
(128, 64, 32, 4, 3) 19.763632
(128, 64, 64, 8, 4) 21.375297
(256, 64, 32, 4, 4) 65.161537
(256, 64, 64, 4, 4) OutOfResources out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.
shape 2
(64, 128, 64, 4, 3) 13.192352
(64, 128, 32, 4, 4) 13.099888
(64, 128, 32, 4, 3) 12.472192
(64, 128, 64, 8, 3) 13.354352
(128, 64, 64, 4, 4) 15.022096
(128, 64, 32, 4, 4) 12.360304
(128, 64, 32, 4, 3) 12.336128
(128, 64, 64, 8, 4) 13.365248
(256, 64, 32, 4, 4) 34.052975
(256, 64, 64, 4, 4) OutOfResources out of resource: shared memory, Required: 147456, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.

Chunk ID: b4cfd9
Wall time: 0.0000 seconds
Process exited with code 2
Original token count: 18
Output:
sed: can't read /workspace/src/hardware.py: No such file or directory

Chunk ID: aab47b
Wall time: 0.0000 seconds
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Output:
/workspace/src/hardware/__pycache__/rtx_pro_6000.cpython-311.pyc
/workspace/src/hardware/__pycache__/__init__.cpython-311.pyc
/workspace/src/hardware/__pycache__/m4_max.cpython-311.pyc
/workspace/src/hardware/m4_max.py
/workspace/src/hardware/__init__.py
/workspace/src/hardware/rtx_pro_6000.py

Chunk ID: 9f4c89
Wall time: 0.5917 seconds
Process exited with code 0
Original token count: 117
Output:
HardwareTarget(name='RTX PRO 6000 Blackwell Workstation', sm='sm_120a', vram_gb=96, peak_bandwidth_gb_s=1800.0, peak_tflops_dense={'fp4': 800.0, 'nvfp4': 800.0, 'mxfp4': 800.0, 'fp6': 800.0, 'fp8': 400.0, 'bf16': 200.0, 'fp16': 200.0, 'tf32': 100.0, 'fp32': 12.0, 'int8': 400.0, 'int4': 800.0})
{'fp4': 800.0, 'nvfp4': 800.0, 'mxfp4': 800.0, 'fp6': 800.0, 'fp8': 400.0, 'bf16': 200.0, 'fp16': 200.0, 'tf32': 100.0, 'fp32': 12.0, 'int8': 400.0, 'int4': 800.0}
1800.0

output:
  Success. Updated the following files:
  M /workspace/problems/06_sonic_moe_swiglu/solution.py
metadata:
  exit_code: 0
  duration_seconds: 0.0

Chunk ID: 767407
Wall time: 1.0010 seconds
Process running with session ID 18846
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Output:

Chunk ID: b2d0fa
Wall time: 120.0014 seconds
Process running with session ID 18846
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Output:

Chunk ID: a7b4a2
Wall time: 44.9357 seconds
Process exited with code 0
Original token count: 2
Output:
PASS

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Output:

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Output:
40719 MiB, 97887 MiB, 6 %

Chunk ID: b2b8d1
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Process running with session ID 28698
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Output:

Chunk ID: 1c0e5e
Wall time: 120.0011 seconds
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Output:

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Wall time: 120.0008 seconds
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Output:

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Output:
44346 MiB, 97887 MiB, 0 %

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Wall time: 0.0000 seconds
Process exited with code 0
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Output:
      1       0 Ssl        19:45  0.0  0.0 43840 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/06_sonic_moe_swiglu I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU 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 op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 

... (5899 more chars)

Chunk ID: 357230
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1847
Output:
      1       0 Ssl        19:49  0.0  0.0 43840 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/06_sonic_moe_swiglu I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU 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 op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 

... (5968 more chars)

Chunk ID: 83f674
Wall time: 56.6852 seconds
Process exited with code 0
Original token count: 249
Output:
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T11:32:30.282761+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T11:32:31.083953+00:00 elapsed_s=0.801 ms=20.642783
shape=0 variant=solution tflops=39.948 gbps=299.088 ms=20.643
shape=0 solution_peak_fraction=0.1997
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T11:32:37.762062+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T11:32:37.778087+00:00 elapsed_s=0.016 ms=0.456320
shape=1 variant=solution tflops=75.297 gbps=1397.121 ms=0.456
shape=1 solution_peak_fraction=0.3765
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T11:33:06.124686+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T11:33:06.524951+00:00 elapsed_s=0.400 ms=13.722272
shape=2 variant=solution tflops=40.063 gbps=273.868 ms=13.722
shape=2 solution_peak_fraction=0.2003
peak_fraction: 0.2470
RESULT: OK

Chunk ID: afe5ff
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1124
Output:
from __future__ import annotations

import torch
import torch.nn as nn

import triton
import triton.language as tl


OP_TYPE = "grouped_gemm_swiglu"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]


@triton.jit
def _swiglu_grouped_kernel(
    x,
    expert_offsets,
    w_gate,
    w_up,
    out,
    H: tl.constexpr,
    I: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    expert = tl.program_id(2)

    start = tl.load(expert_offsets + expert)
    end = tl.load(expert_offsets + expert + 1)
    count = end - start

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

    m_mask = offs_m < count

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), tl.float32)

    for k0 in range(0, H, BLOCK_K):
        k = k0 + offs_k
        x_tile = tl.load(
            x + (start + offs_m[:, None]) * H + k[None, :],
            mask=m_mask[:, None],
            other=0.0,
        )
        wg_tile = tl.load(
            w_gate + expert * H * I + k[:, None] * I + offs_n[None, :],
        )
        wu_tile = tl.load(
            w_up + expert * H * I + k[:, None] * I + offs_n[None, :],
        )
        acc_gate +

... (3101 more chars)

Chunk ID: 54af3c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
triton

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Original token count: 0
Output:

Chunk ID: 06369c
Wall time: 5.0015 seconds
Process running with session ID 59266
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Output:

Chunk ID: e0eb95
Wall time: 10.0019 seconds
Process running with session ID 59266
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Output:

Chunk ID: 48c159
Wall time: 30.0012 seconds
Process running with session ID 59266
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Output:

Chunk ID: 1ecd83
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 178
Output:
   1053      73 Ss         00:58  0.0  0.0  3532 /usr/bin/bash -c uv run python - <<'PY' from pathlib import Path import re, yaml meta=yaml.safe_load(Path('problem.yaml').read_text()) src=Path('solution.py').read_text() for f in meta.get('forbidden', []):     print(f, bool(re.search(re.escape(f), src))) PY
   1054    1053 S          00:58  0.0  0.0  3532 /bin/bash /kbh/bin/gpu-lock-exec uv /usr/local/bin/uv run python -
   1063      73 Ss         00:00  0.0  0.0  3436 /usr/bin/bash -c ps -eo pid,ppid,stat,etime,pcpu,pmem,rss,cmd | rg 'forbidden|problem.yaml|uv run python -|python3 -|gpu-lock'
   1065    1063 R          00:00  0.0  0.0  4184 rg forbidden|problem.yaml|uv run python -|python3 -|gpu-lock

Chunk ID: dbcdd0
Wall time: 5.0011 seconds
Process running with session ID 59266
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Output:

write_stdin failed: stdin is closed for this session; rerun exec_command with tty=true to keep stdin open

Chunk ID: b64f49
Wall time: 0.0000 seconds
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Output:

Chunk ID: 1adf6d
Wall time: 0.0506 seconds
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Output:

Chunk ID: ae47cd
Wall time: 0.0000 seconds
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Output:

Chunk ID: c43784
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1763
Output:
      1       0 Ssl        22:41 node /opt/node/bin/codex exec -m gpt-5.5 -c model_reasoning_effort="xhigh" --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check -C /workspace/problems/06_sonic_moe_swiglu I need you to write the up-projection of a top-K MoE FFN with grouped GEMM and fused SwiGLU 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 op is: per expert e, compute h_e = silu(x_e @ W_gate[e]) * (x_e @ W_up[e]) where x_e is the slice of permuted hidden states routed to expert e. Inputs are hidden_states of shape (T_perm, H) bf16 (already permuted into expert order, T_perm = T_total * K), expert_offsets of shape (E+1,) int32 (prefix sums of token counts per expert, so expert e owns rows [offsets[e]:offsets[e+1]]), and per-expert weights W_gate and W_up each of shape (E, H, I) bf16. You return gated_up of shape (T_perm, I) bf16. Correctness tolerance is 0.02 abs/rel on bf16. The shapes you have to handle are T_total=32768 H=4096 I=1536 E=128 K=8 (the headline sonic-moe configuration, 32K tokens, 128 experts, top

... (5633 more chars)