<skill_content name="kernel-optimization">
# Skill: kernel-optimization

# Kernel Optimization

Unified reference for GPU/TPU kernel work across all platforms. This skill routes to specialized sub-references based on what you need.

## When this skill fires

- Writing or optimizing CUDA, Metal, Triton, HIP, or Pallas kernels
- Porting kernels between platforms (CUDA <-> Metal <-> Triton <-> HIP <-> Pallas)
- Debugging GPU code (OOM, NaN, race conditions, wrong results, hangs)
- Performance profiling and optimization
- Numerical precision issues (FP formats, mixed precision, tolerance)
- AMD MI300/MI350 or TPU v4/v5/v6/v7 specific work

## Sub-references (read on demand)

The following files contain deep platform-specific knowledge. Read the relevant one(s) based on the task:

| File | When to read |
|------|-------------|
| `~/.claude/skills/amd-gpu/SKILL.md` | AMD CDNA/RDNA, ROCm, HIP, MFMA, LDS, chiplet scheduling, HipKittens, AITER |
| `~/.claude/skills/tpu-opt/SKILL.md` | TPU v4-v7, MXU, Pallas kernels, JAX sharding, XLA gotchas, SplashAttention |
| `~/.claude/skills/port-kernel/SKILL.md` | Cross-platform porting: CUDA <-> Metal <-> Triton concept maps, API equivalents, gotchas |
| `~/.claude/skills/debug-gpu/SKILL.md` | OOM diagnosis, NaN tracing, race conditions, compute-sanitizer, FP format specs, tolerance tables |
| `~/.claude/skills/perf-optim/SKILL.md` | Profiling (nsys/ncu/Metal), roofline, occupancy, memory hierarchy, CUTLASS/CuTe, quantization, KernelBench |
| `

... (1462 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax</path>
<type>directory</type>
<entries>
benchmark.py
check.py
problem.yaml
PROMPT.txt
reference.py
shapes.py
sota.py

(7 entries)
</entries>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/reference.py</path>
<type>file</type>
<content>
1: """Naive softmax over the last dim, computed in fp64 for ground-truth.
2: 
3: The reference deliberately runs in float64 so that fp16 / fp32 accumulation
4: drift in agent solutions is exposed by the tight tolerance in problem.yaml.
5: The agent's job is to produce an fp32 softmax whose values match this
6: double-precision reference within atol=rtol=1e-5 — this requires either
7: fp32 accumulation or compensated (Kahan) summation when vocab is large.
8: """
9: import torch
10: import torch.nn as nn
11: 
12: OP_TYPE = "softmax"
13: SUPPORTED_PRECISIONS = ["fp32"]
14: HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
15: 
16: 
17: class Model(nn.Module):
18:     """y = softmax(x, dim=-1) computed in fp64 then returned as fp32.
19: 
20:     No learned parameters — softmax is parameter-free. We still expose an
21:     empty state_dict so the harness's strict load_state_dict matches.
22:     """
23: 
24:     def __init__(self, batch: int, vocab: int):
25:         super().__init__()
26:         self.batch = batch
27:         self.vocab = vocab
28: 
29:     def forward(self, x: torch.Tensor) -> torch.Tensor:
30:         # Promote to fp64 for the ground-truth pathway. Even with double
31:         # precision we still subtract the row-max for stability.
32:         x64 = x.to(torch.float64)
33:         m = x64.amax(dim=-1, keepdim=True)
34:         e = torch.exp

... (592 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/check.py</path>
<type>file</type>
<content>
1: """Correctness runner for Kahan-corrected softmax.
2: 
3: Runs solution.Model vs reference.Model across all shapes in shapes.py, 3
4: seeds each, with the tight (1e-5) fp32 tolerance from problem.yaml. Also
5: rejects forbidden ops via grep.
6: """
7: import re
8: import sys
9: from pathlib import Path
10: 
11: import torch
12: import yaml
13: 
14: # Make the repo's src/ importable
15: REPO_ROOT = Path(__file__).resolve().parents[2]
16: sys.path.insert(0, str(REPO_ROOT))
17: 
18: from src.eval.correctness import check_correctness  # noqa: E402
19: 
20: 
21: def _make_inputs(batch: int, vocab: int, extreme: bool, seed: int) -> torch.Tensor:
22:     g = torch.Generator().manual_seed(seed)
23:     if extreme:
24:         # Adversarial: most logits are mild but a handful per row are huge.
25:         # If the kernel forgets to subtract the row-max before exp, this
26:         # row overflows fp32 and produces NaN/Inf. If it accumulates in
27:         # fp16, the long tail of small exp() values is lost beneath the
28:         # tolerance threshold.
29:         x = torch.randn(batch, vocab, generator=g) * 2.0
30:         # Spike: 4 very large positive logits per row.
31:         idx = torch.randint(0, vocab, (batch, 4), generator=g)
32:         x.scatter_(1, idx, 30.0)
33:     else:
34:         x = torch.randn(batch, vocab, generator=g) * 4.0
35:     return x.to(torc

... (3464 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/benchmark.py</path>
<type>file</type>
<content>
1: """Roofline benchmark for Kahan-corrected softmax.
2: 
3: For each shape: times eager reference, compiled reference, SOTA (if
4: available), and the agent's solution. Reports achieved TFLOPS, GB/s, and
5: peak_fraction. Softmax is memory-bound, so the score is GB/s / peak_dram.
6: 
7: Output lines the harness picks up:
8:   shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
9:   peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
10: """
11: import sys
12: from math import exp, log
13: from pathlib import Path
14: 
15: import torch
16: import yaml
17: 
18: REPO_ROOT = Path(__file__).resolve().parents[2]
19: sys.path.insert(0, str(REPO_ROOT))
20: 
21: from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
22: from src.eval.timing import time_fn  # noqa: E402
23: from src.hardware import get as get_hw  # noqa: E402
24: 
25: 
26: def _eval_formula(expr: str, vars: dict) -> float:
27:     return float(eval(expr, {"__builtins__": {}}, vars))
28: 
29: 
30: def _make_inputs(batch: int, vocab: int, extreme: bool) -> torch.Tensor:
31:     if extreme:
32:         x = torch.randn(batch, vocab) * 2.0
33:         idx = torch.randint(0, vocab, (batch, 4))
34:         x.scatter_(1, idx, 30.0)
35:     else:
36:         x = torch.randn(batch, vocab) * 4.0
37:     return x.to(torch.float32)
38: 
39: 
40: def main():
41:  

... (3706 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/shapes.py</path>
<type>file</type>
<content>
1: """Shape sweep for Kahan-corrected softmax.
2: 
3: The point of this problem is numerical accuracy on long reductions. Shapes
4: mix typical LLM vocab sizes with deliberately adversarial regimes:
5: 
6:   - small vocab (sanity check; naive fp32 should pass)
7:   - Llama3 vocab 128K (real-world, where fp16 accumulation starts to drift)
8:   - 256K (DeepSeek-V3 / Gemma-3 class vocab; naive fp16 sum DOES drift past
9:     the 1e-5 tolerance — this row is what proves Kahan was needed)
10:   - extreme-logit edge case (large positive logits stress max-subtract +
11:     summation; if the implementation accidentally exps before subtracting
12:     max, this row overflows)
13: 
14: The 'extreme' flag is read by check.py to switch input generation to a
15: distribution that produces a few very large logits per row.
16: """
17: 
18: SHAPES = [
19:     {"batch": 32, "vocab": 4096, "extreme": False},      # sanity
20:     {"batch": 16, "vocab": 32768, "extreme": False},     # GPT-2 class
21:     {"batch": 8,  "vocab": 131072, "extreme": False},    # Llama3 vocab
22:     {"batch": 4,  "vocab": 262144, "extreme": False},    # 256K — Kahan needed
23:     {"batch": 8,  "vocab": 131072, "extreme": True},     # extreme logits edge
24: ]

(End of file - total 24 lines)
</content>

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/problem.yaml</path>
<type>file</type>
<content>
1: name: 04_kahan_softmax
2: display_name: "Kahan-corrected Softmax"
3: precision: fp32
4: regime: memory  # softmax is bandwidth-bound: 2 passes over the input tensor
5: 
6: # Softmax FLOPs: per-element exp + 2 reductions + divide. Roughly 5 flops/elt.
7: flops_formula: "5 * batch * vocab"
8: 
9: # Bytes moved: read x once, write y once. Both fp32.
10: bytes_formula: "batch * vocab * 4 + batch * vocab * 4"
11: 
12: hardware: [RTX_PRO_6000]
13: peak_tflops_key: fp32
14: peak_bandwidth_key: dram
15: 
16: # TIGHTER than default (fp32 default is 1e-4). This problem exists
17: # specifically to test whether the agent uses compensated summation, so
18: # we squeeze the tolerance to 1e-5 — naive fp16 sum across 256K elements
19: # drifts past this; fp32 accumulation passes; Kahan/fp32 always passes.
20: tolerance:
21:   "torch.float32": {"atol": 1.0e-5, "rtol": 1.0e-5}
22: 
23: # Forbidden ops — block the obvious "just call the library" cheats. The
24: # agent must implement softmax themselves with explicit (compensated)
25: # summation logic.
26: forbidden:
27:   - "torch.nn.functional.softmax"
28:   - "torch.softmax"
29:   - "F.softmax"
30:   - "liger_kernel.softmax"
31:   - "liger_kernel.transformers.softmax"
32:   - ".softmax("
33: 
34: sota:
35:   name: "Liger-Kernel Softmax (Triton)"
36:   url: "https://github.com/linkedin/Liger-Kernel"
37:   function: "liger_

... (225 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/sota.py</path>
<type>file</type>
<content>
1: """SOTA reference for last-dim softmax.
2: 
3: Preference order:
4:   1. liger-kernel's Triton softmax (LigerSoftmaxFunction) — fused, fp32-acc
5:   2. torch.compile(torch.softmax) — Inductor generates a Triton kernel that
6:      is competitive on bandwidth-bound reductions
7: 
8: Agents are FORBIDDEN from importing either of these in solution.py (see
9: problem.yaml.forbidden). This file is only the benchmark's reference line.
10: """
11: from __future__ import annotations
12: 
13: import torch
14: 
15: _compiled_softmax = None
16: 
17: 
18: def _liger_softmax(x: torch.Tensor) -> torch.Tensor | None:
19:     try:
20:         from liger_kernel.ops.softmax import LigerSoftmaxFunction
21:         return LigerSoftmaxFunction.apply(x)
22:     except Exception:
23:         return None
24: 
25: 
26: def _compiled(x: torch.Tensor) -> torch.Tensor:
27:     global _compiled_softmax
28:     if _compiled_softmax is None:
29:         _compiled_softmax = torch.compile(
30:             lambda t: torch.softmax(t, dim=-1),
31:             mode="reduce-overhead",
32:         )
33:     return _compiled_softmax(x)
34: 
35: 
36: def sota_forward(x: torch.Tensor) -> torch.Tensor:
37:     """Best-available softmax reference. x: (batch, vocab) fp32."""
38:     out = _liger_softmax(x)
39:     if out is not None:
40:         return out
41:     return _compiled(x)
42: 
43: 
44: def is_a

... (128 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/PROMPT.txt</path>
<type>file</type>
<content>
1: I need you to write a numerically tight softmax 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.
2: 
3: The op is softmax along the last dim of a 2D fp32 tensor. The reference computes ground truth in fp64 and casts back to fp32, and you have to match it within atol=rtol=1e-5 — that's a tighter bar than default fp32 (1e-4) on purpose. With long reductions naive fp16 accumulation drifts past this; fp32 accumulation with subtract-max stability is enough on most shapes; on the largest vocabs you may need compensated (Kahan-style) summation to stay under the bar. The shapes you have to handle are batch=32 vocab=4096 (sanity), batch=16 vocab=32768 (GPT-2 class), batch=8 vocab=131072 (Llama-3 vocab), batch=4 vocab=262144 (256K, DeepSeek-V3 / Gemma-3 class — naive fp16 sum drifts past 1e-5 here), and batch=8 vocab=131072 with extreme logits (a few very large positives per row to stress max-subtract — exping before subtracting overflows). The check and benchmark scripts handle the extreme-flag input generation; you just need 

... (949 more chars)

Found 1 matches
/home/infatoshi/cuda/KernelBench-Hard/src/eval/correctness.py:
  Line 61: def check_correctness(

NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 12.0

3.6.0

<path>/home/infatoshi/cuda/KernelBench-Hard/src/eval/correctness.py</path>
<type>file</type>
<content>
1: """Per-dtype correctness tolerance.
2: 
3: Stanford's KernelBench uses 1e-4 atol/rtol for fp32 and 1e-2 for fp16/bf16.
4: We extend to fp8 (0.1, permissive) and int (exact).
5: """
6: from __future__ import annotations
7: 
8: import torch
9: 
10: DEFAULT_TOLERANCE = {
11:     torch.float32: {"atol": 1e-4, "rtol": 1e-4},
12:     torch.float16: {"atol": 1e-2, "rtol": 1e-2},
13:     torch.bfloat16: {"atol": 1e-2, "rtol": 1e-2},
14:     torch.float8_e4m3fn: {"atol": 1e-1, "rtol": 1e-1},
15:     torch.float8_e5m2: {"atol": 1e-1, "rtol": 1e-1},
16:     torch.int8: {"atol": 0, "rtol": 0},
17:     torch.int32: {"atol": 0, "rtol": 0},
18:     torch.int64: {"atol": 0, "rtol": 0},
19: }
20: 
21: 
22: def _coerce_float(x):
23:     """Accept int/float/str scientific-notation as float; pass through dicts."""
24:     if isinstance(x, (int, float)):
25:         return float(x)
26:     if isinstance(x, str):
27:         try:
28:             return float(x)
29:         except ValueError:
30:             return x
31:     return x
32: 
33: 
34: def tolerance_for_dtype(dtype: torch.dtype, override: dict | None = None) -> dict:
35:     """Lookup atol/rtol for a given dtype, with optional per-problem override.
36: 
37:     The override dict is keyed by str(dtype). Values may be:
38:       - a single number (or a string parseable as float, e.g. "5e-2") -> used
39:         as both atol and rtol
4

... (2580 more chars)

content: 'Read all problem files and understand requirements'
status: 'completed'
priority: 'high'
content: 'Write Triton softmax kernel with Kahan summation in solution.py'
status: 'in_progress'
priority: 'high'
content: 'Run check.py to verify correctness across all shapes'
status: 'pending'
priority: 'high'
content: 'Run benchmark.py to measure performance'
status: 'pending'
priority: 'medium'
content: 'Iterate: fix correctness issues or optimize performance'
status: 'pending'
priority: 'medium'

Wrote file successfully.

PASS

shape=0 variant=eager tflops=0.010 gbps=15.504 ms=0.068
shape=0 variant=compiled tflops=0.006 gbps=10.282 ms=0.102
shape=0 variant=sota tflops=0.036 gbps=57.488 ms=0.018
shape=0 variant=solution tflops=0.051 gbps=82.125 ms=0.013
shape=0 solution_peak_fraction=0.0456
shape=1 variant=eager tflops=0.041 gbps=66.298 ms=0.063
shape=1 variant=compiled tflops=0.027 gbps=42.611 ms=0.098
shape=1 variant=sota tflops=0.122 gbps=194.903 ms=0.022
shape=1 variant=solution tflops=0.068 gbps=109.546 ms=0.038
shape=1 solution_peak_fraction=0.0609
shape=2 variant=eager tflops=0.072 gbps=115.533 ms=0.073
shape=2 variant=compiled tflops=0.024 gbps=38.158 ms=0.220
shape=2 variant=sota tflops=0.104 gbps=165.809 ms=0.051
shape=2 variant=solution tflops=0.043 gbps=69.590 ms=0.121
shape=2 solution_peak_fraction=0.0387
shape=3 variant=eager tflops=0.072 gbps=115.890 ms=0.072
shape=3 variant=compiled tflops=0.014 gbps=23.187 ms=0.362
shape=3 variant=sota tflops=0.094 gbps=150.140 ms=0.056
shape=3 variant=solution tflops=0.023 gbps=37.221 ms=0.225
shape=3 solution_peak_fraction=0.0207
shape=4 variant=eager tflops=0.072 gbps=115.533 ms=0.073
shape=4 variant=compiled tflops=0.023 gbps=36.401 ms=0.230
shape=4 variant=sota tflops=0.095 gbps=152.542 ms=0.055
shape=4 variant=solution tflops=0.044 gbps=69.673 ms=0.120
shape=4 solution_peak_fraction=0.0387
peak_fraction: 0.0386
RESULT: LOW

/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/liger_kernel/__init__.py

No files found

No files found

backends
cross_entropy.py
dyt.py
experimental
fused_add_rms_norm.py
fused_linear_cross_entropy.py
fused_linear_jsd.py
fused_neighborhood_attention.py
geglu.py
group_norm.py
grpo_loss.py
__init__.py
jsd.py
kl_div.py
layer_norm.py
llama4_rope.py
multi_token_attention.py
poly_norm.py
__pycache__
qwen2vl_mrope.py
rms_norm.py
rope.py
softmax.py
sparsemax.py
swiglu.py
tiled_mlp.py
tvd.py
utils.py

<path>/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/liger_kernel/ops/softmax.py</path>
<type>file</type>
<content>
1: from typing import Tuple
2: 
3: import torch
4: import triton
5: import triton.language as tl
6: 
7: from liger_kernel.ops.utils import calculate_settings
8: from liger_kernel.ops.utils import ensure_contiguous
9: 
10: 
11: @triton.jit
12: def _softmax_single_block_forward_kernel(
13:     Y_ptr,
14:     Y_row_stride,
15:     X_ptr,
16:     X_row_stride,
17:     n_cols,
18:     BLOCK_SIZE: tl.constexpr,
19: ):
20:     row_id = tl.program_id(0)
21:     offs = tl.arange(0, BLOCK_SIZE)
22:     mask = offs < n_cols
23: 
24:     x = tl.load(X_ptr + row_id * X_row_stride + offs, mask=mask, other=-float("inf"), cache_modifier=".ca")
25:     m = tl.max(x, axis=0)
26:     e = tl.exp(x - m)
27:     d = tl.sum(e, axis=0)
28:     y = e / d
29:     tl.store(Y_ptr + row_id * Y_row_stride + offs, y, mask=mask, cache_modifier=".cs")
30: 
31: 
32: @triton.jit
33: def _softmax_multi_block_forward_kernel(
34:     Y_ptr,
35:     Y_row_stride,
36:     X_ptr,
37:     X_row_stride,
38:     n_cols,
39:     BLOCK_SIZE: tl.constexpr,
40: ):
41:     row_id = tl.program_id(0)
42:     offs = tl.arange(0, BLOCK_SIZE)
43: 
44:     m = tl.float32(-float("inf"))
45:     d = tl.float32(0.0)
46:     for start in tl.range(0, n_cols, BLOCK_SIZE):
47:         idx = start + offs
48:         mask = idx < n_cols
49:         xblk = tl.load(X_ptr + row_id * X_row_stride + idx,

... (5446 more chars)

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/liger_kernel/ops/utils.py", line 51, in calculate_settings
    raise RuntimeError(
RuntimeError: Cannot launch Triton kernel since n = 262144 exceeds the recommended Triton blocksize = 65536.

<path>/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/liger_kernel/ops/utils.py</path>
<type>file</type>
<content>
1: """
2: This file incorporates code from Unsloth licensed under the Apache License, Version 2.0.
3: See the original Unsloth repository at https://github.com/unslothai/unsloth.
4: 
5: The following line
6: https://github.com/linkedin/Liger-Kernel/blob/7382a8761f9af679482b968f9348013d933947c7/src/liger_kernel/ops/utils.py#L23
7: is based on code from Unsloth, located at:
8: https://github.com/unslothai/unsloth/blob/fd753fed99ed5f10ef8a9b7139588d9de9ddecfb/unsloth/kernels/utils.py#L43
9: 
10: Modifications made by Yanning Chen, 2024.
11: """
12: 
13: import functools
14: import importlib
15: import operator
16: 
17: from typing import Callable
18: 
19: import torch
20: import triton
21: import triton.language as tl
22: 
23: from packaging.version import Version
24: 
25: from liger_kernel.utils import infer_device
26: 
27: 
28: def is_hip() -> bool:
29:     return torch.version.hip is not None
30: 
31: 
32: def ensure_contiguous(fn):
33:     @functools.wraps(fn)
34:     def wrapper(ctx, *args, **kwargs):
35:         def maybe_to_contiguous(x):
36:             return x.contiguous() if isinstance(x, torch.Tensor) else x
37: 
38:         args = [maybe_to_contiguous(arg) for arg in args]
39:         kwargs = {k: maybe_to_contiguous(v) for k, v in kwargs.items()}
40:         return fn(ctx, *args, **kwargs)
41: 
42:     return wrapper
43: 
44: 


... (1424 more chars)

Wrote file successfully.

PASS

shape=0 variant=eager tflops=0.010 gbps=15.541 ms=0.067
shape=0 variant=compiled tflops=0.006 gbps=10.208 ms=0.103
shape=0 variant=sota tflops=0.036 gbps=57.187 ms=0.018
shape=0 variant=solution tflops=0.030 gbps=47.906 ms=0.022
shape=0 solution_peak_fraction=0.0266
shape=1 variant=eager tflops=0.041 gbps=66.015 ms=0.064
shape=1 variant=compiled tflops=0.027 gbps=42.841 ms=0.098
shape=1 variant=sota tflops=0.125 gbps=199.957 ms=0.021
shape=1 variant=solution tflops=0.118 gbps=188.729 ms=0.022
shape=1 solution_peak_fraction=0.1048
shape=2 variant=eager tflops=0.072 gbps=114.523 ms=0.073
shape=2 variant=compiled tflops=0.024 gbps=38.435 ms=0.218
shape=2 variant=sota tflops=0.109 gbps=174.588 ms=0.048
shape=2 variant=solution tflops=0.144 gbps=230.052 ms=0.036
shape=2 solution_peak_fraction=0.1278
shape=3 variant=eager tflops=0.073 gbps=116.199 ms=0.072
shape=3 variant=compiled tflops=0.015 gbps=23.332 ms=0.360
shape=3 variant=sota tflops=0.099 gbps=159.165 ms=0.053
shape=3 variant=solution tflops=0.097 gbps=155.714 ms=0.054
shape=3 solution_peak_fraction=0.0865
shape=4 variant=eager tflops=0.072 gbps=114.523 ms=0.073
shape=4 variant=compiled tflops=0.023 gbps=36.841 ms=0.228
shape=4 variant=sota tflops=0.101 gbps=161.270 ms=0.052
shape=4 variant=solution tflops=0.144 gbps=230.964 ms=0.036
shape=4 solution_peak_fraction=0.1283
peak_fraction: 0.0831
RESULT: LOW

Wrote file successfully.

PASS

shape=0 variant=eager tflops=0.010 gbps=15.449 ms=0.068
shape=0 variant=compiled tflops=0.006 gbps=10.172 ms=0.103
shape=0 variant=sota tflops=0.032 gbps=51.725 ms=0.020
shape=0 variant=solution tflops=0.050 gbps=80.215 ms=0.013
shape=0 solution_peak_fraction=0.0446
shape=1 variant=eager tflops=0.041 gbps=65.948 ms=0.064
shape=1 variant=compiled tflops=0.026 gbps=41.816 ms=0.100
shape=1 variant=sota tflops=0.127 gbps=202.428 ms=0.021
shape=1 variant=solution tflops=0.111 gbps=178.087 ms=0.024
shape=1 solution_peak_fraction=0.0989
shape=2 variant=eager tflops=0.071 gbps=113.458 ms=0.074
shape=2 variant=compiled tflops=0.024 gbps=38.252 ms=0.219
shape=2 variant=sota tflops=0.104 gbps=165.757 ms=0.051
shape=2 variant=solution tflops=0.139 gbps=223.006 ms=0.038
shape=2 solution_peak_fraction=0.1239
shape=3 variant=eager tflops=0.071 gbps=114.349 ms=0.073
shape=3 variant=compiled tflops=0.014 gbps=23.171 ms=0.362
shape=3 variant=sota tflops=0.094 gbps=150.441 ms=0.056
shape=3 variant=solution tflops=0.096 gbps=153.480 ms=0.055
shape=3 solution_peak_fraction=0.0853
shape=4 variant=eager tflops=0.071 gbps=113.091 ms=0.074
shape=4 variant=compiled tflops=0.023 gbps=36.406 ms=0.230
shape=4 variant=sota tflops=0.098 gbps=156.224 ms=0.054
shape=4 variant=solution tflops=0.140 gbps=224.150 ms=0.037
shape=4 solution_peak_fraction=0.1245
peak_fraction: 0.0897
RESULT: LOW

Wrote file successfully.

PASS

shape=0 variant=eager tflops=0.010 gbps=15.570 ms=0.067
shape=0 variant=compiled tflops=0.006 gbps=10.100 ms=0.104
shape=0 variant=sota tflops=0.035 gbps=55.351 ms=0.019
shape=0 variant=solution tflops=0.052 gbps=82.852 ms=0.013
shape=0 solution_peak_fraction=0.0460
shape=1 variant=eager tflops=0.041 gbps=64.935 ms=0.065
shape=1 variant=compiled tflops=0.026 gbps=42.384 ms=0.099
shape=1 variant=sota tflops=0.127 gbps=203.844 ms=0.021
shape=1 variant=solution tflops=0.115 gbps=183.703 ms=0.023
shape=1 solution_peak_fraction=0.1021
shape=2 variant=eager tflops=0.072 gbps=114.749 ms=0.073
shape=2 variant=compiled tflops=0.024 gbps=38.384 ms=0.219
shape=2 variant=sota tflops=0.108 gbps=172.918 ms=0.049
shape=2 variant=solution tflops=0.142 gbps=226.572 ms=0.037
shape=2 solution_peak_fraction=0.1259
shape=3 variant=eager tflops=0.072 gbps=114.950 ms=0.073
shape=3 variant=compiled tflops=0.015 gbps=23.300 ms=0.360
shape=3 variant=sota tflops=0.099 gbps=159.165 ms=0.053
shape=3 variant=solution tflops=0.097 gbps=155.437 ms=0.054
shape=3 solution_peak_fraction=0.0864
shape=4 variant=eager tflops=0.071 gbps=114.249 ms=0.073
shape=4 variant=compiled tflops=0.023 gbps=36.743 ms=0.228
shape=4 variant=sota tflops=0.099 gbps=159.068 ms=0.053
shape=4 variant=solution tflops=0.143 gbps=228.548 ms=0.037
shape=4 solution_peak_fraction=0.1270
peak_fraction: 0.0917
RESULT: LOW

Traceback (most recent call last):
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 461, in __init__
    self.raw_src, self.starting_line_number = inspect.getsourcelines(fn)
                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/inspect.py", line 1240, in getsourcelines
    lines, lnum = findsource(object)
                  ^^^^^^^^^^^^^^^^^^
  File "/home/infatoshi/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/inspect.py", line 1077, in findsource
    raise OSError('could not get source code')
OSError: could not get source code

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<string>", line 10, in <module>
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 935, in jit
    return decorator(fn)
           ^^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 923, in decorator
    return JITFunction(
           ^^^^^^^^^^^^
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 756, in __init__
    super().__init__(fn)
  File "/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 463, in __

... (150 more chars)

Wrote file successfully.

=== batch=32 vocab=4096 ===
  BS= 512 NW= 4 nb=   8 ms=0.014 gbps=75
  BS= 512 NW= 8 nb=   8 ms=0.013 gbps=82
  BS= 512 NW=16 nb=   8 ms=0.012 gbps=84
  BS=1024 NW= 4 nb=   4 ms=0.013 gbps=80
  BS=1024 NW= 8 nb=   4 ms=0.013 gbps=83
  BS=1024 NW=16 nb=   4 ms=0.013 gbps=82
  BS=1024 NW=32 nb=   4 ms=0.012 gbps=85
  BS=2048 NW= 4 nb=   2 ms=0.013 gbps=83
  BS=2048 NW= 8 nb=   2 ms=0.013 gbps=79
  BS=2048 NW=16 nb=   2 ms=0.013 gbps=81
  BS=2048 NW=32 nb=   2 ms=0.012 gbps=84
  BS=4096 NW= 4 nb=   1 ms=0.013 gbps=84
  BS=4096 NW= 8 nb=   1 ms=0.013 gbps=82
  BS=4096 NW=16 nb=   1 ms=0.013 gbps=81
  BS=4096 NW=32 nb=   1 ms=0.013 gbps=82

=== batch=16 vocab=32768 ===
  BS= 512 NW= 4 nb=  64 ms=0.013 gbps=330
  BS= 512 NW= 8 nb=  64 ms=0.013 gbps=320
  BS= 512 NW=16 nb=  64 ms=0.020 gbps=208
  BS=1024 NW= 4 nb=  32 ms=0.013 gbps=333
  BS=1024 NW= 8 nb=  32 ms=0.013 gbps=328
  BS=1024 NW=16 nb=  32 ms=0.013 gbps=333
  BS=1024 NW=32 nb=  32 ms=0.014 gbps=296
  BS=2048 NW= 4 nb=  16 ms=0.013 gbps=333
  BS=2048 NW= 8 nb=  16 ms=0.013 gbps=315
  BS=2048 NW=16 nb=  16 ms=0.012 gbps=338
  BS=2048 NW=32 nb=  16 ms=0.013 gbps=322
  BS=4096 NW= 4 nb=   8 ms=0.013 gbps=325
  BS=4096 NW= 8 nb=   8 ms=0.013 gbps=327
  BS=4096 NW=16 nb=   8 ms=0.013 gbps=330
  BS=4096 NW=32 nb=   8 ms=0.013 gbps=324

=== batch=8 vocab=131072 ===
  BS= 512 NW= 4 nb= 256 ms=0.036 gbps=235
  BS= 512 NW= 8 nb= 256 ms=0.061 gbps=137
  BS= 512 NW=16 nb= 256 ms=0.115 gbps=73
  BS=1024 NW= 4 nb= 128 ms=0.013 gbps=644


... (1778 more chars)

<path>/home/infatoshi/cuda/KernelBench-Hard/problems/04_kahan_softmax/solution.py</path>
<type>file</type>
<content>
1: import torch
2: import torch.nn as nn
3: import triton
4: import triton.language as tl
5: 
6: 
7: def _next_power_of_2(n):
8:     return 1 << (n - 1).bit_length()
9: 
10: 
11: @triton.jit
12: def _softmax_single_block_kernel(
13:     X_ptr,
14:     Y_ptr,
15:     stride_xb,
16:     stride_xv,
17:     stride_yb,
18:     stride_yv,
19:     VOCAB,
20:     BLOCK_SIZE: tl.constexpr,
21: ):
22:     row = tl.program_id(0)
23:     offs = tl.arange(0, BLOCK_SIZE)
24:     mask = offs < VOCAB
25: 
26:     x = tl.load(X_ptr + row * stride_xb + offs * stride_xv, mask=mask, other=float('-inf'), cache_modifier='.ca')
27:     m = tl.max(tl.where(mask, x, float('-inf')), axis=0)
28:     e = tl.exp(x - m)
29:     d = tl.sum(tl.where(mask, e, 0.0), axis=0)
30:     y = e / d
31:     tl.store(Y_ptr + row * stride_yb + offs * stride_yv, y, mask=mask, cache_modifier='.cs')
32: 
33: 
34: @triton.jit
35: def _softmax_partial_kernel(
36:     X_ptr,
37:     PARTIAL_MAX_ptr,
38:     PARTIAL_SUM_ptr,
39:     stride_xb,
40:     stride_xv,
41:     stride_pmb,
42:     stride_pmcb,
43:     stride_psb,
44:     stride_pscb,
45:     VOCAB,
46:     BLOCK_SIZE: tl.constexpr,
47: ):
48:     row = tl.program_id(0)
49:     col_block = tl.program_id(1)
50: 
51:     x_base = X_ptr + row * stride_xb
52:     start = col_block * BLOCK_SIZE
53:     offs = start + tl.arange(0, BLOCK_SIZE)
54:     mask = o

... (4660 more chars)

Edit applied successfully.

PASS

shape=0 variant=eager tflops=0.010 gbps=15.523 ms=0.068
shape=0 variant=compiled tflops=0.006 gbps=10.386 ms=0.101
shape=0 variant=sota tflops=0.035 gbps=55.258 ms=0.019
shape=0 variant=solution tflops=0.052 gbps=82.643 ms=0.013
shape=0 solution_peak_fraction=0.0459
shape=1 variant=eager tflops=0.042 gbps=66.483 ms=0.063
shape=1 variant=compiled tflops=0.027 gbps=42.681 ms=0.098
shape=1 variant=sota tflops=0.132 gbps=210.727 ms=0.020
shape=1 variant=solution tflops=0.118 gbps=189.410 ms=0.022
shape=1 solution_peak_fraction=0.1052
shape=2 variant=eager tflops=0.072 gbps=114.548 ms=0.073
shape=2 variant=compiled tflops=0.024 gbps=38.264 ms=0.219
shape=2 variant=sota tflops=0.108 gbps=172.293 ms=0.049
shape=2 variant=solution tflops=0.222 gbps=354.968 ms=0.024
shape=2 solution_peak_fraction=0.1972
shape=3 variant=eager tflops=0.073 gbps=116.044 ms=0.072
shape=3 variant=compiled tflops=0.015 gbps=23.300 ms=0.360
shape=3 variant=sota tflops=0.099 gbps=158.013 ms=0.053
shape=3 variant=solution tflops=0.190 gbps=303.407 ms=0.028
shape=3 solution_peak_fraction=0.1686
shape=4 variant=eager tflops=0.071 gbps=114.299 ms=0.073
shape=4 variant=compiled tflops=0.023 gbps=36.769 ms=0.228
shape=4 variant=sota tflops=0.100 gbps=160.186 ms=0.052
shape=4 variant=solution tflops=0.218 gbps=348.364 ms=0.024
shape=4 solution_peak_fraction=0.1935
peak_fraction: 0.1255
RESULT: OK

content: 'Read all problem files and understand requirements'
status: 'completed'
priority: 'high'
content: 'Write Triton softmax kernel with Kahan summation in solution.py'
status: 'completed'
priority: 'high'
content: 'Run check.py to verify correctness across all shapes'
status: 'completed'
priority: 'high'
content: 'Run benchmark.py to measure performance'
status: 'completed'
priority: 'medium'
content: 'Iterate: fix correctness issues or optimize performance'
status: 'completed'
priority: 'medium'
content: 'Clean up scratch files and run linter'
status: 'in_progress'
priority: 'low'

(no output)

All checks passed!

PASS

content: 'Read all problem files and understand requirements'
status: 'completed'
priority: 'high'
content: 'Write Triton softmax kernel with Kahan summation in solution.py'
status: 'completed'
priority: 'high'
content: 'Run check.py to verify correctness across all shapes'
status: 'completed'
priority: 'high'
content: 'Run benchmark.py to measure performance'
status: 'completed'
priority: 'medium'
content: 'Iterate: fix correctness issues or optimize performance'
status: 'completed'
priority: 'medium'
content: 'Clean up scratch files and run linter'
status: 'completed'
priority: 'low'