solution.py
final answer
benchmark.log
check.log
result.json
import torch
import torch.nn as nn
import triton
import triton.language as tl
from torch.utils.cpp_extension import load_inline
OP_TYPE = "rope"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000"]
_CPP_SRC = r"""
#include <torch/extension.h>
std::vector<torch::Tensor> mrope_forward(
torch::Tensor q,
torch::Tensor k,
torch::Tensor pos_t,
torch::Tensor pos_h,
torch::Tensor pos_w,
torch::Tensor cos_cache,
torch::Tensor sin_cache,
int64_t T,
int64_t Hq,
int64_t Hkv,
int64_t D,
int64_t S0,
int64_t S1);
"""
_CUDA_SRC = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_bf16.h>
#include <stdint.h>
__global__ void mrope_token_kernel(
const __nv_bfloat16* __restrict__ q,
const __nv_bfloat16* __restrict__ k,
const int64_t* __restrict__ pos_t,
const int64_t* __restrict__ pos_h,
const int64_t* __restrict__ pos_w,
const __nv_bfloat16* __restrict__ cos_cache,
const __nv_bfloat16* __restrict__ sin_cache,
__nv_bfloat16* __restrict__ q_out,
__nv_bfloat16* __restrict__ k_out,
int T,
int Hq,
int Hkv,
int D,
int S0,
int S1) {
extern __shared__ __nv_bfloat16 rope[];
__nv_bfloat16* c = rope;
__nv_bfloat16* s = rope + (D >> 1);
const int bt = blockIdx.x;
const int b = bt / T;
const int t = bt - b * T;
const int half = D >> 1;
const int tid = threadIdx.x;
const int64_t pt = pos_t[bt];
const int64_t ph = pos_h[bt];
const int64_t pw = pos_w[bt];
if (tid < half) {
const int64_t p = tid;
const int64_t pos = (p < S0) ? pt : ((p < S0 + S1) ? ph : pw);
const int64_t cache_off = pos * (int64_t)D + p;
c[p] = cos_cache[cache_off];
s[p] = sin_cache[cache_off];
}
__syncthreads();
const int q_pairs = Hq * half;
const int total_pairs = q_pairs + Hkv * half;
for (int idx = tid; idx < total_pairs; idx += blockDim.x) {
const bool is_q = idx < q_pairs;
const int local = is_q ? idx : idx - q_pairs;
const int H = is_q ? Hq : Hkv;
const int h = local / half;
const int p = local - h * half;
const __nv_bfloat16* x = is_q ? q : k;
__nv_bfloat16* y = is_q ? q_out : k_out;
const int64_t in_off = ((int64_t)bt * H + h) * D + p;
const int64_t out_off = (((int64_t)b * H + h) * T + t) * D + p;
const float x0 = __bfloat162float(x[in_off]);
const float x1 = __bfloat162float(x[in_off + half]);
const float cf = __bfloat162float(c[p]);
const float sf = __bfloat162float(s[p]);
y[out_off] = __float2bfloat16_rn(x0 * cf - x1 * sf);
y[out_off + half] = __float2bfloat16_rn(x1 * cf + x0 * sf);
}
}
std::vector<torch::Tensor> mrope_forward(
torch::Tensor q,
torch::Tensor k,
torch::Tensor pos_t,
torch::Tensor pos_h,
torch::Tensor pos_w,
torch::Tensor cos_cache,
torch::Tensor sin_cache,
int64_t T64,
int64_t Hq64,
int64_t Hkv64,
int64_t D64,
int64_t S064,
int64_t S164) {
const int64_t B = q.size(0);
const int T = static_cast<int>(T64);
const int Hq = static_cast<int>(Hq64);
const int Hkv = static_cast<int>(Hkv64);
const int D = static_cast<int>(D64);
const int S0 = static_cast<int>(S064);
const int S1 = static_cast<int>(S164);
auto q_out = torch::empty({B, Hq, T, D}, q.options());
auto k_out = torch::empty({B, Hkv, T, D}, k.options());
const int blocks = static_cast<int>(B) * T;
const int threads = 256;
const size_t shmem = static_cast<size_t>(D) * sizeof(__nv_bfloat16);
mrope_token_kernel<<<blocks, threads, shmem, at::cuda::getCurrentCUDAStream()>>>(
reinterpret_cast<const __nv_bfloat16*>(q.data_ptr<at::BFloat16>()),
reinterpret_cast<const __nv_bfloat16*>(k.data_ptr<at::BFloat16>()),
pos_t.data_ptr<int64_t>(),
pos_h.data_ptr<int64_t>(),
pos_w.data_ptr<int64_t>(),
reinterpret_cast<const __nv_bfloat16*>(cos_cache.data_ptr<at::BFloat16>()),
reinterpret_cast<const __nv_bfloat16*>(sin_cache.data_ptr<at::BFloat16>()),
reinterpret_cast<__nv_bfloat16*>(q_out.data_ptr<at::BFloat16>()),
reinterpret_cast<__nv_bfloat16*>(k_out.data_ptr<at::BFloat16>()),
T,
Hq,
Hkv,
D,
S0,
S1);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return {q_out, k_out};
}
"""
_ext = None
def _get_ext():
global _ext
if _ext is None:
_ext = load_inline(
name="mrope_preattn_cuda_ext_v3",
cpp_sources=_CPP_SRC,
cuda_sources=_CUDA_SRC,
functions=["mrope_forward"],
extra_cuda_cflags=["-O3", "--use_fast_math"],
with_cuda=True,
verbose=False,
)
return _ext
def _build_inv_freq(D: int, base: float = 10000.0) -> torch.Tensor:
return 1.0 / (base ** (torch.arange(0, D, 2, dtype=torch.float32) / D))
@triton.jit
def _mrope_qk_kernel(
q,
k,
pos_t,
pos_h,
pos_w,
cos_cache,
sin_cache,
q_out,
k_out,
T: tl.constexpr,
Hq: tl.constexpr,
Hkv: tl.constexpr,
D: tl.constexpr,
S0: tl.constexpr,
S1: tl.constexpr,
BLOCK_T: tl.constexpr,
BLOCK_H: tl.constexpr,
HALF: tl.constexpr,
):
pid_t = tl.program_id(0)
pid_h = tl.program_id(1)
b = tl.program_id(2)
offs_t = pid_t * BLOCK_T + tl.arange(0, BLOCK_T)
offs_h = pid_h * BLOCK_H + tl.arange(0, BLOCK_H)
offs_p = tl.arange(0, HALF)
t_mask = offs_t < T
hq_mask = offs_h < Hq
cache_mask = t_mask[:, None]
pos_base = b * T + offs_t
pt = tl.load(pos_t + pos_base, mask=t_mask, other=0)
ph = tl.load(pos_h + pos_base, mask=t_mask, other=0)
pw = tl.load(pos_w + pos_base, mask=t_mask, other=0)
pair = offs_p[None, :]
axis_pos = tl.where(pair < S0, pt[:, None], tl.where(pair < S0 + S1, ph[:, None], pw[:, None]))
cache_off = axis_pos * D + pair
c = tl.load(cos_cache + cache_off, mask=cache_mask, other=0.0)
s = tl.load(sin_cache + cache_off, mask=cache_mask, other=0.0)
q_mask = t_mask[:, None, None] & hq_mask[None, :, None]
q_in0 = ((b * T + offs_t[:, None, None]) * Hq + offs_h[None, :, None]) * D + offs_p[None, None, :]
q0 = tl.load(q + q_in0, mask=q_mask, other=0.0)
q1 = tl.load(q + q_in0 + HALF, mask=q_mask, other=0.0)
q_out0 = q0 * c[:, None, :] - q1 * s[:, None, :]
q_out1 = q1 * c[:, None, :] + q0 * s[:, None, :]
q_dst0 = ((b * Hq + offs_h[None, :, None]) * T + offs_t[:, None, None]) * D + offs_p[None, None, :]
tl.store(q_out + q_dst0, q_out0, mask=q_mask)
tl.store(q_out + q_dst0 + HALF, q_out1, mask=q_mask)
hkv_mask = offs_h < Hkv
k_mask = t_mask[:, None, None] & hkv_mask[None, :, None]
k_in0 = ((b * T + offs_t[:, None, None]) * Hkv + offs_h[None, :, None]) * D + offs_p[None, None, :]
k0 = tl.load(k + k_in0, mask=k_mask, other=0.0)
k1 = tl.load(k + k_in0 + HALF, mask=k_mask, other=0.0)
k_out0 = k0 * c[:, None, :] - k1 * s[:, None, :]
k_out1 = k1 * c[:, None, :] + k0 * s[:, None, :]
k_dst0 = ((b * Hkv + offs_h[None, :, None]) * T + offs_t[:, None, None]) * D + offs_p[None, None, :]
tl.store(k_out + k_dst0, k_out0, mask=k_mask)
tl.store(k_out + k_dst0 + HALF, k_out1, mask=k_mask)
class Model(nn.Module):
def __init__(
self,
B: int,
T: int,
Hq: int,
Hkv: int,
D: int,
mrope_section: tuple[int, int, int],
max_pos: int,
):
super().__init__()
assert sum(mrope_section) == D // 2
self.B, self.T = B, T
self.Hq, self.Hkv, self.D = Hq, Hkv, D
self.mrope_section = tuple(mrope_section)
self.max_pos = max_pos
inv_freq = _build_inv_freq(D)
pos = torch.arange(max_pos, dtype=torch.float32)
freqs = torch.outer(pos, inv_freq)
emb = torch.cat([freqs, freqs], dim=-1)
self.register_buffer("cos_cache", emb.cos().to(torch.bfloat16))
self.register_buffer("sin_cache", emb.sin().to(torch.bfloat16))
def forward(
self,
q: torch.Tensor,
k: torch.Tensor,
pos_t: torch.Tensor,
pos_h: torch.Tensor,
pos_w: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
s0, s1, _ = self.mrope_section
if self.D == 64:
q_out, k_out = _get_ext().mrope_forward(
q,
k,
pos_t,
pos_h,
pos_w,
self.cos_cache,
self.sin_cache,
self.T,
self.Hq,
self.Hkv,
self.D,
s0,
s1,
)
return q_out, k_out
B, T, Hq, D = q.shape
Hkv = k.shape[2]
q_out = torch.empty((B, Hq, T, D), device=q.device, dtype=q.dtype)
k_out = torch.empty((B, Hkv, T, D), device=k.device, dtype=k.dtype)
block_t = 16 if T <= 4096 else 8
block_h = 2
grid = (triton.cdiv(T, block_t), triton.cdiv(Hq, block_h), B)
warps = 8 if T == 8192 else 4
_mrope_qk_kernel[grid](
q,
k,
pos_t,
pos_h,
pos_w,
self.cos_cache,
self.sin_cache,
q_out,
k_out,
T,
Hq,
Hkv,
D,
s0,
s1,
block_t,
block_h,
D // 2,
num_warps=warps,
)
return q_out, k_out
B = 1
T = 4096
Hq = 32
Hkv = 8
D = 128
MROPE_SECTION = (16, 24, 24)
MAX_POS = 32768
def get_inputs():
q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16) * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16) * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64)
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64)
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64)
return [q, k, pos_t, pos_h, pos_w]
def get_init_inputs():
return [B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS]
Implemented the optimized prep in [solution.py](/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py).
It uses a hybrid path:
- Triton fused gather + mRoPE mix + rotate + transpose for `D=128`.
- Raw CUDA extension path for `D=64`, one CTA per token with RoPE pairs cached in shared memory.
Validation:
- `uv run python check.py`: `PASS`
- `uv run python benchmark.py`: `peak_fraction: 0.6099`, `RESULT: OK`
- Best final solution lines: shape0 `1047 GB/s`, shape1 `1219 GB/s`, shape2 `852 GB/s`, shape3 `1335 GB/s`
- `uv run ruff check . --fix`: passed
- `uv run pytest`: no tests collected, pytest exit code 5.
shape=0 variant=eager tflops=0.200 gbps=286.728 ms=0.315
shape=0 variant=compiled tflops=0.569 gbps=816.414 ms=0.111
shape=0 variant=solution tflops=0.820 gbps=1176.938 ms=0.077
shape=0 solution_peak_fraction=0.6539
shape=1 variant=eager tflops=0.191 gbps=279.174 ms=0.527
shape=1 variant=compiled tflops=0.528 gbps=771.461 ms=0.191
shape=1 variant=solution tflops=0.906 gbps=1322.298 ms=0.111
shape=1 solution_peak_fraction=0.7346
shape=2 variant=eager tflops=0.097 gbps=151.181 ms=0.146
shape=2 variant=compiled tflops=0.224 gbps=350.152 ms=0.063
shape=2 variant=solution tflops=0.609 gbps=952.066 ms=0.023
shape=2 solution_peak_fraction=0.5289
shape=3 variant=eager tflops=0.177 gbps=253.783 ms=1.423
shape=3 variant=compiled tflops=0.484 gbps=694.344 ms=0.520
shape=3 variant=solution tflops=0.971 gbps=1392.802 ms=0.259
shape=3 solution_peak_fraction=0.7738
peak_fraction: 0.6659
RESULT: OK
{
"problem": "09_fmha_preattn_mrope",
"harness": "codex",
"model": "gpt-5.5",
"reasoning_effort": "xhigh",
"has_solution": true,
"correct": true,
"peak_fraction": 0.6659,
"elapsed_seconds": 876,
"harness_exit_code": 0,
"session_complete": true,
"usage": {"input_tokens": 5873381, "output_tokens": 27998, "cache_read_tokens": 5667840, "cache_creation_tokens": null, "reasoning_tokens": 13407, "total_cost_usd": null}
}
timeline (312 events)
system
session start model=None ctx=?
system
task_started turn=019e0155
system
<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>
<apps_instructions>
## Apps (Connectors)
Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.
An app is equivalent to a set of MCP tools within the `codex_apps` MCP.
An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it.
Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps.
</apps_instructions>
<skills_instructions>
## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /home/infatoshi/.codex/skills/.system/imagegen/SKILL.md)
- openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains. (file: /home/infatoshi/.codex/skills/.system/openai-docs/SKILL.md)
- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, and baseline placeholders you can edit before publishing or testing. Use when Codex needs to create a new local plugin, add optional plugin structure, or generate or update repo-root `.agents/plugins/marketplace.json` entries for plugin ordering and availability metadata. (file: /home/infatoshi/.codex/skills/.system/plugin-creator/SKILL.md)
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/infatoshi/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/infatoshi/.codex/skills/.system/skill-installer/SKILL.md)
- brainstorm: Run interactive brainstorming across verifiers environments, evaluations, GEPA, and RL training. Use when the user wants ideation, literature scanning, concept teaching, roadmap planning, or research program design grounded in local CLI sources, verifiers, and RL trainer code. (file: /home/infatoshi/.prime/skills/brainstorm/SKILL.md)
- browse-environments: Discover and inspect verifiers environments through the Prime ecosystem. Use when asked to find environments on the Hub, compare options, inspect metadata, check action status, pull local copies for inspection, or choose environment starting points before evaluation, training, or migration work. (file: /home/infatoshi/.prime/skills/browse-environments/SKILL.md)
- create-environments: Create or migrate verifiers environments for the Prime Lab ecosystem. Use when asked to build a new environment from scratch, port an eval or benchmark from papers or other libraries, start from an environment on the Hub, or convert existing tasks into a package that exposes load_environment and installs cleanly with prime env install. (file: /home/infatoshi/.prime/skills/create-environments/SKILL.md)
- evaluate-environments: Run and analyze evaluations for verifiers environments using prime eval. Use when asked to smoke-test environments, run benchmark sweeps, resume interrupted evaluations, compare models, inspect sample-level outputs, or produce evaluation summaries suitable for deciding next steps. (file: /home/infatoshi/.prime/skills/evaluate-environments/SKILL.md)
- optimize-environments: Audit and optimize verifiers environments for async performance. Use when asked to profile, speed up, or review an environment for concurrency bottlenecks, event loop blocking, or scaling issues under high rollout counts. (file: /home/infatoshi/.prime/skills/optimize-environments/SKILL.md)
- optimize-with-environments: Optimize environment system prompts with GEPA through prime gepa run. Use when asked to improve prompt performance without gradient training, compare baseline versus optimized prompts, run GEPA from CLI or TOML configs, or interpret GEPA outputs before deployment. (file: /home/infatoshi/.prime/skills/optimize-with-environments/SKILL.md)
- review-environments: Review verifiers environments for correctness, robustness, and ecosystem compatibility. Use when asked for environment code review, quality audit, migration validation, or release readiness checks for local environments or environments pulled from the Hub. (file: /home/infatoshi/.prime/skills/review-environments/SKILL.md)
- train-with-environments: Train models with verifiers environments using hosted RL or prime-rl. Use when asked to configure RL runs, tune key hyperparameters, diagnose instability, set up difficulty filtering and oversampling, or create practical train and eval loops for new environments. (file: /home/infatoshi/.prime/skills/train-with-environments/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
- If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
- Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
- Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
- Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
- When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</skills_instructions>
user
# AGENTS.md instructions for /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope
<INSTRUCTIONS>
<proactive-behavior>
Act without asking permission. Never ask "Should I...?" or "Want me to...?"
Just do it. Report results.
Only involve human when:
1. Uncertain about requirements or major architectural decisions
2. Human eyes needed (visual verification, UI testing)
3. Blocked by something only human can resolve
</proactive-behavior>
<python>
UV is the ONLY way to run Python inside projects.
- `uv run script.py` instead of `python script.py`
- `uv run python --version` instead of `python --version`
- `uv pip install` / `uv add` instead of `pip install`
- `uv venv` instead of `python -m venv`
Never use `--system`. Never use bare `python` or `pip` commands.
Use pipx only for system-level CLI tools.
</python>
<delegation>
External agent delegation (when user says "delegate a cursor/codex/gemini"):
Run headless via Bash and return the result. Use run_in_background for parallel work.
- cursor: `cursor agent --model composer-2 --yolo --print "PROMPT"`
- codex: `codex exec --full-auto "PROMPT"`
- gemini: `gemini -y -m gemini-3.1-pro -o text "PROMPT"`
</delegation>
<principles>
No emojis. No em dashes.
Never guess numbers - benchmark or say "needs measurement".
Do not over-engineer. Minimal changes only.
Do not add features beyond what was requested.
Do not refactor unrelated code.
Do not revert, overwrite, or reformat unrelated user changes.
Non-trivial projects need SPEC.md as north star.
</principles>
<testing>
Tests are mandatory for non-trivial implementation work.
- Run tests: `uv run pytest`
- Lint: `uv run ruff check . --fix`
- Both must pass before declaring work complete.
Comparison rules:
- Integers/exact: bitwise comparison (`==`)
- Floats: `atol`/`rtol` tolerance (IEEE 754)
</testing>
<verification>
Before completing any task:
1. Re-read the original request and make sure the work fully addresses it.
2. If code changed, execute the touched path when practical.
3. If tests exist, run them. If you made non-trivial changes and no tests exist, write them.
4. Review recent command output for unaddressed errors, warnings, or failures.
5. If you edited a file 10+ times without resolving the issue, stop and try a fundamentally different approach.
Do not declare completion until the relevant checks pass or you clearly report the blocker.
</verification>
<environment-discovery>
At the start of complex tasks, map the environment:
- Check installed tools (for Python: `uv run python --version`)
- Check git state (branch, dirty files, recent commits)
- Check disk space if doing large builds
- Read SPEC.md if it exists
- Inspect local project hints in this order: AGENTS.md/CLAUDE.md, SPEC.md, package manager files, Makefile/justfile, README, CI config
</environment-discovery>
<current-sources-and-privacy>
For current docs, pricing, model availability, API behavior, laws, schedules, or vendor claims, verify from live official sources before answering.
When making claims about runtime state, config, installed tools, active services, or failures, base them on a concrete command, log, or file and mention that evidence briefly.
Do not inspect mail, Drive, calendars, social accounts, keyrings, credential files, or secret stores unless the user's request requires it. Prefer narrow queries and summarize without exposing secrets.
</current-sources-and-privacy>
<gpu>
Before running ANY GPU workload, check `nvidia-smi` first. Multiple agents share this machine.
</gpu>
<machines>
macbook: M4 Max (14c: 10P+4E), 36GB unified, 1TB SSD, macOS 26.3, Metal 3
anvil-lan: Ryzen 9 9950X3D (16c/32t), 92GB DDR5, RTX PRO 6000 Blackwell Workstation 96GB (sm_120, 600W TDP), 3.6TB NVMe, Ubuntu 24.04, CUDA 13.2 + nvcc 12.8, Driver 595.58.03
When changing machine-level config or CLI setup that should apply everywhere, apply and verify it on both macbook and `anvil-lan`.
</machines>
<gogcli>
Google CLI for Drive, Gmail, Calendar, Contacts, Sheets, Tasks, and more.
Binary at /usr/local/bin/gog. Keyring password in $GOG_KEYRING_PASSWORD.
Accounts (use -a flag to select):
infatoshi@gmail.com -- personal. Gumroad sales, subscriptions, personal contacts, Google Drive storage.
elliot@arledge.net -- professional. Manning book (CUDA for DL), contracts, invoices, GitHub, work correspondence.
Drive:
gog ls -a EMAIL [--parent FOLDER_ID] [--max N]
gog drive search -a EMAIL "query" [--raw-query "Drive query language"]
gog drive mkdir "name" -a EMAIL [--parent FOLDER_ID]
gog drive move FILE_ID -a EMAIL --parent FOLDER_ID
gog drive delete FILE_ID -a EMAIL -y
gog download FILE_ID -a EMAIL [-o output_path]
gog upload LOCAL_PATH -a EMAIL [--parent FOLDER_ID]
gog drive rename FILE_ID "new name" -a EMAIL
Gmail:
gog gmail search -a EMAIL "gmail query syntax" [--max N]
gog gmail messages read MSG_ID -a EMAIL
gog send -a EMAIL --to ADDR --subject "subj" --body "text"
Calendar:
gog calendar events -a EMAIL [--max N]
gog calendar create CALENDAR_ID -a EMAIL --summary "title" --start "2026-01-01T09:00:00" --end "2026-01-01T10:00:00"
gog calendar search -a EMAIL "query"
Contacts:
gog contacts search -a EMAIL "name"
Add -j for JSON output. Add -p for TSV/plain. Add --max N to limit results.
Gmail query examples: "from:foo newer_than:7d", "subject:invoice is:unread", "has:attachment filename:pdf"
For formatted emails use --body-html with HTML, not markdown in --body. Use --help on any subcommand for full flags.
</gogcli>
<x-cli>
X/Twitter CLI at ~/.local/bin/x-cli. Credentials in ~/x-cli/.env.
x-cli user timeline USERNAME --max N # fetch recent posts
x-cli user get USERNAME # profile lookup
x-cli tweet search "query" --max N # search tweets
x-cli tweet post "text" # post a tweet
x-cli tweet reply TWEET_ID "text" # reply to a tweet
x-cli tweet get TWEET_ID # fetch single tweet
x-cli me mentions --max N # your mentions
x-cli me bookmarks --max N # your bookmarks
x-cli like TWEET_ID # like a tweet
x-cli retweet TWEET_ID # retweet
Add -j for JSON, -md for markdown, -p for TSV. Use --help on any subcommand.
</x-cli>
<project-context-files>
INCLUDE: version pins, build/run/test commands, behavioral overrides,
undiscoverable context, current status, domain gotchas, hard constraints.
EXCLUDE: directory trees, architecture narration, feature specs (use SPEC.md),
enum/type definitions from code, workflow docs, anything findable in <60s.
If something keeps breaking, add the fix to project CLAUDE.md/AGENTS.md.
Fewer constraints with higher signal beats comprehensive documentation.
</project-context-files>
--- project-doc ---
# KernelBench-Hard — Developer Instructions (codex / droid)
This is the codex / droid / cursor-agent equivalent of `CLAUDE.md`. Content is identical; format is plain markdown for any CLI.
See [`CLAUDE.md`](./CLAUDE.md) for the canonical version. All rules there apply.
Summary of the non-negotiables:
- **uv only.** `uv run ...`, `uv add ...`, `uv pip install ...`. Never `pip` or bare `python`.
- **Before committing:** `uv run ruff check . --fix && uv run pytest`.
- **Do not edit `problems/*/solution.py`** — those are agent output.
- **Do not modify `reference.py`, `check.py`, `benchmark.py`, `problem.yaml`, `shapes.py`, or `PROMPT.txt`** of an already-published problem.
- **Apply the torch 2.11 inductor CSE hotfix** via `./scripts/patch_torch.sh` after any `uv sync`.
## Quick actions
```bash
uv sync
./scripts/patch_torch.sh
./scripts/run_hard.sh claude claude-opus-4-7 problems/01_fp8_gemm
```
## Repo layout and adding a new problem
See `CLAUDE.md` — everything there is authoritative.
</INSTRUCTIONS>
<environment_context>
<cwd>/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope</cwd>
<shell>zsh</shell>
<current_date>2026-05-07</current_date>
<timezone>America/Edmonton</timezone>
</environment_context>
user
I need you to optimize the multi-axis RoPE pre-attention prep step 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 the pre-attention prep used in Qwen2-VL-style vision-language models: take q (B, T, Hq, D) and k (B, T, Hkv, D) bf16 plus three int64 position-id tensors pos_t, pos_h, pos_w of shape (B, T) — temporal, height, width axes — gather per-axis cos/sin from a precomputed (max_pos, D) bf16 cache, mix the per-axis values across the head_dim according to mrope_section (a tuple [s0, s1, s2] summing to D/2 — the first s0 pair-indices use the temporal axis, next s1 use height, last s2 use width, mirrored into the second half of D), then apply standard rotate-half rotation (out = x*cos + rotate_half(x)*sin) on q and k. Return q_rot and k_rot in (B, H, T, D) layout — the transpose from (B, T, H, D) to (B, H, T, D) is part of the prep. Correctness tolerance on the bf16 outputs is 1e-2 abs/rel. Both q_rot and k_rot must match the reference within that.
The shapes you have to handle are B=1 T=4096 Hq=32 Hkv=8 D=128 mrope_section=(16,24,24) max_pos=32768 (Qwen2-VL base), B=1 T=8192 Hq=28 Hkv=4 D=128 mrope_section=(16,24,24) max_pos=32768 (long-context GQA), B=2 T=2048 Hq=16 Hkv=2 D=64 mrope_section=(8,12,12) max_pos=16384 (smaller head_dim, batch 2), and B=1 T=16384 Hq=32 Hkv=8 D=128 mrope_section=(16,24,24) max_pos=65536 (very long context).
This needs to be a real custom kernel. Don't reach for transformers' apply_multimodal_rotary_pos_emb, flash_attn.layers.rotary, flashinfer.rope, or flashinfer.apply_rope — they're off-limits and using them fails correctness. Try CUDA C++ via torch.utils.cpp_extension.load_inline, Triton, inline PTX, or whatever fits. The work is bandwidth-bound, so wins come from fusing the gather + per-axis mix + rotate + transpose into one kernel that streams q/k once and emits the rotated (B,H,T,D) layout directly. Anything you're uncertain about — RoPE math, mRoPE per-axis slicing, layout conventions — look up PTX docs, read the transformers Qwen2-VL implementation, browse FlashInfer / flash-attn rotary 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 across multiple seeds, 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.
assistant
I’ll map the local problem and repo rules first, then inspect the reference/check/benchmark harness so the kernel matches exactly what is being measured. Before any GPU run, I’ll check `nvidia-smi` as required.
tool
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Output:
/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope
tool
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Output:
PROMPT.txt
benchmark.py
check.py
problem.yaml
reference.py
shapes.py
sota.py
tool
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Output:
## master...origin/master
?? ./
?? ../10_patch_embed_conv3d_gemm/
?? ../../scripts/sweep_09_10.sh
tool
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Output:
Python 3.11.15
tool
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Output:
Thu May 7 01:27:00 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.58.03 Driver Version: 595.58.03 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA RTX PRO 6000 Blac... On | 00000000:01:00.0 Off | Off |
| 30% 28C P8 11W / 600W | 271MiB / 97887MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID
... (385 more chars)
assistant
The GPU is idle enough to benchmark, and the problem directory is untracked in git, so I’ll avoid touching anything outside this problem unless the harness forces it. Next I’m reading the exact reference, shape definitions, and validation/benchmark rules.
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Output:
"""Naive multi-axis RoPE pre-attention reference (correctness only).
Mirrors the Qwen2-VL apply_multimodal_rotary_pos_emb semantics: three position
axes (temporal, height, width), each contributing a slice of the head_dim via
mrope_section. Standard rotate-half on q and k once per-axis cos/sin have been
mixed.
Layout convention:
q: (B, T, Hq, D) bf16
k: (B, T, Hkv, D) bf16
pos_t, pos_h, pos_w: (B, T) int64
Output: q_rot, k_rot in (B, H, T, D) — i.e. the layout an attention kernel
expects (head dim second). The transpose is part of the "pre-attention prep".
"""
import torch
import torch.nn as nn
OP_TYPE = "rope"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000"]
def _build_inv_freq(D: int, base: float = 10000.0) -> torch.Tensor:
return 1.0 / (base ** (torch.arange(0, D, 2, dtype=torch.float32) / D))
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
return torch.cat((-x[..., half:], x[..., :half]), dim=-1)
class Model(nn.Module):
def __init__(self, B: int, T: int, Hq: int, Hkv: int, D: int,
mrope_section: tuple[int, int, int], max_pos: int):
super().__init__()
assert sum(mrope_section) == D // 2, \
f"mrope_section must sum to D/2, got {mrope_section} sum={sum(mrope_section)} D/2={D//2}"
self.B, self.T = B, T
self.Hq, self.Hkv, self.D = Hq
... (2866 more chars)
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Output:
"""Correctness runner for multi-axis RoPE pre-attention prep.
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
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):
... (2975 more chars)
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Output:
"""Roofline benchmark for multi-axis RoPE pre-attention prep.
For each shape: times eager reference, compiled reference, SOTA (if available),
and the agent's solution. 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 time_fn # 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))
dev
... (3318 more chars)
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Output:
"""Canonical shape sweep for multi-axis RoPE pre-attention.
Sized for Qwen2-VL-style vision-language inference: long T, GQA, mrope_section
splitting D/2 across temporal/height/width axes. Mix of base, long-context,
small-head-dim, and very-long-context.
"""
SHAPES = [
# Qwen2-VL base: T=4k, head_dim=128, GQA 32:8, mrope [16,24,24]
{"B": 1, "T": 4096, "Hq": 32, "Hkv": 8, "D": 128,
"mrope_section": (16, 24, 24), "max_pos": 32768},
# Qwen2-VL 7B-style long context, narrower kv heads
{"B": 1, "T": 8192, "Hq": 28, "Hkv": 4, "D": 128,
"mrope_section": (16, 24, 24), "max_pos": 32768},
# Smaller head_dim and B=2 (tests batch dim and reduced D)
{"B": 2, "T": 2048, "Hq": 16, "Hkv": 2, "D": 64,
"mrope_section": (8, 12, 12), "max_pos": 16384},
# Very long context (16k tokens)
{"B": 1, "T": 16384, "Hq": 32, "Hkv": 8, "D": 128,
"mrope_section": (16, 24, 24), "max_pos": 65536},
]
tool
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Output:
"""SOTA reference for multi-axis RoPE pre-attention.
Wraps transformers' apply_multimodal_rotary_pos_emb. The full pipeline (gather
per-axis cos/sin from cache, then call the vendor rotation) is timed, matching
what the reference and the agent solution do.
If transformers isn't installed or the impl signature has drifted, returns
is_available()=False and the benchmark omits the SOTA line.
"""
from __future__ import annotations
import torch
def _try_transformers():
try:
from transformers.models.qwen2_vl.modeling_qwen2_vl import (
apply_multimodal_rotary_pos_emb as _impl,
)
return _impl
except Exception:
return None
def sota_forward(
q: torch.Tensor, k: torch.Tensor,
pos_t: torch.Tensor, pos_h: torch.Tensor, pos_w: torch.Tensor,
cos_cache: torch.Tensor, sin_cache: torch.Tensor,
mrope_section: tuple[int, int, int],
):
"""q,k: (B, T, H, D); pos_*: (B, T); cos_cache,sin_cache: (max_pos, D)."""
impl = _try_transformers()
if impl is None:
raise RuntimeError("transformers not available for SOTA")
# Gather per-axis cos/sin and stack into (3, B, T, D) as transformers expects.
cos = torch.stack([cos_cache[pos_t], cos_cache[pos_h], cos_cache[pos_w]], dim=0)
sin = torch.stack([sin_cache[pos_t], sin_cache[pos_h], sin_cache[pos_w]], dim=0)
# transformers wants (B, H, T, D) layo
... (256 more chars)
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Output:
name: 09_fmha_preattn_mrope
display_name: "Multi-axis RoPE Pre-attention Prep"
precision: bf16
regime: memory # pointwise rotation + cache gather; bandwidth-bound
# FLOPs per call: rotate-half on q and k.
# Per element: out = a*cos + b*sin -> 3 flops/elem (1 mul + 1 mul + 1 add).
# We do this on q ((B*T*Hq*D) elems) and k ((B*T*Hkv*D) elems).
flops_formula: "3 * B * T * (Hq + Hkv) * D"
# Bytes moved (lower bound):
# read q, k: 2 * B*T*(Hq+Hkv)*D
# read 3 position id arrays: 3 * B*T*8 (int64)
# read cos/sin from cache: 6 * B*T*D*2 (3 axes, 2 caches, bf16)
# write q_rot, k_rot: 2 * B*T*(Hq+Hkv)*D
bytes_formula: "4 * B * T * (Hq + Hkv) * D + 12 * B * T * D + 24 * B * T"
hardware: [RTX_PRO_6000]
peak_tflops_key: bf16 # informational; regime=memory uses bandwidth
peak_bandwidth_key: dram
tolerance:
bfloat16: 0.02
# Forbidden ops -- agent must implement the gather + per-axis mix + rotation.
# Banning vendor mRoPE entry points and obvious shortcuts.
forbidden:
- "transformers.models.qwen2_vl.modeling_qwen2_vl.apply_multimodal_rotary_pos_emb"
- "apply_multimodal_rotary_pos_emb"
- "flash_attn.layers.rotary"
- "flashinfer.rope"
- "flashinfer.apply_rope"
sota:
name: "Qwen2-VL apply_multimodal_rotary_pos_emb (transformers)"
url: "https://github.com/huggingface/transformers"
function: "transformers.models.qwen2
... (76 more chars)
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Output:
"""Centralized GPU timing for benchmark.py files.
All problems' benchmark.py should call `time_fn` from here so we have one
implementation of warmup, L2 flush, and CUDA event capture to maintain.
Methodology:
- 10 warmup calls absorb Triton autotune (typical ~7 configs) and
torch.compile reduce-overhead CUDA-graph capture.
- Between each timed call, `_l2_flush()` writes 128 MB to evict L2
(Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
we measure HBM-load bandwidth, not L2-cached bandwidth).
- GPU timing via cuda.Event with synchronize() AFTER record() but
BEFORE elapsed_time() — the canonical NVIDIA pattern.
- Reported value is the median of `iters` trials, robust to outliers.
Notes / known biases not addressed here:
- torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
On small shapes where launch overhead matters, this gives compile an
artificial advantage. We accept this as the cost of using the agreed
"compiled" baseline policy.
- cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
- Median over a fairly small number of trials (default 30) is fine for
headline numbers but won't catch bimodal distributions. Use --extra
instrumentation if you ever care.
"""
from __future__ import annotations
... (1171 more chars)
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Output:
../09_fmha_preattn_mrope/check.py:103: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../09_fmha_preattn_mrope/check.py:104: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../01_fp8_gemm/check.py:96: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../01_fp8_gemm/check.py:97: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../05_topk_bitonic/check.py:133: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../05_topk_bitonic/check.py:134: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../03_paged_attention/check.py:93: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../03_paged_attention/check.py:94: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../07_w4a16_gemm/check.py:96: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../07_w4a16_gemm/check.py:97: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../02_kda_cutlass/check.py:97: ("triton", r"import\s+triton\b|@triton\.jit|\btl\.dot\b"),
../02_kda_cutlass/check.py:98: ("cuda_raw", r"torch\.utils\.cpp_extension\.load_inline|__global__\s+void"),
../10_patch_embed_conv3d_gemm/check.py:101: ("triton", r"import\s+trit
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Output:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2026 NVIDIA Corporation
Built on Mon_Mar_02_09:52:23_PM_PST_2026
Cuda compilation tools, release 13.2, V13.2.51
Build cuda_13.2.r13.2/compiler.37434383_0
assistant
The reference does exactly the expected gather, axis slice selection, rotate-half, and transpose. I’m going to start with a Triton implementation because the operation is elementwise plus random cache gathers, then use the benchmark output to decide whether a lower-level CUDA path is warranted.
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Output:
triton 3.6.0
torch 2.11.0+cu130
cuda 13.0
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Output:
assistant
I’m adding a fused Triton kernel in `solution.py` now. The first version computes each token block and head directly into `(B, H, T, D)`, loads each RoPE pair once, and handles q plus the matching k head in the same program where possible.
tool
stdout (181 chars)
output:
Success. Updated the following files:
A /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
assistant
The first fused implementation is in place. I’m running the official correctness sweep now, since that is the gate that exercises all four shapes and seeds.
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Output:
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stdout (107 chars)
Chunk ID: b9eb28
Wall time: 1.3196 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
`check.py` printed `PASS`, so the semantics match the reference. I’m timing it against the provided roofline benchmark next, then I’ll iterate on block size/kernel shape if the numbers leave obvious bandwidth on the table.
tool
stdout (113 chars)
Chunk ID: 4af6bc
Wall time: 1.0015 seconds
Process running with session ID 76231
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: e2248a
Wall time: 5.0013 seconds
Process running with session ID 76231
Original token count: 224
Output:
shape=0 variant=eager tflops=0.177 gbps=253.972 ms=0.355
shape=0 variant=compiled tflops=0.489 gbps=701.335 ms=0.129
shape=0 variant=solution tflops=0.741 gbps=1062.969 ms=0.085
shape=0 solution_peak_fraction=0.5905
shape=1 variant=eager tflops=0.175 gbps=255.914 ms=0.574
shape=1 variant=compiled tflops=0.478 gbps=698.444 ms=0.210
shape=1 variant=solution tflops=0.841 gbps=1228.581 ms=0.120
shape=1 solution_peak_fraction=0.6825
shape=2 variant=eager tflops=0.072 gbps=112.518 ms=0.197
shape=2 variant=compiled tflops=0.163 gbps=254.351 ms=0.087
shape=2 variant=solution tflops=0.391 gbps=610.331 ms=0.036
shape=2 solution_peak_fraction=0.3391
shape=3 variant=eager tflops=0.168 gbps=240.897 ms=1.499
shape=3 variant=compiled tflops=0.463 gbps=664.712 ms=0.543
shape=3 variant=solution tflops=0.936 gbps=1343.151 ms=0.269
shape=3 solution_peak_fraction=0.7462
peak_fraction: 0.5651
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 627c57
Wall time: 0.4118 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
The first benchmark is already comfortably faster than eager and compiled, peaking at about 1.34 TB/s by the harness formula on the 16k shape. The remaining obvious cost is reloading the same RoPE cache for every q head, so I’m changing the tile to cover multiple heads per program and reuse the gathered cos/sin across those heads.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: becd53
Wall time: 1.0012 seconds
Process running with session ID 33846
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 9a84a1
Wall time: 5.0016 seconds
Process running with session ID 33846
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 5613b3
Wall time: 2.3679 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
The multi-head tile also passes correctness. I’m benchmarking it against the previous single-head shape to see whether the cos/sin reuse pays for the extra per-program work.
tool
stdout (113 chars)
Chunk ID: 651b52
Wall time: 1.0018 seconds
Process running with session ID 22896
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 6c17b3
Wall time: 5.0058 seconds
Process running with session ID 22896
Original token count: 0
Output:
tool
stdout (1000 chars)
Chunk ID: ab427c
Wall time: 0.6601 seconds
Process exited with code 0
Original token count: 224
Output:
shape=0 variant=eager tflops=0.174 gbps=249.205 ms=0.362
shape=0 variant=compiled tflops=0.509 gbps=730.198 ms=0.124
shape=0 variant=solution tflops=0.773 gbps=1109.803 ms=0.081
shape=0 solution_peak_fraction=0.6166
shape=1 variant=eager tflops=0.177 gbps=258.361 ms=0.569
shape=1 variant=compiled tflops=0.468 gbps=682.768 ms=0.215
shape=1 variant=solution tflops=0.826 gbps=1205.844 ms=0.122
shape=1 solution_peak_fraction=0.6699
shape=2 variant=eager tflops=0.070 gbps=109.393 ms=0.202
shape=2 variant=compiled tflops=0.172 gbps=268.219 ms=0.082
shape=2 variant=solution tflops=0.417 gbps=651.768 ms=0.034
shape=2 solution_peak_fraction=0.3621
shape=3 variant=eager tflops=0.171 gbps=244.663 ms=1.476
shape=3 variant=compiled tflops=0.465 gbps=667.207 ms=0.541
shape=3 variant=solution tflops=0.934 gbps=1340.757 ms=0.269
shape=3 solution_peak_fraction=0.7449
peak_fraction: 0.5777
RESULT: OK
assistant
The two-head tile improved the geometric score slightly, mostly on the smaller shape, but did not move the long-context case. I’m testing one more reuse point, four q heads per tile, because the tradeoff may vary with head count and D.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: ba52ab
Wall time: 1.0017 seconds
Process running with session ID 96145
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: b74079
Wall time: 5.0017 seconds
Process running with session ID 96145
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 0e682b
Wall time: 2.3726 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 7c9d1d
Wall time: 1.0004 seconds
Process running with session ID 90482
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: f1e061
Wall time: 5.0010 seconds
Process running with session ID 90482
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=251.224 ms=0.359
shape=0 variant=compiled tflops=0.513 gbps=735.720 ms=0.123
shape=0 variant=solution tflops=0.765 gbps=1097.926 ms=0.082
shape=0 solution_peak_fraction=0.6100
shape=1 variant=eager tflops=0.175 gbps=254.942 ms=0.577
shape=1 variant=compiled tflops=0.482 gbps=703.740 ms=0.209
shape=1 variant=solution tflops=0.806 gbps=1176.354 ms=0.125
shape=1 solution_peak_fraction=0.6535
shape=2 variant=eager tflops=0.071 gbps=110.938 ms=0.199
shape=2 variant=compiled tflops=0.162 gbps=253.326 ms=0.087
shape=2 variant=solution tflops=0.412 gbps=643.276 ms=0.034
shape=2 solution_peak_fraction=0.3574
shape=3 variant=eager tflops=0.169 gbps=242.440 ms=1.489
shape=3 variant=compiled tflops=0.461 gbps=662.157 ms=0.545
shape=3 variant=solution tflops=0.901 gbps=1293.498 ms=0.279
shape=3 solution_peak_fraction=0.7186
peak_fraction: 0.5656
RESULT: OK
tool
stdout (102 chars)
Chunk ID: f55ff4
Wall time: 1.0555 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
Four heads per tile was a regression, so I’m backing down to two heads and tuning the token-block size instead. Smaller token tiles may improve occupancy and store scheduling on this memory-bound kernel.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: ba4a52
Wall time: 1.0015 seconds
Process running with session ID 46525
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 3e62f4
Wall time: 5.0008 seconds
Process running with session ID 46525
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 5c767d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 3e4ad5
Wall time: 1.0010 seconds
Process running with session ID 47892
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 9afbf2
Wall time: 5.0009 seconds
Process running with session ID 47892
Original token count: 0
Output:
tool
stdout (998 chars)
Chunk ID: 163e82
Wall time: 1.3802 seconds
Process exited with code 0
Original token count: 224
Output:
shape=0 variant=eager tflops=0.174 gbps=249.216 ms=0.362
shape=0 variant=compiled tflops=0.442 gbps=634.887 ms=0.142
shape=0 variant=solution tflops=0.696 gbps=998.980 ms=0.090
shape=0 solution_peak_fraction=0.5550
shape=1 variant=eager tflops=0.166 gbps=242.762 ms=0.606
shape=1 variant=compiled tflops=0.471 gbps=687.417 ms=0.214
shape=1 variant=solution tflops=0.831 gbps=1213.009 ms=0.121
shape=1 solution_peak_fraction=0.6739
shape=2 variant=eager tflops=0.056 gbps=86.856 ms=0.255
shape=2 variant=compiled tflops=0.166 gbps=258.683 ms=0.086
shape=2 variant=solution tflops=0.422 gbps=659.857 ms=0.034
shape=2 solution_peak_fraction=0.3666
shape=3 variant=eager tflops=0.167 gbps=239.608 ms=1.507
shape=3 variant=compiled tflops=0.462 gbps=663.052 ms=0.545
shape=3 variant=solution tflops=0.943 gbps=1352.812 ms=0.267
shape=3 solution_peak_fraction=0.7516
peak_fraction: 0.5666
RESULT: OK
assistant
The smaller token tile helped the longer shapes but hurt the 4k base shape. I’m checking the other side, a larger token tile, so the final heuristic is based on measured results rather than a guess.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: e6a42a
Wall time: 1.0013 seconds
Process running with session ID 72978
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: eafb79
Wall time: 5.0013 seconds
Process running with session ID 72978
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: e4fa1c
Wall time: 1.9896 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 0d2848
Wall time: 1.0013 seconds
Process running with session ID 48503
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: bf1dc1
Wall time: 5.0009 seconds
Process running with session ID 48503
Original token count: 0
Output:
tool
stdout (1000 chars)
Chunk ID: e23127
Wall time: 0.7357 seconds
Process exited with code 0
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=250.978 ms=0.360
shape=0 variant=compiled tflops=0.501 gbps=718.574 ms=0.126
shape=0 variant=solution tflops=0.748 gbps=1072.873 ms=0.084
shape=0 solution_peak_fraction=0.5960
shape=1 variant=eager tflops=0.177 gbps=258.558 ms=0.569
shape=1 variant=compiled tflops=0.474 gbps=692.756 ms=0.212
shape=1 variant=solution tflops=0.815 gbps=1190.377 ms=0.123
shape=1 solution_peak_fraction=0.6613
shape=2 variant=eager tflops=0.070 gbps=109.836 ms=0.201
shape=2 variant=compiled tflops=0.162 gbps=253.280 ms=0.087
shape=2 variant=solution tflops=0.378 gbps=591.275 ms=0.037
shape=2 solution_peak_fraction=0.3285
shape=3 variant=eager tflops=0.168 gbps=241.441 ms=1.496
shape=3 variant=compiled tflops=0.451 gbps=646.490 ms=0.559
shape=3 variant=solution tflops=0.917 gbps=1315.591 ms=0.274
shape=3 solution_peak_fraction=0.7309
peak_fraction: 0.5546
RESULT: OK
assistant
The large tile regressed, especially for the D=64 shape. I’m restoring the measured better token heuristic and checking warp count next; this is usually the last simple Triton knob before considering a CUDA rewrite.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 290646
Wall time: 1.0010 seconds
Process running with session ID 17174
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 2dda4b
Wall time: 5.0022 seconds
Process running with session ID 17174
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 2b8d8e
Wall time: 2.4258 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: b73bd1
Wall time: 1.0015 seconds
Process running with session ID 53196
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: b631fb
Wall time: 5.0013 seconds
Process running with session ID 53196
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=250.410 ms=0.361
shape=0 variant=compiled tflops=0.517 gbps=742.302 ms=0.122
shape=0 variant=solution tflops=0.709 gbps=1017.170 ms=0.089
shape=0 solution_peak_fraction=0.5651
shape=1 variant=eager tflops=0.176 gbps=256.558 ms=0.573
shape=1 variant=compiled tflops=0.468 gbps=683.683 ms=0.215
shape=1 variant=solution tflops=0.842 gbps=1229.897 ms=0.120
shape=1 solution_peak_fraction=0.6833
shape=2 variant=eager tflops=0.074 gbps=115.692 ms=0.191
shape=2 variant=compiled tflops=0.189 gbps=295.006 ms=0.075
shape=2 variant=solution tflops=0.431 gbps=673.028 ms=0.033
shape=2 solution_peak_fraction=0.3739
shape=3 variant=eager tflops=0.171 gbps=244.650 ms=1.476
shape=3 variant=compiled tflops=0.465 gbps=667.109 ms=0.541
shape=3 variant=solution tflops=0.938 gbps=1346.034 ms=0.268
shape=3 solution_peak_fraction=0.7478
peak_fraction: 0.5732
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 114f96
Wall time: 0.3310 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
Eight warps only helped the D=64 case and the 8k case; it hurt the 4k base shape. I’m checking two warps as the lower bound, then I’ll lock in per-shape choices from the measured runs.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 07d044
Wall time: 1.0015 seconds
Process running with session ID 54391
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 857336
Wall time: 5.0053 seconds
Process running with session ID 54391
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 7e0de2
Wall time: 1.8239 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 824613
Wall time: 1.0012 seconds
Process running with session ID 73401
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: 870b10
Wall time: 5.0013 seconds
Process running with session ID 73401
Original token count: 224
Output:
shape=0 variant=eager tflops=0.177 gbps=253.265 ms=0.356
shape=0 variant=compiled tflops=0.492 gbps=705.456 ms=0.128
shape=0 variant=solution tflops=0.764 gbps=1095.793 ms=0.082
shape=0 solution_peak_fraction=0.6088
shape=1 variant=eager tflops=0.176 gbps=256.321 ms=0.573
shape=1 variant=compiled tflops=0.487 gbps=711.149 ms=0.207
shape=1 variant=solution tflops=0.815 gbps=1190.840 ms=0.123
shape=1 solution_peak_fraction=0.6616
shape=2 variant=eager tflops=0.072 gbps=112.610 ms=0.196
shape=2 variant=compiled tflops=0.165 gbps=258.441 ms=0.086
shape=2 variant=solution tflops=0.387 gbps=605.254 ms=0.037
shape=2 solution_peak_fraction=0.3363
shape=3 variant=eager tflops=0.171 gbps=245.046 ms=1.474
shape=3 variant=compiled tflops=0.465 gbps=666.734 ms=0.542
shape=3 variant=solution tflops=0.922 gbps=1322.684 ms=0.273
shape=3 solution_peak_fraction=0.7348
peak_fraction: 0.5617
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 88ae4f
Wall time: 0.7279 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
The Triton path is good, but it still launches tiles per head block. I’m going to test a raw CUDA kernel that assigns one CTA per token, caches the selected RoPE pairs once in shared memory, then rotates all q and k heads for that token. That matches the operation’s reuse pattern more directly.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
assistant
The raw CUDA path is now wired into `forward`; the Triton implementation is still in the file as a fallback candidate while I measure, but it is no longer called. I’m running the official correctness sweep to catch any bf16 or layout mismatch from the CUDA kernel.
tool
stdout (113 chars)
Chunk ID: d5e6bb
Wall time: 1.0005 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 3fe45b
Wall time: 5.0012 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: ec7c7e
Wall time: 5.0011 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 54669f
Wall time: 5.0006 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 9c9dee
Wall time: 5.0012 seconds
Process running with session ID 43031
Original token count: 0
Output:
assistant
The extension compile is still running; no errors have printed yet. I’m letting it finish because this first build is outside the timed benchmark path and determines whether the CUDA implementation is viable.
tool
stdout (113 chars)
Chunk ID: 9355cb
Wall time: 5.0012 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: f80afe
Wall time: 5.0010 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 401bd1
Wall time: 5.0006 seconds
Process running with session ID 43031
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 5edc45
Wall time: 1.1237 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
The CUDA kernel passed all correctness cases. I’m benchmarking it now; this will tell us whether the one-CTA-per-token reuse beats the tuned Triton tile.
tool
stdout (113 chars)
Chunk ID: 546c75
Wall time: 1.0007 seconds
Process running with session ID 23795
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: b78006
Wall time: 5.0014 seconds
Process running with session ID 23795
Original token count: 0
Output:
tool
stdout (1000 chars)
Chunk ID: bbc279
Wall time: 0.6904 seconds
Process exited with code 0
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=250.945 ms=0.360
shape=0 variant=compiled tflops=0.504 gbps=723.457 ms=0.125
shape=0 variant=solution tflops=0.736 gbps=1056.599 ms=0.085
shape=0 solution_peak_fraction=0.5870
shape=1 variant=eager tflops=0.178 gbps=259.265 ms=0.567
shape=1 variant=compiled tflops=0.486 gbps=709.940 ms=0.207
shape=1 variant=solution tflops=0.840 gbps=1226.121 ms=0.120
shape=1 solution_peak_fraction=0.6812
shape=2 variant=eager tflops=0.074 gbps=115.075 ms=0.192
shape=2 variant=compiled tflops=0.166 gbps=259.022 ms=0.085
shape=2 variant=solution tflops=0.520 gbps=813.176 ms=0.027
shape=2 solution_peak_fraction=0.4518
shape=3 variant=eager tflops=0.169 gbps=241.860 ms=1.493
shape=3 variant=compiled tflops=0.460 gbps=660.394 ms=0.547
shape=3 variant=solution tflops=0.911 gbps=1306.755 ms=0.276
shape=3 solution_peak_fraction=0.7260
peak_fraction: 0.6018
RESULT: OK
assistant
The CUDA kernel is much stronger on the D=64 shape but loses some D=128 bandwidth versus the Triton tile. Before building a hybrid, I’m tuning the CUDA CTA size; the D=128 cases have enough pair work per token that more threads may help.
tool
stdout (176 chars)
apply_patch verification failed: Failed to find expected lines in /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py:
const int threads = 256;
tool
stdout (186 chars)
Chunk ID: 77e080
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 21
Output:
128: const int threads = 256;
159: name="mrope_preattn_cuda_ext_v1",
tool
stdout (2106 chars) — TRUNCATED
Chunk ID: fbd2fc
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 501
Output:
int64_t S164) {
const int64_t B = q.size(0);
const int T = static_cast<int>(T64);
const int Hq = static_cast<int>(Hq64);
const int Hkv = static_cast<int>(Hkv64);
const int D = static_cast<int>(D64);
const int S0 = static_cast<int>(S064);
const int S1 = static_cast<int>(S164);
auto q_out = torch::empty({B, Hq, T, D}, q.options());
auto k_out = torch::empty({B, Hkv, T, D}, k.options());
const int blocks = static_cast<int>(B) * T;
const int threads = 256;
const size_t shmem = static_cast<size_t>(D) * sizeof(__nv_bfloat16);
mrope_token_kernel<<<blocks, threads, shmem, at::cuda::getCurrentCUDAStream()>>>(
reinterpret_cast<const __nv_bfloat16*>(q.data_ptr<at::BFloat16>()),
reinterpret_cast<const __nv_bfloat16*>(k.data_ptr<at::BFloat16>()),
pos_t.data_ptr<int64_t>(),
pos_h.data_ptr<int64_t>(),
pos_w.data_ptr<int64_t>(),
reinterpret_cast<const __nv_bfloat16*>(cos_cache.data_ptr<at::BFloat16>()),
reinterpret_cast<const __nv_bfloat16*>(sin_cache.data_ptr<at::BFloat16>()),
reinterpret_cast<__nv_bfloat16*>(q_out.data_ptr<at::BFloat16>()),
reinterpret_cast<__nv_bfloat16*>(k_out.data_ptr<at::BFloat16>()),
T,
Hq,
Hkv,
D,
S0,
S1);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return {q_out, k_out};
}
"""
_ext = None
... (606 more chars)
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 74cec0
Wall time: 1.0010 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 85eead
Wall time: 5.0013 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 01a5be
Wall time: 5.0010 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: cb7188
Wall time: 5.0009 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 05bbe1
Wall time: 5.0010 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 9f9473
Wall time: 5.0014 seconds
Process running with session ID 36257
Original token count: 0
Output:
assistant
The v2 CUDA extension is compiling. This is another one-time build because the launch configuration changed.
tool
stdout (113 chars)
Chunk ID: a544f9
Wall time: 5.0016 seconds
Process running with session ID 36257
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 391e92
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 98f696
Wall time: 1.0012 seconds
Process running with session ID 21634
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: ff4282
Wall time: 5.0013 seconds
Process running with session ID 21634
Original token count: 224
Output:
shape=0 variant=eager tflops=0.176 gbps=252.257 ms=0.358
shape=0 variant=compiled tflops=0.495 gbps=710.968 ms=0.127
shape=0 variant=solution tflops=0.734 gbps=1053.443 ms=0.086
shape=0 solution_peak_fraction=0.5852
shape=1 variant=eager tflops=0.175 gbps=256.021 ms=0.574
shape=1 variant=compiled tflops=0.483 gbps=705.199 ms=0.208
shape=1 variant=solution tflops=0.810 gbps=1183.172 ms=0.124
shape=1 solution_peak_fraction=0.6573
shape=2 variant=eager tflops=0.064 gbps=100.283 ms=0.221
shape=2 variant=compiled tflops=0.165 gbps=257.622 ms=0.086
shape=2 variant=solution tflops=0.557 gbps=870.529 ms=0.025
shape=2 solution_peak_fraction=0.4836
shape=3 variant=eager tflops=0.172 gbps=246.219 ms=1.467
shape=3 variant=compiled tflops=0.465 gbps=667.286 ms=0.541
shape=3 variant=solution tflops=0.897 gbps=1286.714 ms=0.281
shape=3 solution_peak_fraction=0.7148
peak_fraction: 0.6039
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 2d62f9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
The larger CUDA CTA did not help D=128. I’m making the final path hybrid: Triton for D=128 with the best measured token/warp choices, and CUDA for D=64 where the per-token CTA is clearly faster.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 2e926a
Wall time: 1.0010 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: e9f29b
Wall time: 5.0022 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: eff68b
Wall time: 5.0014 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: b9c650
Wall time: 5.0014 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 72d547
Wall time: 5.0015 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: dbf5b5
Wall time: 5.0016 seconds
Process running with session ID 18843
Original token count: 0
Output:
assistant
The hybrid correctness run is still in the compile phase for the CUDA D=64 specialization. No failures have appeared.
tool
stdout (113 chars)
Chunk ID: d8d4dd
Wall time: 5.0029 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 019a88
Wall time: 5.0015 seconds
Process running with session ID 18843
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 361f15
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
assistant
The hybrid path passes `check.py`. I’m running one final full benchmark to verify the measured per-shape choices together.
tool
stdout (113 chars)
Chunk ID: 6d183d
Wall time: 1.0013 seconds
Process running with session ID 45968
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: 903afc
Wall time: 5.0008 seconds
Process running with session ID 45968
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=251.785 ms=0.359
shape=0 variant=compiled tflops=0.493 gbps=707.846 ms=0.128
shape=0 variant=solution tflops=0.768 gbps=1101.785 ms=0.082
shape=0 solution_peak_fraction=0.6121
shape=1 variant=eager tflops=0.177 gbps=258.521 ms=0.569
shape=1 variant=compiled tflops=0.478 gbps=698.444 ms=0.210
shape=1 variant=solution tflops=0.830 gbps=1212.049 ms=0.121
shape=1 solution_peak_fraction=0.6734
shape=2 variant=eager tflops=0.071 gbps=110.751 ms=0.200
shape=2 variant=compiled tflops=0.168 gbps=263.214 ms=0.084
shape=2 variant=solution tflops=0.517 gbps=808.421 ms=0.027
shape=2 solution_peak_fraction=0.4491
shape=3 variant=eager tflops=0.171 gbps=245.830 ms=1.469
shape=3 variant=compiled tflops=0.466 gbps=667.997 ms=0.541
shape=3 variant=solution tflops=0.937 gbps=1344.031 ms=0.269
shape=3 solution_peak_fraction=0.7467
peak_fraction: 0.6097
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 24e2c6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
The hybrid is the best full-run score so far. One detail from earlier measurements: the 8k D=128 shape looked better with the original single-head Triton tile, so I’m testing that as a shape-specific branch.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: b8794a
Wall time: 1.0014 seconds
Process running with session ID 83204
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: 616454
Wall time: 2.0990 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: ec67ca
Wall time: 1.0012 seconds
Process running with session ID 57263
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: e788d0
Wall time: 5.0004 seconds
Process running with session ID 57263
Original token count: 224
Output:
shape=0 variant=eager tflops=0.175 gbps=251.235 ms=0.359
shape=0 variant=compiled tflops=0.501 gbps=719.123 ms=0.126
shape=0 variant=solution tflops=0.764 gbps=1095.793 ms=0.082
shape=0 solution_peak_fraction=0.6088
shape=1 variant=eager tflops=0.177 gbps=258.572 ms=0.568
shape=1 variant=compiled tflops=0.485 gbps=708.735 ms=0.207
shape=1 variant=solution tflops=0.770 gbps=1123.970 ms=0.131
shape=1 solution_peak_fraction=0.6244
shape=2 variant=eager tflops=0.072 gbps=112.564 ms=0.196
shape=2 variant=compiled tflops=0.165 gbps=258.055 ms=0.086
shape=2 variant=solution tflops=0.525 gbps=820.415 ms=0.027
shape=2 solution_peak_fraction=0.4558
shape=3 variant=eager tflops=0.169 gbps=242.862 ms=1.487
shape=3 variant=compiled tflops=0.463 gbps=664.125 ms=0.544
shape=3 variant=solution tflops=0.925 gbps=1327.742 ms=0.272
shape=3 solution_peak_fraction=0.7376
peak_fraction: 0.5979
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 6a79aa
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
That branch was worse on the 8k shape in the full benchmark, so I’m reverting to the two-head Triton branch and keeping the CUDA path only for D=64.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 2f6603
Wall time: 1.0011 seconds
Process running with session ID 85715
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 66f062
Wall time: 5.0012 seconds
Process running with session ID 85715
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: c5b374
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: cfd290
Wall time: 1.0016 seconds
Process running with session ID 65326
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: 3b9dba
Wall time: 5.0014 seconds
Process running with session ID 65326
Original token count: 224
Output:
shape=0 variant=eager tflops=0.173 gbps=248.404 ms=0.363
shape=0 variant=compiled tflops=0.503 gbps=721.422 ms=0.125
shape=0 variant=solution tflops=0.738 gbps=1058.582 ms=0.085
shape=0 solution_peak_fraction=0.5881
shape=1 variant=eager tflops=0.175 gbps=255.793 ms=0.575
shape=1 variant=compiled tflops=0.480 gbps=701.591 ms=0.210
shape=1 variant=solution tflops=0.809 gbps=1181.194 ms=0.124
shape=1 solution_peak_fraction=0.6562
shape=2 variant=eager tflops=0.067 gbps=104.974 ms=0.211
shape=2 variant=compiled tflops=0.167 gbps=260.241 ms=0.085
shape=2 variant=solution tflops=0.522 gbps=815.094 ms=0.027
shape=2 solution_peak_fraction=0.4528
shape=3 variant=eager tflops=0.171 gbps=244.841 ms=1.475
shape=3 variant=compiled tflops=0.457 gbps=656.456 ms=0.550
shape=3 variant=solution tflops=0.930 gbps=1335.125 ms=0.270
shape=3 solution_peak_fraction=0.7417
peak_fraction: 0.6000
RESULT: OK
tool
stdout (102 chars)
Chunk ID: 7d0852
Wall time: 0.5533 seconds
Process exited with code 0
Original token count: 0
Output:
tool
stdout (2597 chars) — TRUNCATED
Chunk ID: 2dd570
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 624
Output:
max_pos: int,
):
super().__init__()
assert sum(mrope_section) == D // 2
self.B, self.T = B, T
self.Hq, self.Hkv, self.D = Hq, Hkv, D
self.mrope_section = tuple(mrope_section)
self.max_pos = max_pos
inv_freq = _build_inv_freq(D)
pos = torch.arange(max_pos, dtype=torch.float32)
freqs = torch.outer(pos, inv_freq)
emb = torch.cat([freqs, freqs], dim=-1)
self.register_buffer("cos_cache", emb.cos().to(torch.bfloat16))
self.register_buffer("sin_cache", emb.sin().to(torch.bfloat16))
def forward(
self,
q: torch.Tensor,
k: torch.Tensor,
pos_t: torch.Tensor,
pos_h: torch.Tensor,
pos_w: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
s0, s1, _ = self.mrope_section
if self.D == 64:
q_out, k_out = _get_ext().mrope_forward(
q,
k,
pos_t,
pos_h,
pos_w,
self.cos_cache,
self.sin_cache,
self.T,
self.Hq,
self.Hkv,
self.D,
s0,
s1,
)
return q_out, k_out
B, T, Hq, D = q.shape
Hkv = k.shape[2]
q_out = torch.empty((B, Hq, T, D), device=q.device, dtype
... (1097 more chars)
assistant
The last run shows some benchmark noise on the Triton shapes, but the conditional eight-warp 8k branch had the best hybrid score. I’m restoring that measured branch and doing a final validation pass.
tool
stdout (181 chars)
output:
Success. Updated the following files:
M /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py
metadata:
exit_code: 0
duration_seconds: 0.0
tool
stdout (113 chars)
Chunk ID: 421ead
Wall time: 1.0019 seconds
Process running with session ID 95241
Original token count: 0
Output:
tool
stdout (113 chars)
Chunk ID: 0fe60e
Wall time: 5.0004 seconds
Process running with session ID 95241
Original token count: 0
Output:
tool
stdout (107 chars)
Chunk ID: feea21
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
PASS
tool
stdout (113 chars)
Chunk ID: 01f0c5
Wall time: 1.0017 seconds
Process running with session ID 16877
Original token count: 0
Output:
tool
stdout (1011 chars)
Chunk ID: 57c2f9
Wall time: 5.0009 seconds
Process running with session ID 16877
Original token count: 224
Output:
shape=0 variant=eager tflops=0.174 gbps=249.878 ms=0.361
shape=0 variant=compiled tflops=0.492 gbps=705.721 ms=0.128
shape=0 variant=solution tflops=0.730 gbps=1047.186 ms=0.086
shape=0 solution_peak_fraction=0.5818
shape=1 variant=eager tflops=0.176 gbps=256.859 ms=0.572
shape=1 variant=compiled tflops=0.487 gbps=711.424 ms=0.207
shape=1 variant=solution tflops=0.835 gbps=1219.125 ms=0.121
shape=1 solution_peak_fraction=0.6773
shape=2 variant=eager tflops=0.072 gbps=112.345 ms=0.197
shape=2 variant=compiled tflops=0.161 gbps=250.844 ms=0.088
shape=2 variant=solution tflops=0.545 gbps=852.281 ms=0.026
shape=2 solution_peak_fraction=0.4735
shape=3 variant=eager tflops=0.169 gbps=242.761 ms=1.487
shape=3 variant=compiled tflops=0.466 gbps=668.809 ms=0.540
shape=3 variant=solution tflops=0.930 gbps=1335.046 ms=0.270
shape=3 solution_peak_fraction=0.7417
peak_fraction: 0.6099
RESULT: OK
tool
stdout (102 chars)
Chunk ID: c6b794
Wall time: 0.2251 seconds
Process exited with code 0
Original token count: 0
Output:
assistant
Final correctness and benchmark are green with a peak_fraction just over 0.61. I’m running the local lint check on this problem directory now, then I’ll do a quick file/status review before closing.
tool
stdout (121 chars)
Chunk ID: cd072d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!
tool
stdout (419 chars)
Chunk ID: 6d1b09
Wall time: 0.0941 seconds
Process exited with code 5
Original token count: 79
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
collected 0 items
============================ no tests ran in 0.01s =============================
assistant
`ruff` passed. `pytest` found no test items in this problem/repo configuration and exited with pytest’s “no tests collected” code; the relevant correctness gate remains `check.py`, which printed `PASS`. I’m doing a final diff/status review.
tool
stdout (175 chars)
Chunk ID: b4b049
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 18
Output:
?? ./
?? ../10_patch_embed_conv3d_gemm/
?? ../../scripts/sweep_09_10.sh
tool
stdout (102 chars)
Chunk ID: 83c930
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:
tool
stdout (869 chars)
Chunk ID: e51d48
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 192
Output:
total 68
drwxrwxr-x 3 infatoshi infatoshi 4096 May 7 01:28 .
drwxrwxr-x 12 infatoshi infatoshi 4096 May 4 13:57 ..
-rw-rw-r-- 1 infatoshi infatoshi 2950 May 4 14:01 PROMPT.txt
drwxrwxr-x 2 infatoshi infatoshi 4096 May 7 01:40 __pycache__
-rw-rw-r-- 1 infatoshi infatoshi 4713 May 4 14:00 benchmark.py
-rw-rw-r-- 1 infatoshi infatoshi 4372 May 4 14:04 check.py
-rw-rw-r-- 1 infatoshi infatoshi 7 May 7 01:40 framework.txt
-rw-rw-r-- 1 infatoshi infatoshi 1472 May 4 13:59 problem.yaml
-rw-rw-r-- 1 infatoshi infatoshi 4263 May 4 13:58 reference.py
-rw-rw-r-- 1 infatoshi infatoshi 938 May 4 13:59 shapes.py
-rw-rw-r-- 1 infatoshi infatoshi 10230 May 7 01:39 solution.py
-rw-rw-r-- 1 infatoshi infatoshi 1652 May 4 14:00 sota.py
tool
stdout (109 chars)
Chunk ID: 295fff
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2
Output:
triton
tool
stdout (10335 chars) — TRUNCATED
Chunk ID: bc7b9d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2558
Output:
import torch
import torch.nn as nn
import triton
import triton.language as tl
from torch.utils.cpp_extension import load_inline
OP_TYPE = "rope"
SUPPORTED_PRECISIONS = ["bf16"]
HARDWARE_REQUIRED = ["RTX_PRO_6000"]
_CPP_SRC = r"""
#include <torch/extension.h>
std::vector<torch::Tensor> mrope_forward(
torch::Tensor q,
torch::Tensor k,
torch::Tensor pos_t,
torch::Tensor pos_h,
torch::Tensor pos_w,
torch::Tensor cos_cache,
torch::Tensor sin_cache,
int64_t T,
int64_t Hq,
int64_t Hkv,
int64_t D,
int64_t S0,
int64_t S1);
"""
_CUDA_SRC = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_bf16.h>
#include <stdint.h>
__global__ void mrope_token_kernel(
const __nv_bfloat16* __restrict__ q,
const __nv_bfloat16* __restrict__ k,
const int64_t* __restrict__ pos_t,
const int64_t* __restrict__ pos_h,
const int64_t* __restrict__ pos_w,
const __nv_bfloat16* __restrict__ cos_cache,
const __nv_bfloat16* __restrict__ sin_cache,
__nv_bfloat16* __restrict__ q_out,
__nv_bfloat16* __restrict__ k_out,
int T,
int Hq,
int Hkv,
int D,
int S0,
int S1) {
extern __shared__ __nv_bfloat16 rope[];
__nv_bfloat16* c = rope;
__nv_bfloat16* s = rope + (D >> 1);
const int bt = blockIdx.x;
const int b = bt /
... (8835 more chars)
tool
stdout (121 chars)
Chunk ID: 74b97d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 5
Output:
All checks passed!
tool
stdout (419 chars)
Chunk ID: 0dd765
Wall time: 0.0388 seconds
Process exited with code 5
Original token count: 79
Output:
============================= test session starts ==============================
platform linux -- Python 3.11.15, pytest-9.0.3, pluggy-1.6.0
rootdir: /home/infatoshi/cuda/KernelBench-Hard
configfile: pyproject.toml
collected 0 items
============================ no tests ran in 0.00s =============================
assistant
Implemented the optimized prep in [solution.py](/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py).
It uses a hybrid path:
- Triton fused gather + mRoPE mix + rotate + transpose for `D=128`.
- Raw CUDA extension path for `D=64`, one CTA per token with RoPE pairs cached in shared memory.
Validation:
- `uv run python check.py`: `PASS`
- `uv run python benchmark.py`: `peak_fraction: 0.6099`, `RESULT: OK`
- Best final solution lines: shape0 `1047 GB/s`, shape1 `1219 GB/s`, shape2 `852 GB/s`, shape3 `1335 GB/s`
- `uv run ruff check . --fix`: passed
- `uv run pytest`: no tests collected, pytest exit code 5.