{
  "todos": [
    {
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "in_progress",
      "priority": "high"
    },
    {
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Check correctness with python check.py",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Profile and optimize iteratively",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Benchmark and finalize",

... (64 more chars)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/reference.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/check.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/shapes.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/benchmark.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/problem.yaml"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/sota.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/src/eval/correctness.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/src/hardware.py"
}

{
  "pattern": "**/hardware.py",
  "path": "/home/infatoshi/cuda/KernelBench-Hard/src"
}

{
  "pattern": "**/hardware/**",
  "path": "/home/infatoshi/cuda/KernelBench-Hard/src"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/src/hardware/rtx_pro_6000.py"
}

{
  "command": "nvidia-smi && uv run python --version && uv run python -c \"import triton; print(f'Triton {triton.__version__}')\" 2>&1 | head -20",
  "description": "Check GPU, Python, Triton availability"
}

{
  "command": "uv run python -c \"import torch; print(f'CUDA {torch.version.cuda}, Torch {torch.__version__}, SM: {torch.cuda.get_device_capability()}')\" 2>&1",
  "description": "Check torch and CUDA version"
}

{
  "command": "<316 chars \u2014 see below>",
  "description": "Check Triton API signatures"
}

uv run python -c "
import triton.language as tl
import inspect
# Check cat signature
try:
    sig = inspect.signature(tl.cat)
    print(f'tl.cat: {sig}')
except:
    print('no signature')
# Check reshape
try:
    sig = inspect.signature(tl.reshape)
    print(f'tl.reshape: {sig}')
except:
    print('no signature')
"

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "in_progress"
    },
    {
      "priority": "high",
      "content": "Check correctness with python check.py",
      "status": "pending"
    },
    {
      "priority": "high",
      "content": "Profile and optimize iteratively",
      "status": "pending"
    },
    {
      "priority": "high",
      "content

... (66 more chars)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<4659 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 | tail -30",
  "description": "Run correctness check on initial Triton kernel",
  "timeout": 120000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "<202 chars \u2014 see below>",
  "newString": "<398 chars \u2014 see below>"
}

    # rotate_half(x) = cat(-x[half:], x[:half])
    x_2d = tl.reshape(x, 2, half_D)
    first = x_2d[0, :]
    second = x_2d[1, :]
    rotated = tl.cat(-second, first)

    out = x * cos + rotated * sin

    # rotate_half(x) = cat(-x[half:], x[:half])
    # Load paired elements; x is already in L1 from the previous load
    pair_offs = tl.where(d_offs < half_D, d_offs + half_D, d_offs - half_D)
    x_pair = tl.load(x_ptr + b * stride_x_b + t * stride_x_t + h * stride_x_h + pair_offs, mask=mask, other=0.0)
    rotated = tl.where(d_offs < half_D, -x_pair, x_pair)

    out = x * cos + rotated * sin

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 | tail -30",
  "description": "Check correctness after fixing rotate_half",
  "timeout": 120000
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check correctness with python check.py",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Profile and optimize iteratively",
      "status": "in_progress"
    },
    {
      "priority": "high",
      "con

... (70 more chars)

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run benchmark.py 2>&1",
  "description": "Run benchmark to see performance baseline",
  "timeout": 300000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<4564 chars \u2014 see diff>"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "<163 chars \u2014 see below>",
  "newString": "<68 chars \u2014 see below>"
}

        num_warps = half_D // 32
        if D == 128:
            num_warps = 4
        elif D == 64:
            num_warps = 2

        _mrope_kernel[(B, Hq, T)](

        num_warps = half_D // 32

        _mrope_kernel[(B, Hq, T)](

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run benchmark.py 2>&1",
  "description": "Benchmark after half_D optimization",
  "timeout": 300000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1",
  "description": "Check correctness after half_D optimization",
  "timeout": 120000
}

{
  "description": "Profile kernel with simple timing breakdown",
  "command": "<2124 chars \u2014 see below>",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch
import triton
import triton.language as tl
from solution import Model, MROPE_SECTION, MAX_POS

B, T, Hq, Hkv, D = 1, 4096, 32, 8, 128
m = Model(B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS).cuda().eval()

q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')

# Warmup
for _ in range(10):
    m(q, k, pos_t, pos_h, pos_w)

# Time each kernel separately
import time
n_iters = 100

# Time q kernel
m.q_only = lambda: m._mrope_kernel[(1, 32, 4096)](
    q, torch.empty(1, 32, 4096, 128, dtype=torch.bfloat16, device='cuda'),
    m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
    q.stride(0), q.stride(1), q.stride(2),
    4096*128, 128, 1,  # out strides
    1, 4096, 32, 128,
    MROPE_SECTION[0], MROPE_SECTION[1], MROPE_SECTION[2],
    half_D=64,
    num_warps=2,
)
torch.cuda.synchronize()
t0 = time.time()
for _ in range(n_iters):
    m.q_only()
torch.cuda.synchronize()
t1 = time.time()
print(f'Q kernel: {(t1-t0)/n_iters*1000:.3f} ms')

# Time k kernel  
m.k_only = lambda: m._mrope_kernel[(1, 8, 4096)](
    k, torch.empty(1, 8, 4096, 128, dtype=torch.bfloat16, device='cuda'),
    m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
    k.stride(0), k.stride(1), k.stride(2),
    4096*128, 128, 1,
    1, 4096, 8, 128,
    MROPE_SECTION[0], MROPE_SECTION[1], MROPE_SECTION[2],
    half_D=64,
    num_warps=2,
)
torch.cuda.synchronize()
t0 = time.time()
for _ in range(n_iters):
    m.k_only()
torch.cuda.synchronize()
t1 = time.time()
print(f'K kernel: {(t1-t0)/n_iters*1000:.3f} ms')

# Time combined
torch.cuda.synchronize()
t0 = time.time()
for _ in range(n_iters):
    m(q, k, pos_t, pos_h, pos_w)
torch.cuda.synchronize()
t1 = time.time()
print(f'Combined: {(t1-t0)/n_iters*1000:.3f} ms')
" 2>&1

{
  "command": "<1097 chars \u2014 see below>",
  "description": "Time combined forward pass",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch
import time
from solution import Model, MROPE_SECTION, MAX_POS

B, T, Hq, Hkv, D = 1, 4096, 32, 8, 128
m = Model(B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS).cuda().eval()

q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')

# Warmup
for _ in range(10):
    m(q, k, pos_t, pos_h, pos_w)

n_iters = 100
torch.cuda.synchronize()
t0 = time.time()
for _ in range(n_iters):
    m(q, k, pos_t, pos_h, pos_w)
torch.cuda.synchronize()
t1 = time.time()
print(f'Combined forward: {(t1-t0)/n_iters*1000:.3f} ms')

# Now let's also check with nsight compute
import subprocess
ret = subprocess.run(['ncu', '--list-sets'], capture_output=True, text=True)
print(ret.stdout[:500])
" 2>&1

{
  "command": "<2618 chars \u2014 see below>",
  "description": "Sweep num_warps for best performance",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch
from solution import Model, MROPE_SECTION, MAX_POS
import time

# Test different num_warps settings
B, T, Hq, Hkv, D = 1, 4096, 32, 8, 128
m = Model(B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS).cuda().eval()

q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')

for nw in [1, 2, 4, 8]:
    import solution
    # Hot-patch num_warps by recompiling
    try:
        from triton.runtime import jit
        # Just directly modify Model.forward
        original_forward = Model.forward
    except:
        pass
    
    out_q = torch.empty(B, Hq, T, D, dtype=torch.bfloat16, device='cuda')
    out_k = torch.empty(B, Hkv, T, D, dtype=torch.bfloat16, device='cuda')
    s0, s1, s2 = MROPE_SECTION
    half_D = 64
    
    # Warmup with specific num_warps
    solution._mrope_kernel[(B, Hq, T)](
        q, out_q, m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
        q.stride(0), q.stride(1), q.stride(2),
        out_q.stride(0), out_q.stride(1), out_q.stride(2),
        B, T, Hq, D, s0, s1, s2,
        half_D=half_D, num_warps=nw,
    )
    solution._mrope_kernel[(B, Hkv, T)](
        k, out_k, m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
        k.stride(0), k.stride(1), k.stride(2),
        out_k.stride(0), out_k.stride(1), out_k.stride(2),
        B, T, Hkv, D, s0, s1, s2,
        half_D=half_D, num_warps=nw,
    )
    torch.cuda.synchronize()
    
    n_iters = 100
    t0 = time.time()
    for _ in range(n_iters):
        solution._mrope_kernel[(B, Hq, T)](
            q, out_q, m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
            q.stride(0), q.stride(1), q.stride(2),
            out_q.stride(0), out_q.stride(1), out_q.stride(2),
            B, T, Hq, D, s0, s1, s2,
            half_D=half_D, num_warps=nw,
        )
        solution._mrope_kernel[(B, Hkv, T)](
            k, out_k, m.cos_cache, m.sin_cache, pos_t, pos_h, pos_w,
            k.stride(0), k.stride(1), k.stride(2),
            out_k.stride(0), out_k.stride(1), out_k.stride(2),
            B, T, Hkv, D, s0, s1, s2,
            half_D=half_D, num_warps=nw,
        )
    torch.cuda.synchronize()
    t1 = time.time()
    ms = (t1-t0)/n_iters*1000
    print(f'num_warps={nw}: {ms:.4f} ms total')
" 2>&1

{
  "command": "cat /home/infatoshi/cuda/KernelBench-Hard/src/eval/timing.py 2>/dev/null || find /home/infatoshi/cuda/KernelBench-Hard -name \"timing.py\" -exec cat {} \\;",
  "description": "Read timing utility"
}

{
  "command": "<1473 chars \u2014 see below>",
  "description": "Measure with L2 flush to match benchmark",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch
import time

from solution import Model, MROPE_SECTION, MAX_POS

B, T, Hq, Hkv, D = 1, 4096, 32, 8, 128
m = Model(B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS).cuda().eval()

q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')

# L2 flush helper
l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_with_flush(fn, n_iters=30):
    for _ in range(10):
        fn()
    torch.cuda.synchronize()
    times = []
    for _ in range(n_iters):
        l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        fn()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    med = sorted(times)[len(times)//2]
    return med

# Test combined
fn = lambda: m(q, k, pos_t, pos_h, pos_w)
ms = time_with_flush(fn)
print(f'Combined (with L2 flush): {ms:.3f} ms')

# For reference: benchmark.py output
print(f'Expected benchmark: ~{ms:.3f} ms (full forward)')
" 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/src/eval/timing.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<4921 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1",
  "description": "Check correctness after combined kernel",
  "timeout": 120000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run benchmark.py 2>&1",
  "description": "Benchmark combined kernel",
  "timeout": 300000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<4861 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1",
  "description": "Check correctness after BLOCK_D=D design",
  "timeout": 120000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run benchmark.py 2>&1",
  "description": "Benchmark BLOCK_D=D design",
  "timeout": 300000
}

{
  "command": "<3463 chars \u2014 see below>",
  "description": "Sweep num_warps for D=64 shape",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch, time
import triton, triton.language as tl

B, T, Hq, Hkv, D = 2, 2048, 16, 2, 64
mrope_section = (8, 12, 12)

q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')

cos_cache = torch.empty(16384, D, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1)
sin_cache = torch.empty(16384, D, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1)

out_q = torch.empty(B, Hq, T, D, dtype=torch.bfloat16, device='cuda')
out_k = torch.empty(B, Hkv, T, D, dtype=torch.bfloat16, device='cuda')

s0, s1, s2 = mrope_section
half_D = D // 2

# Test various num_warps for half_D approach  
@triton.jit
def test_half_kernel(
    q_ptr, k_ptr, out_q_ptr, out_k_ptr, cos_cache_ptr, sin_cache_ptr,
    pos_t_ptr, pos_h_ptr, pos_w_ptr,
    sq_b, sq_t, sq_h, sk_b, sk_t, sk_h,
    soq_b, soq_h, soq_t, sok_b, sok_h, sok_t,
    B, T, Hq, Hkv, D, s0, s1, s2, half_D: tl.constexpr):
    b= tl.program_id(0); hg= tl.program_id(1); t= tl.program_id(2)
    is_q= hg < Hq
    po= b*T+t
    pt=tl.load(pos_t_ptr+po); ph=tl.load(pos_h_ptr+po); pw=tl.load(pos_w_ptr+po)
    do= tl.arange(0, half_D)
    if is_q:
        bx= b*sq_b + t*sq_t + hg*sq_h; bo= b*soq_b + hg*soq_h + t*soq_t
        x1= tl.load(q_ptr+bx+do); x2= tl.load(q_ptr+bx+do+half_D)
    else:
        hk= hg-Hq; bx= b*sk_b + t*sk_t + hk*sk_h; bo= b*sok_b + hk*sok_h + t*sok_t
        x1= tl.load(k_ptr+bx+do); x2= tl.load(k_ptr+bx+do+half_D)
    pa= tl.where(do < s0, pt, tl.where(do < s0+s1, ph, pw))
    c1= tl.load(cos_cache_ptr+pa*D+do); s1_= tl.load(sin_cache_ptr+pa*D+do)
    c2= tl.load(cos_cache_ptr+pa*D+do+half_D); s2_= tl.load(sin_cache_ptr+pa*D+do+half_D)
    o1= x1*c1 + (-x2)*s1_; o2= x2*c2 + x1*s2_
    if is_q: tl.store(out_q_ptr+bo+do, o1); tl.store(out_q_ptr+bo+do+half_D, o2)
    else: tl.store(out_k_ptr+bo+do, o1); tl.store(out_k_ptr+bo+do+half_D, o2)

l2 = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')
for nw in [1, 2, 4]:
    for _ in range(10):
        test_half_kernel[(B, Hq+Hkv, T)](q,k,out_q,out_k,cos_cache,sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,Hq,Hkv,D,s0,s1,s2,half_D=half_D, num_warps=nw)
    torch.cuda.synchronize()
    times=[]
    for _ in range(30):
        l2.zero_(); torch.cuda.synchronize()
        s= torch.cuda.Event(enable_timing=True); e= torch.cuda.Event(enable_timing=True)
        s.record()
        test_half_kernel[(B, Hq+Hkv, T)](q,k,out_q,out_k,cos_cache,sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,Hq,Hkv,D,s0,s1,s2,half_D=half_D, num_warps=nw)
        e.record(); torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    med= sorted(times)[len(times)//2]
    print(f'D=64 half_D approach num_warps={nw}: {med:.3f} ms')
" 2>&1

{
  "description": "Sweep num_warps for D=64 using test file",
  "command": "<3440 chars \u2014 see below>",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && cat > /tmp/test_nw.py << 'PYEOF'
import torch
import triton
import triton.language as tl
import time

@triton.jit
def test_half_kernel(
    q_ptr, k_ptr, out_q_ptr, out_k_ptr, cos_cache_ptr, sin_cache_ptr,
    pos_t_ptr, pos_h_ptr, pos_w_ptr,
    sq_b, sq_t, sq_h, sk_b, sk_t, sk_h,
    soq_b, soq_h, soq_t, sok_b, sok_h, sok_t,
    B, T, Hq, Hkv, D, s0, s1, s2, half_D: tl.constexpr):
    b= tl.program_id(0); hg= tl.program_id(1); t= tl.program_id(2)
    is_q= hg < Hq
    po= b*T+t
    pt=tl.load(pos_t_ptr+po); ph=tl.load(pos_h_ptr+po); pw=tl.load(pos_w_ptr+po)
    do= tl.arange(0, half_D)
    if is_q:
        bx= b*sq_b + t*sq_t + hg*sq_h; bo= b*soq_b + hg*soq_h + t*soq_t
        x1= tl.load(q_ptr+bx+do); x2= tl.load(q_ptr+bx+do+half_D)
    else:
        hk= hg-Hq; bx= b*sk_b + t*sk_t + hk*sk_h; bo= b*sok_b + hk*sok_h + t*sok_t
        x1= tl.load(k_ptr+bx+do); x2= tl.load(k_ptr+bx+do+half_D)
    pa= tl.where(do < s0, pt, tl.where(do < s0+s1, ph, pw))
    c1= tl.load(cos_cache_ptr+pa*D+do); s1_= tl.load(sin_cache_ptr+pa*D+do)
    c2= tl.load(cos_cache_ptr+pa*D+do+half_D); s2_= tl.load(sin_cache_ptr+pa*D+do+half_D)
    o1= x1*c1 + (-x2)*s1_; o2= x2*c2 + x1*s2_
    if is_q: tl.store(out_q_ptr+bo+do, o1); tl.store(out_q_ptr+bo+do+half_D, o2)
    else: tl.store(out_k_ptr+bo+do, o1); tl.store(out_k_ptr+bo+do+half_D, o2)

B, T, Hq, Hkv, D = 2, 2048, 16, 2, 64
s0, s1, s2 = 8, 12, 12
half_D = D // 2
q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, 16384, (B, T), dtype=torch.int64, device='cuda')
cos_cache = torch.empty(16384, D, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1)
sin_cache = torch.empty(16384, D, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1)
out_q = torch.empty(B, Hq, T, D, dtype=torch.bfloat16, device='cuda')
out_k = torch.empty(B, Hkv, T, D, dtype=torch.bfloat16, device='cuda')
l2 = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')

for nw in [1, 2, 4]:
    for _ in range(10):
        test_half_kernel[(B, Hq+Hkv, T)](q,k,out_q,out_k,cos_cache,sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,Hq,Hkv,D,s0,s1,s2,half_D=half_D, num_warps=nw)
    torch.cuda.synchronize()
    times=[]
    for _ in range(30):
        l2.zero_(); torch.cuda.synchronize()
        s= torch.cuda.Event(enable_timing=True); e= torch.cuda.Event(enable_timing=True)
        s.record()
        test_half_kernel[(B, Hq+Hkv, T)](q,k,out_q,out_k,cos_cache,sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,Hq,Hkv,D,s0,s1,s2,half_D=half_D, num_warps=nw)
        e.record(); torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    med= sorted(times)[len(times)//2]
    print(f'D=64 half_D approach num_warps={nw}: {med:.3f} ms')
PYEOF
uv run python /tmp/test_nw.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<5224 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 && uv run benchmark.py 2>&1",
  "description": "Check + benchmark after num_warps fix",
  "timeout": 300000
}

{
  "command": "<767 chars \u2014 see below>",
  "description": "Profile kernel with ncu",
  "timeout": 180000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run ncu --set basic -c 1 --kernel-name _mrope_kernel --page details python -c "
from solution import Model, MROPE_SECTION, MAX_POS
import torch
m = Model(1, 4096, 32, 8, 128, MROPE_SECTION, MAX_POS).cuda().eval()
q = torch.randn(1, 4096, 32, 128, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(1, 4096, 8, 128, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
for _ in range(5): m(q,k,pos_t,pos_h,pos_w)
torch.cuda.synchronize()
" 2>&1 | head -80

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check correctness with python check.py",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Profile and optimize iteratively",
      "status": "in_progress"
    },
    {
      "priority": "high",
      "con

... (70 more chars)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "content": "<6140 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1",
  "description": "Check correctness of head-grouping kernel",
  "timeout": 120000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run benchmark.py 2>&1",
  "description": "Benchmark head-grouping kernel",
  "timeout": 300000
}

{
  "command": "<767 chars \u2014 see below>",
  "description": "ncu profile of new kernel",
  "timeout": 180000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run ncu --set basic -c 1 --kernel-name _mrope_kernel --page details python -c "
from solution import Model, MROPE_SECTION, MAX_POS
import torch
m = Model(1, 4096, 32, 8, 128, MROPE_SECTION, MAX_POS).cuda().eval()
q = torch.randn(1, 4096, 32, 128, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(1, 4096, 8, 128, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
for _ in range(5): m(q,k,pos_t,pos_h,pos_w)
torch.cuda.synchronize()
" 2>&1 | head -80

{
  "command": "<2150 chars \u2014 see below>",
  "description": "Sweep num_warps for head-grouping kernel",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
import torch, time

# Test different num_warps with head-grouping  
from solution import Model, MROPE_SECTION, MAX_POS

B, T, Hq, Hkv, D = 1, 4096, 32, 8, 128
m = Model(B, T, Hq, Hkv, D, MROPE_SECTION, MAX_POS).cuda().eval()
q = torch.randn(B, T, Hq, D, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(B, T, Hkv, D, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (B, T), dtype=torch.int64, device='cuda')

s0, s1, s2 = MROPE_SECTION
half_D = D // 2
out_q = torch.empty(B, Hq, T, D, dtype=torch.bfloat16, device='cuda')
out_k = torch.empty(B, Hkv, T, D, dtype=torch.bfloat16, device='cuda')
l2 = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')

import solution
for nw in [2, 4, 8]:
    for _ in range(10):
        solution._mrope_kernel[(B, T)](
            q,k,out_q,out_k,m.cos_cache,m.sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,D,s0,s1,s2,half_D=half_D,Hq_=Hq,Hkv_=Hkv,num_warps=nw)
    torch.cuda.synchronize()
    times=[]
    for _ in range(30):
        l2.zero_(); torch.cuda.synchronize()
        s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
        s.record()
        solution._mrope_kernel[(B, T)](
            q,k,out_q,out_k,m.cos_cache,m.sin_cache,pos_t,pos_h,pos_w,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            out_q.stride(0),out_q.stride(1),out_q.stride(2),out_k.stride(0),out_k.stride(1),out_k.stride(2),
            B,T,D,s0,s1,s2,half_D=half_D,Hq_=Hq,Hkv_=Hkv,num_warps=nw)
        e.record(); torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    med=sorted(times)[len(times)//2]
    print(f'num_warps={nw}: {med:.3f} ms')
" 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "            num_warps=4,",
  "newString": "            num_warps=2,"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 && uv run benchmark.py 2>&1",
  "description": "Check + benchmark with num_warps=2",
  "timeout": 300000
}

{
  "command": "<879 chars \u2014 see below>",
  "description": "Quick ncu check for num_warps=2",
  "timeout": 180000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run ncu --set basic -c 1 --kernel-name _mrope_kernel --page details python -c "
from solution import Model, MROPE_SECTION, MAX_POS
import torch
m = Model(1, 4096, 32, 8, 128, MROPE_SECTION, MAX_POS).cuda().eval()
q = torch.randn(1, 4096, 32, 128, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(1, 4096, 8, 128, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, MAX_POS, (1, 4096), dtype=torch.int64, device='cuda')
for _ in range(5): m(q,k,pos_t,pos_h,pos_w)
torch.cuda.synchronize()
" 2>&1 | grep -E 'Waves|Partial|Tail|Duration|Memory Throughput|DRAM Throughput|Occupancy|Registers|Block Limit|Warp|num_warps=2'

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "            num_warps=2,",
  "newString": "            num_warps=4,"
}

{
  "command": "<5766 chars \u2014 see below>",
  "description": "Compare in-loop vs hoisted axis selection",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && cat > /tmp/test_inline.py << 'PYEOF'
import torch, triton, triton.language as tl, time

# V1: axis mixing inside head loop (saves regs)
@triton.jit
def _mrope_inline(
    q_ptr, k_ptr, out_q_ptr, out_k_ptr, cos_ptr, sin_ptr,
    pt_ptr, ph_ptr, pw_ptr,
    sq_b,sq_t,sq_h,sk_b,sk_t,sk_h,soq_b,soq_h,soq_t,sok_b,sok_h,sok_t,
    B,T,D,s0,s1,s2,half_D:tl.constexpr,Hq_:tl.constexpr,Hkv_:tl.constexpr):
    b=tl.program_id(0); t=tl.program_id(1)
    po=b*T+t
    pt=tl.load(pt_ptr+po); ph=tl.load(ph_ptr+po); pw=tl.load(pw_ptr+po)
    do=tl.arange(0,half_D)
    cos_t=tl.load(cos_ptr+pt*D+do); sin_t=tl.load(sin_ptr+pt*D+do)
    cos_h=tl.load(cos_ptr+ph*D+do); sin_h=tl.load(sin_ptr+ph*D+do)
    cos_w=tl.load(cos_ptr+pw*D+do); sin_w=tl.load(sin_ptr+pw*D+do)
    cos_t2=tl.load(cos_ptr+pt*D+do+half_D); sin_t2=tl.load(sin_ptr+pt*D+do+half_D)
    cos_h2=tl.load(cos_ptr+ph*D+do+half_D); sin_h2=tl.load(sin_ptr+ph*D+do+half_D)
    cos_w2=tl.load(cos_ptr+pw*D+do+half_D); sin_w2=tl.load(sin_ptr+pw*D+do+half_D)
    is_t=do<s0; is_h=(do>=s0)&(do<(s0+s1))
    for h in range(Hq_):
        bx=b*sq_b+t*sq_t+h*sq_h; bo=b*soq_b+h*soq_h+t*soq_t
        x1=tl.load(q_ptr+bx+do); x2=tl.load(q_ptr+bx+do+half_D)
        c1=tl.where(is_t,cos_t,tl.where(is_h,cos_h,cos_w))
        s1v=tl.where(is_t,sin_t,tl.where(is_h,sin_h,sin_w))
        c2=tl.where(is_t,cos_t2,tl.where(is_h,cos_h2,cos_w2))
        s2v=tl.where(is_t,sin_t2,tl.where(is_h,sin_h2,sin_w2))
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_q_ptr+bo+do,o1); tl.store(out_q_ptr+bo+do+half_D,o2)
    for h in range(Hkv_):
        bx=b*sk_b+t*sk_t+h*sk_h; bo=b*sok_b+h*sok_h+t*sok_t
        x1=tl.load(k_ptr+bx+do); x2=tl.load(k_ptr+bx+do+half_D)
        c1=tl.where(is_t,cos_t,tl.where(is_h,cos_h,cos_w))
        s1v=tl.where(is_t,sin_t,tl.where(is_h,sin_h,sin_w))
        c2=tl.where(is_t,cos_t2,tl.where(is_h,cos_h2,cos_w2))
        s2v=tl.where(is_t,sin_t2,tl.where(is_h,sin_h2,sin_w2))
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_k_ptr+bo+do,o1); tl.store(out_k_ptr+bo+do+half_D,o2)

# V2: axis mixing outside head loop (current approach)
@triton.jit
def _mrope_outside(
    q_ptr, k_ptr, out_q_ptr, out_k_ptr, cos_ptr, sin_ptr,
    pt_ptr, ph_ptr, pw_ptr,
    sq_b,sq_t,sq_h,sk_b,sk_t,sk_h,soq_b,soq_h,soq_t,sok_b,sok_h,sok_t,
    B,T,D,s0,s1,s2,half_D:tl.constexpr,Hq_:tl.constexpr,Hkv_:tl.constexpr):
    b=tl.program_id(0); t=tl.program_id(1)
    po=b*T+t
    pt=tl.load(pt_ptr+po); ph=tl.load(ph_ptr+po); pw=tl.load(pw_ptr+po)
    do=tl.arange(0,half_D)
    cos_t=tl.load(cos_ptr+pt*D+do); sin_t=tl.load(sin_ptr+pt*D+do)
    cos_h=tl.load(cos_ptr+ph*D+do); sin_h=tl.load(sin_ptr+ph*D+do)
    cos_w=tl.load(cos_ptr+pw*D+do); sin_w=tl.load(sin_ptr+pw*D+do)
    cos_t2=tl.load(cos_ptr+pt*D+do+half_D); sin_t2=tl.load(sin_ptr+pt*D+do+half_D)
    cos_h2=tl.load(cos_ptr+ph*D+do+half_D); sin_h2=tl.load(sin_ptr+ph*D+do+half_D)
    cos_w2=tl.load(cos_ptr+pw*D+do+half_D); sin_w2=tl.load(sin_ptr+pw*D+do+half_D)
    is_t=do<s0; is_h=(do>=s0)&(do<(s0+s1))
    c1=tl.where(is_t,cos_t,tl.where(is_h,cos_h,cos_w))
    s1v=tl.where(is_t,sin_t,tl.where(is_h,sin_h,sin_w))
    c2=tl.where(is_t,cos_t2,tl.where(is_h,cos_h2,cos_w2))
    s2v=tl.where(is_t,sin_t2,tl.where(is_h,sin_h2,sin_w2))
    for h in range(Hq_):
        bx=b*sq_b+t*sq_t+h*sq_h; bo=b*soq_b+h*soq_h+t*soq_t
        x1=tl.load(q_ptr+bx+do); x2=tl.load(q_ptr+bx+do+half_D)
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_q_ptr+bo+do,o1); tl.store(out_q_ptr+bo+do+half_D,o2)
    for h in range(Hkv_):
        bx=b*sk_b+t*sk_t+h*sk_h; bo=b*sok_b+h*sok_h+t*sok_t
        x1=tl.load(k_ptr+bx+do); x2=tl.load(k_ptr+bx+do+half_D)
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_k_ptr+bo+do,o1); tl.store(out_k_ptr+bo+do+half_D,o2)

B,T,Hq,Hkv,D = 1,4096,32,8,128
s0,s1,s2 = 16,24,24
hD = D//2
q = torch.randn(B,T,Hq,D, dtype=torch.bfloat16, device='cuda

... (1766 more chars)

{
  "command": "<3697 chars \u2014 see below>",
  "description": "Test with memory hints",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && cp solution.py solution.py.bak && cat > /tmp/test_hints.py << 'PYEOF'
import torch, triton, triton.language as tl, time

# V3: With tl.max_contiguous / tl.multiple_of hints
@triton.jit
def _mrope_hints(
    q_ptr, k_ptr, out_q_ptr, out_k_ptr, cos_ptr, sin_ptr,
    pt_ptr, ph_ptr, pw_ptr,
    sq_b,sq_t,sq_h,sk_b,sk_t,sk_h,soq_b,soq_h,soq_t,sok_b,sok_h,sok_t,
    B,T,D,s0,s1,s2,half_D:tl.constexpr,Hq_:tl.constexpr,Hkv_:tl.constexpr):
    b=tl.program_id(0); t=tl.program_id(1)
    po=b*T+t
    pt=tl.load(pt_ptr+po); ph=tl.load(ph_ptr+po); pw=tl.load(pw_ptr+po)
    do=tl.arange(0,half_D)
    cos_t=tl.load(cos_ptr+pt*D+do); sin_t=tl.load(sin_ptr+pt*D+do)
    cos_h=tl.load(cos_ptr+ph*D+do); sin_h=tl.load(sin_ptr+ph*D+do)
    cos_w=tl.load(cos_ptr+pw*D+do); sin_w=tl.load(sin_ptr+pw*D+do)
    cos_t2=tl.load(cos_ptr+pt*D+do+half_D); sin_t2=tl.load(sin_ptr+pt*D+do+half_D)
    cos_h2=tl.load(cos_ptr+ph*D+do+half_D); sin_h2=tl.load(sin_ptr+ph*D+do+half_D)
    cos_w2=tl.load(cos_ptr+pw*D+do+half_D); sin_w2=tl.load(sin_ptr+pw*D+do+half_D)
    is_t=do<s0; is_h=(do>=s0)&(do<(s0+s1))
    c1=tl.where(is_t,cos_t,tl.where(is_h,cos_h,cos_w))
    s1v=tl.where(is_t,sin_t,tl.where(is_h,sin_h,sin_w))
    c2=tl.where(is_t,cos_t2,tl.where(is_h,cos_h2,cos_w2))
    s2v=tl.where(is_t,sin_t2,tl.where(is_h,sin_h2,sin_w2))
    for h in range(Hq_):
        bx=b*sq_b+t*sq_t+h*sq_h; bo=b*soq_b+h*soq_h+t*soq_t
        x1=tl.load(q_ptr+bx+do); x2=tl.load(q_ptr+bx+do+half_D)
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_q_ptr+bo+do,o1); tl.store(out_q_ptr+bo+do+half_D,o2)
    for h in range(Hkv_):
        bx=b*sk_b+t*sk_t+h*sk_h; bo=b*sok_b+h*sok_h+t*sok_t
        x1=tl.load(k_ptr+bx+do); x2=tl.load(k_ptr+bx+do+half_D)
        o1=x1*c1+(-x2)*s1v; o2=x2*c2+x1*s2v
        tl.store(out_k_ptr+bo+do,o1); tl.store(out_k_ptr+bo+do+half_D,o2)

B,T,Hq,Hkv,D = 1,4096,32,8,128
s0,s1,s2 = 16,24,24; hD=D//2
q=torch.randn(B,T,Hq,D,dtype=torch.bfloat16,device='cuda')*0.5
k=torch.randn(B,T,Hkv,D,dtype=torch.bfloat16,device='cuda')*0.5
pt=torch.randint(0,32768,(B,T),dtype=torch.int64,device='cuda')
ph=torch.randint(0,32768,(B,T),dtype=torch.int64,device='cuda')
pw=torch.randint(0,32768,(B,T),dtype=torch.int64,device='cuda')
cc=torch.empty(32768,D,dtype=torch.bfloat16,device='cuda').uniform_(-1,1)
sc=torch.empty(32768,D,dtype=torch.bfloat16,device='cuda').uniform_(-1,1)
oq=torch.empty(B,Hq,T,D,dtype=torch.bfloat16,device='cuda')
ok=torch.empty(B,Hkv,T,D,dtype=torch.bfloat16,device='cuda')
l2=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')

for nw in [2,4]:
    for _ in range(5):
        _mrope_hints[(B,T)](q,k,oq,ok,cc,sc,pt,ph,pw,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            oq.stride(0),oq.stride(1),oq.stride(2),ok.stride(0),ok.stride(1),ok.stride(2),
            B,T,D,s0,s1,s2,hD,Hq_=Hq,Hkv_=Hkv,num_warps=nw)
    torch.cuda.synchronize()
    times=[]
    for _ in range(30):
        l2.zero_(); torch.cuda.synchronize()
        s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
        s.record()
        _mrope_hints[(B,T)](q,k,oq,ok,cc,sc,pt,ph,pw,
            q.stride(0),q.stride(1),q.stride(2),k.stride(0),k.stride(1),k.stride(2),
            oq.stride(0),oq.stride(1),oq.stride(2),ok.stride(0),ok.stride(1),ok.stride(2),
            B,T,D,s0,s1,s2,hD,Hq_=Hq,Hkv_=Hkv,num_warps=nw)
        e.record(); torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    med=sorted(times)[len(times)//2]
    print(f'hints kernel num_warps={nw}: {med:.3f} ms')
PYEOF
uv run python /tmp/test_hints.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "<433 chars \u2014 see below>",
  "newString": "<393 chars \u2014 see below>"
}

    # --- Per-element axis mixing ---
    axis = tl.where(d_offs < s0, 0, tl.where(d_offs < s0 + s1, 1, 2))
    cos_first = tl.where(axis == 0, cos_t, tl.where(axis == 1, cos_h, cos_w))
    sin_first = tl.where(axis == 0, sin_t, tl.where(axis == 1, sin_h, sin_w))
    cos_second = tl.where(axis == 0, cos_t_2, tl.where(axis == 1, cos_h_2, cos_w_2))
    sin_second = tl.where(axis == 0, sin_t_2, tl.where(axis == 1, sin_h_2, sin_w_2))

    # --- Per-element axis mixing ---
    is_t = d_offs < s0
    is_h = (d_offs < s0 + s1) & (d_offs >= s0)
    cos_first = tl.where(is_t, cos_t, tl.where(is_h, cos_h, cos_w))
    sin_first = tl.where(is_t, sin_t, tl.where(is_h, sin_h, sin_w))
    cos_second = tl.where(is_t, cos_t_2, tl.where(is_h, cos_h_2, cos_w_2))
    sin_second = tl.where(is_t, sin_t_2, tl.where(is_h, sin_h_2, sin_w_2))

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 && for nw in 2 4; do sed -i \"s/num_warps=[0-9]\\+/num_warps=$nw/\" solution.py && uv run benchmark.py 2>&1 | grep -E \"shape=|solution_peak|peak_fraction\"; done",
  "description": "Benchmark both num_warps=2 and num_warps=4",
  "timeout": 600000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "<393 chars \u2014 see below>",
  "newString": "<433 chars \u2014 see below>"
}

    # --- Per-element axis mixing ---
    is_t = d_offs < s0
    is_h = (d_offs < s0 + s1) & (d_offs >= s0)
    cos_first = tl.where(is_t, cos_t, tl.where(is_h, cos_h, cos_w))
    sin_first = tl.where(is_t, sin_t, tl.where(is_h, sin_h, sin_w))
    cos_second = tl.where(is_t, cos_t_2, tl.where(is_h, cos_h_2, cos_w_2))
    sin_second = tl.where(is_t, sin_t_2, tl.where(is_h, sin_h_2, sin_w_2))

    # --- Per-element axis mixing ---
    axis = tl.where(d_offs < s0, 0, tl.where(d_offs < s0 + s1, 1, 2))
    cos_first = tl.where(axis == 0, cos_t, tl.where(axis == 1, cos_h, cos_w))
    sin_first = tl.where(axis == 0, sin_t, tl.where(axis == 1, sin_h, sin_w))
    cos_second = tl.where(axis == 0, cos_t_2, tl.where(axis == 1, cos_h_2, cos_w_2))
    sin_second = tl.where(axis == 0, sin_t_2, tl.where(axis == 1, sin_h_2, sin_w_2))

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "            num_warps=2,",
  "newString": "            num_warps=4,"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && grep -n \"num_warps\" solution.py",
  "description": "Check current num_warps value"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 && uv run benchmark.py 2>&1",
  "description": "Final benchmark with restored axis code + num_warps=4",
  "timeout": 300000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && for i in 1 2 3; do echo \"=== Run $i ===\" && uv run benchmark.py 2>&1 | grep -E \"shape=.*solution|peak_fraction\"; done",
  "description": "Run benchmark 3 times for consistency",
  "timeout": 600000
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && git log --oneline -5 -- solution.py 2>/dev/null; echo \"---\"; git diff HEAD -- solution.py 2>/dev/null | head -80",
  "description": "Check git history for solution.py"
}

{
  "command": "diff /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py.bak /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py 2>/dev/null || echo \"no backup\"",
  "description": "Compare with backup"
}

{
  "command": "nvidia-smi --query-gpu=temperature.gpu,clocks.current.sm,clocks.current.memory,power.draw --format=csv,noheader 2>&1",
  "description": "Check GPU state"
}

{
  "command": "<1364 chars \u2014 see below>",
  "description": "Manual benchmark to verify",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
from solution import Model, MROPE_SECTION, MAX_POS
import torch
m = Model(1, 4096, 32, 8, 128, MROPE_SECTION, MAX_POS).cuda().eval()
q = torch.randn(1, 4096, 32, 128, dtype=torch.bfloat16, device='cuda') * 0.5
k = torch.randn(1, 4096, 8, 128, dtype=torch.bfloat16, device='cuda') * 0.5
pos_t = torch.randint(0, 32768, (1, 4096), dtype=torch.int64, device='cuda')
pos_h = torch.randint(0, 32768, (1, 4096), dtype=torch.int64, device='cuda')
pos_w = torch.randint(0, 32768, (1, 4096), dtype=torch.int64, device='cuda')
l2 = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')

# Warmup
for _ in range(10):
    m(q,k,pos_t,pos_h,pos_w)
torch.cuda.synchronize()

# Measured with L2 flush
import time
times = []
for _ in range(30):
    l2.zero_()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    m(q,k,pos_t,pos_h,pos_w)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
med = sorted(times)[len(times)//2]
print(f'Manual benchmark (with L2 flush): {med:.3f} ms')

B_bytes = 4 * 1 * 4096 * 40 * 128 + 12 * 1 * 4096 * 128 + 24 * 1 * 4096
gbps = B_bytes / med / 1e6
print(f'GB/s: {gbps:.1f}')
print(f'Peak fraction: {gbps/1800*100:.1f}%')
" 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "offset": 55,
  "limit": 45
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "<433 chars \u2014 see below>",
  "newString": "<393 chars \u2014 see below>"
}

    # --- Per-element axis mixing ---
    axis = tl.where(d_offs < s0, 0, tl.where(d_offs < s0 + s1, 1, 2))
    cos_first = tl.where(axis == 0, cos_t, tl.where(axis == 1, cos_h, cos_w))
    sin_first = tl.where(axis == 0, sin_t, tl.where(axis == 1, sin_h, sin_w))
    cos_second = tl.where(axis == 0, cos_t_2, tl.where(axis == 1, cos_h_2, cos_w_2))
    sin_second = tl.where(axis == 0, sin_t_2, tl.where(axis == 1, sin_h_2, sin_w_2))

    # --- Per-element axis mixing ---
    is_t = d_offs < s0
    is_h = (d_offs >= s0) & (d_offs < s0 + s1)
    cos_first = tl.where(is_t, cos_t, tl.where(is_h, cos_h, cos_w))
    sin_first = tl.where(is_t, sin_t, tl.where(is_h, sin_h, sin_w))
    cos_second = tl.where(is_t, cos_t_2, tl.where(is_h, cos_h_2, cos_w_2))
    sin_second = tl.where(is_t, sin_t_2, tl.where(is_h, sin_h_2, sin_w_2))

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py",
  "oldString": "            num_warps=4,",
  "newString": "            num_warps=2,"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run check.py 2>&1 && uv run benchmark.py 2>&1",
  "description": "Final benchmark with boolean+num_warps=2",
  "timeout": 300000
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check correctness with python check.py",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Profile and optimize iteratively",
      "status": "completed"
    },
    {
      "priority": "high",
      "conte

... (72 more chars)

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard && uv run ruff check problems/09_fmha_preattn_mrope/solution.py --fix 2>&1",
  "description": "Lint solution.py"
}

{
  "command": "rm -f /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py.bak /tmp/test_nw.py /tmp/test_inline.py /tmp/test_hints.py",
  "description": "Clean up temp files"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope/solution.py"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && cat framework.txt 2>/dev/null && echo \"---\" && grep -c \"triton\" solution.py && echo \"triton references found\"",
  "description": "Verify framework label"
}

{
  "command": "<592 chars \u2014 see below>",
  "description": "Verify no forbidden ops"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/09_fmha_preattn_mrope && uv run python -c "
# Verify forbidden ops are not present
import re
src = open('solution.py').read()
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',
]
for f in forbidden:
    if re.search(re.escape(f), src):
        print(f'FAIL: forbidden: {f}')
        exit(1)
print('No forbidden ops found')
print(f'Lines: {len(src.splitlines())}')
" 2>&1

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, solution.py, check.py, benchmark.py, and problem.yaml",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Implement baseline Triton kernel for mRoPE prep",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check correctness with python check.py",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Profile and optimize iteratively",
      "status": "completed"
    },
    {
      "priority": "high",
      "conte

... (70 more chars)