{
  "command": "ls -la",
  "description": "List workspace contents"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/reference.py"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/benchmark.py"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/check.py"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/shapes.py"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/problem.yaml"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/sota.py"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/PROMPT.txt"
}

{
  "command": "nvidia-smi 2>&1 | head -20; echo \"---NVCC---\"; which nvcc && nvcc --version 2>&1 | tail -4; echo \"---PYTHON---\"; uv run python -c \"import torch; print('torch', torch.__version__); print('cuda', torch.version.cuda); print('cap', torch.cuda.get_device_capability(0)); print(torch.cuda.get_device_name(0))\" 2>&1 | head -20",
  "description": "Check GPU, nvcc, torch versions"
}

{
  "command": "<621 chars \u2014 see below>",
  "description": "Check triton, cutlass, scoring infra"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "---TRITON---"; uv run python -c "import triton; print('triton', triton.__version__)" 2>&1 | tail -3
echo "---CUTLASS headers---"; find / -name "cutlass" -type d 2>/dev/null | head; find / -path "*cutlass/gemm/collective*" -name "*.h" 2>/dev/null | head -3
echo "---CUTLASS root---"; ls /kbh/ 2>/dev/null; find / -maxdepth 6 -name "CUTLASS*" -type d 2>/dev/null | head
echo "---src eval---"; ls /workspace/src/eval/ 2>/dev/null; find /workspace -name "roofline.py" 2>/dev/null | head
echo "---hardware---"; find /workspace -path "*hardware*" -name "*.py" 2>/dev/null | head

{
  "file_path": "/workspace/src/hardware/rtx_pro_6000.py"
}

{
  "file_path": "/workspace/src/eval/timing.py"
}

{
  "file_path": "/workspace/src/eval/roofline.py"
}

{
  "file_path": "/workspace/src/eval/correctness.py"
}

{
  "file_path": "/workspace/src/eval/numeric_stress.py"
}

{
  "command": "<1346 chars \u2014 see below>",
  "description": "Roofline analysis for each shape"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python - <<'EOF'
# Compute the roofline / minimum-time analysis for each shape.
peak_tflops = 200.0
peak_gbps = 1800.0
shapes = [
    {"T_total":32768,"H":4096,"I":1536,"E":128,"K":8},
    {"T_total":4096 ,"H":2048,"I":1024,"E":64,"K":4},
    {"T_total":16384,"H":2048,"I":4096,"E":64,"K":8},
]
for s in shapes:
    T_perm = s["T_total"]*s["K"]
    n_e = T_perm/s["E"]
    flops_formula = 2*s["T_total"]*s["H"]*(2*s["I"])      # counted FLOPs (no K)
    actual = 2*s["T_total"]*s["K"]*s["H"]*(2*s["I"])      # real FLOPs incl. K
    # bytes (weights once + x + out)
    bytes_moved = T_perm*s["H"]*2 + s["E"]*s["H"]*(2*s["I"])*2 + T_perm*s["I"]*2
    t_compute = actual/(peak_tflops*1e12)*1e3
    t_bw = bytes_moved/(peak_gbps*1e9)*1e3
    print(f"T_perm={T_perm:>7} n_e={n_e:7.1f} | counted_flops={flops_formula:.3e} actual={actual:.3e}")
    print(f"   bytes={bytes_moved/1e9:.3f}GB  AI={actual/bytes_moved:.0f} FLOP/B  t_compute@100%={t_compute:.4f}ms  t_bw={t_bw:.4f}ms")
    # If we hit X% of actual peak, what counted peak_fraction results?
    for frac_actual in (0.3,0.5,0.65,0.8):
        t = actual/(peak_tflops*1e12*frac_actual)*1e3
        pf = (flops_formula/(t*1e-3)/1e12)/peak_tflops
        print(f"     actual_util={frac_actual}: t={t:.4f}ms  counted_peak_fraction={pf:.4f}")
EOF

{
  "subject": "Build v1 Triton grouped-GEMM + fused SwiGLU, pass check.py",
  "description": "Write a correct Triton grouped GEMM kernel that fuses gate+up GEMMs with SwiGLU epilogue. 1D grid with host-precomputed (expert, m_start, n_start) tile mapping cached by offsets.data_ptr. fp32 accumulation, bf16 output. Get `python check.py` to print PASS.",
  "activeForm": "Building v1 Triton grouped-GEMM"
}

{
  "subject": "Benchmark + profile, establish baseline peak_fraction",
  "description": "Run benchmark.py, profile with torch.profiler/ncu. Identify bottlenecks. Record baseline geomean peak_fraction.",
  "activeForm": "Benchmarking and profiling baseline"
}

{
  "subject": "Autotune + optimize to >=65% actual utilization on all shapes",
  "description": "Curate autotune configs (tile sizes, num_warps, num_stages, L2 swizzle). Use TMA/block_ptr on Blackwell. Consider persistent kernel, split-K for small shape. Target geomean peak_fraction >= 0.1 (OK), push higher.",
  "activeForm": "Autotuning and optimizing"
}

{
  "taskId": "1",
  "status": "in_progress"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<8130 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<6092 chars \u2014 see diff>"
}

{
  "command": "<175 chars \u2014 see below>",
  "description": "Inspect Triton Autotuner.run for grid handling"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python - <<'EOF'
import inspect, triton
src = inspect.getsource(triton.runtime.autotuner.Autotuner.run)
print(src[:2600])
EOF

{
  "command": "<448 chars \u2014 see below>",
  "description": "Check Config signature and grid usage"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/bqhzpg4ia.output
echo "=========== grid usages in Autotuner ==========="
uv run python - <<'EOF'
import inspect, triton
src = inspect.getsource(triton.runtime.autotuner.Autotuner)
for ln in src.splitlines():
    if 'grid' in ln.lower() or '__call__' in ln or '__getitem__' in ln:
        print(ln)
EOF

{
  "command": "<420 chars \u2014 see below>",
  "description": "Find grid wiring in autotuner module"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python - <<'EOF'
import inspect, triton
from triton.runtime import autotuner as A
src = inspect.getsource(A)
# Find the autotune decorator and how grid is wired
for i, ln in enumerate(src.splitlines()):
    if any(s in ln for s in ["def autotune", "grid", "def __getitem__", "def __call__", "Autotuner(", "self.grid", "GridEncodable"]):
        print(f"{i:4}: {ln}")
EOF

{
  "command": "<438 chars \u2014 see below>",
  "description": "Check autotune signature for grid support"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python - <<'EOF'
import inspect, triton
print("autotune sig:", inspect.signature(triton.autotune))
print()
print("Autotuner.__init__ sig:", inspect.signature(triton.runtime.autotuner.Autotuner.__init__))
# look at __getitem__ and run dispatch
src = inspect.getsource(triton.runtime.autotuner.Autotuner)
i = src.find("def __getitem__")
print(src[i:i+500] if i>=0 else "no __getitem__")
EOF

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<6187 chars \u2014 see diff>"
}

{
  "command": "<1681 chars \u2014 see below>",
  "description": "Quick correctness sanity check on small shape"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python - <<'EOF'
import torch, time
torch.manual_seed(0)
import reference, solution

# Use the SMALL shape for a fast first correctness check.
for name, (T,H,I,E,K) in {
    "small":(4096,2048,1024,64,4),
}.items():
    reference.T_total,reference.H,reference.I,reference.E,reference.K = T,H,I,E,K
    dev="cuda"
    rm = reference.Model(T,H,I,E,K).to(dev).eval()
    sm = solution.Model(T,H,I,E,K).to(dev).eval()
    sm.load_state_dict(rm.state_dict(), strict=True)
    hs, off = [t.to(dev) for t in reference.get_inputs()]
    with torch.no_grad():
        t0=time.time()
        ro = rm(hs, off)
        so = sm(hs, off)
        torch.cuda.synchronize()
        print(f"{name}: fwd+ref {time.time()-t0:.2f}s")
    diff=(ro.float()-so.float()).abs()
    rel = diff / (ro.float().abs()+1e-6)
    print(f"  shape {name}: max_abs={diff.max().item():.4e} max_rel={rel.max().item():.4e}")
    print(f"  allclose(0.02,0.02):", torch.allclose(ro.float(),so.float(),atol=0.02,rtol=0.02))
    # also test imbalanced routing to be safe
    E2=E
    counts = torch.tensor([10,50,200,5,300,100]+[ (hs.shape[0]-665)//(E2-6) ]*(E2-6), dtype=torch.int32)
    counts = torch.full((E2,), hs.shape[0]//E2, dtype=torch.int32)
    counts[0]+=3; counts[1]-=3; counts[2]+=7; counts[3]-=7  # imbalance
    off2 = torch.zeros(E2+1, dtype=torch.int32)
    off2[1:]=torch.cumsum(counts,0)
    off2=off2.to(dev)
    with torch.no_grad():
        ro2=rm(hs,off2); so2=sm(hs,off2)
    diff2=(ro2.float()-so2.float()).abs()
    print(f"  imbalanced: max_abs={diff2.max().item():.4e} allclose:", torch.allclose(ro2.float(),so2.float(),atol=0.02,rtol=0.02))
EOF

{
  "command": "<86 chars \u2014 see below>",
  "description": "Run full correctness check.py"
}

cd /workspace/problems/06_sonic_moe_swiglu
time uv run python check.py 2>&1 | tail -30

{
  "command": "<246 chars \u2014 see below>",
  "description": "Check correctness result"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/btlojymjv.output 2>/dev/null | tail -6
echo "=== framework label ==="
cat framework.txt 2>/dev/null

{
  "command": "<1759 chars \u2014 see below>",
  "description": "Test each config individually on shape 2 seed 123"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/dbg.py <<'EOF'
import torch, triton, triton.language as tl
import reference, solution

# Shape 2
T,H,I,E,K = 16384,2048,4096,64,8
reference.T_total,reference.H,reference.I,reference.E,reference.K = T,H,I,E,K
dev="cuda"
rm = reference.Model(T,H,I,E,K).to(dev).eval()
sm = solution.Model(T,H,I,E,K).to(dev).eval()
sm.load_state_dict(rm.state_dict(), strict=True)

torch.manual_seed(123); torch.cuda.manual_seed_all(123)
hs, off = [t.to(dev) for t in reference.get_inputs()]
with torch.no_grad():
    ro = rm(hs, off)

# Try the raw kernel with several configs directly
BLOCK_M, BLOCK_N = 128, 256
num_n = (I+BLOCK_N-1)//BLOCK_N
max_ne = int((off[1:]-off[:-1]).max().item())
num_m = (max_ne+BLOCK_M-1)//BLOCK_M
grid = (num_n, num_m, E)

for BK in (32,64,128):
  for nw in (4,8):
    for ns in (2,3,4):
        out = torch.empty(hs.shape[0], I, dtype=torch.bfloat16, device=dev)
        try:
            solution._grouped_swiglu_kernel.fn[grid](
                hs, sm.W_gate, sm.W_up, out, off,
                H, I,
                hs.stride(0), hs.stride(1),
                sm.W_gate.stride(0), sm.W_gate.stride(1), sm.W_gate.stride(2),
                out.stride(0), out.stride(1),
                BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BK, num_warps=nw, num_stages=ns,
            )
            diff = (ro.float()-out.float()).abs()
            bad = (diff > (0.02 + 0.02*ro.float().abs())).sum().item()
            print(f"BK={BK:3} nw={nw} ns={ns}: max_abs={diff.max().item():.4e} bad={bad}/{diff.numel()} {'OK' if bad==0 else 'FAIL'}")
        except Exception as e:
            print(f"BK={BK} nw={nw} ns={ns}: ERR {type(e).__name__}: {str(e)[:80]}")
EOF
uv run python /tmp/dbg.py 2>&1 | tail -40

{
  "command": "<84 chars \u2014 see below>",
  "description": "Run debug from correct dir"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python /tmp/dbg.py 2>&1 | tail -40

{
  "command": "<135 chars \u2014 see below>",
  "description": "Run debug with PYTHONPATH set"
}

cd /workspace/problems/06_sonic_moe_swiglu
PYTHONPATH=/workspace/problems/06_sonic_moe_swiglu uv run python /tmp/dbg.py 2>&1 | tail -40

{
  "command": "<1026 chars \u2014 see below>",
  "description": "Reproduce via forward() through autotuner"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/dbg2.py <<'EOF'
import torch
import reference, solution

T,H,I,E,K = 16384,2048,4096,64,8
reference.T_total,reference.H,reference.I,reference.E,reference.K = T,H,I,E,K
dev="cuda"
rm = reference.Model(T,H,I,E,K).to(dev).eval()
sm = solution.Model(T,H,I,E,K).to(dev).eval()
sm.load_state_dict(rm.state_dict(), strict=True)

for seed in (42, 123):
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    hs, off = [t.to(dev) for t in reference.get_inputs()]
    with torch.no_grad():
        ro = rm(hs, off)
        so = sm(hs, off)   # goes through autotuner
    best = solution._grouped_swiglu_kernel.best_config
    print(f"seed={seed} best_config={best}")
    diff = (ro.float()-so.float()).abs()
    bad = (diff > (0.02 + 0.02*ro.float().abs())).sum().item()
    print(f"   max_abs={diff.max().item():.4e} bad={bad}/{diff.numel()} {'OK' if bad==0 else 'FAIL'}")
EOF
PYTHONPATH=/workspace/problems/06_sonic_moe_swiglu uv run python /tmp/dbg2.py 2>&1 | tail -20

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<673 chars \u2014 see diff>",
  "new_string": "<1031 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<175 chars \u2014 see diff>",
  "new_string": "<191 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<222 chars \u2014 see diff>",
  "new_string": "<214 chars \u2014 see diff>"
}

{
  "command": "<85 chars \u2014 see below>",
  "description": "Re-run check.py after cache-key fix"
}

cd /workspace/problems/06_sonic_moe_swiglu
time uv run python check.py 2>&1 | tail -8

{
  "command": "<357 chars \u2014 see below>",
  "description": "Confirm PASS and locate flashinfer grouped gemm"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b7q6fduox.output 2>/dev/null | tail -4
echo "=== flashinfer grouped gemm sources ==="
find /workspace/.venv -path "*flashinfer*" -iname "*.py" 2>/dev/null | xargs grep -l "grouped\|ragged" 2>/dev/null | head -5

{
  "command": "<179 chars \u2014 see below>",
  "description": "Show PASS/FAIL line"
}

cd /workspace/problems/06_sonic_moe_swiglu
grep -E "PASS|FAIL" /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b7q6fduox.output

{
  "status": "completed",
  "taskId": "1"
}

{
  "status": "in_progress",
  "taskId": "2"
}

{
  "command": "<119 chars \u2014 see below>",
  "description": "Run benchmark.py for baseline (mma.sync path)"
}

cd /workspace/problems/06_sonic_moe_swiglu
time uv run python benchmark.py 2>&1 | grep -E "shape=|peak_fraction|RESULT"

{
  "command": "<87 chars \u2014 see below>",
  "description": "Test TMA scratch kernel correctness and PTX"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python scratch_tma.py 2>&1 | tail -25

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/scratch_mma_probe.py",
  "content": "<2079 chars \u2014 see diff>"
}

{
  "command": "<93 chars \u2014 see below>",
  "description": "Probe tcgen05 emission across configs"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python scratch_mma_probe.py 2>&1 | tail -30

{
  "command": "<992 chars \u2014 see below>",
  "description": "Check CUTLASS sm120 support"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "=== CUTLASS version + sm120/sm100 support ==="
grep -r "CUTLASS_MAJOR\|CUTLASS_MINOR" /usr/include/cutlass/version.h 2>/dev/null | head
echo "--- sm120/sm100 arch files in system cutlass ---"
ls /usr/include/cutlass/arch/ 2>/dev/null | grep -iE "sm10|sm9|blackwell"
echo "--- sm120 collective gemm headers? ---"
find /usr/include/cutlass -iname "*sm10*" -o -iname "*sm12*" 2>/dev/null | head
find /usr/include/cutlass -ipath "*blackwell*" 2>/dev/null | head
echo "--- ao third_party cutlass (newer?) ---"
grep -r "CUTLASS_MAJOR\|CUTLASS_MINOR" /opt/pytorch/ao/third_party/cutlass/include/cutlass/version.h 2>/dev/null | head
ls /opt/pytorch/ao/third_party/cutlass/include/cutlass/arch/ 2>/dev/null | grep -iE "sm10|sm12"
echo "--- sm120 in arch.h? ---"
grep -rn "Sm120\|sm120\|sm_120" /usr/include/cutlass/arch/arch.h 2>/dev/null | head
grep -rn "Sm120\|sm120" /opt/pytorch/ao/third_party/cutlass/include/cutlass/arch/arch.h 2>/dev/null | head

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/scratch_time.py",
  "content": "<2786 chars \u2014 see diff>"
}

{
  "command": "<88 chars \u2014 see below>",
  "description": "Time TMA mma.sync grouped kernel across configs"
}

cd /workspace/problems/06_sonic_moe_swiglu
uv run python scratch_time.py 2>&1 | tail -25

{
  "command": "<368 chars \u2014 see below>",
  "description": "Check if scratch_time is still running"
}

cd /workspace/problems/06_sonic_moe_swiglu
ps aux | grep "[s]cratch_time" | head; echo "---file---"; ls -la /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b4of08gje.output; echo "---content---"; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b4of08gje.output

{
  "command": "<359 chars \u2014 see below>",
  "description": "Read CUTLASS example 75 setup"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "=== example 75 files ==="
ls /opt/pytorch/ao/third_party/cutlass/examples/75_blackwell_grouped_gemm/
echo ""
echo "=== head of 75_blackwell_grouped_gemm.cu (problem setup + kernel selection) ==="
sed -n '1,60p' /opt/pytorch/ao/third_party/cutlass/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu

{
  "command": "<601 chars \u2014 see below>",
  "description": "Check timing result and Gluon/CUTLASS options"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "=== scratch_time result ==="
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b4of08gje.output 2>/dev/null
echo ""
echo "=== Gluon examples / matmul in package ==="
find /workspace/.venv/lib/python3.11/site-packages/triton -path "*gluon*" -name "*.py" | xargs grep -ln "tcgen05_mma\|def matmul\|persistent" 2>/dev/null | head
echo "=== sm120 vs sm100 in CUTLASS 3.9 arch ==="
grep -rn "struct Sm120\|struct Sm100\|struct Sm90" /opt/pytorch/ao/third_party/cutlass/include/cutlass/arch/arch.h | head

{
  "command": "<265 chars \u2014 see below>",
  "description": "Kill slow timing, inspect gluon translator"
}

cd /workspace/problems/06_sonic_moe_swiglu
pkill -f scratch_time 2>/dev/null; sleep 2
echo "=== triton_to_gluon translator helpers (head) ==="
sed -n '1,50p' /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater/translator_helpers.py

{
  "command": "<319 chars \u2014 see below>",
  "description": "Read gluon translator helpers"
}

cd /workspace/problems/06_sonic_moe_swiglu
sed -n '1,60p' /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater/translator_helpers.py 2>/dev/null
echo "=== translator main ==="
find /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater -name "*.py" | head

{
  "command": "<637 chars \u2014 see below>",
  "description": "Find Gluon tcgen05 matmul examples"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "=== gluon test/example files in package ==="
find /workspace/.venv/lib/python3.11/site-packages/triton -path "*gluon*" -name "*.py" | xargs grep -l "tcgen05_mma" 2>/dev/null | head
echo "=== any matmul-like gluon example with full kernel ==="
grep -rln "allocate_tensor_memory\|tcgen05_mma" /workspace/.venv/lib/python3.11/site-packages/triton/experimental/gluon/ 2>/dev/null | head
echo "=== blackwell __init__ matmul helpers ==="
grep -n "def \|class \|matmul\|mma" /workspace/.venv/lib/python3.11/site-packages/triton/experimental/gluon/language/nvidia/blackwell/__init__.py | head -50

{
  "command": "<2388 chars \u2014 see below>",
  "description": "A/B TMA mma.sync timing, single configs"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/ab.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _alloc(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_alloc)

# TMA version
@triton.jit
def k_tma(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    g=tl.zeros((BM,BN),tl.float32);u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nb*BN]),(BK,BN)); wu=tl.reshape(wu_desc.load([e,kb,nb*BN]),(BK,BN))
        g=tl.dot(x,wg,g);u=tl.dot(x,wu,u)
    store_ragged(out_desc,start,ne,[ms,nb*BN],((g*tl.sigmoid(g))*u).to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: k_tma[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}")

for cfg in [(32768,4096,1536,128,8,128,128,64,4,3),(32768,4096,1536,128,8,128,256,32,8,3),(16384,2048,4096,64,8,128,128,64,4,3),(4096,2048,1024,64,4,128,128,64,4,3)]:
    try: bench(*cfg)
    except Exception as ex: print(cfg,"ERR",str(ex)[:70])
EOF
timeout 200 uv run python /tmp/ab.py 2>&1 | tail -12

{
  "command": "<108 chars \u2014 see below>",
  "description": "A/B TMA mma.sync timing with PYTHONPATH"
}

cd /workspace/problems/06_sonic_moe_swiglu
PYTHONPATH=. timeout 240 uv run python /tmp/ab.py 2>&1 | tail -12

{
  "command": "<167 chars \u2014 see below>",
  "description": "Read tcgen05 eligibility and dot impl"
}

cd /workspace/problems/06_sonic_moe_swiglu
sed -n '60,200p' /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater/translator_helpers.py

{
  "command": "<413 chars \u2014 see below>",
  "description": "Inspect gluon translator entry point"
}

cd /workspace/problems/06_sonic_moe_swiglu
echo "=== translator.py entry / usage ==="
grep -n "def \|translate\|argparse\|__main__\|def main\|compile" /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater/translator.py | head -40
echo ""
echo "=== last 60 lines (CLI) ==="
tail -60 /workspace/.venv/lib/python3.11/site-packages/triton/tools/triton_to_gluon_translater/translator.py

{
  "command": "<2213 chars \u2014 see below>",
  "description": "Find mma.sync ceiling on big GEMM vs cuBLAS"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/ceil.py <<'EOF'
import torch, triton, triton.language as tl, time
from triton.tools.tensor_descriptor import TensorDescriptor
def _alloc(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_alloc)

@triton.jit
def matmul_tma(a_desc,b_desc,c_desc,M,N,K,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pm=tl.program_id(0);pn=tl.program_id(1)
    acc=tl.zeros((BM,BN),tl.float32)
    for k in range(tl.cdiv(K,BK)):
        a=a_desc.load([pm*BM,k*BK]); b=b_desc.load([k*BK,pn*BN])
        acc=tl.dot(a,b,acc)
    c_desc.store([pm*BM,pn*BN],acc.to(tl.bfloat16))

def bench(M,N,K,BM,BN,BK,nw,ns):
    a=torch.randn(M,K,dtype=torch.bfloat16,device='cuda');b=torch.randn(K,N,dtype=torch.bfloat16,device='cuda')
    c=torch.empty(M,N,dtype=torch.bfloat16,device='cuda')
    ad=TensorDescriptor(a,[M,K],[K,1],[BM,BK]);bd=TensorDescriptor(b,[K,N],[N,1],[BK,BN]);cd=TensorDescriptor(c,[M,N],[N,1],[BM,BN])
    f=lambda: matmul_tma[(M//BM,N//BN)](ad,bd,cd,M,N,K,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"M{M} N{N} K{K} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={2*M*N*K/ms/1e9:.1f}")

# Big GEMM (shape 0 sizes, no grouping)
M,N,K=262144,1536,4096
for BM,BN,BK,nw,ns in [(128,128,64,4,3),(128,128,64,8,3),(128,256,32,8,3),(64,128,64,4,4),(128,128,32,4,4),(128,64,64,4,3)]:
    try: bench(M,N,K,BM,BN,BK,nw,ns)
    except Exception as ex: print("ERR",str(ex)[:60])
print("--- cuBLAS same ---")
a=torch.randn(M,K,dtype=torch.bfloat16,device='cuda');b=torch.randn(K,N,dtype=torch.bfloat16,device='cuda')
for _ in range(5): y=torch.matmul(a,b)
torch.cuda.synchronize(); ts=[]
for _ in range(20):
    s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();torch.matmul(a,b);e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
print(f"cuBLAS: ms={min(ts):.3f} TF={2*M*N*K/min(ts)/1e9:.1f}")
EOF
PYTHONPATH=. timeout 300 uv run python /tmp/ceil.py 2>&1 | tail -12

{
  "command": "<2499 chars \u2014 see below>",
  "description": "Sweep fused grouped configs with BN=256 on shape 0"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/sweep.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _alloc(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_alloc)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    g=tl.zeros((BM,BN),tl.float32);u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nb*BN]),(BK,BN)); wu=tl.reshape(wu_desc.load([e,kb,nb*BN]),(BK,BN))
        g=tl.dot(x,wg,g);u=tl.dot(x,wu,u)
    store_ragged(out_desc,start,ne,[ms,nb*BN],((g*tl.sigmoid(g))*u).to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}")

print("=== shape 0 (T32768 H4096 I1536 E128) fused ===")
T,H,I,E,K=32768,4096,1536,128,8
for BM,BN,BK,nw,ns in [(128,256,32,4,2),(128,256,32,8,2),(128,256,64,4,2),(128,256,32,4,3),(64,256,32,4,3),(64,256,64,4,2),(128,128,64,4,3)]:
    try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
    except Exception as ex: print(f"BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:50]}")
EOF
PYTHONPATH=. timeout 400 uv run python /tmp/sweep.py 2>&1 | tail -12

{
  "command": "<206 chars \u2014 see below>",
  "description": "Check sweep results again"
}

cd /workspace/problems/06_sonic_moe_swiglu
sleep 90; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b7wlkfuhy.output 2>/dev/null; echo "[done-check]"

{
  "command": "<239 chars \u2014 see below>",
  "description": "Wait for sweep to finish compiling"
}

cd /workspace/problems/06_sonic_moe_swiglu
ps aux | grep "[s]weep.py" | wc -l; sleep 75; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b7wlkfuhy.output 2>/dev/null; echo "[checked]"

{
  "command": "<2368 chars \u2014 see below>",
  "description": "Test grouped single-GEMM ceiling at BN=256"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/single.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _alloc(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_alloc)

@triton.jit
def k1(x_desc,w_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb])
        w=tl.reshape(w_desc.load([e,kb,nb*BN]),(BK,BN))
        acc=tl.dot(x,w,acc)
    store_ragged(out_desc,start,ne,[ms,nb*BN],acc.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns,label=""):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: k1[grid](xd,wd,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(15):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"{label} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={2*T*K*H*I/ms/1e9:.1f} (one GEMM)")

for T,H,I,E,K in [(32768,4096,1536,128,8),(16384,2048,4096,64,8),(4096,2048,1024,64,4)]:
    print(f"--- shape T{T} H{H} I{I} E{E} ---")
    for BM,BN,BK,nw,ns in [(128,256,64,4,3),(128,256,64,8,3),(128,256,32,8,3),(128,256,32,4,4),(64,256,64,4,3)]:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"  BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:50]}")
EOF
PYTHONPATH=. timeout 500 uv run python /tmp/single.py 2>&1 | tail -25

{
  "command": "<241 chars \u2014 see below>",
  "description": "Check ceiling results again"
}

cd /workspace/problems/06_sonic_moe_swiglu
sleep 100; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/bkomp5vu3.output 2>/dev/null; echo "[checked]"; ps aux | grep "[s]ingle.py" | wc -l

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<7744 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<646 chars \u2014 see diff>",
  "new_string": "class Model(nn.Module):"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<181 chars \u2014 see diff>",
  "new_string": "<352 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1407 chars \u2014 see diff>",
  "new_string": "<1465 chars \u2014 see diff>"
}

{
  "status": "in_progress",
  "taskId": "4"
}

{
  "command": "<953 chars \u2014 see below>",
  "description": "Quick correctness check on all shapes"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/qc.py <<'EOF'
import torch, time
import reference, solution
for T,H,I,E,K in [(4096,2048,1024,64,4),(32768,4096,1536,128,8),(16384,2048,4096,64,8)]:
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'
    rm=reference.Model(T,H,I,E,K).to(dev).eval()
    sm=solution.Model(T,H,I,E,K).to(dev).eval()
    sm.load_state_dict(rm.state_dict(),strict=True)
    for seed in (42,123):
        torch.manual_seed(seed);torch.cuda.manual_seed_all(seed)
        hs,off=[t.to(dev) for t in reference.get_inputs()]
        with torch.no_grad(): ro=rm(hs,off); so=sm(hs,off)
        diff=(ro.float()-so.float()).abs(); bad=(diff>(0.02+0.02*ro.float().abs())).sum().item()
        print(f"T{T} H{H} I{I} seed{seed}: max_abs={diff.max().item():.4e} bad={bad}/{diff.numel()} {'OK' if bad==0 else 'FAIL'}")
EOF
PYTHONPATH=. timeout 400 uv run python /tmp/qc.py 2>&1 | tail -12

{
  "command": "<108 chars \u2014 see below>",
  "description": "See full error from qc"
}

cd /workspace/problems/06_sonic_moe_swiglu
PYTHONPATH=. timeout 400 uv run python /tmp/qc.py 2>&1 | tail -30

{
  "command": "<1881 chars \u2014 see below>",
  "description": "Isolate the SwiGLU epilogue compile error"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/err.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def k(x_desc,w_desc,gate_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,FUSE:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32); nstart=nb*BN
    for kk in range(tl.cdiv(H,BK)):
        x=load_ragged(x_desc,start,ne,[ms,kk*BK]); w=tl.reshape(w_desc.load([e,kk*BK,nstart]),(BK,BN))
        acc=tl.dot(x,w,acc)
    if FUSE:
        gate=load_ragged(gate_desc,start,ne,[ms,nstart])
        acc=(gate*tl.sigmoid(gate))*acc
    store_ragged(out_desc,start,ne,[ms,nstart],acc.to(tl.bfloat16))

T,H,I,E,K=4096,2048,1024,64,4
reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
dev='cuda';m=reference.Model(T,H,I,E,K).to(dev).eval()
hs,off=[t.to(dev) for t in reference.get_inputs()]
out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
gate=torch.empty_like(out)
BM,BN,BK=64,128,64
xd=create_ragged_descriptor(hs,[BM,BK]);wd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
gd=create_ragged_descriptor(gate,[BM,BN]);od=create_ragged_descriptor(out,[BM,BN])
grid=((I+BN-1)//BN,((int((off[1:]-off[:-1]).max()))+BM-1)//BM,E)
try:
    k[grid](xd,wd,gd,od,off,H,I,BM=BM,BN=BN,BK=BK,FUSE=True,num_warps=8,num_stages=3)
    print("OK")
except Exception as ex:
    import traceback; traceback.print_exc()
EOF
PYTHONPATH=. timeout 200 uv run python /tmp/err.py 2>&1 | tail -30

{
  "command": "<180 chars \u2014 see below>",
  "description": "Read head of error output for message"
}

cd /workspace/problems/06_sonic_moe_swiglu
head -40 /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/bkzxzxzbq.output 2>/dev/null

{
  "command": "<2487 chars \u2014 see below>",
  "description": "Bisect epilogue expression to find the break"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/bisect.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

def make_variant(expr_code):
    src=f'''
import triton, triton.language as tl
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
@triton.jit
def k(x_desc,w_desc,gate_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32); nstart=nb*BN
    for kk in range(tl.cdiv(H,BK)):
        x=load_ragged(x_desc,start,ne,[ms,kk*BK]); w=tl.reshape(w_desc.load([e,kk*BK,nstart]),(BK,BN))
        acc=tl.dot(x,w,acc)
    gate=load_ragged(gate_desc,start,ne,[ms,nstart])
    {expr_code}
    store_ragged(out_desc,start,ne,[ms,nstart],acc.to(tl.bfloat16))
'''
    ns={}
    exec(src, ns)
    return ns['k']

T,H,I,E,K=4096,2048,1024,64,4
reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
dev='cuda';m=reference.Model(T,H,I,E,K).to(dev).eval()
hs,off=[t.to(dev) for t in reference.get_inputs()]
out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
gate=torch.empty_like(out)
BM,BN,BK=64,128,64
xd=create_ragged_descriptor(hs,[BM,BK]);wd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
gd=create_ragged_descriptor(gate,[BM,BN]);od=create_ragged_descriptor(out,[BM,BN])
grid=((I+BN-1)//BN,((int((off[1:]-off[:-1]).max()))+BM-1)//BM,E)

for name, code in [
    ("mul_only",     "acc = acc * gate"),
    ("to_fp32_mul",  "acc = acc * gate.to(tl.float32)"),
    ("sigmoid_only", "s = tl.sigmoid(gate)"),
    ("silu_fp32",    "gf = gate.to(tl.float32); acc = (gf * tl.sigmoid(gf)) * acc"),
    ("cast_first",   "gf = gate.to(tl.float32); acc = acc * gf"),
]:
    try:
        kk = make_variant(code)
        kk[grid](xd,wd,gd,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=8,num_stages=3)
        torch.cuda.synchronize()
        print(f"{name}: OK")
    except Exception as ex:
        print(f"{name}: ERR {type(ex).__name__}: {str(ex).splitlines()[-1][:120]}")
EOF
PYTHONPATH=. timeout 250 uv run python /tmp/bisect.py 2>&1 | tail -12

{
  "command": "<145 chars \u2014 see below>",
  "description": "Run epilogue probe with renamed file"
}

cd /workspace/problems/06_sonic_moe_swiglu
mv /tmp/bisect.py /tmp/eprobe.py
PYTHONPATH=. timeout 250 uv run python /tmp/eprobe.py 2>&1 | tail -12

{
  "command": "<1171 chars \u2014 see below>",
  "description": "Attempt variant test scaffold"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/var1.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference, sys
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)
EXPR = sys.argv[1] if len(sys.argv)>1 else "acc = acc * gate"

@triton.jit
def k(x_desc,w_desc,gate_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32); nstart=nb*BN
    for kk in range(tl.cdiv(H,BK)):
        x=load_ragged(x_desc,start,ne,[ms,kk*BK]); w=tl.reshape(w_desc.load([e,kk*BK,nstart]),(BK,BN))
        acc=tl.dot(x,w,acc)
    gate=load_ragged(gate_desc,start,ne,[ms,nstart])
    exec_global(EXPR)
    store_ragged(out_desc,start,ne,[ms,nstart],acc.to(tl.bfloat16))
EOF
echo "exec_global won't work in jit. Let me instead just edit the file per variant."

{
  "command": "<1767 chars \u2014 see below>",
  "description": "Test simplest gate*acc epilogue"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/v_mul.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def k(x_desc,w_desc,gate_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32); nstart=nb*BN
    for kk in range(tl.cdiv(H,BK)):
        x=load_ragged(x_desc,start,ne,[ms,kk*BK]); w=tl.reshape(w_desc.load([e,kk*BK,nstart]),(BK,BN))
        acc=tl.dot(x,w,acc)
    gate=load_ragged(gate_desc,start,ne,[ms,nstart])
    acc = acc * gate   # <-- simplest mul
    store_ragged(out_desc,start,ne,[ms,nstart],acc.to(tl.bfloat16))

T,H,I,E,K=4096,2048,1024,64,4
reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
dev='cuda';m=reference.Model(T,H,I,E,K).to(dev).eval()
hs,off=[t.to(dev) for t in reference.get_inputs()]
out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device='cuda')
gate=torch.empty_like(out)
BM,BN,BK=64,128,64
xd=create_ragged_descriptor(hs,[BM,BK]);wd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
gd=create_ragged_descriptor(gate,[BM,BN]);od=create_ragged_descriptor(out,[BM,BN])
grid=((I+BN-1)//BN,((int((off[1:]-off[:-1]).max()))+BM-1)//BM,E)
k[grid](xd,wd,gd,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=8,num_stages=3)
print("OK mul")
EOF
PYTHONPATH=. timeout 150 uv run python /tmp/v_mul.py 2>&1 | tail -8

{
  "command": "<1919 chars \u2014 see below>",
  "description": "Test sigmoid variants in epilogue"
}

cd /workspace/problems/06_sonic_moe_swiglu
for v in "acc = acc * tl.sigmoid(gate)" "gf = gate.to(tl.float32); acc = acc * tl.sigmoid(gf)" "acc = (gate.to(tl.float32) * tl.sigmoid(gate.to(tl.float32))) * acc"; do
cat > /tmp/v_t.py <<EOF
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)
@triton.jit
def k(x_desc,w_desc,gate_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    acc=tl.zeros((BM,BN),tl.float32); nstart=nb*BN
    for kk in range(tl.cdiv(H,BK)):
        x=load_ragged(x_desc,start,ne,[ms,kk*BK]); w=tl.reshape(w_desc.load([e,kk*BK,nstart]),(BK,BN))
        acc=tl.dot(x,w,acc)
    gate=load_ragged(gate_desc,start,ne,[ms,nstart])
    $v
    store_ragged(out_desc,start,ne,[ms,nstart],acc.to(tl.bfloat16))
T,H,I,E,K=4096,2048,1024,64,4
reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
dev='cuda';m=reference.Model(T,H,I,E,K).to(dev).eval()
hs,off=[t.to(dev) for t in reference.get_inputs()]
out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device='cuda'); gate=torch.empty_like(out)
BM,BN,BK=64,128,64
xd=create_ragged_descriptor(hs,[BM,BK]);wd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
gd=create_ragged_descriptor(gate,[BM,BN]);od=create_ragged_descriptor(out,[BM,BN])
grid=((I+BN-1)//BN,((int((off[1:]-off[:-1]).max()))+BM-1)//BM,E)
k[grid](xd,wd,gd,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=8,num_stages=3)
print("OK")
EOF
echo "=== EXPR: $v ==="
PYTHONPATH=. timeout 150 uv run python /tmp/v_t.py 2>&1 | tail -4
done

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<227 chars \u2014 see diff>",
  "new_string": "<393 chars \u2014 see diff>"
}

{
  "command": "<108 chars \u2014 see below>",
  "description": "Re-run correctness sanity check"
}

cd /workspace/problems/06_sonic_moe_swiglu
PYTHONPATH=. timeout 400 uv run python /tmp/qc.py 2>&1 | tail -10

{
  "command": "<114 chars \u2014 see below>",
  "description": "Run full check.py"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 400 uv run python check.py 2>&1 | grep -E "PASS|FAIL" | tail -3

{
  "command": "<126 chars \u2014 see below>",
  "description": "Benchmark two-GEMM solution"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 400 uv run python benchmark.py 2>&1 | grep -E "shape=|peak_fraction|RESULT"

{
  "command": "<1496 chars \u2014 see below>",
  "description": "Profile shape 1 overhead and allocator calls"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/prof.py <<'EOF'
import torch, time
import reference, solution
# Shape 1 (small)
T,H,I,E,K=4096,2048,1024,64,4
reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
dev='cuda'
rm=reference.Model(T,H,I,E,K).to(dev).eval()
sm=solution.Model(T,H,I,E,K).to(dev).eval()
sm.load_state_dict(rm.state_dict(),strict=True)
hs,off=[t.to(dev) for t in reference.get_inputs()]
# warmup
for _ in range(10):
    with torch.no_grad(): sm(hs,off)
torch.cuda.synchronize()
# time full forward (Python overhead included) via perf_counter
N=200
torch.cuda.synchronize(); t0=time.perf_counter()
for _ in range(N):
    with torch.no_grad(): o=sm(hs,off)
torch.cuda.synchronize(); 
wall=(time.perf_counter()-t0)/N*1000
# time pure GPU via events
ts=[]
for _ in range(N):
    s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record()
    with torch.no_grad(): sm(hs,off)
    e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
print(f"shape1 wall/call={wall:.4f}ms  gpu_event/call={min(ts):.4f}ms (median {sorted(ts)[N//2]:.4f})")
print(f"  -> per-call Python+launch overhead ~ {wall-min(ts):.4f}ms")
# Also count allocator calls
import solution as S
orig=S._tma_alloc; cnt=[0]
def counting(size,alignment,stream):
    cnt[0]+=1; return orig(size,alignment,stream)
S.triton.set_allocator(counting)
sm(hs,off); print("allocator calls in one forward:", cnt[0])
EOF
PYTHONPATH=. timeout 150 uv run python /tmp/prof.py 2>&1 | tail -8

{
  "command": "<200 chars \u2014 see below>",
  "description": "Read profiling output (correct id)"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/bj5o3glgp.output 2>/dev/null | tail -8; echo "[done]"

{
  "command": "<2775 chars \u2014 see below>",
  "description": "Test fused single-kernel with bf16 gate at BN=256"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/fused1.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    # gate K-loop -> fp32 acc, then convert to bf16 (frees fp32 regs)
    g=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN))
        g=tl.dot(x,wg,g)
    g_bf16=g.to(tl.bfloat16)
    # up K-loop -> fp32 acc (x reloaded)
    u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN))
        u=tl.dot(x,wu,u)
    out=((g_bf16.to(tl.float32))*tl.sigmoid(g_bf16.to(tl.float32)))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(15):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}")

for T,H,I,E,K in [(32768,4096,1536,128,8),(16384,2048,4096,64,8),(4096,2048,1024,64,4)]:
    for BM,BN,BK,nw,ns in [(128,256,64,8,3),(128,256,32,8,3),(128,256,64,8,2),(64,256,64,8,3)]:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:55]}")
EOF
PYTHONPATH=. timeout 400 uv run python /tmp/fused1.py 2>&1 | tail -16

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<7284 chars \u2014 see diff>"
}

{
  "command": "<114 chars \u2014 see below>",
  "description": "Run check.py on unified fused kernel"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 300 uv run python check.py 2>&1 | grep -E "PASS|FAIL" | tail -2

{
  "command": "<137 chars \u2014 see below>",
  "description": "Benchmark unified fused kernel"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 300 uv run python benchmark.py 2>&1 | grep -E "shape=.*solution|peak_fraction:|RESULT"

{
  "command": "<2984 chars \u2014 see below>",
  "description": "Test true single-pass fusion with num_warps=16"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/true.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_alloc_buf if False else _a)
EOF
# write a proper file (avoid name issues)
cat > /tmp/truefused.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32); u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN)); wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN))
        g=tl.dot(x,wg,g); u=tl.dot(x,wu,u)
    gf=g.to(tl.float32)
    out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns,nc=1):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns,num_ctas=nc)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(15):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns} nc{nc}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}")

print("=== true-fused (x once, 2 fp32 acc) ===")
for T,H,I,E,K in [(32768,4096,1536,128,8),(16384,2048,4096,64,8)]:
    for BM,BN,BK,nw,ns in [(128,256,64,16,3),(128,256,32,16,3),(128,256,64,16,2),(64,256,64,8,3)]:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:55]}")
EOF
PYTHONPATH=. timeout 400 uv run python /tmp/truefused.py 2>&1 | tail -12

{
  "command": "<229 chars \u2014 see below>",
  "description": "Check truefused status and output"
}

cd /workspace/problems/06_sonic_moe_swiglu
ps aux | grep "[t]ruefused" | wc -l
cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b8h9y069f.output 2>/dev/null; echo "===END==="

{
  "command": "<177 chars \u2014 see below>",
  "description": "Benchmark with baselines (eager/compiled/sota)"
}

cd /workspace/problems/06_sonic_moe_swiglu
KBH_BENCHMARK_BASELINES=1 timeout 500 uv run python benchmark.py 2>&1 | grep -E "shape=|peak_fraction:|RESULT|sota|compile" | tail -30

{
  "command": "<2623 chars \u2014 see below>",
  "description": "Test single-buffer x-once true-fused kernel"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/xonce.py <<'EOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    # x loaded ONCE per K-iter; gate and up dots share it. Single weight buffer.
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32); u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK
        x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN))
        g=tl.dot(x,wg,g)
        wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN))
        u=tl.dot(x,wu,u)
    gf=g
    out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(15):
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}")

for T,H,I,E,K in [(32768,4096,1536,128,8),(16384,2048,4096,64,8),(4096,2048,1024,64,4)]:
    for BM,BN,BK,nw,ns in [(64,256,64,8,2),(64,256,64,8,3),(64,256,32,8,3),(128,256,32,8,2),(128,256,64,8,2)]:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:50]}")
EOF
PYTHONPATH=. timeout 400 uv run python /tmp/xonce.py 2>&1 | tail -18

{
  "command": "<239 chars \u2014 see below>",
  "description": "Wait for x-once compilation"
}

cd /workspace/problems/06_sonic_moe_swiglu
ps aux|grep "[x]once"|wc -l; sleep 60; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b03ruj9ls.output 2>/dev/null | tail -18; echo "[end]"

{
  "command": "<240 chars \u2014 see below>",
  "description": "Wait longer for x-once results"
}

cd /workspace/problems/06_sonic_moe_swiglu
sleep 120; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b03ruj9ls.output 2>/dev/null | tail -18; echo "[end]"; ps aux|grep "[x]once"|wc -l

{
  "command": "<2502 chars \u2014 see below>",
  "description": "Lean x-once test, BM64 only, single-iter timing"
}

cd /workspace/problems/06_sonic_moe_swiglu
pkill -f xonce; sleep 2
cat > /tmp/xonce2.py <<'EOF'
import torch, triton, triton.language as tl, signal
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32); u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK
        x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN)); g=tl.dot(x,wg,g)
        wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN)); u=tl.dot(x,wu,u)
    gf=g; out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(3): f()
    torch.cuda.synchronize(); s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize()
    ms=s.elapsed_time(e)
    print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}", flush=True)

# only BM64 (fits registers for 2 acc), various BK/stages
for T,H,I,E,K in [(32768,4096,1536,128,8)]:
    for BM,BN,BK,nw,ns in [(64,256,64,8,2),(64,256,64,8,3),(64,256,32,8,3)]:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:50]}", flush=True)
EOF
PYTHONPATH=. timeout 200 uv run python /tmp/xonce2.py 2>&1 | tail -8

{
  "command": "<120 chars \u2014 see below>",
  "description": "Run lean x-once test cleanly"
}

cd /workspace/problems/06_sonic_moe_swiglu
sleep 3; PYTHONPATH=. timeout 200 uv run python /tmp/xonce2.py 2>&1 | tail -8

{
  "command": "<2365 chars \u2014 see below>",
  "description": "Recreate and run lean x-once test"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/xonce2.py <<'PYEOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32); u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK
        x=load_ragged(x_desc,start,ne,[ms,kb])
        wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN)); g=tl.dot(x,wg,g)
        wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN)); u=tl.dot(x,wu,u)
    gf=g; out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(3): f()
    torch.cuda.synchronize(); s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize()
    ms=s.elapsed_time(e)
    print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f}", flush=True)

for BM,BN,BK,nw,ns in [(64,256,64,8,2),(64,256,64,8,3),(64,256,32,8,3)]:
    try: bench(32768,4096,1536,128,8,BM,BN,BK,nw,ns)
    except Exception as ex: print(f"BM{BM} BK{BK} w{nw} s{ns}: ERR {str(ex)[:50]}", flush=True)
PYEOF
PYTHONPATH=. timeout 200 uv run python /tmp/xonce2.py 2>&1 | tail -6

{
  "command": "<1679 chars \u2014 see below>",
  "description": "Draft cold-L2 bench (had typo)"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/coldbench.py <<'PYEOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)
_flush = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')
def l2flush(): _flush.zero_()

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN))
        g=tl.dot(x,wg,g)
    gb=g.to(tl.bfloat16)
    u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN))
        u=tl.dot(x,wu,u)
    gf=gb.to(tl.float32); out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev')
PYEOF
echo "skip - syntax err, rewrite"

{
  "command": "<3010 chars \u2014 see below>",
  "description": "Cold-L2 benchmark of fused configs with flush"
}

cd /workspace/problems/06_sonic_moe_swiglu
cat > /tmp/coldbench.py <<'PYEOF'
import torch, triton, triton.language as tl
from triton.tools.tensor_descriptor import TensorDescriptor
from triton.tools.ragged_tma import create_ragged_descriptor, load_ragged, store_ragged
import reference
def _a(s,a,st): return torch.empty(s,device='cuda',dtype=torch.int8)
triton.set_allocator(_a)
_flush = torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')
def l2flush(): _flush.zero_()

@triton.jit
def kf(x_desc,wg_desc,wu_desc,out_desc,offs_ptr,H,I,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    nb=tl.program_id(0);mb=tl.program_id(1);e=tl.program_id(2)
    start=tl.load(offs_ptr+e);end=tl.load(offs_ptr+e+1);ne=end-start
    ms=mb*BM
    if ms>=ne: return
    nstart=nb*BN
    g=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wg=tl.reshape(wg_desc.load([e,kb,nstart]),(BK,BN))
        g=tl.dot(x,wg,g)
    gb=g.to(tl.bfloat16)
    u=tl.zeros((BM,BN),tl.float32)
    for kk in range(tl.cdiv(H,BK)):
        kb=kk*BK; x=load_ragged(x_desc,start,ne,[ms,kb]); wu=tl.reshape(wu_desc.load([e,kb,nstart]),(BK,BN))
        u=tl.dot(x,wu,u)
    gf=gb.to(tl.float32); out=(gf*tl.sigmoid(gf))*u
    store_ragged(out_desc,start,ne,[ms,nstart],out.to(tl.bfloat16))

def bench(T,H,I,E,K,BM,BN,BK,nw,ns):
    reference.T_total,reference.H,reference.I,reference.E,reference.K=T,H,I,E,K
    dev='cuda'; m=reference.Model(T,H,I,E,K).to(dev).eval()
    hs,off=[t.to(dev) for t in reference.get_inputs()]
    out=torch.empty(hs.shape[0],I,dtype=torch.bfloat16,device=dev)
    xd=create_ragged_descriptor(hs,[BM,BK]); wgd=TensorDescriptor(m.W_gate,[E,H,I],[H*I,I,1],[1,BK,BN])
    wud=TensorDescriptor(m.W_up,[E,H,I],[H*I,I,1],[1,BK,BN]); od=create_ragged_descriptor(out,[BM,BN])
    mne=int((off[1:]-off[:-1]).max().item()); grid=((I+BN-1)//BN,(mne+BM-1)//BM,E)
    f=lambda: kf[grid](xd,wgd,wud,od,off,H,I,BM=BM,BN=BN,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(5): f()
    torch.cuda.synchronize(); ts=[]
    for _ in range(15):
        l2flush(); torch.cuda.synchronize()
        s=torch.cuda.Event(True);e=torch.cuda.Event(True);s.record();f();e.record();torch.cuda.synchronize();ts.append(s.elapsed_time(e))
    ms=min(ts); print(f"T{T} BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ms={ms:.3f} TF={4*T*K*H*I/ms/1e9:.1f} pf={8.25e11/1e12 if T==32768 else (3.44e10/1e12 if T==4096 else 5.5e11/1e12)/(ms/1e3)/200:.4f}", flush=True)

cfgs=[(128,256,64,8,3),(128,256,64,8,4),(128,256,32,8,3),(128,256,32,8,4),(64,256,64,8,3),(128,128,64,8,3)]
for T,H,I,E,K in [(32768,4096,1536,128,8),(16384,2048,4096,64,8),(4096,2048,1024,64,4)]:
    print(f"--- T{T} H{H} I{I} ---", flush=True)
    for BM,BN,BK,nw,ns in cfgs:
        try: bench(T,H,I,E,K,BM,BN,BK,nw,ns)
        except Exception as ex: print(f"  BM{BM} BN{BN} BK{BK} w{nw} s{ns}: ERR {str(ex)[:45]}", flush=True)
PYEOF
PYTHONPATH=. timeout 500 uv run python /tmp/coldbench.py 2>&1 | tail -24

{
  "command": "<243 chars \u2014 see below>",
  "description": "Wait for cold-L2 sweep"
}

cd /workspace/problems/06_sonic_moe_swiglu
ps aux|grep "[c]oldbench"|wc -l; sleep 90; cat /tmp/claude-1002/-workspace-problems-06-sonic-moe-swiglu/14c43ffe-a76a-4efc-a274-acb672fe92ae/tasks/b1yo52le4.output 2>/dev/null | tail -24; echo "[end]"

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<384 chars \u2014 see diff>",
  "new_string": "<302 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<192 chars \u2014 see diff>",
  "new_string": "<250 chars \u2014 see diff>"
}

{
  "command": "<114 chars \u2014 see below>",
  "description": "Verify hardcoded solution correctness"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 200 uv run python check.py 2>&1 | grep -E "PASS|FAIL" | tail -1

{
  "command": "<92 chars \u2014 see below>",
  "description": "Run check.py and show tail"
}

cd /workspace/problems/06_sonic_moe_swiglu
timeout 300 uv run python check.py 2>&1 | tail -5