{
  "command": "ls -la && echo \"---REFERENCE---\" && cat reference.py",
  "description": "List dir and show reference.py"
}

{
  "command": "echo \"---CHECK---\" && cat check.py && echo \"---BENCHMARK---\" && cat benchmark.py",
  "description": "Show check.py and benchmark.py"
}

{
  "command": "echo \"---SHAPES---\" && cat shapes.py && echo \"---PROBLEM.YAML---\" && cat problem.yaml && echo \"---SOTA---\" && cat sota.py && echo \"---PROMPT---\" && cat PROMPT.txt",
  "description": "Show shapes, problem.yaml, sota, prompt"
}

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

{
  "command": "cd /workspace && find . -path ./.venv -prune -o -name \"*.py\" -path \"*hardware*\" -print 2>/dev/null; echo \"---\"; find . -path ./.venv -prune -o -name \"*.py\" -path \"*eval*\" -print 2>/dev/null | head; echo \"---ROOFLINE---\"; cat src/hardware/*.py 2>/dev/null | head -100",
  "description": "Find hardware and eval source"
}

{
  "command": "cat /workspace/src/eval/numeric_stress.py; echo \"---CORRECTNESS---\"; cat /workspace/src/eval/correctness.py; echo \"---ROOFLINE---\"; cat /workspace/src/eval/roofline.py; echo \"---TIMING---\"; sed -n '1,80p' /workspace/src/eval/timing.py",
  "description": "Show numeric_stress, correctness, roofline, timing"
}

{
  "command": "uv run python -c \"import triton; print('triton', triton.__version__); import triton.language as tl\" 2>&1 | tail -5; echo \"---net---\"; timeout 8 curl -sI https://github.com 2>&1 | head -3",
  "description": "Check triton version and network"
}

{
  "command": "<1446 chars \u2014 see below>",
  "description": "Quick triton bf16 matmul perf test"
}

cat > /tmp/tt.py <<'EOF'
import torch, triton, triton.language as tl

@triton.jit
def mm(a,b,c,M,N,K,sm,sk,sn,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GM:tl.constexpr):
    pid=tl.program_id(0)
    nm=tl.cdiv(M,BM); nn=tl.cdiv(N,BN)
    npg=GM*nn; gid=pid//npg; first=gid*GM
    gsz=min(GM,nm-first); 
    pm=first+((pid%npg)%gsz); pn=(pid%npg)//gsz
    rm=(pm*BM+tl.arange(0,BM))%M
    rn=(pn*BN+tl.arange(0,BN))%N
    rk=tl.arange(0,BK)
    ap=a+rm[:,None]*sm+rk[None,:]*sk
    bp=b+rk[:,None]*sk+rn[None,:]*sn
    acc=tl.zeros((BM,BN),tl.float32)
    for k in range(0,K,BK):
        av=tl.load(ap); bv=tl.load(bp)
        acc=tl.dot(av,bv,acc)
        ap+=BK*sk; bp+=BK*sn
    c+=(pm*BM+tl.arange(0,BM))[:,None]*N+(pn*BN+tl.arange(0,BN))[None,:]
    tl.store(c, acc.to(tl.bfloat16))

M=N=K=4096
a=torch.randn(M,K,device='cuda',dtype=torch.bfloat16)
b=torch.randn(K,N,device='cuda',dtype=torch.bfloat16)
c=torch.empty(M,N,device='cuda',dtype=torch.bfloat16)
BM,BN,BK,GM=128,128,64,8
grid=(triton.cdiv(M,BM)*triton.cdiv(N,BN),)
for _ in range(5): mm[grid](a,b,c,M,N,K,K,1,1,BM,BN,BK,GM)
torch.cuda.synchronize()
import time
t=time.time()
n=50
for _ in range(n): mm[grid](a,b,c,M,N,K,K,1,1,BM,BN,BK,GM)
torch.cuda.synchronize()
ms=(time.time()-t)/n*1e3
print(f"{ms:.3f} ms, {2*M*N*K/(ms*1e-3)/1e12:.1f} TFLOPS")
# verify
ref=(a.float()@b.float())
print("err",(ref-c.float()).abs().max().item())
EOF
uv run python /tmp/tt.py 2>&1 | tail -15

{
  "query": "select:TaskCreate",
  "max_results": 3
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<5973 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<494 chars \u2014 see diff>",
  "new_string": "<427 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<86 chars \u2014 see diff>",
  "new_string": "<100 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<172 chars \u2014 see diff>",
  "new_string": "_BM_SCHED = _BM"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<132 chars \u2014 see diff>",
  "new_string": "<151 chars \u2014 see diff>"
}

{
  "command": "timeout 1200 uv run python check.py 2>&1 | tail -30",
  "description": "Run check.py"
}

{
  "command": "timeout 1200 uv run python benchmark.py 2>&1 | tail -20",
  "description": "Run benchmark.py",
  "run_in_background": true
}

{
  "command": "<605 chars \u2014 see below>",
  "description": "Measure true bf16 HW peak via cuBLAS"
}

cat > /tmp/peak.py <<'EOF'
import torch, time
# Use cuBLAS (just to measure HW peak, not part of solution) for ground truth peak.
for M,N,K in [(8192,8192,8192),(4096,3072,4096),(16384,8192,2048)]:
    a=torch.randn(M,K,device='cuda',dtype=torch.bfloat16)
    b=torch.randn(K,N,device='cuda',dtype=torch.bfloat16)
    for _ in range(10): c=a@b
    torch.cuda.synchronize()
    t=time.time(); n=50
    for _ in range(n): c=a@b
    torch.cuda.synchronize()
    ms=(time.time()-t)/n*1e3
    print(f"{M}x{N}x{K}: {ms:.3f} ms, {2*M*N*K/(ms*1e-3)/1e12:.1f} TFLOPS")
EOF
uv run python /tmp/peak.py 2>&1 | tail -6

{
  "command": "<1729 chars \u2014 see below>",
  "description": "Tune standalone Triton matmul for per-expert shape"
}

cat > /tmp/tune.py <<'EOF'
import torch, triton, triton.language as tl, time

@triton.jit
def mm(a,b,c,M,N,K,BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GM:tl.constexpr):
    pid=tl.program_id(0)
    nm=tl.cdiv(M,BM); nn=tl.cdiv(N,BN)
    npg=GM*nn; gid=pid//npg; first=gid*GM
    gsz=min(GM,nm-first)
    pm=first+((pid%npg)%gsz); pn=(pid%npg)//gsz
    rm=pm*BM+tl.arange(0,BM)
    rn=pn*BN+tl.arange(0,BN)
    rk=tl.arange(0,BK)
    ap=a+rm[:,None]*K+rk[None,:]
    bp=b+rk[:,None]*N+rn[None,:]
    acc=tl.zeros((BM,BN),tl.float32)
    for k in range(0,K,BK):
        acc=tl.dot(tl.load(ap),tl.load(bp),acc)
        ap+=BK; bp+=BK*N
    cp=c+rm[:,None]*N+rn[None,:]
    tl.store(cp,acc.to(tl.bfloat16))

def bench(M,N,K,BM,BN,BK,GM,w,s):
    a=torch.randn(M,K,device='cuda',dtype=torch.bfloat16)
    b=torch.randn(K,N,device='cuda',dtype=torch.bfloat16)
    c=torch.empty(M,N,device='cuda',dtype=torch.bfloat16)
    grid=(triton.cdiv(M,BM)*triton.cdiv(N,BN),)
    try:
        for _ in range(5): mm[grid](a,b,c,M,N,K,BM,BN,BK,GM,num_warps=w,num_stages=s)
        torch.cuda.synchronize()
        t=time.time(); n=30
        for _ in range(n): mm[grid](a,b,c,M,N,K,BM,BN,BK,GM,num_warps=w,num_stages=s)
        torch.cuda.synchronize()
        ms=(time.time()-t)/n*1e3
        return 2*M*N*K/(ms*1e-3)/1e12
    except Exception as ex:
        return None

M,N,K=2048,3072,4096
best=0;bc=None
for BM,BN in [(128,128),(128,256),(256,128),(256,256),(128,64),(64,256)]:
  for BK in [64,128,256]:
    for w in [4,8]:
      for s in [3,4,5]:
        t=bench(M,N,K,BM,BN,BK,8,w,s)
        if t and t>best: best=t;bc=(BM,BN,BK,w,s)
print(f"M{M}N{N}K{K} best {best:.1f} TFLOPS cfg={bc}")
EOF
uv run python /tmp/tune.py 2>&1 | tail -5

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<415 chars \u2014 see diff>",
  "new_string": "<422 chars \u2014 see diff>"
}

{
  "command": "timeout 1800 uv run python benchmark.py 2>&1 | grep -E \"solution tflops|peak_fraction\" ",
  "description": "Benchmark with broadened configs",
  "run_in_background": true
}

{
  "command": "<3614 chars \u2014 see below>",
  "description": "Custom L2-flushed tuner for fused grouped kernel",
  "run_in_background": true
}

cat > /tmp/tg.py <<'EOF'
import torch, triton, triton.language as tl, statistics

@triton.jit
def kern(X,Wg,Wu,Out,offsets,tile_expert,tile_row0,H,I,
    sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(tile_expert+pid_m)
    if e<0: return
    row0=tl.load(tile_row0+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    wb=e*swe
    wg_ptrs=Wg+wb+offs_k[:,None]*swk+offs_n[None,:]*swn
    wu_ptrs=Wu+wb+offs_k[:,None]*swk+offs_n[None,:]*swn
    ag=tl.zeros((BM,BN),tl.float32); au=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0)
        wg=tl.load(wg_ptrs); wu=tl.load(wu_ptrs)
        ag=tl.dot(x,wg,ag); au=tl.dot(x,wu,au)
        x_ptrs+=BK*sxk; wg_ptrs+=BK*swk; wu_ptrs+=BK*swk
    g=ag; out=(g*tl.sigmoid(g))*au
    o_ptrs=Out+offs_m[:,None]*som+offs_n[None,:]*son
    tl.store(o_ptrs,out.to(tl.bfloat16),mask=mask_m[:,None])

def build_sched(offsets,E,BM,MAX_MT,device):
    counts=offsets[1:]-offsets[:-1]
    mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device=device,dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]
    row0=offsets[ec]+within*BM
    te=torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32))
    return te,row0.to(torch.int32)

_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()

def time_cfg(X,Wg,Wu,offs,E,H,I,BM,BN,BK,w,s,MAX_MT):
    dev=X.device
    te,tr=build_sched(offs,E,BM,MAX_MT,dev)
    Out=torch.empty(X.shape[0],I,dtype=torch.bfloat16,device=dev)
    grid=(MAX_MT,triton.cdiv(I,BN))
    args=(X,Wg,Wu,Out,offs,te,tr,H,I,X.stride(0),X.stride(1),
          Wg.stride(0),Wg.stride(1),Wg.stride(2),Out.stride(0),Out.stride(1))
    try:
        for _ in range(3): kern[grid](*args,BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
        torch.cuda.synchronize()
    except Exception as ex:
        return None
    ts=[]
    for _ in range(15):
        flush()
        st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); kern[grid](*args,BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s); en.record()
        torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)

shapes=[(32768,4096,1536,128,8),(4096,2048,1024,64,4),(16384,2048,4096,64,8)]
for (T,H,I,E,K) in shapes:
    Tp=T*K
    X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
    Wg=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
    Wu=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
    base=Tp//E; rem=Tp-base*E
    counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
    offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
    flops=2*T*H*2*I
    best=1e9;bc=None
    for BM in [64,128]:
      MAX_MT=Tp//BM+E+1
      for BN in [32,64,128,256]:
        for BK in [64,128]:
          for w in [4,8]:
            for s in [2,3,4]:
              ms=time_cfg(X,Wg,Wu,offs,E,H,I,BM,BN,BK,w,s,MAX_MT)
              if ms and ms<best: best=ms;bc=(BM,BN,BK,w,s)
    print(f"T{T}H{H}I{I}E{E}K{K}: best {best:.3f}ms realTF={flops*K/(best*1e-3)/1e12:.0f} reportTF={flops/(best*1e-3)/1e12:.2f} cfg={bc}",flush=True)
EOF
timeout 1700 uv run python /tmp/tg.py 2>&1 | tail -8

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<501 chars \u2014 see diff>",
  "new_string": "<310 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<529 chars \u2014 see diff>",
  "new_string": "<607 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<469 chars \u2014 see diff>",
  "new_string": "<325 chars \u2014 see diff>"
}

{
  "command": "timeout 600 uv run python check.py 2>&1 | tail -5",
  "description": "Run check.py with pinned configs",
  "run_in_background": true
}

{
  "command": "timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution tflops|peak_fraction\"",
  "description": "Benchmark pinned configs",
  "run_in_background": true
}

{
  "file_path": "/home/agent/.claude/projects/-workspace-problems-06-sonic-moe-swiglu/memory/sonic-moe-metric.md",
  "content": "<1429 chars \u2014 see diff>"
}

{
  "command": "<300 chars \u2014 see below>",
  "description": "Write MEMORY.md, check ncu"
}

cat > /workspace/problems/06_sonic_moe_swiglu/MEMORY.md <<'EOF'
- [sonic-moe-metric](sonic-moe-metric.md) — metric quirks (reported tflops = real/K, true peak ~390 not 200), tuning notes for grouped GEMM
EOF
echo done; which ncu ncu-ui 2>/dev/null; ls /usr/local/cuda*/bin/ncu 2>/dev/null; echo "---"

{
  "command": "<599 chars \u2014 see below>",
  "description": "Profile shape0 kernel with ncu"
}

cat > /tmp/drv.py <<'EOF'
import torch, solution as S, triton
T,H,I,E,K=32768,4096,1536,128,8
Tp=T*K
X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
m=S.Model(T,H,I,E,K).cuda()
base=Tp//E; rem=Tp-base*E
import torch as t
counts=t.full((E,),base,dtype=t.int32); counts[:rem]+=1
offs=t.zeros(E+1,dtype=t.int32); offs[1:]=t.cumsum(counts,0); offs=offs.cuda()
for _ in range(3): o=m(X,offs)
torch.cuda.synchronize()
o=m(X,offs)
torch.cuda.synchronize()
EOF
timeout 300 /kbh/bin/ncu --set basic --launch-skip 0 --launch-count 1 -k regex:grouped_swiglu uv run python /tmp/drv.py 2>&1 | tail -45

{
  "command": "timeout 300 /kbh/bin/ncu --set basic --launch-count 1 -k regex:grouped_swiglu uv run python /tmp/drv.py 2>&1 | tail -50",
  "description": "Profile from problem dir"
}

{
  "command": "<176 chars \u2014 see below>",
  "description": "Profile from problem dir with local drv"
}

cp /tmp/drv.py /workspace/problems/06_sonic_moe_swiglu/drv.py
timeout 300 /kbh/bin/ncu --set basic --launch-count 1 -k regex:grouped_swiglu uv run python drv.py 2>&1 | tail -50

{
  "command": "timeout 300 /kbh/bin/ncu --set basic --launch-count 1 -k regex:grouped_swiglu uv run python drv.py 2>&1 | grep -iE \"Duration|Compute \\(SM\\)|Memory Throughput|DRAM Throughput|Throughput|register|Achieved Occ|Tensor|Issued Ipc|pipe\" | head -40",
  "description": "Extract SOL metrics"
}

{
  "command": "<3026 chars \u2014 see below>",
  "description": "Tune single-acc grouped GEMM ceiling on shape0",
  "run_in_background": true
}

cat > /tmp/single.py <<'EOF'
import torch, triton, triton.language as tl, statistics

@triton.jit
def kern1(X,W,Out,offsets,tile_expert,tile_row0,H,N,
    sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(tile_expert+pid_m)
    if e<0: return
    row0=tl.load(tile_row0+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    o_ptrs=Out+offs_m[:,None]*som+offs_n[None,:]*son
    tl.store(o_ptrs,a.to(tl.bfloat16),mask=mask_m[:,None])

def build_sched(offsets,E,BM,MAX_MT,device):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device=device,dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]
    row0=offsets[ec]+within*BM
    te=torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32))
    return te,row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()

T,H,I,E,K=32768,4096,1536,128,8
Tp=T*K; N=I
X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
W=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
base=Tp//E; rem=Tp-base*E
counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
flops=2*Tp*H*N  # single gemm real flops
res=[]
for BM in [64,128]:
  MAX_MT=Tp//BM+E+1
  te,tr=build_sched(offs,E,BM,MAX_MT,'cuda')
  Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
  for BN in [64,128,256]:
    for BK in [64,128]:
      for w in [4,8]:
        for s in [2,3,4]:
          grid=(MAX_MT,triton.cdiv(N,BN))
          args=(X,W,Out,offs,te,tr,H,N,X.stride(0),X.stride(1),W.stride(0),W.stride(1),W.stride(2),Out.stride(0),Out.stride(1))
          try:
            for _ in range(3): kern1[grid](*args,BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
            torch.cuda.synchronize()
          except Exception: continue
          ts=[]
          for _ in range(15):
            flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
            st.record(); kern1[grid](*args,BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s); en.record()
            torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
          ms=statistics.median(ts); res.append((flops/(ms*1e-3)/1e12,ms,(BM,BN,BK,w,s)))
res.sort(reverse=True)
for tf,ms,c in res[:8]: print(f"{tf:.0f} TFLOPS {ms:.3f}ms {c}")
EOF
timeout 800 uv run python /tmp/single.py 2>&1 | tail -10

{
  "command": "<3645 chars \u2014 see below>",
  "description": "Tune two-pass gate+up+swiglu design per shape",
  "run_in_background": true
}

cat > /tmp/tg2.py <<'EOF'
import torch, triton, triton.language as tl, statistics

@triton.jit
def kern(X,W,Out,G,offsets,tile_expert,tile_row0,H,N,
    sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,APPLY:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(tile_expert+pid_m)
    if e<0: return
    row0=tl.load(tile_row0+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    oo=offs_m[:,None]*som+offs_n[None,:]*son
    if APPLY:
        g=tl.load(G+oo,mask=mask_m[:,None],other=0.0).to(tl.float32)
        a=(g*tl.sigmoid(g))*a
    tl.store(Out+oo,a.to(tl.bfloat16),mask=mask_m[:,None])

def build_sched(offsets,E,BM,MAX_MT,device):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device=device,dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]
    row0=offsets[ec]+within*BM
    te=torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32))
    return te,row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()

shapes=[(32768,4096,1536,128,8),(4096,2048,1024,64,4),(16384,2048,4096,64,8)]
for (T,H,I,E,K) in shapes:
  Tp=T*K; N=I
  X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
  Wg=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  Wu=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  base=Tp//E; rem=Tp-base*E
  counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
  offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
  flops=2*T*H*2*I  # reported formula
  res=[]
  for BM in [64,128]:
    MAX_MT=Tp//BM+E+1
    te,tr=build_sched(offs,E,BM,MAX_MT,'cuda')
    bufG=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    for BN in [128,256]:
      for BK in [64,128]:
        for w in [4,8]:
          for s in [2,3,4]:
            grid=(MAX_MT,triton.cdiv(N,BN))
            def run():
              kern[grid](X,Wg,bufG,bufG,offs,te,tr,H,N,X.stride(0),X.stride(1),Wg.stride(0),Wg.stride(1),Wg.stride(2),bufG.stride(0),bufG.stride(1),BM=BM,BN=BN,BK=BK,APPLY=False,num_warps=w,num_stages=s)
              kern[grid](X,Wu,Out,bufG,offs,te,tr,H,N,X.stride(0),X.stride(1),Wu.stride(0),Wu.stride(1),Wu.stride(2),Out.stride(0),Out.stride(1),BM=BM,BN=BN,BK=BK,APPLY=True,num_warps=w,num_stages=s)
            try:
              for _ in range(3): run()
              torch.cuda.synchronize()
            except Exception as ex: continue
            ts=[]
            for _ in range(15):
              flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
              st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
            ms=statistics.median(ts); res.append((flops/(ms*1e-3)/1e12,ms,(BM,BN,BK,w,s)))
  res.sort(reverse=True)
  print(f"=== T{T}H{H}I{I}E{E}K{K} ===")
  for tf,ms,c in res[:5]: print(f"  report {tf:.2f} TF, {ms:.3f}ms cfg={c} frac={tf/200:.3f}")
EOF
timeout 1700 uv run python /tmp/tg2.py 2>&1 | tail -25

{
  "command": "<3885 chars \u2014 see below>",
  "description": "Tune 3-kernel pipeline (2 GEMM + swiglu)",
  "run_in_background": true
}

cat > /tmp/tg3.py <<'EOF'
import torch, triton, triton.language as tl, statistics

@triton.jit
def gemm(X,W,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16),mask=mask_m[:,None])

@triton.jit
def swiglu(G,U,O,n,BLK:tl.constexpr):
    pid=tl.program_id(0); off=pid*BLK+tl.arange(0,BLK)
    m=off<n
    g=tl.load(G+off,mask=m,other=0.0).to(tl.float32)
    u=tl.load(U+off,mask=m,other=0.0).to(tl.float32)
    tl.store(O+off,((g*tl.sigmoid(g))*u).to(tl.bfloat16),mask=m)

def build_sched(offsets,E,BM,MAX_MT,device):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device=device,dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]
    row0=offsets[ec]+within*BM
    te=torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32))
    return te,row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()

shapes=[(32768,4096,1536,128,8),(4096,2048,1024,64,4),(16384,2048,4096,64,8)]
for (T,H,I,E,K) in shapes:
  Tp=T*K; N=I
  X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
  Wg=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  Wu=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  base=Tp//E; rem=Tp-base*E
  counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
  offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
  flops=2*T*H*2*I
  res=[]
  numel=Tp*N; BLK=8192; sg=(triton.cdiv(numel,BLK),)
  for BM in [64,128]:
    MAX_MT=Tp//BM+E+1
    te,tr=build_sched(offs,E,BM,MAX_MT,'cuda')
    bufG=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    bufU=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    for BN in [128,256]:
      for BK in [64,128]:
        for w in [8]:
          for s in [2,3,4]:
            grid=(MAX_MT,triton.cdiv(N,BN))
            def run():
              gemm[grid](X,Wg,bufG,offs,te,tr,H,N,X.stride(0),X.stride(1),Wg.stride(0),Wg.stride(1),Wg.stride(2),bufG.stride(0),bufG.stride(1),BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
              gemm[grid](X,Wu,bufU,offs,te,tr,H,N,X.stride(0),X.stride(1),Wu.stride(0),Wu.stride(1),Wu.stride(2),bufU.stride(0),bufU.stride(1),BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
              swiglu[sg](bufG,bufU,Out,numel,BLK=BLK,num_warps=4)
            try:
              for _ in range(3): run()
              torch.cuda.synchronize()
            except Exception: continue
            ts=[]
            for _ in range(15):
              flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
              st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
            ms=statistics.median(ts); res.append((flops/(ms*1e-3)/1e12,ms,(BM,BN,BK,w,s)))
  res.sort(reverse=True)
  print(f"=== T{T}H{H}I{I} === best of 3-kernel:")
  for tf,ms,c in res[:4]: print(f"  report {tf:.2f} TF {ms:.3f}ms cfg={c} frac={tf/200:.3f}")
EOF
timeout 1700 uv run python /tmp/tg3.py 2>&1 | tail -20

{
  "command": "<4072 chars \u2014 see below>",
  "description": "Tune merged-2I GEMM + swiglu",
  "run_in_background": true
}

cat > /tmp/tg4.py <<'EOF'
import torch, triton, triton.language as tl, statistics

@triton.jit
def gemm2(X,Wg,Wu,Buf,offsets,te,tr,H,I,sxm,sxk,swe,swk,swn,sbm,sbn,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    n0=pid_n*BN
    offs_m=row0+tl.arange(0,BM); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    # select gate/up weight by n-tile (uniform branch across block)
    if n0<I:
        wn=n0+tl.arange(0,BN)
        w_ptrs=Wg+e*swe+offs_k[:,None]*swk+wn[None,:]*swn
    else:
        wn=(n0-I)+tl.arange(0,BN)
        w_ptrs=Wu+e*swe+offs_k[:,None]*swk+wn[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    offs_n=n0+tl.arange(0,BN)
    tl.store(Buf+offs_m[:,None]*sbm+offs_n[None,:]*sbn,a.to(tl.bfloat16),mask=mask_m[:,None])

@triton.jit
def swiglu(Buf,O,Tp,I,sbm,sbn,som,son,BM:tl.constexpr,BN:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    rm=pid_m*BM+tl.arange(0,BM); rn=pid_n*BN+tl.arange(0,BN)
    mm=rm<Tp
    g=tl.load(Buf+rm[:,None]*sbm+rn[None,:]*sbn,mask=mm[:,None],other=0.).to(tl.float32)
    u=tl.load(Buf+rm[:,None]*sbm+(rn+I)[None,:]*sbn,mask=mm[:,None],other=0.).to(tl.float32)
    tl.store(O+rm[:,None]*som+rn[None,:]*son,((g*tl.sigmoid(g))*u).to(tl.bfloat16),mask=mm[:,None])

def build_sched(offsets,E,BM,MAX_MT,device):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device=device,dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]
    row0=offsets[ec]+within*BM
    te=torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32))
    return te,row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()

shapes=[(32768,4096,1536,128,8),(4096,2048,1024,64,4),(16384,2048,4096,64,8)]
for (T,H,I,E,K) in shapes:
  Tp=T*K
  X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
  Wg=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
  Wu=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
  base=Tp//E; rem=Tp-base*E
  counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
  offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
  flops=2*T*H*2*I
  res=[]
  for BM in [64,128]:
    MAX_MT=Tp//BM+E+1
    te,tr=build_sched(offs,E,BM,MAX_MT,'cuda')
    Buf=torch.empty(Tp,2*I,dtype=torch.bfloat16,device='cuda')
    Out=torch.empty(Tp,I,dtype=torch.bfloat16,device='cuda')
    sgrid=(triton.cdiv(Tp,64),triton.cdiv(I,256))
    for BN in [128,256]:
      for BK in [64,128]:
        for s in [2,3,4]:
          grid=(MAX_MT,triton.cdiv(2*I,BN))
          def run():
            gemm2[grid](X,Wg,Wu,Buf,offs,te,tr,H,I,X.stride(0),X.stride(1),Wg.stride(0),Wg.stride(1),Wg.stride(2),Buf.stride(0),Buf.stride(1),BM=BM,BN=BN,BK=BK,num_warps=8,num_stages=s)
            swiglu[sgrid](Buf,Out,Tp,I,Buf.stride(0),Buf.stride(1),Out.stride(0),Out.stride(1),BM=64,BN=256,num_warps=4)
          try:
            for _ in range(3): run()
            torch.cuda.synchronize()
          except Exception: continue
          ts=[]
          for _ in range(15):
            flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
            st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
          ms=statistics.median(ts); res.append((flops/(ms*1e-3)/1e12,ms,(BM,BN,BK,s)))
  res.sort(reverse=True)
  print(f"=== T{T}H{H}I{I} === merged-2I+swiglu:")
  for tf,ms,c in res[:4]: print(f"  report {tf:.2f} TF {ms:.3f}ms cfg={c} frac={

... (72 more chars)

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<9780 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<526 chars \u2014 see diff>",
  "new_string": "<534 chars \u2014 see diff>"
}

{
  "command": "rm -f drv.py && timeout 600 uv run python check.py 2>&1 | tail -5",
  "description": "Run check.py on hybrid solution",
  "run_in_background": true
}

{
  "command": "timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution tflops|peak_fraction\"",
  "description": "Benchmark hybrid",
  "run_in_background": true
}

{
  "command": "timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution_peak_fraction|peak_fraction:\"",
  "description": "Re-run benchmark for variance",
  "run_in_background": true
}

{
  "command": "<2046 chars \u2014 see below>",
  "description": "Re-tune shape1 fused",
  "run_in_background": true
}

cat > /tmp/t1.py <<'EOF'
import torch, triton, triton.language as tl, statistics
import importlib.util
spec=importlib.util.spec_from_file_location("sol","/workspace/problems/06_sonic_moe_swiglu/solution.py")
sol=importlib.util.module_from_spec(spec); spec.loader.exec_module(sol)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()
def build(offsets,E,BM,MAX_MT):
    return sol._build_schedule(offsets,E,BM,MAX_MT,'cuda')

T,H,I,E,K=4096,2048,1024,64,4
Tp=T*K
X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
Wg=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
Wu=torch.randn(E,H,I,device='cuda',dtype=torch.bfloat16)*0.02
base=Tp//E; rem=Tp-base*E
counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
flops=2*T*H*2*I
def timeit(run):
    for _ in range(5): run()
    torch.cuda.synchronize()
    ts=[]
    for _ in range(30):
        flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)
res=[]
# fused
for BM in [32,64,128]:
  MAX_MT=Tp//BM+E+1; te,tr=build(offs,E,BM,MAX_MT)
  out=torch.empty(Tp,I,dtype=torch.bfloat16,device='cuda')
  for BN in [128,256]:
    for BK in [64,128]:
      for w in [4,8]:
        for s in [2,3,4]:
          g=(MAX_MT,triton.cdiv(I,BN))
          def run(BM=BM,BN=BN,BK=BK,w=w,s=s,te=te,tr=tr,out=out):
            sol._fused_kernel[g](X,Wg,Wu,out,offs,te,tr,H,I,X.stride(0),X.stride(1),Wg.stride(0),Wg.stride(1),Wg.stride(2),out.stride(0),out.stride(1),BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
          try: ms=timeit(run)
          except Exception: continue
          res.append((flops/(ms*1e-3)/1e12,ms,"fused",(BM,BN,BK,w,s)))
res.sort(reverse=True)
for tf,ms,k,c in res[:6]: print(f"{k} report {tf:.2f} {ms:.3f}ms cfg={c} frac={tf/200:.3f}")
EOF
timeout 700 uv run python /tmp/t1.py 2>&1 | tail -8

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<138 chars \u2014 see diff>",
  "new_string": "<1044 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<304 chars \u2014 see diff>",
  "new_string": "<265 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<289 chars \u2014 see diff>",
  "new_string": "<254 chars \u2014 see diff>"
}

{
  "command": "timeout 600 uv run python check.py 2>&1 | tail -3",
  "description": "Check with caching",
  "run_in_background": true
}

{
  "command": "timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution tflops|peak_fraction\"",
  "description": "Benchmark with caching",
  "run_in_background": true
}

{
  "replace_all": false,
  "file_path": "/home/agent/.claude/projects/-workspace-problems-06-sonic-moe-swiglu/memory/sonic-moe-metric.md",
  "old_string": "<179 chars \u2014 see diff>",
  "new_string": "<845 chars \u2014 see diff>"
}

{
  "command": "<3002 chars \u2014 see below>",
  "description": "Big-tile single-acc GEMM sweep",
  "run_in_background": true
}

cat > /tmp/big.py <<'EOF'
import torch, triton, triton.language as tl, statistics
@triton.jit
def gemm(X,W,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16),mask=mask_m[:,None])
def build(offsets,E,BM,MAX_MT):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device='cuda',dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]; row0=offsets[ec]+within*BM
    return torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32)),row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()
def timeit(run):
    for _ in range(4): run()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)
for (T,H,I,E,K) in [(32768,4096,1536,128,8),(16384,2048,4096,64,8)]:
  Tp=T*K; N=I
  X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
  W=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  base=Tp//E; rem=Tp-base*E
  counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
  offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
  flops=2*Tp*H*N; res=[]
  for BM in [128,256]:
    MAX_MT=Tp//BM+E+1; te,tr=build(offs,E,BM,MAX_MT)
    Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    for BN in [128,256]:
      for BK in [64,128,256]:
        for w in [8,16]:
          for s in [3,4,5]:
            g=(MAX_MT,triton.cdiv(N,BN))
            def run(BM=BM,BN=BN,BK=BK,w=w,s=s,te=te,tr=tr,Out=Out):
              gemm[g](X,W,Out,offs,te,tr,H,N,X.stride(0),X.stride(1),W.stride(0),W.stride(1),W.stride(2),Out.stride(0),Out.stride(1),BM=BM,BN=BN,BK=BK,num_warps=w,num_stages=s)
            try: ms=timeit(run)
            except Exception: continue
            res.append((flops/(ms*1e-3)/1e12,(BM,BN,BK,w,s)))
  res.sort(reverse=True)
  print(f"=== H{H}I{I} single-acc GEMM TFLOPS ===")
  for tf,c in res[:6]: print(f"  {tf:.0f} cfg={c}")
EOF
timeout 1500 uv run python /tmp/big.py 2>&1 | tail -16

{
  "command": "<4205 chars \u2014 see below>",
  "description": "Test two-pass-epilogue for shapes 0/2",
  "run_in_background": true
}

cat > /tmp/t2.py <<'EOF'
import torch, triton, triton.language as tl, statistics
@triton.jit
def gemm(X,W,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16),mask=mask_m[:,None])
@triton.jit
def gemm_up(X,W,G,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,sgm,sgn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in range(0,H,BK):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    g=tl.load(G+offs_m[:,None]*sgm+offs_n[None,:]*sgn,mask=mask_m[:,None],other=0.0).to(tl.float32)
    out=(g*tl.sigmoid(g))*a
    tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,out.to(tl.bfloat16),mask=mask_m[:,None])
def build(offsets,E,BM,MAX_MT):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device='cuda',dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]; row0=offsets[ec]+within*BM
    return torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32)),row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()
def timeit(run):
    for _ in range(5): run()
    torch.cuda.synchronize(); ts=[]
    for _ in range(30):
        flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)
for (T,H,I,E,K) in [(32768,4096,1536,128,8),(16384,2048,4096,64,8)]:
  Tp=T*K; N=I
  X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
  Wg=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  Wu=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
  base=Tp//E; rem=Tp-base*E
  counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
  offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
  flops=2*T*H*2*I; res=[]
  for BM in [128]:
    MAX_MT=Tp//BM+E+1; te,tr=build(offs,E,BM,MAX_MT)
    G=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda'); Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
    for BN in [128,256]:
      for s in [3,4]:
        g=(MAX_MT,triton.cdiv(N,BN))
        def run(BM=BM,BN=BN,s=s,te=te,tr=tr,G=G,Out=Out):
          gemm[g](X,Wg,G,offs,te,tr,H,N,X.stride(0),X.stride(1),Wg.stride(0),Wg.stride(1),Wg.stride(2),G.stride(0),G.stride(1),BM=BM,BN=BN,BK=64,num_warps=8,num_stages=s)
          gemm_up[g](X,Wu,G,Out,offs,te,tr,H,N,X.stride(0),X.stride(1),Wu.stride(0),Wu.stride(1),Wu.stride(2),G.stride(0),G.stride(1),Out.stride(0),Out.stride(1),BM=BM,BN=BN,BK=64,num_warps=8,num_stages=s)
        try: ms=timeit(run)
        except Exception as ex: print("err",ex); continue
        res.append((flops/(ms*1e-3)/1e12,ms,(BM,BN,s)))
  res

... (205 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<2209 chars \u2014 see diff>",
  "new_string": "<1707 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1049 chars \u2014 see diff>",
  "new_string": "<1432 chars \u2014 see diff>"
}

{
  "command": "timeout 600 uv run python check.py 2>&1 | tail -3",
  "description": "Check two-pass-epi solution",
  "run_in_background": true
}

{
  "command": "for i in 1 2; do timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution_peak_fraction|peak_fraction:\"; echo \"---run $i done---\"; done",
  "description": "Benchmark two-pass-epi twice",
  "run_in_background": true
}

{
  "command": "<693 chars \u2014 see below>",
  "description": "Re-measure cuBLAS peak at current clock"
}

cat > /tmp/peak2.py <<'EOF'
import torch, time
def bench(M,N,K,n=50):
    a=torch.randn(M,K,device='cuda',dtype=torch.bfloat16); b=torch.randn(K,N,device='cuda',dtype=torch.bfloat16)
    for _ in range(10): c=a@b
    torch.cuda.synchronize(); t=time.time()
    for _ in range(n): c=a@b
    torch.cuda.synchronize(); ms=(time.time()-t)/n*1e3
    return 2*M*N*K/(ms*1e-3)/1e12
print("cuBLAS 8192^3:", round(bench(8192,8192,8192),1))
print("cuBLAS per-expert 2048x1536x4096:", round(bench(2048,1536,4096),1))
print("cuBLAS big M 16384x1536x4096:", round(bench(16384,1536,4096),1))
print("cuBLAS 32768x3072x4096:", round(bench(32768,3072,4096,20),1))
EOF
uv run python /tmp/peak2.py 2>&1 | tail -6

{
  "command": "<3004 chars \u2014 see below>",
  "description": "Test warp specialization on GEMM",
  "run_in_background": true
}

cat > /tmp/ws.py <<'EOF'
import torch, triton, triton.language as tl, statistics
@triton.jit
def gemm(X,W,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,WS:tl.constexpr,NS:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mask_m=offs_m<m_hi
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    for k in tl.range(0,H,BK,warp_specialize=WS,num_stages=NS):
        x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
        a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
    tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16),mask=mask_m[:,None])
def build(offsets,E,BM,MAX_MT):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device='cuda',dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]; row0=offsets[ec]+within*BM
    return torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32)),row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()
def timeit(run):
    for _ in range(4): run()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)
T,H,I,E,K=32768,4096,1536,128,8
Tp=T*K; N=I
X=torch.randn(Tp,H,device='cuda',dtype=torch.bfloat16)*0.1
W=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
base=Tp//E; rem=Tp-base*E
counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
flops=2*Tp*H*N; res=[]
for BM in [128]:
  MAX_MT=Tp//BM+E+1; te,tr=build(offs,E,BM,MAX_MT)
  Out=torch.empty(Tp,N,dtype=torch.bfloat16,device='cuda')
  for BN in [128,256]:
    for BK in [64,128]:
      for WS in [True,False]:
        for NS in [2,3,4]:
          for w in [4,8]:
            g=(MAX_MT,triton.cdiv(N,BN))
            def run(BM=BM,BN=BN,BK=BK,WS=WS,NS=NS,w=w,te=te,tr=tr,Out=Out):
              gemm[g](X,W,Out,offs,te,tr,H,N,X.stride(0),X.stride(1),W.stride(0),W.stride(1),W.stride(2),Out.stride(0),Out.stride(1),BM=BM,BN=BN,BK=BK,WS=WS,NS=NS,num_warps=w)
            try: ms=timeit(run)
            except Exception: continue
            res.append((flops/(ms*1e-3)/1e12,(BM,BN,BK,'WS' if WS else 'noWS',NS,w)))
res.sort(reverse=True)
for tf,c in res[:10]: print(f"  {tf:.0f} cfg={c}")
EOF
timeout 900 uv run python /tmp/ws.py 2>&1 | tail -12

{
  "command": "which nvcc; nvcc --version 2>&1 | tail -4; echo \"---cutlass pip?---\"; uv run python -c \"import cutlass; print(cutlass.__version__)\" 2>&1 | tail -2; echo \"---nvidia cutlass dsl?---\"; uv run pip list 2>/dev/null | grep -iE \"cutlass|nvidia-cut\" | head; echo \"---cuda toolkit---\"; ls /usr/local/ | grep cuda",
  "description": "Check nvcc and cutlass availability"
}

{
  "command": "<2904 chars \u2014 see below>",
  "description": "Measure mask overhead in GEMM"
}

cat > /tmp/nomask.py <<'EOF'
import torch, triton, triton.language as tl, statistics
@triton.jit
def gemm(X,W,Out,offsets,te,tr,H,N,sxm,sxk,swe,swk,swn,som,son,
    BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,MASK:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    e=tl.load(te+pid_m)
    if e<0: return
    row0=tl.load(tr+pid_m); m_hi=tl.load(offsets+e+1)
    offs_m=row0+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    x_ptrs=X+offs_m[:,None]*sxm+offs_k[None,:]*sxk
    w_ptrs=W+e*swe+offs_k[:,None]*swk+offs_n[None,:]*swn
    a=tl.zeros((BM,BN),tl.float32)
    if MASK:
        mask_m=offs_m<m_hi
        for k in range(0,H,BK):
            x=tl.load(x_ptrs,mask=mask_m[:,None],other=0.0); w=tl.load(w_ptrs)
            a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
        tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16),mask=mask_m[:,None])
    else:
        for k in range(0,H,BK):
            x=tl.load(x_ptrs); w=tl.load(w_ptrs)
            a=tl.dot(x,w,a); x_ptrs+=BK*sxk; w_ptrs+=BK*swk
        tl.store(Out+offs_m[:,None]*som+offs_n[None,:]*son,a.to(tl.bfloat16))
def build(offsets,E,BM,MAX_MT):
    counts=offsets[1:]-offsets[:-1]; mt=(counts+(BM-1))//BM
    mtc=torch.cumsum(mt,0).to(torch.int32); mte=mtc-mt
    tids=torch.arange(MAX_MT,device='cuda',dtype=torch.int32)
    eot=torch.searchsorted(mtc,tids,right=True); valid=eot<E
    ec=torch.clamp(eot,max=E-1); within=tids-mte[ec]; row0=offsets[ec]+within*BM
    return torch.where(valid,ec.to(torch.int32),torch.full_like(ec,-1,dtype=torch.int32)),row0.to(torch.int32)
_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
def flush(): _scr.zero_()
def timeit(run):
    for _ in range(4): run()
    torch.cuda.synchronize(); ts=[]
    for _ in range(20):
        flush(); st=torch.cuda.Event(True); en=torch.cuda.Event(True)
        st.record(); run(); en.record(); torch.cuda.synchronize(); ts.append(st.elapsed_time(en))
    return statistics.median(ts)
T,H,I,E,K=32768,4096,1536,128,8
Tp=T*K; N=I
X=torch.randn(Tp+256,H,device='cuda',dtype=torch.bfloat16)*0.1
W=torch.randn(E,H,N,device='cuda',dtype=torch.bfloat16)*0.02
base=Tp//E; rem=Tp-base*E
counts=torch.full((E,),base,dtype=torch.int32); counts[:rem]+=1
offs=torch.zeros(E+1,dtype=torch.int32); offs[1:]=torch.cumsum(counts,0); offs=offs.cuda()
flops=2*Tp*H*N
for MASK in [True,False]:
  BM,BN,BK=128,256,64
  MAX_MT=Tp//BM+E+1; te,tr=build(offs,E,BM,MAX_MT)
  Out=torch.empty(Tp+256,N,dtype=torch.bfloat16,device='cuda')
  g=(MAX_MT,triton.cdiv(N,BN))
  def run(): gemm[g](X,W,Out,offs,te,tr,H,N,X.stride(0),X.stride(1),W.stride(0),W.stride(1),W.stride(2),Out.stride(0),Out.stride(1),BM=BM,BN=BN,BK=BK,MASK=MASK,num_warps=8,num_stages=3)
  ms=timeit(run)
  print(f"MASK={MASK}: {flops/(ms*1e-3)/1e12:.0f} TFLOPS {ms:.3f}ms")
EOF
timeout 300 uv run python /tmp/nomask.py 2>&1 | tail -4

{
  "command": "timeout 180 git clone --depth 1 https://github.com/NVIDIA/cutlass.git 2>&1 | tail -3; echo \"EXIT $?\"; ls cutlass/include/cutlass/arch/ 2>/dev/null | head; echo \"---sm120 grep---\"; grep -rl \"Sm120\\|sm_120\\|SM120\" cutlass/include/cutlass/gemm/collective/ 2>/dev/null | head; echo \"---version---\"; cat cutlass/include/cutlass/version.h 2>/dev/null | grep -iE \"major|minor|patch\" | head -4",
  "description": "Clone CUTLASS and check sm120 support"
}

{
  "command": "ls cutlass/examples/ | grep -iE \"120|group|blackwell\" ; echo \"---grouped examples---\"; ls cutlass/examples/ | grep -iE \"grouped|group_gemm|moe\" ; echo \"---sm120 builder content---\"; grep -rl \"Sm120\" cutlass/include/cutlass/gemm/collective/builders/ | head; echo \"---check non-blockscaled sm120 bf16 builder---\"; ls cutlass/include/cutlass/gemm/collective/builders/ | grep 120",
  "description": "Find sm120 grouped examples and builders"
}

{
  "command": "echo \"===92 MoE files===\"; ls cutlass/examples/92_blackwell_moe_gemm/; echo \"===92 README===\"; sed -n '1,60p' cutlass/examples/92_blackwell_moe_gemm/README.md 2>/dev/null; echo \"===79 files===\"; ls cutlass/examples/79_blackwell_geforce_gemm/",
  "description": "Inspect MoE and geforce examples"
}

{
  "command": "echo \"===92 CMake===\"; cat cutlass/examples/92_blackwell_moe_gemm/CMakeLists.txt | head -40; echo \"===92 grouped arch tag===\"; grep -nE \"Sm1[0-9][0-9]|ArchTag|KernelSchedule|cutlass::bfloat16|ElementA|TileShape\" cutlass/examples/92_blackwell_moe_gemm/92_blackwell_moe_gemm_grouped.cu | head -30",
  "description": "Check 92 arch and config"
}

{
  "command": "echo \"=== sm120 builder: array/group/bf16 support ===\"; grep -nE \"Array|Group|Ptr|KernelSchedule|bfloat|GmmaMajor|is_2sm|cp_async|Warp\" cutlass/include/cutlass/gemm/collective/builders/sm120_mma_builder.inl | head -40",
  "description": "Check sm120 builder grouped support"
}

{
  "replace_all": false,
  "file_path": "/home/agent/.claude/projects/-workspace-problems-06-sonic-moe-swiglu/memory/sonic-moe-metric.md",
  "old_string": "<142 chars \u2014 see diff>",
  "new_string": "<1093 chars \u2014 see diff>"
}

{
  "command": "<382 chars \u2014 see below>",
  "description": "Clean up scratch, verify framework label"
}

rm -rf cutlass drv.py framework.txt 2>/dev/null; ls; echo "=== framework detection ==="; uv run python -c "
import re
from pathlib import Path
code=Path('solution.py').read_text()
for name,pat in [('ptx',r'asm\s+volatile|mma\.sync'),('triton',r'import\s+triton|@triton\.jit|tl\.dot'),('cuda_raw',r'load_inline|__global__')]:
    if re.search(pat,code): print('label:',name); break
"

{
  "command": "<383 chars \u2014 see below>",
  "description": "Move MEMORY.md to memory dir"
}

rm -f /workspace/problems/06_sonic_moe_swiglu/MEMORY.md
cat > /home/agent/.claude/projects/-workspace-problems-06-sonic-moe-swiglu/memory/MEMORY.md <<'EOF'
- [sonic-moe-metric](sonic-moe-metric.md) — 06_sonic_moe_swiglu: metric quirks (reported tflops = real/K, true peak ~390 not 200), Triton grouped-GEMM tuning, CUTLASS dead-end on sm120 bf16. Final geomean ~0.2155.
EOF
echo done

{
  "command": "timeout 600 uv run python check.py 2>&1 | tail -3",
  "description": "Final correctness check",
  "run_in_background": true
}

{
  "command": "timeout 600 uv run python benchmark.py 2>&1 | grep -E \"solution tflops|solution_peak_fraction|peak_fraction:|RESULT\"",
  "description": "Final benchmark",
  "run_in_background": true
}