{
  "command": "ls -la && echo \"---GPU---\" && nvidia-smi 2>/dev/null | head -20",
  "description": "List workspace and check GPU"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/reference.py"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/benchmark.py"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/check.py"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/shapes.py"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/sota.py"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/problem.yaml"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/PROMPT.txt"
}

{
  "command": "<290 chars \u2014 see below>",
  "description": "Check repo root and uv python"
}

echo "=== REPO_ROOT ===" && python3 -c "from pathlib import Path; p=Path('check.py').resolve().parents[2]; print(p, p.exists())" 2>&1
echo "=== which uv ===" && which uv 2>&1
echo "=== uv python ===" && uv run python -c "import sys; print(sys.version); print(sys.executable)" 2>&1 | tail -5

{
  "command": "<307 chars \u2014 see below>",
  "description": "Check torch/triton/cuda versions and device props"
}

uv run python -c "
import torch, triton
print('torch', torch.__version__)
print('cuda', torch.version.cuda)
print('triton', triton.__version__)
print('cc', torch.cuda.get_device_capability())
print('device', torch.cuda.get_device_name())
print('props', torch.cuda.get_device_properties(0))
" 2>&1 | tail -20

{
  "command": "cd /workspace && find . -path ./problems -prune -o -name \"*.py\" -print 2>/dev/null | grep -E \"eval|hardware\" | head -40",
  "description": "Find eval/hardware modules in repo"
}

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

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

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

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

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

{
  "command": "<772 chars \u2014 see below>",
  "description": "Compute theoretical bandwidth-fraction ceiling per shape"
}

uv run python -c "
shapes=[(1,12288,4096),(32,12288,4096),(256,12288,4096),(1,4096,4096),(16,14336,4096)]
bw=1800e9; bf16=200e12
for i,(M,N,K) in enumerate(shapes):
    ng=K//128
    b = M*K*2 + (K//2)*N + ng*N*2 + ng*N*2 + M*N*2
    fl = 2*M*N*K
    t_bw = b/bw
    t_cp = fl/bf16
    t = max(t_bw, t_cp)
    frac = (b/t)/bw
    print(f'shape{i} M={M:3d} N={N:5d}: bytes={b/1e6:6.2f}MB bwmin={t_bw*1e6:6.1f}us compmin={t_cp*1e6:7.1f}us -> tmin={t*1e6:7.1f}us fracmax={frac:.3f}  ({\"mem\" if t_bw>=t_cp else \"COMPUTE\"})')
import math
fracs=[(( (M*K*2 + (K//2)*N + (K//128)*N*2*2 + M*N*2))/ ( max((M*K*2 + (K//2)*N + (K//128)*N*2*2 + M*N*2)/bw, (2*M*N*K)/bf16) )/bw) for M,N,K in shapes]
print('geomean ceiling =', math.exp(sum(math.log(f) for f in fracs)/len(fracs)))
"

{
  "subject": "Implement correct Triton W4A16 GEMM kernel",
  "description": "Write solution.py with fused unpack+dequant+GEMM tl.dot kernel. Match reference dequant (bf16). Get check.py to PASS first.",
  "activeForm": "Implementing correct Triton W4A16 GEMM"
}

{
  "subject": "Add dedicated decode GEMV kernel (M=1)",
  "description": "For M=1 memory-bound shapes (0,3): pure-bandwidth GEMV with split-K, no tensor-core waste. Highest leverage shapes.",
  "activeForm": "Building decode GEMV kernel"
}

{
  "subject": "Tune GEMM for compute-bound M=256",
  "description": "Shape 2 is compute-bound (capped 0.15). Optimize tl.dot tiling/split-K to maximize bf16 throughput.",
  "activeForm": "Tuning GEMM for M=256"
}

{
  "subject": "Profile, benchmark, iterate to maximize peak_fraction",
  "description": "Run benchmark.py, use torch.profiler/ncu to find bottlenecks, iterate configs. Verify check.py stays PASS.",
  "activeForm": "Profiling and iterating"
}

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

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "content": "<4201 chars \u2014 see diff>"
}

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

{
  "command": "uv run python -c \"import triton; print([n for n in dir(triton) if 'pow' in n.lower() or 'cdiv' in n.lower()])\"",
  "description": "Find triton helper functions"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<76 chars \u2014 see diff>",
  "new_string": "    BM = max(16, min(64, triton.next_power_of_2(M)))"
}

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

{
  "command": "uv run python benchmark.py 2>&1 | grep -E \"shape=|peak_fraction|RESULT\"",
  "description": "Run benchmark baseline"
}

{
  "command": "<597 chars \u2014 see below>",
  "description": "Test Triton atomic_add support for fp32/bf16/fp16"
}

uv run python -c "
import torch, triton, triton.language as tl
@triton.jit
def k(x_ptr, v_ptr, N, BLOCK: tl.constexpr):
    pid=tl.program_id(0)
    off=pid*BLOCK+tl.arange(0,BLOCK)
    tl.atomic_add(x_ptr+off, v_ptr+off)
for dt in [torch.float32, torch.bfloat16, torch.float16]:
    try:
        x=torch.zeros(128, dtype=dt, device='cuda'); v=torch.ones(128,dtype=dt,device='cuda')
        k[(4,)](x,v,128,BLOCK=32)
        torch.cuda.synchronize()
        print(dt, 'atomic OK', x[:4])
    except Exception as e:
        print(dt, 'atomic FAIL', type(e).__name__, str(e)[:120])
" 2>&1 | tail -10

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/brlzwi4r5.output 2>/dev/null | tail -10; echo \"---done---\"",
  "description": "Read atomic test output again"
}

{
  "command": "<686 chars \u2014 see below>",
  "description": "Test atomic support via file"
}

cat > /workspace/problems/07_w4a16_gemm/scratch_atomic.py <<'EOF'
import torch, triton, triton.language as tl
@triton.jit
def k(x_ptr, v_ptr, N, BLOCK: tl.constexpr):
    pid=tl.program_id(0)
    off=pid*BLOCK+tl.arange(0,BLOCK)
    tl.atomic_add(x_ptr+off, v_ptr+off)
for dt in [torch.float32, torch.bfloat16, torch.float16]:
    try:
        x=torch.zeros(128, dtype=dt, device='cuda'); v=torch.ones(128,dtype=dt,device='cuda')
        k[(4,)](x,v,128,BLOCK=32)
        torch.cuda.synchronize()
        print(dt, 'atomic OK', x[:4].tolist())
    except Exception as e:
        print(dt, 'atomic FAIL', type(e).__name__, str(e)[:100])
EOF
uv run python scratch_atomic.py 2>&1 | tail -6

{
  "command": "uv run python scratch_atomic.py 2>&1 | grep -E \"atomic (OK|FAIL)\"",
  "description": "Filter atomic test results"
}

{
  "command": "uv run python scratch_atomic.py 2>&1 | grep -iE \"error|atomic_add|not supported|requires|TypeError\" | head -10",
  "description": "Get atomic error details"
}

{
  "command": "<611 chars \u2014 see below>",
  "description": "Test atomic with mask and full traceback"
}

cat > /workspace/problems/07_w4a16_gemm/scratch_atomic2.py <<'EOF'
import torch, triton, triton.language as tl
@triton.jit
def k(x_ptr, v_ptr, N, BLOCK: tl.constexpr):
    pid=tl.program_id(0)
    off=pid*BLOCK+tl.arange(0,BLOCK)
    tl.atomic_add(x_ptr+off, v_ptr+off, mask=off<N)
dt=torch.float32
try:
    x=torch.zeros(128, dtype=dt, device='cuda'); v=torch.ones(128,dtype=dt,device='cuda')
    k[(4,)](x,v,128,BLOCK=32)
    torch.cuda.synchronize()
    print('atomic OK', x[:4].tolist())
except Exception as e:
    import traceback; traceback.print_exc()
EOF
uv run python scratch_atomic2.py 2>&1 | tail -15

{
  "command": "<740 chars \u2014 see below>",
  "description": "Test atomic with proper value load"
}

cat > /workspace/problems/07_w4a16_gemm/scratch_atomic2.py <<'EOF'
import torch, triton, triton.language as tl
@triton.jit
def k(x_ptr, v_ptr, N, BLOCK: tl.constexpr):
    pid=tl.program_id(0)
    off=pid*BLOCK+tl.arange(0,BLOCK)
    m=off<N
    tl.atomic_add(x_ptr+off, tl.load(v_ptr+off, mask=m), mask=m)
for dt in [torch.float32, torch.bfloat16, torch.float16]:
    try:
        x=torch.zeros(128, dtype=dt, device='cuda'); v=torch.full((128,),0.5,dtype=dt,device='cuda')
        k[(4,)](x,v,128,BLOCK=32)
        torch.cuda.synchronize()
        print(dt, 'atomic OK', x[:4].tolist())
    except Exception as e:
        print(dt, 'atomic FAIL', type(e).__name__, str(e)[:80])
EOF
uv run python scratch_atomic2.py 2>&1 | grep -E "atomic"

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "content": "<7528 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -5",
  "description": "Run correctness check"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<2856 chars \u2014 see diff>",
  "new_string": "<3535 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<767 chars \u2014 see diff>",
  "new_string": "<1217 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -5",
  "description": "Run correctness check"
}

{
  "command": "uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution|peak_fraction|RESULT\"",
  "description": "Benchmark with GEMV path"
}

{
  "command": "<1169 chars \u2014 see below>",
  "description": "Profile decode kernels and launch overhead"
}

cat > prof_decode.py <<'EOF'
import torch, time
import solution, reference
from torch.profiler import profile, ProfilerActivity

device='cuda:0'
for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval()
    sm=solution.Model(M,N,K).to(device).eval()
    sm.load_state_dict(rm.state_dict(), strict=True)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    # warmup
    for _ in range(20): sm(x)
    torch.cuda.synchronize()
    # time
    NITER=200
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    s.record()
    for _ in range(NITER): sm(x)
    e.record(); torch.cuda.synchronize()
    per= s.elapsed_time(e)/NITER*1000  # us
    print(f"M={M} N={N}: {per:.2f} us/call ({NITER} iters)")
    # profile kernels
    with profile(activities=[ProfilerActivity.CUDA]) as prof:
        for _ in range(50): sm(x)
    torch.cuda.synchronize()
    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=6))
    print("="*70)
EOF
uv run python prof_decode.py 2>&1 | grep -vE "benchmark_event|WARNING|UserWarning|warnings.warn" | tail -60

{
  "command": "<1026 chars \u2014 see below>",
  "description": "Probe raw copy bandwidth for weight tensor"
}

cat > bw_probe.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
# Raw copy bandwidth of the weight tensor (K//2, N) uint8, the dominant bytes
for K,N in [(4096,12288),(4096,4096)]:
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    out=torch.empty_like(wq)
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    for _ in range(10): out.copy_(wq)
    torch.cuda.synchronize()
    s.record()
    for _ in range(100): out.copy_(wq)
    e.record(); torch.cuda.synchronize()
    ms=s.elapsed_time(e)/100
    bytes_=(K//2)*N
    print(f"copy wq {K//2}x{N}: {ms*1000:.1f}us  {bytes_/ms/1e6:.0f} GB/s")
# Also a strided read pattern: sum reduce of wq over axis 0 (mimics reading columns)
@triton.jit
def copy_k(x_ptr,o_ptr,NP,N,BLOCK:tl.constexpr):
    pid=tl.program_id(0)
    off=pid*BLOCK+tl.arange(0,BLOCK)
    for r in range(NP):
        v=tl.load(x_ptr+r*N+off)
        tl.store(o_ptr+r*N+off,v)
EOF
uv run python bw_probe.py 2>&1 | grep -E "copy|GB/s"

{
  "command": "<880 chars \u2014 see below>",
  "description": "Probe DRAM copy bandwidth with L2 flush"
}

cat > bw_probe2.py <<'EOF'
import torch
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def do_flush():
    flush.zero_()
for K,N in [(4096,12288),(4096,4096)]:
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    out=torch.empty_like(wq)
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    for _ in range(10):
        do_flush(); out.copy_(wq)
    torch.cuda.synchronize()
    times=[]
    for _ in range(50):
        do_flush(); torch.cuda.synchronize()
        s.record(); out.copy_(wq); e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    times.sort(); ms=times[len(times)//2]
    bytes_=(K//2)*N
    print(f"DRAM copy wq {K//2}x{N}: {ms*1000:.1f}us  {bytes_/ms/1e6:.0f} GB/s (flushed)")
EOF
uv run python bw_probe2.py 2>&1 | grep -E "DRAM|GB/s"

{
  "command": "<3547 chars \u2014 see below>",
  "description": "Sweep GEMV configs"
}

cat > sweep_gemv.py <<'EOF'
import torch, triton, triton.language as tl, time
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()

GROUP=128
@triton.jit
def gemv(x_ptr,wq_ptr,s_ptr,z_ptr,p_ptr,N,K,stride_wpk,stride_wn,stride_sg,stride_sn,stride_pk,
         SPLIT_K:tl.constexpr,BN:tl.constexpr,GROUP:tl.constexpr,PROC:tl.constexpr):
    # PROC: groups processed per inner iter (PROC*GROUP K), must tile SPLIT_K
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_n=pid_n*BN+tl.arange(0,BN); mask_n=offs_n<N
    k_start=pid_k*SPLIT_K
    ng:tl.constexpr = SPLIT_K//GROUP
    GH:tl.constexpr=GROUP//2
    acc=tl.zeros((BN,),dtype=tl.float32)
    for gi in tl.static_range(0,ng):
        k0=k_start+gi*GROUP; g=k0//GROUP
        x=tl.load(x_ptr+k0+tl.arange(0,GROUP))
        xr=tl.reshape(x,(GH,2)); xlo,xhi=tl.split(xr)
        pk=(k0//2)+tl.arange(0,GH)
        wp=wq_ptr+pk[:,None]*stride_wpk+offs_n[None,:]*stride_wn
        wpc=tl.load(wp,mask=mask_n[None,:],other=0)
        wlo=(wpc&0xF).to(tl.bfloat16); whi=((wpc>>4)&0xF).to(tl.bfloat16)
        s=tl.load(s_ptr+g*stride_sg+offs_n*stride_sn,mask=mask_n,other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+g*stride_sg+offs_n*stride_sn,mask=mask_n,other=0.0).to(tl.bfloat16)
        wlo=(wlo-z[None,:])*s[None,:]; whi=(whi-z[None,:])*s[None,:]
        acc+=tl.sum(xlo.to(tl.float32)[:,None]*wlo.to(tl.float32),axis=0)
        acc+=tl.sum(xhi.to(tl.float32)[:,None]*whi.to(tl.float32),axis=0)
    tl.store(p_ptr+pid_k*stride_pk+offs_n,acc,mask=mask_n)

def run(M,N,K,BN,nw,ns,target_mult):
    ng=K//GROUP
    nnt=triton.cdiv(N,BN)
    ts=max(1,(target_mult*188)//nnt)
    nsplit=1
    for c in (32,16,8,4,2,1):
        if c<=ng and c<=ts: nsplit=c;break
    SK=K//nsplit
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
    def f():
        gemv[(nnt,nsplit)](x,wq,sc,zr,part,N,K,wq.stride(0),wq.stride(1),sc.stride(0),sc.stride(1),part.stride(0),
                           SPLIT_K=SK,BN=BN,GROUP=GROUP,PROC=1,num_warps=nw,num_stages=ns)
    for _ in range(20): f()
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    ts_list=[]
    for _ in range(50):
        l2flush(); torch.cuda.synchronize()
        s.record(); f(); e.record(); torch.cuda.synchronize()
        ts_list.append(s.elapsed_time(e))
    ts_list.sort(); ms=ts_list[len(ts_list)//2]
    by= (K//2)*N + ng*N*2*2 + M*K*2  # approx read bytes
    return ms*1000, by/ms/1e6, nsplit, nnt*nsplit

for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    print(f"=== M={M} N={N} K={K} (theoretical read bw target ~1500+ GB/s) ===")
    best=None
    for BN in [64,128,256]:
        for nw in [2,4,8]:
            for ns in [2,3,4]:
                for tm in [4,8,12]:
                    try:
                        us,gbw,nsplit,nblk=run(M,N,K,BN,nw,ns,tm)
                        if best is None or us<best[0]: best=(us,gbw,BN,nw,ns,tm,nsplit,nblk)
                    except Exception as ex:
                        pass
    print(f"  BEST: {best[0]:.1f}us {best[1]:.0f}GB/s  BN={best[2]} nw={best[3]} ns={best[4]} tmult={best[5]} nsplit={best[6]} nblk={best[7]}")
EOF
timeout 600 uv run python sweep_gemv.py 2>&1 | grep -E "===|BEST"

{
  "command": "<3907 chars \u2014 see below>",
  "description": "Sweep tl.dot split-K GEMM for M=1"
}

cat > sweep_dot.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()
GROUP=128

# tl.dot split-K GEMM with M padded to BM. Writes fp32 partials (nsplit, M, N) -> reduce later (we just time the GEMM).
@triton.jit
def dot_gemm(x_ptr,wq_ptr,s_ptr,z_ptr,p_ptr,M,N,K,
             sxm,sxk,wpk,wn,sg,sn,spm,spn,
             BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GROUP:tl.constexpr,NSPLIT:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1); pid_k=tl.program_id(2)
    offs_m=pid_m*BM+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN)
    offs_k=tl.arange(0,BK)
    mm=offs_m<M; mn=offs_n<N
    acc=tl.zeros((BM,BN),dtype=tl.float32)
    BKH:tl.constexpr=BK//2
    gbase=pid_k*(K//NSPLIT//GROUP)
    ngl:tl.constexpr=(K//NSPLIT)//GROUP
    for gi in tl.static_range(0,ngl):
        g=gbase+gi; k0=g*GROUP
        xp=x_ptr+offs_m[:,None]*sxm+(k0+offs_k)[None,:]*sxk
        x=tl.load(xp,mask=mm[:,None],other=0.0)
        xr=tl.reshape(x,(BM,BKH,2)); xlo,xhi=tl.split(xr)
        pk=(k0//2)+tl.arange(0,BKH)
        wp=wq_ptr+pk[:,None]*wpk+offs_n[None,:]*wn
        wpc=tl.load(wp,mask=mn[None,:],other=0)
        wlo=(wpc&0xF).to(tl.bfloat16); whi=((wpc>>4)&0xF).to(tl.bfloat16)
        s=tl.load(s_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        wlo=(wlo-z[None,:])*s[None,:]; whi=(whi-z[None,:])*s[None,:]
        acc=tl.dot(xlo,wlo,acc=acc,allow_tf32=False)
        acc=tl.dot(xhi,whi,acc=acc,allow_tf32=False)
    pp=p_ptr+pid_k*spm+offs_m[:,None]*spm+offs_n[None,:]*spn if False else p_ptr+(pid_k*M+offs_m[:,None])*spn+offs_n[None,:]*0+ (p_ptr)
    # simpler: store to partial (NSPLIT, M, N) row-major
    out_ptr=p_ptr+(pid_k*M)*spn
    tl.store(out_ptr+offs_m[:,None]*spn+offs_n[None,:]*0, acc.to(tl.bfloat16), mask=mm[:,None]&mn[None,:])

def run(M,N,K,BM,BN,BK,nw,ns,nsplit):
    ng=K//GROUP
    nnt=triton.cdiv(N,BN); nnm=triton.cdiv(M,BM)
    if (K//nsplit)%GROUP!=0: return None
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device); zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    part=torch.empty((nsplit,M,N),dtype=torch.bfloat16,device=device)
    def f():
        dot_gemm[(nnm,nnt,nsplit)](x,wq,sc,zr,part,M,N,K,x.stride(0),1,wq.stride(0),1,sc.stride(0),1,
            part.stride(1),part.stride(2),BM=BM,BN=BN,BK=BK,GROUP=GROUP,NSPLIT=nsplit,num_warps=nw,num_stages=ns)
    try:
        for _ in range(15): f()
    except Exception as ex:
        return None
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    tl_list=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize()
        s.record(); f(); e.record(); torch.cuda.synchronize()
        tl_list.append(s.elapsed_time(e))
    tl_list.sort(); ms=tl_list[len(tl_list)//2]
    by=(K//2)*N+ng*N*2*2+M*K*2
    return ms*1000, by/ms/1e6, nnm*nnt*nsplit

for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    print(f"=== tl.dot split-K GEMM, M={M} N={N} ===")
    best=None
    for BM in [16,32]:
        for BN in [64,128,256]:
            for BK in [128,256]:
                for nw in [4,8]:
                    for ns in [3,4]:
                        for nspl in [2,4,8,16]:
                            r=run(M,N,K,BM,BN,BK,nw,ns,nspl)
                            if r and (best is None or r[0]<best[0]): best=(r[0],r[1],BM,BN,BK,nw,ns,nspl,r[2])
    print(f"  BEST: {best[0]:.1f}us {best[1]:.0f}GB/s BM={best[2]} BN={best[3]} BK={best[4]} nw={best[5]} ns={best[6]} nspl={best[7]} nblk={best[8]}")
EOF
timeout 900 uv run python sweep_dot.py 2>&1 | grep -E "===|BEST|Error|error" | head -40

{
  "command": "<3928 chars \u2014 see below>",
  "description": "Sweep tl.dot split-K GEMM (fixed)"
}

cat > sweep_dot.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()
GROUP=128

@triton.jit
def dot_gemm(x_ptr,wq_ptr,s_ptr,z_ptr,p_ptr,M,N,K,
             sxk,wpk,wn,sg,sn,
             BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GROUP:tl.constexpr,NSPLIT:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_m=tl.arange(0,BM)            # M=BM (padded)
    offs_n=pid_n*BN+tl.arange(0,BN)
    offs_k=tl.arange(0,BK)
    mn=offs_n<N
    acc=tl.zeros((BM,BN),dtype=tl.float32)
    BKH:tl.constexpr=BK//2
    gbase=pid_k*(K//NSPLIT//GROUP)
    ngl:tl.constexpr=(K//NSPLIT)//GROUP
    for gi in tl.static_range(0,ngl):
        g=gbase+gi; k0=g*GROUP
        xp=x_ptr+offs_m[:,None]*K+(k0+offs_k)[None,:]*sxk
        x=tl.load(xp)   # M padded: rows>0 may be junk but we only keep row 0 later; mask not needed if x has M rows
        xr=tl.reshape(x,(BM,BKH,2)); xlo,xhi=tl.split(xr)
        pk=(k0//2)+tl.arange(0,BKH)
        wp=wq_ptr+pk[:,None]*wpk+offs_n[None,:]*wn
        wpc=tl.load(wp,mask=mn[None,:],other=0)
        wlo=(wpc&0xF).to(tl.bfloat16); whi=((wpc>>4)&0xF).to(tl.bfloat16)
        s=tl.load(s_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        wlo=(wlo-z[None,:])*s[None,:]; whi=(whi-z[None,:])*s[None,:]
        acc=tl.dot(xlo,wlo,acc=acc,allow_tf32=False)
        acc=tl.dot(xhi,whi,acc=acc,allow_tf32=False)
    # partial (NSPLIT, M, N) row-major; store row0 only matters but store all BM rows (cheap)
    base=p_ptr+(pid_k*BM)*N
    tl.store(base+offs_m[:,None]*N+offs_n[None,:], acc.to(tl.bfloat16), mask=mn[None,:])

def run(M,N,K,BM,BN,BK,nw,ns,nsplit):
    if (K//nsplit)%GROUP!=0: return None
    ng=K//GROUP
    nnt=triton.cdiv(N,BN)
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device); zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    part=torch.empty((nsplit,BM,N),dtype=torch.bfloat16,device=device)
    def f():
        dot_gemm[(nnt,nsplit)](x,wq,sc,zr,part,M,N,K,1,wq.stride(0),1,sc.stride(0),1,
            BM=BM,BN=BN,BK=BK,GROUP=GROUP,NSPLIT=nsplit,num_warps=nw,num_stages=ns)
    try:
        for _ in range(10): f()
    except Exception as ex:
        return ("ERR",str(ex)[:80])
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True)
    tl_list=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize()
        s.record(); f(); e.record(); torch.cuda.synchronize()
        tl_list.append(s.elapsed_time(e))
    tl_list.sort(); ms=tl_list[len(tl_list)//2]
    by=(K//2)*N+ng*N*2*2+M*K*2
    return (ms*1000, by/ms/1e6, nnt*nsplit)

for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    print(f"=== tl.dot split-K GEMM, M={M} N={N} ===", flush=True)
    best=None; first_err=None
    for BM in [16,32]:
        for BN in [64,128,256]:
            for BK in [128,256]:
                for nw in [4,8]:
                    for ns in [3,4]:
                        for nspl in [2,4,8,16]:
                            r=run(M,N,K,BM,BN,BK,nw,ns,nspl)
                            if r is None: continue
                            if r[0]=="ERR":
                                if first_err is None: first_err=r[1]
                                continue
                            if best is None or r[0]<best[0]: best=(r[0],r[1],BM,BN,BK,nw,ns,nspl,r[2])
    if first_err: print("  first err:", first_err)
    if best: print(f"  BEST: {best[0]:.1f}us {best[1]:.0f}GB/s BM={best[2]} BN={best[3]} BK={best[4]} nw={best[5]} ns={best[6]} nspl={best[7]} nblk={best[8]}")
EOF
timeout 900 uv run python sweep_dot.py 2>&1 | grep -E "===|BEST|err" | head -20

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bdnegzgxc.output 2>/dev/null | grep -E \"===|BEST|err\"",
  "description": "Read dot sweep results"
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bdnegzgxc.output 2>/dev/null | grep -A8 \"first err\" | head -20",
  "description": "Read full error"
}

{
  "command": "<2163 chars \u2014 see below>",
  "description": "Reproduce dot kernel error"
}

cat > /workspace/problems/07_w4a16_gemm/dot_err.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
GROUP=128
@triton.jit
def dot_gemm(x_ptr,wq_ptr,s_ptr,z_ptr,p_ptr,M,N,K,
             sxk,wpk,wn,sg,sn,
             BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GROUP:tl.constexpr,NSPLIT:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_m=tl.arange(0,BM)
    offs_n=pid_n*BN+tl.arange(0,BN)
    offs_k=tl.arange(0,BK)
    mn=offs_n<N
    acc=tl.zeros((BM,BN),dtype=tl.float32)
    BKH:tl.constexpr=BK//2
    gbase=pid_k*(K//NSPLIT//GROUP)
    ngl:tl.constexpr=(K//NSPLIT)//GROUP
    for gi in tl.static_range(0,ngl):
        g=gbase+gi; k0=g*GROUP
        xp=x_ptr+offs_m[:,None]*K+(k0+offs_k)[None,:]*sxk
        x=tl.load(xp)
        xr=tl.reshape(x,(BM,BKH,2)); xlo,xhi=tl.split(xr)
        pk=(k0//2)+tl.arange(0,BKH)
        wp=wq_ptr+pk[:,None]*wpk+offs_n[None,:]*wn
        wpc=tl.load(wp,mask=mn[None,:],other=0)
        wlo=(wpc&0xF).to(tl.bfloat16); whi=((wpc>>4)&0xF).to(tl.bfloat16)
        s=tl.load(s_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+g*sg+offs_n*sn,mask=mn,other=0.0).to(tl.bfloat16)
        wlo=(wlo-z[None,:])*s[None,:]; whi=(whi-z[None,:])*s[None,:]
        acc=tl.dot(xlo,wlo,acc=acc,allow_tf32=False)
        acc=tl.dot(xhi,whi,acc=acc,allow_tf32=False)
    base=p_ptr+(pid_k*BM)*N
    tl.store(base+offs_m[:,None]*N+offs_n[None,:], acc.to(tl.bfloat16), mask=mn[None,:])
try:
    N,K,M=12288,4096,1; BM,BN,BK=16,128,128; ng=K//GROUP
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device); zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    part=torch.empty((4,BM,N),dtype=torch.bfloat16,device=device)
    dot_gemm[(triton.cdiv(N,BN),4)](x,wq,sc,zr,part,M,N,K,1,wq.stride(0),1,sc.stride(0),1,BM=BM,BN=BN,BK=BK,GROUP=GROUP,NSPLIT=4,num_warps=4,num_stages=3)
    torch.cuda.synchronize(); print("OK")
except Exception as e:
    import traceback; traceback.print_exc()
EOF
uv run python dot_err.py 2>&1 | tail -20

{
  "command": "<253 chars \u2014 see below>",
  "description": "Fix static_range and test compile"
}

sed -i 's/for gi in tl.static_range(0,ngl):/for gi in range(0,ngl):/' sweep_dot.py dot_err.py
# also fix dot_err to use range and re-test compile
sed -i 's/dot_gemm\[(triton.cdiv/dot_gemm[(triton.cdiv/' dot_err.py
uv run python dot_err.py 2>&1 | tail -6

{
  "command": "timeout 700 uv run python sweep_dot.py 2>&1 | grep -E \"===|BEST\"",
  "description": "Run dot GEMM sweep"
}

{
  "command": "<2423 chars \u2014 see below>",
  "description": "Probe minimal reduction bandwidth"
}

cat > bw_reduce.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()

# Minimal: read wq (K//2, N) uint8, compute y[n]=sum over K//2 of byte, split-K, fp32 partials.
# No dequant, no even/odd split. Pure bandwidth probe of the reduction access pattern.
@triton.jit
def red(wq_ptr,p_ptr,N,KHALF,Nstride,BN:tl.constexpr,SK:tl.constexpr,BK:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_n=pid_n*BN+tl.arange(0,BN); mn=offs_n<N
    acc=tl.zeros((BN,),dtype=tl.float32)
    niter=SK//BK
    for it in range(0,niter):
        r0=pid_k*SK+it*BK
        pk=r0+tl.arange(0,BK)
        wp=wq_ptr+pk[:,None]*Nstride+offs_n[None,:]
        v=tl.load(wp,mask=mn[None,:],other=0).to(tl.float32)
        acc+=tl.sum(v,axis=0)
    tl.store(p_ptr+pid_k*N+offs_n,acc,mask=mn)

def run(N,KHALF,BN,nw,ns,nspl,BK,target_mult=8):
    nnt=triton.cdiv(N,BN); ts=max(1,(target_mult*188)//nnt); 
    nsplit=1
    for c in (32,16,8,4,2,1):
        if c*KHALF*2<=KHALF*2*2 and c<=ts and (KHALF//BK)%c==0: nsplit=c;break
    # nsplit must divide KHALF//BK
    SK=BK*(KHALF//BK//nsplit)
    wq=torch.randint(0,256,(KHALF,N),dtype=torch.uint8,device=device)
    part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
    def f(): red[(nnt,nsplit)](wq,part,N,KHALF,N,BN=BN,SK=SK,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(15): f()
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize(); s.record(); f(); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
    L.sort(); ms=L[len(L)//2]
    by=KHALF*N
    return ms*1000, by/ms/1e6

for KH,N in [(2048,12288),(2048,4096)]:
    print(f"=== minimal reduce wq {KH}x{N} (read-only) ===")
    best=None
    for BN in [64,128,256,512]:
        for BK in [32,64,128]:
            for nw in [2,4,8]:
                for ns in [2,3,4]:
                    try:
                        r=run(N,KH,BN,nw,ns,4,BK)
                        if best is None or r[0]<best[0]: best=(r[0],r[1],BN,BK,nw,ns)
                    except: pass
    print(f"  BEST {best[0]:.1f}us {best[1]:.0f}GB/s BN={best[2]} BK={best[3]} nw={best[4]} ns={best[5]}")
EOF
timeout 500 uv run python bw_reduce.py 2>&1 | grep -E "===|BEST"

{
  "command": "<2487 chars \u2014 see below>",
  "description": "Probe transposed layout reduction bandwidth"
}

cat > bw_transpose.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()

# Layout B: weight stored (N, KHALF) row-major. Block handles BN output rows.
# y[n] = sum over k2 of byte[n,k2]. Split-K over k2.
@triton.jit
def redB(wq_ptr,p_ptr,N,KHALF,BN:tl.constexpr,SK:tl.constexpr,BK:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    n0=pid_n*BN
    offs_n=n0+tl.arange(0,BN); mn=offs_n<N
    acc=tl.zeros((BN,),dtype=tl.float32)
    niter=SK//BK
    for it in range(0,niter):
        k0=pid_k*SK+it*BK
        offs_k=k0+tl.arange(0,BK)
        # wq[n, k2] at n*KHALF + k2  -> (BN, BK) tile, contiguous along k2
        wp=wq_ptr+offs_n[:,None]*KHALF+offs_k[None,:]
        v=tl.load(wp,mask=mn[:,None],other=0).to(tl.float32)   # (BN, BK)
        acc+=tl.sum(v,axis=1)   # reduce over k2 -> (BN,)
    tl.store(p_ptr+pid_k*N+offs_n,acc,mask=mn)

def run(N,KHALF,BN,nw,ns,nspl,BK):
    nnt=triton.cdiv(N,BN)
    if (KHALF//BK)%nspl!=0: return None
    nsplit=nspl; SK=BK*(KHALF//BK//nsplit)
    wq=torch.randint(0,256,(N,KHALF),dtype=torch.uint8,device=device)
    part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
    def f(): redB[(nnt,nsplit)](wq,part,N,KHALF,BN=BN,SK=SK,BK=BK,num_warps=nw,num_stages=ns)
    try:
        for _ in range(15): f()
    except Exception as e: return None
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize(); s.record(); f(); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
    L.sort(); ms=L[len(L)//2]
    return ms*1000, KHALF*N/ms/1e6

for N,KH in [(12288,2048),(4096,2048)]:
    print(f"=== LayoutB (N,K//2) reduce {N}x{KH} ===")
    best=None
    for BN in [8,16,32,64]:
        for BK in [128,256,512,1024]:
            for nw in [4,8]:
                for ns in [3,4,5]:
                    for nspl in [2,4,8,16,32]:
                        try:
                            r=run(N,KH,BN,nw,ns,nspl,BK)
                            if r and (best is None or r[0]<best[0]): best=(r[0],r[1],BN,BK,nw,ns,nspl)
                        except: pass
    if best: print(f"  BEST {best[0]:.1f}us {best[1]:.0f}GB/s BN={best[2]} BK={best[3]} nw={best[4]} ns={best[5]} nspl={best[6]}")
EOF
timeout 600 uv run python bw_transpose.py 2>&1 | grep -E "===|BEST"

{
  "command": "which ncu nsys 2>&1; ls /usr/local/cuda*/bin/ 2>/dev/null | grep -E \"ncu|nsys\" | head; echo \"---nvcc---\"; which nvcc; nvcc --version 2>&1 | tail -2",
  "description": "Check profiler and nvcc availability"
}

{
  "command": "<1323 chars \u2014 see below>",
  "description": "ncu roofline on reduce kernel"
}

cat > ncu_probe.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
@triton.jit
def redA(wq_ptr,p_ptr,N,KHALF,Nstride,BN:tl.constexpr,SK:tl.constexpr,BK:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_n=pid_n*BN+tl.arange(0,BN); mn=offs_n<N
    acc=tl.zeros((BN,),dtype=tl.float32)
    niter=SK//BK
    for it in range(0,niter):
        r0=pid_k*SK+it*BK
        pk=r0+tl.arange(0,BK)
        wp=wq_ptr+pk[:,None]*Nstride+offs_n[None,:]
        v=tl.load(wp,mask=mn[None,:],other=0).to(tl.float32)
        acc+=tl.sum(v,axis=0)
    tl.store(p_ptr+pid_k*N+offs_n,acc,mask=mn)
N,KH,BN,SK,BK=12288,2048,64,2048,128; nsplit=KH//(SK//(BK)); nsplit=max(1,KH//BK// (KH//(SK//(BK)) or 1))
nsplit=2; nnt=triton.cdiv(N,BN)
wq=torch.randint(0,256,(KH,N),dtype=torch.uint8,device=device)
part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
def f(): redA[(nnt,nsplit)](wq,part,N,KH,N,BN=BN,SK=KH//nsplit,BK=BK,num_warps=8,num_stages=3)
for _ in range(3): f()
torch.cuda.synchronize()
f(); torch.cuda.synchronize()
print("ran")
EOF
ncu --set roofline --kernel-name regex:redA --launch-skip 2 --launch-count 1 --target-processes all uv run python ncu_probe.py 2>&1 | grep -iE "DRAM|Memory Throughput|Compute|SM Busy|Occupancy|Achieved|cycles|stall|warps per|Theoretical" | head -30

{
  "command": "<1474 chars \u2014 see below>",
  "description": "Get occupancy and test aggressive stages"
}

ncu --set full --kernel-name regex:redA --launch-skip 2 --launch-count 1 --target-processes all uv run python ncu_probe.py 2>&1 | grep -iE "Registers Per|Warps per|Theoretical Occupancy|Achieved Occupancy|Block Size|Active Warps|Stack" | head -15
echo "=== try aggressive num_stages on reduce ==="
cat >> bw_reduce.py <<'EOF'

def run2(N,KHALF,BN,nw,ns,BK,nspl):
    nnt=triton.cdiv(N,BN); nsplit=nspl; SK=BK*(KHALF//BK//nsplit)
    wq=torch.randint(0,256,(KHALF,N),dtype=torch.uint8,device=device)
    part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
    def f(): red(wq,part,N,KHALF,N,BN=BN,SK=SK,BK=BK,num_warps=nw,num_stages=ns)
    for _ in range(15): f()
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize(); s.record(); f(); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
    L.sort(); return L[len(L)//2]*1000, KHALF*N/(L[len(L)//2]/1e3)/1e6
for KH,N in [(2048,12288)]:
    for (BN,BK,nw,ns,nspl) in [(64,128,8,6,2),(64,128,8,8,2),(128,256,8,6,2),(128,256,8,8,2),(256,256,8,6,2),(64,512,8,6,2),(128,128,8,6,4)]:
        try:
            us,gbw=run2(N,KH,BN,nw,ns,BK,nspl); print(f"  {us:.1f}us {gbw:.0f}GB/s  BN={BN} BK={BK} nw={nw} ns={ns} nspl={nspl}")
        except Exception as ex: print("  err",str(ex)[:60])
EOF
timeout 300 uv run python -c "import bw_reduce" 2>&1 | grep -E "us|GB" | head -10

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bliuwgwx8.output 2>/dev/null | grep -iE \"Registers|Warps per|Occupancy|Block Size|Stack|GB/s\"",
  "description": "Read full occupancy results"
}

{
  "command": "<3841 chars \u2014 see below>",
  "description": "Sweep LayoutB GEMV with dequant"
}

cat > sweep_gemvB.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()
GROUP_BYTES=64  # one quant group = 64 bytes (128 K)

@triton.jit
def gemvB(x_ptr,wqt_ptr,s_ptr,z_ptr,p_ptr,N,KH,  # KH=K//2; wqt is (N,KH)
          SKB:tl.constexpr,  # split bytes (multiple of 64)
          BK:tl.constexpr,BN:tl.constexpr,GPT:tl.constexpr):  # GPT groups per iter; BK=64*GPT
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_n=pid_n*BN+tl.arange(0,BN); mn=offs_n<N
    kb_start=pid_k*SKB
    acc=tl.zeros((BN,),dtype=tl.float32)
    niters=SKB//BK
    for it in range(0,niters):
        kb=kb_start+it*BK
        gbase=kb//GROUP_BYTES
        off_kb=kb+tl.arange(0,BK)
        w=tl.load(wqt_ptr+offs_n[:,None]*KH+off_kb[None,:],mask=mn[:,None],other=0)  # (BN,BK) u8
        wlo=(w&0xF).to(tl.bfloat16); whi=((w>>4)&0xF).to(tl.bfloat16)
        # reshape to (BN, GPT, 64) to apply per-group s/z
        wlo=tl.reshape(wlo,(BN,GPT,64)); whi=tl.reshape(whi,(BN,GPT,64))
        gg=gbase+tl.arange(0,GPT)   # (GPT,)
        s=tl.load(s_ptr+gg[:,None]*N+offs_n[None,:],mask=mn[None,:],other=0.0).to(tl.bfloat16)  # (GPT,BN)
        z=tl.load(z_ptr+gg[:,None]*N+offs_n[None,:],mask=mn[None,:],other=0.0).to(tl.bfloat16)
        s=tl.permute(s,(1,0)); z=tl.permute(z,(1,0))  # (BN,GPT)
        wlo=(wlo-z[:,:,None])*s[:,:,None]; whi=(whi-z[:,:,None])*s[:,:,None]
        wlo=tl.reshape(wlo,(BN,BK)); whi=tl.reshape(whi,(BN,BK))
        xk=(2*kb)+tl.arange(0,2*BK)
        xf=tl.load(x_ptr+xk)  # (2BK,)
        xr=tl.reshape(xf,(BK,2)); xe,xo=tl.split(xr)  # (BK,)
        acc+=tl.sum(xe[None,:].to(tl.float32)*wlo.to(tl.float32),axis=1)
        acc+=tl.sum(xo[None,:].to(tl.float32)*whi.to(tl.float32),axis=1)
    tl.store(p_ptr+pid_k*N+offs_n,acc,mask=mn)

def run(N,KH,BN,BK,GPT,nw,ns,nspl):
    nnt=triton.cdiv(N,BN); SKB=(KH//nspl); SKB=(SKB//BK)*BK
    if SKB==0 or (KH//SKB)<1: return None
    nsplit=KH//SKB
    wq=torch.randint(0,256,(KH,N),dtype=torch.uint8,device=device)
    wqt=wq.t().contiguous()
    ng=KH//64
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device); zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(1,2*KH,dtype=torch.bfloat16,device=device)
    part=torch.empty((nsplit,N),dtype=torch.float32,device=device)
    def f(): gemvB[(nnt,nsplit)](x,wqt,sc,zr,part,N,KH,SKB=SKB,BK=BK,BN=BN,GPT=GPT,num_warps=nw,num_stages=ns)
    try:
        for _ in range(15): f()
    except Exception as e: return ("ERR",str(e)[:70])
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize(); s.record(); f(); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
    L.sort(); ms=L[len(L)//2]
    by=KH*N + ng*N*2*2 + 2*KH*2  # wq + scales+zeros + x
    return (ms*1000, by/ms/1e6, nnt*nsplit)

for N,KH in [(12288,2048),(4096,2048)]:
    print(f"=== LayoutB GEMV dequant N={N} ===")
    best=None
    for GPT in [1,2,4]:
        BK=64*GPT
        for BN in [8,16,32,64]:
            for nw in [4,8]:
                for ns in [3,4,5,6]:
                    for nspl in [1,2,4,8,16,32]:
                        if KH//(64*GPT* (KH//(64*GPT)) <1: continue
                        r=run(N,KH,BN,BK,GPT,nw,ns,nspl)
                        if r is None: continue
                        if r[0]=="ERR": continue
                        if best is None or r[0]<best[0]: best=(r[0],r[1],BN,BK,GPT,nw,ns,nspl,r[2])
    print(f"  BEST {best[0]:.1f}us {best[1]:.0f}GB/s BN={best[2]} BK={best[3]} GPT={best[4]} nw={best[5]} ns={best[6]} nspl={best[7]} nblk={best[8]}")
EOF
timeout 700 uv run python sweep_gemvB.py 2>&1 | grep -E "===|BEST|ERR" | head -10

{
  "command": "timeout 700 uv run python sweep_gemvB.py 2>&1 | tail -8",
  "description": "Run LayoutB GEMV sweep"
}

{
  "command": "<122 chars \u2014 see below>",
  "description": "Fix syntax and run sweep"
}

sed -i '/if KH\/\/(64\*GPT/d' sweep_gemvB.py
timeout 700 uv run python sweep_gemvB.py 2>&1 | grep -E "===|BEST" | head -10

{
  "command": "timeout 200 uv run python sweep_gemvB.py 2>&1 | grep -iE \"ERR|error|traceback\" | head -5",
  "description": "Find GEMV B errors"
}

{
  "command": "<2164 chars \u2014 see below>",
  "description": "Debug GEMV B compile"
}

cat > gemvB_err.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
GROUP_BYTES=64
@triton.jit
def gemvB(x_ptr,wqt_ptr,s_ptr,z_ptr,p_ptr,N,KH,
          SKB:tl.constexpr,BK:tl.constexpr,BN:tl.constexpr,GPT:tl.constexpr):
    pid_n=tl.program_id(0); pid_k=tl.program_id(1)
    offs_n=pid_n*BN+tl.arange(0,BN); mn=offs_n<N
    kb_start=pid_k*SKB
    acc=tl.zeros((BN,),dtype=tl.float32)
    niters=SKB//BK
    for it in range(0,niters):
        kb=kb_start+it*BK
        gbase=kb//GROUP_BYTES
        off_kb=kb+tl.arange(0,BK)
        w=tl.load(wqt_ptr+offs_n[:,None]*KH+off_kb[None,:],mask=mn[:,None],other=0)
        wlo=(w&0xF).to(tl.bfloat16); whi=((w>>4)&0xF).to(tl.bfloat16)
        wlo=tl.reshape(wlo,(BN,GPT,64)); whi=tl.reshape(whi,(BN,GPT,64))
        gg=gbase+tl.arange(0,GPT)
        s=tl.load(s_ptr+gg[:,None]*N+offs_n[None,:],mask=mn[None,:],other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+gg[:,None]*N+offs_n[None,:],mask=mn[None,:],other=0.0).to(tl.bfloat16)
        s=tl.permute(s,(1,0)); z=tl.permute(z,(1,0))
        wlo=(wlo-z[:,:,None])*s[:,:,None]; whi=(whi-z[:,:,None])*s[:,:,None]
        wlo=tl.reshape(wlo,(BN,BK)); whi=tl.reshape(whi,(BN,BK))
        xk=(2*kb)+tl.arange(0,2*BK)
        xf=tl.load(x_ptr+xk)
        xr=tl.reshape(xf,(BK,2)); xe,xo=tl.split(xr)
        acc+=tl.sum(xe[None,:].to(tl.float32)*wlo.to(tl.float32),axis=1)
        acc+=tl.sum(xo[None,:].to(tl.float32)*whi.to(tl.float32),axis=1)
    tl.store(p_ptr+pid_k*N+offs_n,acc,mask=mn)
try:
    N,KH=12288,2048; BN,BK,GPT=32,64,1; nw,ns=8,4; nspl=4
    SKB=KH//nspl
    wqt=torch.randint(0,256,(N,KH),dtype=torch.uint8,device=device)
    sc=torch.randn(KH//64,N,dtype=torch.bfloat16,device=device); zr=torch.randn(KH//64,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(1,2*KH,dtype=torch.bfloat16,device=device)
    part=torch.empty((nspl,N),dtype=torch.float32,device=device)
    gemvB[(triton.cdiv(N,BN),nspl)](x,wqt,sc,zr,part,N,KH,SKB=SKB,BK=BK,BN=BN,GPT=GPT,num_warps=nw,num_stages=ns)
    torch.cuda.synchronize(); print("OK")
except Exception:
    import traceback; traceback.print_exc()
EOF
uv run python gemvB_err.py 2>&1 | tail -12

{
  "command": "<120 chars \u2014 see below>",
  "description": "Fix global constexpr and test"
}

sed -i 's/gbase=kb\/\/GROUP_BYTES/gbase=kb\/\/64/' sweep_gemvB.py gemvB_err.py
uv run python gemvB_err.py 2>&1 | tail -6

{
  "command": "timeout 700 uv run python sweep_gemvB.py 2>&1 | grep -E \"===|BEST\" | head -8",
  "description": "Run LayoutB GEMV sweep"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "content": "<9217 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -3 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution|peak_fraction|RESULT\"",
  "description": "Check correctness and benchmark"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<3635 chars \u2014 see diff>",
  "new_string": "<4522 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "content": "<9877 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark v4"
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bgo3wtsq0.output 2>/dev/null | grep -E \"PASS|FAIL|shape=[0-9] variant=solution|peak_fraction|RESULT\"",
  "description": "Read v4 results again"
}

{
  "command": "<577 chars \u2014 see below>",
  "description": "Debug shape3 TypeError"
}

cat > dbg_s3.py <<'EOF'
import torch, traceback
import solution, reference
device='cuda:0'
M,N,K=1,4096,4096
reference.M,reference.N,reference.K=M,N,K
rm=reference.Model(M,N,K).to(device).eval()
sm=solution.Model(M,N,K).to(device).eval()
sm.load_state_dict(rm.state_dict(),strict=True)
x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
try:
    sm._ensure_setup(); print("plan", sm._plan, "partial", None if sm._partial is None else sm._partial.shape)
    y=sm(x); print("OK", y.shape)
except Exception:
    traceback.print_exc()
EOF
uv run python dbg_s3.py 2>&1 | tail -15

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b163m0sg0.output 2>/dev/null | tail -16",
  "description": "Read shape3 debug output"
}

{
  "command": "timeout 60 uv run python dbg_s3.py 2>&1 | tail -16",
  "description": "Run shape3 debug directly"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<1708 chars \u2014 see diff>",
  "new_string": "<1168 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<263 chars \u2014 see diff>",
  "new_string": "<214 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark fixed v4"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<141 chars \u2014 see diff>",
  "new_string": "<141 chars \u2014 see diff>"
}

{
  "command": "uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Benchmark with 6x SMs split"
}

{
  "command": "<4827 chars \u2014 see below>",
  "description": "Sweep GEMM with split-K for M=16,32,256"
}

cat > sweep_gemm.py <<'EOF'
import torch, triton, triton.language as tl
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def l2flush(): flush.zero_()
GROUP=128

@triton.jit
def gemm_sk(x_ptr,wq_ptr,s_ptr,z_ptr,p_ptr,M,N,K,sxm,sxk,wpk,wn,sg,sn,spm,spn,
            BM:tl.constexpr,BN:tl.constexpr,BK:tl.constexpr,GROUP:tl.constexpr,NSPLIT:tl.constexpr,DIRECT:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    ng=K//GROUP; ng_per=ng//NSPLIT
    if DIRECT:
        g0=0
    else:
        pid_k=tl.program_id(2); g0=pid_k*ng_per
    offs_m=pid_m*BM+tl.arange(0,BM); offs_n=pid_n*BN+tl.arange(0,BN); offs_k=tl.arange(0,BK)
    mm=offs_m<M; nn=offs_n<N
    acc=tl.zeros((BM,BN),dtype=tl.float32); BKH:tl.constexpr=BK//2
    for gi in range(0,ng_per):
        g=g0+gi; k0=g*GROUP
        x=tl.load(x_ptr+offs_m[:,None]*sxm+(k0+offs_k)[None,:]*sxk,mask=mm[:,None],other=0.0)
        xr=tl.reshape(x,(BM,BKH,2)); xlo,xhi=tl.split(xr)
        pk=(k0//2)+tl.arange(0,BKH)
        wpc=tl.load(wq_ptr+pk[:,None]*wpk+offs_n[None,:]*wn,mask=nn[None,:],other=0)
        wlo=(wpc&0xF).to(tl.bfloat16); whi=((wpc>>4)&0xF).to(tl.bfloat16)
        s=tl.load(s_ptr+g*sg+offs_n*sn,mask=nn,other=0.0).to(tl.bfloat16)
        z=tl.load(z_ptr+g*sg+offs_n*sn,mask=nn,other=0.0).to(tl.bfloat16)
        wlo=(wlo-z[None,:])*s[None,:]; whi=(whi-z[None,:])*s[None,:]
        acc=tl.dot(xlo,wlo,acc=acc,allow_tf32=False); acc=tl.dot(xhi,whi,acc=acc,allow_tf32=False)
    if DIRECT:
        tl.store(p_ptr+offs_m[:,None]*spn+offs_n[None,:],acc.to(tl.bfloat16),mask=mm[:,None]&nn[None,:])
    else:
        base=p_ptr+(pid_k*M)*spn
        tl.store(base+offs_m[:,None]*spn+offs_n[None,:],acc.to(tl.float32),mask=mm[:,None]&nn[None,:])

@triton.jit
def reduce2d(p_ptr,y_ptr,M,N,NSPLIT,spm,spn,BM:tl.constexpr,BN:tl.constexpr):
    pid_m=tl.program_id(0); pid_n=tl.program_id(1)
    om=pid_m*BM+tl.arange(0,BM); on=pid_n*BN+tl.arange(0,BN)
    mm=om<M; nn=on<N
    acc=tl.zeros((BM,BN),dtype=tl.float32)
    for i in range(NSPLIT):
        acc+=tl.load(p_ptr+(i*M+om[:,None])*spn+on[None,:],mask=mm[:,None]&nn[None,:],other=0.0)
    tl.store(y_ptr+om[:,None]*spn+on[None,:],acc.to(tl.bfloat16),mask=mm[:,None]&nn[None,:])

def run(M,N,K,BM,BN,nw,ns,nsplit):
    ng=K//GROUP
    if nsplit==1:
        grid=(triton.cdiv(M,BM),triton.cdiv(N,BN)); DIRECT=True
        part=torch.empty((M,N),dtype=torch.bfloat16,device=device)
    else:
        if ng%nsplit!=0: return None
        grid=(triton.cdiv(M,BM),triton.cdiv(N,BN),nsplit); DIRECT=False
        part=torch.empty((nsplit,M,N),dtype=torch.float32,device=device)
    wq=torch.randint(0,256,(K//2,N),dtype=torch.uint8,device=device)
    sc=torch.randn(ng,N,dtype=torch.bfloat16,device=device); zr=torch.randn(ng,N,dtype=torch.bfloat16,device=device)
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    BK=GROUP
    def f():
        gemm_sk[grid](x,wq,sc,zr,part,M,N,K,x.stride(0),1,wq.stride(0),1,sc.stride(0),1,part.stride(1) if not DIRECT else N,part.stride(2) if not DIRECT else 1,
                      BM=BM,BN=BN,BK=BK,GROUP=GROUP,NSPLIT=nsplit,DIRECT=DIRECT,num_warps=nw,num_stages=ns)
    try:
        for _ in range(10): f()
    except Exception as e: return ("ERR",str(e)[:60])
    torch.cuda.synchronize()
    y=torch.empty((M,N),dtype=torch.bfloat16,device=device)
    def full():
        f()
        if not DIRECT:
            reduce2d[(triton.cdiv(M,BM),triton.cdiv(N,BN))](part,y,M,N,nsplit,part.stride(1),part.stride(2),BM=BM,BN=BN,num_warps=4,num_stages=1)
    for _ in range(5): full()
    torch.cuda.synchronize()
    s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
    for _ in range(40):
        l2flush(); torch.cuda.synchronize(); s.record(); full(); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
    L.sort(); ms=L[len(L)//2]
    by=M*K*2+(K//2)*N+ng*N*2*2+M*N*2
    return (ms*1000, by/ms/1e6, grid)

for M,N,K in [(32,12288,4096),(1

... (827 more chars)

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/by4kjutlj.output 2>/dev/null | grep -E \"===|BEST\"",
  "description": "Read GEMM sweep BEST lines"
}

{
  "command": "<3123 chars \u2014 see below>",
  "description": "Test CUDA GEMV correctness"
}

cat > test_cuda_gemv.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <cstdint>
#include <torch/extension.h>

__global__ void w4a16_gemv_kernel(
    const __nv_bfloat16* __restrict__ x,
    const uint8_t* __restrict__ wqt,
    const __nv_bfloat16* __restrict__ sc,
    const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y,
    int N, int K, int KH) {
    int wpb = blockDim.x >> 5;
    int warp = threadIdx.x >> 5;
    int lane = threadIdx.x & 31;
    int n = blockIdx.x * wpb + warp;
    if (n >= N) return;
    const uint8_t* wrow = wqt + (size_t)n * KH;
    float acc = 0.f;
    for (int b = 0; b < KH; b += 128) {
        uint32_t p = *reinterpret_cast<const uint32_t*>(wrow + b + 4*lane);
        int k0 = 2*b + 8*lane;
        int g = b/64 + (lane >> 4);
        float s = __bfloat162float(sc[(size_t)g*N + n]);
        float zf = __bfloat162float(zz[(size_t)g*N + n]);
        uint4 xv = *reinterpret_cast<const uint4*>(x + k0);
        __nv_bfloat16 xb[8];
        *reinterpret_cast<uint4*>(xb) = xv;
        #pragma unroll
        for (int j = 0; j < 4; j++) {
            unsigned int bv = (p >> (8*j)) & 0xFFu;
            float wl = ((float)(bv & 0xFu) - zf) * s;
            float wh = ((float)((bv >> 4) & 0xFu) - zf) * s;
            float xe = __bfloat162float(xb[2*j]);
            float xo = __bfloat162float(xb[2*j+1]);
            acc += xe*wl + xo*wh;
        }
    }
    #pragma unroll
    for (int off=16; off>0; off>>=1) acc += __shfl_xor_sync(0xffffffff, acc, off);
    if (lane == 0) y[n] = __float2bfloat16(acc);
}

void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb) {
    int N = out.size(1); int K = x.size(1); int KH = K/2;
    int block = wpb*32;
    int grid_n = (N + wpb - 1)/wpb;
    w4a16_gemv_kernel<<<grid_n, block>>>(
        reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
        reinterpret_cast<const uint8_t*>(wqt.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(out.data_ptr()), N, K, KH);
}
'''
mod = load_inline(name="w4gemv_test", cpp_sources="void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb);",
                  cuda_sources=src, functions=["launch"], verbose=False)
print("compiled OK")

import reference
device='cuda:0'
for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval()
    wq=rm.w_q; sc=rm.scales; zr=rm.zeros
    wqt=wq.t().contiguous()
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
    mod.launch(x,wqt,sc,zr,out,8)
    ref=rm(x)
    diff=(out.float()-ref.float()).abs().max().item()
    print(f"M={M} N={N}: max_diff={diff:.2e}", "PASS" if diff<0.1 else "FAIL")
EOF
timeout 180 uv run python test_cuda_gemv.py 2>&1 | grep -vE "WARNING|warning|warn" | tail -12

{
  "command": "<1116 chars \u2014 see below>",
  "description": "Benchmark CUDA GEMV"
}

cat >> test_cuda_gemv.py <<'EOF'

# benchmark
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device='cuda')
def lf(): flush.zero_()
for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval()
    wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
    out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
    for wpb in [4,8,16]:
        for _ in range(20): mod.launch(x,wqt,sc,zr,out,wpb)
        torch.cuda.synchronize()
        s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
        for _ in range(50):
            lf(); torch.cuda.synchronize(); s.record(); mod.launch(x,wqt,sc,zr,out,wpb); e.record(); torch.cuda.synchronize(); L.append(s.elapsed_time(e))
        L.sort(); ms=L[len(L)//2]
        by=M*K*2+(K//2)*N+(K//128)*N*2*2+M*N*2
        print(f"M={M} N={N} wpb={wpb}: {ms*1000:.1f}us {by/ms/1e6:.0f}GB/s frac={by/ms/1e6/1800:.3f}")
EOF
timeout 120 uv run python test_cuda_gemv.py 2>&1 | grep -E "wpb=" | tail -10

{
  "command": "<918 chars \u2014 see below>",
  "description": "ncu profile CUDA GEMV"
}

cat > ncu_cuda.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
exec(open('test_cuda_gemv.py').read().split('# benchmark')[0])  # reuse compiled mod + import reference
import reference
device='cuda:0'
M,N,K=1,12288,4096
reference.M,reference.N,reference.K=M,N,K
rm=reference.Model(M,N,K).to(device).eval()
wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
x=torch.randn(M,K,dtype=torch.bfloat16,device=device); out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
for _ in range(3): mod.launch(x,wqt,sc,zr,out,8)
torch.cuda.synchronize()
mod.launch(x,wqt,sc,zr,out,8); torch.cuda.synchronize()
EOF
ncu --set full --kernel-name regex:w4a16_gemv --launch-skip 2 --launch-count 1 --target-processes all uv run python ncu_cuda.py 2>&1 | grep -iE "DRAM Throughput|Memory Throughput|Compute.*Throughput|Achieved Occupancy|Eligible Warps|Active Warps Per Sched|Registers Per|Stall" | head -20

{
  "command": "<4600 chars \u2014 see below>",
  "description": "Test pipelined uint4 CUDA GEMV"
}

cat > test_cuda2.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <cstdint>
#include <torch/extension.h>

// One warp per output. Each lane reads 16 bytes (uint4) per K-iteration,
// covering 32 K-elements; warp covers 512 bytes = 1024 K = 8 groups per iter.
// Next iteration is prefetched to hide DRAM latency.
__global__ void w4a16_gemv_kernel(
    const __nv_bfloat16* __restrict__ x,
    const uint8_t* __restrict__ wqt,
    const __nv_bfloat16* __restrict__ sc,
    const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y,
    int N, int K, int KH) {
    int wpb = blockDim.x >> 5;
    int warp = threadIdx.x >> 5;
    int lane = threadIdx.x & 31;
    int n = blockIdx.x * wpb + warp;
    if (n >= N) return;
    const uint8_t* wrow = wqt + (size_t)n * KH;
    float acc = 0.f;
    int niters = KH >> 9;   // /512 bytes per warp-iter
    uint4 wn = *reinterpret_cast<const uint4*>(wrow + 16*lane);  // prefetch iter 0
    #pragma unroll 4
    for (int it = 0; it < niters; it++) {
        uint4 wv = wn;
        int b = it * 512;
        if (it + 1 < niters)
            wn = *reinterpret_cast<const uint4*>(wrow + b + 512 + 16*lane);
        int g = b/64 + (lane >> 2);
        float s = __bfloat162float(sc[(size_t)g*N + n]);
        float zf = __bfloat162float(zz[(size_t)g*N + n]);
        int k0 = 2*b + 32*lane;
        uint4 xv0 = *reinterpret_cast<const uint4*>(x + k0);
        uint4 xv1 = *reinterpret_cast<const uint4*>(x + k0 + 8);
        __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)    = xv0;
        *reinterpret_cast<uint4*>(xb + 8) = xv1;
        const uint32_t* wp = reinterpret_cast<const uint32_t*>(&wv);
        #pragma unroll
        for (int q = 0; q < 4; q++) {
            uint32_t p = wp[q];
            int base = 8*q;
            #pragma unroll
            for (int j = 0; j < 4; j++) {
                unsigned int bv = (p >> (8*j)) & 0xFFu;
                float xe = __bfloat162float(xb[base + 2*j]);
                float xo = __bfloat162float(xb[base + 2*j + 1]);
                float wl = ((float)(bv & 0xFu) - zf) * s;
                float wh = ((float)((bv >> 4) & 0xFu) - zf) * s;
                acc += xe*wl + xo*wh;
            }
        }
    }
    #pragma unroll
    for (int off=16; off>0; off>>=1) acc += __shfl_xor_sync(0xffffffff, acc, off);
    if (lane == 0) y[n] = __float2bfloat16(acc);
}

void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb) {
    int N = out.size(1); int K = x.size(1); int KH = K/2;
    int block = wpb*32;
    int grid_n = (N + wpb - 1)/wpb;
    w4a16_gemv_kernel<<<grid_n, block>>>(
        reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
        reinterpret_cast<const uint8_t*>(wqt.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(out.data_ptr()), N, K, KH);
}
'''
mod = load_inline(name="w4gemv2", cpp_sources="void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb);",
                  cuda_sources=src, functions=["launch"], verbose=False)
import reference
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def lf(): flush.zero_()
for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval()
    wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device); out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
    mod.launch(x,wqt,sc,zr,out,8)
    ref=rm(x); diff=(out.float()-ref.float()).abs().max().item()
    best=None
    for wpb in [4,8,16]:
        for _ in range(20): mod.launch(x,wqt,sc,zr,out,wpb)
        torch.cuda.synchronize()
        s=torch.cuda.Event(enable_timing=True); e=

... (600 more chars)

{
  "command": "<1192 chars \u2014 see below>",
  "description": "Fix x-load and re-test"
}

sed -i 's|        uint4 xv0 = \*reinterpret_cast<const uint4\*>(x + k0);\n        uint4 xv1 = \*reinterpret_cast<const uint4\*>(x + k0 + 8);|XX|' test_cuda2.py
# simpler: rewrite the x-load block via python
uv run python - <<'PY'
import re
s=open('test_cuda2.py').read()
old='''        int k0 = 2*b + 32*lane;
        uint4 xv0 = *reinterpret_cast<const uint4*>(x + k0);
        uint4 xv1 = *reinterpret_cast<const uint4*>(x + k0 + 8);
        __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)    = xv0;
        *reinterpret_cast<uint4*>(xb + 8) = xv1;'''
new='''        int k0 = 2*b + 32*lane;
        __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)      = *reinterpret_cast<const uint4*>(x + k0);
        *reinterpret_cast<uint4*>(xb + 8)  = *reinterpret_cast<const uint4*>(x + k0 + 8);
        *reinterpret_cast<uint4*>(xb + 16) = *reinterpret_cast<const uint4*>(x + k0 + 16);
        *reinterpret_cast<uint4*>(xb + 24) = *reinterpret_cast<const uint4*>(x + k0 + 24);'''
assert old in s, "pattern not found"
s=s.replace(old,new)
open('test_cuda2.py','w').write(s)
print("patched")
PY
timeout 180 uv run python test_cuda2.py 2>&1 | grep -E "wpb=|best|Error" | tail -10

{
  "command": "sleep 20; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/br8z2mlwf.output 2>/dev/null | grep -E \"wpb=|best|Error\" | tail -12",
  "description": "Read fixed CUDA results v2"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<172 chars \u2014 see diff>",
  "new_string": "<4110 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<373 chars \u2014 see diff>",
  "new_string": "<660 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -3 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark with CUDA GEMV"
}

{
  "command": "sleep 30; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/brwj538vv.output 2>/dev/null | grep -E \"PASS|FAIL|shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Read CUDA GEMV benchmark v2"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<985 chars \u2014 see diff>",
  "new_string": "<1109 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<673 chars \u2014 see diff>",
  "new_string": "<703 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "        return _gemm(x, self.w_q, self.scales, self.zeros, M, self.N, self.K)",
  "new_string": "<226 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<79 chars \u2014 see diff>",
  "new_string": "<157 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark with cached outputs"
}

{
  "command": "<5332 chars \u2014 see below>",
  "description": "Test split-K CUDA GEMV"
}

cat > test_cuda3.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <cstdint>
#include <torch/extension.h>

// split-K: grid.y = nsplit. Each warp does K/nsplit of one output, writes fp32 partial.
__global__ void w4a16_gemv_sk_kernel(
    const __nv_bfloat16* __restrict__ x, const uint8_t* __restrict__ wqt,
    const __nv_bfloat16* __restrict__ sc, const __nv_bfloat16* __restrict__ zz,
    float* __restrict__ p, int N, int K, int KH, int KperSplit) {
    int wpb = blockDim.x >> 5;
    int warp = threadIdx.x >> 5;
    int lane = threadIdx.x & 31;
    int n = blockIdx.x * wpb + warp;
    int kid = blockIdx.y;
    if (n >= N) return;
    const uint8_t* wrow = wqt + (size_t)n * KH;
    int b_start = kid * (KperSplit >> 1);   // bytes
    int b_end = b_start + (KperSplit >> 1);
    float acc = 0.f;
    int niters = (b_end - b_start) >> 9;    // 512 bytes/iter
    uint4 wn = *reinterpret_cast<const uint4*>(wrow + b_start + 16*lane);
    for (int it = 0; it < niters; it++) {
        uint4 wv = wn;
        int b = b_start + it*512;
        if (it+1 < niters) wn = *reinterpret_cast<const uint4*>(wrow + b + 512 + 16*lane);
        int g = b/64 + (lane >> 2);
        float s = __bfloat162float(sc[(size_t)g*N + n]);
        float zf = __bfloat162float(zz[(size_t)g*N + n]);
        int k0 = 2*b + 32*lane;
        __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)      = *reinterpret_cast<const uint4*>(x + k0);
        *reinterpret_cast<uint4*>(xb + 8)  = *reinterpret_cast<const uint4*>(x + k0 + 8);
        *reinterpret_cast<uint4*>(xb + 16) = *reinterpret_cast<const uint4*>(x + k0 + 16);
        *reinterpret_cast<uint4*>(xb + 24) = *reinterpret_cast<const uint4*>(x + k0 + 24);
        const uint32_t* wp = reinterpret_cast<const uint32_t*>(&wv);
        #pragma unroll
        for (int q=0;q<4;q++){uint32_t pp=wp[q];int base=8*q;
        #pragma unroll
        for(int j=0;j<4;j++){unsigned int bv=(pp>>(8*j))&0xFFu;
            float xe=__bfloat162float(xb[base+2*j]);float xo=__bfloat162float(xb[base+2*j+1]);
            float wl=((float)(bv&0xFu)-zf)*s;float wh=((float)((bv>>4)&0xFu)-zf)*s;
            acc+=xe*wl+xo*wh;}}}
    #pragma unroll
    for (int off=16;off>0;off>>=1) acc += __shfl_xor_sync(0xffffffff,acc,off);
    if (lane==0) p[(size_t)kid*N + n] = acc;
}

__global__ void reduce_kernel(const float* __restrict__ p, __nv_bfloat16* __restrict__ y,
                              int N, int nsplit) {
    int n = blockIdx.x * blockDim.x + threadIdx.x;
    if (n >= N) return;
    float a = 0.f;
    for (int i = 0; i < nsplit; i++) a += p[(size_t)i*N + n];
    y[n] = __float2bfloat16(a);
}

void launch_sk(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz,
               torch::Tensor partial, torch::Tensor out, int wpb, int nsplit) {
    int N = out.size(1); int K = x.size(1); int KH = K/2;
    int KperSplit = K / nsplit;
    int block = wpb*32; int grid_n = (N + wpb - 1)/wpb;
    w4a16_gemv_sk_kernel<<<dim3(grid_n, nsplit), block>>>(
        reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
        reinterpret_cast<const uint8_t*>(wqt.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()),
        reinterpret_cast<float*>(partial.data_ptr()), N, K, KH, KperSplit);
    int rblock=128; int rgrid=(N+rblock-1)/rblock;
    reduce_kernel<<<rgrid, rblock>>>(
        reinterpret_cast<const float*>(partial.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(out.data_ptr()), N, nsplit);
}
'''
mod = load_inline(name="w4gemvsk", cpp_sources="void launch_sk(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor partial, torch::Tensor out, int wpb, int nsplit);",
                  cuda_sources=src, functions=["launch_sk"], verbose=False)
import reference
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)


... (1332 more chars)

{
  "command": "sleep 25; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b60v1r547.output 2>/dev/null | grep -E \"nsplit=|Error|error\" | tail -12",
  "description": "Read split-K results v2"
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b60v1r547.output"
}

{
  "command": "<874 chars \u2014 see below>",
  "description": "ncu shape3 kernel"
}

cat > ncu_s3.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
exec(open('test_cuda2.py').read().split('import reference')[0])  # compile mod
import reference
device='cuda:0'
M,N,K=1,4096,4096
reference.M,reference.N,reference.K=M,N,K
rm=reference.Model(M,N,K).to(device).eval()
wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
x=torch.randn(M,K,dtype=torch.bfloat16,device=device); out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
for _ in range(3): mod.launch(x,wqt,sc,zr,out,8)
torch.cuda.synchronize()
mod.launch(x,wqt,sc,zr,out,8); torch.cuda.synchronize()
EOF
ncu --set full --kernel-name regex:w4a16_gemv --launch-skip 2 --launch-count 1 --target-processes all uv run python ncu_s3.py 2>&1 | grep -iE "DRAM Throughput|Memory Throughput Gbyte|Compute.*Throughput.*%|Achieved Occupancy|Eligible Warps|Stall|Registers Per" | head -12

{
  "command": "<4285 chars \u2014 see below>",
  "description": "Test 2-deep prefetch CUDA GEMV"
}

cat > test_cuda4.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <cstdint>
#include <torch/extension.h>

// 2-deep prefetch: keep two weight loads in flight ahead of compute.
__global__ void w4a16_gemv_kernel(
    const __nv_bfloat16* __restrict__ x, const uint8_t* __restrict__ wqt,
    const __nv_bfloat16* __restrict__ sc, const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y, int N, int K, int KH) {
    int wpb = blockDim.x >> 5;
    int warp = threadIdx.x >> 5;
    int lane = threadIdx.x & 31;
    int n = blockIdx.x * wpb + warp;
    if (n >= N) return;
    const uint8_t* wrow = wqt + (size_t)n * KH;
    float acc = 0.f;
    int niters = KH >> 9;
    uint4 w0 = *reinterpret_cast<const uint4*>(wrow + 0 + 16*lane);
    uint4 w1 = (niters > 1) ? *reinterpret_cast<const uint4*>(wrow + 512 + 16*lane) : w0;
    for (int it = 0; it < niters; it++) {
        uint4 wv = w0;
        w0 = w1;
        if (it + 2 < niters) w1 = *reinterpret_cast<const uint4*>(wrow + (it+2)*512 + 16*lane);
        int b = it * 512;
        int g = b/64 + (lane >> 2);
        float s = __bfloat162float(sc[(size_t)g*N + n]);
        float zf = __bfloat162float(zz[(size_t)g*N + n]);
        int k0 = 2*b + 32*lane;
        __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)      = *reinterpret_cast<const uint4*>(x + k0);
        *reinterpret_cast<uint4*>(xb + 8)  = *reinterpret_cast<const uint4*>(x + k0 + 8);
        *reinterpret_cast<uint4*>(xb + 16) = *reinterpret_cast<const uint4*>(x + k0 + 16);
        *reinterpret_cast<uint4*>(xb + 24) = *reinterpret_cast<const uint4*>(x + k0 + 24);
        const uint32_t* wp = reinterpret_cast<const uint32_t*>(&wv);
        #pragma unroll
        for (int q=0;q<4;q++){uint32_t pp=wp[q];int base=8*q;
        #pragma unroll
        for(int j=0;j<4;j++){unsigned int bv=(pp>>(8*j))&0xFFu;
            float xe=__bfloat162float(xb[base+2*j]);float xo=__bfloat162float(xb[base+2*j+1]);
            float wl=((float)(bv&0xFu)-zf)*s;float wh=((float)((bv>>4)&0xFu)-zf)*s;
            acc+=xe*wl+xo*wh;}}}
    #pragma unroll
    for (int off=16;off>0;off>>=1) acc += __shfl_xor_sync(0xffffffff,acc,off);
    if (lane==0) y[n] = __float2bfloat16(acc);
}
void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb) {
    int N=out.size(1); int K=x.size(1); int KH=K/2; int block=wpb*32; int grid_n=(N+wpb-1)/wpb;
    w4a16_gemv_kernel<<<grid_n,block>>>(
        reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
        reinterpret_cast<const uint8_t*>(wqt.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(out.data_ptr()),N,K,KH);
}
'''
mod = load_inline(name="w4gemv4", cpp_sources="void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb);",
                  cuda_sources=src, functions=["launch"], verbose=False)
import reference
device='cuda:0'
flush=torch.empty(128*1024*1024//4, dtype=torch.float32, device=device)
def lf(): flush.zero_()
for M,N,K in [(1,12288,4096),(1,4096,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval()
    wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device); out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
    mod.launch(x,wqt,sc,zr,out,8); ref=rm(x); diff=(out.float()-ref.float()).abs().max().item()
    for wpb in [4,8]:
        for _ in range(20): mod.launch(x,wqt,sc,zr,out,wpb)
        torch.cuda.synchronize()
        s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
        for _ in range(50):
            lf(); torch.cuda.synchronize(); s.record(); mod.launch(x,wqt,sc,zr,out,wpb); e.record(); torch.cuda.synchronize(); L.append(s.elapse

... (285 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<3527 chars \u2014 see diff>",
  "new_string": "<4773 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<146 chars \u2014 see diff>",
  "new_string": "<174 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<149 chars \u2014 see diff>",
  "new_string": "<162 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark with depth dispatch"
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7jocymiu.output 2>/dev/null | grep -E \"PASS|FAIL|shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Read depth dispatch benchmark v2"
}

{
  "command": "sleep 20; tail -30 /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7jocymiu.output 2>/dev/null",
  "description": "Read full benchmark output"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<4380 chars \u2014 see diff>",
  "new_string": "<6062 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Check and benchmark with two hardcoded kernels"
}

{
  "command": "sleep 25; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bxv4e2mi6.output 2>/dev/null | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Read benchmark numbers"
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bxv4e2mi6.output 2>/dev/null | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Read benchmark numbers v2"
}

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

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

{
  "command": "<705 chars \u2014 see below>",
  "description": "ncu M=32 GEMM"
}

cat > ncu_gemm.py <<'EOF'
import torch, solution, reference
device='cuda:0'
M,N,K=32,12288,4096
reference.M,reference.N,reference.K=M,N,K
rm=reference.Model(M,N,K).to(device).eval()
sm=solution.Model(M,N,K).to(device).eval()
sm.load_state_dict(rm.state_dict(),strict=True)
x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
for _ in range(5): sm(x)
torch.cuda.synchronize()
sm(x); torch.cuda.synchronize()
EOF
ncu --set full --kernel-name regex:w4a16_gemm_kernel --launch-skip 3 --launch-count 1 --target-processes all uv run python ncu_gemm.py 2>&1 | grep -iE "DRAM Throughput|Memory Throughput Gbyte|Compute.*Throughput.*%|Achieved Occupancy|Eligible Warps|Registers Per|Stall.*scoreboard" | head -10

{
  "command": "<332 chars \u2014 see below>",
  "description": "Re-sweep GEMM with ns=2 for M=32,16"
}

sed -i 's/for ns in \[3,4,5\]:/for ns in [2,3,4,5]:/' sweep_gemm.py
# only sweep M=32 and M=16 (M=256 already good)
sed -i 's/for M,N,K in \[(32,12288,4096),(16,14336,4096),(256,12288,4096)\]:/for M,N,K in [(32,12288,4096),(16,14336,4096)]:/' sweep_gemm.py
timeout 500 uv run python sweep_gemm.py 2>&1 | grep -E "===|BEST" | head -6

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<161 chars \u2014 see diff>",
  "new_string": "<161 chars \u2014 see diff>"
}

{
  "command": "<4030 chars \u2014 see below>",
  "description": "Test 3-deep prefetch for decode"
}

cat > test_d3.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <cstdint>
#include <torch/extension.h>
__global__ void gemv_d3(const __nv_bfloat16* __restrict__ x, const uint8_t* __restrict__ wqt,
    const __nv_bfloat16* __restrict__ sc, const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y, int N, int K, int KH) {
    int wpb=blockDim.x>>5, warp=threadIdx.x>>5, lane=threadIdx.x&31;
    int n=blockIdx.x*wpb+warp; if(n>=N) return;
    const uint8_t* wrow=wqt+(size_t)n*KH; float acc=0.f; int niters=KH>>9;
    uint4 w0=*reinterpret_cast<const uint4*>(wrow+0+16*lane);
    uint4 w1=(niters>1)?*reinterpret_cast<const uint4*>(wrow+512+16*lane):w0;
    uint4 w2=(niters>2)?*reinterpret_cast<const uint4*>(wrow+1024+16*lane):w1;
    _Pragma("unroll 4")
    for(int it=0;it<niters;it++){
        uint4 wv=w0; w0=w1; w1=w2;
        if(it+3<niters) w2=*reinterpret_cast<const uint4*>(wrow+(it+3)*512+16*lane);
        int b=it*512; int g=b/64+(lane>>2);
        float s=__bfloat162float(sc[(size_t)g*N+n]); float zf=__bfloat162float(zz[(size_t)g*N+n]);
        int k0=2*b+32*lane; __nv_bfloat16 xb[32];
        *reinterpret_cast<uint4*>(xb)=*reinterpret_cast<const uint4*>(x+k0);
        *reinterpret_cast<uint4*>(xb+8)=*reinterpret_cast<const uint4*>(x+k0+8);
        *reinterpret_cast<uint4*>(xb+16)=*reinterpret_cast<const uint4*>(x+k0+16);
        *reinterpret_cast<uint4*>(xb+24)=*reinterpret_cast<const uint4*>(x+k0+24);
        const uint32_t* wp=reinterpret_cast<const uint32_t*>(&wv);
        _Pragma("unroll")
        for(int q=0;q<4;q++){uint32_t p=wp[q];int base=8*q;
        _Pragma("unroll")
        for(int j=0;j<4;j++){unsigned int bv=(p>>(8*j))&0xFFu;
            float xe=__bfloat162float(xb[base+2*j]);float xo=__bfloat162float(xb[base+2*j+1]);
            float wl=((float)(bv&0xFu)-zf)*s;float wh=((float)((bv>>4)&0xFu)-zf)*s;
            acc+=xe*wl+xo*wh;}}}
    _Pragma("unroll")
    for(int off=16;off>0;off>>=1) acc+=__shfl_xor_sync(0xffffffff,acc,off);
    if(lane==0) y[n]=__float2bfloat16(acc);
}
void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb) {
    int N=out.size(1);int K=x.size(1);int KH=K/2;int block=wpb*32;int grid_n=(N+wpb-1)/wpb;
    gemv_d3<<<grid_n,block>>>(reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
        reinterpret_cast<const uint8_t*>(wqt.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
        reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(out.data_ptr()),N,K,KH);
}
'''
mod=load_inline(name="w4d3",cpp_sources="void launch(torch::Tensor x, torch::Tensor wqt, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int wpb);",cuda_sources=src,functions=["launch"],verbose=False)
import reference
device='cuda:0'; flush=torch.empty(128*1024*1024//4,dtype=torch.float32,device=device)
def lf(): flush.zero_()
for M,N,K in [(1,4096,4096),(1,12288,4096)]:
    reference.M,reference.N,reference.K=M,N,K
    rm=reference.Model(M,N,K).to(device).eval(); wqt=rm.w_q.t().contiguous(); sc=rm.scales; zr=rm.zeros
    x=torch.randn(M,K,dtype=torch.bfloat16,device=device); out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
    mod.launch(x,wqt,sc,zr,out,4); ref=rm(x); diff=(out.float()-ref.float()).abs().max().item()
    for wpb in [4,8]:
        for _ in range(20): mod.launch(x,wqt,sc,zr,out,wpb)
        torch.cuda.synchronize(); s=torch.cuda.Event(enable_timing=True); e=torch.cuda.Event(enable_timing=True); L=[]
        for _ in range(50): lf();torch.cuda.synchronize();s.record();mod.launch(x,wqt,sc,zr,out,wpb);e.record();torch.cuda.synchronize();L.append(s.elapsed_time(e))
        L.sort();ms=L[len(L)//2];by=M*K*2+(K//2)*N+(K//128)*N*2*2+M*N*2
        print(f"M={M} N={N} wpb={wpb}: {ms*1000:.1f}us {by/ms/1e6:.0f}GB/s frac={by/ms/1e6/1800:.3f} diff={diff:.1e}")
EOF
timeout 150 uv run python test_d3.py 2>&1 | 

... (30 more chars)

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/bwp4l733q.output"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Full benchmark with ns=5 GEMM"
}

{
  "command": "sleep 25; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output 2>/dev/null | grep -E \"PASS|FAIL|shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Read ns=5 benchmark v2"
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output"
}

{
  "command": "sleep 30; tail -20 /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output 2>/dev/null",
  "description": "Read benchmark tail"
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output",
  "description": "Cat benchmark output"
}

{
  "command": "echo \"test shell\"",
  "description": "Test shell responsiveness"
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output"
}

{
  "command": "nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader 2>&1; ps aux | grep -c \"[b]enchmark\\|[p]ython\" ",
  "description": "Check GPU processes",
  "timeout": 30000
}

{
  "command": "sleep 120; echo done-waiting",
  "description": "Long wait for shell to recover",
  "timeout": 180000
}

{
  "command": "pkill -9 -f benchmark.py; pkill -9 -f \"uv run\"; pkill -9 -f test_cuda; pkill -9 -f check.py; sleep 3; echo cleaned",
  "description": "Kill stuck python processes",
  "timeout": 60000
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b4kz7tmrb.output"
}

{
  "command": "sleep 60; nvidia-smi --query-gpu=memory.used,utilization.gpu --format=csv,noheader 2>&1 | head -1",
  "description": "Check GPU after wait",
  "timeout": 90000
}

{
  "command": "echo hi",
  "description": "Minimal shell test",
  "timeout": 15000
}

{
  "command": "sleep 200; echo ok",
  "description": "Extended recovery wait",
  "timeout": 240000
}

{
  "command": "free -m 2>&1 | head -3",
  "description": "Check memory",
  "timeout": 20000
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b8f5nwbum.output"
}

{
  "command": "timeout 200 uv run python benchmark.py 2>&1 | tail -40",
  "description": "Run benchmark cleanly to see errors",
  "timeout": 220000
}

{
  "command": "for i in 1 2; do echo \"=== RUN $i ===\"; timeout 200 uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction\"; done",
  "description": "Two benchmark runs for stability",
  "timeout": 450000
}

{
  "command": "<4767 chars \u2014 see below>",
  "description": "Test wmma GEMM correctness",
  "timeout": 240000
}

cat > test_wmma.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
src = r'''
#include <cuda_bf16.h>
#include <mma.h>
#include <cstdint>
using namespace nvcuda;

// W4A16 GEMM via wmma tensor cores. Block tile (BM, BN=64), one warp per
// 16x16 output fragment. Per group: dequant weight into shared, then 8 wmma
// K-steps (16 each). fp32 accumulator.
template<int BM>
__global__ void w4a16_gemm_kernel(
    const __nv_bfloat16* __restrict__ x, const uint8_t* __restrict__ wq,
    const __nv_bfloat16* __restrict__ sc, const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y, int M, int N, int K) {
    constexpr int BN = 64, GROUP = 128, WK = 16;
    constexpr int WN_T = BN / 16;            // 4
    constexpr int WM_T = (BM == 32) ? 2 : 1; // 1 or 2
    constexpr int WARPS = WM_T * WN_T;
    int warp = threadIdx.x >> 5;
    int lane = threadIdx.x & 31;
    int wm = warp / WN_T;
    int wn = warp % WN_T;
    int m0 = blockIdx.y * BM;
    int n0 = blockIdx.x * BN;

    __shared__ __nv_bfloat16 xs[BM][GROUP];          // (BM, 128)
    __shared__ __nv_bfloat16 ws[GROUP][BN];          // (128, BN) dequanted

    wmma::fragment<wmma::accumulator, 16, 16, 16, float> acc;
    wmma::fill_fragment(acc, 0.f);
    wmma::fragment<wmma::matrix_a, 16, 16, 16, __nv_bfloat16, wmma::row_major> a;
    wmma::fragment<wmma::matrix_b, 16, 16, 16, __nv_bfloat16, wmma::row_major> b;

    int ng = K / GROUP;
    for (int g = 0; g < ng; g++) {
        int k0 = g * GROUP;
        // load x[m0:m0+BM, k0:k0+128] -> xs
        for (int i = lane; i < BM * GROUP; i += WARPS * 32) {
            int r = i / GROUP, c = i % GROUP;
            xs[r][c] = x[(m0 + r) * K + (k0 + c)];
        }
        // load+dequant wq[k0/2 : +64, n0:n0+BN] -> ws[0:128][0:BN]
        // scale/zero for this group, this N-tile
        __nv_bfloat16 sbuf[BN], zbuf[BN];
        // load scale/zero collaboratively into ws reuse? load per-thread below.
        for (int i = lane; i < 64 * BN; i += WARPS * 32) {
            int kb = i / BN;          // 0..63
            int n = i % BN;           // 0..BN-1
            float s = __bfloat162float(sc[g * N + n0 + n]);
            float z = __bfloat162float(zz[g * N + n0 + n]);
            unsigned int bytev = wq[(k0 / 2 + kb) * N + n0 + n];
            float lo = (float)(bytev & 0xFu);
            float hi = (float)((bytev >> 4) & 0xFu);
            ws[2 * kb][n]     = __float2bfloat16((lo - z) * s);
            ws[2 * kb + 1][n] = __float2bfloat16((hi - z) * s);
        }
        __syncthreads();
        #pragma unroll
        for (int ks = 0; ks < GROUP / WK; ks++) {   // 8 steps
            wmma::load_matrix_sync(a, &xs[wm * 16][ks * WK], GROUP);
            wmma::load_matrix_sync(b, &ws[ks * WK][wn * 16], BN);
            wmma::mma_sync(acc, a, b, acc);
        }
        __syncthreads();
    }
    wmma::store_matrix_sync(&y[(m0 + wm * 16) * N + n0 + wn * 16], acc, N, wmma::mem_row_major);
}

void launch(torch::Tensor x, torch::Tensor wq, torch::Tensor sc, torch::Tensor zz, torch::Tensor out, int bm) {
    int M = out.size(0), N = out.size(1), K = x.size(1);
    if (bm == 32) {
        w4a16_gemm_kernel<32><<<dim3(N/64, (M+31)/32), 256>>>(reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
            reinterpret_cast<const uint8_t*>(wq.data_ptr()), reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
            reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()), reinterpret_cast<__nv_bfloat16*>(out.data_ptr()), M, N, K);
    } else {
        w4a16_gemm_kernel<16><<<dim3(N/64, (M+15)/16), 128>>>(reinterpret_cast<const __nv_bfloat16*>(x.data_ptr()),
            reinterpret_cast<const uint8_t*>(wq.data_ptr()), reinterpret_cast<const __nv_bfloat16*>(sc.data_ptr()),
            reinterpret_cast<const __nv_bfloat16*>(zz.data_ptr()), reinterpret_cast<__nv_bfloat16*>(out.data_ptr()), M, N, K);
    }
}
'''
mod = load_inline(name="w4wmma", cpp_sources="void launch(torch::Tensor x, torch::Tensor wq, torch::Te

... (767 more chars)

{
  "command": "<633 chars \u2014 see below>",
  "description": "Fix store and test wmma",
  "timeout": 240000
}

uv run python - <<'PY'
s=open('test_wmma.py').read()
s=s.replace(
"    wmma::store_matrix_sync(&y[(m0 + wm * 16) * N + n0 + wn * 16], acc, N, wmma::mem_row_major);\n}",
"    __shared__ float csm[BM][BN];\n"
"    wmma::store_matrix_sync(&csm[wm * 16][wn * 16], acc, BN, wmma::mem_row_major);\n"
"    __syncthreads();\n"
"    for (int i = lane; i < BM * BN; i += WARPS * 32) {\n"
"        int r = i / BN, c = i % BN;\n"
"        y[(m0 + r) * N + n0 + c] = __float2bfloat16(csm[r][c]);\n"
"    }\n}")
open('test_wmma.py','w').write(s)
print("patched")
PY
timeout 200 uv run python test_wmma.py 2>&1 | grep -E "M=|error|Error" | tail -10

{
  "command": "<412 chars \u2014 see below>",
  "description": "Undef conversions macro and test",
  "timeout": 240000
}

uv run python - <<'PY'
s=open('test_wmma.py').read()
s=s.replace("#include <cuda_bf16.h>\n#include <mma.h>",
            "#undef __CUDA_NO_BFLOAT16_CONVERSIONS__\n#undef __CUDA_NO_HALF_OPERATORS__\n#undef __CUDA_NO_HALF2_OPERATORS__\n#include <cuda_bf16.h>\n#include <mma.h>")
open('test_wmma.py','w').write(s)
print("patched")
PY
timeout 200 uv run python test_wmma.py 2>&1 | grep -E "M=|error|Error" | tail -10

{
  "command": "<544 chars \u2014 see below>",
  "description": "Force sm_120 arch for wmma",
  "timeout": 240000
}

uv run python - <<'PY'
s=open('test_wmma.py').read()
# add env set + extra flags before load_inline call
s=s.replace("mod = load_inline(name=\"w4wmma\",",
            "import os\nos.environ.setdefault('TORCH_CUDA_ARCH_LIST','12.0')\nmod = load_inline(\n    extra_cuda_cflags=['-arch=sm_120','--use_fast_math'],\n    name=\"w4wmma\",")
s=s.replace('cpp_sources="void launch','cpp_sources="void launch')  # noop
open('test_wmma.py','w').write(s)
print("patched")
PY
timeout 200 uv run python test_wmma.py 2>&1 | grep -E "M=|error|Error" | tail -8

{
  "command": "<1079 chars \u2014 see below>",
  "description": "Debug wmma element-wise",
  "timeout": 150000
}

cat > debug_wmma.py <<'EOF'
import torch
from torch.utils.cpp_extension import load_inline
exec(open('test_wmma.py').read().split('import reference')[0])  # compile mod
import reference
device='cuda:0'
M,N,K=32,64,128   # 1 tile, 1 group
reference.M,reference.N,reference.K=M,N,K
rm=reference.Model(M,N,K).to(device).eval()
wq=rm.w_q; sc=rm.scales; zr=rm.zeros
x=torch.randn(M,K,dtype=torch.bfloat16,device=device)
out=torch.empty(M,N,dtype=torch.bfloat16,device=device)
mod.launch(x,wq,sc,zr,out,32)
ref=rm(x)
print("out[0,:6]",out[0,:6].tolist())
print("ref[0,:6]",ref[0,:6].tolist())
print("out[4,:6]",out[4,:6].tolist())
print("ref[4,:6]",ref[4,:6].tolist())
# also check: is out == x @ ref_w? compute ref_w dequant
import torch
ng=K//128
wu=torch.empty(M if False else (K,N),dtype=torch.uint8,device=device)
wu[0::2]=wq&0xF; wu[1::2]=(wq>>4)&0xF
sf=sc.repeat_interleave(128,0); zf=zr.repeat_interleave(128,0)
wbf=(wu.to(torch.bfloat16)-zf)*sf
ref2=x@wbf
print("ref2[0,:6]",ref2[0,:6].tolist())
EOF
timeout 120 uv run python debug_wmma.py 2>&1 | grep -E "out\[|ref" | tail -8

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/buc4hgkcf.output"
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "offset": 232,
  "limit": 52
}

{
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "limit": 48,
  "offset": 285
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<1131 chars \u2014 see diff>",
  "new_string": "<1291 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<181 chars \u2014 see diff>",
  "new_string": "<186 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"===BENCH===\" && timeout 200 uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Test BK=64 GEMM",
  "timeout": 300000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/07_w4a16_gemm/solution.py",
  "old_string": "<186 chars \u2014 see diff>",
  "new_string": "<188 chars \u2014 see diff>"
}

{
  "command": "rm -f scratch_*.py test_cuda*.py test_wmma.py test_d3.py debug_wmma.py bw_*.py sweep_*.py prof_decode.py ncu_*.py dot_err.py gemvB_err.py dbg_s3.py 2>/dev/null; ls *.py",
  "description": "Remove scratch files",
  "timeout": 30000
}

{
  "command": "uv run python check.py 2>&1 | tail -2 && echo \"=== FINAL BENCH ===\" && timeout 200 uv run python benchmark.py 2>&1 | grep -E \"shape=[0-9] variant=solution |peak_fraction|RESULT\"",
  "description": "Final check and benchmark",
  "timeout": 300000
}

{
  "command": "<4572 chars \u2014 see below>",
  "description": "Test wmma with row vs col major for B",
  "timeout": 260000
}

cat > test_wmma2.py <<'EOF'
import torch, os
os.environ.setdefault('TORCH_CUDA_ARCH_LIST','12.0')
from torch.utils.cpp_extension import load_inline
src = r'''
#undef __CUDA_NO_BFLOAT16_CONVERSIONS__
#include <cuda_bf16.h>
#include <mma.h>
#include <cstdint>
using namespace nvcuda;
template<int BM, int BLAYOUT>  // BLAYOUT 0=row,1=col for B
__global__ void gemm(const __nv_bfloat16* __restrict__ x, const uint8_t* __restrict__ wq,
    const __nv_bfloat16* __restrict__ sc, const __nv_bfloat16* __restrict__ zz,
    __nv_bfloat16* __restrict__ y, int M, int N, int K) {
    constexpr int BN=64, GROUP=128, WK=16, WN_T=4;
    constexpr int WM_T = (BM==32)?2:1;
    constexpr int WARPS=WM_T*WN_T;
    int warp=threadIdx.x>>5, lane=threadIdx.x&31;
    int wm=warp/WN_T, wn=warp%WN_T;
    int m0=blockIdx.y*BM, n0=blockIdx.x*BN;
    __shared__ __nv_bfloat16 xs[BM][GROUP];
    __shared__ __nv_bfloat16 ws[GROUP][BN];
    __shared__ float csm[BM][BN];
    wmma::fragment<wmma::accumulator,16,16,16,float> acc;
    wmma::fill_fragment(acc,0.f);
    wmma::fragment<wmma::matrix_a,16,16,16,__nv_bfloat16,wmma::row_major> a;
    int ng=K/GROUP;
    for(int g=0;g<ng;g++){
        int k0=g*GROUP;
        for(int i=lane;i<BM*GROUP;i+=WARPS*32){int r=i/GROUP,c=i%GROUP; xs[r][c]=x[(m0+r)*K+(k0+c)];}
        for(int i=lane;i<64*BN;i+=WARPS*32){
            int kb=i/BN,n=i%BN;
            float s=__bfloat162float(sc[g*N+n0+n]); float z=__bfloat162float(zz[g*N+n0+n]);
            unsigned int bv=wq[(k0/2+kb)*N+n0+n];
            ws[2*kb][n]=__float2bfloat16(((float)(bv&0xFu)-z)*s);
            ws[2*kb+1][n]=__float2bfloat16(((float)((bv>>4)&0xFu)-z)*s);
        }
        __syncthreads();
        #pragma unroll
        for(int ks=0;ks<GROUP/WK;ks++){
            wmma::load_matrix_sync(a,&xs[wm*16][ks*WK],GROUP);
            if(BLAYOUT==0){
                wmma::fragment<wmma::matrix_b,16,16,16,__nv_bfloat16,wmma::row_major> b;
                wmma::load_matrix_sync(b,&ws[ks*WK][wn*16],BN);
                wmma::mma_sync(acc,a,b,acc);
            } else {
                wmma::fragment<wmma::matrix_b,16,16,16,__nv_bfloat16,wmma::col_major> b;
                wmma::load_matrix_sync(b,&ws[ks*WK][wn*16],GROUP);
                wmma::mma_sync(acc,a,b,acc);
            }
        }
        __syncthreads();
    }
    wmma::store_matrix_sync(&csm[wm*16][wn*16],acc,BN,wmma::mem_row_major);
    __syncthreads();
    for(int i=lane;i<BM*BN;i+=WARPS*32){int r=i/BN,c=i%BN; y[(m0+r)*N+n0+c]=__float2bfloat16(csm[r][c]);}
}
void launch(torch::Tensor x,torch::Tensor wq,torch::Tensor sc,torch::Tensor zz,torch::Tensor out,int bm,int bl){
    int M=out.size(0),N=out.size(1),K=x.size(1);
    if(bm==32){ if(bl==0) gemm<32,0><<<dim3(N/64,M/32),256>>>(r(x),r(wq),r(sc),r(zz),r(out),M,N,K); else gemm<32,1><<<dim3(N/64,M/32),256>>>(r(x),r(wq),r(sc),r(zz),r(out),M,N,K);}
    else { if(bl==0) gemm<16,0><<<dim3(N/64,M/16),128>>>(r(x),r(wq),r(sc),r(zz),r(out),M,N,K); else gemm<16,1><<<dim3(N/64,M/16),128>>>(r(x),r(wq),r(sc),r(zz),r(out),M,N,K);}
}
#define r(p) reinterpret_cast<const __nv_bfloat16*>(p.data_ptr())
'''
# fix: r() macro must be before launch. redo properly:
src=src.replace('#define r(p) reinterpret_cast<const __nv_bfloat16*>(p.data_ptr())\n','')
src=src.replace('void launch(torch::Tensor x,torch::Tensor wq,torch::Tensor sc,torch::Tensor zz,torch::Tensor out,int bm,int bl){',
'#define R(p) reinterpret_cast<const __nv_bfloat16*>(p.data_ptr())\nvoid launch(torch::Tensor x,torch::Tensor wq,torch::Tensor sc,torch::Tensor zz,torch::Tensor out,int bm,int bl){')
src=src.replace('r(out),M,N,K)','R(out),M,N,K)').replace('r(x),r(wq),r(sc),r(zz),','R(x),R(wq),R(sc),R(zz),')
mod=load_inline(name="w4wmma2",cpp_sources="void launch(torch::Tensor,torch::Tensor,torch::Tensor,torch::Tensor,torch::Tensor,int,int);",
    cuda_sources=src,functions=["launch"],extra_cuda_cflags=['-arch=sm_120'],verbose=False)
import reference
device='cuda:0'
for M,N,K in [(32,12288,4096),(16,14336,4096)]:
    reference.

... (572 more chars)

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b2jy68nt1.output"
}

{
  "command": "sleep 60; tail -8 /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b2jy68nt1.output 2>/dev/null",
  "description": "Read wmma layout test v2",
  "timeout": 90000
}

{
  "command": "cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b2jy68nt1.output 2>/dev/null | tail -8",
  "description": "Cat wmma test output",
  "timeout": 30000
}

{
  "command": "sleep 120; echo wait-done",
  "description": "Wait for compile to finish",
  "timeout": 150000
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b2jy68nt1.output"
}

{
  "command": "sleep 180; tail -8 /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b2jy68nt1.output 2>/dev/null",
  "description": "Patient wait for wmma test",
  "timeout": 220000
}

{
  "command": "rm -f test_wmma2.py 2>/dev/null; uv run python check.py 2>&1 | tail -2",
  "description": "Clean up and final correctness check",
  "timeout": 120000
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7yejupms.output"
}

{
  "command": "sleep 45; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7yejupms.output 2>/dev/null | tail -3",
  "description": "Read final check result v2",
  "timeout": 60000
}

{
  "command": "sleep 150; cat /tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7yejupms.output 2>/dev/null | tail -3",
  "description": "Patient wait for check result",
  "timeout": 180000
}

{
  "file_path": "/tmp/claude-1002/-workspace-problems-07-w4a16-gemm/db2ed347-d777-45ce-8cd2-4829108fa6f1/tasks/b7yejupms.output"
}