{
  "name": "kernel-optimization"
}

{
  "name": "moe-training"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/reference.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/check.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/benchmark.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/shapes.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/problem.yaml"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/sota.py"
}

{
  "todos": [
    {
      "content": "Check GPU availability and environment (nvidia-smi, CUDA, nvcc)",
      "status": "in_progress",
      "priority": "high"
    },
    {
      "content": "Decide approach: CUTLASS grouped GEMM vs Triton vs raw CUDA",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Implement grouped GEMM + fused SwiGLU kernel",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Run check.py for correctness",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Run benchmark.py fo

... (205 more chars)

{
  "command": "nvidia-smi --query-gpu=name,compute_cap,memory.total,driver_version --format=csv,noheader",
  "description": "Check GPU info"
}

{
  "command": "nvcc --version 2>/dev/null || echo \"nvcc not found\"",
  "description": "Check nvcc version"
}

{
  "command": "uv run python -c \"import torch; print(torch.__version__); print('CUDA:', torch.version.cuda); print('BF16:', torch.cuda.get_device_properties(0))\"",
  "description": "Check PyTorch CUDA"
}

{
  "command": "pip list 2>/dev/null | grep -i triton; uv run python -c \"import triton; print(triton.__version__)\" 2>/dev/null || echo \"triton not installed\"",
  "description": "Check triton availability"
}

{
  "command": "ls /usr/local/cuda*/include/cutlass 2>/dev/null || ls /usr/include/cutlass 2>/dev/null || find /home/infatoshi -maxdepth 4 -name \"cutlass\" -type d 2>/dev/null | head -5 || echo \"no cutlass found\"",
  "description": "Check CUTLASS availability"
}

{
  "command": "which ncu 2>/dev/null || echo \"ncu not found\"; which nsys 2>/dev/null || echo \"nsys not found\"",
  "description": "Check profiling tools"
}

{
  "todos": [
    {
      "content": "Check GPU availability and environment (nvidia-smi, CUDA, nvcc)",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Decide approach: CUTLASS grouped GEMM vs Triton vs raw CUDA",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Implement grouped GEMM + fused SwiGLU kernel (v1)",
      "status": "in_progress",
      "priority": "high"
    },
    {
      "content": "Run check.py for correctness",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Run benchm

... (214 more chars)

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard && ./scripts/patch_torch.sh 2>&1 | tail -5",
  "description": "Apply torch inductor CSE hotfix"
}

{
  "command": "uv run python -c \"import triton; import triton.language as tl; help(tl.dot)\" 2>&1 | head -60",
  "description": "Check tl.dot signature"
}

{
  "command": "uv run python -c \"import triton.language as tl; help(tl.sigmoid)\" 2>&1 | head -15",
  "description": "Check tl.sigmoid signature"
}

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

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

{
  "content": "<5867 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py"
}

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "oldString": "<790 chars \u2014 see below>",
  "newString": "<778 chars \u2014 see below>"
}

    m_start = 0
    n_start = n_tile * BN
    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I

    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        row_offs = m_start + tl.arange(0, BM)
        load_row_mask = row_offs < num_rows
        row_ptrs = row_offs * stride_xm

        x_ptrs = x_ptr + row_ptrs[:, None] + k_offs[None, :] * stride_xk_unused
        load_mask = load_row_mask[:, None] & k_mask[None, :]
        x = tl.load(x_ptrs, mask=load_mask, other=0.0)

    m_start = 0
    n_start = n_tile * BN
    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows
    row_ptrs = row_offs * stride_xm

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I

    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + row_ptrs[:, None] + k_offs[None, :] * stride_xk_unused
        load_mask = load_row_mask[:, None] & k_mask[None, :]
        x = tl.load(x_ptrs, mask=load_mask, other=0.0)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "oldString": "<234 chars \u2014 see below>",
  "newString": "<169 chars \u2014 see below>"
}

    gate_silu = gate * tl.sigmoid(gate)
    out_fp32 = gate_silu * up
    result = out_fp32.to(tl.bfloat16)

    store_row_mask = row_offs < num_rows
    out_ptrs = out_ptr + row_offs[:, None] * stride_om + n_offs[None, :] * stride_oi

    gate_silu = gate * tl.sigmoid(gate)
    out_fp32 = gate_silu * up
    result = out_fp32.to(tl.bfloat16)

    out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check v2"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "newString": "<118 chars \u2014 see below>",
  "oldString": "<119 chars \u2014 see below>"
}

    out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]

    out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]
    store_mask = store_row_mask[:, None] & n_mask[None, :]

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check v3"
}

{
  "command": "<594 chars \u2014 see below>",
  "description": "Test tl.dot bf16 accumulation precision"
}

uv run python -c "
import torch
import triton
import triton.language as tl

@triton.jit
def test_dot_kernel(out_ptr, BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr):
    a = tl.full((BM, BK), 1.0, dtype=tl.bfloat16)
    b = tl.full((BK, BN), 1.0, dtype=tl.bfloat16)
    acc = tl.zeros((BM, BN), dtype=tl.float32)
    c = tl.dot(a, b, acc=acc)
    # store first element
    tl.store(out_ptr, c[0, 0])

out = torch.empty(1, dtype=torch.float32, device='cuda')
BM, BK, BN = 16, 16, 16
test_dot_kernel[(1,)](out, BM=BM, BK=BK, BN=BN)
print('Result:', out.item(), '(expected:', BK, ')')
" 2>&1

{
  "command": "<1206 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug correctness on small shape"
}

uv run python -c "
import sys, torch
sys.path.insert(0, '.')
import shapes, reference, solution

# Use smallest shape for speed
shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape['T_total'], shape['H'], shape['I'], shape['E'], shape['K']
reference.T_total = T_total
reference.H = H
reference.I = I
reference.E = E
reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]

with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

ref_f = ref_out.float()
sol_f = sol_out.float()
diff = (ref_f - sol_f).abs()
print(f'  max_abs_diff: {diff.max().item():.6f}')
print(f'  mean_abs_diff: {diff.mean().item():.6f}')
print(f'  allclose(atol=0.02, rtol=0.02): {torch.allclose(ref_f, sol_f, atol=0.02, rtol=0.02)}')
print(f'  shapes: ref={ref_out.shape}, sol={sol_out.shape}')
print(f'  ref range: [{ref_f.min():.3f}, {ref_f.max():.3f}]')
print(f'  sol range: [{sol_f.min():.3f}, {sol_f.max():.3f}]')
" 2>&1

{
  "command": "<1059 chars \u2014 see below>",
  "timeout": 60000,
  "description": "Test tl.dot precision with bf16/fp32 acc"
}

cat > /tmp/test_dot_prec.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def _test_bf16_dot(out_ptr, N: tl.constexpr):
    pid = tl.program_id(0)
    a_data = tl.full((N, N), 0.001, dtype=tl.bfloat16)
    b_data = tl.full((N, N), 0.001, dtype=tl.bfloat16)
    acc_bf16 = tl.zeros((N, N), dtype=tl.bfloat16)
    acc_fp32 = tl.zeros((N, N), dtype=tl.float32)
    
    c1 = tl.dot(a_data, b_data, acc=acc_bf16)
    c1 = tl.dot(a_data, b_data, acc=c1)
    
    c2 = tl.dot(a_data, b_data, acc=acc_fp32)
    c2 = tl.dot(a_data, b_data, acc=c2)
    
    if pid == 0:
        tl.store(out_ptr, c1[0, 0])
        tl.store(out_ptr + 1, c2[0, 0])

out = torch.empty(2, dtype=torch.float32, device='cuda')
_test_bf16_dot[(1,)](out, N=16)
print("bf16 acc result:", out[0].item())
print("fp32 acc result:", out[1].item())
# expected: 2 * 16 * 0.001 * 0.001 = 3.2e-5 (approx)
print("expected:", 2 * 16 * 0.001 * 0.001)
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu && uv run python /tmp/test_dot_prec.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 85
}

{
  "content": "<5445 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py"
}

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check with fp32 accumulation"
}

{
  "command": "<1505 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug per-expert correctness"
}

uv run python -c "
import sys, torch
sys.path.insert(0, '.')
import shapes, reference, solution

# Use smallest shape for speed
shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape['T_total'], shape['H'], shape['I'], shape['E'], shape['K']
reference.T_total = T_total
reference.H = H
reference.I = I
reference.E = E
reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]
hidden_states, expert_offsets = inputs

# Check per-expert correctness
with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

for e in range(min(5, E)):
    start = int(expert_offsets[e].item())
    end = int(expert_offsets[e+1].item())
    if end == start:
        continue
    ref_e = ref_out[start:end]
    sol_e = sol_out[start:end]
    diff = (ref_e.float() - sol_e.float()).abs()
    print(f'expert {e}: M_e={end-start}, max_diff={diff.max():.6f}, mean_diff={diff.mean():.6f}')

# Also check: what's inside each expert's region?
# Do sol outputs look like zeros or random values?
print()
print('ref_out stats:', ref_out.float().mean(), ref_out.float().std())
print('sol_out stats:', sol_out.float().mean(), sol_out.float().std())
print('sol_out zeros?', (sol_out == 0).sum().item(), 'out of', sol_out.numel())
" 2>&1

{
  "content": "<5701 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "newString": "<381 chars \u2014 see below>",
  "oldString": "<358 chars \u2014 see below>"
}

    for m_tile in range(max_m_tiles):
        skip = m_tile >= m_tiles
        if skip:
            continue

        m_start = m_tile * BM

        row_offs = m_start + tl.arange(0, BM)
        load_row_mask = row_offs < num_rows
        row_ptrs = row_offs * stride_xm

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for m_tile in range(max_m_tiles):
        if m_tile >= m_tiles:
            break

        m_start = m_tile * BM

        row_offs = m_start + tl.arange(0, BM)
        load_row_mask = row_offs < num_rows
        row_ptrs = row_offs * stride_xm

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "newString": "<194 chars \u2014 see below>",
  "oldString": "<51 chars \u2014 see below>"
}

    E: tl.constexpr,
    num_n_tiles: tl.constexpr,
    BM: tl.constexpr,
    BK: tl.constexpr,
    BN: tl.constexpr,
    num_k_tiles: tl.constexpr,
    max_m_tiles: tl.constexpr,
    stride_xm,

    E: tl.constexpr,
    num_n_tiles: tl.constexpr,

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 55
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "newString": "<195 chars \u2014 see below>",
  "oldString": "<338 chars \u2014 see below>"
}

    num_n_tiles: tl.constexpr,
    BM: tl.constexpr,
    BK: tl.constexpr,
    BN: tl.constexpr,
    num_k_tiles: tl.constexpr,
    max_m_tiles: tl.constexpr,
    stride_xm,
    stride_xk_unused,

    num_n_tiles: tl.constexpr,
    BM: tl.constexpr,
    BK: tl.constexpr,
    BN: tl.constexpr,
    num_k_tiles: tl.constexpr,
    max_m_tiles: tl.constexpr,
    stride_xm,
    BM: tl.constexpr,
    BK: tl.constexpr,
    BN: tl.constexpr,
    num_k_tiles: tl.constexpr,
    max_m_tiles: tl.constexpr,
    stride_xm,
    stride_xk_unused,

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "offset": 110,
  "limit": 50
}

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check with M tile loop"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 50,
  "offset": 57
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py",
  "newString": "<1704 chars \u2014 see below>",
  "oldString": "<1664 chars \u2014 see below>"
}

    for m_tile in range(max_m_tiles):
        if m_tile < m_tiles:
            m_start = m_tile * BM

            row_offs = m_start + tl.arange(0, BM)
            load_row_mask = row_offs < num_rows
            row_ptrs = row_offs * stride_xm

            gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
            up_acc = tl.zeros((BM, BN), dtype=tl.float32)

            for k_tile in range(num_k_tiles):
                k_start = k_tile * BK
                k_offs = k_start + k_offs_base
                k_mask = k_offs < H

                x_ptrs = x_ptr + row_ptrs[:, None] + k_offs[None, :] * stride_xk_unused
                load_mask = load_row_mask[:, None] & k_mask[None, :]
                x_bf16 = tl.load(x_ptrs, mask=load_mask, other=0.0)
                x = x_bf16.to(tl.float32)

                wg_ptrs = w_gate_ptr + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
                wg_mask = k_mask[:, None] & n_mask[None, :]
                wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
                wg = wg_bf16.to(tl.float32)

                wu_ptrs = w_up_ptr + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
                wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
                wu = wu_bf16.to(tl.float32)

                gate_acc += tl.dot(x, wg)
                up_acc += tl.dot(x, wu)

            gate_silu = gate_acc * tl.sigmoid(gate_acc)
            out_fp32 = gate_silu * up_acc
            result = out_fp32.to(tl.bfloat16)

            out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]
            store_mask = load_row_mask[:, None] & n_mask[None, :]
            tl.store(out_ptrs, result, mask=store_mask)

    for m_tile in range(max_m_tiles):
        skip = m_tile >= m_tiles
        if skip:
            continue

        m_start = m_tile * BM

        row_offs = m_start + tl.arange(0, BM)
        load_row_mask = row_offs < num_rows
        row_ptrs = row_offs * stride_xm

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

        for k_tile in range(num_k_tiles):
            k_start = k_tile * BK
            k_offs = k_start + k_offs_base
            k_mask = k_offs < H

            x_ptrs = x_ptr + row_ptrs[:, None] + k_offs[None, :] * stride_xk_unused
            load_mask = load_row_mask[:, None] & k_mask[None, :]
            x_bf16 = tl.load(x_ptrs, mask=load_mask, other=0.0)
            x = x_bf16.to(tl.float32)

            wg_ptrs = w_gate_ptr + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
            wg_mask = k_mask[:, None] & n_mask[None, :]
            wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
            wg = wg_bf16.to(tl.float32)

            wu_ptrs = w_up_ptr + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
            wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
            wu = wu_bf16.to(tl.float32)

            gate_acc += tl.dot(x, wg)
            up_acc += tl.dot(x, wu)

        gate = gate_acc
        up = up_acc

        gate_silu = gate * tl.sigmoid(gate)
        out_fp32 = gate_silu * up
        result = out_fp32.to(tl.bfloat16)

        out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]
        store_mask = load_row_mask[:, None] & n_mask[None, :]
        tl.store(out_ptrs, result, mask=store_mask)

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check v2"
}

{
  "command": "<1449 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug correctness details"
}

uv run python -c "
import sys, torch
sys.path.insert(0, '.')
import shapes, reference, solution

shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape['T_total'], shape['H'], shape['I'], shape['E'], shape['K']
reference.T_total = T_total
reference.H = H
reference.I = I
reference.E = E
reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]

with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

ref_f = ref_out.float()
sol_f = sol_out.float()
print('ref range:', ref_f.min(), ref_f.max(), 'std:', ref_f.std())
print('sol range:', sol_f.min(), sol_f.max(), 'std:', sol_f.std())
print('sol zeros:', (sol_out == 0).sum().item(), '/ total:', sol_out.numel())
print('allclose(0.02):', torch.allclose(ref_f, sol_f, atol=0.02, rtol=0.02))

# Check per-expert
for e in range(min(5, E)):
    start = int(inputs[1][e].item())
    end = int(inputs[1][e+1].item())
    if end == start: continue
    ref_e = ref_out[start:end]
    sol_e = sol_out[start:end]
    diff = (ref_e.float() - sol_e.float()).abs()
    print(f'  expert {e} M_e={end-start}: max={diff.max():.6f} mean={diff.mean():.6f} sol_zero={((sol_e==0).sum()/2):.0f}/{end-start}')
" 2>&1

{
  "command": "<2203 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug kernel parameters and per-row stats"
}

uv run python -c "
import sys, torch
sys.path.insert(0, '.')
import shapes, reference, solution

shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape['T_total'], shape['H'], shape['I'], shape['E'], shape['K']
reference.T_total = T_total
reference.H = H
reference.I = I
reference.E = E
reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]
hidden_states, expert_offsets = inputs

# Check kernel parameters
T_perm = hidden_states.shape[0]
tokens_per_expert = expert_offsets[1:].diff()
max_m = int(tokens_per_expert.max().item())
BM, BK_, BN_ = 128, 64, 128
num_k_tiles = (H + BK_ - 1) // BK_
num_n_tiles = (I + BN_ - 1) // BN_
max_m_tiles = (max_m + BM - 1) // BM

print(f'T_perm={T_perm}, H={H}, I={I}, E={E}')
print(f'M per expert: min={tokens_per_expert.min()}, max={tokens_per_expert.max()}, mean={tokens_per_expert.float().mean():.1f}')
print(f'num_k_tiles={num_k_tiles}, num_n_tiles={num_n_tiles}, max_m_tiles={max_m_tiles}')
print(f'total_programs={num_n_tiles * E}')

with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

# Check how many elements differ
ref_f = ref_out.float()
sol_f = sol_out.float()
total = sol_out.numel()
zeros = (sol_out == 0).sum().item()
ref_zeros = (ref_out == 0).sum().item()
print(f'sol zeros: {zeros}/{total} ({100*zeros/total:.1f}%)')
print(f'ref zeros: {ref_zeros}/{total} ({100*ref_zeros/total:.1f}%)')

# Check a small region manually
e = 0
start = int(expert_offsets[e].item())
end = int(expert_offsets[e+1].item())
print(f'\\nExpert 0: rows {start}:{end}, M_e={end-start}')

# Check per-row correctness
ref_e = ref_out[start:end].float()  # (M_e, I)
sol_e = sol_out[start:end].float()
row_diff = (ref_e - sol_e).abs().max(dim=1).values  # (M_e,)
for i in range(0, end-start, 16):
    chunk_diff = row_diff[i:min(i+16, end-start)]
    print(f'  rows {i}-{min(i+15,end-start-1)}: max_diff={chunk_diff.max():.6f}, mean={chunk_diff.mean():.6f}')
" 2>&1

{
  "content": "<5620 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py"
}

{
  "command": "timeout 120 uv run python check.py 2>&1",
  "timeout": 180000,
  "description": "Run correctness check v3"
}

{
  "command": "<1451 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Check zeros and diff distribution"
}

uv run python -c "
import sys, torch
sys.path.insert(0, '.')
import shapes, reference, solution

shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape['T_total'], shape['H'], shape['I'], shape['E'], shape['K']
reference.T_total = T_total
reference.H = H
reference.I = I
reference.E = E
reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]

with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

ref_f = ref_out.float()
sol_f = sol_out.float()
total = sol_out.numel()
zeros = (sol_out == 0).sum().item()
print(f'sol zeros: {zeros}/{total} ({100*zeros/total:.1f}%)')
print(f'ref zeros: {(ref_out==0).sum().item()}/{total} ({100*(ref_out==0).sum().item()/total:.1f}%)')

diff = (ref_f - sol_f).abs()
print(f'max_diff: {diff.max():.6f}')
print(f'mean_diff: {diff.mean():.6f}')
print(f'shape: ref={ref_out.shape} sol={sol_out.shape}')
print(f'ref range: [{ref_f.min():.6f}, {ref_f.max():.6f}]')
print(f'sol range: [{sol_f.min():.6f}, {sol_f.max():.6f}]')
print(f'N elements where diff > 0.02: {(diff > 0.02).sum().item()} ({100*(diff>0.02).sum().item()/total:.1f}%)')
print(f'N elements where diff > 0.05: {(diff > 0.05).sum().item()}')
" 2>&1

{
  "command": "<2134 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Manually verify grid and kernel"
}

uv run python -c "
import sys, torch, triton
sys.path.insert(0, '.')
import shapes, reference

shape = shapes.SHAPES[1]
T_total, H, I, E, K = shape.values()
reference.T_total = T_total; reference.H = H; reference.I = I; reference.E = E; reference.K = K

device = torch.device('cuda:0')
init_args = reference.get_init_inputs()
ref_model = reference.Model(*init_args).to(device).eval()
torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]
hidden_states, expert_offsets = inputs

# Manually check grid calculation
T_perm = hidden_states.shape[0]
BM, BK, BN = 128, 64, 128
num_k_tiles_val = triton.cdiv(H, BK)
num_n_tiles_val = triton.cdiv(I, BN)
max_tokens = int(expert_offsets[1:].diff().max().item())
max_m_tiles_val = triton.cdiv(max_tokens, BM)
total_programs = E * num_n_tiles_val * max_m_tiles_val

print(f'H={H}, I={I}, E={E}, K={K}')
print(f'num_k_tiles={num_k_tiles_val}, num_n_tiles={num_n_tiles_val}, max_m_tiles={max_m_tiles_val}')
print(f'total_programs = {E}*{num_n_tiles_val}*{max_m_tiles_val} = {total_programs}')

# Manually launch the kernel
import solution
sol_model = solution.Model(*init_args).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

out = torch.empty((T_perm, I), dtype=torch.bfloat16, device=device)

from solution import _grouped_gemm_swiglu_kernel
_grouped_gemm_swiglu_kernel[(total_programs,)](
    hidden_states, ref_model.W_gate, ref_model.W_up, out, expert_offsets,
    H=H, I=I, E=E,
    num_n_tiles=num_n_tiles_val,
    max_m_tiles=max_m_tiles_val,
    BM=BM, BK=BK, BN=BN,
    num_k_tiles=num_k_tiles_val,
    stride_xm=H, stride_xk_unused=1, stride_wg_h=I, stride_wg_i=1,
    stride_om=I, stride_oi=1,
)
torch.cuda.synchronize()

with torch.no_grad():
    ref_out = ref_model(*inputs)

ref_f = ref_out.float()
out_f = out.float()
print(f'sol zeros: {(out==0).sum().item()}/{out.numel()}')
print(f'ref range: [{ref_f.min():.4f}, {ref_f.max():.4f}]')
print(f'sol range: [{out_f.min():.4f}, {out_f.max():.4f}]')
diff = (ref_f - out_f).abs()
print(f'max_diff: {diff.max():.6f}')
print(f'allclose(0.02): {torch.allclose(ref_f, out_f, 0.02, 0.02)}')
" 2>&1

{
  "command": "<2321 chars \u2014 see below>",
  "timeout": 60000,
  "description": "Debug pid decomposition"
}

cat > /tmp/debug_sol.py << 'PYEOF'
import torch
import triton
import triton.language as tl

BM = 128
BK = 64
BN = 128

@triton.jit
def _debug_kernel(out_ptr, offsets_ptr, pid_dbg_ptr,
                  H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                  num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                  BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                  num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)

    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    # Log pid info
    idx = pid * 3
    tl.store(pid_dbg_ptr + idx, tl.cast(tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert), tl.int32))
    tl.store(pid_dbg_ptr + idx + 1, n_tile)
    tl.store(pid_dbg_ptr + idx + 2, m_tile_pid)

    # Just write a marker value to verify which pid writes where
    if pid == 0:
        # Write a special marker
        tl.store(out_ptr, tl.cast(99.0, tl.bfloat16))
    if pid == 250:
        # Write another marker  
        tl.store(out_ptr + 1, tl.cast(88.0, tl.bfloat16))

def test():
    E, H, I, num_n_tiles, max_m_tiles = 64, 2048, 1024, 8, 2
    total_programs = E * num_n_tiles * max_m_tiles
    
    offsets = torch.zeros(E + 1, dtype=torch.int32, device='cuda')
    for i in range(E + 1):
        offsets[i] = i * 256
    
    out = torch.zeros((16384, I), dtype=torch.bfloat16, device='cuda')
    pid_dbg = torch.zeros((total_programs * 3,), dtype=torch.int32, device='cuda')

    _debug_kernel[(total_programs,)](
        out, offsets, pid_dbg,
        H=H, I=I, E=E,
        num_n_tiles=num_n_tiles, max_m_tiles=max_m_tiles,
        BM=BM, BK=BK, BN=BN,
        num_k_tiles=triton.cdiv(H, BK),
    )
    torch.cuda.synchronize()

    # Check pid decomposition
    print(f"Grid size: {total_programs}")
    dbg = pid_dbg.cpu()
    for p in [0, 15, 16, 250, 1023]:
        m_e = dbg[p*3].item()
        nt = dbg[p*3+1].item()
        mt = dbg[p*3+2].item()
        print(f"  pid {p}: M_e={m_e}, n_tile={nt}, m_tile={mt}")
    
    print(f"\nOutput markers: out[0,0]={out[0,0].item()}, out[0,1]={out[0,1].item()}")
    print(f"Non-zero output count: {(out != 0).sum().item()}")

test()
PYEOF
uv run python /tmp/debug_sol.py 2>&1

{
  "command": "<4911 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug kernel with markers"
}

cat > /tmp/debug_sol2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

BM = 128
BK = 64
BN = 128

@triton.jit
def _debug_gemm_kernel(out_ptr, offsets_ptr, marker_ptr,
                       x_ptr, w_gate_ptr, w_up_ptr,
                       H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                       num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                       BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                       num_k_tiles: tl.constexpr,
                       stride_xm, stride_xk_unused, stride_wg_h, stride_wg_i,
                       stride_om, stride_oi):
    pid = tl.program_id(0)

    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    
    # Mark whether this pid enters or skips
    early_return = m_tile_pid >= m_tiles_needed
    if early_return:
        tl.store(marker_ptr + pid, 0)
        return
    
    tl.store(marker_ptr + pid, 1)

    m_start = m_tile_pid * BM
    n_start = n_tile * BN

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I

    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + expert * stride_xm + row_offs[:, None] + k_offs[None, :] * stride_xk_unused
        load_mask = load_row_mask[:, None] & k_mask[None, :]
        x_bf16 = tl.load(x_ptrs, mask=load_mask, other=0.0)
        x = x_bf16.to(tl.float32)

        wg_ptrs = w_gate_ptr + expert * stride_wg_h * H + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
        wg = wg_bf16.to(tl.float32)

        wu_ptrs = w_up_ptr + expert * stride_wg_h * H + k_offs[:, None] * stride_wg_h + n_offs[None, :] * stride_wg_i
        wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
        wu = wu_bf16.to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    out_fp32 = gate_silu * up_acc
    result = out_fp32.to(tl.bfloat16)

    row_ptrs = row_offs * stride_om
    out_ptrs = out_ptr + row_ptrs[:, None] + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    E, H, I, K_routing = 64, 2048, 1024, 4
    T_total, T_perm = 4096, 16384
    
    num_n_tiles = 8
    max_m_tiles = 2
    num_k_tiles = triton.cdiv(H, BK)
    total_programs = E * num_n_tiles * max_m_tiles
    
    offsets = torch.zeros(E + 1, dtype=torch.int32, device='cuda')
    for i in range(E + 1):
        offsets[i] = i * 256  # 256 tokens per expert
    
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')
    marker = torch.zeros(total_programs, dtype=torch.int32, device='cuda')

    _debug_gemm_kernel[(total_programs,)](
        out, offsets, marker, hidden, W_gate, W_up,
        H=H, I=I, E=E,
        num_n_tiles=num_n_tiles, max_m_tiles=max_m_tiles,
        BM=BM, BK=BK, BN=BN,
        num_k_tiles=num_k_tiles,
        stride_xm=H, stride_xk_unused=1, stride_wg_h=I, stride_wg_i=1,
        stride_om=I, stride_oi=1,
    )
    torch.cuda.synchronize()

    m_cpu = marker.cpu()
    entered = (m_cpu == 1).sum().item()
    skipped = (m

... (911 more chars)

{
  "command": "<4747 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug with small shapes"
}

rm -rf /tmp/triton_* 2>/dev/null; rm -rf __pycache__ 2>/dev/null
cat > /tmp/debug_sol3.py << 'PYEOF'
import torch, triton, triton.language as tl
import torch.nn.functional as F

BM = 64
BK = 64
BN = 128

@triton.jit
def _test_kernel(out_ptr, offsets_ptr, x_ptr, wg_ptr, wu_ptr,
                 H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                 num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                 BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                 num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    if m_tile_pid >= m_tiles_needed:
        return

    m_start = m_tile_pid * BM
    n_start = n_tile * BN

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows
    row_ptrs_x = row_offs * H
    row_ptrs_out = row_offs * I

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        wg_base = expert.to(tl.int64) * H.to(tl.int64) * I.to(tl.int64)
        x_base = tl.cast(0, tl.int64)  # already included in row_ptrs

        x_ptrs = x_ptr + row_ptrs_x[:, None] + k_offs[None, :]
        x_mask = load_row_mask[:, None] & k_mask[None, :]
        x_bf16 = tl.load(x_ptrs, mask=x_mask, other=0.0)
        x = x_bf16.to(tl.float32)

        wg_ptrs = wg_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
        wg = wg_bf16.to(tl.float32)

        wu_ptrs = wu_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
        wu = wu_bf16.to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    combined = gate_silu * up_acc
    result = combined.to(tl.bfloat16)

    out_ptrs = out_ptr + row_ptrs_out[:, None] + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    E, H, I, K_routing = 4, 256, 128, 2
    T_total = 128
    T_perm = T_total * K_routing  # 256
    tokens_per_expert = T_perm // E  # 64
    
    # Even split
    offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

    max_m_tiles_val = triton.cdiv(tokens_per_expert, BM)  # ceil(64/64)=1
    num_n_tiles_val = triton.cdiv(I, BN)  # ceil(128/128)=1
    num_k_tiles_val = triton.cdiv(H, BK)  # ceil(256/64)=4
    total = E * num_n_tiles_val * max_m_tiles_val  # 4*1*1=4

    _test_kernel[(total,)](out, offsets, hidden, W_gate, W_up,
                           H=H, I=I, E=E, num_n_tiles=num_n_tiles_val,
                           max_m_tiles=max_m_tiles_val, BM=BM, BK=BK, BN=BN,
                           num_k_tiles=num_k_tiles_val)
    torch.cuda.synchronize()

    # Reference
    ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        x_e = hidden[s:e_end].float()
        g = x_e @ W_gate[e].float()
        u = x_e @ W_up[e].float()
        ref[s:e_end] = (F.silu(g) * u).bfloat16()

   

... (747 more chars)

{
  "command": "<4467 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug with small shapes in local dir"
}

cat > debug_sol3.py << 'PYEOF'
import torch, triton, triton.language as tl
import torch.nn.functional as F

BM = 64
BK = 64
BN = 128

@triton.jit
def _test_kernel(out_ptr, offsets_ptr, x_ptr, wg_ptr, wu_ptr,
                 H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                 num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                 BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                 num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    if m_tile_pid >= m_tiles_needed:
        return

    m_start = m_tile_pid * BM
    n_start = n_tile * BN

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        x_mask = load_row_mask[:, None] & k_mask[None, :]
        x_bf16 = tl.load(x_ptrs, mask=x_mask, other=0.0)
        x = x_bf16.to(tl.float32)

        wg_base = expert * H * I
        wg_ptrs = wg_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
        wg = wg_bf16.to(tl.float32)

        wu_ptrs = wu_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
        wu = wu_bf16.to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    combined = gate_silu * up_acc
    result = combined.to(tl.bfloat16)

    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    E, H, I, K_routing = 4, 256, 128, 2
    T_total = 128
    T_perm = T_total * K_routing
    tokens_per_expert = T_perm // E
    
    offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

    max_m_tiles_val = triton.cdiv(tokens_per_expert, BM)
    num_n_tiles_val = triton.cdiv(I, BN)
    num_k_tiles_val = triton.cdiv(H, BK)
    total = E * num_n_tiles_val * max_m_tiles_val
    print(f"E={E}, H={H}, I={I}, T_perm={T_perm}, tokens_per_expert={tokens_per_expert}")
    print(f"num_n_tiles={num_n_tiles_val}, max_m_tiles={max_m_tiles_val}, num_k_tiles={num_k_tiles_val}")
    print(f"total_programs={total}")

    _test_kernel[(total,)](out, offsets, hidden, W_gate, W_up,
                           H=H, I=I, E=E, num_n_tiles=num_n_tiles_val,
                           max_m_tiles=max_m_tiles_val, BM=BM, BK=BK, BN=BN,
                           num_k_tiles=num_k_tiles_val)
    torch.cuda.synchronize()

    ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        x_e = hidden[s:e_end].float()
        g = x_e @ W_gate[e].float()
        u = x_e @ W_up[e].float()
        ref[s:e_end] = (F.silu(g) * u).bfloat16()

    zeros = (out == 0).sum().item()
    print(f"Non-zero: {out.numel() - zeros}/{out.numel()}")
    for e in range(E):
  

... (467 more chars)

{
  "command": "<4226 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug without constexpr types"
}

cat > debug_sol4.py << 'PYEOF'
import torch, triton, triton.language as tl
import torch.nn.functional as F

BM = 64; BK = 64; BN = 128

@triton.jit
def _test_kernel(out_ptr, offsets_ptr, x_ptr, wg_ptr, wu_ptr,
                 H, I, E, num_n_tiles, max_m_tiles, BM_c, BK_c, BN_c,
                 num_k_tiles):
    pid = tl.program_id(0)
    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert * H + 1) - tl.load(offsets_ptr + expert * H)
    m_tiles_needed = tl.cdiv(num_rows, BM_c)
    if m_tile_pid >= m_tiles_needed:
        return

    m_start = m_tile_pid * BM_c
    n_start = n_tile * BN_c

    row_offs = m_start + tl.arange(0, BM_c)
    load_row_mask = row_offs < num_rows

    n_offs = n_start + tl.arange(0, BN_c)
    n_mask = n_offs < I

    k_offs_base = tl.arange(0, BK_c)

    gate_acc = tl.zeros((BM_c, BN_c), dtype=tl.float32)
    up_acc = tl.zeros((BM_c, BN_c), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK_c
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        x_mask = load_row_mask[:, None] & k_mask[None, :]
        x_bf16 = tl.load(x_ptrs, mask=x_mask, other=0.0)
        x = x_bf16.to(tl.float32)

        wg_base = expert.to(tl.int64) * H.to(tl.int64) * I.to(tl.int64)
        wg_ptrs = wg_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
        wg = wg_bf16.to(tl.float32)

        wu_ptrs = wu_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
        wu = wu_bf16.to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    combined = gate_silu * up_acc
    result = combined.to(tl.bfloat16)

    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    E, H, I, K_routing = 4, 256, 128, 2
    T_total = 128; T_perm = T_total * K_routing
    tokens_per_expert = T_perm // E
    
    offsets = torch.zeros(E + 1, dtype=torch.int32, device='cuda')
    for i in range(E + 1):
        offsets[i] = i * tokens_per_expert
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

    max_m_tiles_val = (tokens_per_expert + BM - 1) // BM
    num_n_tiles_val = (I + BN - 1) // BN
    num_k_tiles_val = (H + BK - 1) // BK
    total = E * num_n_tiles_val * max_m_tiles_val

    print(f"Grid: {total}, E={E}, num_n={num_n_tiles_val}, max_m={max_m_tiles_val}, num_k={num_k_tiles_val}")

    _test_kernel[(total,)](out, offsets, hidden, W_gate, W_up,
                           H, I, E, num_n_tiles_val, max_m_tiles_val,
                           BM, BK, BN, num_k_tiles_val)
    torch.cuda.synchronize()

    ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        x_e = hidden[s:e_end].float()
        g = x_e @ W_gate[e].float()
        u = x_e @ W_up[e].float()
        ref[s:e_end] = (F.silu(g) * u).bfloat16()

    zeros = (out == 0).sum().item()
    print(f"Non-zero: {out.numel() - zeros}/{out.numel()} ({100*(out.numel()-zeros)/out.numel():.0f}%)")
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        o = out[s:e_end]
        r = ref[s:e_end]
        z = (o == 0).sum().item()
        d = (o.float() - r.float()).abs()
        print(f"

... (226 more chars)

{
  "command": "<4484 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Debug with 8 experts"
}

cat > debug_sol5.py << 'PYEOF'
import torch, triton, triton.language as tl
import torch.nn.functional as F

BM = 64; BK = 64; BN = 128

@triton.jit
def _test_kernel(out_ptr, offsets_ptr, x_ptr, wg_ptr, wu_ptr,
                 H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                 num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                 BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                 num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    if m_tile_pid >= m_tiles_needed:
        return

    m_start = m_tile_pid * BM
    n_start = n_tile * BN

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows

    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I

    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        x_mask = load_row_mask[:, None] & k_mask[None, :]
        x_bf16 = tl.load(x_ptrs, mask=x_mask, other=0.0)
        x = x_bf16.to(tl.float32)

        wg_base = expert.to(tl.int64) * H.to(tl.int64) * I.to(tl.int64)
        wg_ptrs = wg_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg_bf16 = tl.load(wg_ptrs, mask=wg_mask, other=0.0)
        wg = wg_bf16.to(tl.float32)

        wu_ptrs = wu_ptr + wg_base + k_offs[:, None] * I + n_offs[None, :]
        wu_bf16 = tl.load(wu_ptrs, mask=wg_mask, other=0.0)
        wu = wu_bf16.to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    combined = gate_silu * up_acc
    result = combined.to(tl.bfloat16)

    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    E, H, I, K_routing = 8, 256, 128, 2
    T_total = 256; T_perm = T_total * K_routing
    tokens_per_expert = T_perm // E  # 64
    
    offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

    max_m_tiles_val = (tokens_per_expert + BM - 1) // BM  
    num_n_tiles_val = (I + BN - 1) // BN
    num_k_tiles_val = (H + BK - 1) // BK
    total = E * num_n_tiles_val * max_m_tiles_val

    print(f"E={E}, H={H}, I={I}, T_perm={T_perm}, M_per_exp={tokens_per_expert}")
    print(f"Grid: {total} = {E} * {num_n_tiles_val} * {max_m_tiles_val}")

    _test_kernel[(total,)](out, offsets, hidden, W_gate, W_up,
                           H=H, I=I, E=E, num_n_tiles=num_n_tiles_val,
                           max_m_tiles=max_m_tiles_val, BM=BM, BK=BK, BN=BN,
                           num_k_tiles=num_k_tiles_val)
    torch.cuda.synchronize()

    ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        x_e = hidden[s:e_end].float()
        g = x_e @ W_gate[e].float()
        u = x_e @ W_up[e].float()
        ref[s:e_end] = (F.silu(g) * u).bfloat16()

    zeros = (out == 0).sum().item()
    print(f"Non-zero: {out.numel() - zeros}/{out.numel()} ({100*(out.numel()-zeros)/out.numel():.0f}%)")
    for e

... (484 more chars)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol5.py",
  "newString": "        o = out[s:e_end]",
  "oldString": "        o = out[s:e_end]\\n        r = ref[s:e_end]"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol5.py",
  "limit": 20,
  "offset": 95
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol5.py",
  "newString": "<91 chars \u2014 see below>",
  "oldString": "<66 chars \u2014 see below>"
}

        o = out[s:e_end]
        r = ref[s:e_end]
        d = (o.float() - r.float()).abs()

        o = out[s:e_end]
        d = (o.float() - r.float()).abs()

{
  "command": "uv run python debug_sol5.py 2>&1",
  "timeout": 120000,
  "description": "Debug with 8 experts"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol5.py",
  "newString": "        wg_base = expert.to(tl.int64) * H * I",
  "oldString": "        wg_base = expert.to(tl.int64) * H.to(tl.int64) * I.to(tl.int64)"
}

{
  "command": "uv run python debug_sol5.py 2>&1",
  "timeout": 120000,
  "description": "Debug with 8 experts v2"
}

{
  "command": "<4468 chars \u2014 see below>",
  "timeout": 180000,
  "description": "Test corrected kernel with expert offset"
}

cat > debug_sol6.py << 'PYEOF'
import torch, triton, triton.language as tl
import torch.nn.functional as F

BM = 64; BK = 64; BN = 128

@triton.jit
def _correct_kernel(out_ptr, offsets_ptr, x_ptr, wg_ptr, wu_ptr,
                    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
                    num_n_tiles: tl.constexpr, max_m_tiles: tl.constexpr,
                    BM: tl.constexpr, BK: tl.constexpr, BN: tl.constexpr,
                    num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    if m_tile_pid >= m_tiles_needed:
        return

    m_start = m_tile_pid * BM
    n_start = n_tile * BN

    row_offs = m_start + tl.arange(0, BM)
    load_row_mask = row_offs < num_rows
    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
    up_acc = tl.zeros((BM, BN), dtype=tl.float32)

    expert_weight_off = expert.to(tl.int64) * H * I

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base
        k_mask = k_offs < H

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        x_mask = load_row_mask[:, None] & k_mask[None, :]
        x = tl.load(x_ptrs, mask=x_mask, other=0.0).to(tl.float32)

        wg_ptrs = wg_ptr + expert_weight_off + k_offs[:, None] * I + n_offs[None, :]
        wg_mask = k_mask[:, None] & n_mask[None, :]
        wg = tl.load(wg_ptrs, mask=wg_mask, other=0.0).to(tl.float32)

        wu_ptrs = wu_ptr + expert_weight_off + k_offs[:, None] * I + n_offs[None, :]
        wu = tl.load(wu_ptrs, mask=wg_mask, other=0.0).to(tl.float32)

        gate_acc += tl.dot(x, wg)
        up_acc += tl.dot(x, wu)

    gate_silu = gate_acc * tl.sigmoid(gate_acc)
    result = (gate_silu * up_acc).to(tl.bfloat16)

    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    store_mask = load_row_mask[:, None] & n_mask[None, :]
    tl.store(out_ptrs, result, mask=store_mask)

def test():
    for E, H, I, T_total, K_r in [(64, 2048, 1024, 4096, 4),
                                   (128, 4096, 1536, 32768, 8)]:
        T_perm = T_total * K_r
        base = T_perm // E
        offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * base
        hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
        W_gate = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
        W_up = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
        out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

        max_m = (base + BM - 1) // BM
        num_n = (I + BN - 1) // BN
        num_k = (H + BK - 1) // BK
        total = E * num_n * max_m
        print(f"\n=== E={E}, H={H}, I={I}, T_perm={T_perm} ===")
        print(f"Grid: {total}, num_n={num_n}, max_m={max_m}, num_k={num_k}")

        _correct_kernel[(total,)](out, offsets, hidden, W_gate, W_up,
                                   H=H, I=I, E=E, num_n_tiles=num_n,
                                   max_m_tiles=max_m, BM=BM, BK=BK, BN=BN,
                                   num_k_tiles=num_k)
        torch.cuda.synchronize()

        ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
        for e in range(E):
            s, e_end = int(offsets[e]), int(offsets[e+1])
            x_e = hidden[s:e_end].float()
            g = x_e @ W_gate[e].float()
            u = x_e @ W_up[e].float()
            ref[s:e_end] = (F.silu(g) * u).bfloat16()

        zeros = (out == 0).sum().item()
        print(f"Non-zero: {out.numel() - zeros}/{out.numel()} ({100*(out.numel()-zeros)/out.numel():.1f}%)")
        
        # Check first 3 experts
        for e in ra

... (468 more chars)

{
  "command": "<1565 chars \u2014 see below>",
  "timeout": 60000,
  "description": "Test Triton grid size with markers"
}

cat > debug_sol7.py << 'PYEOF'
"""Test if Triton skips programs with pid > some limit"""
import torch, triton, triton.language as tl

@triton.jit
def _marker_kernel(markers, num_n: tl.constexpr, max_m: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // (num_n * max_m)
    rem = pid % (num_n * max_m)
    n_tile = rem // max_m
    m_tile = rem % max_m
    # Write pid info
    idx = pid * 4
    markers[idx + 0] = pid
    markers[idx + 1] = expert
    markers[idx + 2] = n_tile
    markers[idx + 3] = m_tile

# Test with same constexprs as E=64 case
num_n = 8; max_m = 4; E = 64
total = E * num_n * max_m
markers = torch.zeros(total * 4, dtype=torch.int32, device='cuda')
print(f"Testing pid decomposition: E={E}, num_n={num_n}, max_m={max_m}, total={total}")

_marker_kernel[(total,)](markers, num_n, max_m)
torch.cuda.synchronize()

m = markers.cpu()
# Check all pids were processed
pid_set = set()
for i in range(0, total * 4, 4):
    p, e, n, mt = int(m[i]), int(m[i+1]), int(m[i+2]), int(m[i+3])
    pid_set.add(p)
    if p < 5 or p > total - 5 or e == 1:
        pass  # print(f"pid {p}: expert={e}, n_tile={n}, m_tile={mt}")
    
# Count per-expert
exp_count = {}
for i in range(0, total * 4, 4):
    e = int(m[i+1])
    exp_count[e] = exp_count.get(e, 0) + 1

print(f"Unique pids: {len(pid_set)}/{total}")
print(f"Experts covered: {len(exp_count)}/64")
print(f"Expert 0 programs: {exp_count.get(0, 0)}")
print(f"Expert 1 programs: {exp_count.get(1, 0)}")
print(f"Expert 63 programs: {exp_count.get(63, 0)}")
PYEOF
uv run python debug_sol7.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol7.py",
  "newString": "<191 chars \u2014 see below>",
  "oldString": "<155 chars \u2014 see below>"
}

    # Write pid info
    idx = pid * 4
    tl.store(markers + idx, pid)
    tl.store(markers + idx + 1, expert)
    tl.store(markers + idx + 2, n_tile)
    tl.store(markers + idx + 3, m_tile)

    # Write pid info
    idx = pid * 4
    markers[idx + 0] = pid
    markers[idx + 1] = expert
    markers[idx + 2] = n_tile
    markers[idx + 3] = m_tile

{
  "command": "uv run python debug_sol7.py 2>&1",
  "timeout": 60000,
  "description": "Test Triton grid size"
}

{
  "command": "<2684 chars \u2014 see below>",
  "timeout": 60000,
  "description": "Test early return behavior"
}

cat > debug_sol8.py << 'PYEOF'
"""Test if Triton correctly evaluates early return based on program pid"""
import torch, triton, triton.language as tl

@triton.jit
def _early_return_kernel(out_ptr, offsets_ptr, marker_ptr,
                         BM: tl.constexpr, max_m_tiles: tl.constexpr):
    pid = tl.program_id(0)
    num_n_tiles: tl.constexpr = 8

    expert = pid // (num_n_tiles * max_m_tiles)
    rem = pid % (num_n_tiles * max_m_tiles)
    n_tile = rem // max_m_tiles
    m_tile_pid = rem % max_m_tiles

    num_rows = tl.load(offsets_ptr + expert + 1) - tl.load(offsets_ptr + expert)
    m_tiles_needed = tl.cdiv(num_rows, BM)
    
    # Log what happens for each pid
    marker_ptr[pid * 3 + 0] = tl.cast(num_rows, tl.int32)
    marker_ptr[pid * 3 + 1] = tl.cast(m_tiles_needed, tl.int32)
    marker_ptr[pid * 3 + 2] = tl.cast(m_tile_pid, tl.int32)

    if m_tile_pid >= m_tiles_needed:
        marker_ptr[pid * 3 + 2] = 1000 + m_tile_pid  # mark as "returned early"
        out_ptr[pid] = -99  # sentinel for early return
        return

    out_ptr[pid] = pid  

E = 64; num_n = 8; max_m = 4; BM_val = 64
total = E * num_n * max_m  # 2048

base = 256
offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * base
out = torch.zeros(total, dtype=torch.int32, device='cuda')
markers = torch.zeros(total * 3, dtype=torch.int32, device='cuda')

print(f"Grid: {total}, BM={BM_val}")
_early_return_kernel[(total,)](out, offsets, markers, BM=BM_val, max_m_tiles=max_m)
torch.cuda.synchronize()

o = out.cpu()
m = markers.cpu()

# Check how many returned early
early_ret = (o == -99).sum().item()
no_ret = ((o != -99) & (o != 0)).sum().item()
zero_vals = (o == 0).sum().item()
print(f"Results: early_return={early_ret}, no_ret={no_ret}, zero_vals={zero_vals}")

# Check first few and a few expert boundaries
for p in [0, 1, 2, 3, 31, 32, 33, 63, 64, 127, 128]:
    mrows = m[p*3].item()
    mneeded = m[p*3+1].item()
    mtile = m[p*3+2].item()
    oval = o[p].item()
    print(f"  pid={p}: num_rows={mrows}, m_needed={mneeded}, m_tile={mtile}, out={oval}")

# Count per expert
exp_data = {}
for p in range(total):
    e = p // (num_n * max_m)
    if e not in exp_data:
        exp_data[e] = {'early': 0, 'proc': 0}
    if o[p].item() == -99:
        exp_data[e]['early'] += 1
    else:
        exp_data[e]['proc'] += 1

print(f"\nPer-expert processing:")
for e in sorted(exp_data.keys())[:3]:
    print(f"  Expert {e}: early={exp_data[e]['early']}, proc={exp_data[e]['proc']}")
print(f"  ...")
e_last = sorted(exp_data.keys())[-1]
print(f"  Expert {e_last}: early={exp_data[e_last]['early']}, proc={exp_data[e_last]['proc']}")
PYEOF
uv run python debug_sol8.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/debug_sol8.py",
  "newString": "<429 chars \u2014 see below>",
  "oldString": "<430 chars \u2014 see below>"
}

    # Log what happens for each pid
    tl.store(marker_ptr + pid * 3, tl.cast(num_rows, tl.int32))
    tl.store(marker_ptr + pid * 3 + 1, tl.cast(m_tiles_needed, tl.int32))
    tl.store(marker_ptr + pid * 3 + 2, tl.cast(m_tile_pid, tl.int32))

    if m_tile_pid >= m_tiles_needed:
        tl.store(marker_ptr + pid * 3 + 2, 1000 + m_tile_pid)
        tl.store(out_ptr + pid, -99)
        return

    tl.store(out_ptr + pid, pid)

    # Log what happens for each pid
    marker_ptr[pid * 3 + 0] = tl.cast(num_rows, tl.int32)
    marker_ptr[pid * 3 + 1] = tl.cast(m_tiles_needed, tl.int32)
    marker_ptr[pid * 3 + 2] = tl.cast(m_tile_pid, tl.int32)

    if m_tile_pid >= m_tiles_needed:
        marker_ptr[pid * 3 + 2] = 1000 + m_tile_pid  # mark as "returned early"
        out_ptr[pid] = -99  # sentinel for early return
        return

    out_ptr[pid] = pid

{
  "command": "uv run python debug_sol8.py 2>&1",
  "timeout": 60000,
  "description": "Test early return behavior"
}

{
  "command": "<4104 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Test single and expert-indexed GEMM"
}

cat > debug_sol9.py << 'PYEOF'
"""Minimal test: single program GEMM with known data"""
import torch, triton, triton.language as tl
import torch.nn.functional as F

@triton.jit
def _single_gemm(out_ptr, x_ptr, w_ptr,
                 M: tl.constexpr, K: tl.constexpr, N: tl.constexpr):
    BM: tl.constexpr = M
    BK: tl.constexpr = 64
    BN: tl.constexpr = N

    row_offs = tl.arange(0, BM)
    n_offs = tl.arange(0, BN)
    k_offs_base = tl.arange(0, BK)
    num_k_tiles = K // BK

    acc = tl.zeros((BM, BN), dtype=tl.float32)
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base

        x_ptrs = x_ptr + row_offs[:, None] * K + k_offs[None, :]
        w_ptrs = w_ptr + k_offs[:, None] * N + n_offs[None, :]

        x = tl.load(x_ptrs).to(tl.float32)
        w = tl.load(w_ptrs).to(tl.float32)

        acc += tl.dot(x, w)

    result = acc.to(tl.bfloat16)
    out_ptrs = out_ptr + row_offs[:, None] * N + n_offs[None, :]
    tl.store(out_ptrs, result)

def test_single_gemm():
    M, K, N = 64, 256, 128
    x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda') * 0.1
    w = torch.randn(K, N, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros(M, N, dtype=torch.bfloat16, device='cuda')

    _single_gemm[(1,)](out, x, w, M=M, K=K, N=N)
    torch.cuda.synchronize()

    ref = (x.float() @ w.float()).bfloat16()
    diff = (out.float() - ref.float()).abs()
    print(f"M={M}, K={K}, N={N}")
    print(f"max_diff={diff.max():.6f}, mean_diff={diff.mean():.6f}")
    print(f"allclose(0.02)={torch.allclose(out.float(), ref.float(), 0.02, 0.02)}")

test_single_gemm()

# Now test with expert indexing
@triton.jit
def _expert_gemm(out_ptr, x_ptr, w_ptr, offsets_ptr,
                 H: tl.constexpr, I: tl.constexpr):
    pid = tl.program_id(0)
    BM: tl.constexpr = 64
    BK: tl.constexpr = 64
    BN: tl.constexpr = 128
    num_k_tiles = H // BK

    num_rows = tl.load(offsets_ptr + pid + 1) - tl.load(offsets_ptr + pid)
    m_start = 0

    row_offs = m_start + tl.arange(0, BM)
    n_offs = tl.arange(0, BN)
    k_offs_base = tl.arange(0, BK)

    acc = tl.zeros((BM, BN), dtype=tl.float32)
    expert_off = pid.to(tl.int64) * H * I

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        w_ptrs = w_ptr + expert_off + k_offs[:, None] * I + n_offs[None, :]

        x = tl.load(x_ptrs).to(tl.float32)
        w = tl.load(w_ptrs).to(tl.float32)

        acc += tl.dot(x, w)

    result = acc.to(tl.bfloat16)
    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    tl.store(out_ptrs, result)

def test_expert_gemm():
    E, H, I = 4, 256, 128
    tokens_per_expert = 64
    total = E * tokens_per_expert

    offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
    x = torch.randn(total, H, dtype=torch.bfloat16, device='cuda') * 0.1
    w = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros(total, I, dtype=torch.bfloat16, device='cuda')

    _expert_gemm[(E,)](out, x, w, offsets, H=H, I=I)
    torch.cuda.synchronize()

    ref = torch.empty(total, I, dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        ref[s:e_end] = (x[s:e_end].float() @ w[e].float()).bfloat16()

    zeros = (out == 0).sum().item()
    print(f"\nExpert GEMM: E={E}, H={H}, I={I}, M_per_exp={tokens_per_expert}")
    print(f"Non-zero: {out.numel() - zeros}/{out.numel()}")
    for e in range(E):
        s, e_end = int(offsets[e]), int(offsets[e+1])
        d = (out[s:e_end].float() - ref[s:e_end].float()).abs()
        z = (out[s:e_end] == 0).sum().item()
        print(f"  Expert {e}: diff_max={d.max():.6f} zeros={z}/{(e_end-s)*I}")
    
    diff = (out.float() - ref.float()).abs()
    print(f"Overall: max_diff={diff.max():.6f} allclose(0.02)={torch.allc

... (104 more chars)

{
  "command": "<3778 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Test expert indexing with pid"
}

cat > debug_sol10.py << 'PYEOF'
import torch, triton, triton.language as tl

@triton.jit
def _expert_gemm(out_ptr, x_ptr, w_ptr, offsets_ptr,
                 H: tl.constexpr, I: tl.constexpr):
    pid = tl.program_id(0)
    BM: tl.constexpr = 64
    BK: tl.constexpr = 64
    BN: tl.constexpr = 128
    num_k_tiles = H // BK

    num_rows = tl.load(offsets_ptr + pid + 1) - tl.load(offsets_ptr + pid)
    row_offs = tl.arange(0, BM)
    n_offs = tl.arange(0, BN)
    k_offs_base = tl.arange(0, BK)

    acc = tl.zeros((BM, BN), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BK
        k_offs = k_start + k_offs_base

        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        # Expert weight offset using pid
        expert_weight_offset = pid * H * I
        w_ptrs = w_ptr + expert_weight_offset + k_offs[:, None] * I + n_offs[None, :]

        x = tl.load(x_ptrs).to(tl.float32)
        w = tl.load(w_ptrs).to(tl.float32)

        acc += tl.dot(x, w)

    result = acc.to(tl.bfloat16)
    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    tl.store(out_ptrs, result)

E, H, I = 4, 256, 128
tokens_per_expert = 64
total_rows = E * tokens_per_expert

offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
x = torch.randn(total_rows, H, dtype=torch.bfloat16, device='cuda') * 0.1
w = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.zeros(total_rows, I, dtype=torch.bfloat16, device='cuda')

import torch.nn.functional as F
ref = torch.empty(total_rows, I, dtype=torch.bfloat16, device='cuda')
for e in range(E):
    s, e_end = int(offsets[e]), int(offsets[e+1])
    ref[s:e_end] = (x[s:e_end].float() @ w[e].float()).bfloat16()

_expert_gemm[(E,)](out, x, w, offsets, H=H, I=I)
torch.cuda.synchronize()

zeros = (out == 0).sum().item()
print(f"Non-zero: {out.numel() - zeros}/{out.numel()}")
for e in range(E):
    s, e_end = int(offsets[e]), int(offsets[e+1])
    d = (out[s:e_end].float() - ref[s:e_end].float()).abs()
    z = (out[s:e_end] == 0).sum().item()
    print(f"  Expert {e}: diff_max={d.max():.6f} zeros={z}")

# Also test with pid as constexpr parameter
@triton.jit
def _expert_gemm_param(out_ptr, x_ptr, w_ptr, expert_id,
                       H: tl.constexpr, I: tl.constexpr):
    BM: tl.constexpr = 64; BK: tl.constexpr = 64; BN: tl.constexpr = 128
    row_offs = tl.arange(0, BM); n_offs = tl.arange(0, BN); k_offs_base = tl.arange(0, BK)

    acc = tl.zeros((BM, BN), dtype=tl.float32)
    for k_tile in range(H // BK):
        k_offs = k_tile * BK + k_offs_base
        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        expert_w_off = expert_id * H * I
        w_ptrs = w_ptr + expert_w_off + k_offs[:, None] * I + n_offs[None, :]
        acc += tl.dot(tl.load(x_ptrs).to(tl.float32), tl.load(w_ptrs).to(tl.float32))
    
    result = acc.to(tl.bfloat16)
    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    tl.store(out_ptrs, result)

out2 = torch.zeros(total_rows, I, dtype=torch.bfloat16, device='cuda')
# Launch each expert separately with expert_id as constexpr
for e in range(E):
    _expert_gemm_param[(1,)](out2, x, w, e, H=H, I=I)
torch.cuda.synchronize()

zeros2 = (out2 == 0).sum().item()
print(f"\nWith param: Non-zero: {out2.numel() - zeros2}/{out2.numel()}")
for e in range(E):
    s, e_end = int(offsets[e]), int(offsets[e+1])
    d = (out2[s:e_end].float() - ref[s:e_end].float()).abs()
    print(f"  Expert {e}: diff_max={d.max():.6f} zeros={(out2[s:e_end]==0).sum().item()}")
    
diff = (out2.float() - ref.float()).abs()
print(f"Overall: max_diff={diff.max():.6f} allclose(0.02)={torch.allclose(out2.float(), ref.float(), 0.02, 0.02)}")
PYEOF
uv run python debug_sol10.py 2>&1

{
  "command": "<2264 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Test double-bug fix"
}

cat > debug_sol11.py << 'PYEOF'
"""Test GEMM with CORRECT x pointer offset using offsets array"""
import torch, triton, triton.language as tl
import torch.nn.functional as F

@triton.jit
def _fixed_kernel(out_ptr, x_ptr, w_ptr, offsets_ptr,
                  H: tl.constexpr, I: tl.constexpr):
    pid = tl.program_id(0)
    BM: tl.constexpr = 64; BK: tl.constexpr = 64; BN: tl.constexpr = 128

    start = tl.load(offsets_ptr + pid)
    end = tl.load(offsets_ptr + pid + 1)

    row_offs = start + tl.arange(0, BM)
    n_offs = tl.arange(0, BN)
    k_offs_base = tl.arange(0, BK)

    acc = tl.zeros((BM, BN), dtype=tl.float32)
    expert_w_off = pid * H * I

    for k_tile in range(H // BK):
        k_offs = k_tile * BK + k_offs_base
        x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
        w_ptrs = w_ptr + expert_w_off + k_offs[:, None] * I + n_offs[None, :]
        acc += tl.dot(tl.load(x_ptrs).to(tl.float32), tl.load(w_ptrs).to(tl.float32))

    result = acc.to(tl.bfloat16)
    out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
    tl.store(out_ptrs, result)

E, H, I = 4, 256, 128
tokens_per_expert = 64
total_rows = E * tokens_per_expert

offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * tokens_per_expert
x = torch.randn(total_rows, H, dtype=torch.bfloat16, device='cuda') * 0.1
w = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.zeros(total_rows, I, dtype=torch.bfloat16, device='cuda')

ref = torch.empty(total_rows, I, dtype=torch.bfloat16, device='cuda')
for e in range(E):
    s, e_end = int(offsets[e]), int(offsets[e+1])
    ref[s:e_end] = (x[s:e_end].float() @ w[e].float()).bfloat16()

_fixed_kernel[(E,)](out, x, w, offsets, H=H, I=I)
torch.cuda.synchronize()

zeros = (out == 0).sum().item()
print(f"Non-zero: {out.numel() - zeros}/{out.numel()}")
for e in range(E):
    s, e_end = int(offsets[e]), int(offsets[e+1])
    d = (out[s:e_end].float() - ref[s:e_end].float()).abs()
    print(f"  Expert {e}: diff_max={d.max():.6f} zeros={(out[s:e_end]==0).sum().item()}")
diff = (out.float() - ref.float()).abs()
print(f"Overall: max_diff={diff.max():.6f} allclose(0.02)={torch.allclose(out.float(), ref.float(), 0.02, 0.02)}")
PYEOF
uv run python debug_sol11.py 2>&1

{
  "content": "<4855 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/06_sonic_moe_swiglu/solution.py"
}

{
  "command": "timeout 300 uv run python check.py 2>&1",
  "timeout": 360000,
  "description": "Run correctness check with fixed kernel"
}

{
  "todos": [
    {
      "content": "Check GPU availability and environment (nvidia-smi, CUDA, nvcc)",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Decide approach: CUTLASS grouped GEMM vs Triton vs raw CUDA",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Implement grouped GEMM + fused SwiGLU kernel (v1)",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Run check.py for correctness",
      "status": "completed",
      "priority": "high"
    },
    {
      "content": "Run benchm

... (218 more chars)

{
  "command": "timeout 300 uv run python benchmark.py 2>&1",
  "timeout": 360000,
  "description": "Run benchmark"
}

{
  "command": "<6103 chars \u2014 see below>",
  "timeout": 120000,
  "description": "Compare bf16 tensor cores vs fp32"
}

cat > debug_perf.py << 'PYEOF'
"""Compare kernel performance with different precision settings"""
import torch, triton, triton.language as tl
import torch.nn.functional as F

# Test bf16 tensor cores vs fp32
@triton.jit
def _kernel_bf16(out_ptr, x_ptr, wg_ptr, wu_ptr, offsets_ptr,
                 H: tl.constexpr, I: tl.constexpr, BM: tl.constexpr,
                 BK: tl.constexpr, BN: tl.constexpr, num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // tl.cdiv(I, BN)
    n_tile = pid % tl.cdiv(I, BN)
    
    start = tl.load(offsets_ptr + expert)
    end = tl.load(offsets_ptr + expert + 1)
    num_rows = end - start
    m_tiles = tl.cdiv(num_rows, BM)

    n_start = n_tile * BN
    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    for m_tile_id in range(m_tiles):
        m_start = start + m_tile_id * BM
        row_offs = m_start + tl.arange(0, BM)
        row_mask = row_offs < end

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

        for k_tile in range(num_k_tiles):
            k_offs = k_tile * BK + k_offs_base
            k_mask = k_offs < H
            x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
            wg_ptrs = wg_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            wu_ptrs = wu_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            mask2d = row_mask[:, None] & k_mask[None, :]
            maskw = k_mask[:, None] & n_mask[None, :]

            # Keep bf16, accumulate in fp32 via out_dtype
            x = tl.load(x_ptrs, mask=mask2d, other=0.0)
            wg = tl.load(wg_ptrs, mask=maskw, other=0.0)
            wu = tl.load(wu_ptrs, mask=maskw, other=0.0)
            gate_acc += tl.dot(x, wg, out_dtype=tl.float32)
            up_acc += tl.dot(x, wu, out_dtype=tl.float32)

        gate_silu = gate_acc * tl.sigmoid(gate_acc)
        result = (gate_silu * up_acc).to(tl.bfloat16)
        out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
        tl.store(out_ptrs, result, mask=row_mask[:, None] & n_mask[None, :])

@triton.jit
def _kernel_mixed(out_ptr, x_ptr, wg_ptr, wu_ptr, offsets_ptr,
                  H: tl.constexpr, I: tl.constexpr, BM: tl.constexpr,
                  BK: tl.constexpr, BN: tl.constexpr, num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    expert = pid // tl.cdiv(I, BN)
    n_tile = pid % tl.cdiv(I, BN)
    
    start = tl.load(offsets_ptr + expert)
    end = tl.load(offsets_ptr + expert + 1)
    num_rows = end - start
    m_tiles = tl.cdiv(num_rows, BM)

    n_start = n_tile * BN
    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    for m_tile_id in range(m_tiles):
        m_start = start + m_tile_id * BM
        row_offs = m_start + tl.arange(0, BM)
        row_mask = row_offs < end

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

        for k_tile in range(num_k_tiles):
            k_offs = k_tile * BK + k_offs_base
            k_mask = k_offs < H
            x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
            wg_ptrs = wg_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            wu_ptrs = wu_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            mask2d = row_mask[:, None] & k_mask[None, :]
            maskw = k_mask[:, None] & n_mask[None, :]

            x = tl.load(x_ptrs, mask=mask2d, other=0.0)
            wg = tl.load(wg_ptrs, mask=maskw, other=0.0)
            wu = tl.load(wu_ptrs, mask=maskw, other=0.0)
            
            # Use large k blocks (BK=128) with fp32, but try tf32 for speed
            gate_acc += tl.dot(x.to(tl.float32), wg.to(tl.float32), input_precision="tf32")
            up_acc += tl.dot(x.to(tl.float32), wu.to(tl.float32), input_precision="tf32")

        gate_silu = gate_acc * tl.sigmoid(gate_acc)
     

... (2103 more chars)

{
  "command": "<4349 chars \u2014 see below>",
  "timeout": 180000,
  "description": "Test bf16 tensor cores on all shapes"
}

cat > debug_perf2.py << 'PYEOF'
"""Test bf16 tensor cores on all 3 shapes"""
import torch, triton, triton.language as tl, torch.nn.functional as F

@triton.jit
def _bf16tc(out_ptr, x_ptr, wg_ptr, wu_ptr, offsets_ptr,
            H: tl.constexpr, I: tl.constexpr, BM: tl.constexpr,
            BK: tl.constexpr, BN: tl.constexpr, num_k_tiles: tl.constexpr):
    pid = tl.program_id(0)
    n_per_expert = I // BN if I % BN == 0 else (I + BN - 1) // BN
    expert = pid // n_per_expert
    n_tile = pid % n_per_expert
    
    start = tl.load(offsets_ptr + expert)
    end = tl.load(offsets_ptr + expert + 1)
    num_rows = end - start
    m_tiles = tl.cdiv(num_rows, BM)

    n_start = n_tile * BN
    n_offs = n_start + tl.arange(0, BN)
    n_mask = n_offs < I
    k_offs_base = tl.arange(0, BK)

    for m_tile_id in range(m_tiles):
        m_start = start + m_tile_id * BM
        row_offs = m_start + tl.arange(0, BM)
        row_mask = row_offs < end

        gate_acc = tl.zeros((BM, BN), dtype=tl.float32)
        up_acc = tl.zeros((BM, BN), dtype=tl.float32)

        for k_tile in range(num_k_tiles):
            k_offs = k_tile * BK + k_offs_base
            k_mask = k_offs < H
            x_ptrs = x_ptr + row_offs[:, None] * H + k_offs[None, :]
            wg_ptrs = wg_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            wu_ptrs = wu_ptr + expert * H * I + k_offs[:, None] * I + n_offs[None, :]
            maskx = row_mask[:, None] & k_mask[None, :]
            maskw = k_mask[:, None] & n_mask[None, :]

            x = tl.load(x_ptrs, mask=maskx, other=0.0)
            wg = tl.load(wg_ptrs, mask=maskw, other=0.0)
            wu = tl.load(wu_ptrs, mask=maskw, other=0.0)
            
            gate_acc += tl.dot(x, wg, out_dtype=tl.float32)
            up_acc += tl.dot(x, wu, out_dtype=tl.float32)

        gate_silu = gate_acc * tl.sigmoid(gate_acc)
        result = (gate_silu * up_acc).to(tl.bfloat16)
        out_ptrs = out_ptr + row_offs[:, None] * I + n_offs[None, :]
        tl.store(out_ptrs, result, mask=row_mask[:, None] & n_mask[None, :])

def test(shape_idx, BM, BK, BN):
    shapes = [
        {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8},
        {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4},
        {"T_total": 16384, "H": 2048, "I": 4096, "E": 64, "K": 8},
    ]
    s = shapes[shape_idx]
    T_total, H, I, E, K = s["T_total"], s["H"], s["I"], s["E"], s["K"]
    T_perm = T_total * K
    base = T_perm // E
    offsets = torch.arange(0, E + 1, dtype=torch.int32, device='cuda') * base
    hidden = torch.randn(T_perm, H, dtype=torch.bfloat16, device='cuda') * 0.1
    Wg = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    Wu = torch.randn(E, H, I, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.zeros((T_perm, I), dtype=torch.bfloat16, device='cuda')

    num_n = triton.cdiv(I, BN)
    num_k = triton.cdiv(H, BK)
    total = E * num_n

    ref = torch.empty((T_perm, I), dtype=torch.bfloat16, device='cuda')
    for e in range(E):
        s_e, e_end = int(offsets[e]), int(offsets[e+1])
        g = hidden[s_e:e_end].float() @ Wg[e].float()
        u = hidden[s_e:e_end].float() @ Wu[e].float()
        ref[s_e:e_end] = (F.silu(g) * u).bfloat16()

    # Warmup
    for _ in range(3):
        _bf16tc[(total,)](out, hidden, Wg, Wu, offsets, H=H, I=I, BM=BM, BK=BK, BN=BN, num_k_tiles=num_k)
    torch.cuda.synchronize()

    # Time
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(5):
        _bf16tc[(total,)](out, hidden, Wg, Wu, offsets, H=H, I=I, BM=BM, BK=BK, BN=BN, num_k_tiles=num_k)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 5

    diff = (out.float() - ref.float()).abs()
    ok = torch.allclose(out.float(), ref.float(), 0.02, 0.02)
    # FLOPs = 2 * T_total * H * (2 * I)
    flops = 2 * T_total * H * (2 * I)
    tflops = flops / (ms * 1e-3) / 1

... (349 more chars)