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

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

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

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

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

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

{
  "command": "ls -la /workspace/problems/06_sonic_moe_swiglu/",
  "description": "List workspace contents"
}

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

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

{
  "command": "echo \"=== Python ===\" && uv run python --version && echo \"=== PyTorch ===\" && uv run python -c \"import torch; print(f'PyTorch {torch.__version__}, CUDA {torch.version.cuda}, GPU: {torch.cuda.get_device_name(0)}')\" && echo \"=== CUDA Capability ===\" && uv run python -c \"import torch; print(f'compute capability: {torch.cuda.get_device_capability(0)}')\" && echo \"=== CUTLASS ===\" && python -c \"import cutlass; print(dir(cutlass))\" 2>&1 || echo \"no cutlass via system python\" && uv run python -c \"import cutlass; print(dir(cutlass))\" 2>&1 || echo \"no cutlass via uv\" 

... (288 more chars)

{
  "subject": "Implement grouped GEMM + fused SwiGLU kernel for SM120",
  "description": "Write a high-performance solution.py with a Triton-based grouped GEMM kernel that fuses SwiGLU activation. Must pass check.py (0.02 tolerance) and achieve good benchmark scores across all 3 shapes.",
  "activeForm": "Implementing grouped GEMM + SwiGLU kernel"
}

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

{
  "command": "<715 chars \u2014 see below>",
  "description": "Check GPU specifications in detail"
}

uv run python -c "
import torch
print('GPU:', torch.cuda.get_device_name(0))
print('Compute Capability:', torch.cuda.get_device_capability(0))
print('Total Memory:', torch.cuda.get_device_properties(0).total_mem / 1e9, 'GB')
print('SM Count:', torch.cuda.get_device_properties(0).multi_processor_count)
print('Max Threads per SM:', torch.cuda.get_device_properties(0).max_threads_per_multi_processor)
print('L2 Cache Size:', torch.cuda.get_device_properties(0).l2_cache_size / 1024, 'KB')
print('Max Shared Memory per Block:', torch.cuda.get_device_properties(0).shared_mem_per_block / 1024, 'KB')
print('Max Shared Memory per SM:', torch.cuda.get_device_properties(0).shared_mem_per_multiprocessor / 1024, 'KB')
"

{
  "command": "<592 chars \u2014 see below>",
  "description": "Verify Triton works on SM120 with basic kernel"
}

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

@triton.jit
def test_kernel(x_ptr, y_ptr, N: tl.constexpr, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < N
    x = tl.load(x_ptr + offs, mask=mask)
    y = x * x + x
    tl.store(y_ptr + offs, y, mask=mask)

x = torch.randn(1024, device='cuda', dtype=torch.bfloat16)
y = torch.empty(1024, device='cuda', dtype=torch.bfloat16)
test_kernel[(32,)](x, y, 1024, BLOCK=32)
print('Triton basic test:', 'PASS' if torch.allclose(y, x*x + x, atol=0.01) else 'FAIL')
" 2>&1

{
  "command": "<871 chars \u2014 see below>",
  "description": "Fix GPU properties query and check details"
}

uv run python -c "
import torch
p = torch.cuda.get_device_properties(0)
print('GPU:', torch.cuda.get_device_name(0))
print('Compute Capability:', torch.cuda.get_device_capability(0))
print('Total Memory:', p.total_memory / 1e9, 'GB')
print('SM Count:', p.multi_processor_count)
print('Max Threads per SM:', p.max_threads_per_multi_processor)
print('L2 Cache:', p.l2_cache_size / 1024, 'KB')
print('Max Shared Memory/Block:', p.shared_mem_per_block / 1024, 'KB')
print('Max Shared Memory/SM:', p.shared_mem_per_multiprocessor / 1024, 'KB')
print('Max Registers/Block:', p.regs_per_block)
print('Warp Size:', p.warp_size)
print('Max Threads/Block:', p.max_threads_per_block)
print('Max Grid Size:', p.max_grid)
print('Clock Rate:', p.clock_rate / 1e6, 'GHz')
print('Memory Clock:', p.memory_clock_rate / 1e6, 'GHz')
print('Memory Bus Width:', p.memory_bus_width, 'bits')
"

{
  "command": "<755 chars \u2014 see below>",
  "description": "Fix GPU properties query"
}

uv run python -c "
import torch
p = torch.cuda.get_device_properties(0)
print('GPU:', torch.cuda.get_device_name(0))
print('Compute Capability:', torch.cuda.get_device_capability(0))
print('Total Memory:', p.total_memory / 1e9, 'GB')
print('SM Count:', p.multi_processor_count)
print('Max Threads per SM:', p.max_threads_per_multi_processor)
print('L2 Cache:', p.L2_cache_size / 1024, 'KB')
print('Max Shared Memory/Block:', p.shared_mem_per_block / 1024, 'KB')
print('Max Shared Memory/SM:', p.shared_mem_per_multiprocessor / 1024, 'KB')
print('Registers/Block:', p.regs_per_block)
print('Warp Size:', p.warp_size)
print('Max Threads/Block:', p.max_threads_per_block)
print('Max Grid Size:', p.max_grid)
print('Clock Rate:', p.clock_rate / 1e6, 'MHz')
"

{
  "command": "<282 chars \u2014 see below>",
  "description": "Print all GPU properties"
}

uv run python -c "
import torch
p = torch.cuda.get_device_properties(0)
for attr in dir(p):
    if not attr.startswith('_'):
        try:
            val = getattr(p, attr)
            if not callable(val):
                print(f'{attr}: {val}')
        except:
            pass
"

{
  "command": "<958 chars \u2014 see below>",
  "description": "Test Triton basic kernel via file"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_basic.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def sq_add_kernel(x_ptr, y_ptr, N, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < N
    x = tl.load(x_ptr + offs, mask=mask)
    y = x * x + x
    tl.store(y_ptr + offs, y, mask=mask)

def test():
    x = torch.randn(1024, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(1024, device='cuda', dtype=torch.bfloat16)
    grid = lambda meta: (triton.cdiv(1024, meta['BLOCK']),)
    sq_add_kernel[grid](x, y, 1024, BLOCK=64)
    expected = x * x + x
    ok = torch.allclose(y, expected, atol=0.01)
    print(f'Triton basic bf16 test: {"PASS" if ok else "FAIL"}')
    if not ok:
        diff = (y - expected).abs()
        print(f'Max diff: {diff.max().item()}')

if __name__ == '__main__':
    test()
PYEOF
uv run python test_triton_basic.py

{
  "command": "<3048 chars \u2014 see below>",
  "description": "Test Triton with f32, bf16, and dot product",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_basic.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def sq_add_f32(x_ptr, y_ptr, N, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < N
    x = tl.load(x_ptr + offs, mask=mask)
    y = x * x + x
    tl.store(y_ptr + offs, y, mask=mask)

@triton.jit
def sq_add_bf16(x_ptr, y_ptr, N, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < N
    x = tl.load(x_ptr + offs, mask=mask)
    y = x * x + x
    tl.store(y_ptr + offs, y, mask=mask)

@triton.jit
def dot_test(x_ptr, w_ptr, y_ptr, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    x_ptrs = x_ptr + rm[:, None] * K + rk[None, :]
    w_ptrs = w_ptr + rk[:, None] * N + rn[None, :]
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        x = tl.load(x_ptrs, mask=(rm[:, None] < M) & (rk[None, :] < K - k))
        w = tl.load(w_ptrs, mask=(rk[:, None] < K - k) & (rn[None, :] < N))
        acc += tl.dot(x, w)
        x_ptrs += BLOCK_K
        w_ptrs += BLOCK_K
    y = acc.to(tl.bfloat16)
    y_ptrs = y_ptr + rm[:, None] * N + rn[None, :]
    tl.store(y_ptrs, y, mask=rm[:, None] < M)

def test_f32():
    N = 1024
    x = torch.randn(N, device='cuda', dtype=torch.float32)
    y = torch.empty(N, device='cuda', dtype=torch.float32)
    grid = lambda meta: (triton.cdiv(N, meta['BLOCK']),)
    sq_add_f32[grid](x, y, N, BLOCK=128)
    expected = x * x + x
    ok = torch.allclose(y, expected, atol=1e-5)
    print(f'Triton f32: {"PASS" if ok else "FAIL"} (max diff: {(y-expected).abs().max().item():.6f})')

def test_bf16():
    N = 1024
    x = torch.randn(N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(N, device='cuda', dtype=torch.bfloat16)
    grid = lambda meta: (triton.cdiv(N, meta['BLOCK']),)
    sq_add_bf16[grid](x, y, N, BLOCK=128)
    expected = x.float() * x.float() + x.float()
    expected = expected.bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    print(f'Triton bf16: {"PASS" if ok else "FAIL"} (max diff: {(y.float()-expected.float()).abs().max().item():.6f})')

def test_dot():
    M, N, K = 64, 64, 128
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (1,)
    dot_test[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    print(f'Triton dot bf16: {"PASS" if ok else "FAIL"} (max diff: {(y.float()-expected.float()).abs().max().item():.6f})')

if __name__ == '__main__':
    test_f32()
    test_bf16()
    test_dot()
PYEOF
uv run python test_triton_basic.py

{
  "command": "<4230 chars \u2014 see below>",
  "description": "Debug Triton dot product on SM120",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_test_v1(x_ptr, w_ptr, y_ptr, M, N, K, 
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    for k in range(0, K, BLOCK_K):
        k_remaining = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        
        x_mask = (rm[:, None] < M) & (rk[None, :] < k_remaining)
        w_mask = (rk[:, None] < k_remaining) & (rn[None, :] < N)
        
        x = tl.load(x_ptr + x_off, mask=x_mask, other=0.0)
        w = tl.load(w_ptr + w_off, mask=w_mask, other=0.0)
        acc += tl.dot(x, w)
    
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64}, num_warps=4),
    ],
    key=['M', 'N', 'K'],
)
@triton.jit
def dot_test_v2(x_ptr, w_ptr, y_ptr, M, N, K, 
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    for k in range(0, K, BLOCK_K):
        k_remaining = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        
        x_mask = (rm[:, None] < M) & (rk[None, :] < k_remaining)
        w_mask = (rk[:, None] < k_remaining) & (rn[None, :] < N)
        
        x = tl.load(x_ptr + x_off, mask=x_mask, other=0.0)
        w = tl.load(w_ptr + w_off, mask=w_mask, other=0.0)
        acc += tl.dot(x, w)
    
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with small exact sizes
def test_exact():
    M, N, K = 64, 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    
    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_M']),)
    dot_test_v2[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
    
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    max_diff = (y.float() - expected.float()).abs().max().item()
    print(f'Triton dot exact 64x64x64: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
    
    if not ok:
        print(f'y[0,:8]: {y[0,:8]}')
        print(f'exp[0,:8]: {expected[0,:8]}')

# Test with smaller blocks  
def test_smaller():
    M, N, K = 32, 32, 32
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    
    grid = (1,)
    dot_test_v1[grid](x, w, y, M, N, K, BLOCK_M=32, BLOCK_N=32, BLOCK_K=32)
    
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    max_diff = (y.float() - expected.float()).abs().max().item()
    print(f'Triton dot exact 32x32x32: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')

# Test with f32
def test_f32():
    M, N, K = 64, 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.float32)
    w = torch.randn(K, N, device='cuda', dtype=torch.float32)
    y = torch.empty(M, N, device='cuda', dtype=torch.float32)
    
    grid = (1,)
    dot_test_v1[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32)
    
    expected = x @ w
    ok = torch.allclose(y, expected, atol=1e-4)
    max_diff = (y - expected).abs()

... (230 more chars)

{
  "command": "<2735 chars \u2014 see below>",
  "description": "Debug Triton dot product with simpler tests",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_test(x_ptr, w_ptr, y_ptr, M, N, K, 
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    for k in range(0, K, BLOCK_K):
        k_remaining = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        
        x_mask = (rm[:, None] < M) & (rk[None, :] < k_remaining)
        w_mask = (rk[:, None] < k_remaining) & (rn[None, :] < N)
        
        x = tl.load(x_ptr + x_off, mask=x_mask, other=0.0)
        w = tl.load(w_ptr + w_off, mask=w_mask, other=0.0)
        acc += tl.dot(x, w)
    
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with small exact sizes, single block
def test_single():
    for M, N, K in [(16,16,16), (32,32,32), (64,64,64), (64,64,128), (128,64,128)]:
        x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
        w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
        y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
        
        grid = (1,)
        dot_test[grid](x, w, y, M, N, K, BLOCK_M=M, BLOCK_N=N, BLOCK_K=32)
        
        expected = (x.float() @ w.float()).bfloat16()
        ok = torch.allclose(y, expected, atol=0.02)
        max_diff = (y.float() - expected.float()).abs().max().item()
        print(f'Triton dot bf16 {M}x{K}x{N}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
        if not ok:
            print(f'  y[0,:4]: {y[0,:4]}')
            print(f'  exp[0,:4]: {expected[0,:4]}')
            break

# Test with f32
def test_f32():
    for M, N, K in [(16,16,16), (32,32,32), (64,64,64)]:
        x = torch.randn(M, K, device='cuda', dtype=torch.float32)
        w = torch.randn(K, N, device='cuda', dtype=torch.float32)
        y = torch.empty(M, N, device='cuda', dtype=torch.float32)
        
        grid = (1,)
        dot_test[grid](x, w, y, M, N, K, BLOCK_M=M, BLOCK_N=N, BLOCK_K=16)
        
        expected = x @ w
        ok = torch.allclose(y, expected, atol=1e-4)
        max_diff = (y - expected).abs().max().item()
        print(f'Triton dot f32 {M}x{K}x{N}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')

if __name__ == '__main__':
    print("=== bf16 tests ===")
    test_single()
    print("\n=== f32 tests ===")
    test_f32()
PYEOF
uv run python test_triton_dot.py 2>&1

{
  "command": "<2033 chars \u2014 see below>",
  "description": "Check what PTX instructions Triton generates for bf16 dot on SM120",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_ttgir.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16(x_ptr, w_ptr, y_ptr, M, N, K,
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        k_rem = K - k
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test and print compilation info
BLOCK_M, BLOCK_N, BLOCK_K = 128, 64, 64
M, N, K = 128, 64, 256
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)

# Compile and get PTX/IR
compiled = dot_bf16[(1,)](x, w, y, M, N, K, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K)

# Check if Triton has asm property
try:
    asm = compiled.asm
    # Just check a snippet for wgmma
    if 'wgmma' in asm.get('ptx', ''):
        print("USING wgmma instructions!")
    elif 'mma.sync' in asm.get('ptx', ''):
        print("Using mma.sync instructions")
    print("PTX keys:", list(asm.keys()) if isinstance(asm, dict) else type(asm))
except Exception as e:
    print(f"Could not get asm: {e}")

# Verify correctness
expected = (x.float() @ w.float()).bfloat16()
ok = torch.allclose(y, expected, atol=0.02)
print(f'Correctness: {"PASS" if ok else "FAIL"} (max diff: {(y.float()-expected.float()).abs().max().item():.6f})')
PYEOF
uv run python test_ttgir.py 2>&1

{
  "command": "<1860 chars \u2014 see below>",
  "description": "Test Triton dot with problem-relevant sizes",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16(x_ptr, w_ptr, y_ptr, M, N, K,
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with various sizes relevant to the problem
test_configs = [
    (128, 64, 2048),   # typical expert tile for headline shape
    (256, 64, 2048),
    (64, 128, 4096),
    (128, 128, 4096),
    (64, 64, 4096),
    (256, 128, 4096),
]
for M, N, K in test_configs:
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    num_m_tiles = triton.cdiv(M, 64)
    grid = (num_m_tiles,)
    dot_bf16[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=N, BLOCK_K=64)
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    max_diff = (y.float() - expected.float()).abs().max().item()
    print(f'M={M:4d} N={N:3d} K={K:4d}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
PYEOF
uv run python test_triton_dot.py 2>&1

{
  "command": "<2430 chars \u2014 see below>",
  "description": "Debug Triton dot with various K sizes",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16(x_ptr, w_ptr, y_ptr, M, N, K,
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

for K in [64, 128, 256, 512, 1024, 2048]:
    M, N = 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    dot_bf16[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    max_diff = (y.float() - expected.float()).abs().max().item()
    print(f'K={K:4d}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')

# Now check with torch.matmul as sanity
print('\nSanity check:')
M, N, K = 64, 64, 4096
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
expected = torch.mm(x.float(), w.float()).bfloat16()
triton_y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, 64),)
dot_bf16[grid](x, w, triton_y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
print(f'torch.mm: expected[0,:4] = {expected[0,:4]}')
print(f'triton:   y[0,:4] = {triton_y[0,:4]}')
print(f'Max diff: {(triton_y.float()-expected.float()).abs().max().item():.6f}')

# Let me try a manual K loop to check
print('\nManual check of first element:')
dot_prod = (x[0].float() * w[:, 0].float()).sum()
print(f'  expected = {dot_prod}')
print(f'  triton = {triton_y[0,0].float()}')
PYEOF
uv run python test_triton_dot2.py 2>&1

{
  "command": "<2146 chars \u2014 see below>",
  "description": "Test different tile configurations for correctness",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot3.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16(x_ptr, w_ptr, y_ptr, M, N, K, BLOCK_M: tl.constexpr,
             BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, NUM_WARPS: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x_mask = (rm[:, None] < M) & (rk[None, :] < k_rem)
        w_mask = (rk[:, None] < k_rem) & (rn[None, :] < N)
        x = tl.load(x_ptr + x_off, mask=x_mask, other=0.0)
        w = tl.load(w_ptr + w_off, mask=w_mask, other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

M, N, K = 256, 128, 2048
N_TILES = triton.cdiv(N, 128)
M_TILES = triton.cdiv(M, 256)
grid = (M_TILES,)

# Test different configs
configs = [
    (256, 128, 32, 8),
    (256, 128, 64, 8),
    (128, 128, 32, 4),
    (128, 128, 64, 4),
    (64, 64, 32, 4),
    (64, 64, 64, 4),
    (128, 64, 32, 4),
    (128, 64, 64, 4),
]

for BM, BN, BK, NW in configs:
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM),)
    try:
        dot_bf16[grid](x, w, y, M, N, K, BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=NW)
        expected = (x.float() @ w.float()).bfloat16()
        ok = torch.allclose(y, expected, atol=0.02)
        max_diff = (y.float() - expected.float()).abs().max().item()
        print(f'BM={BM:3d} BN={BN:3d} BK={BK:2d} NW={NW}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
    except Exception as e:
        print(f'BM={BM:3d} BN={BN:3d} BK={BK:2d} NW={NW}: ERROR: {e}')
PYEOF
uv run python test_triton_dot3.py 2>&1

{
  "command": "<1993 chars \u2014 see below>",
  "description": "Test different tile sizes with correct num_warps usage",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot3.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16(x_ptr, w_ptr, y_ptr, M, N, K,
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x_mask = (rm[:, None] < M) & (rk[None, :] < k_rem)
        w_mask = (rk[:, None] < k_rem) & (rn[None, :] < N)
        x = tl.load(x_ptr + x_off, mask=x_mask, other=0.0)
        w = tl.load(w_ptr + w_off, mask=w_mask, other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

M, N, K = 128, 64, 2048

configs = [
    (64, 64, 64, 4),
    (128, 64, 64, 4),
    (64, 128, 64, 4),
    (128, 128, 64, 8),
    (64, 64, 32, 4),
    (128, 64, 32, 4),
    (128, 64, 128, 4),
]

for BM, BN, BK, NW in configs:
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM),)
    try:
        dot_bf16[grid](x, w, y, M, N, K, BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=NW)
        expected = (x.float() @ w.float()).bfloat16()
        ok = torch.allclose(y, expected, atol=0.02)
        max_diff = (y.float() - expected.float()).abs().max().item()
        print(f'BM={BM:3d} BN={BN:3d} BK={BK:2d} NW={NW}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
    except Exception as e:
        print(f'BM={BM:3d} BN={BN:3d} BK={BK:2d} NW={NW}: ERROR: {e}')
PYEOF
uv run python test_triton_dot3.py 2>&1

{
  "command": "<4234 chars \u2014 see below>",
  "description": "Compare tl.dot vs software path vs float16",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot4.py << 'PYEOF'
import torch
import triton
import triton.language as tl

# Manual dot product using FMA (no tl.dot)
@triton.jit
def dot_manual(x_ptr, w_ptr, y_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        kr = k + tl.arange(0, BLOCK_K)
        k_rem = K - k
        x_off = rm[:, None] * K + kr[None, :]
        w_off = kr[:, None] * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (kr[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(kr[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        # Manual FMA: acc[m,n] += x[m,k] * w[k,n]
        acc += tl.dot(x, w)  # still using tl.dot
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Without tl.dot - using full software path
@triton.jit
def dot_sw(x_ptr, w_ptr, y_ptr, M, N, K,
           BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    offs_m = pid * BLOCK_M + tl.arange(0, BLOCK_M)  # (BLOCK_M,)
    offs_n = tl.arange(0, BLOCK_N)  # (BLOCK_N,)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(K):
        x = tl.load(x_ptr + offs_m[:, None] * K + k, mask=offs_m[:, None] < M).to(tl.float32)  # (BLOCK_M, 1)
        w = tl.load(w_ptr + k * N + offs_n[None, :], mask=offs_n[None, :] < N).to(tl.float32)  # (1, BLOCK_N)
        acc += x * w
    y = acc.to(tl.bfloat16)
    y_off = offs_m[:, None] * N + offs_n[None, :]
    tl.store(y_ptr + y_off, y, mask=offs_m[:, None] < M)

# Test with different sizes
M, N, K = 64, 64, 2048
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
expected = (x.float() @ w.float()).bfloat16()

# Test tl.dot version
y1 = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, 64),)
dot_manual[grid](x, w, y1, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
diff1 = (y1.float() - expected.float()).abs().max().item()
print(f'tl.dot:   max diff = {diff1:.6f}, PASS={diff1 < 0.02}')

# Test software version
y2 = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, 64),)
dot_sw[grid](x, w, y2, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=1, num_warps=4)
diff2 = (y2.float() - expected.float()).abs().max().item()
print(f'software: max diff = {diff2:.6f}, PASS={diff2 < 0.02}')

# Also test with f16
x_f16 = x.to(torch.float16)
w_f16 = w.to(torch.float16)
expected_f16 = (x_f16.float() @ w_f16.float()).to(torch.float16)

@triton.jit
def dot_f16(x_ptr, w_ptr, y_ptr, M, N, K,
            BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.float16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

y3 = torch.empty(M, N, device='cuda', dtype=torch.float16)
grid = (triton.cdiv(M, 64),)
dot_f16[grid](x_f16, w_f16, y3, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
diff3 = (y3.float() - expected_f16.float()).abs().max().item()
print(f'f16 tl.dot: max diff = {diff3:.6f}, PASS={diff3 < 0.02}')

# Also tes

... (234 more chars)

{
  "command": "<1540 chars \u2014 see below>",
  "description": "Check generated PTX for MMA instructions on SM120",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot5.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_bf16_check(x_ptr, w_ptr, y_ptr, M, N, K,
                   BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Check PTX
M, N, K = 64, 64, 64
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, 64),)
kernel = dot_bf16_check[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
asm = kernel.asm
ptx = asm['ptx']

# Find MMA-related instructions
for line in ptx.split('\n'):
    if 'mma' in line.lower() or 'wgmma' in line.lower():
        print(line.strip())
PYEOF
uv run python test_triton_dot5.py 2>&1

{
  "command": "<1826 chars \u2014 see below>",
  "description": "Test Triton dot with f32 conversion before MMA",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot6.py << 'PYEOF'
import torch
import triton
import triton.language as tl

# Try converting to f32 before dot
@triton.jit
def dot_f32_via_convert(x_ptr, w_ptr, y_ptr, M, N, K,
                        BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        # Convert to f32 explicitly
        x_f32 = x.to(tl.float32)
        w_f32 = w.to(tl.float32)
        acc += tl.dot(x_f32, w_f32)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with varying K
for K in [64, 128, 256, 512, 1024, 2048, 4096]:
    M, N = 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    dot_f32_via_convert[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    expected = (x.float() @ w.float()).bfloat16()
    ok = torch.allclose(y, expected, atol=0.02)
    max_diff = (y.float() - expected.float()).abs().max().item()
    print(f'K={K:4d}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.6f})')
PYEOF
uv run python test_triton_dot6.py 2>&1

{
  "command": "<4321 chars \u2014 see below>",
  "description": "Systematic test of Triton dot on SM120 with various configs",
  "timeout": 180000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_triton_dot7.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def dot_basic(x_ptr, w_ptr, y_ptr, M, N, K,
              BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test different BLOCK_K and num_warps for K=4096
M, N, K = 64, 64, 4096
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
expected = (x.float() @ w.float()).bfloat16()

# The key question: does error depend on num K iterations?
for BK in [16, 32, 64, 128]:
    n_iters = K // BK
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    try:
        dot_basic[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=BK, num_warps=4)
        max_diff = (y.float() - expected.float()).abs().max().item()
        print(f'BK={BK:3d} (n_iters={n_iters:3d}): max diff = {max_diff:.6f}, PASS={max_diff < 0.02}')
    except Exception as e:
        print(f'BK={BK:3d}: ERROR: {e}')

# Also test with num_warps=8 for larger tiles
@triton.jit
def dot_big(x_ptr, w_ptr, y_ptr, M, N, K,
            BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Try larger tiles with more warps
M2, N2, K2 = 128, 64, 4096
x2 = torch.randn(M2, K2, device='cuda', dtype=torch.bfloat16)
w2 = torch.randn(K2, N2, device='cuda', dtype=torch.bfloat16)
expected2 = (x2.float() @ w2.float()).bfloat16()
grid2 = (triton.cdiv(M2, 128),)
y2 = torch.empty(M2, N2, device='cuda', dtype=torch.bfloat16)
try:
    dot_big[grid2](x2, w2, y2, M2, N2, K2, BLOCK_M=128, BLOCK_N=64, BLOCK_K=64, num_warps=8)
    max_diff = (y2.float() - expected2.float()).abs().max().item()
    print(f'BM=128 BN=64 BK=64 NW=8: max diff = {max_diff:.6f}, PASS={max_diff < 0.02}')
except Exception as e:
    print(f'BM=128 BN=64 BK=64 NW=8: ERROR: {e}')

# Software path reference
@triton.jit
def dot_sw_ref(x_ptr, w_ptr, y_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
    pid = tl.program_id(0)
    offs_m = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(K):
        x = tl.load(x_ptr + offs_m[:, None] * K + k, mask=offs_m[:, None] < M)
        w = tl.load(w_ptr + k * N + offs_n[None, :], mask=offs_n[None, :] < N)
        acc += x.to(tl.float32) * w.to(tl.float32)
    y = acc.to(tl.bfloat16)
    y_off = offs_m[:, None] * N + offs_n[None, :]
    tl.store(y_ptr + y_off, y, mask=offs_m[:, None] < M)

y3 = torch.empty(M, N, device='

... (321 more chars)

{
  "command": "<1296 chars \u2014 see below>",
  "description": "Test CUDA inline compilation with load_inline",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_cuda_inline.py << 'PYEOF'
"""Test if torch.utils.cpp_extension.load_inline works for CUDA kernels."""
import torch
from torch.utils.cpp_extension import load_inline

cuda_src = """
__global__ void add_kernel(const float* a, const float* b, float* c, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}

torch::Tensor add(torch::Tensor a, torch::Tensor b) {
    auto n = a.numel();
    auto c = torch::empty_like(a);
    const int threads = 256;
    const int blocks = (n + threads - 1) / threads;
    add_kernel<<<blocks, threads>>>(a.data_ptr<float>(), b.data_ptr<float>(), c.data_ptr<float>(), n);
    return c;
}
"""

cpp_src = "torch::Tensor add(torch::Tensor a, torch::Tensor b);"

module = load_inline(
    name='test_cuda',
    cpp_sources=cpp_src,
    cuda_sources=cuda_src,
    functions=['add'],
    verbose=True,
    extra_cuda_cflags=['-arch=sm_120', '--expt-relaxed-constexpr'],
)

a = torch.randn(1024, device='cuda', dtype=torch.float32)
b = torch.randn(1024, device='cuda', dtype=torch.float32)
c = module.add(a, b)
expected = a + b
print(f'CUDA inline test: {"PASS" if torch.allclose(c, expected) else "FAIL"}')
PYEOF
uv run python test_cuda_inline.py 2>&1

{
  "command": "<3189 chars \u2014 see below>",
  "description": "Test raw MMA PTX on SM120",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_mma_ptx.py << 'PYEOF'
"""Test raw mma.sync on SM120 via inline PTX."""
import torch
from torch.utils.cpp_extension import load_inline

cuda_src = r"""
#include <cuda_bf16.h>
#include <cuda_fp16.h>

__global__ void test_mma_bf16(const __nv_bfloat16* a, const __nv_bfloat16* b, float* c) {
    // Simple test: 16x8x16 MMA
    // A: 16x16 bf16 (M=16, K=16), row-major
    // B: 16x8 bf16 (K=16, N=8), row-major
    // C: 16x8 f32 (M=16, N=8), row-major
    
    // Load A into registers (16x16 bf16 = 256 elements = 8 32-bit regs for a, 8 for b)
    uint32_t a_regs[4];  // 4x 32-bit = 8 bf16 values per lane
    uint32_t b_regs[2];  // 2x 32-bit = 4 bf16 values per lane
    
    float c_regs[4];  // accumulators, 4 per thread = 4*32 = 128 f32 values / 32 threads = 4 per thread
    
    // Load A (16x16): each thread loads 8 values of A
    int tid = threadIdx.x;
    int row_a = tid / 4;    // which 4-row group (0-3)
    int col_a = tid % 4;    // which 4-column group (0-3)
    
    // For mma.sync.aligned.m16n8k16, each thread:
    // A: holds 4 bf16 values (2 regs) from 4 rows x 4 cols of the 16xK matrix
    // Actually the exact mapping depends on the matrix layout
    
    // Simpler test: just use the instruction
    // For m16n8k16, the warp is divided into 4 groups of 8 threads
    // Each thread in group i holds element i%4 of A
    
    // Let me just use the simpler approach: inline asm with proper setup
    
    // Initialize accumulators to zero
    for (int i = 0; i < 4; i++) c_regs[i] = 0.0f;
    
    // Load A and B as the MMA expects
    // For m16n8k16 with row-major A and col-major B:
    // Thread mapping follows the CUDA PTX ISA spec
    
    // Actually let's use a different approach - test with wmma instead which is simpler
    asm volatile(
        "{\n\t"
        ".reg .b32 a0, a1, a2, a3;\n\t"
        ".reg .b32 b0, b1;\n\t"
        ".reg .f32 c0, c1, c2, c3;\n\t"
        
        // Load A: 16x16 bf16 matrix, each thread in warp loads different elements
        // Thread t loads from A at specific coordinates based on MMA layout
        "mov.b32 c0, 0;\n\t"
        "mov.b32 c1, 0;\n\t"
        "mov.b32 c2, 0;\n\t"
        "mov.b32 c3, 0;\n\t"
        
        // Load a0-a3 from A and b0-b1 from B
        // This requires understanding the exact MMA thread-to-data mapping
        "}"
    );
}

torch::Tensor test(torch::Tensor a, torch::Tensor b) {
    auto c = torch::zeros({16, 8}, torch::dtype(torch::kFloat32).device(torch::kCUDA));
    test_mma_bf16<<<1, 32>>>(
        reinterpret_cast<__nv_bfloat16*>(a.data_ptr()),
        reinterpret_cast<__nv_bfloat16*>(b.data_ptr()),
        c.data_ptr<float>()
    );
    return c;
}
"""

cpp_src = "torch::Tensor test(torch::Tensor a, torch::Tensor b);"

try:
    module = load_inline(
        name='test_mma',
        cpp_sources=cpp_src,
        cuda_sources=cuda_src,
        functions=['test'],
        verbose=True,
        extra_cuda_cflags=['-arch=sm_120', '--expt-relaxed-constexpr'],
    )
    print("Compilation successful")
except Exception as e:
    print(f"Compilation error: {e}")
PYEOF
uv run python test_mma_ptx.py 2>&1

{
  "command": "<3584 chars \u2014 see below>",
  "description": "Test single vs multi-kernel tl.dot calls",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_dot_single.py << 'PYEOF'
"""Test single tl.dot call vs multiple (same K total)."""
import torch
import triton
import triton.language as tl

@triton.jit
def dot_multi_k(x_ptr, w_ptr, y_ptr, M, N, K,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Single-shot: K=K_tile, one tl.dot call
@triton.jit
def dot_single_k(x_ptr, w_ptr, y_ptr, M, N, K,
                 BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    
    # Only use first BLOCK_K of K
    x_off = rm[:, None] * K + rk[None, :]
    w_off = rk[:, None] * N + rn[None, :]
    x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < K), other=0.0)
    w = tl.load(w_ptr + w_off, mask=(rk[:, None] < K) & (rn[None, :] < N), other=0.0)
    acc = tl.dot(x, w)  # single tl.dot call!
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with K=64 -> single tl.dot
M, N, K = 64, 64, 64
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
expected = (x.float() @ w.float()).bfloat16()

grid = (1,)
y1 = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
dot_multi_k[grid](x, w, y1, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
diff1 = (y1.float() - expected.float()).abs().max().item()

y2 = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
dot_single_k[grid](x, w, y2, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
diff2 = (y2.float() - expected.float()).abs().max().item()

print(f'K=64, single tl.dot: {diff1:.8f}')
print(f'K=64, one iter multi: {diff2:.8f}')

# Now test with K=1024, BLOCK_K=64 -> 16 tl.dot calls
K2 = 1024
x2 = torch.randn(M, K2, device='cuda', dtype=torch.bfloat16)
w2 = torch.randn(K2, N, device='cuda', dtype=torch.bfloat16)
expected2 = (x2.float() @ w2.float()).bfloat16()

y3 = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
dot_multi_k[grid](x2, w2, y3, M, N, K2, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64, num_warps=4)
diff3 = (y3.float() - expected2.float()).abs().max().item()
print(f'K=1024, 16 iter multi: {diff3:.8f}')

# Check which elements are wrong
if diff3 > 0.01:
    diff = (y3.float() - expected2.float()).abs()
    wrong = (diff > 0.01).sum().item()
    print(f'  {wrong}/{M*N} elements have diff > 0.01')
    print(f'  Wrong positions: {torch.nonzero(diff > 0.01).tolist()[:10]}')
    # Check pattern
    print(f'  Errors per row: {[(diff[i] > 0.01).sum().item() for i in range(M)]}')
    # Check if it's specific columns
    print(f'  Errors per col: {[(diff[:, i] > 0.01).sum().item() for i in range(N)]}')
PYEOF
uv run python test_dot_single.py 2>&1

{
  "command": "<3313 chars \u2014 see below>",
  "description": "Test f16 MMA and bf16-to-f16 conversion approach",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_f16_mma.py << 'PYEOF'
"""Test whether tl.dot with f16 works correctly on SM120 for large K."""
import torch
import triton
import triton.language as tl

@triton.jit
def dot_f16_mma(x_ptr, w_ptr, y_ptr, M, N, K,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.float16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with f16 for K=4096
for K in [64, 256, 1024, 4096]:
    M, N = 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.float16)
    w = torch.randn(K, N, device='cuda', dtype=torch.float16)
    y = torch.empty(M, N, device='cuda', dtype=torch.float16)
    expected = (x.float() @ w.float()).to(torch.float16)
    
    grid = (triton.cdiv(M, 64),)
    dot_f16_mma[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    max_diff = (y.float() - expected.float()).abs().max().item()
    ok = max_diff < 0.02
    print(f'f16 K={K:4d}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.8f})')

# Now test bf16->f16 conversion approach
@triton.jit
def dot_bf16_to_f16(x_ptr, w_ptr, y_ptr, M, N, K,
                    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        # Convert bf16 -> f16 then mma
        x_f16 = x.to(tl.float16)
        w_f16 = w.to(tl.float16)
        acc += tl.dot(x_f16, w_f16)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

print()
for K in [64, 256, 1024, 2048, 4096]:
    M, N = 64, 64
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    expected = (x.float() @ w.float()).bfloat16()
    
    grid = (triton.cdiv(M, 64),)
    dot_bf16_to_f16[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    max_diff = (y.float() - expected.float()).abs().max().item()
    ok = max_diff < 0.02
    print(f'bf16->f16 K={K:4d}: {"PASS" if ok else "FAIL"} (max diff: {max_diff:.8f})')
PYEOF
uv run python test_f16_mma.py 2>&1

{
  "command": "<3681 chars \u2014 see below>",
  "description": "Test if MMA errors are deterministic",
  "timeout": 60000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_mma_deterministic.py << 'PYEOF'
"""Test if MMA errors are deterministic on SM120."""
import torch
import triton
import triton.language as tl

torch.manual_seed(42)
torch.cuda.manual_seed(42)

@triton.jit
def dot_kernel(x_ptr, w_ptr, y_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

M, N, K = 64, 64, 1024
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
expected = (x.float() @ w.float()).bfloat16()

# Run 5 times with same data
print("Same data, multiple runs:")
for i in range(5):
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    dot_kernel[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    max_diff = (y.float() - expected.float()).abs().max().item()
    # Count how many elements are wrong
    wrong = ((y.float() - expected.float()).abs() > 0.01).sum().item()
    print(f'  Run {i}: max_diff={max_diff:.6f}, wrong_elements={wrong}/{M*N}')

# Try different num_warps
print("\nDifferent num_warps:")
for nw in [4, 8]:
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    try:
        dot_kernel[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=nw)
        max_diff = (y.float() - expected.float()).abs().max().item()
        wrong = ((y.float() - expected.float()).abs() > 0.01).sum().item()
        print(f'  num_warps={nw}: max_diff={max_diff:.6f}, wrong_elements={wrong}/{M*N}')
    except Exception as e:
        print(f'  num_warps={nw}: ERROR: {e}')

# Try with smaller BLOCK_M
print("\nSmaller BLOCK_M=32:")
@triton.jit
def dot_small(x_ptr, w_ptr, y_ptr, M, N, K,
              BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, 32),)
dot_small[grid](x, w, y, M, N, K, BLOCK_M=32, BLOCK_N=64, BLOCK_K=32, num_warps=4)
max_diff = (y.float() - expected.float()).abs().max().item()
wrong = ((y.float() - expected.float()).abs() > 0.01).sum().item()
print(f'  max_diff={max_diff:.6f}, wrong_elements={wrong}/{M*N}')
PYEOF
uv run python test_mma_deterministic.py 2>&1

{
  "command": "<2620 chars \u2014 see below>",
  "description": "Test if MMA errors depend on data values",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_mma_data_dep.py << 'PYEOF'
"""Test if MMA errors depend on data values on SM120."""
import torch
import triton
import triton.language as tl

@triton.jit
def dot_kernel(x_ptr, w_ptr, y_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

M, N, K = 64, 64, 2048

for seed in [0, 1, 42, 123, 456, 1000, 9999]:
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.1
    expected = (x.float() @ w.float()).bfloat16()
    
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    dot_kernel[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    max_diff = (y.float() - expected.float()).abs().max().item()
    wrong = ((y.float() - expected.float()).abs() > 0.01).sum().item()
    print(f'  seed={seed:4d}: max_diff={max_diff:.8f}, wrong={wrong}/{M*N}')

# Also test with very small values vs large values
print("\nValue scale test:")
for scale in [0.001, 0.01, 0.1, 1.0, 10.0]:
    torch.manual_seed(42)
    x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * scale
    w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * scale
    expected = (x.float() @ w.float()).bfloat16()
    
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    dot_kernel[grid](x, w, y, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)
    max_diff = (y.float() - expected.float()).abs().max().item()
    wrong = ((y.float() - expected.float()).abs() > 0.01).sum().item()
    ok = torch.allclose(y, expected, atol=0.02)
    print(f'  scale={scale:6.3f}: max_diff={max_diff:.8f}, wrong={wrong}/{M*N}, {"PASS" if ok else "FAIL"}')
PYEOF
uv run python test_mma_data_dep.py 2>&1

{
  "command": "<3641 chars \u2014 see below>",
  "description": "Test tl.dot with real problem data distribution",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/basic_triton_test.py << 'PYEOF'
"""Quick check: does tl.dot produce correct results for real problem data?"""
import torch
import triton
import triton.language as tl

# Simulate problem data
H, I, E, K, T_total = 4096, 1536, 128, 8, 32768
T_perm = T_total * K  # 262144

torch.manual_seed(42)
hidden_states = torch.randn(T_perm, H, dtype=torch.bfloat16) * 0.1
W_gate = torch.randn(E, H, I, dtype=torch.bfloat16) * 0.02
W_up = torch.randn(E, H, I, dtype=torch.bfloat16) * 0.02

# Generate expert offsets
base = T_perm // E
rem = T_perm - base * E
counts = torch.full((E,), base, dtype=torch.int32)
counts[:rem] += 1
offsets = torch.zeros(E + 1, dtype=torch.int32)
offsets[1:] = torch.cumsum(counts, dim=0)

# Test one expert with largest token count
max_count = counts.max().item()
max_e = counts.argmax().item()
print(f"Expert {max_e} has {max_count} tokens")

# Check: does a simple Triton GEMM for one expert match torch.mm?
start = offsets[max_e].item()
end = offsets[max_e + 1].item()
x_e = hidden_states[start:end].cuda()  # (n_e, H)
w_g = W_gate[max_e].cuda()  # (H, I)
w_u = W_up[max_e].cuda()  # (H, I)

n_e = end - start
print(f"Problem size: M={n_e}, K={H}, N={I}")

ref_gate = (x_e.float() @ w_g.float()).bfloat16()
ref_up = (x_e.float() @ w_u.float()).bfloat16()
ref_out = (torch.nn.functional.silu(ref_gate.float()) * ref_up.float()).bfloat16()

@triton.jit
def gemm_kernel(x_ptr, w_ptr, y_ptr, M, N, K,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_rem = K - k
        x_off = rm[:, None] * K + (k + rk[None, :])
        w_off = (k + rk[:, None]) * N + rn[None, :]
        x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
        w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
        acc += tl.dot(x, w)
    y = acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test gate GEMM only
M, N, Kdim = n_e, I, H
y_triton = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
grid = (triton.cdiv(M, 64),)
gemm_kernel[grid](x_e.cuda(), w_g, y_triton, M, N, Kdim, 
                  BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)

diff = (y_triton.float() - ref_gate.float()).abs()
max_diff = diff.max().item()
wrong = (diff > 0.02).sum().item()
total = diff.numel()
print(f"Gate GEMM: max_diff={max_diff:.6f}, wrong={wrong}/{total} ({100*wrong/total:.2f}%)")
# Also check mean of reference values
print(f"  Reference value range: [{ref_gate.float().min():.3f}, {ref_gate.float().max():.3f}]")
print(f"  Reference value mean abs: {ref_gate.float().abs().mean():.3f}")

# Also test with a smaller slice (first 512 rows only)
M_small = min(512, n_e)
y_triton_small = torch.empty(M_small, N, dtype=torch.bfloat16, device='cuda')
grid_small = (triton.cdiv(M_small, 64),)
gemm_kernel[grid_small](x_e[:M_small].cuda(), w_g, y_triton_small, M_small, N, Kdim,
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=32, num_warps=4)

diff_small = (y_triton_small.float() - ref_gate[:M_small].float()).abs()
max_diff_small = diff_small.max().item()
wrong_small = (diff_small > 0.02).sum().item()
total_small = diff_small.numel()
print(f"Gate GEMM (512 rows): max_diff={max_diff_small:.6f}, wrong={wrong_small}/{total_small}")
PYEOF
uv run python basic_triton_test.py 2>&1

{
  "command": "<3570 chars \u2014 see below>",
  "description": "Test accumulator reset workaround",
  "timeout": 180000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_acc_reset.py << 'PYEOF'
"""Test if resetting accumulators periodically helps."""
import torch
import triton
import triton.language as tl

torch.manual_seed(42)
torch.cuda.manual_seed(42)

@triton.jit
def dot_reset_acc(x_ptr, w_ptr, y_ptr, M, N, K,
                  BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                  RESET_EVERY: tl.constexpr):
    """Reset accumulators every RESET_EVERY K iterations."""
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    
    # Final accumulator in f32 (software)
    final_acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    # Process K in chunks, resetting MMA accumulators each chunk
    for k_outer in range(0, K, BLOCK_K * RESET_EVERY):
        mma_acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
        k_end = min(k_outer + BLOCK_K * RESET_EVERY, K)
        for k in range(k_outer, k_end, BLOCK_K):
            k_rem = K - k
            rk = tl.arange(0, BLOCK_K)
            x_off = rm[:, None] * K + (k + rk[None, :])
            w_off = (k + rk[:, None]) * N + rn[None, :]
            x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
            w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
            mma_acc += tl.dot(x, w)
        # Add chunk result to final accumulator using software addition
        final_acc += mma_acc.to(tl.float32)
    
    y = final_acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

# Test with problem sizes
M, N, K = 64, 64, 4096
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
expected = (x.float() @ w.float()).bfloat16()

for reset in [1, 2, 4, 8, 16, 32, 64, 128]:
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    try:
        dot_reset_acc[grid](x, w, y, M, N, K,
                           BLOCK_M=64, BLOCK_N=64, BLOCK_K=32,
                           RESET_EVERY=reset, num_warps=4)
        max_diff = (y.float() - expected.float()).abs().max().item()
        wrong = ((y.float() - expected.float()).abs() > 0.02).sum().item()
        total = M * N
        print(f'reset_every={reset:3d} (MMA accs per reset={reset}): max_diff={max_diff:.6f}, wrong={wrong}/{total} {"PASS" if wrong==0 else "FAIL"}')
    except Exception as e:
        print(f'reset_every={reset:3d}: ERROR: {e}')

# Now test with full problem-like dimensions
print("\n--- Full expert test (M=2048, N=1536, K=4096) ---")
M2, N2, K2 = 512, 256, 4096  # use a subset for speed
x2 = torch.randn(M2, K2, device='cuda', dtype=torch.bfloat16) * 0.1
w2 = torch.randn(K2, N2, device='cuda', dtype=torch.bfloat16) * 0.02
expected2 = (x2.float() @ w2.float()).bfloat16()

for reset in [2, 4, 8]:
    y2 = torch.empty(M2, N2, device='cuda', dtype=torch.bfloat16)
    grid2 = (triton.cdiv(M2, 64),)
    dot_reset_acc[grid2](x2, w2, y2, M2, N2, K2,
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=32,
                        RESET_EVERY=reset, num_warps=4)
    max_diff2 = (y2.float() - expected2.float()).abs().max().item()
    wrong2 = ((y2.float() - expected2.float()).abs() > 0.02).sum().item()
    total2 = M2 * N2
    print(f'reset_every={reset:3d}: max_diff={max_diff2:.6f}, wrong={wrong2}/{total2} {"PASS" if wrong2==0 else "FAIL"}')
PYEOF
uv run python test_acc_reset.py 2>&1

{
  "command": "<4100 chars \u2014 see below>",
  "description": "Find safe RESET_EVERY settings for various BLOCK_K",
  "timeout": 180000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_reset_config.py << 'PYEOF'
"""Find good RESET_EVERY configs for various BLOCK_K."""
import torch
import triton
import triton.language as tl

torch.manual_seed(42)
torch.cuda.manual_seed(42)

@triton.jit
def dot_reset_acc(x_ptr, w_ptr, y_ptr, M, N, K,
                  BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                  RESET_EVERY: tl.constexpr):
    pid = tl.program_id(0)
    rm = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = tl.arange(0, BLOCK_N)
    
    final_acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    for k_outer in range(0, K, BLOCK_K * RESET_EVERY):
        mma_acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
        k_end = min(k_outer + BLOCK_K * RESET_EVERY, K)
        for k in range(k_outer, k_end, BLOCK_K):
            k_rem = K - k
            rk = tl.arange(0, BLOCK_K)
            x_off = rm[:, None] * K + (k + rk[None, :])
            w_off = (k + rk[:, None]) * N + rn[None, :]
            x = tl.load(x_ptr + x_off, mask=(rm[:, None] < M) & (rk[None, :] < k_rem), other=0.0)
            w = tl.load(w_ptr + w_off, mask=(rk[:, None] < k_rem) & (rn[None, :] < N), other=0.0)
            mma_acc += tl.dot(x, w)
        final_acc += mma_acc
    
    y = final_acc.to(tl.bfloat16)
    y_off = rm[:, None] * N + rn[None, :]
    tl.store(y_ptr + y_off, y, mask=rm[:, None] < M)

M, N, K = 64, 64, 4096
x = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
w = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
expected = (x.float() @ w.float()).bfloat16()

print("BLOCK_K=64:")
for reset in [1, 2, 4, 8, 16, 32]:
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    try:
        dot_reset_acc[grid](x, w, y, M, N, K,
                           BLOCK_M=64, BLOCK_N=64, BLOCK_K=64,
                           RESET_EVERY=reset, num_warps=4)
        max_diff = (y.float() - expected.float()).abs().max().item()
        wrong = ((y.float() - expected.float()).abs() > 0.02).sum().item()
        print(f'  reset_every={reset:2d} (K_per_acc={reset*64}): max_diff={max_diff:.8f}, wrong={wrong}/4096 {"PASS" if wrong==0 else "FAIL"}')
    except Exception as e:
        print(f'  reset_every={reset:2d}: ERROR: {e}')

print("BLOCK_K=128:")
for reset in [1, 2, 4, 8, 16]:
    y = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    grid = (triton.cdiv(M, 64),)
    try:
        dot_reset_acc[grid](x, w, y, M, N, K,
                           BLOCK_M=64, BLOCK_N=64, BLOCK_K=128,
                           RESET_EVERY=reset, num_warps=8)
        max_diff = (y.float() - expected.float()).abs().max().item()
        wrong = ((y.float() - expected.float()).abs() > 0.02).sum().item()
        print(f'  reset_every={reset:2d} (K_per_acc={reset*128}): max_diff={max_diff:.8f}, wrong={wrong}/4096 {"PASS" if wrong==0 else "FAIL"}')
    except Exception as e:
        print(f'  reset_every={reset:2d}: ERROR: {e}')

# Test with larger M (128) which requires BLOCK_N=128 and num_warps=8
print("\nBLOCK_M=128, BLOCK_N=128, BLOCK_K=32, num_warps=8:")
M2, N2, K2 = 128, 128, 4096
x2 = torch.randn(M2, K2, device='cuda', dtype=torch.bfloat16) * 0.1
w2 = torch.randn(K2, N2, device='cuda', dtype=torch.bfloat16) * 0.02
expected2 = (x2.float() @ w2.float()).bfloat16()

for reset in [2, 4, 8, 16]:
    y2 = torch.empty(M2, N2, device='cuda', dtype=torch.bfloat16)
    grid2 = (triton.cdiv(M2, 128),)
    try:
        dot_reset_acc[grid2](x2, w2, y2, M2, N2, K2,
                            BLOCK_M=128, BLOCK_N=128, BLOCK_K=32,
                            RESET_EVERY=reset, num_warps=8)
        max_diff = (y2.float() - expected2.float()).abs().max().item()
        wrong = ((y2.float() - expected2.float()).abs() > 0.02).sum().item()
        total = M2 * N2
        print(f'  reset_every={reset:2d}: max_diff={max_diff:.8f}, wrong={wrong}/{total} {"PASS" if wrong==0 else "FAIL"}')
    except Exception as e:
 

... (100 more chars)

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

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<13139 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<9419 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Run correctness check",
  "timeout": 300000
}

{
  "command": "uv run python benchmark.py 2>&1",
  "description": "Run performance benchmark",
  "timeout": 600000
}

{
  "activeForm": "Optimizing kernel performance",
  "description": "Improve TFLOPS from ~34 to >50. Key ideas: reduce grid blocks by looping N internally, increase BLOCK_K, reduce register pressure, tune tile sizes per shape.",
  "subject": "Optimize kernel for >0.1 peak fraction"
}

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

{
  "command": "<1381 chars \u2014 see below>",
  "description": "Profile kernel to identify bottlenecks",
  "timeout": 300000
}

cat > /workspace/problems/06_sonic_moe_swiglu/profile_kernel.py << 'PYEOF'
"""Quick profile of the kernel to identify bottlenecks."""
import torch
import sys
sys.path.insert(0, '.')
import solution

# Headline shape
solution.T_total = 32768
solution.H = 4096
solution.I = 1536
solution.E = 128
solution.K = 8

device = torch.device("cuda:0")
init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]

# Warm up
with torch.no_grad():
    for _ in range(3):
        model(*inputs)
torch.cuda.synchronize()

# Profile with torch.profiler
with torch.profiler.profile(
    activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
    record_shapes=True,
    with_stack=True,
) as prof:
    with torch.no_grad():
        out = model(*inputs)
    torch.cuda.synchronize()

print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))

# Also print kernel specific info
for evt in prof.key_averages():
    if 'triton' in evt.key.lower() or 'kernel' in evt.key.lower():
        print(f"  {evt.key}: cuda_time={evt.cuda_time_total/1e3:.2f}us, count={evt.count}")

# Check occupancy estimate
print(f"\nGPU: {torch.cuda.get_device_name(0)}")
print(f"SMs: {torch.cuda.get_device_properties(0).multi_processor_count}")
PYEOF
uv run python profile_kernel.py 2>&1

{
  "command": "<158 chars \u2014 see below>",
  "description": "Find ncu profiler"
}

which ncu 2>&1 || echo "ncu not found in PATH"
find /usr/local/cuda* -name "ncu" -type f 2>/dev/null | head -5
dpkg -l | grep -i nsight 2>/dev/null | head -5

{
  "command": "<1717 chars \u2014 see below>",
  "description": "Profile kernel with ncu for occupancy and throughput metrics",
  "timeout": 300000
}

cat > /workspace/problems/06_sonic_moe_swiglu/profile_ncu.py << 'PYEOF'
"""Run the kernel for ncu profiling."""
import torch
import sys
sys.path.insert(0, '.')
import solution

# Headline shape
solution.T_total = 32768
solution.H = 4096
solution.I = 1536
solution.E = 128
solution.K = 8

device = torch.device("cuda:0")
init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]

# Warm up
with torch.no_grad():
    model(*inputs)
torch.cuda.synchronize()

# Run once more for profiling
with torch.no_grad():
    out = model(*inputs)
torch.cuda.synchronize()
PYEOF

# Profile with ncu for kernel metrics
ncu --kernel-name _grouped_gemm_swiglu --launch-skip 0 --launch-count 1 \
    --set full \
    --metrics \
    sm__warps_active.avg.pct_of_peak_sustained_active,\
sm__maximum_warps_per_active_cycle,\
sm__throughput.avg.pct_of_peak_sustained_elapsed,\
dram__throughput.avg.pct_of_peak_sustained_elapsed,\
l1tex__throughput.avg.pct_of_peak_sustained_elapsed,\
lts__throughput.avg.pct_of_peak_sustained_elapsed,\
sm__pipe_tensor_cycles_active.avg.pct_of_peak_sustained_elapsed,\
sm__pipe_fma_cycles_active.avg.pct_of_peak_sustained_elapsed,\
sm__inst_executed.avg.pct_of_peak_sustained_elapsed,\
launch__occupancy_limit_registers,\
launch__occupancy_limit_shared_mem,\
launch__occupancy_per_register_count,\
launch__occupancy_per_shared_mem_size,\
sm__warps_active.avg.per_cycle_active,\
sm__sass_inst_executed.avg.per_cycle_active,\
sm__sass_inst_executed_op_integer.avg.pct_of_peak_sustained_elapsed,\
gpu__compute_memory_access_throughput.avg.pct_of_peak_sustained_elapsed \
    uv run python profile_ncu.py 2>&1 | head -100

{
  "command": "<678 chars \u2014 see below>",
  "description": "Profile kernel with corrected name filter",
  "timeout": 300000
}

ncu --kernel-name _grouped_gemm_swiglu_kernel --launch-skip 0 --launch-count 1 \
    --set full \
    --metrics \
    sm__warps_active.avg.pct_of_peak_sustained_active,\
sm__throughput.avg.pct_of_peak_sustained_elapsed,\
dram__throughput.avg.pct_of_peak_sustained_elapsed,\
l1tex__throughput.avg.pct_of_peak_sustained_elapsed,\
sm__pipe_tensor_cycles_active.avg.pct_of_peak_sustained_elapsed,\
launch__occupancy_limit_registers,\
launch__occupancy_limit_shared_mem,\
launch__occupancy_per_register_count,\
launch__occupancy_per_shared_mem_size \
    uv run python profile_ncu.py 2>&1 | grep -E "kernel:|_grouped|metric|occupancy|throughput|tensor|warps_active|registers|shared"

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<11123 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Verify correctness of sequential approach",
  "timeout": 300000
}

{
  "command": "uv run python benchmark.py 2>&1",
  "description": "Benchmark sequential approach",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<11290 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Verify combined weight approach",
  "timeout": 300000
}

{
  "command": "<3397 chars \u2014 see below>",
  "description": "Debug combined weight approach",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_combined.py << 'PYEOF'
"""Debug the combined weight approach."""
import torch
import sys
sys.path.insert(0, '.')
import solution

# Small test
solution.T_total = 128
solution.H = 256
solution.I = 128
solution.E = 8
solution.K = 2

device = torch.device("cuda:0")
torch.manual_seed(42)
torch.cuda.manual_seed(42)

init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]

hidden_states, expert_offsets = inputs

# Compute reference
import reference
reference.T_total = 128
reference.H = 256
reference.I = 128
reference.E = 8
reference.K = 2

ref_init = reference.get_init_inputs()
ref_model = reference.Model(*ref_init).to(device).eval()
ref_model.load_state_dict(model.state_dict())
with torch.no_grad():
    ref_out = ref_model(*inputs)

# Compute solution
with torch.no_grad():
    sol_out = model(*inputs)

diff = (sol_out.float() - ref_out.float()).abs()
max_diff = diff.max().item()
wrong = (diff > 0.02).sum().item()
total = diff.numel()
print(f"Combined approach: max_diff={max_diff:.6f}, wrong={wrong}/{total}")

if wrong > 0:
    # Find some wrong elements
    wrong_indices = torch.nonzero(diff > 0.02)
    for idx in wrong_indices[:5]:
        i, j = idx.tolist()
        print(f"  [{i},{j}]: ref={ref_out[i,j].float():.6f} sol={sol_out[i,j].float():.6f} diff={diff[i,j]:.6f}")
        # Check gate and up separately
        e_found = None
        for e in range(8):
            s = int(expert_offsets[e].item())
            e2 = int(expert_offsets[e+1].item())
            if s <= i < e2:
                e_found = e
                break
        print(f"    expert={e_found}, row_in_expert={i - int(expert_offsets[e_found].item())}")
        
        # Check W_gate and W_up values
        if e_found is not None:
            x_e = hidden_states[i:i+1]
            wg = model.W_gate[e_found]
            wu = model.W_up[e_found]
            gate_ref = (x_e.float() @ wg.float())[0, j]
            up_ref = (x_e.float() @ wu.float())[0, j]
            import torch.nn.functional as F
            expected = (F.silu(gate_ref) * up_ref).item()
            print(f"    gate_ref={gate_ref:.6f} up_ref={up_ref:.6f} expected={expected:.6f}")

# Also test just the GEMM part (intermediate combined tensor)
W_combined = torch.cat([model.W_gate, model.W_up], dim=-1)
I_combined = 2 * model.I
combined_ref = (hidden_states.float() @ W_combined.float().view(model.E, model.H, I_combined).transpose(1,2)[0].t()).bfloat16()
# Actually, reference is per-expert, not one big gemm
# Let me verify the combined gemm produces correct intermediate values

# Manual check: compute combined for one expert
e0_start = int(expert_offsets[0].item())
e0_end = int(expert_offsets[1].item())
x0 = hidden_states[e0_start:e0_end]
combined_ref_e0 = (x0.float() @ W_combined[0].float()).bfloat16()
# Split and fuse
gate_ref_e0 = combined_ref_e0[:, :128]
up_ref_e0 = combined_ref_e0[:, 128:]
expected_e0 = (torch.nn.functional.silu(gate_ref_e0.float()) * up_ref_e0.float()).bfloat16()

# Compare with solution output for expert 0
sol_e0 = sol_out[e0_start:e0_end]
diff_e0 = (sol_e0.float() - expected_e0.float()).abs()
print(f"\nExpert 0: max_diff={diff_e0.max().item():.6f}, wrong={(diff_e0>0.02).sum().item()}/{diff_e0.numel()}")
PYEOF
uv run python debug_combined.py 2>&1

{
  "command": "<2975 chars \u2014 see below>",
  "description": "Debug headline shape failure",
  "timeout": 300000
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_headline.py << 'PYEOF'
"""Debug the headline shape failure."""
import torch
import sys
sys.path.insert(0, '.')
import solution

# Headline shape 
solution.T_total = 32768
solution.H = 4096
solution.I = 1536
solution.E = 128
solution.K = 8

device = torch.device("cuda:0")
torch.manual_seed(42)
torch.cuda.manual_seed(42)

init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]
hidden_states, expert_offsets = inputs

# Reference
import reference
reference.T_total = 32768
reference.H = 4096
reference.I = 1536
reference.E = 128
reference.K = 8
ref_init = reference.get_init_inputs()
ref_model = reference.Model(*ref_init).to(device).eval()
ref_model.load_state_dict(model.state_dict())

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

diff = (sol_out.float() - ref_out.float()).abs()
max_diff = diff.max().item()
wrong = (diff > 0.02).sum().item()
total = diff.numel()
print(f"Headline: max_diff={max_diff:.6f}, wrong={wrong}/{total} ({100*wrong/total:.2f}%)")

if wrong > 0:
    # Check first expert
    e0_start = int(expert_offsets[0].item())
    e0_end = int(expert_offsets[1].item())
    print(f"Expert 0: tokens {e0_start}:{e0_end} ({e0_end-e0_start} tokens)")
    diff_e0 = diff[e0_start:e0_end]
    wrong_e0 = (diff_e0 > 0.02).sum().item()
    print(f"  wrong in expert 0: {wrong_e0}/{diff_e0.numel()}")
    
    # Check last expert
    e_last = 127
    e_start = int(expert_offsets[e_last].item())
    e_end = int(expert_offsets[e_last+1].item())
    print(f"Expert {e_last}: tokens {e_start}:{e_end} ({e_end-e_start} tokens)")
    diff_elast = diff[e_start:e_end]
    wrong_elast = (diff_elast > 0.02).sum().item()
    print(f"  wrong in expert {e_last}: {wrong_elast}/{diff_elast.numel()}")

    # Check the combined intermediate
    W_combined = torch.cat([model.W_gate, model.W_up], dim=-1)
    # Compute reference combined for expert 0
    x0 = hidden_states[e0_start:e0_end]
    combined_ref = (x0.float() @ W_combined[0].float()).bfloat16()
    # Check what the solution computed for expert 0
    combined_sol = torch.empty(e0_end-e0_start, 2*solution.I, dtype=torch.bfloat16, device=device)
    
    # Let's check the striding
    print(f"\nModel.W_gate shape: {model.W_gate.shape}, strides: {model.W_gate.stride()}")
    print(f"Model.W_up shape: {model.W_up.shape}, strides: {model.W_up.stride()}")
    print(f"W_combined shape: {W_combined.shape}, strides: {W_combined.stride()}")
    print(f"W_combined.is_contiguous(): {W_combined.is_contiguous()}")
    
    # Verify cat is correct
    ok_gate = torch.allclose(W_combined[0, :, :solution.I], model.W_gate[0])
    ok_up = torch.allclose(W_combined[0, :, solution.I:], model.W_up[0])
    print(f"cat gate section matches: {ok_gate}")
    print(f"cat up section matches: {ok_up}")
PYEOF
uv run python debug_headline.py 2>&1

{
  "command": "<4763 chars \u2014 see below>",
  "description": "Debug GEMM vs fusion for headline shape",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_headline2.py << 'PYEOF'
"""Test GEMM part vs fusion part separately for headline shape."""
import torch
import triton
import triton.language as tl
import sys
sys.path.insert(0, '.')
import solution

solution.T_total = 32768
solution.H = 4096
solution.I = 1536
solution.E = 128
solution.K = 8

device = torch.device("cuda:0")
torch.manual_seed(42)
torch.cuda.manual_seed(42)

init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]
hidden_states, expert_offsets = inputs

# Compute the combined GEMM reference
W_combined = torch.cat([model.W_gate, model.W_up], dim=-1).contiguous()
I_combined = 2 * solution.I

# Compute combined reference per-expert
combined_ref = torch.empty(hidden_states.shape[0], I_combined, dtype=torch.bfloat16, device=device)
for e in range(solution.E):
    start = int(expert_offsets[e].item())
    end = int(expert_offsets[e + 1].item())
    if end > start:
        x_e = hidden_states[start:end]
        w_e = W_combined[e]
        combined_ref[start:end] = (x_e.float() @ w_e.float()).bfloat16()

# Now compute with our triton kernel (just the GEMM part)
T_perm = hidden_states.shape[0]
H_dim = solution.H
E_dim = solution.E

BLOCK_M, BLOCK_N, BLOCK_K = 64, 256, 64
NUM_WARPS = 4
RESET_EVERY = 8

t2e, trs, tnr, num_m_tiles = solution._build_tile_map(expert_offsets, E_dim, BLOCK_M, device)

n_ntiles = (I_combined + BLOCK_N - 1) // BLOCK_N
grid = (num_m_tiles * n_ntiles,)

stride_hs_m = H_dim
stride_hs_k = 1
stride_w_e = H_dim * I_combined
stride_w_k = I_combined
stride_w_n = 1
stride_out_m = I_combined
stride_out_n = 1

combined_triton = torch.empty(T_perm, I_combined, dtype=torch.bfloat16, device=device)

solution._grouped_gemm_kernel[grid](
    hidden_states, W_combined, combined_triton,
    t2e, trs, tnr,
    H_dim, I_combined,
    stride_hs_m, stride_hs_k,
    stride_w_e, stride_w_k, stride_w_n,
    stride_out_m, stride_out_n,
    n_ntiles,
    BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
    RESET_EVERY=RESET_EVERY,
    num_warps=NUM_WARPS,
)

# Compare GEMM output
diff_gemm = (combined_triton.float() - combined_ref.float()).abs()
max_diff_gemm = diff_gemm.max().item()
wrong_gemm = (diff_gemm > 0.02).sum().item()
total_gemm = diff_gemm.numel()
print(f"GEMM only: max_diff={max_diff_gemm:.6f}, wrong={wrong_gemm}/{total_gemm} ({100*wrong_gemm/total_gemm:.2f}%)")

# Now test the fusion kernel
# Use reference combined for fusion
I_dim = solution.I
out_ref = torch.empty(T_perm, I_dim, dtype=torch.bfloat16, device=device)
import torch.nn.functional as F
for e in range(solution.E):
    start = int(expert_offsets[e].item())
    end = int(expert_offsets[e + 1].item())
    if end > start:
        gate_ref = combined_ref[start:end, :I_dim]
        up_ref = combined_ref[start:end, I_dim:]
        out_ref[start:end] = (F.silu(gate_ref.float()) * up_ref.float()).bfloat16()

# Fusion with triton combined output  
out_triton = torch.empty(T_perm, I_dim, dtype=torch.bfloat16, device=device)
fuse_grid = (triton.cdiv(T_perm, 128),)
solution._swiglu_split_fuse_kernel[fuse_grid](
    combined_triton, out_triton,
    I_dim,
    I_combined, 1, I_dim, 1,
    T_perm,
    BLOCK_M=128, BLOCK_N=64,
)

diff_fuse = (out_triton.float() - out_ref.float()).abs()
max_diff_fuse = diff_fuse.max().item()
wrong_fuse = (diff_fuse > 0.02).sum().item()
total_fuse = diff_fuse.numel()
print(f"Fusion on triton GEMM: max_diff={max_diff_fuse:.6f}, wrong={wrong_fuse}/{total_fuse} ({100*wrong_fuse/total_fuse:.2f}%)")

# Also test fusion on REFERENCE combined
out_triton2 = torch.empty(T_perm, I_dim, dtype=torch.bfloat16, device=device)
solution._swiglu_split_fuse_kernel[fuse_grid](
    combined_ref, out_triton2,
    I_dim,
    I_combined, 1, I_dim, 1,
    T_perm,
    BLOCK_M=128, BLOCK_N=64,
)
diff_fuse2 = (out_triton2.float() - out_ref.float()).abs()
max_diff_fuse2 = diff_fuse2.max().item()
wrong_fuse2 = (diff_fuse2

... (763 more chars)

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "offset": 94,
  "limit": 60
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1453 chars \u2014 see diff>",
  "new_string": "<1512 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<559 chars \u2014 see diff>",
  "new_string": "<627 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Verify fixed fusion kernel",
  "timeout": 300000
}

{
  "command": "uv run python benchmark.py 2>&1",
  "description": "Benchmark fixed combined approach",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<9955 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Check correctness and benchmark optimized dual-accumulator",
  "timeout": 600000
}

{
  "command": "<1292 chars \u2014 see below>",
  "description": "Profile new config with ncu",
  "timeout": 300000
}

cat > /workspace/problems/06_sonic_moe_swiglu/profile_ncu2.py << 'PYEOF'
"""Run the kernel for ncu profiling with the new config."""
import torch
import sys
sys.path.insert(0, '.')
import solution
solution.T_total = 32768
solution.H = 4096
solution.I = 1536
solution.E = 128
solution.K = 8
device = torch.device("cuda:0")
init_args = solution.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]
with torch.no_grad():
    model(*inputs)
torch.cuda.synchronize()
with torch.no_grad():
    out = model(*inputs)
torch.cuda.synchronize()
PYEOF

ncu --kernel-name _grouped_gemm_swiglu_kernel --launch-skip 1 --launch-count 1 \
    --set full \
    --metrics \
    sm__warps_active.avg.pct_of_peak_sustained_active,\
sm__throughput.avg.pct_of_peak_sustained_elapsed,\
dram__throughput.avg.pct_of_peak_sustained_elapsed,\
l1tex__throughput.avg.pct_of_peak_sustained_elapsed,\
sm__pipe_tensor_cycles_active.avg.pct_of_peak_sustained_elapsed,\
launch__occupancy_limit_registers,\
launch__occupancy_per_register_count,\
launch__occupancy_per_shared_mem_size,\
launch__occupancy_per_register_file \
    uv run python profile_ncu2.py 2>&1 | grep -A5 "kernel\|metric\|occupancy\|throughput\|warps_active\|registers\|shared\|tensor"

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<11952 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<633 chars \u2014 see diff>",
  "new_string": "<585 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Check correctness of sequential approach",
  "timeout": 300000
}

{
  "command": "uv run python benchmark.py 2>&1",
  "description": "Benchmark sequential single-accumulator approach",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<9883 chars \u2014 see diff>"
}

{
  "command": "<379 chars \u2014 see below>",
  "description": "Check peak TFLOPS values for RTX PRO 6000",
  "timeout": 30000
}

uv run python -c "
import sys
sys.path.insert(0, '/workspace/problems/06_sonic_moe_swiglu/../..')
from src.hardware import get as get_hw
hw = get_hw('RTX_PRO_6000')
print('peak_tflops_dense keys:', list(hw.peak_tflops_dense.keys()))
for k, v in hw.peak_tflops_dense.items():
    print(f'  {k}: {v} TFLOPS')
print()
print('peak_bandwidth_gb_s:', hw.peak_bandwidth_gb_s, 'GB/s')
"

{
  "command": "<1141 chars \u2014 see below>",
  "description": "Profile sequential single-accumulator kernel occupancy",
  "timeout": 300000
}

cat > /workspace/problems/06_sonic_moe_swiglu/profile_sequential.py << 'PYEOF'
"""Profile the sequential (single-accumulator) kernel for occupancy."""
import torch, sys
sys.path.insert(0, '.')
import solution
solution.T_total = 32768; solution.H = 4096; solution.I = 1536; solution.E = 128; solution.K = 8
device = torch.device("cuda:0")
model = solution.Model(*solution.get_init_inputs()).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]
# warmup
with torch.no_grad(): model(*inputs); torch.cuda.synchronize()
with torch.no_grad(): out = model(*inputs); torch.cuda.synchronize()
PYEOF

echo "=== Gemm kernel ===" 
ncu --kernel-name _gemm_kernel --launch-skip 1 --launch-count 1 \
    --metrics \
    sm__warps_active.avg.pct_of_peak_sustained_active,\
launch__occupancy_limit_registers,\
launch__occupancy_per_register_count,\
launch__occupancy_per_shared_mem_size,\
sm__pipe_tensor_cycles_active.avg.pct_of_peak_sustained_elapsed,\
sm__throughput.avg.pct_of_peak_sustained_elapsed \
    uv run python profile_sequential.py 2>&1 | grep -E "kernel|warps_active|occupancy|tensor|throughput|register|shared" | head -20

{
  "command": "<2451 chars \u2014 see below>",
  "description": "Test CUDA wmma on SM120",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_wmma.cu << 'CUEOF'
#include <cuda_bf16.h>
#include <mma.h>
#include <cuda_fp16.h>

using namespace nvcuda;

__global__ void wmma_bf16_test(
    const __nv_bfloat16* a, const __nv_bfloat16* b, float* c, int K)
{
    // Test a simple 16x16x16 wmma
    wmma::fragment<wmma::matrix_a, 16, 16, 16, __nv_bfloat16, wmma::row_major> a_frag;
    wmma::fragment<wmma::matrix_b, 16, 16, 16, __nv_bfloat16, wmma::col_major> b_frag;
    wmma::fragment<wmma::accumulator, 16, 16, 16, float> c_frag;
    
    wmma::fill_fragment(c_frag, 0.0f);
    
    // Load A (16x16 row-major)
    wmma::load_matrix_sync(a_frag, a, 16);
    // Load B (16x16 col-major)
    wmma::load_matrix_sync(b_frag, b, 16);
    // MMA
    wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
    // Store
    wmma::store_matrix_sync(c, c_frag, 16, wmma::mem_row_major);
}

// Test: 16x16 A @ 16x16 B^T = 16x16 C (but B is col-major so A @ B)
// We pass A in row-major (16xK), B in col-major (Kx16), output C in row-major (16x16)
extern "C" void launch_wmma_test(
    const __nv_bfloat16* a, const __nv_bfloat16* b, float* c, int K)
{
    wmma_bf16_test<<<1, 32>>>(a, b, c, K);
}
CUEOF

cat > /workspace/problems/06_sonic_moe_swiglu/test_wmma.py << 'PYEOF'
import torch
from torch.utils.cpp_extension import load_inline

cuda_src = open('test_wmma.cu').read()

cpp_src = """
void launch_wmma_test(const __nv_bfloat16* a, const __nv_bfloat16* b, float* c, int K);
"""

module = load_inline(
    name='test_wmma',
    cpp_sources=cpp_src,
    cuda_sources=cuda_src,
    functions=['launch_wmma_test'],
    verbose=False,
    extra_cuda_cflags=['-arch=sm_120', '-std=c++17'],
)

# Test: A (16, 16) @ B (16, 16) in row-major, output (16, 16)
# wmma uses B in col-major, so we pass B^T
torch.manual_seed(42)
M, N, K = 16, 16, 16
a = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
b = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
c = torch.zeros(M, N, device='cuda', dtype=torch.float32)

# For wmma with col_major B: b should be in column-major = (N, K) i.e., transposed
b_col = b.t().contiguous()  # (N, K) stored as N rows of K elements -> col-major of orig

module.launch_wmma_test(
    a.data_ptr(), b_col.data_ptr(), c.data_ptr(), K
)

expected = (a.float() @ b.float())
max_diff = (c - expected).abs().max().item()
print(f"WMMA bf16 test: max_diff={max_diff:.6f}, PASS={max_diff < 0.02}")
PYEOF
uv run python test_wmma.py 2>&1

{
  "command": "<276 chars \u2014 see below>",
  "description": "Check available libraries for grouped GEMM",
  "timeout": 30000
}

uv run python -c "import quack_kernels; print(dir(quack_kernels))" 2>&1 || echo "quack_kernels not available"
uv run python -c "import sonic_moe; print(dir(sonic_moe))" 2>&1 || echo "sonic_moe not available"
uv pip list 2>/dev/null | grep -i "quack\|sonic\|cutlass\|moe" 2>&1

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<8531 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test cached tile map approach",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<183 chars \u2014 see diff>",
  "new_string": "<260 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1159 chars \u2014 see diff>",
  "new_string": "<1156 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test with reduced shared memory configs",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<260 chars \u2014 see diff>",
  "new_string": "<412 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test optimized per-shape configs",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<8782 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1370 chars \u2014 see diff>",
  "new_string": "<1472 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1205 chars \u2014 see diff>",
  "new_string": "@triton.jit"
}

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

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<7277 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test small-tile high-occupancy config",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<772 chars \u2014 see diff>",
  "new_string": "<599 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test best config with num_stages=2",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<599 chars \u2014 see diff>",
  "new_string": "<348 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<138 chars \u2014 see diff>",
  "new_string": "<111 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<106 chars \u2014 see below>",
  "new_string": "<106 chars \u2014 see below>"
}

            BM, BN, BK, NW, RE = 64, 128, 32, 8, 16

        t2e, trs, tnr, num_m_tiles = _build_tile_map(

            BM, BN, BK, NW, RE = 64, 128, 32, 8, 16

        t2e, trs, tnr, num_m_tiles = _build_tile_map(

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test without explicit num_stages",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<8006 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test single-accumulator kernel with output buffer reuse",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<303 chars \u2014 see diff>",
  "new_string": "<589 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test shape-optimized configs",
  "timeout": 600000
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<6264 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Test cuBLAS approach correctness",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1960 chars \u2014 see diff>",
  "new_string": "<2425 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1",
  "description": "Test fixed cuBLAS approach",
  "timeout": 600000
}

{
  "command": "<2108 chars \u2014 see below>",
  "description": "Test minimal cuBLAS GEMM",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_cublas_simple.py << 'PYEOF'
import torch
from torch.utils.cpp_extension import load_inline

cuda_src = r"""
#include <cublas_v2.h>
#include <cuda_bf16.h>
#include <torch/extension.h>

void simple_gemm(
    torch::Tensor a,       // (M, K) bf16 row-major
    torch::Tensor b,       // (K, N) bf16 row-major
    torch::Tensor c)       // (M, N) bf16 row-major
{
    int M = a.size(0);
    int K = a.size(1);
    int N = b.size(1);

    auto a_ptr = reinterpret_cast<__nv_bfloat16*>(a.data_ptr());
    auto b_ptr = reinterpret_cast<__nv_bfloat16*>(b.data_ptr());
    auto c_ptr = reinterpret_cast<__nv_bfloat16*>(c.data_ptr());

    cublasHandle_t handle;
    cublasCreate(&handle);

    float one = 1.0f, zero = 0.0f;

    // PyTorch row-major (M,K) = col-major (K,M) with ld=K
    // PyTorch row-major (K,N) = col-major (N,K) with ld=N
    // We want col-major: C[N,M] = A[N,K] @ B[K,M]
    // A = b in col-major (N,K), lda=N
    // B = a in col-major (K,M), ldb=K
    // C = c in col-major (N,M), ldc=N
    cublasGemmEx(
        handle,
        CUBLAS_OP_N, CUBLAS_OP_N,
        N, M, K,
        &one,
        b_ptr, CUDA_R_16BF, N,
        a_ptr, CUDA_R_16BF, K,
        &zero,
        c_ptr, CUDA_R_16BF, N,
        CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP
    );

    cublasDestroy(handle);
}
"""

cpp_src = "void simple_gemm(torch::Tensor, torch::Tensor, torch::Tensor);"

mod = load_inline(
    name='test_cublas',
    cpp_sources=cpp_src,
    cuda_sources=cuda_src,
    functions=['simple_gemm'],
    extra_cuda_cflags=['-arch=sm_120', '-std=c++17'],
    extra_ldflags=['-lcublas'],
)

M, K, N = 64, 256, 128
a = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
b = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
c = torch.zeros(M, N, device='cuda', dtype=torch.bfloat16)

mod.simple_gemm(a, b, c)

expected = (a.float() @ b.float()).bfloat16()
diff = (c.float() - expected.float()).abs().max().item()
print(f"cuBLAS simple GEMM: max_diff={diff:.6f}, {'PASS' if diff < 0.02 else 'FAIL'}")
PYEOF
uv run python test_cublas_simple.py 2>&1

{
  "command": "<3016 chars \u2014 see below>",
  "description": "Test cuBLAS loop over experts",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_cublas_loop.py << 'PYEOF'
import torch
from torch.utils.cpp_extension import load_inline

cuda_src = r"""
#include <cublas_v2.h>
#include <cuda_bf16.h>
#include <torch/extension.h>

void grouped_gemm_cublas(
    torch::Tensor hidden_states,   // (T_perm, H) bf16
    torch::Tensor weight,          // (E, H, I) bf16
    torch::Tensor out,             // (T_perm, I) bf16
    torch::Tensor expert_offsets)  // (E+1,) int32
{
    int E = weight.size(0);
    int H_dim = weight.size(1);
    int I_dim = weight.size(2);

    auto hs_ptr = reinterpret_cast<__nv_bfloat16*>(hidden_states.data_ptr());
    auto w_ptr  = reinterpret_cast<__nv_bfloat16*>(weight.data_ptr());
    auto o_ptr  = reinterpret_cast<__nv_bfloat16*>(out.data_ptr());
    auto off_ptr = expert_offsets.data_ptr<int32_t>();

    cublasHandle_t handle;
    cublasCreate(&handle);

    float one = 1.0f, zero = 0.0f;

    for (int e = 0; e < E; ++e) {
        int start = off_ptr[e];
        int end   = off_ptr[e + 1];
        int n_e   = end - start;
        if (n_e == 0) continue;

        cublasGemmEx(
            handle,
            CUBLAS_OP_N, CUBLAS_OP_N,
            I_dim, n_e, H_dim,
            &one,
            w_ptr + e * H_dim * I_dim, CUDA_R_16BF, I_dim,
            hs_ptr + start * H_dim,     CUDA_R_16BF, H_dim,
            &zero,
            o_ptr + start * I_dim,      CUDA_R_16BF, I_dim,
            CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP
        );
    }

    cublasDestroy(handle);
}
"""

cpp_src = "void grouped_gemm_cublas(torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor);"

mod = load_inline(
    name='test_cublas_loop',
    cpp_sources=cpp_src,
    cuda_sources=cuda_src,
    functions=['grouped_gemm_cublas'],
    extra_cuda_cflags=['-arch=sm_120', '-std=c++17'],
    extra_ldflags=['-lcublas'],
)

# Small test
T_total, H_dim, I_dim, E, K = 128, 256, 128, 8, 2
T_perm = T_total * K
device = 'cuda'

torch.manual_seed(42)
hidden_states = torch.randn(T_perm, H_dim, dtype=torch.bfloat16, device=device) * 0.1
weight = torch.randn(E, H_dim, I_dim, dtype=torch.bfloat16, device=device) * 0.02
out = torch.zeros(T_perm, I_dim, dtype=torch.bfloat16, device=device)

# Build offsets
base = T_perm // E
rem = T_perm - base * E
counts = torch.full((E,), base, dtype=torch.int32)
counts[:rem] += 1
offsets = torch.zeros(E + 1, dtype=torch.int32)
offsets[1:] = torch.cumsum(counts, dim=0)

mod.grouped_gemm_cublas(hidden_states, weight, out, offsets)

# Reference
ref_out = torch.zeros(T_perm, I_dim, dtype=torch.bfloat16, device=device)
for e in range(E):
    start = int(offsets[e].item())
    end = int(offsets[e+1].item())
    if start < end:
        x_e = hidden_states[start:end]
        w_e = weight[e]
        ref_out[start:end] = (x_e.float() @ w_e.float()).bfloat16()

diff = (out.float() - ref_out.float()).abs().max().item()
print(f"Grouped cuBLAS: max_diff={diff:.6f}, {'PASS' if diff < 0.02 else 'FAIL'}")
PYEOF
uv run python test_cublas_loop.py 2>&1

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<2425 chars \u2014 see diff>",
  "new_string": "<1550 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Test and benchmark cuBLAS approach",
  "timeout": 600000
}

{
  "command": "<590 chars \u2014 see below>",
  "description": "Test model forward pass standalone",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_model.py << 'PYEOF'
import torch, sys
sys.path.insert(0, '.')
import solution
solution.T_total = 32768; solution.H = 4096; solution.I = 1536; solution.E = 128; solution.K = 8
device = torch.device("cuda:0")
model = solution.Model(*solution.get_init_inputs()).to(device).eval()
inputs = [t.to(device) for t in solution.get_inputs()]
with torch.no_grad():
    out = model(*inputs)
print("Output shape:", out.shape)
print("Output stats: min", out.min().item(), "max", out.max().item())
print("SUCCESS")
PYEOF
uv run python test_model.py 2>&1

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1550 chars \u2014 see diff>",
  "new_string": "<1824 chars \u2014 see diff>"
}

{
  "command": "uv run python test_model.py 2>&1",
  "description": "Test cuBLAS model with contiguous tensors",
  "timeout": 120000
}

{
  "command": "<914 chars \u2014 see below>",
  "description": "Debug cuBLAS model with error catching",
  "timeout": 120000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_model_debug.py << 'PYEOF'
import torch, sys, traceback
sys.path.insert(0, '.')
try:
    import solution
    solution.T_total = 32768; solution.H = 4096; solution.I = 1536; solution.E = 128; solution.K = 8
    device = torch.device("cuda:0")
    print("Creating model...")
    model = solution.Model(*solution.get_init_inputs()).to(device).eval()
    print("Getting inputs...")
    inputs = [t.to(device) for t in solution.get_inputs()]
    print(f"hidden_states shape: {inputs[0].shape}, device: {inputs[0].device}")
    print(f"expert_offsets shape: {inputs[1].shape}, device: {inputs[1].device}")
    print("Running forward...")
    with torch.no_grad():
        out = model(*inputs)
    print("Output shape:", out.shape)
    print("SUCCESS")
except Exception as e:
    print(f"ERROR: {e}")
    traceback.print_exc()
PYEOF
uv run python test_model_debug.py 2>&1

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1824 chars \u2014 see diff>",
  "new_string": "<1650 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<6615 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py 2>&1 && uv run python benchmark.py 2>&1",
  "description": "Final check and benchmark",
  "timeout": 600000
}

{
  "command": "<305 chars \u2014 see below>",
  "description": "Check framework label"
}

cat /workspace/problems/06_sonic_moe_swiglu/framework.txt 2>/dev/null || echo "no framework.txt"
uv run python -c "import sys; sys.path.insert(0, '.'); import check; check._emit_framework_label()" 2>&1
cat /workspace/problems/06_sonic_moe_swiglu/framework.txt 2>/dev/null || echo "still no framework.txt"

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

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

{
  "command": "rm -f /workspace/problems/06_sonic_moe_swiglu/test_*.py /workspace/problems/06_sonic_moe_swiglu/test_*.cu /workspace/problems/06_sonic_moe_swiglu/profile_*.py /workspace/problems/06_sonic_moe_swiglu/debug_*.py /workspace/problems/06_sonic_moe_swiglu/basic_triton_test.py 2>/dev/null; ls /workspace/problems/06_sonic_moe_swiglu/",
  "description": "Clean up scratch files"
}