{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/reference.py"
}

{
  "raw": "{\"path\": \"/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/check.py"
}

{
  "raw": "{\"path\": \"/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a"
}

{
  "raw": "{\"path\": \"/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/problem.yaml"
}

{
  "raw": "{\"path\": \"/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a"
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/sota.py"
}

{
  "raw": "{\"command\": \"cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm &&"
}

{
  "command": "python -c \"import triton; print(triton.__version__); import triton.language as tl; print('Triton OK')\""
}

{
  "raw": "{\"command\": \"ls -"
}

{
  "raw": ""
}

{
  "path": "/home/infatoshi/.claude/skills/kernel/SKILL.md"
}

{
  "path": "/home/infatoshi/.claude/skills/perf-optim/SKILL.md"
}

{
  "command": "<352 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python -c "
import torch
print('CUDA version:', torch.version.cuda)
print('Triton available:', end=' ')
try:
    import triton
    print(triton.__version__)
except:
    print('No')
print('CUTLASS available:', end=' ')
try:
    import cutlass
    print('Yes')
except:
    print('No')
"

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1 | head -40",
  "timeout": 120
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<6942 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 120
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<4969 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 120
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 120
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<6835 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<6478 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<121 chars \u2014 see below>"
}

python -c "
import triton.language as tl
print([x for x in dir(tl) if not x.startswith('_')])
" | tr ',' '\n' | head -80

{
  "command": "<79 chars \u2014 see below>"
}

python -c "
import triton.language as tl
help(tl.interleave)
" 2>&1 | head -40

{
  "raw": "{\"command\": \"python -c \\\"\\nimport triton.language"
}

{
  "command": "python -c \"import triton.language as tl; print(hasattr(tl, 'trans'))\""
}

{
  "raw": ""
}

{
  "command": "<689 chars \u2014 see below>"
}

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

@triton.jit
def test_join(a_ptr, b_ptr, out_ptr):
    a = tl.load(a_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :])
    b = tl.load(b_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :])
    joined = tl.join(a, b)  # (4, 4, 2)
    # Try reshape to (8, 4)
    c = tl.reshape(joined, (8, 4))
    tl.store(out_ptr + tl.arange(0, 8)[:, None] * 4 + tl.arange(0, 4)[None, :], c)

a = torch.arange(16).reshape(4, 4).float().cuda()
b = torch.arange(16, 32).reshape(4, 4).float().cuda()
out = torch.empty(8, 4).float().cuda()
test_join[(1,)](a, b, out)
print('a:', a)
print('b:', b)
print('out:', out)
"

{
  "command": "<713 chars \u2014 see below>"
}

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

@triton.jit
def test_join(a_ptr, b_ptr, out_ptr):
    a = tl.load(a_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :])
    b = tl.load(b_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :])
    joined = tl.join(a, b)  # (4, 4, 2)
    c = tl.reshape(joined, (8, 4))
    tl.store(out_ptr + tl.arange(0, 8)[:, None] * 4 + tl.arange(0, 4)[None, :], c)

a = torch.arange(16).reshape(4, 4).float().cuda()
b = torch.arange(16, 32).reshape(4, 4).float().cuda()
out = torch.empty(8, 4).float().cuda()
test_join[(1,)](a, b, out)
print('a:', a)
print('b:', b)
print('out:', out)
EOF
python /tmp/test_triton.py

{
  "command": "<845 chars \u2014 see below>"
}

cat > /tmp/test_triton2.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_join2(a_ptr, b_ptr, out_ptr):
    a = tl.load(a_ptr + tl.arange(0, 2)[:, None] * 4 + tl.arange(0, 4)[None, :])
    b = tl.load(b_ptr + tl.arange(0, 2)[:, None] * 4 + tl.arange(0, 4)[None, :])
    a_t = tl.trans(a)
    b_t = tl.trans(b)
    joined = tl.join(a_t, b_t)  # (4, 2, 2)
    c = tl.reshape(joined, (4, 4))
    d = tl.trans(c)
    tl.store(out_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], d)

a = torch.arange(8).reshape(2, 4).float().cuda()
b = torch.arange(8, 16).reshape(2, 4).float().cuda()
out = torch.empty(4, 4).float().cuda()
test_join2[(1,)](a, b, out)
print('a:', a)
print('b:', b)
print('out:', out)
print('Expected: row0=a[0], row1=b[0], row2=a[1], row3=b[1]')
EOF
python /tmp/test_triton2.py

{
  "command": "<2154 chars \u2014 see below>"
}

cat > /tmp/test_triton3.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K):
    BLOCK_M = 4
    BLOCK_N = 4
    BLOCK_K = 4
    GROUP_SIZE = 4
    
    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    k_start = 0
    pk_start = 0
    offs_pk = tl.arange(0, BLOCK_K // 2)
    
    w_packed = tl.load(w_ptr + offs_pk[:, None] * 4 + offs_n[None, :])
    lo = w_packed & 0xF
    hi = (w_packed >> 4) & 0xF
    
    s = tl.load(s_ptr + offs_n[None, :]).to(tl.bfloat16)
    z = tl.load(z_ptr + offs_n[None, :]).to(tl.bfloat16)
    
    lo_bf = lo.to(tl.bfloat16)
    hi_bf = hi.to(tl.bfloat16)
    
    lo_t = tl.trans(lo_bf)
    hi_t = tl.trans(hi_bf)
    joined = tl.join(lo_t, hi_t)
    w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
    w_unpacked = tl.trans(w_reshaped)
    
    w_deq = (w_unpacked - z) * s
    
    x = tl.load(x_ptr + offs_m[:, None] * K + offs_k[None, :]).to(tl.bfloat16)
    acc += tl.dot(x, w_deq)
    
    tl.store(out_ptr + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16))

# Test data
M, N, K = 4, 4, 4
x = torch.tensor([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], dtype=torch.bfloat16).cuda()
w_q = torch.tensor([[0x01, 0x23, 0x45, 0x67]], dtype=torch.uint8).cuda()  # K//2=2, but we need 2x4 for BLOCK_K=4
# Actually need (K//2, N) = (2, 4)
w_q = torch.tensor([[0x10, 0x32, 0x54, 0x76], [0x98, 0xBA, 0xDC, 0xFE]], dtype=torch.uint8).cuda()
s = torch.ones(1, N, dtype=torch.bfloat16).cuda()
z = torch.zeros(1, N, dtype=torch.bfloat16).cuda()
out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

test_w4a16[(1,)](x, w_q, s, z, out, M, N, K)

# Reference: unpack and matmul
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_q & 0xF
w_unpacked[1::2] = (w_q >> 4) & 0xF
w_bf = w_unpacked.to(torch.bfloat16)
ref = x @ w_bf

print('out:', out)
print('ref:', ref)
print('match:', torch.allclose(out.float(), ref.float(), atol=0.1))
EOF
python /tmp/test_triton3.py

{
  "command": "<2090 chars \u2014 see below>"
}

cat > /tmp/test_triton3.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    k_start = 0
    pk_start = 0
    offs_pk = tl.arange(0, BLOCK_K // 2)
    
    w_packed = tl.load(w_ptr + offs_pk[:, None] * 4 + offs_n[None, :])
    lo = w_packed & 0xF
    hi = (w_packed >> 4) & 0xF
    
    s = tl.load(s_ptr + offs_n[None, :]).to(tl.bfloat16)
    z = tl.load(z_ptr + offs_n[None, :]).to(tl.bfloat16)
    
    lo_bf = lo.to(tl.bfloat16)
    hi_bf = hi.to(tl.bfloat16)
    
    lo_t = tl.trans(lo_bf)
    hi_t = tl.trans(hi_bf)
    joined = tl.join(lo_t, hi_t)
    w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
    w_unpacked = tl.trans(w_reshaped)
    
    w_deq = (w_unpacked - z) * s
    
    x = tl.load(x_ptr + offs_m[:, None] * K + offs_k[None, :]).to(tl.bfloat16)
    acc += tl.dot(x, w_deq)
    
    tl.store(out_ptr + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16))

# Test data
M, N, K = 4, 4, 4
x = torch.tensor([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], dtype=torch.bfloat16).cuda()
w_q = torch.tensor([[0x10, 0x32, 0x54, 0x76], [0x98, 0xBA, 0xDC, 0xFE]], dtype=torch.uint8).cuda()
s = torch.ones(1, N, dtype=torch.bfloat16).cuda()
z = torch.zeros(1, N, dtype=torch.bfloat16).cuda()
out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

test_w4a16[(1,)](x, w_q, s, z, out, M, N, K, BLOCK_M=4, BLOCK_N=4, BLOCK_K=4, GROUP_SIZE=4)

# Reference: unpack and matmul
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_q & 0xF
w_unpacked[1::2] = (w_q >> 4) & 0xF
w_bf = w_unpacked.to(torch.bfloat16)
ref = x @ w_bf

print('out:', out)
print('ref:', ref)
print('match:', torch.allclose(out.float(), ref.float(), atol=0.1))
EOF
python /tmp/test_triton3.py

{
  "command": "<2085 chars \u2014 see below>"
}

cat > /tmp/test_triton3.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    k_start = 0
    offs_pk = tl.arange(0, BLOCK_K // 2)
    
    w_packed = tl.load(w_ptr + offs_pk[:, None] * N + offs_n[None, :])
    lo = w_packed & 0xF
    hi = (w_packed >> 4) & 0xF
    
    s = tl.load(s_ptr + offs_n[None, :]).to(tl.bfloat16)
    z = tl.load(z_ptr + offs_n[None, :]).to(tl.bfloat16)
    
    lo_bf = lo.to(tl.bfloat16)
    hi_bf = hi.to(tl.bfloat16)
    
    lo_t = tl.trans(lo_bf)
    hi_t = tl.trans(hi_bf)
    joined = tl.join(lo_t, hi_t)
    w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
    w_unpacked = tl.trans(w_reshaped)
    
    w_deq = (w_unpacked - z) * s
    
    x = tl.load(x_ptr + offs_m[:, None] * K + offs_k[None, :]).to(tl.bfloat16)
    acc += tl.dot(x, w_deq)
    
    tl.store(out_ptr + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16))

# Test data
M, N, K = 4, 4, 16
x = torch.ones(M, K, dtype=torch.bfloat16).cuda()
w_q = torch.ones(K // 2, N, dtype=torch.uint8).cuda()
# Make w_q have recognizable values: low nibble = 1, high nibble = 2
w_q[:, :] = 0x21  # lo=1, hi=2
s = torch.ones(1, N, dtype=torch.bfloat16).cuda()
z = torch.zeros(1, N, dtype=torch.bfloat16).cuda()
out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

test_w4a16[(1,)](x, w_q, s, z, out, M, N, K, BLOCK_M=4, BLOCK_N=4, BLOCK_K=16, GROUP_SIZE=16)

# Reference: unpack and matmul
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_q & 0xF
w_unpacked[1::2] = (w_q >> 4) & 0xF
w_bf = w_unpacked.to(torch.bfloat16)
ref = x @ w_bf

print('out:', out)
print('ref:', ref)
print('match:', torch.allclose(out.float(), ref.float(), atol=0.1))
EOF
python /tmp/test_triton3.py

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7000 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 120
}

{
  "command": "<4067 chars \u2014 see below>"
}

cat > /tmp/test_triton4.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    num_k_tiles = K // BLOCK_K
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)
        
        w_ptrs = w_ptr + (offs_pk[:, None] * (N // 2) + offs_n[None, :] * 1)  # stride_wk = N//2, stride_wn = 1
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)
        
        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
        
        lo_t = tl.trans(lo)
        hi_t = tl.trans(hi)
        joined = tl.join(lo_t, hi_t)
        w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
        w_unpacked = tl.trans(w_reshaped)
        
        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * N + offs_n[None, :]
        z_ptrs = z_ptr + group_idx * N + offs_n[None, :]
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        
        w_deq = (w_unpacked - z) * s
        
        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * K + offs_k[None, :])
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)
        
        acc += tl.dot(x, w_deq)
    
    out_ptrs = out_ptr + (offs_m[:, None] * N + offs_n[None, :])
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)

# Match shape 4: M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

# Generate weights like reference
torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32) * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

# Pack
w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Reference
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_packed & 0xF
w_unpacked[1::2] = (w_packed >> 4) & 0xF
s_full = scales_2d.repeat_interleave(GROUP_SIZE, dim=0)
z_full = zeros_2d.repeat_interleave(GROUP_SIZE, dim=0)
w_bf = (w_unpacked.to(torch.bfloat16) - z_full) * s_full
ref = x @ w_bf

# Test kernel
for BLOCK_M in [8, 16]:
    for BLOCK_N in [128, 256]:
        for BLOCK_K in [128, 256]:
            if M % BLOCK_M != 0 or N % BLOCK_N != 0 or K % BLOCK_K != 0:
                continue
            test_w4a16[(M//BLOCK_M, N//BLOCK_N)](x, w_packed, scales_2d, zeros_2d, out, M, N, K,
                                                   BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_SIZE=GROUP_SIZE)
            max_diff = (out - ref).abs().max().item()
            mean_diff = (out - ref).abs().mean().item()
            print(f"BM={BLOCK_M}, BN={BLOCK_N}, BK={BLOCK_K}: max_diff={max_diff

... (67 more chars)

{
  "command": "<4047 chars \u2014 see below>"
}

cat > /tmp/test_triton4.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    num_k_tiles = K // BLOCK_K
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)
        
        w_ptrs = w_ptr + (offs_pk[:, None] * (N // 2) + offs_n[None, :] * 1)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)
        
        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
        
        lo_t = tl.trans(lo)
        hi_t = tl.trans(hi)
        joined = tl.join(lo_t, hi_t)
        w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
        w_unpacked = tl.trans(w_reshaped)
        
        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * N + offs_n[None, :]
        z_ptrs = z_ptr + group_idx * N + offs_n[None, :]
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        
        w_deq = (w_unpacked - z) * s
        
        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * K + offs_k[None, :])
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)
        
        acc += tl.dot(x, w_deq)
    
    out_ptrs = out_ptr + (offs_m[:, None] * N + offs_n[None, :])
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)

# Match shape 4: M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

# Generate weights like reference
torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

# Pack
w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Reference
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_packed & 0xF
w_unpacked[1::2] = (w_packed >> 4) & 0xF
s_full = scales_2d.repeat_interleave(GROUP_SIZE, dim=0)
z_full = zeros_2d.repeat_interleave(GROUP_SIZE, dim=0)
w_bf = (w_unpacked.to(torch.bfloat16) - z_full) * s_full
ref = x @ w_bf

# Test kernel
for BLOCK_M in [8, 16]:
    for BLOCK_N in [128, 256]:
        for BLOCK_K in [128, 256]:
            if M % BLOCK_M != 0 or N % BLOCK_N != 0 or K % BLOCK_K != 0:
                continue
            test_w4a16[(M//BLOCK_M, N//BLOCK_N)](x, w_packed, scales_2d, zeros_2d, out, M, N, K,
                                                   BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_SIZE=GROUP_SIZE)
            max_diff = (out - ref).abs().max().item()
            mean_diff = (out - ref).abs().mean().item()
            print(f"BM={BLOCK_M}, BN={BLOCK_N}, BK={BLOCK_K}: max_diff={max_diff:.4f}, mean_diff={me

... (47 more chars)

{
  "command": "python -c \"import triton.language as tl; help(tl.dot_scaled)\" 2>&1 | head -40"
}

{
  "command": "python -c \"import triton.language as tl; print(hasattr(tl, 'split'))\""
}

{
  "command": "<1048 chars \u2014 see below>"
}

cat > /tmp/test_triton5.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_split(x_ptr, out1_ptr, out2_ptr):
    x = tl.load(x_ptr + tl.arange(0, 4)[:, None] * 8 + tl.arange(0, 8)[None, :])
    # x shape: (4, 8)
    # We want to split along last dimension into (4, 4, 2) and extract each slice
    # Actually, split takes a tensor and splits it along a dimension into equal halves
    a, b = tl.split(x, axis=1)  # Split along axis 1 (N dimension) into two (4, 4) tensors
    # But this splits [0,1,2,3,4,5,6,7] into [0,1,2,3] and [4,5,6,7]
    # We want even/odd: [0,2,4,6] and [1,3,5,7]
    tl.store(out1_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], a)
    tl.store(out2_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], b)

x = torch.arange(32).reshape(4, 8).float().cuda()
out1 = torch.empty(4, 4).float().cuda()
out2 = torch.empty(4, 4).float().cuda()
test_split[(1,)](x, out1, out2)
print('x:', x)
print('out1:', out1)
print('out2:', out2)
EOF
python /tmp/test_triton5.py

{
  "command": "python -c \"import triton.language as tl; help(tl.split)\" 2>&1 | head -40"
}

{
  "command": "<750 chars \u2014 see below>"
}

cat > /tmp/test_triton6.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_reshape_split(x_ptr, out1_ptr, out2_ptr):
    x = tl.load(x_ptr + tl.arange(0, 4)[:, None] * 8 + tl.arange(0, 8)[None, :])
    x_3d = tl.reshape(x, (4, 4, 2))
    a, b = tl.split(x_3d)
    tl.store(out1_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], a)
    tl.store(out2_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], b)

x = torch.arange(32).reshape(4, 8).float().cuda()
out1 = torch.empty(4, 4).float().cuda()
out2 = torch.empty(4, 4).float().cuda()
test_reshape_split[(1,)](x, out1, out2)
print('x:', x)
print('out1 (even cols):', out1)
print('out2 (odd cols):', out2)
EOF
python /tmp/test_triton6.py

{
  "command": "<4014 chars \u2014 see below>"
}

cat > /tmp/test_triton7.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    num_k_tiles = K // BLOCK_K
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)
        
        w_ptrs = w_ptr + (offs_pk[:, None] * (N // 2) + offs_n[None, :] * 1)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)
        
        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
        
        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * N + offs_n[None, :]
        z_ptrs = z_ptr + group_idx * N + offs_n[None, :]
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        
        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s
        
        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * K + offs_k[None, :])
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)
        
        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)
        
        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)
    
    out_ptrs = out_ptr + (offs_m[:, None] * N + offs_n[None, :])
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)

# Match shape 4: M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

# Generate weights like reference
torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

# Pack
w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Reference
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_packed & 0xF
w_unpacked[1::2] = (w_packed >> 4) & 0xF
s_full = scales_2d.repeat_interleave(GROUP_SIZE, dim=0)
z_full = zeros_2d.repeat_interleave(GROUP_SIZE, dim=0)
w_bf = (w_unpacked.to(torch.bfloat16) - z_full) * s_full
ref = x @ w_bf

# Test kernel
for BLOCK_M in [8, 16]:
    for BLOCK_N in [128, 256]:
        for BLOCK_K in [128, 256]:
            if M % BLOCK_M != 0 or N % BLOCK_N != 0 or K % BLOCK_K != 0:
                continue
            test_w4a16[(M//BLOCK_M, N//BLOCK_N)](x, w_packed, scales_2d, zeros_2d, out, M, N, K,
                                                   BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_SIZE=GROUP_SIZE)
            max_diff = (out - ref).abs().max().item()
            mean_diff = (out - ref).abs().mean().item()
            print(f"BM={BLOCK_M}, BN={BLOCK_N}, BK={BLOCK_K}: max_diff={max_diff:.4f}, mean_diff={mean_diff:.6f}")
EOF
python /tmp/te

... (14 more chars)

{
  "command": "<4031 chars \u2014 see below>"
}

cat > /tmp/test_triton7.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    num_k_tiles = K // BLOCK_K
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)
        
        w_ptrs = w_ptr + (offs_pk[:, None] * N + offs_n[None, :] * 1)  # FIXED: stride_wk = N
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)
        
        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
        
        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * N + offs_n[None, :]
        z_ptrs = z_ptr + group_idx * N + offs_n[None, :]
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        
        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s
        
        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * K + offs_k[None, :])
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)
        
        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)
        
        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)
    
    out_ptrs = out_ptr + (offs_m[:, None] * N + offs_n[None, :])
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)

# Match shape 4: M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

# Generate weights like reference
torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

# Pack
w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Reference
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_packed & 0xF
w_unpacked[1::2] = (w_packed >> 4) & 0xF
s_full = scales_2d.repeat_interleave(GROUP_SIZE, dim=0)
z_full = zeros_2d.repeat_interleave(GROUP_SIZE, dim=0)
w_bf = (w_unpacked.to(torch.bfloat16) - z_full) * s_full
ref = x @ w_bf

# Test kernel
for BLOCK_M in [8, 16]:
    for BLOCK_N in [128, 256]:
        for BLOCK_K in [128, 256]:
            if M % BLOCK_M != 0 or N % BLOCK_N != 0 or K % BLOCK_K != 0:
                continue
            test_w4a16[(M//BLOCK_M, N//BLOCK_N)](x, w_packed, scales_2d, zeros_2d, out, M, N, K,
                                                   BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_SIZE=GROUP_SIZE)
            max_diff = (out - ref).abs().max().item()
            mean_diff = (out - ref).abs().mean().item()
            print(f"BM={BLOCK_M}, BN={BLOCK_N}, BK={BLOCK_K}: max_diff={max_diff:.4f}, mean_diff={mean_diff:.6f}")
E

... (31 more chars)

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<6415 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 120
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<4079 chars \u2014 see below>"
}

cat > /tmp/test_triton8.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_w4a16_interleave(x_ptr, w_ptr, s_ptr, z_ptr, out_ptr, M, N, K,
                          BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP_SIZE: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    
    num_k_tiles = K // BLOCK_K
    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)
        
        w_ptrs = w_ptr + (offs_pk[:, None] * N + offs_n[None, :] * 1)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)
        
        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)
        
        lo_t = tl.trans(lo)
        hi_t = tl.trans(hi)
        joined = tl.join(lo_t, hi_t)
        w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
        w_unpacked = tl.trans(w_reshaped)
        
        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * N + offs_n[None, :]
        z_ptrs = z_ptr + group_idx * N + offs_n[None, :]
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        
        w_deq = (w_unpacked - z) * s
        
        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * K + offs_k[None, :])
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)
        
        acc += tl.dot(x, w_deq)
    
    out_ptrs = out_ptr + (offs_m[:, None] * N + offs_n[None, :])
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)

# Match shape 4: M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

# Generate weights like reference
torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

# Pack
w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Reference
w_unpacked = torch.empty(K, N, dtype=torch.uint8, device='cuda')
w_unpacked[0::2] = w_packed & 0xF
w_unpacked[1::2] = (w_packed >> 4) & 0xF
s_full = scales_2d.repeat_interleave(GROUP_SIZE, dim=0)
z_full = zeros_2d.repeat_interleave(GROUP_SIZE, dim=0)
w_bf = (w_unpacked.to(torch.bfloat16) - z_full) * s_full
ref = x @ w_bf

# Test kernel
for BLOCK_M in [8, 16]:
    for BLOCK_N in [128, 256]:
        for BLOCK_K in [128]:
            if M % BLOCK_M != 0 or N % BLOCK_N != 0 or K % BLOCK_K != 0:
                continue
            test_w4a16_interleave[(M//BLOCK_M, N//BLOCK_N)](x, w_packed, scales_2d, zeros_2d, out, M, N, K,
                                                              BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_SIZE=GROUP_SIZE)
            max_diff = (out - ref).abs().max().item()
            mean_diff = (out - ref).abs().mean().item()
            print(f"BM={BLOCK_M}, BN={BLOCK_N}, BK={BLOCK_K}: max_di

... (79 more chars)

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<6619 chars \u2014 see diff>"
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 120
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<1045 chars \u2014 see below>"
}

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

@triton.jit
def test_permute(a_ptr, b_ptr, out_ptr):
    a = tl.load(a_ptr + tl.arange(0, 2)[:, None] * 4 + tl.arange(0, 4)[None, :])
    b = tl.load(b_ptr + tl.arange(0, 2)[:, None] * 4 + tl.arange(0, 4)[None, :])
    # a, b are (2, 4)
    # Stack them: (2, 4, 2)
    stacked = tl.join(a, b)  # Actually join creates (2, 4, 2) with last dim = 2
    # permute from (2, 4, 2) to (4, 4)
    # Wait, permute changes axis order. (2, 4, 2) -> permute to what?
    # We want (4, 4) where result[2*i+c, n] = a[i, n] if c=0 else b[i, n]
    # Let's try reshape first: (2, 4, 2) -> (4, 4)
    c = tl.reshape(stacked, (4, 4))
    tl.store(out_ptr + tl.arange(0, 4)[:, None] * 4 + tl.arange(0, 4)[None, :], c)

a = torch.arange(8).reshape(2, 4).float().cuda()
b = torch.arange(8, 16).reshape(2, 4).float().cuda()
out = torch.empty(4, 4).float().cuda()
test_permute[(1,)](a, b, out)
print('a:', a)
print('b:', b)
print('out:', out)
EOF
python /tmp/test_permute.py

{
  "command": "<725 chars \u2014 see below>"
}

cat > /tmp/check_configs.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import shapes
import torch

device = torch.device('cuda')

for shape in shapes.SHAPES:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Warmup / autotune
    for _ in range(3):
        _ = model(x)
    
    print(f"Shape {shape}: best config = {solution.w4a16_gemm_kernel.best_config}")
EOF
python /tmp/check_configs.py

{
  "command": "<5179 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# Generate test data
M, N, K = 256, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Time different configs
import time

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    grid = (M // BLOCK_M, N // BLOCK_N)
    # Warmup
    for _ in range(5):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    start = time.time()
    for _ in range(50):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w

... (1179 more chars)

{
  "command": "<5241 chars \u2014 see below>"
}

cat > /tmp/test_perf_m1.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# Generate test data for M=1, N=12288, K=4096
M, N, K = 1, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

# Time different configs
import time

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    grid = (M // BLOCK_M, N // BLOCK_N)
    # Warmup
    for _ in range(5):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    start = time.time()
    for _ in range(100):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0),

... (1241 more chars)

{
  "command": "<5184 chars \u2014 see below>"
}

cat > /tmp/test_perf_m1_sq.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import time

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(5):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    start = time.time()
    for _ in range(100):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.strid

... (1184 more chars)

{
  "command": "<5254 chars \u2014 see below>"
}

cat > /tmp/test_perf_m16.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=16, N=14336, K=4096
M, N, K = 16, 14336, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import time

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(5):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    start = time.time()
    for _ in range(100):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.str

... (1254 more chars)

{
  "command": "<5148 chars \u2014 see below>"
}

cat > /tmp/test_perf_m32.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=32, N=12288, K=4096
M, N, K = 32, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import time

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(5):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    start = time.time()
    for _ in range(100):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.str

... (1148 more chars)

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7268 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<733 chars \u2014 see below>"
}

cat > /tmp/check_configs2.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import shapes
import torch

device = torch.device('cuda')

for shape in shapes.SHAPES:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Warmup / autotune
    for _ in range(3):
        _ = model(x)
    
    config = solution.w4a16_gemm_kernel.best_config
    print(f"Shape {shape}: {config}")
EOF
python /tmp/check_configs2.py

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/src/eval/timing.py"
}

{
  "command": "<5761 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        
        # Use block pointer for weights
        w_block_ptr = tl.make_block_ptr(
            base=w_ptr,
            shape=(K // 2, N),
            strides=(stride_wk, stride_wn),
            offsets=(pk_start, pid_n * BLOCK_N),
            block_shape=(BLOCK_K // 2, BLOCK_N),
            order=(1, 0),
        )
        w_packed = tl.load(w_block_ptr, boundary_check=(0, 1))

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        # Block pointer for x
        x_block_ptr = tl.make_block_ptr(
            base=x_ptr,
            shape=(M, K),
            strides=(stride_xm, stride_xk),
            offsets=(pid_m * BLOCK_M, k_start),
            block_shape=(BLOCK_M, BLOCK_K),
            order=(1, 0),
        )
        x = tl.load(x_block_ptr, boundary_check=(0, 1))

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=12288, K=4096
M, N, K = 1, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import time
import statistics

# L2 flush
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    # Warmup
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_

... (1761 more chars)

{
  "command": "<5385 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        
        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(

... (1385 more chars)

{
  "command": "<5433 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(e

... (1433 more chars)

{
  "command": "<1777 chars \u2014 see below>"
}

cat > /tmp/test_actual_model.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import torch
import statistics

_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_fn(fn, inputs, iters=50, warmup=10):
    for _ in range(warmup):
        with torch.no_grad():
            fn(*inputs)
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        with torch.no_grad():
            fn(*inputs)
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)

device = torch.device('cuda')

for shape in [
    {"M": 1, "N": 12288, "K": 4096},
    {"M": 1, "N": 4096, "K": 4096},
    {"M": 256, "N": 12288, "K": 4096},
]:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    ms = time_fn(model, [x], iters=50, warmup=10)
    
    M, N, K = shape['M'], shape['N'], shape['K']
    bytes_moved = M*K*2 + (K/2)*N + (K/128)*N*2 + (K/128)*N*2 + M*N*2
    gbps = bytes_moved / (ms * 1e-3) / 1e9
    tflops = 2 * M * N * K / (ms * 1e-3) / 1e12
    
    config = solution.w4a16_gemm_kernel.best_config
    print(f"Shape {shape}: {ms:.3f}ms, {gbps:.1f} GB/s, {tflops:.1f} TFLOPS, config={config}")
EOF
rm -rf ~/.triton/cache && python /tmp/test_actual_model.py

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7592 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7580 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7278 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<261 chars \u2014 see below>"
}

python -c "
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard')
from src.hardware import get as get_hw
hw = get_hw('RTX_PRO_6000')
print(f'Peak TFLOPS (bf16): {hw.peak_tflops_dense}')
print(f'Peak bandwidth: {hw.peak_bandwidth_gb_s} GB/s')
"

{
  "command": "cat /home/infatoshi/cuda/KernelBench-Hard/src/eval/roofline.py"
}

{
  "command": "<319 chars \u2014 see below>"
}

python -c "
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard')
from src.eval.roofline import peak_fraction, compute_tflops

flops = 2 * 256 * 12288 * 4096
ms = 0.165
tflops = compute_tflops(flops, ms)
print(f'TFLOPS: {tflops}')
print(f'peak_fraction(155.916, 200): {peak_fraction(155.916, 200)}')
"

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7625 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<733 chars \u2014 see below>"
}

cat > /tmp/check_configs3.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import shapes
import torch

device = torch.device('cuda')

for shape in shapes.SHAPES:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Warmup / autotune
    for _ in range(3):
        _ = model(x)
    
    config = solution.w4a16_gemm_kernel.best_config
    print(f"Shape {shape}: {config}")
EOF
python /tmp/check_configs3.py

{
  "command": "<1537 chars \u2014 see below>"
}

cat > /tmp/test_shape3.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import torch
import statistics

_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_fn(fn, inputs, iters=50, warmup=10):
    for _ in range(warmup):
        with torch.no_grad():
            fn(*inputs)
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        with torch.no_grad():
            fn(*inputs)
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)

device = torch.device('cuda')

shape = {"M": 1, "N": 4096, "K": 4096}
reference.M = shape['M']
reference.N = shape['N']
reference.K = shape['K']

init_args = reference.get_init_inputs()
model = solution.Model(*init_args).to(device).eval()

x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)

ms = time_fn(model, [x], iters=50, warmup=10)

M, N, K = shape['M'], shape['N'], shape['K']
bytes_moved = M*K*2 + (K/2)*N + (K/128)*N*2 + (K/128)*N*2 + M*N*2
gbps = bytes_moved / (ms * 1e-3) / 1e9

config = solution.w4a16_gemm_kernel.best_config
print(f"Shape {shape}: {ms:.3f}ms, {gbps:.1f} GB/s, config={config}")
EOF
rm -rf ~/.triton/cache && python /tmp/test_shape3.py

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<7630 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<733 chars \u2014 see below>"
}

cat > /tmp/check_configs4.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import shapes
import torch

device = torch.device('cuda')

for shape in shapes.SHAPES:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Warmup / autotune
    for _ in range(3):
        _ = model(x)
    
    config = solution.w4a16_gemm_kernel.best_config
    print(f"Shape {shape}: {config}")
EOF
python /tmp/check_configs4.py

{
  "command": "<5520 chars \u2014 see below>"
}

cat > /tmp/test_single_dot_m1.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel_single(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        # Interleave along K dimension
        lo_t = tl.trans(lo)
        hi_t = tl.trans(hi)
        joined = tl.join(lo_t, hi_t)
        w_reshaped = tl.reshape(joined, (BLOCK_N, BLOCK_K))
        w_unpacked = tl.trans(w_reshaped)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        w_deq = (w_unpacked - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        acc += tl.dot(x, w_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel_single[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
    

... (1520 more chars)

{
  "command": "<5611 chars \u2014 see below>"
}

cat > /tmp/test_strided_m1.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel_strided(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        # Strided x loads
        offs_k_even = k_start + 2 * tl.arange(0, BLOCK_K // 2)
        offs_k_odd = k_start + 2 * tl.arange(0, BLOCK_K // 2) + 1
        x_even_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k_even[None, :] * stride_xk)
        x_odd_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k_odd[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M)
        x_even = tl.load(x_even_ptrs, mask=x_mask, other=0.0)
        x_odd = tl.load(x_odd_ptrs, mask=x_mask, other=0.0)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel_strided[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
     

... (1611 more chars)

{
  "command": "<5891 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel_v2(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        offs_k = k_start + tl.arange(0, BLOCK_K)

        w_ptrs = w_ptr + (offs_k[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_k[:, None] < K) & (offs_n[None, :] < N)
        w_unpacked = tl.load(w_ptrs, mask=w_mask, other=0)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        w_deq = (w_unpacked.to(tl.bfloat16) - z) * s

        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        acc += tl.dot(x, w_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


def unpack_weights(w_packed, K, N):
    w_unpacked = torch.empty(K, N, dtype=torch.uint8, device=w_packed.device)
    w_unpacked[0::2] = w_packed & 0xF
    w_unpacked[1::2] = (w_packed >> 4) & 0xF
    return w_unpacked


# Test for M=1, N=4096 and M=256, N=12288
for M, N, K in [(1, 4096, 4096), (256, 12288, 4096)]:
    GROUP_SIZE = 128
    n_groups = K // GROUP_SIZE

    torch.manual_seed(42)
    x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

    torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
    w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
    w_g = w_full.view(n_groups, GROUP_SIZE, N)
    w_min = w_g.min(dim=1, keepdim=True).values
    w_max = w_g.max(dim=1, keepdim=True).values
    scales = (w_max - w_min).clamp_min(1e-8) / 15.0
    zeros = (-w_min / scales).round().clamp(0, 15)
    w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
    w_q = w_q.view(K, N)
    scales_2d = scales.squeeze(1).to(torch.bfloat16)
    zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

    w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
    w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)
    
    w_unpacked = unpack_weights(w_packed, K, N)

    out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

    import statistics
    _l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

    def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
        grid = (M // BLOCK_M, N // BLOCK_N)
        for _ in range(10):
            w4a16_gemm_kernel_v2[grid](
                x, w_unpacked, scales_2d, zeros_2d, out,
                M, N, K,
                x.stride(0), x.stride(1),
                w_unpacked.stride(0), w_unpacked.stride(1),
                scales_2d.stride(0), scales_2d.stride(1),
                zeros_2d.stride(0), zeros_2d.stride(1),
                out.stride(0), out.stride(1),
                BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                num_warps=num_warps, num_stages=num_stages,
            )
        torch.cuda.synchronize()


... (1891 more chars)

{
  "command": "<5439 chars \u2014 see below>"
}

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

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=4096, K=4096
M, N, K = 1, 4096, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(e

... (1439 more chars)

{
  "command": "<5429 chars \u2014 see below>"
}

cat > /tmp/test_m1_large.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=1, N=12288, K=4096
M, N, K = 1, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(ena

... (1429 more chars)

{
  "command": "<5511 chars \u2014 see below>"
}

cat > /tmp/test_m256_stages4.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


# M=256, N=12288, K=4096
M, N, K = 256, 12288, 4096
GROUP_SIZE = 128
n_groups = K // GROUP_SIZE

torch.manual_seed(42)
x = torch.randn(M, K, dtype=torch.bfloat16).cuda()

torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
w_g = w_full.view(n_groups, GROUP_SIZE, N)
w_min = w_g.min(dim=1, keepdim=True).values
w_max = w_g.max(dim=1, keepdim=True).values
scales = (w_max - w_min).clamp_min(1e-8) / 15.0
zeros = (-w_min / scales).round().clamp(0, 15)
w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
w_q = w_q.view(K, N)
scales_2d = scales.squeeze(1).to(torch.bfloat16)
zeros_2d = zeros.squeeze(1).to(torch.bfloat16)

w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)

out = torch.empty(M, N, dtype=torch.bfloat16).cuda()

import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.E

... (1511 more chars)

{
  "command": "<5635 chars \u2014 see below>"
}

cat > /tmp/test_m16_m32.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def w4a16_gemm_kernel(
    x_ptr, w_ptr, s_ptr, z_ptr, out_ptr,
    M, N, K,
    stride_xm, stride_xk,
    stride_wk, stride_wn,
    stride_sg, stride_sn,
    stride_zg, stride_zn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_SIZE: tl.constexpr = 128,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    num_k_tiles = K // BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k_tile in range(num_k_tiles):
        k_start = k_tile * BLOCK_K
        pk_start = k_start // 2
        offs_pk = pk_start + tl.arange(0, BLOCK_K // 2)

        w_ptrs = w_ptr + (offs_pk[:, None] * stride_wk + offs_n[None, :] * stride_wn)
        w_mask = (offs_pk[:, None] < K // 2) & (offs_n[None, :] < N)
        w_packed = tl.load(w_ptrs, mask=w_mask, other=0)

        lo = (w_packed & 0xF).to(tl.bfloat16)
        hi = ((w_packed >> 4) & 0xF).to(tl.bfloat16)

        group_idx = k_start // GROUP_SIZE
        s_ptrs = s_ptr + group_idx * stride_sg + offs_n[None, :] * stride_sn
        z_ptrs = z_ptr + group_idx * stride_zg + offs_n[None, :] * stride_zn
        s = tl.load(s_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)
        z = tl.load(z_ptrs, mask=offs_n[None, :] < N, other=0.0).to(tl.bfloat16)

        lo_deq = (lo - z) * s
        hi_deq = (hi - z) * s

        offs_k = k_start + tl.arange(0, BLOCK_K)
        x_ptrs = x_ptr + (offs_m[:, None] * stride_xm + offs_k[None, :] * stride_xk)
        x_mask = (offs_m[:, None] < M) & (offs_k[None, :] < K)
        x = tl.load(x_ptrs, mask=x_mask, other=0.0)

        x_3d = tl.reshape(x, (BLOCK_M, BLOCK_K // 2, 2))
        x_even, x_odd = tl.split(x_3d)

        acc += tl.dot(x_even, lo_deq)
        acc += tl.dot(x_odd, hi_deq)

    out_ptrs = out_ptr + (offs_m[:, None] * stride_om + offs_n[None, :] * stride_on)
    out_mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(out_ptrs, acc.to(tl.bfloat16), mask=out_mask)


import statistics
_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_kernel(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, num_warps, num_stages, iters=50):
    GROUP_SIZE = 128
    n_groups = K // GROUP_SIZE
    
    torch.manual_seed(42)
    x = torch.randn(M, K, dtype=torch.bfloat16).cuda()
    
    torch.manual_seed(0xC0DE ^ (M * 1315423911 + N * 2654435761 + K))
    w_full = torch.randn(K, N, dtype=torch.float32, device='cuda') * 0.02
    w_g = w_full.view(n_groups, GROUP_SIZE, N)
    w_min = w_g.min(dim=1, keepdim=True).values
    w_max = w_g.max(dim=1, keepdim=True).values
    scales = (w_max - w_min).clamp_min(1e-8) / 15.0
    zeros = (-w_min / scales).round().clamp(0, 15)
    w_q = ((w_g / scales) + zeros).round().clamp(0, 15).to(torch.uint8)
    w_q = w_q.view(K, N)
    scales_2d = scales.squeeze(1).to(torch.bfloat16)
    zeros_2d = zeros.squeeze(1).to(torch.bfloat16)
    
    w_packed = torch.empty(K // 2, N, dtype=torch.uint8, device='cuda')
    w_packed[:, :] = (w_q[0::2] & 0xF) | ((w_q[1::2] & 0xF) << 4)
    
    out = torch.empty(M, N, dtype=torch.bfloat16).cuda()
    
    grid = (M // BLOCK_M, N // BLOCK_N)
    for _ in range(10):
        w4a16_gemm_kernel[grid](
            x, w_packed, scales_2d, zeros_2d, out,
            M, N, K,
            x.stride(0), x.stride(1),
            w_packed.stride(0), w_packed.stride(1),
            scales_2d.stride(0), scales_2d.stride(1),
            zeros_2d.stride(0), zeros_2d.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps, num_stages=num_stages,
        )
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch

... (1635 more chars)

{
  "path": "/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm/solution.py",
  "content": "<8234 chars \u2014 see diff>"
}

{
  "command": "rm -rf ~/.triton/cache && cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python check.py 2>&1",
  "timeout": 180
}

{
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm && python benchmark.py 2>&1",
  "timeout": 300
}

{
  "command": "<733 chars \u2014 see below>"
}

cat > /tmp/check_configs5.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import shapes
import torch

device = torch.device('cuda')

for shape in shapes.SHAPES:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Warmup / autotune
    for _ in range(3):
        _ = model(x)
    
    config = solution.w4a16_gemm_kernel.best_config
    print(f"Shape {shape}: {config}")
EOF
python /tmp/check_configs5.py

{
  "command": "<1946 chars \u2014 see below>"
}

cat > /tmp/test_compile.py << 'EOF'
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/07_w4a16_gemm')
import solution
import reference
import torch
import statistics

_l2_scratch = torch.empty(128 * 1024 * 1024 // 4, dtype=torch.float32, device='cuda')

def time_fn(fn, inputs, iters=50, warmup=10):
    for _ in range(warmup):
        with torch.no_grad():
            fn(*inputs)
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        _l2_scratch.zero_()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        with torch.no_grad():
            fn(*inputs)
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)

device = torch.device('cuda')

for shape in [
    {"M": 1, "N": 4096, "K": 4096},
    {"M": 1, "N": 12288, "K": 4096},
]:
    reference.M = shape['M']
    reference.N = shape['N']
    reference.K = shape['K']
    
    init_args = reference.get_init_inputs()
    model = solution.Model(*init_args).to(device).eval()
    
    x = torch.randn(shape['M'], shape['K'], dtype=torch.bfloat16, device=device)
    
    # Baseline
    ms_base = time_fn(model, [x], iters=50, warmup=10)
    
    # Compiled
    try:
        compiled = torch.compile(model, mode="reduce-overhead")
        ms_comp = time_fn(compiled, [x], iters=50, warmup=10)
    except Exception as e:
        ms_comp = None
        print(f"Compile failed: {e}")
    
    M, N, K = shape['M'], shape['N'], shape['K']
    bytes_moved = M*K*2 + (K/2)*N + (K/128)*N*2 + (K/128)*N*2 + M*N*2
    
    print(f"Shape {shape}:")
    print(f"  Baseline: {ms_base:.3f}ms, {bytes_moved/(ms_base*1e-3)/1e9:.1f} GB/s")
    if ms_comp:
        print(f"  Compiled: {ms_comp:.3f}ms, {bytes_moved/(ms_comp*1e-3)/1e9:.1f} GB/s")
EOF
python /tmp/test_compile.py