{
  "name": "kernel-optimization"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/reference.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/check.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/benchmark.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/shapes.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/problem.yaml"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/sota.py"
}

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

{
  "command": "nvcc --version",
  "description": "Check CUDA version"
}

{
  "command": "python -c \"import torch; print(f'Torch {torch.__version__}'); print(f'CUDA avail: {torch.cuda.is_available()}'); cc = torch.cuda.get_device_capability(); print(f'Compute Capability: {cc}')\"",
  "description": "Check PyTorch and compute capability"
}

{
  "command": "python -c \"import cutlass; print(cutlass.__version__)\" 2>/dev/null || echo \"CUTLASS not installed\"",
  "description": "Check if CUTLASS is installed"
}

{
  "command": "pip list 2>/dev/null | grep -iE \"triton|flashinfer|cutlass|cublas|curand\"",
  "description": "Check GPU-related python packages"
}

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

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/src/hardware/rtx_pro_6000.py"
}

{
  "todos": [
    {
      "content": "Write first-pass Triton FP8 GEMM kernel in solution.py",
      "status": "in_progress",
      "priority": "high"
    },
    {
      "content": "Run check.py to verify correctness",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Run benchmark.py to measure performance",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Profile with ncu/nsys and optimize",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Handle all shapes correctly (especially K=4127 and M

... (70 more chars)

{
  "command": "<1731 chars \u2014 see below>",
  "description": "Test Triton fp8 dot on SM120",
  "timeout": 30000
}

cat > /tmp/test_triton_fp8.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def test_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bn, stride_bk, stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    
    m_mask = offs_m < M
    n_mask = offs_n < N
    
    a = tl.load(a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak,
                mask=m_mask[:, None], other=0.0)
    b = tl.load(b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk,
                mask=n_mask[None, :], other=0.0)
    
    c = tl.dot(a, b)
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             c.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

M, N, K = 128, 128, 128
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = (torch.rand(N, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
test_kernel[grid](x, w, out, M, N, K,
                  x.stride(0), x.stride(1),
                  w.stride(0), w.stride(1),
                  out.stride(0), out.stride(1),
                  BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref = x.to(torch.bfloat16) @ w.to(torch.bfloat16).T
diff = (ref - out).abs().max().item()
print(f"Max diff: {diff:.6f}")
print(f"Triton fp8 dot works: {diff < 0.15}")
PYEOF
python /tmp/test_triton_fp8.py

{
  "command": "<6145 chars \u2014 see below>",
  "description": "Debug Triton fp8/tl.dot issue",
  "timeout": 30000
}

cat > /tmp/test_triton_fp16.py << 'PYEOF'
import torch
import triton
import triton.language as tl

# First test if basic tl.dot works at all
@triton.jit
def test_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bn, stride_bk, stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a = tl.load(a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak)
    b = tl.load(b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    c = tl.dot(a, b.to(tl.float16))  # both fp16
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn, c.to(tl.bfloat16))

M, N, K = 128, 128, 128
x = torch.randn(M, K, dtype=torch.float16, device='cuda')
w = torch.randn(N, K, dtype=torch.float16, device='cuda')
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
test_kernel[grid](x, w, out, M, N, K,
                  x.stride(0), x.stride(1),
                  w.stride(0), w.stride(1),
                  out.stride(0), out.stride(1),
                  BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref = (x @ w.T).to(torch.bfloat16)
diff = (ref - out).abs().max().item()
print(f"fp16 test - Max diff: {diff:.6f}")

# Now test with explicit tl.float8e4nv type
@triton.jit
def test_fp8_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                    stride_am, stride_ak, stride_bn, stride_bk, stride_cm, stride_cn,
                    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a = tl.load(a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak,
                mask=m_mask[:, None], other=0.0).to(tl.float8e4nv)
    b = tl.load(b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk,
                mask=n_mask[None, :], other=0.0).to(tl.float8e4nv)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc = tl.dot(a, b, acc, input_precision="ieee")
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

x_fp8 = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w_fp8 = (torch.rand(N, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
out2 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
test_fp8_kernel[grid](x_fp8, w_fp8, out2, M, N, K,
                      x_fp8.stride(0), x_fp8.stride(1),
                      w_fp8.stride(0), w_fp8.stride(1),
                      out2.stride(0), out2.stride(1),
                      BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref2 = x_fp8.to(torch.bfloat16) @ w_fp8.to(torch.bfloat16).T
diff2 = (ref2 - out2).abs().max().item()
print(f"fp8 test - Max diff: {diff2:.6f}")

# Try without explicit types, just let Triton infer
@triton.jit
def test_fp8_kernel2(a_ptr, b_ptr, c_ptr, M, N, K,
                     stride_am, stride_ak, stride_bn, stride_bk, stride_cm, stride_cn,
                     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a = tl.load(a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak,
                mask=m_mask[:, None], other=0.0)
    b = tl.

... (2145 more chars)

{
  "command": "<3595 chars \u2014 see below>",
  "description": "Test simple Triton matmul from tutorial",
  "timeout": 30000
}

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

# Minimal test from official Triton tutorials
@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    c = acc.to(tl.float16)
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    tl.store(c_ptrs, c)

# Test with fp16
M, N, K = 128, 128, 128
device = 'cuda'
a = torch.randn(M, K, dtype=torch.float16, device=device)
b = torch.randn(N, K, dtype=torch.float16, device=device)
c = torch.empty(M, N, dtype=torch.float16, device=device)

# b is (N, K), we need b.T which is (K, N)
# So b_ptrs should access b as (K, N)
# stride_bk = b.stride(1) = 1 (K dim in the transposed view)
# stride_bn = b.stride(0) = K (N dim)

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
matmul_kernel[grid](a, b.T.contiguous(), c, M, N, K,
                    a.stride(0), a.stride(1),
                    b.T.stride(0), b.T.stride(1),
                    c.stride(0), c.stride(1),
                    BLOCK_M=32, BLOCK_N=32, BLOCK_K=32)

ref = a @ b.T
diff = (ref - c).abs().max().item()
print(f"fp16 pre-transposed: {diff:.6f}")

# Now test with b in (N, K) format, accessing as (K, N) in kernel
c2 = torch.empty(M, N, dtype=torch.float16, device=device)
@triton.jit
def matmul_kernel2(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,  # b has shape (N, K), we want (K, N) access
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: 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)
    offs_k = tl.arange(0, BLOCK_K)
    
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    # b is (N, K), we want to access as (K, N)
    # b[n, k] = ptr + n * K + k
    # We want (k, n) element: ptr + n * stride_bn + k * stride_bk
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    tl.store(c_ptrs, c.to(tl.float16))

matmul_kernel2[grid](a, b, c2, M, N, K,
                     a.stride(0), a.stride(1),
                     b.stride(0), b.stride(1),
                     c2.stride(0), c2.stride(1),
                     BLOCK_M=32, BLOCK_N=32, BLOCK_K=32)
diff2 = (ref - c2).abs().max().item()
print(f"fp16 in-kernel transpose: {diff2:.6f}")
PYEOF
python /tmp/test_triton_simple.py

{
  "command": "<2516 chars \u2014 see below>",
  "description": "Test Triton matmul more carefully",
  "timeout": 30000
}

cat > /tmp/test_triton_v2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

# Minimal test: just load and store, see if basic triton works
@triton.jit
def simple_copy(x_ptr, y_ptr, N, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < N
    x = tl.load(x_ptr + offs, mask=mask)
    tl.store(y_ptr + offs, x, mask=mask)

N = 256
x = torch.randn(N, dtype=torch.float16, device='cuda')
y = torch.empty(N, dtype=torch.float16, device='cuda')
grid = lambda meta: (triton.cdiv(N, meta['BLOCK']),)
simple_copy[grid](x, y, N, BLOCK=128)
print(f"copy matches: {torch.allclose(x, y)}")

# Now test matmul with latest Triton API
@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,  # b is pre-transposed to (K, N)
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        d = tl.dot(a, b)
        acc = acc + d
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn, acc.to(tl.float16))

M, N, K = 64, 64, 64
a = torch.randn(M, K, dtype=torch.float16, device='cuda')
b = torch.randn(K, N, dtype=torch.float16, device='cuda')  # b is already (K, N)
c = torch.empty(M, N, dtype=torch.float16, device='cuda')

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
matmul_kernel[grid](a, b, c, M, N, K,
                    a.stride(0), a.stride(1),
                    b.stride(0), b.stride(1),
                    c.stride(0), c.stride(1),
                    BLOCK_M=16, BLOCK_N=16, BLOCK_K=16)

ref = a @ b
diff = (ref - c).abs().max().item()
print(f"fp16 matmul 64x64x64: diff={diff:.6f}")
print(f"ref[0,0]={ref[0,0]}, c[0,0]={c[0,0]}")
print(f"ref min={ref.min()}, c min={c.min()}")
print(f"ref max={ref.max()}, c max={c.max()}")

# Check entire matrix
print(f"ref:\n{ref}")
print(f"c:\n{c}")
PYEOF
python /tmp/test_triton_v2.py

{
  "command": "<2891 chars \u2014 see below>",
  "description": "Test Triton fp8 matmul v2",
  "timeout": 30000
}

cat > /tmp/test_triton_fp8_v2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

@triton.jit
def matmul_fp8_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,  # b is pre-transposed to (K, N)
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    
    m_mask = offs_m < M
    n_mask = offs_n < N
    
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        d = tl.dot(a, b)
        acc = acc + d
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test fp8: a is (M, K) fp8, b.T is (K, N) fp8
M, N, K = 128, 128, 128
a_fp8 = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b_fp8 = (torch.rand(N, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b_fp8_t = b_fp8.T.contiguous()  # (K, N) fp8

c = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

grid = lambda meta: (M // meta['BLOCK_M'], N // meta['BLOCK_N'])
matmul_fp8_kernel[grid](a_fp8, b_fp8_t, c, M, N, K,
                        a_fp8.stride(0), a_fp8.stride(1),
                        b_fp8_t.stride(0), b_fp8_t.stride(1),
                        c.stride(0), c.stride(1),
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref = a_fp8.to(torch.bfloat16) @ b_fp8.to(torch.bfloat16).T
diff = (ref - c).abs().max().item()
print(f"fp8 matmul 128x128x128: diff={diff:.6f}")
print(f"PASS: {diff < 0.15}")

# Test with non-power-of-2 K
K2 = 160
a2 = (torch.rand(M, K2) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b2 = (torch.rand(N, K2) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b2t = b2.T.contiguous()
c2 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

matmul_fp8_kernel[grid](a2, b2t, c2, M, N, K2,
                        a2.stride(0), a2.stride(1),
                        b2t.stride(0), b2t.stride(1),
                        c2.stride(0), c2.stride(1),
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref2 = a2.to(torch.bfloat16) @ b2.to(torch.bfloat16).T
diff2 = (ref2 - c2).abs().max().item()
print(f"fp8 matmul 128x128x160 (non-pow2 K): diff={diff2:.6f}")
print(f"PASS: {diff2 < 0.15}")
PYEOF
python /tmp/test_triton_fp8_v2.py

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py",
  "content": "<3088 chars \u2014 see diff>"
}

{
  "description": "Run correctness check",
  "command": "python /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/check.py",
  "workdir": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm",
  "timeout": 120000
}

{
  "command": "<7011 chars \u2014 see below>",
  "description": "Debug fp8 precision issue",
  "timeout": 60000
}

cat > /tmp/debug_fp8.py << 'PYEOF'
import torch

# Test precision loss: bf16 -> fp8 -> bf16
torch.manual_seed(42)
weight = torch.randn(4096, 4096, dtype=torch.bfloat16, device='cuda') * 0.02
w_fp8 = weight.to(torch.float8_e4m3fn)
w_back = w_fp8.to(torch.bfloat16)
diff = (weight - w_back).abs().max().item()
print(f"bf16->fp8->bf16 max diff: {diff:.6f}")
print(f"weight range: [{weight.min()}, {weight.max()}]")

# Test GEMM: fp8 matmul vs bf16 matmul
x_fp8 = (torch.rand(4096, 4096, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
weight_bf16 = torch.randn(4096, 4096, dtype=torch.bfloat16, device='cuda') * 0.02

# Reference: bf16 matmul
ref = x_fp8.to(torch.bfloat16) @ weight_bf16.to(torch.bfloat16).T

# FP8 matmul: convert weight to fp8, do fp8 matmul
w_fp8 = weight_bf16.to(torch.float8_e4m3fn)
x_bf = x_fp8.to(torch.bfloat16)
w_bf = w_fp8.to(torch.bfloat16)
fp8_result = x_bf @ w_bf.T

diff_vs_ref = (ref - fp8_result).abs().max().item()
print(f"bf16 ref vs bf16-from-fp8 matmul max diff: {diff_vs_ref:.6f}")

# What about: do the matmul in fp8 tensor cores (via Triton)?
import triton
import triton.language as tl

@triton.jit
def test_gemm(a_ptr, b_ptr, c_ptr, M, N, K,
              stride_am, stride_ak, stride_bk, stride_bn,
              stride_cm, stride_cn,
              BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test 1: fp8 inputs, Triton tl.dot
a_fp8 = (torch.rand(256, 256, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
b_fp8 = (torch.rand(256, 256, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
b_fp8_t = b_fp8.T.contiguous()
c1 = torch.empty(256, 256, dtype=torch.bfloat16, device='cuda')
grid = (256//64, 256//64)
test_gemm[grid](a_fp8, b_fp8_t, c1, 256, 256, 256,
                a_fp8.stride(0), a_fp8.stride(1),
                b_fp8_t.stride(0), b_fp8_t.stride(1),
                c1.stride(0), c1.stride(1),
                BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

ref1 = a_fp8.to(torch.bfloat16) @ b_fp8.to(torch.bfloat16).T
print(f"Test 1 (fp8 input * fp8 input via tl.dot): max diff = {(ref1 - c1).abs().max().item():.6f}")

# Test 2: bf16 inputs, Triton tl.dot (should be exact)
a_bf = a_fp8.to(torch.bfloat16)
b_bf = b_fp8.to(torch.bfloat16)
b_bf_t = b_bf.T.contiguous()
c2 = torch.empty(256, 256, dtype=torch.bfloat16, device='cuda')
test_gemm[grid](a_bf, b_bf_t, c2, 256, 256, 256,
                a_bf.stride(0), a_bf.stride(1),
                b_bf_t.stride(0), b_bf_t.stride(1),
                c2.stride(0), c2.stride(1),
                BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
print(f"Test 2 (bf16 input * bf16 input via tl.dot): max diff = {(ref1 - c2).abs().max().item():.6f}")

# Test 3: unaligned K
K3 = 160
a3 = (torch.rand(128, K3) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b3 = (torch.rand(128, K3) * 8 - 4).to(torch.float8_e4m3fn).cuda()
b3t = b3.T.contiguous()
c3 = torch.empty(128, 128, dtype=torch.bfloat16, device='cuda')
grid3 = (128//64, 128//64)
test_gemm[grid3](a3, b3t, c3, 128, 128, K3,
                 a3.stride(0), a3.stride(1),
                 b3t.stride(0), b3t.stride(1),
            

... (3011 more chars)

{
  "command": "<6152 chars \u2014 see below>",
  "description": "Test fp8 vs bf16 on larger shapes",
  "timeout": 120000
}

cat > /tmp/debug_fp8_large.py << 'PYEOF'
import torch
import triton
import triton.language as tl

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

@triton.jit
def gemm_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bk, stride_bn,
                stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                GROUP_M: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

@triton.jit
def bf16_gemm_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                     stride_am, stride_ak, stride_bn, stride_bk,
                     stride_cm, stride_cn,
                     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                     GROUP_M: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0).to(tl.bfloat16)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

M, N, K = 512, 512, 512

# Test 1: fp8 input * fp8-from-bf16 weight (fp8 tensor cores)
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02

w_fp8 = w.to(torch.float8_e4m3fn)
w_fp8_t = w_fp8.T.contiguous()

out_fp8 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
grid = (triton.cdiv(M, 128), triton.cdiv(N, 128))
gemm_kernel[grid](x, w_fp8_t, out_fp8, M, N, K,
                  x.stride(0), x.stride(1),
                  w_fp8_t.stride(0), w_fp8_t.stride(1),
                  out_fp8.stride(0), out_fp8.stride(1),
                  BLOCK_M=128, BLOCK_N=128, BLOCK_K=128, GROUP_M=8)

ref = x.to(torch.bfloat16) @ w.T
diff_fp8 = (ref - out_

... (2152 more chars)

{
  "command": "<654 chars \u2014 see below>",
  "description": "Debug tolerance lookup path"
}

cat > /tmp/debug_tolerance.py << 'PYEOF'
# Check if the tolerance file is correct and trace the path
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard')
from src.eval.correctness import tolerance_for_dtype, DEFAULT_TOLERANCE
import torch

print("Default tol for bf16:", DEFAULT_TOLERANCE[torch.bfloat16])
override = {"fp8_e4m3fn": 0.15}
print("Override:", override)
print("type_str for bf16:", str(torch.bfloat16))
print("short_str for bf16:", str(torch.bfloat16).split('.')[-1])
result = tolerance_for_dtype(torch.bfloat16, override)
print("tolerance_for_dtype(torch.bfloat16, override):", result)
PYEOF
python /tmp/debug_tolerance.py

{
  "command": "<5769 chars \u2014 see below>",
  "description": "Debug bf16 precision",
  "timeout": 120000
}

cat > /tmp/debug_bf16.py << 'PYEOF'
import torch
import triton
import triton.language as tl

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

@triton.jit
def bf16_gemm_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                     stride_am, stride_ak, stride_bn, stride_bk,
                     stride_cm, stride_cn,
                     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                     GROUP_M: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test 1: both bf16 inputs, bf16 kernel
M, N, K = 4096, 4096, 4096
x_bf16 = (torch.rand(M, K) * 8 - 4).to(torch.bfloat16).cuda()
w_bf16 = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02

out1 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
grid = (triton.cdiv(M, 128), triton.cdiv(N, 128))
bf16_gemm_kernel[grid](x_bf16, w_bf16, out1, M, N, K,
                       x_bf16.stride(0), x_bf16.stride(1),
                       w_bf16.stride(0), w_bf16.stride(1),
                       out1.stride(0), out1.stride(1),
                       BLOCK_M=128, BLOCK_N=128, BLOCK_K=128, GROUP_M=8)

ref = x_bf16 @ w_bf16.T
diff1 = (ref - out1).abs().max().item()
mean_diff1 = (ref - out1).abs().mean().item()
print(f"bf16 * bf16: max_diff={diff1:.6f}, mean_diff={mean_diff1:.6f}")

# Test 2: fp8 input -> cast to bf16 in kernel, bf16 weight
x_fp8 = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w_bf16_2 = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02

@triton.jit
def fp8_to_bf16_gemm(a_ptr, b_ptr, c_ptr, M, N, K,
                     stride_am, stride_ak, stride_bn, stride_bk,
                     stride_cm, stride_cn,
                     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
                     GROUP_M: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0).to(tl.bfloat16)
        b = tl.load(b_ptrs, mask=k_mask[:, None

... (1769 more chars)

{
  "command": "<5284 chars \u2014 see below>",
  "description": "Test Triton autotune and block sizes",
  "timeout": 120000
}

cat > /tmp/test_blocks.py << 'PYEOF'
import torch
import triton
import triton.language as tl
from triton.runtime import driver

# Check SM120 shared memory limits
device = torch.cuda.current_device()
props = driver.active.utils.get_device_properties(device)
print(f"Device: {props.name}")
print(f"SM count: {props.multiprocessor_count}")
print(f"Shared mem per SM: {props.shared_mem_per_block}")
print(f"Max shared mem: {props.max_shared_mem}")

# Try kernel with explicit num_stages
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=8),
    ],
    key=[],
)
@triton.jit
def bf16_gemm_auto(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    GROUP_M = 8
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

M, N, K = 512, 512, 512
x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda')
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
bf16_gemm_auto[(triton.cdiv(M,128), triton.cdiv(N,128))](
    x, w, out, M, N, K,
    x.stride(0), x.stride(1),
    w.stride(0), w.stride(1),
    out.stride(0), out.stride(1),
)
ref = x @ w.T
diff = (ref - out).abs().max().item()
print(f"bf16 auto kernel: max_diff={diff:.6f}")

# Now test fp8 -> bf16 conversion in kernel with autotune
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=4),
    ],
    key=[],
)
@triton.jit
def fp8_to_bf16_auto(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    GROUP_M = 8
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    f

... (1284 more chars)

{
  "command": "<6286 chars \u2014 see below>",
  "description": "Test Triton autotune and shapes",
  "timeout": 120000
}

cat > /tmp/test_blocks2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

# Check SM120 shared memory limits
device = torch.cuda.current_device()
props = triton.runtime.driver.utils.get_device_properties(device)
print(f"Props keys: {props.keys() if isinstance(props, dict) else 'not dict'}")
print(f"Props: {props}")

# Try kernel with explicit num_stages
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64},
                       num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=4),
    ],
    key=[],
)
@triton.jit
def bf16_gemm_auto(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    GROUP_M = 8
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    k_mask_full = offs_k < K  # compile-time constant in loop, will be handled via k check

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

M, N, K = 512, 512, 512
x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda')
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
bf16_gemm_auto[(triton.cdiv(M,128), triton.cdiv(N,128))](
    x, w, out, M, N, K,
    x.stride(0), x.stride(1),
    w.stride(0), w.stride(1),
    out.stride(0), out.stride(1),
)
ref = x @ w.T
diff = (ref - out).abs().max().item()
print(f"bf16 auto kernel: max_diff={diff:.6f}")

# Now test fp8 -> bf16 with autotune
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64},
                       num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=4),
    ],
    key=[],
)
@triton.jit
def fp8_to_bf16_auto(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    GROUP_M = 8
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:,

... (2286 more chars)

{
  "command": "<3095 chars \u2014 see below>",
  "description": "Test kernel with all shapes",
  "timeout": 180000
}

cat > /tmp/test_kernel.py << 'PYEOF'
import torch
import triton
import triton.language as tl
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm')
import shapes

# Design the kernel that will be in solution.py
# fp8 activation -> cast to bf16 in kernel, bf16 weight with in-kernel transpose
# This avoids weight quantization noise and produces exact match

@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64},
                       num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64},
                       num_stages=3, num_warps=4),
        triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 64},
                       num_stages=3, num_warps=4),
    ],
    key=['M', 'N', 'K'],
)
@triton.jit
def fp8_gemm_auto(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    GROUP_M = 8
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0).to(tl.bfloat16)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

for s in shapes.SHAPES:
    M, N, K = s['M'], s['N'], s['K']
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
    w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
    
    bm = 128 if M >= 128 else (64 if M >= 64 else 32)
    bn = 128 if M >= 64 else 256
    grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
    
    fp8_gemm_auto[grid](
        x, w, out, M, N, K,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        out.stride(0), out.stride(1),
    )
    ref = x.to(torch.bfloat16) @ w.T
    diff = (ref - out).abs().max().item()
    print(f"Shape {M}x{N}x{K}: max_diff={diff:.6f}  PASS={diff < 0.01}")
PYEOF
python /tmp/test_kernel.py

{
  "command": "<3417 chars \u2014 see below>",
  "description": "Test with fixed block sizes matching grid",
  "timeout": 180000
}

cat > /tmp/test_kernel2.py << 'PYEOF'
import torch
import triton
import triton.language as tl
import sys
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm')
import shapes

# Fixed block sizes, no autotune, matching grid
@triton.jit
def fp8_gemm_fixed(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bn, stride_bk,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    num_pid_m = tl.cdiv(M, BLOCK_M)
    num_pid_n = tl.cdiv(N, BLOCK_N)
    group_id = pid_m // GROUP_M
    first_pid_m = group_id * GROUP_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
    pid_m = first_pid_m + ((pid_n * group_size_m) // num_pid_n)
    pid_n = (pid_n * group_size_m) % num_pid_n

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_n[None, :] * stride_bn + offs_k[:, None] * stride_bk

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        k_mask = (k + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        a_bf16 = a.to(tl.bfloat16)
        d = tl.dot(a_bf16, b)
        acc += d
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test all shapes with correct grid sizes
for s in shapes.SHAPES:
    M, N, K = s['M'], s['N'], s['K']
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
    w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
    out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
    
    # Block sizes to try - start small for correctness
    BLOCK_M = 64
    BLOCK_N = 64
    BLOCK_K = 64
    GROUP_M = 4
    
    grid = (triton.cdiv(M, BLOCK_M), triton.cdiv(N, BLOCK_N))
    
    fp8_gemm_fixed[grid](
        x, w, out, M, N, K,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        out.stride(0), out.stride(1),
        BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
        GROUP_M=GROUP_M,
    )
    ref = x.to(torch.bfloat16) @ w.T
    diff = (ref - out).abs().max().item()
    print(f"Shape {M}x{N}x{K}: max_diff={diff:.8f}  PASS={diff < 0.01}")
    
    # If failed, try smaller blocks
    if diff >= 0.01:
        BLOCK_M2 = 32
        BLOCK_N2 = 32
        BLOCK_K2 = 32
        grid2 = (triton.cdiv(M, BLOCK_M2), triton.cdiv(N, BLOCK_N2))
        out2 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
        fp8_gemm_fixed[grid2](
            x, w, out2, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            out2.stride(0), out2.stride(1),
            BLOCK_M=BLOCK_M2, BLOCK_N=BLOCK_N2, BLOCK_K=BLOCK_K2,
            GROUP_M=2,
        )
        diff2 = (ref - out2).abs().max().item()
        print(f"  => 32x32x32: max_diff={diff2:.8f}  PASS={diff2 < 0.01}")
PYEOF
python /tmp/test_kernel2.py

{
  "command": "<4640 chars \u2014 see below>",
  "description": "Debug Triton bf16 accuracy",
  "timeout": 60000
}

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

# Ultra-simple test: does Triton tl.dot with bf16 match PyTorch matmul?
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

@triton.jit
def simple_bf16_gemm(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        d = tl.dot(a, b)
        acc = acc + d
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test 1: Small, both bf16
M, N, K = 128, 128, 128
a = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
b = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')  # pre-transposed
c = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
grid = (M//32, N//32)
simple_bf16_gemm[grid](a, b, c, M, N, K,
                       a.stride(0), a.stride(1),
                       b.stride(0), b.stride(1),
                       c.stride(0), c.stride(1),
                       BLOCK_M=32, BLOCK_N=32, BLOCK_K=32)
ref = a @ b
diff = (ref - c).abs().max().item()
print(f"Test 1 (bf16 128x128x128): max_diff={diff:.10f}")

# Test 2: Single block, medium
M2, N2, K2 = 64, 64, 256
a2 = torch.randn(M2, K2, dtype=torch.bfloat16, device='cuda')
b2 = torch.randn(K2, N2, dtype=torch.bfloat16, device='cuda')
c2 = torch.empty(M2, N2, dtype=torch.bfloat16, device='cuda')
grid2 = (1, 1)  # single block
simple_bf16_gemm[grid2](a2, b2, c2, M2, N2, K2,
                        a2.stride(0), a2.stride(1),
                        b2.stride(0), b2.stride(1),
                        c2.stride(0), c2.stride(1),
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
ref2 = a2 @ b2
diff2 = (ref2 - c2).abs().max().item()
print(f"Test 2 (bf16 64x64x256, 1 block): max_diff={diff2:.10f}")

# Test 3: K=64, single block  
M3, N3, K3 = 64, 64, 64
a3 = torch.randn(M3, K3, dtype=torch.bfloat16, device='cuda')
b3 = torch.randn(K3, N3, dtype=torch.bfloat16, device='cuda')
c3 = torch.empty(M3, N3, dtype=torch.bfloat16, device='cuda')
grid3 = (1, 1)
simple_bf16_gemm[grid3](a3, b3, c3, M3, N3, K3,
                        a3.stride(0), a3.stride(1),
                        b3.stride(0), b3.stride(1),
                        c3.stride(0), c3.stride(1),
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
ref3 = a3 @ b3
diff3 = (ref3 - c3).abs().max().item()
print(f"Test 3 (bf16 64x64x64, 1 block): max_diff={diff3:.10f}")

# Test 4: Single block, K=8192
M4, N4, K4 = 64, 64, 8192
a4 = torch.randn(M4, K4, dtype=torch.bfloat16, device='cuda')
b4 = torch.randn(K4, N4, dtype=torch.bfloat16, device='cuda')
c4 = torch.empty(M4, N4, dtype=torch.bfloat16, device='cuda')
simple_bf16_gemm[grid3](a4, b4, c4, M4, N4, K4,
                        a4.stride(0), a4.stride(1),
                        b4.stride(0), b4.stride(1),
                        c4.stride(0), c4.stride(1),
                        BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
ref4 = a4 @ b4
diff4 = (ref4 - c4).abs().max().item()
print(f"Test 4 (bf16 64x64x8192, 1 block): max_diff={diff4:.10f}")

# Test 5: Multiple blocks, K=8192
M5, N5, K5 = 256, 256, 8192
a5 = tor

... (640 more chars)

{
  "command": "<6191 chars \u2014 see below>",
  "description": "Debug bf16/fp16 accuracy vs K",
  "timeout": 60000
}

cat > /tmp/debug_accuracy2.py << 'PYEOF'
import torch
import triton
import triton.language as tl

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

@triton.jit
def gemm_kernel(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)  # USE accum directly
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test with accumulate vs without
@triton.jit
def gemm_kernel_noacc(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N

    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        d = tl.dot(a, b)
        acc = acc + d
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test fp16 too
@triton.jit
def gemm_fp16(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

@triton.jit
def gemm_fp16_noacc(
    a_ptr, b_ptr, c_ptr, M, N, K,
    stride_am, stride_ak, stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.a

... (2191 more chars)

{
  "command": "<6017 chars \u2014 see below>",
  "description": "Compare cuBLAS vs Triton accuracy",
  "timeout": 60000
}

cat > /tmp/debug_cublas.py << 'PYEOF'
import torch

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

# Compare cuBLAS bf16 vs fp32 vs Triton
M, N, K = 64, 64, 8192

a_bf16 = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
b_bf16 = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')

# cuBLAS bf16 matmul (the reference)
c_bf16 = a_bf16 @ b_bf16

# fp32 matmul (the "ground truth")
a_fp32 = a_bf16.float()
b_fp32 = b_bf16.float()
c_fp32 = a_fp32 @ b_fp32

# Compare cuBLAS bf16 vs fp32
diff_cublas = (c_bf16.float() - c_fp32).abs().max().item()
print(f"cuBLAS bf16 vs fp32: max_diff={diff_cublas:.6f}")

# Compare cuBLAS matmul on same precision multiple times
c_bf16_2 = a_bf16 @ b_bf16
diff_same = (c_bf16 - c_bf16_2).abs().max().item()
print(f"cuBLAS self-consistency: max_diff={diff_same:.6f}")

# Now test Triton
import triton
import triton.language as tl

@triton.jit
def test_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bk, stride_bn,
                stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=a_mask, other=0.0)
        b = tl.load(b_ptrs, mask=b_mask, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn, acc.to(tl.bfloat16))

# Fixed: proper masks
@triton.jit
def test_kernel2(a_ptr, b_ptr, c_ptr, M, N, K,
                 stride_am, stride_ak, stride_bk, stride_bn,
                 stride_cm, stride_cn,
                 BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

c_triton = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
a_t = a_bf16  # Use pre-transposed b
b_t = b_bf16  # b is (K, N)
test_kernel2[(1,1)](a_t, b_t, c_triton, M, N, K,
                    a_t.stride(0), a_t.stride(1),
                    b_t.stride(0), b_t.stride(1),
                    c_triton.stride(0), c_triton.stride(1),
                    BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)

diff_triton_vs_cublas = (c_triton.float() - c_bf16.float()).abs().max().item()
print(f"Triton vs cuBLAS bf16: max_diff={diff_triton_vs_cublas:.6f}")

# What about fp32 with tf32? (input_precision ieee)
@triton.jit
def test_fp32_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                     stride_am, stride_ak, stride_bk, stride_bn,
                     stride_cm, stride_cn,
                     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_

... (2017 more chars)

{
  "command": "pip install --upgrade cutlass 2>&1 | tail -5",
  "description": "Install CUTLASS Python package",
  "timeout": 120000
}

{
  "command": "uv pip install --upgrade cutlass 2>&1 | tail -10",
  "description": "Install CUTLASS via uv",
  "timeout": 120000
}

{
  "command": "python -c \"import cutlass; print(dir(cutlass))\" 2>&1",
  "description": "Check CUTLASS Python API",
  "timeout": 30000
}

{
  "command": "pip list 2>/dev/null | grep -i cutlass && python -c \"import cutlass; print(cutlass.__version__)\" 2>&1",
  "description": "Check CUTLASS import"
}

{
  "command": "pip show cutlass 2>&1 && uv pip show cutlass 2>&1",
  "description": "Check cutlass package info"
}

{
  "command": "uv pip install cutlass 2>&1",
  "description": "Install cutlass package properly via uv",
  "timeout": 120000
}

{
  "command": "<6129 chars \u2014 see below>",
  "description": "Test different precision approaches",
  "timeout": 60000
}

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

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

# Test: does BLOCK_K=16 (matching tensor core tile) help?
@triton.jit
def kernel_vary_bk(a_ptr, b_ptr, c_ptr, M, N, K,
                   stride_am, stride_ak, stride_bk, stride_bn,
                   stride_cm, stride_cn,
                   BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

# Test with no accumulate (just compute in one go for single K-block)
M, N, K = 64, 64, 4096
a_bf = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
b_bf = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')
ref_bf = a_bf @ b_bf

for bk in [16, 32, 64, 128]:
    c = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
    kernel_vary_bk[(1,1)](a_bf, b_bf, c, M, N, K,
                          a_bf.stride(0), a_bf.stride(1),
                          b_bf.stride(0), b_bf.stride(1),
                          c.stride(0), c.stride(1),
                          BLOCK_M=64, BLOCK_N=64, BLOCK_K=bk)
    diff = (c.float() - ref_bf.float()).abs().max().item()
    print(f"BLOCK_K={bk:3d}: max_diff={diff:.6f}")

# Test with fp32 cast
@triton.jit
def kernel_fp32(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bk, stride_bn,
                stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc += tl.dot(a, b, input_precision="ieee")
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

c_fp32 = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
a_f32 = a_bf.float()
b_f32 = b_bf.float()
kernel_fp32[(1,1)](a_f32, b_f32, c_fp32, M, N, K,
                   a_f32.stride(0), a_f32.stride(1),
                   b_f32.stride(0), b_f32.stride(1),
                   c_fp32.stride(0), c_fp32.stride(1),
                   BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
ref_f32 = a_f32 @ b_f32
diff_fp32 = (c_fp32.float() - ref_f32.float()).abs().max().item()
print(f"fp32 tl.dot (ieee): max_diff={diff_fp32:.6f}")

# Compare fp32 tl.dot with bf16 reference
diff_fp32_vs_bf16 = (c_fp32.float() - ref_bf.float()).abs().max().item()
print(f"fp32 tl.dot vs cuBLAS bf16: max_diff={diff_fp32_vs_bf1

... (2129 more chars)

{
  "command": "<5137 chars \u2014 see below>",
  "description": "Test torch.compile and precision",
  "timeout": 120000
}

cat > /tmp/test_torchcompile.py << 'PYEOF'
import torch

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

M, N, K = 64, 64, 4096
a = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
b = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')

# Reference: eager bf16 matmul (cuBLAS)
ref = a @ b

# Compiled: torch.compile the matmul
@torch.compile
def compiled_mm(x, y):
    return x @ y

try:
    c_comp = compiled_mm(a, b)
    diff_comp = (c_comp.float() - ref.float()).abs().max().item()
    print(f"torch.compile bf16 matmul vs cuBLAS: max_diff={diff_comp:.6f}")
except Exception as e:
    print(f"torch.compile failed: {e}")

# What about just a manual fp32 matmul?
c_f32 = (a.float() @ b.float()).to(torch.bfloat16)
diff_f32 = (c_f32.float() - ref.float()).abs().max().item()
print(f"fp32 matmul vs cuBLAS bf16: max_diff={diff_f32:.6f}")

# fp32 matmul self-consistency
c_f32_ref = a.float() @ b.float()
diff_f32_self = (c_f32.float() - c_f32_ref.float()).abs().max().item()
print(f"fp32 matmul self-consistency: max_diff={diff_f32_self:.6f}")

# So the reference cuBLAS bf16 differs from fp32...
# The question is: what precision does the reference actually use?
# Let's trace: reference does x @ w.T where both are bf16
# cuBLAS receives bf16 inputs and internally calls the bf16 tensor core path
# This path uses fp32 accumulation but the inputs are bf16

# The diff of 1.0 for K=4096 between Triton and cuBLAS means:
# cuBLAS and Triton bf16 tensor cores produce different results

# Let me check if there's a version where they match
import triton
import triton.language as tl

@triton.jit
def simple_dot(a_ptr, b_ptr, c_ptr, M, N, K,
               stride_am, stride_ak, stride_bk, stride_bn,
               stride_cm, stride_cn,
               BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=a_mask, other=0.0)
        b = tl.load(b_ptrs, mask=b_mask, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn, acc.to(tl.bfloat16))

# Just try different BLOCK_K to see if any matches cuBLAS
@triton.jit
def test_dot(a_ptr, b_ptr, c_ptr, M, N, K,
             stride_am, stride_ak, stride_bk, stride_bn,
             stride_cm, stride_cn,
             BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    offs_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    m_mask = offs_m < M
    n_mask = offs_n < N
    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k_ in range(0, K, BLOCK_K):
        k_mask = (k_ + offs_k) < K
        a = tl.load(a_ptrs, mask=m_mask[:, None] & k_mask[None, :], other=0.0)
        b = tl.load(b_ptrs, mask=k_mask[:, None] & n_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    tl.store(c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn,
             acc.to(tl.bfloat16), mask=m_mask[:, None] & n_mask[None, :])

for K_val in [512, 1024, 2048, 4096]:
    M2, N2 = 64, 64
    a2 = torch.randn(M2, K_val, dtype=torch.bfloat16, device='cuda')
    b2 = torch.randn(K_val, N2, dtype=torch.bfloat16, device='cuda')


... (1137 more chars)

{
  "command": "pip show cutlass 2>&1; uv pip show cutlass 2>&1; find /home/infatoshi/cuda/KernelBench-Hard/.venv -name \"*cutlass*\" -o -name \"*CUTLASS*\" 2>/dev/null",
  "description": "Find cutlass package",
  "timeout": 30000
}

{
  "command": "<580 chars \u2014 see below>",
  "description": "Try importing CUTLASS DSL"
}

cd /home/infatoshi/cuda/KernelBench-Hard && python -c "
import sys
sys.path.insert(0, '.venv/lib/python3.11/site-packages/cutlass')
print('cutlass dir contents:', __import__('os').listdir('.venv/lib/python3.11/site-packages/cutlass'))
try:
    from cutlass import *
    print('cutlass imported successfully')
except ImportError as e:
    print(f'cutlass import failed: {e}')
try:
    import nvidia_cutlass_dsl
    print('nvidia_cutlass_dsl imported')
    print(dir(nvidia_cutlass_dsl))
except ImportError as e:
    print(f'nvidia_cutlass_dsl import failed: {e}')
" 2>&1 | head -30

{
  "description": "Investigate nvidia_cutlass_dsl",
  "command": "ls /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/nvidia_cutlass_dsl/ && cat /home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/nvidia_cutlass_dsl.pth"
}

{
  "command": "<597 chars \u2014 see below>",
  "description": "Try cutlass DSL import"
}

python -c "
import sys
# Check if .pth is loaded
for p in sys.path:
    if 'cutlass' in p.lower() or 'nvidia' in p:
        print(p)

# Try importing from the nvidia_cutlass_dsl packages
sys.path.insert(0, '/home/infatoshi/cuda/KernelBench-Hard/.venv/lib/python3.11/site-packages/nvidia_cutlass_dsl/python_packages')
try:
    import cutlass
    print('import cutlass OK')
    print(dir(cutlass))
except ImportError as e:
    print(f'import cutlass failed: {e}')

try:
    import cute
    print('import cute OK')
    print(dir(cute))
except ImportError as e:
    print(f'import cute failed: {e}')
"

{
  "command": "<4576 chars \u2014 see below>",
  "description": "Test CUDA C++ fp32 kernel with load_inline",
  "timeout": 60000
}

cat > /tmp/test_cuda_wmma.py << 'PYEOF'
import torch
from torch.utils.cpp_extension import load_inline
import os

cuda_source = """
#include <cuda_bf16.h>
#include <cuda_fp8.h>

__global__ void fp8_gemm_kernel(
    const __nv_fp8_e4m3* __restrict__ A,
    const __nv_bfloat16* __restrict__ B,
    __nv_bfloat16* __restrict__ C,
    int M, int N, int K
) {
    // Each thread handles a tile of the output
    // Simple 1D block: each thread computes one element of C
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= M * N) return;
    int row = idx / N;
    int col = idx % N;
    
    float sum = 0.0f;
    for (int k = 0; k < K; k++) {
        float a_val = __bfloat162float(__nv_bfloat16(__nv_fp8_e4m3(A[row * K + k])));
        float b_val = __bfloat162float(B[col * K + k]);
        sum += a_val * b_val;
    }
    C[row * N + col] = __float2bfloat16(sum);
}

__global__ void fp8_gemm_tiled(
    const __nv_fp8_e4m3* __restrict__ A,
    const __nv_bfloat16* __restrict__ B,
    __nv_bfloat16* __restrict__ C,
    int M, int N, int K
) {
    // Tile-based: each block handles a (BM, BN) tile, threads work on BM/4 x BN/4 elements each
    const int BM = 64, BN = 64, BK = 16;
    __shared__ __nv_bfloat16 As[BM][BK];
    __shared__ __nv_bfloat16 Bs[BK][BN];
    
    int base_m = blockIdx.x * BM;
    int base_n = blockIdx.y * BN;
    int tid = threadIdx.x;
    
    float acc[4][4] = {{0}};
    
    for (int k0 = 0; k0 < K; k0 += BK) {
        // Load tile of A into shared memory (cooperative)
        int a_idx = tid;
        while (a_idx < BM * BK) {
            int m = a_idx / BK;
            int k = a_idx % BK;
            int gm = base_m + m;
            int gk = k0 + k;
            if (gm < M && gk < K) {
                As[m][k] = __nv_bfloat16(__nv_fp8_e4m3(A[gm * K + gk]));
            } else {
                As[m][k] = __nv_bfloat16(0.0f);
            }
            a_idx += blockDim.x;
        }
        
        // Load tile of B into shared memory
        int b_idx = tid;
        while (b_idx < BK * BN) {
            int k = b_idx / BN;
            int n = b_idx % BN;
            int gk = k0 + k;
            int gn = base_n + n;
            if (gk < K && gn < N) {
                Bs[k][n] = B[gn * K + gk];
            } else {
                Bs[k][n] = __nv_bfloat16(0.0f);
            }
            b_idx += blockDim.x;
        }
        __syncthreads();
        
        // Compute: each thread handles 4x4 elements
        for (int k = 0; k < BK; k++) {
            for (int i = 0; i < 4; i++) {
                int row_in_tile = threadIdx.y * 4 + i;
                __nv_bfloat16 a_row[4];
                // Actually need to load from shared...
            }
        }
        
        __syncthreads();
    }
}

__global__ void fp8_gemm_simple(
    const __nv_fp8_e4m3* __restrict__ A,
    const __nv_bfloat16* __restrict__ B,
    __nv_bfloat16* __restrict__ C,
    int M, int N, int K
) {
    // Each thread computes one output element using fp32 arithmetic
    int row = blockIdx.x * blockDim.x + threadIdx.x;
    int col = blockIdx.y * blockDim.y + threadIdx.y;
    
    if (row >= M || col >= N) return;
    
    float sum = 0.0f;
    for (int k = 0; k < K; k++) {
        float a_val = __bfloat162float(__nv_bfloat16(__nv_fp8_e4m3(A[row * K + k])));
        float b_val = __bfloat162float(B[col * K + k]);
        sum += a_val * b_val;
    }
    C[row * N + col] = __float2bfloat16(sum);
}
"""

# Load the module
try:
    module = load_inline(
        name="fp8_gemm_simple",
        cpp_sources="",
        cuda_sources=cuda_source,
        functions=["fp8_gemm_simple"],
        extra_cuda_cflags=["--std=c++17", "-O3"],
        verbose=False
    )
    
    M, N, K = 64, 64, 4096
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
    w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
    
    # Reference
    ref = x.to(torch.bfloat16) @ w.T
    
    # Custom kernel
    out = torch.empty(M, N, dtype=to

... (576 more chars)

{
  "todos": [
    {
      "priority": "high",
      "content": "Write CUDA C++ kernel with load_inline: fp8->bf16 + cuBLAS bf16 gemm",
      "status": "in_progress"
    },
    {
      "priority": "high",
      "content": "Run check.py to verify correctness across all shapes",
      "status": "pending"
    },
    {
      "priority": "high",
      "content": "Benchmark with benchmark.py and optimize",
      "status": "pending"
    },
    {
      "priority": "medium",
      "content": "Profile with ncu/nsys if performance is low",
      "status": "pending"
    }
  ]
}

{
  "content": "<4712 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "command": "<704 chars \u2014 see below>",
  "description": "Test CUDA C++ kernel compilation",
  "timeout": 60000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && python -c "
import torch
from solution import _get_cuda_gemm

mod = _get_cuda_gemm()

# Quick test
M, N, K = 64, 64, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

grid = (M // 64, N // 64)
block = (16, 16)
mod.fp8_gemm_fp32_acc(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K, grid=grid, block=block)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'CUDA fp32 acc test (64x64x256): max_diff={diff:.6f}')
print(f'PASS = {diff < 0.01}')
" 2>&1

{
  "content": "<4773 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "command": "<629 chars \u2014 see below>",
  "description": "Test CUDA tiled kernel",
  "timeout": 60000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && python -c "
from solution import _get_cuda_gemm
import torch

mod = _get_cuda_gemm()

M, N, K = 64, 64, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

mod.tiled_wrapper(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K, 64, 64, 16)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'CUDA tiled test (64x64x256): max_diff={diff:.6f}')
print(f'PASS = {diff < 0.01}')
" 2>&1

{
  "content": "<5043 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "command": "<3136 chars \u2014 see below>",
  "description": "Test manual fp32 vs cuBLAS accuracy",
  "timeout": 120000
}

cat > /tmp/test_fp32_manual.py << 'PYEOF'
import torch

# Test: simple fp32 manual matmul vs cuBLAS bf16 matmul
torch.manual_seed(42)
M, N, K = 64, 64, 4096

x_fp8 = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
w = torch.randn(N, K, dtype=torch.bfloat16) * 0.02

# Reference: cuBLAS bf16 matmul (same as what the reference does)
x_bf = x_fp8.to(torch.bfloat16)
ref = x_bf @ w.T  # cuBLAS bf16 matmul

# Manual fp32 matmul: convert to fp32, multiply, accumulate
x_f32 = x_bf.float()
w_f32 = w.float()
manual = torch.zeros(M, N, dtype=torch.bfloat16)

for i in range(M):
    for j in range(N):
        s = 0.0
        for k in range(K):
            s += x_f32[i, k].item() * w_f32[j, k].item()
        manual[i, j] = s

diff = (manual.float() - ref.float()).abs().max().item()
print(f"Manual fp32 sequential vs cuBLAS bf16: max_diff={diff:.12f}")

# What about using torch.mm with fp32?
mm_f32 = (x_f32 @ w_f32.T).to(torch.bfloat16)
diff2 = (mm_f32.float() - ref.float()).abs().max().item()
print(f"torch.mm fp32 vs cuBLAS bf16: max_diff={diff2:.12f}")

# What about sequential bf16?
manual_bf16 = torch.zeros(M, N, dtype=torch.bfloat16)
for i in range(M):
    for j in range(N):
        s = 0.0
        for k in range(K):
            s += float(x_bf[i, k]) * float(w[j, k])
        manual_bf16[i, j] = s

diff3 = (manual_bf16.float() - ref.float()).abs().max().item()
print(f"Sequential bf16 -> fp32 acc vs cuBLAS bf16: max_diff={diff3:.12f}")

# Try reducing the sum in a tree-like fashion (simulating cuBLAS)
# cuBLAS likely uses some kind of tree reduction
# Manual sequential is just one possible approach
# The key is: the diff between sequential and cuBLAS is ~0.031 for this seed

# Now test: is the diff due to bf16 rounding or accumulation order?
manual_f32_fma = torch.zeros(M, N, dtype=torch.bfloat16)
for i in range(M):
    for j in range(N):
        s = 0.0
        for k in range(K):
            # Simulate bf16 FMA: convert to bf16, multiply in fp32
            a_bf = x_bf[i, k]
            b_bf = w[j, k]
            s += float(a_bf) * float(b_bf)  # same as reference
        manual_f32_fma[i, j] = s

diff4 = (manual_f32_fma.float() - ref.float()).abs().max().item()
print(f"BF16 input sequential vs cuBLAS: max_diff={diff4:.12f}")
# Same as manual_bf16

# What if we use the same reduction as the reference?
# We don't know the reference's reduction order...
# But it must be some parallel reduction

# Final check: is the diff 0 for K small?
for K_test in [64, 128, 256, 512, 1024, 2048, 4096]:
    x2 = (torch.rand(M, K_test) * 8 - 4).to(torch.float8_e4m3fn)
    w2 = torch.randn(N, K_test, dtype=torch.bfloat16) * 0.02
    
    ref2 = x2.to(torch.bfloat16) @ w2.T
    
    manual2 = torch.zeros(M, N, dtype=torch.bfloat16)
    for i in range(M):
        for j in range(N):
            s = 0.0
            for k in range(K_test):
                s += float(x2.to(torch.bfloat16)[i, k]) * float(w2[j, k])
            manual2[i, j] = s
    
    diff = (manual2.float() - ref2.float()).abs().max().item()
    print(f"K={K_test:5d}: sequential vs cuBLAS max_diff={diff:.8f}")
PYEOF
python /tmp/test_fp32_manual.py

{
  "content": "<15826 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "content": "<9824 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "description": "Test CUDA cuBLAS kernel compilation",
  "command": "<679 chars \u2014 see below>",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && timeout 120 python -c "
import torch
from solution import _get_module

mod = _get_module()
print('Module compiled successfully!')

# Test on small case
M, N, K = 128, 128, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

mod.fp8_gemm_forward(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'Test (128x128x256): max_diff={diff:.8f}')
print(f'PASS = {diff < 0.01}')
" 2>&1

{
  "content": "<3222 chars \u2014 see diff>",
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py"
}

{
  "description": "Compile and test CUDA kernel",
  "command": "<645 chars \u2014 see below>",
  "timeout": 120000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && timeout 120 python -c "
import torch
from solution import _get_module

mod = _get_module()
print('Module compiled!')

# Quick test
M, N, K = 128, 128, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

mod.fp8_gemm_forward(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'Test: max_diff={diff:.10f}')
print(f'PASS = {diff < 0.01}')
" 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py",
  "oldString": "<301 chars \u2014 see below>",
  "newString": "<361 chars \u2014 see below>"
}

extern "C" {

void fp8_gemm_forward(
    const void* A_fp8_ptr,
    const void* B_ptr,
    void* C_ptr,
    int M, int N, int K
) {
    const __nv_fp8_e4m3* A_fp8 = (const __nv_fp8_e4m3*)A_fp8_ptr;
    const __nv_bfloat16* B = (const __nv_bfloat16*)B_ptr;
    __nv_bfloat16* C = (__nv_bfloat16*)C_ptr;

extern "C" {

void fp8_gemm_forward(
    unsigned long long A_fp8_ptr,
    unsigned long long B_ptr,
    unsigned long long C_ptr,
    int M, int N, int K
) {
    const __nv_fp8_e4m3* A_fp8 = (const __nv_fp8_e4m3*)(uintptr_t)A_fp8_ptr;
    const __nv_bfloat16* B = (const __nv_bfloat16*)(uintptr_t)B_ptr;
    __nv_bfloat16* C = (__nv_bfloat16*)(uintptr_t)C_ptr;

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py",
  "oldString": "<127 chars \u2014 see below>",
  "newString": "<154 chars \u2014 see below>"
}

extern "C" {
    void fp8_gemm_forward(
        const void* A_fp8, const void* B, void* C,
        int M, int N, int K
    );
}

extern "C" {
    void fp8_gemm_forward(
        unsigned long long A_fp8, unsigned long long B, unsigned long long C,
        int M, int N, int K
    );
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm/solution.py",
  "oldString": "<67 chars \u2014 see below>",
  "newString": "<86 chars \u2014 see below>"
}

#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <cublas_v2.h>

#include <cstdint>
#include <cuda_bf16.h>
#include <cuda_fp8.h>
#include <cublas_v2.h>

{
  "command": "<726 chars \u2014 see below>",
  "timeout": 120000
}

rm -rf /home/infatoshi/.cache/torch_extensions/py312_cu130/fp8gemm/ 2>/dev/null; cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && timeout 120 python -c "
import torch
from solution import _get_module

mod = _get_module()
print('Module compiled!')

# Quick test
M, N, K = 128, 128, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

mod.fp8_gemm_forward(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'Test: max_diff={diff:.10f}')
print(f'PASS = {diff < 0.01}')
" 2>&1

{
  "description": "Test CUDA kernel with correct types",
  "command": "<710 chars \u2014 see below>",
  "timeout": 120000
}

rm -rf /home/infatoshi/.cache/torch_extensions/py312_cu130/fp8gemm/ 2>/dev/null; cd /home/infatoshi/cuda/KernelBench-Hard/problems/01_fp8_gemm && timeout 120 python -c "
import torch
from solution import _get_module
mod = _get_module()
print('Module compiled!')
M, N, K = 128, 128, 256
x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
w = torch.randn(N, K, dtype=torch.bfloat16, device='cuda') * 0.02
out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
mod.fp8_gemm_forward(x.data_ptr(), w.data_ptr(), out.data_ptr(), M, N, K)
ref = x.to(torch.bfloat16) @ w.T
diff = (out.float() - ref.float()).abs().max().item()
print(f'Test: max_diff={diff:.10f}')
print(f'PASS = {diff < 0.01}')
" 2>&1