{
  "command": "ls -la /workspace/problems/01_fp8_gemm/",
  "description": "List workspace contents"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/reference.py"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/benchmark.py"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/check.py"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/shapes.py"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/sota.py"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/problem.yaml"
}

{
  "file_path": "/workspace/problems/01_fp8_gemm/PROMPT.txt"
}

{
  "command": "nvidia-smi",
  "description": "Check GPU info"
}

{
  "command": "uv run python -c \"import torch; print(torch.__version__); print(torch.cuda.get_device_capability()); print(torch.cuda.get_device_name())\"",
  "description": "Check torch + GPU details"
}

{
  "command": "uv run python -c \"import triton; print('triton', triton.__version__)\"",
  "description": "Check triton version"
}

{
  "command": "uv run python -c \"import torch; print('FP8 mm? ' + str(hasattr(torch, '_scaled_mm'))); print(torch.cuda.get_device_properties(0))\"",
  "description": "Check torch and GPU details"
}

{
  "command": "find /workspace -name \"timing.py\" -path \"*/eval/*\" 2>/dev/null",
  "description": "Find timing.py"
}

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

{
  "command": "find /workspace -name \"hardware*\" -o -name \"rtx_pro*\" 2>/dev/null | head -20",
  "description": "Find hardware config"
}

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

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

{
  "command": "<142 chars \u2014 see below>",
  "description": "Check triton API"
}

uv run python -c "
import triton
import triton.language as tl
import torch
print('triton', triton.__version__)
print(dir(tl))" 2>&1 | head -30

{
  "command": "<135 chars \u2014 see below>",
  "description": "Check tl.dot signature"
}

uv run python -c "
import triton
import triton.language as tl
import torch
# Check if tl.dot supports fp8
help(tl.dot)" 2>&1 | head -50

{
  "command": "<147 chars \u2014 see below>",
  "description": "Check fp8 dtypes in triton"
}

uv run python -c "
import triton.language as tl
print([s for s in dir(tl) if 'fp8' in s.lower() or 'e4m3' in s.lower() or 'float8' in s.lower()])
"

{
  "command": "<2126 chars \u2014 see below>",
  "description": "Test triton fp8 gemm"
}

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

@triton.jit
def fp8_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,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // grid_n
    pid_n = pid % grid_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)
    
    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, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        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, acc.to(tl.bfloat16))

# Test
M, N, K = 4096, 4096, 4096
BLOCK_M, BLOCK_N, BLOCK_K = 128, 256, 64

# Cast input to fp8
x = torch.randn(M, K, device='cuda')
w = torch.randn(N, K, device='cuda')
x_fp8 = x.to(torch.float8_e4m3fn)
w_fp8 = w.to(torch.float8_e4m3fn)
c = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)

grid = (triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N),)
fp8_gemm_kernel[grid](
    x_fp8, w_fp8, c,
    M, N, K,
    x_fp8.stride(0), x_fp8.stride(1),
    w_fp8.stride(0), w_fp8.stride(1),
    c.stride(0), c.stride(1),
    BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
)

# Check
ref = (x.float() @ w.float().T).to(torch.bfloat16)
print('shapes', c.shape, ref.shape)
print('max abs diff:', (c.float() - ref.float()).abs().max())
print('mean abs diff:', (c.float() - ref.float()).abs().mean())
EOF
uv run python /tmp/test_triton_fp8.py

{
  "command": "<2161 chars \u2014 see below>",
  "description": "Test triton fp8 gemm - fixed strides"
}

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

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,  # NOTE: switched to (N, K) layout
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // grid_n
    pid_n = pid % grid_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)
    
    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, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        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, acc.to(tl.bfloat16))

# Test
M, N, K = 4096, 4096, 4096
BLOCK_M, BLOCK_N, BLOCK_K = 128, 256, 64

# Cast input to fp8
x = torch.randn(M, K, device='cuda')
w = torch.randn(N, K, device='cuda')
x_fp8 = x.to(torch.float8_e4m3fn)
w_fp8 = w.to(torch.float8_e4m3fn)
c = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)

grid = (triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N),)
fp8_gemm_kernel[grid](
    x_fp8, w_fp8, c,
    M, N, K,
    x_fp8.stride(0), x_fp8.stride(1),
    w_fp8.stride(0), w_fp8.stride(1),
    c.stride(0), c.stride(1),
    BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
)

# Check
ref = (x.float() @ w.float().T).to(torch.bfloat16)
print('shapes', c.shape, ref.shape)
print('max abs diff:', (c.float() - ref.float()).abs().max())
print('mean abs diff:', (c.float() - ref.float()).abs().mean())
EOF
uv run python /tmp/test_triton_fp8.py

{
  "command": "<2766 chars \u2014 see below>",
  "description": "Test triton fp8 with proper reference-style data"
}

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

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,  # per-N scale
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid // grid_n
    pid_n = pid % grid_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)
    
    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, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    # Apply per-channel scale
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    tl.store(c_ptrs, acc.to(tl.bfloat16))

# Test
torch.manual_seed(0)
M, N, K = 4096, 4096, 4096
BLOCK_M, BLOCK_N, BLOCK_K = 128, 256, 64

# Cast input to fp8 (reference Model)
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
torch.nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)  # (N,1)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
weight_scale = s.squeeze(1).to(torch.float32)

# Activation
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

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

grid = (triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N),)
fp8_gemm_kernel[grid](
    x, w_fp8, c,
    M, N, K,
    x.stride(0), x.stride(1),
    w_fp8.stride(0), w_fp8.stride(1),
    weight_scale,
    c.stride(0), c.stride(1),
    BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
)

# Reference
x_bf = x.to(torch.bfloat16)
w_bf = w_fp8.to(torch.bfloat16)
y = (x_bf @ w_bf.T).float()
y = y * weight_scale[None, :]
ref = y.to(torch.bfloat16)

print('shapes', c.shape, ref.shape)
print('max abs diff:', (c.float() - ref.float()).abs().max())
print('mean abs diff:', (c.float() - ref.float()).abs().mean())
print('max rel diff:', ((c.float() - ref.float()).abs() / ref.float().abs().clamp_min(1e-3)).max())
print('max ref val:', ref.float().abs().max())
EOF
uv run python /tmp/test_triton_fp8.py

{
  "command": "<3910 chars \u2014 see below>",
  "description": "Test initial solution with check.py"
}

cat > /tmp/solution_v1.py << 'EOF'
"""FP8 GEMM with Triton — basic version."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0


@triton.autotune(
    configs=[
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 64, "GROUP_M": 8}, num_stages=4, num_warps=8),
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 64, "GROUP_M": 8}, num_stages=4, num_warps=4),
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "GROUP_M": 8}, num_stages=4, num_warps=4),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "GROUP_M": 8}, num_stages=4, num_warps=4),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "BLOCK_K": 64, "GROUP_M": 8}, num_stages=4, num_warps=4),
    ],
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    # Swizzle for better L2 locality
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale):
    """x: (M, K) fp8, w: (N, K) fp8, weight_scale: (N,) fp32. Returns (M, N) bf16."""
    M, K = x.shape
    N, K2 = w.shape
    assert K == K2
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
    fp8_gemm_kernel[grid](
        x, w, c,
        M, N, K,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return fp8_gemm(x, self.weight, self.weight_scale)


M = 4096
N = 4096
K = 4096


def get_inputs():
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]


def get_init_inputs():
    return [M, N, K]
EOF
cp /tmp/solution_v1.py /workspace/problems/01_fp8_gemm/solution.py
uv run python check.py 2>&1 | head -50

{
  "command": "uv run python benchmark.py 2>&1 | head -40",
  "description": "Benchmark v1 solution"
}

{
  "command": "<3946 chars \u2014 see below>",
  "description": "Test with broader autotune"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0


def _autotune_configs():
    cfgs = []
    for bm in (64, 128):
        for bn in (64, 128, 256):
            for bk in (32, 64, 128):
                for nw in (4, 8):
                    for ns in (3, 4, 5):
                        # Avoid impossible combinations
                        if bm * bn > 32 * 1024: continue
                        if bm * bn // (32 * nw) < 16: continue  # too small per warp
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    K_full = (K // BLOCK_K) * BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K_full, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    if K_full < K:
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - K_full, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - K_full, other=0.0)
        acc = tl.dot(a, b, acc)
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale):
    M, K = x.shape
    N, K2 = w.shape
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
    fp8_gemm_kernel[grid](
        x, w, c,
        M, N, K,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return fp8_gemm(x, self.weight, self.weight_scale)


M = 4096
N = 4096
K = 4096


def get_inputs():
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn)
    return [x]


def get_init_inputs():
    return [M, N, K]
EOF
uv run python check.py 2>&1 | head -20

{
  "command": "uv run python benchmark.py 2>&1 | tail -25",
  "description": "Benchmark v2"
}

{
  "command": "<4217 chars \u2014 see below>",
  "description": "Test with wider autotune"
}

cat > /tmp/solution_v2.py << 'EOF'
"""FP8 GEMM with Triton — better autotune."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0


def _autotune_configs():
    cfgs = []
    for bm in (32, 64, 128, 256):
        for bn in (32, 64, 128, 256):
            for bk in (32, 64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        # Constraints
                        if bm * bn > 64 * 1024: continue
                        if bm * bn // (32 * nw) < 8: continue
                        # Don't try too-large BLOCK_K
                        if bk > 128 and bm * bn * bk > 32 * 1024: continue
                        # Avoid wasteful small N tiles
                        if bn < 32: continue
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    K_full = (K // BLOCK_K) * BLOCK_K
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K_full, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    if K_full < K:
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - K_full, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - K_full, other=0.0)
        acc = tl.dot(a, b, acc)
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale):
    M, K = x.shape
    N, K2 = w.shape
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
    fp8_gemm_kernel[grid](
        x, w, c,
        M, N, K,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return fp8_gemm(x, self.weight, self.weight_scale)


M = 4096
N = 4096
K = 4096


def get_inputs():
    x = (tor

... (217 more chars)

{
  "task_id": "b4j09a4xi",
  "block": true,
  "timeout": 300000
}

{
  "command": "uv run python benchmark.py 2>&1 | tail -30",
  "description": "Benchmark v2 - extended autotune"
}

{
  "command": "<614 chars \u2014 see below>",
  "description": "Check best configs for all shapes"
}

cat > /tmp/check_autotune2.py << 'EOF'
import torch
import torch.nn as nn
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution

for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    torch.manual_seed(0)
    model = solution.Model(M, N, K).cuda()
    x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()
    _ = model(x)
    torch.cuda.synchronize()
    best = solution.fp8_gemm_kernel.best_config
    print(f"Shape (M={M},N={N},K={K}):")
    print(f"  Best config: {best}")
EOF
uv run python /tmp/check_autotune2.py 2>&1 | tail -15

{
  "command": "<107 chars \u2014 see below>",
  "description": "Tune shape 2 with wider filter"
}

sed -i 's/256 \* 1024/1024 \* 1024/g' /tmp/tune_shape2.py
uv run python /tmp/tune_shape2.py 2>&1 | tail -25

{
  "command": "<4269 chars \u2014 see below>",
  "description": "Tune shape 2 with L2 flush"
}

cat > /tmp/tune_shape2_l2flush.py << 'EOF'
"""Tune for shape 2 (skinny M) with L2 flush."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

# L2 flush
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None

def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Test configs
results = []
for BM in (16, 32, 64):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8):
                for ns in (3, 4, 5):
                    if BM * BN // (32 * nw) < 8: continue
                    if BM * BN * BK > 1024 * 1024: continue
                    try:
                        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, 1, nw, ns)
                        flops = 2 * M * N * K
                        tflops = flops / (t * 1e9)
                        bytes_moved = M*K + K*N + M*N*2
                        gbps = bytes_moved / (t * 1e6)
                        results.append((t, tflops, gbps, (BM, BN, BK, nw, ns)))
                    except Exception as e:
                        pass

... (269 more chars)

{
  "command": "<4672 chars \u2014 see below>",
  "description": "Test SplitK for shape 2"
}

cat > /tmp/tune_shape2_splitk.py << 'EOF'
"""SplitK version for skinny M."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None

def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_splitk_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    SPLIT_K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_mn = tl.program_id(0)
    pid_k = tl.program_id(1)
    
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid_mn // grid_n
    pid_n = pid_mn % grid_n
    
    K_per_split = tl.cdiv(K, SPLIT_K)
    K_start = pid_k * K_per_split
    K_end = tl.minimum(K_start + K_per_split, K)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = K_start + tl.arange(0, BLOCK_K)
    
    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_off in range(K_start, K_end, BLOCK_K):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K_end, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K_end, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    if pid_k == 0:
        scale = tl.load(scale_ptr + offs_n)
        acc = acc * scale[None, :]
        c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
        mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
        tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)
    else:
        c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
        mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
        tl.atomic_add(c_ptrs, acc.to(tl.bfloat16), mask=mask, sem='relaxed')


def run_kernel_splitk(x, w, ws, M, N, K, SPLIT_K, BM, BN, BK, nw, ns, iters=20):
    c = torch.zeros((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN), SPLIT_K)
    
    def call():
        _l2_flush()
        fp8_gemm_splitk_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            SPLIT_K=SPLIT_K,
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

results = []
for SPLIT_K in (2, 4, 8):
    for BM in (32,):
        for BN in (64, 128, 256):
            for BK in (128, 256, 512):
                for nw in (4, 8):
                    for ns in (3, 4, 5):
                        if BM * BN // (32 * nw) < 8: continue
                        if BM * BN * BK > 1024 * 1024: continue
                        t = ru

... (672 more chars)

{
  "command": "<4895 chars \u2014 see below>",
  "description": "Test persistent kernel for shape 2"
}

cat > /tmp/tune_shape2_persistent.py << 'EOF'
"""Persistent kernel for shape 2."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None

def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_persistent_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    NUM_SMS: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    total_tiles = grid_m * grid_n
    
    offs_k = tl.arange(0, BLOCK_K)
    a_ptr_base = a_ptr
    b_ptr_base = b_ptr
    scale_base = scale_ptr
    c_ptr_base = c_ptr
    
    K_full = (K // BLOCK_K) * BLOCK_K
    
    for tile_id in range(pid, total_tiles, NUM_SMS):
        # Swizzle
        width = GROUP_M * grid_n
        group_id = tile_id // width
        group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
        pid_m = group_id * GROUP_M + (tile_id % group_size)
        pid_n = (tile_id % width) // group_size
        
        offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
        offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
        
        a_ptrs = a_ptr_base + (offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak)
        b_ptrs = b_ptr_base + (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_full, BLOCK_K):
            a = tl.load(a_ptrs)
            b = tl.load(b_ptrs)
            acc = tl.dot(a, b, acc)
            a_ptrs += BLOCK_K * stride_ak
            b_ptrs += BLOCK_K * stride_bk
        
        if K_full < K:
            a = tl.load(a_ptrs, mask=offs_k[None, :] < K - K_full, other=0.0)
            b = tl.load(b_ptrs, mask=offs_k[:, None] < K - K_full, other=0.0)
            acc = tl.dot(a, b, acc)
        
        scale = tl.load(scale_base + offs_n)
        acc = acc * scale[None, :]
        
        c_ptrs = c_ptr_base + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
        mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
        tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    NUM_SMS = torch.cuda.get_device_properties(0).multi_processor_count
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (NUM_SMS,)
    
    def call():
        _l2_flush()
        fp8_gemm_persistent_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            NUM_SMS=NUM_SMS,
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

results = []
for BM in (16, 32, 64):
    for BN in (32, 64,

... (895 more chars)

{
  "command": "<2781 chars \u2014 see below>",
  "description": "Check cuBLAS baseline for shape 2"
}

cat > /tmp/check_cublas.py << 'EOF'
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference

# L2 flush
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

# Test shape 2 with cuBLAS
M, N, K = 32, 8192, 8192
reference.M, reference.N, reference.K = M, N, K
ref_model = reference.Model(M, N, K).cuda()
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Warmup
for _ in range(10):
    y = ref_model(x)
torch.cuda.synchronize()

# Time with L2 flush
times = []
for _ in range(20):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = ref_model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))

t = statistics.median(times)
flops = 2 * M * N * K
tflops = flops / (t * 1e9)
bytes_moved = M*K + K*N + M*N*2
gbps = bytes_moved / (t * 1e6)
print(f"Shape 2 reference (bf16 cuBLAS): t={t:.4f}ms tflops={tflops:.1f} gbps={gbps:.0f}")

# Now with fp8 torch._scaled_mm (forbidden in solution but OK for reference)
x_bf = x.to(torch.bfloat16)
w_bf = ref_model.weight.to(torch.bfloat16)

# Warmup
for _ in range(10):
    y = torch.matmul(x_bf, w_bf.T)
torch.cuda.synchronize()

times = []
for _ in range(20):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = torch.matmul(x_bf, w_bf.T)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
tflops = flops / (t * 1e9)
gbps = bytes_moved / (t * 1e6)
print(f"Shape 2 bf16 matmul: t={t:.4f}ms tflops={tflops:.1f} gbps={gbps:.0f}")

# Try fp8 scaled mm
x_fp8 = x
w_fp8 = ref_model.weight.T.contiguous().T  # ensure fp8
scale = torch.tensor(1.0, device='cuda')
for _ in range(10):
    y = torch._scaled_mm(x_fp8, w_fp8.T, scale_a=scale, scale_b=scale, out_dtype=torch.bfloat16)
torch.cuda.synchronize()
times = []
for _ in range(20):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = torch._scaled_mm(x_fp8, w_fp8.T, scale_a=scale, scale_b=scale, out_dtype=torch.bfloat16)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
tflops = flops / (t * 1e9)
gbps = bytes_moved / (t * 1e6)
print(f"Shape 2 fp8 _scaled_mm: t={t:.4f}ms tflops={tflops:.1f} gbps={gbps:.0f}")
EOF
uv run python /tmp/check_cublas.py 2>&1 | tail -10

{
  "command": "<4235 chars \u2014 see below>",
  "description": "Tune shape 2 with smaller BN"
}

cat > /tmp/tune_shape2_v2.py << 'EOF'
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Try many configs
results = []
for BM in (16, 32, 64):
    for BN in (16, 32, 64, 128, 256):
        for BK in (64, 128, 256, 512):
            for nw in (2, 4, 8):
                for ns in (2, 3, 4, 5, 6):
                    if BM * BN // (32 * nw) < 4: continue
                    if BM * BN * BK > 4 * 1024 * 1024: continue
                    t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, 1, nw, ns)
                    if t is None: continue
                    flops = 2 * M * N * K
                    tflops = flops / (t * 1e9)
                    bytes_moved = M*K + K*N + M*N*2
                    gbps = bytes_moved / (t * 1e6)
                    results.append((t, tflops, gbps, (BM, BN, BK, nw, ns)))

results.sort(

... (235 more chars)

{
  "command": "<2279 chars \u2014 see below>",
  "description": "Test L2 effect on shape 2"
}

cat > /tmp/test_l2_effect.py << 'EOF'
"""Test L2 effect on shape 2."""
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution

# L2 flush
_L2_FLUSH_BYTES = 256 * 1024 * 1024  # bigger
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

torch.manual_seed(0)
M, N, K = 32, 8192, 8192
model = solution.Model(M, N, K).cuda()
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Warmup
for _ in range(10):
    y = model(x)
torch.cuda.synchronize()

# Time without flush (hot)
times = []
for _ in range(20):
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t_hot = statistics.median(times)

# Time with 128MB flush
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
times = []
for _ in range(20):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t_flush_128 = statistics.median(times)

# Time with 256MB flush
_L2_FLUSH_BYTES = 256 * 1024 * 1024
_l2_scratch = None
times = []
for _ in range(20):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t_flush_256 = statistics.median(times)

print(f"Shape 2 hot: {t_hot:.4f} ms ({64.75e6 / (t_hot*1e6):.0f} GB/s)")
print(f"Shape 2 128MB flush: {t_flush_128:.4f} ms ({64.75e6 / (t_flush_128*1e6):.0f} GB/s)")
print(f"Shape 2 256MB flush: {t_flush_256:.4f} ms ({64.75e6 / (t_flush_256*1e6):.0f} GB/s)")

# Memory: 32*8192 + 8192*8192 + 32*8192*2 = 262144 + 67108864 + 524288 = 67.9MB
print(f"Memory traffic: {M*K + K*N + M*N*2} bytes = {(M*K + K*N + M*N*2)/1024/1024:.2f} MB")
EOF
uv run python /tmp/test_l2_effect.py 2>&1 | tail -10

{
  "command": "<4130 chars \u2014 see below>",
  "description": "Tune shape 0"
}

cat > /tmp/tune_shape0.py << 'EOF'
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 4096, 4096, 4096
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8, 16):
                for ns in (3, 4, 5):
                    if BM * BN // (32 * nw) < 8: continue
                    if BM * BN * BK > 4 * 1024 * 1024: continue
                    for GM in (1, 4, 8):
                        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
                        if t is None: continue
                        flops = 2 * M * N * K
                        tflops = flops / (t * 1e9)
                        results.append((t, tflops, (BM, BN, BK, GM, nw, ns)))

results.sort()
print(f"Top 30 configs for shape 0 (M={M},N={N},K={K}):")
for t, tflops, cfg 

... (130 more chars)

{
  "command": "<4388 chars \u2014 see below>",
  "description": "Test with pre-transposed w for all shapes"
}

cat > /tmp/test_transposed_w.py << 'EOF'
"""Test with pre-transposed weight."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_kernel_t(
    a_ptr, b_ptr, c_ptr,  # b is (K, N) layout now
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,  # K, N strides
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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, tl.cdiv(K, BLOCK_K)):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w_t, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel_t[grid](
            x, w_t, c, M, N, K,
            x.stride(0), x.stride(1),
            w_t.stride(0), w_t.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)

# Test all 4 shapes
for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
    nn.init.normal_(w, std=0.02)
    s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
    w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
    w_t = w_fp8.T.contiguous()  # (K, N)
    ws = s.squeeze(1).to(torch.float32)
    x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
    
    print(f"\nShape (M={M},N={N},K={K}) with transposed w:")
    results = []
    for BM in (64, 128, 256):
        for BN in (64, 128, 256):
            for BK in (64, 128, 256):
                for nw in (4, 8):
                    for ns in (3, 4, 5):
                        if BM * BN // (32 * nw) < 8: continue
                        if BM * BN * BK > 4 * 1024 * 1024: continue
                        t = run_kernel(x, w_t, ws, M, N, K, BM, BN, BK, 1, nw, ns)
           

... (388 more chars)

{
  "command": "<6274 chars \u2014 see below>",
  "description": "Test with K_ALIGNED flag"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — heuristic + autotune."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
    K_ALIGNED: tl.constexpr,  # K is multiple of BLOCK_K
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    if K_ALIGNED:
        for k in range(0, K, BLOCK_K):
            a = tl.load(a_ptrs)
            b = tl.load(b_ptrs)
            acc = tl.dot(a, b, acc)
            a_ptrs += BLOCK_K * stride_ak
            b_ptrs += BLOCK_K * stride_bk
    else:
        K_full = (K // BLOCK_K) * BLOCK_K
        for k in range(0, K_full, BLOCK_K):
            a = tl.load(a_ptrs)
            b = tl.load(b_ptrs)
            acc = tl.dot(a, b, acc)
            a_ptrs += BLOCK_K * stride_ak
            b_ptrs += BLOCK_K * stride_bk
        # Tail
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - K_full, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - K_full, other=0.0)
        acc = tl.dot(a, b, acc)
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        if bm * bn // (32 * nw) < 8: continue
                        if bm * bn * bk > 4 * 1024 * 1024: continue
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K", "K_ALIGNED"],
)
@triton.jit
def fp8_gemm_autotune_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
    K_ALIGNED: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_n[None, :] * stride_bn + offs_k[:, None] * stride_bk)
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    if K_ALIGNED:


... (2274 more chars)

{
  "command": "<5743 chars \u2014 see below>",
  "description": "Test with K padding"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — pad K to BLOCK_K boundary for unaligned cases."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        if bm * bn // (32 * nw) < 8: continue
                        if bm * bn * bk > 4 * 1024 * 1024: continue
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K_PAD"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale):
    """x: (M, K) fp8, w: (N, K) fp

... (1743 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -25",
  "description": "Benchmark with K padding"
}

{
  "command": "<5985 chars \u2014 see below>",
  "description": "Test with K padding in init"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K is padded to multiple of 128 in init."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA (no masked tail).
K_BLOCK_ALIGN = 128


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        if bm * bn // (32 * nw) < 8: continue
                        if bm * bn * bk > 4 * 1024 * 1024: continue
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class Model(nn.Module):
    def __init__(self, M: int, N: int,

... (1985 more chars)

{
  "command": "<4400 chars \u2014 see below>",
  "description": "Test with K padding in init"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K is padded to multiple of 128."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        if bm * bn // (32 * nw) < 8: continue
                        if bm * bn * bk > 4 * 1024 * 1024: continue
                        cfgs.append(triton.Config(
                            {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": 8},
                            num_warps=nw, num_stages=ns,
                        ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale, w_padded, K, K_pad, M, N):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    
    if K_pad != K:
        x_pad = torch.zeros((M, K_pad), device=x.device, dtype=x.dtype)
        x_pad[:, :K].copy_(x)
        x_use = x_pad
    else:
        x_use = x
    
    grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
    fp8_gemm_kernel_aligned[grid](
        x_use, w_padded, c, M, N, K_pad,
        x_use.stride(0), x_use.stride(1),
        w_padded.stride(0), w_padded.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        # Pad K to multiple of 128 for efficient kernel
        self._K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), device=self.weight.device, dtype=se

... (400 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -25",
  "description": "Benchmark with K padding"
}

{
  "command": "<651 chars \u2014 see below>",
  "description": "Check autotune choices"
}

cat > /tmp/check_autotune3.py << 'EOF'
import torch
import torch.nn as nn
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution

for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    torch.manual_seed(0)
    model = solution.Model(M, N, K).cuda()
    x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
    _ = model(x)
    torch.cuda.synchronize()
    best = solution.fp8_gemm_kernel_aligned.best_config
    print(f"Shape (M={M},N={N},K={K}): K_pad={model._K_pad}")
    print(f"  Best config: {best}")
EOF
uv run python /tmp/check_autotune3.py 2>&1 | tail -10

{
  "command": "<4195 chars \u2014 see below>",
  "description": "Tune padded K=4224 with L2 flush"
}

cat > /tmp/tune_shape1_padded_v2.py << 'EOF'
"""Test padded approach for K=4127 with proper L2 flush."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_kernel_padded(
    a_ptr, b_ptr, c_ptr,
    M, N, K_PADDED,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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_PADDED, BLOCK_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel_padded(x_pad, w_pad, ws, M, N, K, K_pad, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x_pad.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel_padded[grid](
            x_pad, w_pad, c, M, N, K_pad,
            x_pad.stride(0), x_pad.stride(1),
            w_pad.stride(0), w_pad.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

K_pad = 4224
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

results = []
for BM, BN, BK, nw, ns in [
    (128, 256, 128, 8, 3),
    (128, 256, 64, 8, 4),
    (128, 256, 64, 8, 3),
    (128, 128, 128, 8, 3),
    (128, 128, 64, 16, 4),
    (128, 256, 64, 16, 3),
    (128, 128, 64, 8, 4),
    (128, 256, 128, 4, 3),
    (256, 128, 128, 8, 3),
    (256, 128, 64, 8, 4),
    (64, 256, 128, 8, 3),
    (64, 128, 128, 8, 3),
    (64, 128, 64, 4, 4),
]:
    t = run_kernel_padded(x_pad, w_pad, ws, M, N, K, K_pad, BM, BN, BK, 8, nw, ns)
    flops = 2 * M * N * K  # original FLOPs (not padded)
    tflops = flops / (t * 1e9)
    results.append((t, tflops, (BM, BN, BK, nw

... (195 more chars)

{
  "command": "<4391 chars \u2014 see below>",
  "description": "Compare shapes with L2 flush"
}

cat > /tmp/compare_shapes.py << 'EOF'
"""Compare all shapes with L2 flush."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=20):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)

# Test the actual best config found for each shape
best_configs = {
    (4096, 4096, 4096): (128, 256, 128, 8, 3),
    (4096, 4096, 4127): (128, 256, 128, 8, 3),  # padded K=4224
    (32, 8192, 8192): (32, 16, 512, 2, 4),  # best from earlier
    (4096, 14336, 4096): (128, 256, 128, 8, 3),
}

for M, N, K, cfg in [
    (4096, 4096, 4096, (128, 256, 128, 8, 3)),
    (4096, 4096, 4127, (128, 256, 128, 8, 3)),
    (32, 8192, 8192, (32, 64, 256, 8, 3)),
    (4096, 14336, 4096, (128, 256, 128, 8, 3)),
]:
    w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
    nn.init.normal_(w, std=0.02)
    s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
    w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
    ws = s.squeeze(1).to(torch.float32)
    x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
    
    if K == 4127:
        K_pad = 4224
        x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
        x_pad[:, :K] = x
        w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
        w_pad[:, :K] = w_fp8
        K_run = K_pad
        x_use = x_pad
  

... (391 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -25",
  "description": "Re-benchmark current solution"
}

{
  "command": "<1578 chars \u2014 see below>",
  "description": "Debug L2 effect on shape 0"
}

cat > /tmp/debug_l2.py << 'EOF'
"""Debug the L2 effect on shape 0 timing."""
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')

import solution

# L2 flush exactly like benchmark
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device="cuda")
    _l2_scratch.zero_()

torch.manual_seed(0)
M, N, K = 4096, 4096, 4096
model = solution.Model(M, N, K).cuda()
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Warmup
for _ in range(10):
    y = model(x)
torch.cuda.synchronize()

# Time with L2 flush (cold L2)
times = []
for _ in range(30):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t_cold = statistics.median(times)
flops = 2 * M * N * K
print(f"Shape 0 cold: t={t_cold:.4f}ms tflops={flops/(t_cold*1e9):.1f}")

# Time without flush (hot L2)
times = []
for _ in range(30):
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t_hot = statistics.median(times)
print(f"Shape 0 hot: t={t_hot:.4f}ms tflops={flops/(t_hot*1e9):.1f}")
EOF
uv run python /tmp/debug_l2.py 2>&1 | tail -5

{
  "command": "<4214 chars \u2014 see below>",
  "description": "Tune shape 1 with BK=256"
}

cat > /tmp/tune_shape1_bk256.py << 'EOF'
"""Try BLOCK_K=256 for shape 1."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(5): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (128, 256):
            for nw in (4, 8, 16):
                for ns in (3, 4, 5):
                    if BM * BN // (32 * nw) < 8: continue
                    if BM * BN * BK > 4 * 1024 * 1024: continue
                    t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, BM, BN, BK, 8, nw, ns)
                    if t is None: continue
                    flops = 2 * M * N * K
                    tflops = flops / (t * 1e9)
                    results.append((t, tflops, (BM, BN, BK, nw, ns)))

results.sort()
print(f"Top 20

... (214 more chars)

{
  "command": "<4453 chars \u2014 see below>",
  "description": "Test K_pad effect on shape 0 vs 1"
}

cat > /tmp/test_kpad_effect.py << 'EOF'
"""Test if K_pad=4224 is the issue, by padding K=4096 to 4224."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(5): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)

# Setup with K=4096 padded to 4224
M, N, K = 4096, 4096, 4096
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

# Test unpadded
t = run_kernel(x, w_fp8, ws, M, N, K, 128, 256, 128, 8, 8, 3)
flops = 2 * M * N * K
print(f"K=4096 unpadded: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Test padded
t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, 128, 256, 128, 8, 8, 3)
print(f"K=4096 padded to 4224: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Test K=4127 padded
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K,

... (453 more chars)

{
  "command": "<1501 chars \u2014 see below>",
  "description": "Check cuBLAS bf16 baseline for all shapes"
}

cat > /tmp/check_cublas2.py << 'EOF'
"""Check cuBLAS bf16 baseline for shape 2."""
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference

# L2 flush
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

# Test all 4 shapes with reference (bf16)
for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    torch.manual_seed(0)
    reference.M, reference.N, reference.K = M, N, K
    ref_model = reference.Model(M, N, K).cuda()
    x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
    
    for _ in range(10): _ = ref_model(x)
    torch.cuda.synchronize()
    
    times = []
    for _ in range(30):
        _l2_flush()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        y = ref_model(x)
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    t = statistics.median(times)
    flops = 2 * M * N * K
    bytes_moved = M*K + K*N + M*N*2
    tflops = flops / (t * 1e9)
    gbps = bytes_moved / (t * 1e6)
    print(f"Shape (M={M},N={N},K={K}) bf16: t={t:.4f}ms tflops={tflops:.1f} gbps={gbps:.0f}")
EOF
uv run python /tmp/check_cublas2.py 2>&1 | tail -10

{
  "command": "<3716 chars \u2014 see below>",
  "description": "Test L2 cache hints"
}

cat > /tmp/test_l2_cache_hint.py << 'EOF'
"""Test with cache modifier for shape 2."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_kernel_cache(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, cache_modifier=".ca")  # cache at all levels
        b = tl.load(b_ptrs, cache_modifier=".ca")
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask, cache_modifier=".cs")  # streaming


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel_cache[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(5): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

for BM, BN, BK, nw, ns in [(32, 64, 128, 4, 3), (32, 64, 128, 8, 3), (32, 64, 256, 8, 3), (32, 64, 256, 4, 4), (32, 32, 256, 4, 4), (32, 32, 128, 4, 4)]:
    try:
        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, 1, nw, ns)
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        print(f"  cache.ca: t={t:.4f}ms  tflops={tflops:.1f}  cfg={(BM, BN, BK, nw, ns)}")
    except Exception as e:
        print(f"  FAIL: {e}")
EOF
uv run python /tmp/test_l2_cache_hint.py 2>&1 | tail -10

{
  "command": "<4835 chars \u2014 see below>",
  "description": "Test SplitK with reduction kernel for shape 2"
}

cat > /tmp/test_splitk2.py << 'EOF'
"""SplitK for shape 2."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_splitk(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    stride_cm, stride_cn,
    SPLIT_K: tl.constexpr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_mn = tl.program_id(0)
    pid_k = tl.program_id(1)
    
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    pid_m = pid_mn // grid_n
    pid_n = pid_mn % grid_n
    
    K_per_split = tl.cdiv(K, SPLIT_K)
    K_start = pid_k * K_per_split
    K_end = tl.minimum(K_start + K_per_split, K)
    
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = K_start + tl.arange(0, BLOCK_K)
    
    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_off in range(K_start, K_end, BLOCK_K):
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K_end, other=0.0)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K_end, other=0.0)
        acc = tl.dot(a, b, acc)
        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
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.atomic_add(c_ptrs, acc, mask=mask, sem='relaxed')


@triton.jit
def scale_and_convert_kernel(
    c_ptr, scale_ptr, out_ptr,
    M, N,
    stride_cm, stride_cn,
    stride_om, stride_on,
    BLOCK_M: tl.constexpr,
    BLOCK_N: 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)
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    val = tl.load(c_ptrs, mask=mask, other=0.0)
    
    scale = tl.load(scale_ptr + offs_n)
    val = val * scale[None, :]
    
    o_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
    tl.store(o_ptrs, val.to(tl.bfloat16), mask=mask)


def run_splitk(x, w, ws, M, N, K, SPLIT_K, BM, BN, BK, nw, ns, iters=30):
    c_fp32 = torch.zeros((M, N), device=x.device, dtype=torch.float32)
    out = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN), SPLIT_K)
    
    def call():
        _l2_flush()
        c_fp32.zero_()
        fp8_gemm_splitk[grid](
            x, w, c_fp32, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            c_fp32.stride(0), c_fp32.stride(1),
            SPLIT_K=SPLIT_K,
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK,
            num_warps=nw, num_stages=ns,
        )
        scale_grid = (triton.cdiv(M, 32), triton.cdiv(N, 32))
        scale_and_convert_kernel[scale_grid](
            c_fp32, ws, out, M, N,
            c_fp32.stride(0), c_fp32.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=32, BLOCK_N=32,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=t

... (835 more chars)

{
  "command": "<1860 chars \u2014 see below>",
  "description": "Test torch._scaled_mm for shape 2"
}

cat > /tmp/test_scaled_mm.py << 'EOF'
"""Test torch._scaled_mm for shape 2."""
import torch
import statistics

# L2 flush
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

torch.manual_seed(0)
M, N, K = 32, 8192, 8192

# Try different layouts
x_fp8 = torch.randn(M, K, device='cuda').to(torch.float8_e4m3fn)
w_fp8 = torch.randn(N, K, device='cuda').to(torch.float8_e4m3fn)
w_fp8_T = w_fp8.T.contiguous()  # (K, N)
ws = torch.randn(N, device='cuda', dtype=torch.float32).abs() + 0.01

# Try various invocations
configs = [
    ('w.T', x_fp8, w_fp8.T, ws, ws.new_ones(())),  # x @ w.T
    ('w_T', x_fp8, w_fp8_T, ws, ws.new_ones(())),  # x @ w_T
    ('bmm', x_fp8, w_fp8.T, ws, torch.tensor(1.0, device='cuda')),
]

for name, x, w, sx, sw in configs:
    try:
        # Warmup
        for _ in range(5):
            y = torch._scaled_mm(x, w, scale_a=sx, scale_b=sw, out_dtype=torch.bfloat16)
        torch.cuda.synchronize()
        
        # Time
        times = []
        for _ in range(20):
            _l2_flush()
            torch.cuda.synchronize()
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            y = torch._scaled_mm(x, w, scale_a=sx, scale_b=sw, out_dtype=torch.bfloat16)
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        t = statistics.median(times)
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        print(f"Config {name}: t={t:.4f}ms tflops={tflops:.1f}")
    except Exception as e:
        print(f"Config {name}: FAIL {e}")
EOF
uv run python /tmp/test_scaled_mm.py 2>&1 | tail -10

{
  "command": "<1708 chars \u2014 see below>",
  "description": "Test torch._scaled_mm with rowwise scales"
}

cat > /tmp/test_scaled_mm2.py << 'EOF'
"""Test torch._scaled_mm for shape 2 with proper scales."""
import torch
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

torch.manual_seed(0)
M, N, K = 32, 8192, 8192

x_fp8 = torch.randn(M, K, device='cuda').to(torch.float8_e4m3fn)
w_fp8 = torch.randn(N, K, device='cuda').to(torch.float8_e4m3fn)

# RowWise: scale_a (M, 1), scale_b (1, N)
sa = torch.ones(M, 1, device='cuda', dtype=torch.float32)
sb = torch.ones(1, N, device='cuda', dtype=torch.float32)

for name, w, sb_use in [
    ('w.T', w_fp8.T, sb),
    ('w_T', w_fp8.T.contiguous(), sb),
]:
    try:
        for _ in range(5):
            y = torch._scaled_mm(x_fp8, w, scale_a=sa, scale_b=sb_use, out_dtype=torch.bfloat16)
        torch.cuda.synchronize()
        times = []
        for _ in range(20):
            _l2_flush()
            torch.cuda.synchronize()
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            y = torch._scaled_mm(x_fp8, w, scale_a=sa, scale_b=sb_use, out_dtype=torch.bfloat16)
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        t = statistics.median(times)
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        print(f"Config {name}: t={t:.4f}ms tflops={tflops:.1f}")
    except Exception as e:
        print(f"Config {name}: FAIL {str(e)[:200]}")
EOF
uv run python /tmp/test_scaled_mm2.py 2>&1 | tail -5

{
  "command": "<1777 chars \u2014 see below>",
  "description": "Test torch._scaled_mm for all shapes"
}

cat > /tmp/test_scaled_mm_all.py << 'EOF'
"""Test torch._scaled_mm for all shapes."""
import torch
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

torch.manual_seed(0)

for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    x_fp8 = torch.randn(M, K, device='cuda').to(torch.float8_e4m3fn)
    w_fp8 = torch.randn(N, K, device='cuda').to(torch.float8_e4m3fn)
    sa = torch.ones(M, 1, device='cuda', dtype=torch.float32)
    sb = torch.ones(1, N, device='cuda', dtype=torch.float32)
    
    try:
        for _ in range(5):
            y = torch._scaled_mm(x_fp8, w_fp8.T, scale_a=sa, scale_b=sb, out_dtype=torch.bfloat16)
        torch.cuda.synchronize()
        times = []
        for _ in range(20):
            _l2_flush()
            torch.cuda.synchronize()
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            y = torch._scaled_mm(x_fp8, w_fp8.T, scale_a=sa, scale_b=sb, out_dtype=torch.bfloat16)
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        t = statistics.median(times)
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        bytes_moved = M*K + K*N + M*N*2
        gbps = bytes_moved / (t * 1e6)
        print(f"Shape (M={M},N={N},K={K}): t={t:.4f}ms tflops={tflops:.1f} gbps={gbps:.0f}")
    except Exception as e:
        print(f"Shape (M={M},N={N},K={K}): FAIL {str(e)[:200]}")
EOF
uv run python /tmp/test_scaled_mm_all.py 2>&1 | tail -10

{
  "command": "<3950 chars \u2014 see below>",
  "description": "Tune shape 3 thoroughly"
}

cat > /tmp/tune_shape3.py << 'EOF'
"""Tune shape 3 with L2 flush."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(5): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 14336, 4096
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8, 16):
                for ns in (3, 4, 5):
                    if BM * BN // (32 * nw) < 8: continue
                    if BM * BN * BK > 4 * 1024 * 1024: continue
                    for GM in (1, 4, 8):
                        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
                        if t is None: continue
                        flops = 2 * M * N * K
                        tflops = flops / (t * 1e9)
                        results.append((t, tflops, (BM, BN, BK, GM, nw, ns)))

results.sort()
print(f"Top 20 configs for shape 3 (M={M},N={N},K={K}):")
for t, tflops, cfg in results[:20]:
    print(f"  t={t:.4f}ms  tflops={tflops:.1f}  cfg={cfg}")
EOF
uv run python /tmp/tune_shape3.py 2>&1 | tail -25

{
  "command": "<4171 chars \u2014 see below>",
  "description": "Tune shape 3 v2"
}

cat > /tmp/tune_shape3_v2.py << 'EOF'
"""Tune shape 3 with smaller configs."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 14336, 4096
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Restrict to configs that fit in shared memory
results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8):
                for ns in (3, 4, 5):
                    if BM * BN // (32 * nw) < 8: continue
                    if BM * BN * BK * (1.0 + ns) * 2 > 100 * 1024: continue  # rough shared mem check
                    for GM in (4, 8):
                        try:
                            t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
                            if t is None: continue
                            flops = 2 * M * N * K
                            tflops = flops / (t * 1e9)
                            results.append((t, tflops, (BM, BN, BK, GM, nw, ns)))
                        except Exception as e:
                            pass

results.sort()
print(f"Top 20 configs for shape 3 (M={

... (171 more chars)

{
  "command": "<148 chars \u2014 see below>",
  "description": "Run with smaller mem constraint"
}

cat /tmp/tune_shape3_v2.py | wc -l; sed -i 's/100 \* 1024/200 \* 1024/g' /tmp/tune_shape3_v2.py
uv run python /tmp/tune_shape3_v2.py 2>&1 | tail -25

{
  "command": "<4164 chars \u2014 see below>",
  "description": "Try specific configs for shape 3"
}

cat > /tmp/tune_shape3_v3.py << 'EOF'
"""Tune shape 3 - focus on key configs."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 14336, 4096
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Specific configs to try
configs = [
    (128, 256, 128, 8, 8, 3),
    (128, 256, 128, 8, 8, 4),
    (128, 256, 128, 8, 8, 2),
    (128, 256, 128, 4, 8, 3),
    (128, 256, 64, 8, 8, 4),
    (256, 128, 128, 8, 8, 3),
    (256, 128, 64, 8, 8, 4),
    (128, 128, 128, 8, 8, 3),
    (128, 128, 128, 8, 8, 4),
    (128, 256, 256, 8, 8, 2),
    (128, 256, 256, 8, 8, 3),
    (256, 256, 64, 8, 8, 3),
    (64, 256, 128, 8, 8, 3),
    (64, 128, 128, 8, 8, 3),
    (128, 128, 64, 8, 8, 5),
    (128, 256, 64, 16, 16, 3),
    (256, 128, 64, 8, 8, 3),
    (128, 256, 64, 8, 16, 3),
]

results = []
for cfg in configs:
    BM, BN, BK, GM, nw, ns = cfg
    try:
        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        results.append((t, tflops, cfg))
        print(f"  t={t:.4f}ms  tflops={tflops:.1f}  cfg={cfg}")
    except Excep

... (164 more chars)

{
  "command": "<4374 chars \u2014 see below>",
  "description": "Test with GROUP_M tunable"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, autotune, GROUP_M tunable."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm(x, w, weight_scale, K, K_pad, M, N):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    
    if K_pad != K:
        x_pad = torch.zeros((M, K_pad), device=x.device, dtype=x.dtype)
        x_pad[:, :K].copy_(x)
        x_use = x_pad
    else:
        x_use = x
    
    grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
    fp8_gemm_kernel_aligned[grid](
        x_use, w, c, M, N, K_pad,
        x_use.stride(0), x_use.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        self._K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), device=self.weight.device, dtype=self.weight.dt

... (374 more chars)

{
  "command": "<3753 chars \u2014 see below>",
  "description": "Test TMA for fp8 GEMM"
}

cat > /tmp/test_tma.py << 'EOF'
"""Test TMA (Tensor Memory Accelerator) for shape 0."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()


@triton.jit
def fp8_gemm_tma(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    scale_ptr,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    a_desc = tl.make_tensor_descriptor(
        a_ptr, shape=[M, K], strides=[K, 1], block_shape=[BLOCK_M, BLOCK_K]
    )
    b_desc = tl.make_tensor_descriptor(
        b_ptr, shape=[N, K], strides=[K, 1], block_shape=[BLOCK_N, BLOCK_K]
    )
    c_desc = tl.make_tensor_descriptor(
        c_ptr, shape=[M, N], strides=[N, 1], block_shape=[BLOCK_M, BLOCK_N]
    )
    
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = a_desc.load([pid_m * BLOCK_M, k])
        b = b_desc.load([pid_n * BLOCK_N, k])
        acc = tl.dot(a, b, acc)
    
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    c_desc.store([pid_m * BLOCK_M, pid_n * BLOCK_N], acc.to(tl.bfloat16))


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_tma[grid](
            x, w, c, M, N, K,
            ws,
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(3): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return f"FAIL: {str(e)[:100]}"


torch.manual_seed(0)
M, N, K = 4096, 4096, 4096
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Set up TMA descriptors
import triton
triton.set_allocator(lambda size, alignment, stream: torch.empty(size, device='cuda', dtype=torch.int8))

for cfg in [
    (128, 256, 128, 1, 8, 3),
    (128, 256, 128, 1, 8, 4),
    (256, 128, 128, 1, 8, 3),
    (256, 128, 128, 1, 8, 4),
    (128, 128, 128, 1, 8, 3),
    (128, 128, 256, 1, 8, 3),
    (128, 256, 64, 1, 8, 4),
    (128, 256, 64, 1, 8, 5),
]:
    BM, BN, BK, GM, nw, ns = cfg
    t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
    if isinstance(t, float):
        flops = 2 * M * N * K
        tflops = flops / (t * 1e9)
        print(f"  TMA t={t:.4f}ms  tflops={tflops:.1f}  cfg={cfg}")
    else:
        print(f"  TMA FAIL cfg={cfg}: {t}")
EOF
uv run python /tmp/test_tma.py 2>&1 | tail -15

{
  "command": "<4698 chars \u2014 see below>",
  "description": "Verify shape 1 perf with different configs"
}

cat > /tmp/verify_shape1.py << 'EOF'
"""Verify shape 1 perf with different configs."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

# Try the autotune's choice
t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, 256, 128, 128, 1, 8, 3)
flops = 2 * M * N * K
print(f"256x128x128 GM=1 8w 3st: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Try the shape 0 best
t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, 128, 256, 128, 1, 8, 3)
print(f"128x256x128 GM=1 8w 3st: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Try with GM=4
t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, 128, 256, 128, 4, 8, 3)
print(f"128x256x128 GM=4 8w 3st: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Try with GM=8
t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, 128, 256, 128, 8, 8, 3)
print(f"128x256x128 GM=8 8w 3st: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Compare with shape 

... (698 more chars)

{
  "command": "<1322 chars \u2014 see below>",
  "description": "Verify benchmark methodology"
}

cat > /tmp/verify_bench.py << 'EOF'
"""Verify the benchmark methodology for shape 1."""
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')

import solution
import reference

# L2 flush like benchmark
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

# Time with bench methodology
M, N, K = 4096, 4096, 4127
torch.manual_seed(0)
sol_model = solution.Model(M, N, K).cuda()
ref_model = reference.Model(M, N, K).cuda()
sol_model.load_state_dict(ref_model.state_dict())
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Warmup (10)
for _ in range(10):
    y = sol_model(x)
torch.cuda.synchronize()

# Time (30 with L2 flush)
times = []
for _ in range(30):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    y = sol_model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
flops = 2 * M * N * K
print(f"Shape 1: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")
EOF
uv run python /tmp/verify_bench.py 2>&1 | tail -3

{
  "command": "<1471 chars \u2014 see below>",
  "description": "Verify all shapes with bench methodology"
}

cat > /tmp/verify_all.py << 'EOF'
"""Verify all shapes."""
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')

import solution
import reference

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

for M, N, K in [(4096, 4096, 4096), (4096, 4096, 4127), (32, 8192, 8192), (4096, 14336, 4096)]:
    torch.manual_seed(0)
    sol_model = solution.Model(M, N, K).cuda()
    ref_model = reference.Model(M, N, K).cuda()
    sol_model.load_state_dict(ref_model.state_dict())
    x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
    
    for _ in range(10):
        y = sol_model(x)
    torch.cuda.synchronize()
    
    times = []
    for _ in range(30):
        _l2_flush()
        torch.cuda.synchronize()
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        y = sol_model(x)
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    t = statistics.median(times)
    flops = 2 * M * N * K
    print(f"Shape (M={M},N={N},K={K}): t={t:.4f}ms tflops={flops/(t*1e9):.1f}")
    best = solution.fp8_gemm_kernel_aligned.best_config
    print(f"  Best config: {best}")
EOF
uv run python /tmp/verify_all.py 2>&1 | tail -15

{
  "command": "<3773 chars \u2014 see below>",
  "description": "Verify shape 1 GROUP_M"
}

cat > /tmp/verify_shape1_gm.py << 'EOF'
"""Verify shape 1 with different GROUP_M."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=30):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(3): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

flops = 2 * M * N * K
print("With K_pad=4224:")
for BM, BN, BK, GM, nw, ns in [
    (128, 256, 128, 1, 8, 3),
    (128, 256, 128, 4, 8, 3),
    (128, 256, 128, 8, 8, 3),
    (256, 128, 128, 1, 8, 3),
    (256, 128, 128, 4, 8, 3),
    (256, 128, 128, 8, 8, 3),
]:
    t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, BM, BN, BK, GM, nw, ns)
    print(f"  t={t:.4f}ms tflops={flops/(t*1e9):.1f} cfg={(BM, BN, BK, GM, nw, ns)}")
EOF
uv run python /tmp/verify_shape1_gm.py 2>&1 | tail -10

{
  "command": "<6137 chars \u2014 see below>",
  "description": "Test with pre-padded w and x_pad buffer"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, w pre-padded, x padded in-place."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class 

... (2137 more chars)

{
  "command": "<4807 chars \u2014 see below>",
  "description": "Test with pre-padded w and x_pad buffer"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, w pre-padded, x padded in-place."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        # Pre-pad w if K is not multiple of 128
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype, device=w_fp8.device)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad)
        else:
            self._w_padded = w_fp8
        # Pre-allocate x_pad buffer (will move with the model)
        if K_pad != K:
            x_pad = torch.zeros((M, K_pad), dtype=w_fp8.dtype, device='cpu')
            self._x_pad = x_pad
        else:
            self._x_pad = None

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        
        if K_pad != K:
            if self._x_pad.device != x.device:
                self._x_pad = self._x_pad.to(x.device)
            self._x_pad[:, :K]

... (807 more chars)

{
  "command": "<510 chars \u2014 see below>",
  "description": "Debug device of tensors"
}

cat > /tmp/debug_solution.py << 'EOF'
import torch
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import importlib
import solution
importlib.reload(solution)

model = solution.Model(4096, 4096, 4127).cuda()
print('weight device:', model.weight.device)
print('weight_scale device:', model.weight_scale.device)
print('_w_padded device:', model._w_padded.device)
print('_K_pad:', model._K_pad)
print('_x_pad device:', model._x_pad.device)
EOF
uv run python /tmp/debug_solution.py 2>&1 | tail -10

{
  "command": "<4941 chars \u2014 see below>",
  "description": "Test with lazy x_pad allocation"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, w pre-padded, x padded in-place."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        # Pre-pad w if K is not multiple of 128
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        # _w_padded is a buffer so it moves with the model
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad)
        else:
            # Use the original weight (no padding needed)
            self._w_padded = None  # signal to use weight
        # x_pad: lazy alloc on first call to GPU

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        
        if K_pad != K:
            # Allocate x_pad lazily on the right device
            x_pad = getattr(self, '_x_pad', None)
            if x_pad is None or x_pad.device != x.device:
                x_pad = torch.zeros((M, 

... (941 more chars)

{
  "command": "<1709 chars \u2014 see below>",
  "description": "Debug correctness with bf16 padded version"
}

cat > /tmp/test_correctness2.py << 'EOF'
import torch
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference
import solution

torch.manual_seed(42)
M, N, K = 4096, 4096, 4127
ref_model = reference.Model(M, N, K).cuda()
sol_model = solution.Model(M, N, K).cuda()
sol_model.load_state_dict(ref_model.state_dict())

x = (torch.rand(M, K) * 8 - 4).to(torch.float8_e4m3fn).cuda()

# Reference
with torch.no_grad():
    x_bf = x.to(torch.bfloat16)
    w_bf = ref_model.weight.to(torch.bfloat16)
    y = (x_bf @ w_bf.T).float()
    y_ref = (y * ref_model.weight_scale[None, :]).to(torch.bfloat16)
    
    # Sol
    y_sol = sol_model(x)
    
    # Compare
    diff = (y_ref - y_sol).abs()
    print('Max abs diff:', diff.max().item())
    print('Mean abs diff:', diff.mean().item())
    
    # Check worst indices
    flat_idx = diff.argmax()
    i, j = flat_idx // N, flat_idx % N
    print(f'Worst at ({i.item()}, {j.item()}): ref={y_ref[i,j].item()}, sol={y_sol[i,j].item()}')
    
    # Compute the bf16 version of my padded kernel
    x_pad = torch.zeros((M, 4224), device='cuda', dtype=torch.float8_e4m3fn)
    x_pad[:, :K] = x
    w_pad = torch.zeros((N, 4224), device='cuda', dtype=torch.float8_e4m3fn)
    w_pad[:, :K] = ref_model.weight
    x_pad_bf = x_pad.to(torch.bfloat16)
    w_pad_bf = w_pad.to(torch.bfloat16)
    y_pad_ref = (x_pad_bf @ w_pad_bf.T).float() * ref_model.weight_scale[None, :]
    y_pad_ref = y_pad_ref.to(torch.bfloat16)
    
    diff_pad = (y_ref - y_pad_ref).abs()
    print('Max abs diff (padded bf16):', diff_pad.max().item())
    print('Mean abs diff (padded bf16):', diff_pad.mean().item())
EOF
uv run python /tmp/test_correctness2.py 2>&1 | tail -10

{
  "command": "<1310 chars \u2014 see below>",
  "description": "Debug state_dict loading"
}

cat > /tmp/test_load_state.py << 'EOF'
import torch
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference
import solution

torch.manual_seed(42)
M, N, K = 4096, 4096, 4127
ref_model = reference.Model(M, N, K).cuda()
sol_model = solution.Model(M, N, K).cuda()

print('Before load_state_dict:')
print('  sol weight device:', sol_model.weight.device)
print('  sol _w_padded device:', sol_model._w_padded.device if sol_model._w_padded is not None else 'None')
print('  sol _w_padded shape:', sol_model._w_padded.shape if sol_model._w_padded is not None else 'None')
print('  sol _w_padded sum:', sol_model._w_padded.sum().item() if sol_model._w_padded is not None else 'None')

# Load ref state_dict
sd = ref_model.state_dict()
print('state_dict keys:', list(sd.keys()))
sol_model.load_state_dict(sd, strict=True)

print('\nAfter load_state_dict:')
print('  sol weight sum:', sol_model.weight.sum().item())
print('  sol _w_padded sum:', sol_model._w_padded.sum().item() if sol_model._w_padded is not None else 'None')

# Compare
print('\n  ref weight sum:', ref_model.weight.sum().item())
print('  ref weight_scale sum:', ref_model.weight_scale.sum().item())
print('  sol weight_scale sum:', sol_model.weight_scale.sum().item())
EOF
uv run python /tmp/test_load_state.py 2>&1 | tail -20

{
  "command": "<1348 chars \u2014 see below>",
  "description": "Check w_padded values"
}

cat > /tmp/test_load_state2.py << 'EOF'
import torch
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference
import solution

torch.manual_seed(42)
M, N, K = 4096, 4096, 4127
ref_model = reference.Model(M, N, K).cuda()
sol_model = solution.Model(M, N, K).cuda()

print('Before load_state_dict:')
print('  sol _w_padded device:', sol_model._w_padded.device)
print('  sol _w_padded shape:', sol_model._w_padded.shape)
print('  sol _w_padded first 5 vals (row 0):', sol_model._w_padded[0, :5].float().tolist())
print('  sol _w_padded K=4096 to 4100:', sol_model._w_padded[0, 4095:4100].float().tolist())
print('  sol _w_padded K=4120 to 4127:', sol_model._w_padded[0, 4120:4224].float().tolist())

# Load ref state_dict
sd = ref_model.state_dict()
sol_model.load_state_dict(sd, strict=True)

print('\nAfter load_state_dict:')
print('  sol weight device:', sol_model.weight.device)
print('  sol _w_padded first 5 vals (row 0):', sol_model._w_padded[0, :5].float().tolist())
print('  sol _w_padded K=4096 to 4100:', sol_model._w_padded[0, 4095:4100].float().tolist())
print('  sol _w_padded K=4120 to 4127:', sol_model._w_padded[0, 4120:4224].float().tolist())

# Check the actual stored values
print('\n  ref weight[0, :5]:', ref_model.weight[0, :5].float().tolist())
EOF
uv run python /tmp/test_load_state2.py 2>&1 | tail -20

{
  "command": "<5641 chars \u2014 see below>",
  "description": "Test with re-pad on every forward"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, w pre-padded, x padded in-place, with load_state_dict hook."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        # Pre-pad w if K is not multiple of 128
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        # Register hook to re-pad after weight is loaded
        self._register_load_state_dict_pre_hook(self._repad_hook)
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad, persistent=False)
        else:
            self._w_padded = None

    def _repad_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
        # Re-pad w after load
        K = self.K
        K_pad = self._K_pad
        if K_pad != K:
            w = state_dict[prefix + "weight"]
            # The w_padded buffer is on the same device as weight (after .cuda())
            self._w_padded[:, :K].copy_(w.to(self

... (1641 more chars)

{
  "command": "<1294 chars \u2014 see below>",
  "description": "Test small weight stress for shape 1"
}

cat > /tmp/test_small_weight_shape1.py << 'EOF'
import torch
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import reference
import solution
from src.eval.numeric_stress import numeric_stress_context

torch.manual_seed(42)
M, N, K = 4096, 4096, 4127  # shape 1
ref_model = reference.Model(M, N, K).cuda()
sol_model = solution.Model(M, N, K).cuda()
sol_model.load_state_dict(ref_model.state_dict())

x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

for case_name, state_scales, input_scales in [
    ('nominal', {}, {}),
    ('small_weight', {'weight': 1e-2}, {}),
]:
    case = type('Case', (), {'name': case_name, 'state_scales': state_scales, 'input_scales': input_scales, 'tolerance': None})()
    with numeric_stress_context(ref_model, sol_model, [x], case) as inputs:
        with torch.no_grad():
            ref_out = ref_model(*inputs)
            sol_out = sol_model(*inputs)
    diff = (ref_out.float() - sol_out.float()).abs()
    print(f"{case_name}: max abs diff = {diff.max().item():.4g}, mean abs diff = {diff.mean().item():.4g}")
    print(f"  ref max abs = {ref_out.float().abs().max().item():.4g}")
    print(f"  sol max abs = {sol_out.float().abs().max().item():.4g}")
EOF
uv run python /tmp/test_small_weight_shape1.py 2>&1 | tail -10

{
  "command": "<4915 chars \u2014 see below>",
  "description": "Re-pad on every forward to handle weight updates"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — K padded, w re-padded on every call to handle weight updates."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


def _autotune_configs():
    cfgs = []
    for bm in (64, 128, 256):
        for bn in (64, 128, 256):
            for bk in (64, 128, 256):
                for nw in (4, 8, 16):
                    for ns in (3, 4, 5):
                        for gm in (1, 4, 8):
                            if bm * bn // (32 * nw) < 8: continue
                            if bm * bn * bk > 4 * 1024 * 1024: continue
                            cfgs.append(triton.Config(
                                {"BLOCK_M": bm, "BLOCK_N": bn, "BLOCK_K": bk, "GROUP_M": gm},
                                num_warps=nw, num_stages=ns,
                            ))
    return cfgs


@triton.autotune(
    configs=_autotune_configs(),
    key=["M", "N", "K"],
)
@triton.jit
def fp8_gemm_kernel_aligned(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        # Pre-pad w if K is not multiple of 128 (non-persistent)
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad, persistent=False)
        else:
            self._w_padded = None

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        
        if K_pad != K:
            # Re-pad w from current weight (handles load_state_dict and stress-test scaling)
            self._w_padded[:, :K].copy_(self.weight)
            # Pad x
            x_pad = getattr(self, '_x_pad', None)
            if x_pad is None or x_pad.device != x.device:
                x_pad = torch.zeros((M, K_pad), device=x.device, d

... (915 more chars)

{
  "command": "<4211 chars \u2014 see below>",
  "description": "Find best config for shape 1"
}

cat > /tmp/tune_shape1_v3.py << 'EOF'
"""Tune shape 1 with L2 flush, more configs."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=50):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    for _ in range(5): call()
    torch.cuda.synchronize()
    times = []
    for _ in range(iters):
        s = torch.cuda.Event(enable_timing=True)
        e = torch.cuda.Event(enable_timing=True)
        s.record()
        call()
        e.record()
        torch.cuda.synchronize()
        times.append(s.elapsed_time(e))
    return statistics.median(times)


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

flops = 2 * M * N * K
results = []
for BM, BN, BK, GM, nw, ns in [
    (128, 256, 128, 4, 8, 3),
    (128, 256, 128, 1, 8, 3),
    (128, 256, 128, 8, 8, 3),
    (128, 128, 128, 4, 8, 3),
    (128, 128, 128, 4, 4, 3),
    (128, 128, 128, 4, 4, 4),
    (128, 128, 64, 4, 8, 4),
    (128, 128, 64, 4, 4, 5),
    (64, 256, 128, 4, 8, 3),
    (64, 256, 128, 4, 4, 3),
    (256, 128, 128, 4, 8, 3),
    (256, 128, 128, 4, 8, 4),
    (128, 256, 64, 4, 8, 4),
    (128, 256, 64, 4, 4, 5),
    (128, 256, 64, 4, 8, 5),
    (64, 128, 128, 4, 4, 3),
    (256, 128, 64, 4, 8, 5),
]:
    t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, BM, BN, BK, GM, nw, ns)
    tflops = flops / (t * 1e9)
    results.append((t, tflops, 

... (211 more chars)

{
  "command": "<4180 chars \u2014 see below>",
  "description": "Comprehensive tune for shape 1"
}

cat > /tmp/tune_shape1_v4.py << 'EOF'
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=50):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(5): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

flops = 2 * M * N * K
results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8):
                for ns in (3, 4, 5):
                    for GM in (1, 4, 8):
                        if BM * BN // (32 * nw) < 8: continue
                        if BM * BN * BK > 4 * 1024 * 1024: continue
                        t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, BM, BN, BK, GM, nw, ns)
                        if t is None: continue
                        tflops = flops / (t * 1e9)
                        results.append((t, tflops, (BM, BN, BK, GM, nw, ns)))

results.sort()
pr

... (180 more chars)

{
  "command": "<254 chars \u2014 see below>",
  "description": "Test tensor _version"
}

cat > /tmp/test_version.py << 'EOF'
import torch
x = torch.zeros(10)
print('version:', x._version)
x.copy_(torch.ones(10))
print('version after copy_:', x._version)
x.add_(1)
print('version after add_:', x._version)
EOF
uv run python /tmp/test_version.py

{
  "command": "<4490 chars \u2014 see below>",
  "description": "Test with version-based repad"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — heuristic + version-based repad."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm_dispatch(x, w, weight_scale, M, N, K_pad):
    if M <= 32:
        BM, BN, BK, GM, NW, NS = 64, 64, 256, 4, 4, 4
    else:
        BM, BN, BK, GM, NW, NS = 128, 256, 128, 4, 8, 3
    
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    fp8_gemm_kernel[grid](
        x, w, c, M, N, K_pad,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad, persistent=False)
            self._w_version = -1  # initial
        else:
            self._w_padded = None
            self._w_version = -1

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        
        if K_pad != K:
            # Re-pad w only if weight changed (using _version)
            if self.weight._version != self._w_version:
                self._w_padded[:, :K].copy_(self.weight)
                self._w_version = self.weight._version
            # Pad x
            x_pad = getattr(self, '_x_pad', None)
            if x_pad is None or x_pad.device != x.device:
                x_pad = torch.zeros((M, K_pad), device=x.device, dtype=x.dtype)
                self._x_pad = x_pad
            x_pad

... (490 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -25",
  "description": "Verify stability"
}

{
  "command": "<3987 chars \u2014 see below>",
  "description": "Comprehensive tune for shape 2"
}

cat > /tmp/tune_shape2_v3.py << 'EOF'
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=50):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(5): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

flops = 2 * M * N * K
results = []
for BM in (16, 32, 64):
    for BN in (32, 64, 128, 256):
        for BK in (128, 256, 512):
            for nw in (2, 4, 8):
                for ns in (2, 3, 4, 5, 6):
                    for GM in (1, 4, 8):
                        if BM * BN // (32 * nw) < 4: continue
                        if BM * BN * BK > 4 * 1024 * 1024: continue
                        t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
                        if t is None: continue
                        tflops = flops / (t * 1e9)
                        results.append((t, tflops, (BM, BN, BK, GM, nw, ns)))

results.sort()
print(f"Top 15 for shape 2:")
for t, tflops, cfg in results[:15]:
    print(f"  t={t:.4f}ms  tflops={tflops:.1f}  cfg={cfg}")
EOF
uv run python /tmp/tune_shape2_v3.py 2>&1 | tail -20

{
  "command": "<1447 chars \u2014 see below>",
  "description": "Match benchmark exactly"
}

cat > /tmp/verify_shape2_v4.py << 'EOF'
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution
import reference

# Use the exact benchmark methodology
torch.manual_seed(2026)
M, N, K = 32, 8192, 8192
reference.M, reference.N, reference.K = M, N, K
ref_model = reference.Model(M, N, K).cuda().eval()
sol_model = solution.Model(M, N, K).cuda().eval()
sd = ref_model.state_dict()
sol_model.load_state_dict(sd, strict=True)
inputs = [t.cuda() for t in reference.get_inputs()]

# Warmup exactly like benchmark
for _ in range(10):
    with torch.no_grad():
        sol_model(*inputs)
torch.cuda.synchronize()

# L2 flush exactly like benchmark
_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

# Timed calls
times = []
for _ in range(30):
    _l2_flush()
    torch.cuda.synchronize()
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    with torch.no_grad():
        sol_model(*inputs)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
flops = 2 * M * N * K
print(f"Shape 2 (benchmark-style): t={t:.4f}ms tflops={flops/(t*1e9):.1f}")
EOF
uv run python /tmp/verify_shape2_v4.py 2>&1 | tail -3

{
  "command": "<1855 chars \u2014 see below>",
  "description": "Test no_grad and eval effect"
}

cat > /tmp/verify_shape2_v5.py << 'EOF'
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution
import reference

torch.manual_seed(2026)
M, N, K = 32, 8192, 8192
reference.M, reference.N, reference.K = M, N, K
ref_model = reference.Model(M, N, K).cuda().eval()
sol_model = solution.Model(M, N, K).cuda().eval()
sd = ref_model.state_dict()
sol_model.load_state_dict(sd, strict=True)
inputs = [t.cuda() for t in reference.get_inputs()]

# Test: no_grad vs with grad
import contextlib

for use_nograd in [True, False]:
    for use_eval in [True, False]:
        if not use_eval:
            sol_model.train()
        else:
            sol_model.eval()
        
        # Warmup
        ctx = torch.no_grad() if use_nograd else contextlib.nullcontext()
        with ctx:
            for _ in range(10):
                sol_model(*inputs)
        torch.cuda.synchronize()
        
        _L2_FLUSH_BYTES = 128 * 1024 * 1024
        _l2_scratch = None
        def _l2_flush():
            global _l2_scratch
            if _l2_scratch is None:
                _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
            _l2_scratch.zero_()
        
        times = []
        for _ in range(30):
            _l2_flush()
            torch.cuda.synchronize()
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            with ctx:
                sol_model(*inputs)
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        t = statistics.median(times)
        flops = 2 * M * N * K
        print(f"nograd={use_nograd}, eval={use_eval}: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")
EOF
uv run python /tmp/verify_shape2_v5.py 2>&1 | tail -10

{
  "command": "<4359 chars \u2014 see below>",
  "description": "Compare v3 and v4 style side by side"
}

cat > /tmp/verify_shape2_v6.py << 'EOF'
import torch
import statistics
import sys
sys.path.insert(0, '/workspace/problems/01_fp8_gemm')
import solution
import reference
import triton

torch.manual_seed(0)
M, N, K = 32, 8192, 8192

# Setup v3 style: no load_state_dict
print("=== v3 style: no load_state_dict ===")
model = solution.Model(M, N, K).cuda()
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

# Warmup
for _ in range(10):
    y = model(x)
torch.cuda.synchronize()

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

# Direct kernel call
from solution import fp8_gemm_kernel
BM, BN, BK, GM, NW, NS = 64, 64, 256, 4, 4, 4
weight = model.weight
ws = model.weight_scale
c = torch.empty((M, N), device='cuda', dtype=torch.bfloat16)
grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)

for _ in range(5):
    _l2_flush()
    fp8_gemm_kernel[grid](
        x, weight, c, M, N, K,
        x.stride(0), x.stride(1),
        weight.stride(0), weight.stride(1),
        ws, c.stride(0), c.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
torch.cuda.synchronize()

times = []
for _ in range(30):
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    _l2_flush()
    fp8_gemm_kernel[grid](
        x, weight, c, M, N, K,
        x.stride(0), x.stride(1),
        weight.stride(0), weight.stride(1),
        ws, c.stride(0), c.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
flops = 2 * M * N * K
print(f"  Direct kernel: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Now with model
times = []
for _ in range(30):
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    _l2_flush()
    y = model(x)
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
print(f"  Model: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

print("\n=== v4 style: with load_state_dict ===")
torch.manual_seed(2026)
reference.M, reference.N, reference.K = M, N, K
ref_model = reference.Model(M, N, K).cuda().eval()
sol_model = solution.Model(M, N, K).cuda().eval()
sd = ref_model.state_dict()
sol_model.load_state_dict(sd, strict=True)
inputs = [t.cuda() for t in reference.get_inputs()]
x_v4 = inputs[0]

# Warmup
for _ in range(10):
    with torch.no_grad():
        sol_model(*inputs)
torch.cuda.synchronize()

# Direct kernel
weight_v4 = sol_model.weight
ws_v4 = sol_model.weight_scale
c_v4 = torch.empty((M, N), device='cuda', dtype=torch.bfloat16)

for _ in range(5):
    _l2_flush()
    fp8_gemm_kernel[grid](
        x_v4, weight_v4, c_v4, M, N, K,
        x_v4.stride(0), x_v4.stride(1),
        weight_v4.stride(0), weight_v4.stride(1),
        ws_v4, c_v4.stride(0), c_v4.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
torch.cuda.synchronize()

times = []
for _ in range(30):
    s = torch.cuda.Event(enable_timing=True)
    e = torch.cuda.Event(enable_timing=True)
    s.record()
    _l2_flush()
    fp8_gemm_kernel[grid](
        x_v4, weight_v4, c_v4, M, N, K,
        x_v4.stride(0), x_v4.stride(1),
        weight_v4.stride(0), weight_v4.stride(1),
        ws_v4, c_v4.stride(0), c_v4.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
    e.record()
    torch.cuda.synchronize()
    times.append(s.elapsed_time(e))
t = statistics.median(times)
print(f"  Direct kernel: t={t:.4f}ms tflops={flops/(t*1e9):.1f}")

# Model
times = []
for _ in range(30):
    s = torch.cuda.Event(enable_timing=True)

... (359 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -20",
  "description": "Re-run benchmark"
}

{
  "command": "<4396 chars \u2014 see below>",
  "description": "Test with BM=32 for shape 2"
}

cat > /workspace/problems/01_fp8_gemm/solution.py << 'EOF'
"""FP8 GEMM with Triton — heuristic + version-based repad."""
import torch
import torch.nn as nn
import triton
import triton.language as tl

OP_TYPE = "gemm"
SUPPORTED_PRECISIONS = ["fp8_e4m3"]
HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
E4M3_MAX = 448.0

# Pad K to multiple of 128 for efficient MMA.
K_BLOCK_ALIGN = 128


@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def fp8_gemm_dispatch(x, w, weight_scale, M, N, K_pad):
    if M <= 32:
        BM, BN, BK, GM, NW, NS = 32, 64, 256, 4, 4, 2
    else:
        BM, BN, BK, GM, NW, NS = 128, 256, 128, 4, 8, 3
    
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    fp8_gemm_kernel[grid](
        x, w, c, M, N, K_pad,
        x.stride(0), x.stride(1),
        w.stride(0), w.stride(1),
        weight_scale,
        c.stride(0), c.stride(1),
        BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
        num_warps=NW, num_stages=NS,
    )
    return c


class Model(nn.Module):
    def __init__(self, M: int, N: int, K: int):
        super().__init__()
        self.M, self.N, self.K = M, N, K
        w = torch.empty(N, K, dtype=torch.bfloat16)
        nn.init.normal_(w, std=0.02)
        s = (w.float().abs().amax(dim=1, keepdim=True) / E4M3_MAX).clamp(min=1e-12)
        w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
        self.register_buffer("weight", w_fp8)
        self.register_buffer("weight_scale", s.squeeze(1).to(torch.float32))
        K_pad = ((K + K_BLOCK_ALIGN - 1) // K_BLOCK_ALIGN) * K_BLOCK_ALIGN
        self._K_pad = K_pad
        if K_pad != K:
            w_pad = torch.zeros((N, K_pad), dtype=w_fp8.dtype)
            w_pad[:, :K].copy_(w_fp8)
            self.register_buffer("_w_padded", w_pad, persistent=False)
            self._w_version = -1
        else:
            self._w_padded = None
            self._w_version = -1

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        K = self.K
        K_pad = self._K_pad
        M, N = self.M, self.N
        
        if K_pad != K:
            if self.weight._version != self._w_version:
                self._w_padded[:, :K].copy_(self.weight)
                self._w_version = self.weight._version
            x_pad = getattr(self, '_x_pad', None)
            if x_pad is None or x_pad.device != x.device:
                x_pad = torch.zeros((M, K_pad), device=x.device, dtype=x.dtype)
                self._x_pad = x_pad
            x_pad[:, :K].copy_(x)
            x_use = x_pad
            w_use = self._w_padded
        else:
  

... (396 more chars)

{
  "command": "uv run python benchmark.py 2>&1 | tail -20",
  "description": "Final benchmark"
}

{
  "command": "<4023 chars \u2014 see below>",
  "description": "Try BM=16 for shape 2"
}

cat > /tmp/try_shape2_bm16.py << 'EOF'
"""Try BM=16 for shape 2."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=50):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(5): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 32, 8192, 8192
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)

flops = 2 * M * N * K
print("With L2 flush (cold L2):")
for BM, BN, BK, GM, nw, ns in [
    (16, 32, 256, 4, 4, 2),
    (16, 32, 256, 1, 4, 2),
    (16, 64, 128, 1, 4, 2),
    (16, 64, 256, 1, 4, 2),
    (16, 64, 256, 4, 4, 2),
    (16, 64, 128, 4, 4, 2),
    (32, 32, 256, 4, 4, 2),
    (32, 32, 512, 1, 4, 2),
    (32, 32, 256, 1, 4, 2),
    (32, 64, 256, 4, 4, 2),
    (64, 32, 256, 4, 4, 2),
    (32, 16, 512, 1, 4, 2),
    (16, 128, 64, 1, 4, 2),
]:
    t = run_kernel(x, w_fp8, ws, M, N, K, BM, BN, BK, GM, nw, ns)
    if t is None:
        print(f"  FAIL cfg={(BM, BN, BK, GM, nw, ns)}")
        continue
    tflops = flops / (t * 1e9)
    gbps = (M*K + K*N + M*N*2) / (t * 1e6)
    print(f"  t={t:.4f}ms  tflops={tflops:.1f}  gbps={gbps:.0f}  cfg={(BM, BN, BK, GM, nw, ns)}")
EOF
uv run python /tmp/try_shape2_

... (23 more chars)

{
  "command": "<4246 chars \u2014 see below>",
  "description": "Try different configs for shape 1 with cold L2"
}

cat > /tmp/try_shape1_v2.py << 'EOF'
"""Try different configs for shape 1 (cold L2)."""
import torch
import torch.nn as nn
import triton
import triton.language as tl
import statistics

_L2_FLUSH_BYTES = 128 * 1024 * 1024
_l2_scratch = None
def _l2_flush():
    global _l2_scratch
    if _l2_scratch is None:
        _l2_scratch = torch.empty(_L2_FLUSH_BYTES // 4, dtype=torch.float32, device='cuda')
    _l2_scratch.zero_()

@triton.jit
def fp8_gemm_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bn, stride_bk,
    scale_ptr,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
    GROUP_M: tl.constexpr,
):
    pid = tl.program_id(0)
    grid_m = tl.cdiv(M, BLOCK_M)
    grid_n = tl.cdiv(N, BLOCK_N)
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // group_size
    
    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_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, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    
    scale = tl.load(scale_ptr + offs_n)
    acc = acc * scale[None, :]
    
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc.to(tl.bfloat16), mask=mask)


def run_kernel(x, w, ws, M, N, K, BM, BN, BK, GM, nw, ns, iters=50):
    c = torch.empty((M, N), device=x.device, dtype=torch.bfloat16)
    grid = (triton.cdiv(M, BM) * triton.cdiv(N, BN),)
    
    def call():
        _l2_flush()
        fp8_gemm_kernel[grid](
            x, w, c, M, N, K,
            x.stride(0), x.stride(1),
            w.stride(0), w.stride(1),
            ws, c.stride(0), c.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, GROUP_M=GM,
            num_warps=nw, num_stages=ns,
        )
    
    try:
        for _ in range(5): call()
        torch.cuda.synchronize()
        times = []
        for _ in range(iters):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            s.record()
            call()
            e.record()
            torch.cuda.synchronize()
            times.append(s.elapsed_time(e))
        return statistics.median(times)
    except Exception as e:
        return None


torch.manual_seed(0)
M, N, K = 4096, 4096, 4127
K_pad = 4224
w = torch.empty(N, K, dtype=torch.bfloat16, device='cuda')
nn.init.normal_(w, std=0.02)
s = (w.float().abs().amax(dim=1, keepdim=True) / 448.0).clamp(min=1e-12)
w_fp8 = (w.float() / s).to(torch.float8_e4m3fn)
ws = s.squeeze(1).to(torch.float32)
x = (torch.rand(M, K, device='cuda') * 8 - 4).to(torch.float8_e4m3fn)
x_pad = torch.zeros(M, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
x_pad[:, :K] = x
w_pad = torch.zeros(N, K_pad, device='cuda', dtype=torch.float8_e4m3fn)
w_pad[:, :K] = w_fp8

flops = 2 * M * N * K
results = []
for BM in (64, 128, 256):
    for BN in (64, 128, 256):
        for BK in (64, 128, 256):
            for nw in (4, 8):
                for ns in (2, 3, 4, 5):
                    for GM in (1, 4, 8, 16):
                        if BM * BN // (32 * nw) < 8: continue
                        if BM * BN * BK > 4 * 1024 * 1024: continue
                        t = run_kernel(x_pad, w_pad, ws, M, N, K_pad, BM, BN, BK, GM, nw, ns)
                        if t is None: continue
                        tflops = flops / (t * 1e9)
                        results.append(

... (246 more chars)

{
  "file_path": "/workspace/problems/01_fp8_gemm/solution.py"
}

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

{
  "command": "<101 chars \u2014 see below>",
  "description": "Check framework label and files"
}

cat /workspace/problems/01_fp8_gemm/framework.txt 2>/dev/null
ls -la /workspace/problems/01_fp8_gemm/