{
  "todos": [
    {
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "in_progress",
      "priority": "high"
    },
    {
      "content": "Read existing solution.py if any",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Design and implement optimized CUDA kernel for 3D patch embedding",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Test correctness with check.py",
      "status": "pending",
      "priority": "high"
    },
    {
      "content": "Benchmark p

... (229 more chars)

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/reference.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/check.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/benchmark.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/shapes.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/problem.yaml"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/sota.py"
}

{
  "pattern": "problems/10_patch_embed_conv3d_gemm/solution.py"
}

{
  "command": "nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader 2>&1 | head -5",
  "description": "Check GPU info"
}

{
  "command": "nvcc --version 2>&1 | head -3",
  "description": "Check CUDA version"
}

{
  "command": "python -c \"import triton; print(triton.__version__)\" 2>&1 || echo \"no triton\"",
  "description": "Check Triton availability"
}

{
  "command": "python -c \"import torch; print(torch.__version__, torch.version.cuda)\" 2>&1",
  "description": "Check PyTorch and CUDA versions"
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Read existing solution.py if any",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check existing examples and hardware config for patterns",
      "status": "in_progress"
    },
    {
      "priority": "high",
      "content": "Design and implement optimized CUDA kernel for 3D patch embedding",
      "status": "pending"
    },
    {

... (375 more chars)

{
  "description": "Check repo structure and find other examples",
  "command": "ls problems/ | head -20"
}

{
  "description": "Check hardware config for RTX PRO 6000",
  "command": "python -c \"from src.hardware import get; hw = get('RTX_PRO_6000'); print(f'Peak bf16 TFLOPS: {hw.peak_tflops_dense}'); print(f'Peak bandwidth: {hw.peak_bandwidth_gb_s} GB/s'); print(f'SM count etc: {hw.__dict__ if hasattr(hw, \\\"__dict__\\\") else dir(hw)}')\"",
  "workdir": "/home/infatoshi/cuda/KernelBench-Hard"
}

{
  "pattern": "problems/*/solution.py"
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Read existing solution.py if any",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check existing examples and hardware config for patterns",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Design and implement optimized Triton kernel for 3D patch embedding",
      "status": "in_progress"
    },
 

... (379 more chars)

{
  "description": "Test reshape logic correctness",
  "command": "<1154 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 -c "
import torch
import torch.nn as nn

# Quick test: verify reshape -> gemm -> reshape matches Conv3d
B, C, T, H, W = 1, 3, 2, 16, 16
kT, kH, kW = 2, 4, 4
embed_dim = 8

torch.manual_seed(42)
x = torch.randn(B, C, T, H, W, dtype=torch.bfloat16) * 0.5

conv = nn.Conv3d(C, embed_dim, kernel_size=(kT, kH, kW), stride=(kT, kH, kW), bias=False, dtype=torch.bfloat16)
nn.init.normal_(conv.weight, std=0.02)

ref_out = conv(x)

# Reshape approach
nT, nH, nW = T // kT, H // kH, W // kW
N = embed_dim
K = C * kT * kH * kW
M = B * nT * nH * nW

# Reshape input to patches
x_reshaped = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
x_reshaped = x_reshaped.permute(0, 2, 4, 6, 1, 3, 5, 7)
x_patches = x_reshaped.contiguous().reshape(M, K)

# Reshape weight
w = conv.weight.reshape(N, K).t().contiguous()

# Manual GEMM
out_2d = x_patches @ w  # (M, K) @ (K, N) = (M, N)
out = out_2d.reshape(B, nT, nH, nW, N).permute(0, 4, 1, 2, 3).contiguous()

print('Max diff:', (ref_out - out).abs().max().item())
print('All close:', torch.allclose(ref_out, out, atol=1e-2, rtol=1e-2))
" 2>&1

{
  "description": "Check Triton GEMM capabilities on this GPU",
  "command": "<712 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 -c "
import triton
import triton.language as tl
import torch

@triton.jit
def test_dot_kernel(a_ptr, b_ptr, c_ptr, N: tl.constexpr, BLOCK_N: tl.constexpr):
    offs = tl.arange(0, BLOCK_N)
    a = tl.load(a_ptr + offs)
    b = tl.load(b_ptr + offs)
    c = tl.dot(a[None, :], b[:, None])
    tl.store(c_ptr + offs, tl.ravel(c))

a = torch.ones(64, device='cuda', dtype=torch.bfloat16)
b = torch.ones(64, device='cuda', dtype=torch.bfloat16)
c = torch.zeros(64, device='cuda', dtype=torch.bfloat16)
test_dot_kernel[(1,)](a, b, c, 64, 64)
print('tl.dot works:', c[0].item())
print('Triton version:', triton.__version__)
" 2>&1

{
  "description": "Test Triton GEMM from a file",
  "command": "<2059 chars \u2014 see below>"
}

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

@triton.jit
def test_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_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak
    b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs, mask=(rm[:, None] < M) & (rk[None, :] + k < K), other=0.0)
        b = tl.load(b_ptrs, mask=(rk[:, None] + k < K) & (rn[None, :] < N), other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    c = acc.to(tl.bfloat16)
    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
    tl.store(c_ptrs, c, mask=(rm[:, None] < M) & (rn[None, :] < N))

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

BLOCK_M, BLOCK_N, BLOCK_K = 64, 64, 32
grid = lambda meta: (triton.cdiv(M, meta['BLOCK_M']), triton.cdiv(N, meta['BLOCK_N']))
test_gemm_kernel[grid](
    a, b, c_triton,
    M, N, K,
    a.stride(0), a.stride(1),
    b.stride(0), b.stride(1),
    c_triton.stride(0), c_triton.stride(1),
    BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
    num_warps=4,
)

c_ref = a @ b
print("Max diff:", (c_triton.float() - c_ref.float()).abs().max().item())
print("All close:", torch.allclose(c_triton.float(), c_ref.float(), atol=1e-2, rtol=1e-2))
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/test_triton.py 2>&1

{
  "description": "Benchmark different Triton GEMM configs",
  "command": "<3982 chars \u2014 see below>",
  "timeout": 180000
}

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

@triton.jit
def bench_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_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak
    b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs, mask=(rm[:, None] < M) & (rk[None, :] + k < K), other=0.0)
        b = tl.load(b_ptrs, mask=(rk[:, None] + k < K) & (rn[None, :] < N), other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    c = acc.to(tl.bfloat16)
    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
    tl.store(c_ptrs, c, mask=(rm[:, None] < M) & (rn[None, :] < N))

def time_kernel(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, num_warps=4, iters=200):
    a = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
    b = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')
    c = torch.zeros(M, N, dtype=torch.bfloat16, device='cuda')
    
    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_M']), triton.cdiv(N, meta['BLOCK_N']))
    
    # Warmup
    for _ in range(20):
        bench_gemm_kernel[grid](
            a, b, c, M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps,
        )
    torch.cuda.synchronize()
    
    start = time.perf_counter()
    for _ in range(iters):
        bench_gemm_kernel[grid](
            a, b, c, M, N, K,
            a.stride(0), a.stride(1),
            b.stride(0), b.stride(1),
            c.stride(0), c.stride(1),
            BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
            num_warps=num_warps,
        )
    torch.cuda.synchronize()
    elapsed = (time.perf_counter() - start) / iters * 1000
    return elapsed

# Test shapes from the problem
shapes = [
    ("shape0 B1C3T2H224W224", 1*1*16*16, 1280, 3*2*14*14),  # M=256, N=1280, K=1176
    ("shape1 B2C3T4H224W224", 2*2*16*16, 1280, 3*2*14*14),  # M=1024, N=1280, K=1176
    ("shape2 B1C3T8H336W336", 1*4*24*24, 1280, 3*2*14*14),  # M=2304, N=1280, K=1176
    ("shape3 B4C3T1H224W224", 4*1*14*14, 768, 3*1*16*16),   # M=784, N=768, K=768
]

configs = [
    # (BLOCK_M, BLOCK_N, BLOCK_K, num_warps)
    (32, 32, 32, 4),
    (64, 32, 32, 4),
    (32, 64, 32, 4),
    (64, 64, 32, 4),
    (64, 64, 64, 4),
    (128, 64, 32, 4),
    (64, 128, 32, 4),
    (128, 128, 32, 4),
    (128, 128, 64, 4),
    (64, 64, 32, 8),
    (128, 64, 32, 8),
    (64, 128, 32, 8),
    (128, 128, 32, 8),
    (128, 128, 64, 8),
    (128, 256, 32, 8),
    (256, 128, 32, 8),
]

for name, M, N, K in shapes:
    print(f"\n--- {name}: M={M}, N={N}, K={K} ---")
    best_ms = float('inf')
    best_cfg = None
    for cfg in configs:
        BM, BN, BK, nw = cfg
        if BM > M or BN > N:
            continue
        try:
            ms = time_kernel(M, N, K, BM, BN, BK, nw, iters=100)
            if ms < best_ms:
                best_ms = ms
                best_cfg = cfg
            print(f"  BM={BM:3d} BN={BN:3d} BK={BK:3d} nw={nw}: {ms:.4f} ms")
        except Exception as e:
            print(f"  BM={BM:3d} BN={BN:3d} BK={BK:3d} nw={nw}: ERROR {e}")
    print(f"  BEST: BM={best_cfg[0]} BN={best_cfg[1]} BK={best_cfg[2]} nw={best_cfg[3]} = {best_ms:.4f} ms")
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/bench_configs.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<4724 chars \u2014 see diff>"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "oldString": "<226 chars \u2014 see below>",
  "newString": "<568 chars \u2014 see below>"
}

        K_dim = C * kT * kH * kW
        w = self.conv.weight.reshape(embed_dim, K_dim).t().contiguous()
        self.register_buffer("_w_reshaped", w, persistent=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        K_dim = C * kT * kH * kW
        w = self.conv.weight.reshape(embed_dim, K_dim).t().contiguous()
        self.register_buffer("_w_reshaped", w, persistent=False)

    def load_state_dict(self, state_dict, strict=True):
        result = super().load_state_dict(state_dict, strict=strict)
        K_dim = self.C * self.kT * self.kH * self.kW
        w = self.conv.weight.reshape(self.embed_dim, K_dim).t().contiguous()
        self.register_buffer("_w_reshaped", w, persistent=False)
        return result

    def forward(self, x: torch.Tensor) -> torch.Tensor:

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Read existing solution.py if any",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check existing examples and hardware config for patterns",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Design and implement optimized Triton kernel for 3D patch embedding",
      "status": "completed"
    },
   

... (381 more chars)

{
  "description": "Run correctness check",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1",
  "timeout": 300000
}

{
  "description": "Run benchmark",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "description": "Inspect time_fn implementation",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard && python3 -c \"from src.eval.timing import time_fn; import inspect; print(inspect.getsource(time_fn))\" 2>&1"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "offset": 93,
  "limit": 20
}

{
  "description": "Check if reshape without contiguous creates a copy",
  "command": "<792 chars \u2014 see below>"
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 -c "
import torch
B,C,T,H,W = 1,3,2,16,16
kT,kH,kW = 2,4,4
nT,nH,nW = T//kT, H//kH, W//kW
K = C * kT * kH * kW
M = B * nT * nH * nW

x = torch.randn(B,C,T,H,W)
x1 = x.reshape(B,C,nT,kT,nH,kH,nW,kW)
x2 = x1.permute(0,2,4,6,1,3,5,7)
print('After permute - contiguous:', x2.is_contiguous())
print('After permute - strides:', x2.stride())

x3 = x2.reshape(M, K)
print('After reshape - contiguous:', x3.is_contiguous())
print('After reshape - strides:', x3.stride())
print('After reshape - data_ptr same as x:', x3.data_ptr() == x.data_ptr())

# Try without reshape - just view_as
x4 = x2.contiguous().reshape(M, K)
print('After contiguous+reshape - data_ptr same as x:', x4.data_ptr() == x.data_ptr())
" 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py"
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<6470 chars \u2014 see diff>"
}

{
  "description": "Test correctness of fused kernel",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1",
  "timeout": 300000
}

{
  "description": "Benchmark fused kernel",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<5544 chars \u2014 see diff>"
}

{
  "description": "Test correctness and benchmark optimized version",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1 && echo \"---\" && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "description": "Check what config autotuner picked",
  "command": "<855 chars \u2014 see below>",
  "timeout": 60000
}

cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 -c "
import torch
import triton
import solution

# Force one shape through to see autotune
B,C,T,H,W = 1,3,8,336,336
kT,kH,kW = 2,14,14
embed_dim = 1280
solution.B, solution.C, solution.T, solution.H, solution.W = B,C,T,H,W
solution.kT, solution.kH, solution.kW = kT,kH,kW
solution.embed_dim = embed_dim

model = solution.Model(B,C,T,H,W,kT,kH,kW,embed_dim).cuda().eval()
x = solution.get_inputs()[0].cuda()

# Run once to trigger autotune
with torch.no_grad():
    model(x)
torch.cuda.synchronize()

import os
cache_dir = os.path.expanduser('~/.triton/cache')
import json
# Find the autotune result
for root, dirs, files in os.walk(cache_dir):
    for f in files:
        if 'patch_gemm' in root or 'gemm_kernel' in root:
            print(root, f)
" 2>&1 | head -30

{
  "description": "Benchmark specific manual configs",
  "command": "<3779 chars \u2014 see below>",
  "timeout": 300000
}

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

@triton.jit
def bench_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_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak
    b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs, mask=(rm[:, None] < M) & (rk[None, :] + k < K), other=0.0)
        b = tl.load(b_ptrs, mask=(rk[:, None] + k < K) & (rn[None, :] < N), other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    c = acc.to(tl.bfloat16)
    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
    tl.store(c_ptrs, c, mask=(rm[:, None] < M) & (rn[None, :] < N))

def time_full(M, N, K, BM, BN, BK, nw, nT, nH, nW, kT, kH, kW, iters=200):
    # Simulate the full forward pass including im2col copy
    C = K // (kT * kH * kW)
    B = M // (nT * nH * nW)
    
    x = torch.randn(B, C, nT*kT, nH*kH, nW*kW, dtype=torch.bfloat16, device='cuda')
    w = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')
    
    def forward():
        # im2col copy
        x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
        x_p = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
        x_p = x_p.contiguous().reshape(M, K)
        out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
        grid = (triton.cdiv(M, BM), triton.cdiv(N, BN))
        bench_gemm_kernel[grid](
            x_p, w, out, M, N, K,
            x_p.stride(0), x_p.stride(1),
            w.stride(0), w.stride(1),
            out.stride(0), out.stride(1),
            BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=nw,
        )
        return out
    
    for _ in range(30):
        forward()
    torch.cuda.synchronize()
    
    start = time.perf_counter()
    for _ in range(iters):
        forward()
    torch.cuda.synchronize()
    return (time.perf_counter() - start) / iters * 1000

shapes = [
    ("shape0", 256, 1280, 1176, 1, 16, 16, 2, 14, 14),
    ("shape1", 1024, 1280, 1176, 2, 16, 16, 2, 14, 14),
    ("shape2", 2304, 1280, 1176, 1, 24, 24, 2, 14, 14),
    ("shape3", 784, 768, 768, 4, 14, 14, 1, 16, 16),
]

print("Testing full forward (copy + GEMM):")
for name, M, N, K, B, nH, nW, kT, kH, kW in shapes:
    print(f"\n--- {name}: M={M}, N={N}, K={K} ---")
    
    configs_to_try = [
        (64, 64, 32, 4),
        (64, 64, 64, 4),
        (64, 128, 32, 4),
        (128, 64, 32, 4),
        (64, 64, 32, 8),
        (64, 64, 64, 8),
        (64, 128, 32, 8),
        (128, 64, 32, 8),
        (128, 128, 32, 8),
        (128, 128, 64, 8),
    ]
    
    best = float('inf')
    best_cfg = None
    for BM, BN, BK, nw in configs_to_try:
        if BM > M or BN > N:
            continue
        try:
            ms = time_full(M, N, K, BM, BN, BK, nw, nT=B*nH*nW, nH=nH, nW=nW, kT=kT, kH=kH, kW=kW, iters=100)
            if ms < best:
                best = ms
                best_cfg = (BM, BN, BK, nw)
            print(f"  BM={BM:3d} BN={BN:3d} BK={BK:3d} nw={nw}: {ms:.4f} ms")
        except Exception as e:
            print(f"  BM={BM:3d} BN={BN:3d} BK={BK:3d} nw={nw}: ERROR {e}")
    print(f"  BEST: {best_cfg} = {best:.4f} ms")
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/bench_manual.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<5105 chars \u2014 see diff>"
}

{
  "description": "Test correctness then benchmark",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1 && echo \"---\" && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "description": "Test different configs for shape0 specifically",
  "command": "<3338 chars \u2014 see below>",
  "timeout": 120000
}

cat > /tmp/bench_shape0.py << 'PYEOF'
import torch
import triton
import triton.language as tl
import time

@triton.jit
def gemm_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak
    b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs, mask=(rm[:, None] < M) & (rk[None, :] + k < K), other=0.0)
        b = tl.load(b_ptrs, mask=(rk[:, None] + k < K) & (rn[None, :] < N), other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    c = acc.to(tl.bfloat16)
    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
    tl.store(c_ptrs, c, mask=(rm[:, None] < M) & (rn[None, :] < N))

# shape0 parameters
M, N, K = 256, 1280, 1176
B, C, nT, nH, nW, kT, kH, kW = 1, 3, 1, 16, 16, 2, 14, 14

x = torch.randn(B, C, nT*kT, nH*kH, nW*kW, dtype=torch.bfloat16, device='cuda') * 0.5
w = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')

# Pre-warm to avoid first-run overhead
for _ in range(50):
    x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
    x_p = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
    x_p = x_p.contiguous().reshape(M, K)
torch.cuda.synchronize()

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

best = float('inf')
best_cfg = None
for BM, BN, BK, nw in configs:
    if BM > M or BN > N:
        continue
    
    # Full forward sim: alloc x_p, ge_alloc out, GEMM
    def forward():
        x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
        x_p = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
        x_p = x_p.contiguous().reshape(M, K)
        out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
        grid = (triton.cdiv(M, BM), triton.cdiv(N, BN))
        gemm_kernel[grid](x_p, w, out, M, N, K,
                          x_p.stride(0), x_p.stride(1),
                          w.stride(0), w.stride(1),
                          out.stride(0), out.stride(1),
                          BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=nw)
        return out
    
    for _ in range(30):
        forward()
    torch.cuda.synchronize()
    
    start = time.perf_counter()
    iters = 200
    for _ in range(iters):
        forward()
    torch.cuda.synchronize()
    ms = (time.perf_counter() - start) / iters * 1000
    
    if ms < best:
        best = ms
        best_cfg = (BM, BN, BK, nw)
    print(f"  BM={BM:3d} BN={BN:3d} BK={BK:3d} nw={nw}: {ms:.4f} ms")
print(f"BEST: {best_cfg} = {best:.4f} ms")
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/bench_shape0.py 2>&1

{
  "description": "Test pre-allocation approach",
  "command": "<3350 chars \u2014 see below>",
  "timeout": 30000
}

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

@triton.jit
def gemm_kernel(a_ptr, b_ptr, c_ptr, M, N, K,
                stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn,
                BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    a_ptrs = a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak
    b_ptrs = b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(a_ptrs, mask=(rm[:, None] < M) & (rk[None, :] + k < K), other=0.0)
        b = tl.load(b_ptrs, mask=(rk[:, None] + k < K) & (rn[None, :] < N), other=0.0)
        acc = tl.dot(a, b, acc)
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk
    c = acc.to(tl.bfloat16)
    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
    tl.store(c_ptrs, c, mask=(rm[:, None] < M) & (rn[None, :] < N))

# shape0
M, N, K = 256, 1280, 1176
B, C, nT, nH, nW, kT, kH, kW = 1, 3, 1, 16, 16, 2, 14, 14
BM, BN, BK, nw = 64, 64, 64, 4
grid = (triton.cdiv(M, BM), triton.cdiv(N, BN))

x = torch.randn(B, C, nT*kT, nH*kH, nW*kW, dtype=torch.bfloat16, device='cuda') * 0.5
w = torch.randn(K, N, dtype=torch.bfloat16, device='cuda')

# --- Method 1: allocate per call (current) ---
def forward_alloc():
    x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
    x_p = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
    x_p = x_p.contiguous().reshape(M, K)
    out = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')
    gemm_kernel[grid](x_p, w, out, M, N, K,
                      x_p.stride(0), x_p.stride(1),
                      w.stride(0), w.stride(1),
                      out.stride(0), out.stride(1),
                      BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=nw)
    return out

# --- Method 2: pre-allocated buffers ---
buf_a = torch.empty(M, K, dtype=torch.bfloat16, device='cuda')
buf_c = torch.empty(M, N, dtype=torch.bfloat16, device='cuda')

def forward_prealloc():
    src_view = x.reshape(B, C, nT, kT, nH, kH, nW, kW).permute(0, 2, 4, 6, 1, 3, 5, 7)
    buf_a.view(-1)[:M*K].copy_(src_view.reshape(-1)[:M*K])
    gemm_kernel[grid](buf_a, w, buf_c, M, N, K,
                      buf_a.stride(0), buf_a.stride(1),
                      w.stride(0), w.stride(1),
                      buf_c.stride(0), buf_c.stride(1),
                      BLOCK_M=BM, BLOCK_N=BN, BLOCK_K=BK, num_warps=nw)
    return buf_c

def time_it(fn, warmup=30, iters=200):
    for _ in range(warmup):
        fn()
    torch.cuda.synchronize()
    start = time.perf_counter()
    for _ in range(iters):
        fn()
    torch.cuda.synchronize()
    return (time.perf_counter() - start) / iters * 1000

print(f"alloc:   {time_it(forward_alloc):.4f} ms")
print(f"prealloc: {time_it(forward_prealloc):.4f} ms")

# Verify correctness
out1 = forward_alloc()
out2 = forward_prealloc()
print(f"match: {torch.allclose(out1, out2, atol=1e-2, rtol=1e-2)}")
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/bench_prealloc.py 2>&1

{
  "description": "Isolate the copy overhead precisely",
  "command": "<2013 chars \u2014 see below>",
  "timeout": 30000
}

cat > /tmp/bench_copy.py << 'PYEOF'
import torch
import time

M, K = 256, 1176
B, C, nT, nH, nW, kT, kH, kW = 1, 3, 1, 16, 16, 2, 14, 14

x = torch.randn(B, C, nT*kT, nH*kH, nW*kW, dtype=torch.bfloat16, device='cuda') * 0.5

# Test: time just the copy
def copy_only():
    x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
    x_p = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
    return x_p.contiguous().reshape(M, K)

# Warmup
for _ in range(100):
    copy_only()
torch.cuda.synchronize()

# Time the copy
start = time.perf_counter()
for _ in range(500):
    copy_only()
torch.cuda.synchronize()
ms_copy = (time.perf_counter() - start) / 500 * 1000
print(f"Copy only: {ms_copy:.4f} ms")

# Test: time empty allocation
def alloc_only():
    return torch.empty(M, K, dtype=torch.bfloat16, device='cuda')

for _ in range(100):
    alloc_only()
torch.cuda.synchronize()

start = time.perf_counter()
for _ in range(500):
    alloc_only()
torch.cuda.synchronize()
ms_alloc = (time.perf_counter() - start) / 500 * 1000
print(f"empty alloc: {ms_alloc:.4f} ms")

# Test: time empty + copy
def alloc_copy():
    out = torch.empty(M, K, dtype=torch.bfloat16, device='cuda')
    x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW)
    x_view = x_p.permute(0, 2, 4, 6, 1, 3, 5, 7)
    out.view(-1)[:M*K].copy_(x_view.reshape(-1)[:M*K])
    return out

for _ in range(100):
    alloc_copy()
torch.cuda.synchronize()

start = time.perf_counter()
for _ in range(500):
    alloc_copy()
torch.cuda.synchronize()
ms_ac = (time.perf_counter() - start) / 500 * 1000
print(f"alloc+copy: {ms_ac:.4f} ms")

# Check data_ptr to confirm copy
x_p = x.reshape(B, C, nT, kT, nH, kH, nW, kW).permute(0, 2, 4, 6, 1, 3, 5, 7)
result = x_p.contiguous().reshape(M, K)
print(f"data_ptr same as x: {result.data_ptr() == x.data_ptr()}")
print(f"result contiguous: {result.is_contiguous()}")
print(f"result size: {result.numel() * 2 / 1024:.1f} KB")
PYEOF
cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 /tmp/bench_copy.py 2>&1

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<5094 chars \u2014 see diff>"
}

{
  "description": "Test correctness and benchmark with micro-optimizations",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1 && echo \"---\" && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "filePath": "/home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm/solution.py",
  "content": "<5283 chars \u2014 see diff>"
}

{
  "description": "Final correctness and benchmark",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1 && echo \"---\" && python3 benchmark.py 2>&1",
  "timeout": 300000
}

{
  "description": "Verify stability and framework label",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 benchmark.py 2>&1 | grep -E \"(peak_fraction|RESULT)\" && echo \"---\" && cat framework.txt",
  "timeout": 120000
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Read existing solution.py if any",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check existing examples and hardware config for patterns",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Design and implement optimized Triton kernel for 3D patch embedding",
      "status": "completed"
    },
   

... (373 more chars)

{
  "description": "Run linting on solution",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard && uv run ruff check problems/10_patch_embed_conv3d_gemm/solution.py --fix 2>&1"
}

{
  "description": "Final ruff check",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard && uv run ruff check problems/10_patch_embed_conv3d_gemm/solution.py 2>&1"
}

{
  "description": "Final correctness check",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 check.py 2>&1"
}

{
  "description": "Final benchmark run",
  "command": "cd /home/infatoshi/cuda/KernelBench-Hard/problems/10_patch_embed_conv3d_gemm && python3 benchmark.py 2>&1",
  "timeout": 120000
}

{
  "todos": [
    {
      "priority": "high",
      "content": "Read reference.py, check.py, benchmark.py, and understand problem structure",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Read existing solution.py if any",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Check existing examples and hardware config for patterns",
      "status": "completed"
    },
    {
      "priority": "high",
      "content": "Design and implement optimized Triton kernel for 3D patch embedding",
      "status": "completed"
    },
   

... (371 more chars)