{
  "subject": "Explore workspace and reference implementation",
  "description": "Read reference.py, check.py, benchmark.py and understand the interfaces, shapes, and correctness requirements",
  "activeForm": "Exploring workspace and reference implementation"
}

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

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

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

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

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

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

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

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

{
  "command": "python --version && nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv",
  "description": "Check Python and GPU info"
}

{
  "command": "uv run python -c \"import torch; print(torch.__version__); print('CUDA available:', torch.cuda.is_available()); print('CUDA capability:', torch.cuda.get_device_capability(0) if torch.cuda.is_available() else 'N/A'); print('CUDA version:', torch.version.cuda)\"",
  "description": "Check PyTorch CUDA info"
}

{
  "command": "uv run python -c \"import triton; print('Triton version:', triton.__version__)\" 2>&1 | head -50",
  "description": "Check Triton availability"
}

{
  "command": "find /usr -name \"cutlass\" -type d 2>/dev/null | head -20; find /workspace -name \"cutlass\" -type d 2>/dev/null | head -20; python -c \"import cutlass; print(cutlass.__version__)\" 2>&1 | head -10",
  "description": "Check CUTLASS availability"
}

{
  "command": "which ncu nsys nvcc ninjia 2>/dev/null; nvcc --version 2>&1 | head -5",
  "description": "Check CUDA toolkit and profiling tools"
}

{
  "command": "ls -la /usr/include/cutlass | head -20; grep -r \"Sm120\" /usr/include/cutlass --include=\"*.h\" -l 2>/dev/null | head -10; grep -r \"class.*GroupedGEMM\" /usr/include/cutlass --include=\"*.h\" -l 2>/dev/null | head -20",
  "description": "Inspect CUTLASS headers for Sm120 and grouped GEMM"
}

{
  "command": "grep -r \"grouped\" /usr/include/cutlass/gemm/kernel --include=\"*.h\" -l 2>/dev/null | head -20; ls /usr/include/cutlass/gemm/kernel/ | grep -i group",
  "description": "Look for grouped GEMM kernels in CUTLASS"
}

{
  "command": "grep -E \"CUTLASS_VERSION|version\" /usr/include/cutlass/version.h 2>/dev/null | head -20; cat /usr/include/cutlass/version.h 2>/dev/null | head -30",
  "description": "Check CUTLASS version"
}

{
  "command": "find /usr/include/cutlass -name \"sm120*\" -o -name \"*sm_120*\" -o -name \"*blackwell*\" 2>/dev/null | head -20; ls /usr/include/cutlass/gemm/collective 2>/dev/null | head -20; ls /usr/include/cutlass/gemm/dispatch_policy 2>/dev/null | head -20",
  "description": "Check for CUTLASS 3 / Blackwell support"
}

{
  "command": "<1902 chars \u2014 see below>",
  "description": "Test Triton on Blackwell"
}

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

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

n = 1024
x = torch.randn(n, device='cuda', dtype=torch.bfloat16)
y = torch.randn(n, device='cuda', dtype=torch.bfloat16)
out = torch.empty_like(x)
add_kernel[(triton.cdiv(n, 256),)](x, y, out, n, BLOCK=256)
print('Triton basic kernel OK:', torch.allclose(out, x + y))

# Test matmul via Triton
@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

M, N, K = 512, 512, 512
A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16)
B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16)
C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
matmul_kernel[(triton.cdiv(M, 64), triton.cdiv(N, 64))](A, B, C, M, N, K, BLOCK_M=64, BLOCK_N=64, BLOCK_K=64)
C_ref = A @ B
print('Triton matmul close:', torch.allclose(C, C_ref, atol=1e-2, rtol=1e-2))
EOF
uv run python /tmp/test_triton.py

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

{
  "activeForm": "Writing Triton grouped GEMM + SwiGLU kernel",
  "description": "Implement solution.py with a Triton grouped GEMM kernel that fuses SwiGLU epilogue, matches reference interface, and avoids forbidden ops.",
  "subject": "Implement Triton grouped GEMM + SwiGLU kernel"
}

{
  "activeForm": "Verifying correctness with check.py",
  "description": "Run python check.py to verify all shapes and seeds pass within tolerance.",
  "subject": "Verify correctness with check.py"
}

{
  "activeForm": "Profiling and optimizing performance",
  "description": "Run benchmark.py, profile with ncu/nsys/torch.profiler, and iterate on autotune/block sizes to improve peak_fraction.",
  "subject": "Profile and optimize performance"
}

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

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "content": "<7893 chars \u2014 see diff>"
}

{
  "command": "uv run python -c \"import solution; print('import OK')\"",
  "description": "Import solution module"
}

{
  "command": "uv run python check.py",
  "description": "Run correctness check on all shapes",
  "timeout": 600000
}

{
  "command": "<1612 chars \u2014 see below>",
  "description": "Debug correctness failure"
}

cat > /tmp/debug_sol.py << 'EOF'
import torch
import reference
import solution

shape = {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4}
for k, v in shape.items():
    setattr(reference, k, v)
    setattr(solution, k, v)

device = 'cuda'
ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()
sol_model = solution.Model(*solution.get_init_inputs()).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]
with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

diff = (ref_out - sol_out).abs()
print('ref_out shape', ref_out.shape, 'sol_out shape', sol_out.shape)
print('max abs diff', diff.max().item())
print('mean abs diff', diff.mean().item())
print('ref max', ref_out.abs().max().item(), 'sol max', sol_out.abs().max().item())

# Find worst index
worst = diff.argmax().item()
row, col = worst // diff.shape[1], worst % diff.shape[1]
print('worst idx', row, col)
print('ref', ref_out[row, col].item(), 'sol', sol_out[row, col].item())

# Check expert boundaries
expert_offsets = inputs[1]
print('expert_offsets[:10]', expert_offsets[:10].cpu())

# Compare a single expert manually
for e in range(shape['E']):
    start = expert_offsets[e].item()
    end = expert_offsets[e+1].item()
    if end > start:
        d = (ref_out[start:end] - sol_out[start:end]).abs()
        print(f'expert {e}: rows {start}-{end}, max diff {d.max().item():.6f}, mean diff {d.mean().item():.6f}')
        if d.max() > 0.1:
            break
EOF
uv run python /tmp/debug_sol.py

{
  "command": "uv run python /tmp/debug_sol.py",
  "description": "Debug correctness failure from workspace"
}

{
  "command": "cp /tmp/debug_sol.py /workspace/problems/06_sonic_moe_swiglu/debug_sol.py && uv run python debug_sol.py",
  "description": "Debug correctness failure from workspace"
}

{
  "command": "<1473 chars \u2014 see below>",
  "description": "Test standalone Triton matmul accuracy"
}

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

@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

M, N, K = 256, 1024, 2048
A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
matmul_kernel[(triton.cdiv(M, 64), triton.cdiv(N, 128))](A, B, C, M, N, K, BLOCK_M=64, BLOCK_N=128, BLOCK_K=64)
C_ref = A @ B
diff = (C - C_ref).abs()
print('matmul max diff', diff.max().item())
print('matmul mean diff', diff.mean().item())
print('C max', C.abs().max().item(), 'C_ref max', C_ref.abs().max().item())
EOF
uv run python debug_matmul.py

{
  "command": "<3959 chars \u2014 see below>",
  "description": "Test grouped gate accuracy only"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_grouped.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
from typing import List, Tuple

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

import reference
shape = {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4}
for k, v in shape.items():
    setattr(reference, k, v)

device = 'cuda'
model = reference.Model(*reference.get_init_inputs()).to(device).eval()
torch.manual_seed(42)
hidden, offsets = [t.to(device) for t in reference.get_inputs()]
T_perm, H = hidden.shape
I = shape['I']

# Reference gate
out_ref = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
for e in range(shape['E']):
    s = int(offsets[e].item())
    en = int(offsets[e+1].item())
    if en > s:
        out_ref[s:en] = hidden[s:en] @ model.W_gate[e]

# Triton grouped gate
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, BLOCK_M, device)
out_sol = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
grid = (num_tiles * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, model.W_gate, out_sol, tile_expert, tile_row_start, offsets,
                          H=H, I=I, E=shape['E'], BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

diff = (out_ref - out_sol).abs()
print('grouped gate max diff', diff.max().item())
print('grouped gate mean diff', diff.mean().item())
EOF
uv run python debug_grouped.py

{
  "command": "<2940 chars \u2014 see below>",
  "description": "Test grouped gate with one expert"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_one_expert.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

device = 'cuda'
M, H, I = 256, 2048, 1024
E = 1
hidden = torch.randn(M, H, device=device, dtype=torch.bfloat16) * 0.1
W_gate = torch.randn(E, H, I, device=device, dtype=torch.bfloat16) * 0.02
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]

tile_expert = torch.tensor([0], dtype=torch.int32, device=device)
tile_row_start = torch.tensor([0], dtype=torch.int32, device=device)
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
grid = (1 * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, W_gate, out_sol, tile_expert, tile_row_start, offsets,
                          H=H, I=I, E=E, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

diff = (out_ref - out_sol).abs()
print('one expert max diff', diff.max().item())
print('one expert mean diff', diff.mean().item())
EOF
uv run python debug_one_expert.py

{
  "command": "<2779 chars \u2014 see below>",
  "description": "Test grouped gate with hardcoded expert"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_hardcode.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = 0
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

device = 'cuda'
M, H, I = 256, 2048, 1024
hidden = torch.randn(M, H, device=device, dtype=torch.bfloat16) * 0.1
W_gate = torch.randn(1, H, I, device=device, dtype=torch.bfloat16) * 0.02
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]

tile_row_start = torch.tensor([0], dtype=torch.int32, device=device)
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
grid = (1 * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, W_gate, out_sol, tile_row_start, offsets,
                          H=H, I=I, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

diff = (out_ref - out_sol).abs()
print('hardcoded expert max diff', diff.max().item())
print('hardcoded expert mean diff', diff.mean().item())
EOF
uv run python debug_hardcode.py

{
  "command": "<1477 chars \u2014 see below>",
  "description": "Test standalone matmul with same block sizes"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_matmul2.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) 6 (offs_n[None, :] < N))

M, N, K = 256, 1024, 2048
A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
matmul_kernel[(triton.cdiv(M, 64), triton.cdiv(N, 128))](A, B, C, M, N, K, BLOCK_M=64, BLOCK_N=128, BLOCK_K=64)
C_ref = A @ B
diff = (C - C_ref).abs()
print('matmul2 max diff', diff.max().item())
print('matmul2 mean diff', diff.mean().item())
print('C max', C.abs().max().item(), 'C_ref max', C_ref.abs().max().item())
EOF
uv run python debug_matmul2.py

{
  "command": "sed -i 's/< M) 6 /< M) & /' /workspace/problems/06_sonic_moe_swiglu/debug_matmul2.py && uv run python debug_matmul2.py",
  "description": "Fix syntax and rerun matmul test"
}

{
  "command": "<1477 chars \u2014 see below>",
  "description": "Rerun fixed matmul test"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_matmul2.py << 'EOF'
import torch
import triton
import triton.language as tl

@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

M, N, K = 256, 1024, 2048
A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
matmul_kernel[(triton.cdiv(M, 64), triton.cdiv(N, 128))](A, B, C, M, N, K, BLOCK_M=64, BLOCK_N=128, BLOCK_K=64)
C_ref = A @ B
diff = (C - C_ref).abs()
print('matmul2 max diff', diff.max().item())
print('matmul2 mean diff', diff.mean().item())
print('C max', C.abs().max().item(), 'C_ref max', C_ref.abs().max().item())
EOF
uv run python debug_matmul2.py

{
  "command": "<2899 chars \u2014 see below>",
  "description": "Test grouped kernel with identity-like weights"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_identity.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = 0
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

device = 'cuda'
M, H, I = 64, 128, 128
hidden = torch.zeros(M, H, device=device, dtype=torch.bfloat16)
hidden[:, 0] = 1.0
W_gate = torch.zeros(1, H, I, device=device, dtype=torch.bfloat16)
W_gate[0, 0, :] = 1.0
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]
print('out_ref[0,:5]', out_ref[0, :5].cpu())
print('out_ref[1,:5]', out_ref[1, :5].cpu())

tile_row_start = torch.tensor([0], dtype=torch.int32, device=device)
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
grid = (1 * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, W_gate, out_sol, tile_row_start, offsets,
                          H=H, I=I, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

print('out_sol[0,:5]', out_sol[0, :5].cpu())
print('out_sol[1,:5]', out_sol[1, :5].cpu())
print('diff max', (out_ref - out_sol).abs().max().item())
EOF
uv run python debug_identity.py

{
  "command": "<2813 chars \u2014 see below>",
  "description": "Test grouped kernel with H=2048 identity pattern"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_identity2.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = 0
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

device = 'cuda'
M, H, I = 64, 2048, 1024
hidden = torch.zeros(M, H, device=device, dtype=torch.bfloat16)
hidden[:, 0] = 1.0
W_gate = torch.zeros(1, H, I, device=device, dtype=torch.bfloat16)
W_gate[0, 0, :] = 1.0
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]
print('out_ref[0,:5]', out_ref[0, :5].cpu())

tile_row_start = torch.tensor([0], dtype=torch.int32, device=device)
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
grid = (1 * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, W_gate, out_sol, tile_row_start, offsets,
                          H=H, I=I, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

print('out_sol[0,:5]', out_sol[0, :5].cpu())
print('diff max', (out_ref - out_sol).abs().max().item())
EOF
uv run python debug_identity2.py

{
  "command": "<3000 chars \u2014 see below>",
  "description": "Compare grouped kernel to reference for random data"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_compare.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gate_kernel(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
    EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = 0
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * H * I
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

device = 'cuda'
torch.manual_seed(42)
M, H, I = 64, 2048, 1024
hidden = torch.randn(M, H, device=device, dtype=torch.bfloat16) * 0.1
W_gate = torch.randn(1, H, I, device=device, dtype=torch.bfloat16) * 0.02
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]

tile_row_start = torch.tensor([0], dtype=torch.int32, device=device)
BLOCK_M, BLOCK_N, BLOCK_K = 64, 128, 64
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
grid = (1 * math.ceil(I / BLOCK_N),)
grouped_gate_kernel[grid](hidden, W_gate, out_sol, tile_row_start, offsets,
                          H=H, I=I, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
                          EVEN_K=(H % BLOCK_K == 0))

diff = (out_ref - out_sol).abs()
print('diff max', diff.max().item(), 'mean', diff.mean().item())

# Find worst columns
worst_cols = diff.max(dim=0).values
print('worst 10 cols', worst_cols.topk(10).indices.cpu().tolist())
print('max diff per col tile (128 cols):')
for t in range(0, I, BLOCK_N):
    print(t, diff[:, t:t+BLOCK_N].max().item())
EOF
uv run python debug_compare.py

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

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<204 chars \u2014 see diff>",
  "new_string": "<224 chars \u2014 see diff>"
}

{
  "command": "<863 chars \u2014 see below>",
  "description": "Test fix on one shape"
}

cat > /workspace/problems/06_sonic_moe_swiglu/debug_fixed.py << 'EOF'
import torch
import reference
import solution

shape = {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4}
for k, v in shape.items():
    setattr(reference, k, v)
    setattr(solution, k, v)

device = 'cuda'
ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()
sol_model = solution.Model(*solution.get_init_inputs()).to(device).eval()
sol_model.load_state_dict(ref_model.state_dict())

torch.manual_seed(42)
inputs = [t.to(device) for t in reference.get_inputs()]
with torch.no_grad():
    ref_out = ref_model(*inputs)
    sol_out = sol_model(*inputs)

diff = (ref_out - sol_out).abs()
print('max abs diff', diff.max().item())
print('mean abs diff', diff.mean().item())
print('rel max', (diff / (ref_out.abs() + 1e-6)).max().item())
EOF
uv run python debug_fixed.py

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

{
  "status": "in_progress",
  "taskId": "3"
}

{
  "command": "uv run python check.py",
  "description": "Run full correctness check",
  "timeout": 600000
}

{
  "status": "completed",
  "taskId": "3"
}

{
  "status": "in_progress",
  "taskId": "4"
}

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

{
  "command": "<910 chars \u2014 see below>",
  "description": "Benchmark current config on shape 0"
}

cat > /workspace/problems/06_sonic_moe_swiglu/bench_configs.py << 'EOF'
import torch
import reference
import solution
import time

shape = {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8}
for k, v in shape.items():
    setattr(reference, k, v)
    setattr(solution, k, v)

device = 'cuda'
model = solution.Model(*solution.get_init_inputs()).to(device).eval()
torch.manual_seed(2026)
inputs = [t.to(device) for t in reference.get_inputs()]

# warmup
torch.cuda.synchronize()
for _ in range(5):
    _ = model(*inputs)
torch.cuda.synchronize()

for _ in range(20):
    _ = model(*inputs)
torch.cuda.synchronize()

start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for _ in range(20):
    _ = model(*inputs)
end.record()
torch.cuda.synchronize()
ms = start.elapsed_time(end) / 20
print(f'shape0 current ms={ms:.3f}')
EOF
uv run python bench_configs.py

{
  "command": "<5534 chars \u2014 see below>",
  "description": "Benchmark different block configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_configs.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time
from typing import List, Tuple

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    even_k = (H % block_k) == 0
    # warmup
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, EVEN_K=even_k)
    torch.cuda.synchronize()
    start = torch.cuda.Event(en

... (1534 more chars)

{
  "command": "<5146 chars \u2014 see below>",
  "description": "Benchmark smaller block configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_configs2.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    even_k = (H % block_k) == 0
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, EVEN_K=even_k)
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Even

... (1146 more chars)

{
  "command": "<3331 chars \u2014 see below>",
  "description": "Benchmark per-expert loop approach",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_perexpert.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def swiglu_matmul_kernel(
    A, B_gate, B_up, C,
    M, N, K,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b_gate = tl.load(B_gate + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        b_up = tl.load(B_up + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], out, mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

def run_per_expert(hidden, offsets, W_gate, W_up, block_m=64, block_n=128, block_k=64, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    # warmup
    for _ in range(3):
        for e in range(E):
            s = int(offsets[e].item())
            en = int(offsets[e+1].item())
            if en > s:
                m = en - s
                grid = (math.ceil(m / block_m), math.ceil(I / block_n))
                swiglu_matmul_kernel[grid](hidden[s:en], W_gate[e], W_up[e], out[s:en], m, I, H,
                    BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k)
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(iters):
        for e in range(E):
            s = int(offsets[e].item())
            en = int(offsets[e+1].item())
            if en > s:
                m = en - s
                grid = (math.ceil(m / block_m), math.ceil(I / block_n))
                swiglu_matmul_kernel[grid](hidden[s:en], W_gate[e], W_up[e], out[s:en], m, I, H,
                    BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k)
    end.record()
    torch.cuda.synchronize()
    return start.elapsed_time(end) / iters

import reference
shape = {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8}
for k, v in shape.items():
    setattr(reference, k, v)
device = 'cuda'
model = reference.Model(*reference.get_init_inputs()).to(device).eval()
torch.manual_seed(2026)
hidden, offsets = [t.to(device) for t in reference.get_inputs()]

for bm, bn, bk in [(64,128,64), (64,128,32), (128,128,64), (128,64,64)]:
    try:
        ms = run_per_expert(hidden, offsets, model.W_gate, model.W_up, bm, bn, bk, iters=10)
        print(f'per-expert ({bm},{bn},{bk}) ms={ms:.3f}')
    except Exception as e:
        print(f'per-expert ({bm},{bn},{bk}) ERROR: {str(e)[:80]}')
EOF
uv run python test_perexpert.py

{
  "command": "<5730 chars \u2014 see below>",
  "description": "Benchmark num_warps and num_stages configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_stages.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=4, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    even_k = (H % block_k) == 0
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, EVEN_K=even_k,
            num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchro

... (1730 more chars)

{
  "command": "ncu --version",
  "description": "Check ncu availability"
}

{
  "command": "<793 chars \u2014 see below>",
  "description": "Profile kernel with ncu",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/profile_run.py << 'EOF'
import torch
import reference
import solution

shape = {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8}
for k, v in shape.items():
    setattr(reference, k, v)
    setattr(solution, k, v)

device = 'cuda'
model = solution.Model(*solution.get_init_inputs()).to(device).eval()
torch.manual_seed(2026)
inputs = [t.to(device) for t in reference.get_inputs()]

# Just run once
with torch.no_grad():
    out = model(*inputs)
print('done', out.shape)
EOF
uv run ncu --kernel-name regex:grouped --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed,sm__memory_throughput.avg.pct_of_peak_sustained_elapsed,dram__throughput.avg.pct_of_peak_sustained_elapsed,gpu__time_duration.avg python profile_run.py 2>&1 | tail -50

{
  "command": "pip show nvidia-cutlass 2>&1 | head -20; python -c \"import nvidia.cutlass; print(nvidia.cutlass.__file__)\" 2>&1 | head -5",
  "description": "Check for nvidia-cutlass package"
}

{
  "command": "find / -name \"cutlass\" -type d 2>/dev/null | grep -E \"(cutlass|include)\" | head -30",
  "description": "Find all CUTLASS installations"
}

{
  "command": "grep -r \"CUTLASS_VERSION\" /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/version.h 2>/dev/null | head -10; cat /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/version.h 2>/dev/null | head -40",
  "description": "Check PyTorch CUTLASS version"
}

{
  "command": "find /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/gemm/collective -name \"*sm120*\" -o -name \"*blackwell*\" -o -name \"*tcgen05*\" 2>/dev/null | head -20; ls /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/gemm/collective | head -30",
  "description": "Check PyTorch CUTLASS for Blackwell support"
}

{
  "command": "grep -r \"Sm120\\|sm_120\\|sm120\" /opt/pytorch/pytorch/third_party/cutlass/include --include=\"*.h\" -l 2>/dev/null | head -20; grep -r \"Sm100\" /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/arch --include=\"*.h\" -l 2>/dev/null | head -10; ls /opt/pytorch/pytorch/third_party/cutlass/include/cutlass/arch | head -30",
  "description": "Check for SM120 support in CUTLASS 3.8"
}

{
  "command": "<5122 chars \u2014 see below>",
  "description": "Benchmark 2D grid version",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_2dgrid.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel_2d(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
):
    tile_m = tl.program_id(0)
    tile_n = tl.program_id(1)
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles, math.ceil(I / block_n))
    even_k = (H % block_k) == 0
    for _ in range(3):
        grouped_gemm_swiglu_kernel_2d[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, EVEN_K=even_k)
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(iters)

... (1122 more chars)

{
  "command": "<392 chars \u2014 see below>",
  "description": "Check hardware peak specs"
}

cat > /tmp/check_hw.py << 'EOF'
import sys
from pathlib import Path
sys.path.insert(0, str(Path('/workspace/problems/06_sonic_moe_swiglu').resolve().parents[2]))
from src.hardware import get as get_hw
hw = get_hw('RTX_PRO_6000')
print('peak_tflops_dense:', hw.peak_tflops_dense)
print('peak_bandwidth_gb_s:', hw.peak_bandwidth_gb_s)
print('attrs:', dir(hw))
EOF
uv run python /tmp/check_hw.py

{
  "command": "<5085 chars \u2014 see below>",
  "description": "Benchmark transposed weight layout",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_transpose.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
):
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    # W_gate is transposed: shape (E, I, H), stride (I*H, H, 1)
    b_gate_ptr = W_gate_ptr + expert * I * H + n_start * H
    b_up_ptr = W_up_ptr + expert * I * H + n_start * H
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, W_gate_t, W_up_t, offsets, block_m, block_n, block_k, iters=20):
    T_perm, H = hidden.shape
    I = W_gate_t.shape[1]
    E = W_gate_t.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    even_k = (H % block_k) == 0
    for _ in range(3):
        grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, W_up_t, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, EVEN_K=even_k)
    to

... (1085 more chars)

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

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<219 chars \u2014 see diff>",
  "new_string": "<704 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<456 chars \u2014 see diff>",
  "new_string": "<455 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<1139 chars \u2014 see diff>",
  "new_string": "<1590 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<172 chars \u2014 see diff>",
  "new_string": "<192 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<309 chars \u2014 see diff>",
  "new_string": "<275 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<455 chars \u2014 see diff>",
  "new_string": "<261 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py",
  "description": "Run correctness check after optimizations",
  "timeout": 600000
}

{
  "command": "uv run python benchmark.py",
  "description": "Run benchmark after optimizations",
  "timeout": 600000
}

{
  "command": "<4730 chars \u2014 see below>",
  "description": "Benchmark separated gate/up matmuls"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_separate.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_matmul_kernel(
    hidden_ptr, W_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_separate(hidden, offsets, W_gate, W_up, block_m=64, block_n=128, block_k=64, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    gate_out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    up_out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_matmul_kernel[grid](hidden, W_gate, gate_out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k)
        grouped_matmul_kernel[grid](hidden, W_up, up_out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k)
        out.copy_(gate_out)  # placeholder
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(iters):
        grouped_matmul_kernel[grid](hidden, W_gate, gate_out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k)
 

... (730 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<261 chars \u2014 see diff>",
  "new_string": "<457 chars \u2014 see diff>"
}

{
  "command": "uv run python benchmark.py",
  "description": "Run benchmark with more autotune configs",
  "timeout": 600000
}

{
  "command": "<2526 chars \u2014 see below>",
  "description": "Benchmark dense Triton matmul for comparison",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_dense.py << 'EOF'
import torch
import triton
import triton.language as tl
import time

@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

M, N, K = 32768, 3072, 4096  # Effective dense: T_total=32768, 2*I=3072, H=4096
A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)

for bm, bn, bk in [(64,128,64), (128,128,64), (256,128,64), (128,256,64)]:
    try:
        grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
        for _ in range(3):
            matmul_kernel[grid](A, B, C, M, N, K, BLOCK_M=bm, BLOCK_N=bn, BLOCK_K=bk)
        torch.cuda.synchronize()
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        start.record()
        for _ in range(20):
            matmul_kernel[grid](A, B, C, M, N, K, BLOCK_M=bm, BLOCK_N=bn, BLOCK_K=bk)
        end.record()
        torch.cuda.synchronize()
        ms = start.elapsed_time(end) / 20
        flops = 2 * M * N * K / 1e12
        tflops = flops / (ms / 1000)
        print(f'dense ({bm},{bn},{bk}) ms={ms:.3f} tflops={tflops:.1f} peak_frac={tflops/200:.3f}')
    except Exception as e:
        print(f'dense ({bm},{bn},{bk}) ERROR: {str(e)[:80]}')

# Also compare torch.matmul
C_ref = A @ B
torch.cuda.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for _ in range(20):
    C_ref = A @ B
end.record()
torch.cuda.synchronize()
ms = start.elapsed_time(end) / 20
flops = 2 * M * N * K / 1e12
tflops = flops / (ms / 1000)
print(f'torch.matmul ms={ms:.3f} tflops={tflops:.1f} peak_frac={tflops/200:.3f}')
EOF
uv run python test_dense.py

{
  "command": "<1905 chars \u2014 see below>",
  "description": "Benchmark small dense matmul sizes"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_small_dense.py << 'EOF'
import torch
import triton
import triton.language as tl
import time

@triton.jit
def matmul_kernel(A, B, C, M, N, K, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        a = tl.load(A + offs_m[:, None] * K + (k + offs_k)[None, :], mask=offs_m[:, None] < M, other=0.0)
        b = tl.load(B + (k + offs_k)[:, None] * N + offs_n[None, :], mask=offs_n[None, :] < N, other=0.0)
        acc += tl.dot(a, b)
    tl.store(C + offs_m[:, None] * N + offs_n[None, :], acc.to(tl.bfloat16), mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

for M, N, K in [(2048, 1536, 4096), (256, 1536, 4096), (2048, 1024, 2048), (512, 4096, 2048)]:
    A = torch.randn(M, K, device='cuda', dtype=torch.bfloat16) * 0.1
    B = torch.randn(K, N, device='cuda', dtype=torch.bfloat16) * 0.02
    C = torch.empty(M, N, device='cuda', dtype=torch.bfloat16)
    bm, bn, bk = 64, 128, 64
    grid = (triton.cdiv(M, bm), triton.cdiv(N, bn))
    for _ in range(3):
        matmul_kernel[grid](A, B, C, M, N, K, BLOCK_M=bm, BLOCK_N=bn, BLOCK_K=bk)
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(20):
        matmul_kernel[grid](A, B, C, M, N, K, BLOCK_M=bm, BLOCK_N=bn, BLOCK_K=bk)
    end.record()
    torch.cuda.synchronize()
    ms = start.elapsed_time(end) / 20
    flops = 2 * M * N * K / 1e12
    tflops = flops / (ms / 1000)
    print(f'MNK={M},{N},{K} ms={ms:.3f} tflops={tflops:.1f}')
EOF
uv run python test_small_dense.py

{
  "command": "<5607 chars \u2014 see below>",
  "description": "Benchmark BLOCK_M=128 configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_blockm128.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=4, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Ev

... (1607 more chars)

{
  "command": "<5487 chars \u2014 see below>",
  "description": "Benchmark BLOCK_M=128 BLOCK_N=64 with BLOCK_K variations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_blockm128_k32.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=4, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cud

... (1487 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<457 chars \u2014 see diff>",
  "new_string": "<457 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<276 chars \u2014 see diff>",
  "new_string": "<277 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<82 chars \u2014 see diff>",
  "new_string": "<81 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "offset": 175,
  "limit": 60
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<121 chars \u2014 see diff>",
  "new_string": "<81 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py",
  "description": "Run correctness check with new config",
  "timeout": 600000
}

{
  "command": "uv run python benchmark.py",
  "description": "Run benchmark with BLOCK_M=128 config",
  "timeout": 600000
}

{
  "command": "<5605 chars \u2014 see below>",
  "description": "Benchmark BLOCK_M=256 configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_blockm256.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=4, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Ev

... (1605 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<457 chars \u2014 see diff>",
  "new_string": "<359 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<114 chars \u2014 see diff>",
  "new_string": "<114 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<359 chars \u2014 see diff>",
  "new_string": "<457 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py",
  "description": "Run correctness check with BLOCK_M=256",
  "timeout": 600000
}

{
  "command": "uv run python benchmark.py",
  "description": "Run benchmark with BLOCK_M=256 autotune",
  "timeout": 600000
}

{
  "command": "<5643 chars \u2014 see below>",
  "description": "Benchmark more BLOCK_M=256 configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_blockm256_v2.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda

... (1643 more chars)

{
  "command": "<5606 chars \u2014 see below>",
  "description": "Benchmark BLOCK_M=512 configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_blockm512.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Ev

... (1606 more chars)

{
  "command": "<5413 chars \u2014 see below>",
  "description": "Focused search around (256,64,64)",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_focus.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Event(

... (1413 more chars)

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<457 chars \u2014 see diff>",
  "new_string": "<655 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Run correctness and benchmark with expanded autotune",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<655 chars \u2014 see diff>",
  "new_string": "<457 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Run correctness and benchmark with reduced autotune",
  "timeout": 600000
}

{
  "command": "<5092 chars \u2014 see below>",
  "description": "Benchmark BLOCK_K=128 configurations",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_k128.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Event(e

... (1092 more chars)

{
  "command": "uv run python benchmark.py",
  "description": "Final benchmark run",
  "timeout": 600000
}

{
  "command": "<5194 chars \u2014 see below>",
  "description": "Benchmark 2D grid with BLOCK_M=256",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_2d_256.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel_2d(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    tile_m = tl.program_id(0)
    tile_n = tl.program_id(1)
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles, math.ceil(I / block_n))
    for _ in range(3):
        grouped_gemm_swiglu_kernel_2d[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=Tr

... (1194 more chars)

{
  "command": "<5543 chars \u2014 see below>",
  "description": "Test BLOCK_M=128 with num_stages=4",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_128_ns4.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, offsets, W_gate, W_up, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate.shape[2]
    E = W_gate.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel[grid](hidden, W_gate, W_up, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()
    start = torch.cuda.Even

... (1543 more chars)

{
  "command": "uv run ncu --kernel-name regex:grouped --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed,sm__memory_throughput.avg.pct_of_peak_sustained_elapsed,dram__throughput.avg.pct_of_peak_sustained_elapsed,gpu__time_duration.avg python profile_run.py 2>&1 | tail -30",
  "description": "Profile optimized kernel with ncu",
  "timeout": 600000
}

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

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<157 chars \u2014 see diff>",
  "new_string": "<157 chars \u2014 see diff>"
}

{
  "command": "ls /workspace/problems/06_sonic_moe_swiglu/",
  "description": "List files for cleanup"
}

{
  "command": "rm -f /workspace/problems/06_sonic_moe_swiglu/bench_configs.py /workspace/problems/06_sonic_moe_swiglu/debug_*.py /workspace/problems/06_sonic_moe_swiglu/profile_run.py /workspace/problems/06_sonic_moe_swiglu/test_*.py /workspace/problems/06_sonic_moe_swiglu/framework.txt && rm -rf /workspace/problems/06_sonic_moe_swiglu/__pycache__ && ls /workspace/problems/06_sonic_moe_swiglu/",
  "description": "Clean up scratch files"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Final correctness and benchmark verification",
  "timeout": 600000
}

{
  "status": "completed",
  "taskId": "4"
}

{
  "command": "<5472 chars \u2014 see below>",
  "description": "Test transposed weights with BLOCK_M=256",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_256.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    # W_gate is transposed: shape (E, I, H), stride (I*H, H, 1)
    b_gate_ptr = W_gate_ptr + expert * I * H + n_start * H
    b_up_ptr = W_up_ptr + expert * I * H + n_start * H
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, W_gate_t, W_up_t, offsets, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate_t.shape[1]
    E = W_gate_t.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, W_up_t, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, nu

... (1472 more chars)

{
  "command": "<4263 chars \u2014 see below>",
  "description": "Verify transposed weight correctness"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_correct.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * I * H + n_start * H
    b_up_ptr = W_up_ptr + expert * I * H + n_start * H
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

import reference
shape = {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4}
for k, v in shape.items():
    setattr(reference, k, v)
device = 'cuda'
ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()

torch.manual_seed(42)
hidden, offsets = [t.to(device) for t in reference.get_inputs()]
T_perm, H = hidden.shape
I = shape['I']
E = shape['E']
out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
W_gate_t = ref_model.W_gate.transpose(1, 2)
W_up_t = ref_model.W_up.transpose(1, 2)

tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, 256, device)
grid = (num_tiles * math.ceil(I / 64),)
grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, W_up_t, out, tile_expert, tile_row_start, offse

... (263 more chars)

{
  "command": "<3597 chars \u2014 see below>",
  "description": "Test transposed identity"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_identity.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * I * H + n_start * H
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + offs_n[None, :] * H + (k + offs_k)[:, None], mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

device = 'cuda'
M, H, I = 256, 256, 256
hidden = torch.zeros(M, H, device=device, dtype=torch.bfloat16)
hidden[:, 0] = 1.0
W_gate = torch.zeros(1, H, I, device=device, dtype=torch.bfloat16)
W_gate[0, 0, :] = 1.0
W_gate_t = W_gate.transpose(1, 2)
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]
print('out_ref[0,:5]', out_ref[0, :5].cpu())

out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, 256, device)
grid = (num_tiles * math.ceil(I / 64),)
grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, out_sol, tile_expert, tile_row_start, offsets,
    H=H, I=I, E=1, BLOCK_M=256, BLOCK_N=64, BLOCK_K=64, num_warps=8, num_stages=3)
print('out_sol[0,:5]', out_sol[0, :5].cpu())
print('diff max', (out_ref - out_sol).abs().max().item())
EOF
uv run python test_trans_identity.py

{
  "command": "<4242 chars \u2014 see below>",
  "description": "Test fixed transposed indexing"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_fix.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_ptr = W_gate_ptr + expert * I * H + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b = tl.load(b_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b = tl.load(b_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc = tl.dot(a, b, acc)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], acc.to(tl.bfloat16), mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

# Identity test
device = 'cuda'
M, H, I = 256, 256, 256
hidden = torch.zeros(M, H, device=device, dtype=torch.bfloat16)
hidden[:, 0] = 1.0
W_gate = torch.zeros(1, H, I, device=device, dtype=torch.bfloat16)
W_gate[0, 0, :] = 1.0
W_gate_t = W_gate.transpose(1, 2)
offsets = torch.tensor([0, M], dtype=torch.int32, device=device)

out_ref = hidden @ W_gate[0]
out_sol = torch.empty(M, I, dtype=torch.bfloat16, device=device)
tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, 256, device)
grid = (num_tiles * math.ceil(I / 64),)
grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, out_sol, tile_expert, tile_row_start, offsets,
    H=H, I=I, E=1, BLOCK_M=256, BLOCK_N=64, BLOCK_K=64, num_warps=8, num_stages=3)
print('identity diff max', (out_ref - out_sol).abs().max().item())

# Random test
import reference
shape = {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4}
for k, v in shape.items():
    setattr(reference, k, v)
ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()
torch.manual_seed(42)
hidden, offsets = [t.to(device) for t in reference.get_inputs()]
T_perm, H = hidden.shape
I = shape['I']
E = shape['E']
out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
W_gate_t = ref_model.W_gate.transpose(1, 2)
W_up_t = ref_model.W_up.transpose(1, 

... (242 more chars)

{
  "command": "<4494 chars \u2014 see below>",
  "description": "Verify full SwiGLU transposed correctness on all shapes",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_full.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * I * H + n_start
    b_up_ptr = W_up_ptr + expert * I * H + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * H, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

import reference
for shape in [
    {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8},
    {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4},
    {"T_total": 16384, "H": 2048, "I": 4096, "E": 64, "K": 8},
]:
    print(f"\nShape {shape}")
    for k, v in shape.items():
        setattr(reference, k, v)
    device = 'cuda'
    ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()
    torch.manual_seed(42)
    hidden, offsets = [t.to(device) for t in reference.get_inputs()]
    T_perm, H = hidden.shape
    I = shape['I']
    E = shape['E']
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
    W_gate_t = ref_model.W_gate.transpose(1, 2)
    W_up_t = ref_model.W_up.transpose(1, 2)
    tile_expert, til

... (494 more chars)

{
  "command": "<4496 chars \u2014 see below>",
  "description": "Verify fixed transposed SwiGLU correctness"
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_full2.py << 'EOF'
import torch
import triton
import triton.language as tl
import math

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

import reference
for shape in [
    {"T_total": 32768, "H": 4096, "I": 1536, "E": 128, "K": 8},
    {"T_total": 4096, "H": 2048, "I": 1024, "E": 64, "K": 4},
    {"T_total": 16384, "H": 2048, "I": 4096, "E": 64, "K": 8},
]:
    print(f"\nShape {shape}")
    for k, v in shape.items():
        setattr(reference, k, v)
    device = 'cuda'
    ref_model = reference.Model(*reference.get_init_inputs()).to(device).eval()
    torch.manual_seed(42)
    hidden, offsets = [t.to(device) for t in reference.get_inputs()]
    T_perm, H = hidden.shape
    I = shape['I']
    E = shape['E']
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=device)
    W_gate_t = ref_model.W_gate.transpose(1, 2)
    W_up_t = ref_model.W_up.transpose(1, 2)
    tile_expert, ti

... (496 more chars)

{
  "command": "<5410 chars \u2014 see below>",
  "description": "Benchmark corrected transposed kernel",
  "timeout": 600000
}

cat > /workspace/problems/06_sonic_moe_swiglu/test_trans_perf.py << 'EOF'
import torch
import triton
import triton.language as tl
import math
import time

@triton.jit
def grouped_gemm_swiglu_kernel_trans(
    hidden_ptr, W_gate_ptr, W_up_ptr, out_ptr,
    tile_expert_ptr, tile_row_start_ptr, expert_offsets_ptr,
    H: tl.constexpr, I: tl.constexpr, E: tl.constexpr,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    EVEN_K: tl.constexpr = (H % BLOCK_K == 0)
    pid = tl.program_id(0)
    num_n_tiles = tl.cdiv(I, BLOCK_N)
    tile_m = pid // num_n_tiles
    tile_n = pid % num_n_tiles
    expert = tl.load(tile_expert_ptr + tile_m).to(tl.int32)
    row_start = tl.load(tile_row_start_ptr + tile_m).to(tl.int32)
    expert_end = tl.load(expert_offsets_ptr + expert + 1).to(tl.int32)
    row_end = tl.minimum(row_start + BLOCK_M, expert_end)
    n_start = tile_n * BLOCK_N

    a_ptr = hidden_ptr + row_start * H
    b_gate_ptr = W_gate_ptr + expert * H * I + n_start
    b_up_ptr = W_up_ptr + expert * H * I + n_start
    c_ptr = out_ptr + row_start * I + n_start

    offs_m = tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)
    row_mask = offs_m < (row_end - row_start)
    col_mask = offs_n < (I - n_start)

    acc_gate = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    acc_up = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, H, BLOCK_K):
        if EVEN_K:
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=col_mask[None, :], other=0.0)
        else:
            k_mask = (k + offs_k) < H
            a = tl.load(a_ptr + offs_m[:, None] * H + (k + offs_k)[None, :], mask=row_mask[:, None] & k_mask[None, :], other=0.0)
            b_gate = tl.load(b_gate_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
            b_up = tl.load(b_up_ptr + offs_n[None, :] + (k + offs_k)[:, None] * I, mask=k_mask[:, None] & col_mask[None, :], other=0.0)
        acc_gate = tl.dot(a, b_gate, acc_gate)
        acc_up = tl.dot(a, b_up, acc_up)
    gate = acc_gate
    up = acc_up
    silu = gate * tl.sigmoid(gate)
    out = (silu * up).to(tl.bfloat16)
    tl.store(c_ptr + offs_m[:, None] * I + offs_n[None, :], out, mask=row_mask[:, None] & col_mask[None, :])

def build_tile_metadata(offsets, block_m, device):
    offsets = offsets.cpu().tolist()
    E = len(offsets) - 1
    tile_expert = []
    tile_row_start = []
    for e in range(E):
        start = int(offsets[e])
        end = int(offsets[e + 1])
        count = end - start
        if count <= 0:
            continue
        num_tiles = math.ceil(count / block_m)
        for t in range(num_tiles):
            tile_expert.append(e)
            tile_row_start.append(start + t * block_m)
    return (torch.tensor(tile_expert, dtype=torch.int32, device=device),
            torch.tensor(tile_row_start, dtype=torch.int32, device=device),
            len(tile_expert))

def run_config(hidden, W_gate_t, W_up_t, offsets, block_m, block_n, block_k, num_warps=8, num_stages=3, iters=20):
    T_perm, H = hidden.shape
    I = W_gate_t.shape[1]
    E = W_gate_t.shape[0]
    out = torch.empty(T_perm, I, dtype=torch.bfloat16, device=hidden.device)
    tile_expert, tile_row_start, num_tiles = build_tile_metadata(offsets, block_m, hidden.device)
    if num_tiles == 0:
        return 0
    grid = (num_tiles * math.ceil(I / block_n),)
    for _ in range(3):
        grouped_gemm_swiglu_kernel_trans[grid](hidden, W_gate_t, W_up_t, out, tile_expert, tile_row_start, offsets,
            H=H, I=I, E=E, BLOCK_M=block_m, BLOCK_N=block_n, BLOCK_K=block_k, num_warps=num_warps, num_stages=num_stages)
    torch.cuda.synchronize()


... (1410 more chars)

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 140
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<154 chars \u2014 see diff>",
  "new_string": "<319 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<224 chars \u2014 see diff>",
  "new_string": "<327 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<365 chars \u2014 see below>",
  "new_string": "<365 chars \u2014 see below>"
}

            b_gate = tl.load(
                b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :],
                mask=col_mask[None, :],
                other=0.0,
            )
            b_up = tl.load(
                b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :],
                mask=col_mask[None, :],
                other=0.0,
            )

            b_gate = tl.load(
                b_gate_ptr + (k + offs_k)[:, None] * I + offs_n[None, :],
                mask=col_mask[None, :],
                other=0.0,
            )
            b_up = tl.load(
                b_up_ptr + (k + offs_k)[:, None] * I + offs_n[None, :],
                mask=col_mask[None, :],
                other=0.0,
            )

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<275 chars \u2014 see diff>",
  "new_string": "<307 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<386 chars \u2014 see diff>",
  "new_string": "<344 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 80,
  "offset": 60
}

{
  "command": "rm -f /workspace/problems/06_sonic_moe_swiglu/test_*.py /workspace/problems/06_sonic_moe_swiglu/debug_*.py /workspace/problems/06_sonic_moe_swiglu/profile_run.py && uv run python check.py && uv run python benchmark.py",
  "description": "Clean and run final check/benchmark with transposed weights",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<262 chars \u2014 see diff>",
  "new_string": "<268 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Run with CPU cached offsets",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<276 chars \u2014 see diff>",
  "new_string": "<313 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Run with CPU comparison fix",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<705 chars \u2014 see diff>",
  "new_string": "<219 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<307 chars \u2014 see diff>",
  "new_string": "<431 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Benchmark hardcoded transposed config",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<123 chars \u2014 see diff>",
  "new_string": "<123 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Benchmark hardcoded BLOCK_K=32",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<123 chars \u2014 see diff>",
  "new_string": "<123 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Final verification with hardcoded config",
  "timeout": 600000
}

{
  "command": "grep -E \"torch\\.matmul|torch\\.bmm|torch\\.nn\\.functional\\.linear|F\\.linear|from sonic_moe|import sonic_moe\" /workspace/problems/06_sonic_moe_swiglu/solution.py",
  "description": "Verify no forbidden ops"
}

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

{
  "command": "uv run python check.py",
  "description": "Final correctness check",
  "timeout": 600000
}

{
  "command": "ls /workspace/problems/06_sonic_moe_swiglu/",
  "description": "List final workspace files"
}

{
  "command": "rm -rf /workspace/problems/06_sonic_moe_swiglu/__pycache__ && ls /workspace/problems/06_sonic_moe_swiglu/",
  "description": "Remove pycache"
}

{
  "command": "uv run python benchmark.py",
  "description": "Final benchmark",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<583 chars \u2014 see diff>",
  "new_string": "<476 chars \u2014 see diff>"
}

{
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "limit": 50,
  "offset": 185
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<583 chars \u2014 see diff>",
  "new_string": "<476 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<175 chars \u2014 see diff>",
  "new_string": "<159 chars \u2014 see diff>"
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<313 chars \u2014 see diff>",
  "new_string": "<214 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Benchmark with data_ptr cache key",
  "timeout": 600000
}

{
  "replace_all": false,
  "file_path": "/workspace/problems/06_sonic_moe_swiglu/solution.py",
  "old_string": "<450 chars \u2014 see diff>",
  "new_string": "<925 chars \u2014 see diff>"
}

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Benchmark with cached transposed views",
  "timeout": 600000
}

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

{
  "command": "uv run python check.py && uv run python benchmark.py",
  "description": "Final verification",
  "timeout": 600000
}

{
  "command": "rm -rf /workspace/problems/06_sonic_moe_swiglu/__pycache__",
  "description": "Final cleanup"
}