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import pytest
import torch
import triton
import triton.language as tl
from sgl_kernel import moe_align_block_size, moe_sum
def is_hip() -> bool:
return torch.version.hip is not None
_is_hip = is_hip()
def ceil_div(a, b):
return (a + b - 1) // b
@triton.jit
def moe_align_block_size_stage1(
topk_ids_ptr,
tokens_cnts_ptr,
num_experts: tl.constexpr,
numel: tl.constexpr,
tokens_per_thread: tl.constexpr,
):
pid = tl.program_id(0)
start_idx = pid * tokens_per_thread
off_c = (pid + 1) * num_experts
for i in range(tokens_per_thread):
if start_idx + i < numel:
idx = tl.load(topk_ids_ptr + start_idx + i)
token_cnt = tl.load(tokens_cnts_ptr + off_c + idx)
tl.store(tokens_cnts_ptr + off_c + idx, token_cnt + 1)
@triton.jit
def moe_align_block_size_stage2(
tokens_cnts_ptr,
num_experts: tl.constexpr,
):
pid = tl.program_id(0)
last_cnt = 0
for i in range(1, num_experts + 1):
token_cnt = tl.load(tokens_cnts_ptr + i * num_experts + pid)
last_cnt = last_cnt + token_cnt
tl.store(tokens_cnts_ptr + i * num_experts + pid, last_cnt)
@triton.jit
def moe_align_block_size_stage3(
total_tokens_post_pad_ptr,
tokens_cnts_ptr,
cumsum_ptr,
num_experts: tl.constexpr,
block_size: tl.constexpr,
):
last_cumsum = 0
off_cnt = num_experts * num_experts
for i in range(1, num_experts + 1):
token_cnt = tl.load(tokens_cnts_ptr + off_cnt + i - 1)
last_cumsum = last_cumsum + tl.cdiv(token_cnt, block_size) * block_size
tl.store(cumsum_ptr + i, last_cumsum)
tl.store(total_tokens_post_pad_ptr, last_cumsum)
@triton.jit
def moe_align_block_size_stage4(
topk_ids_ptr,
sorted_token_ids_ptr,
expert_ids_ptr,
tokens_cnts_ptr,
cumsum_ptr,
num_experts: tl.constexpr,
block_size: tl.constexpr,
numel: tl.constexpr,
tokens_per_thread: tl.constexpr,
):
pid = tl.program_id(0)
start_idx = tl.load(cumsum_ptr + pid)
end_idx = tl.load(cumsum_ptr + pid + 1)
for i in range(start_idx, end_idx, block_size):
tl.store(expert_ids_ptr + i // block_size, pid)
start_idx = pid * tokens_per_thread
off_t = pid * num_experts
for i in range(start_idx, tl.minimum(start_idx + tokens_per_thread, numel)):
expert_id = tl.load(topk_ids_ptr + i)
token_cnt = tl.load(tokens_cnts_ptr + off_t + expert_id)
rank_post_pad = token_cnt + tl.load(cumsum_ptr + expert_id)
tl.store(sorted_token_ids_ptr + rank_post_pad, i)
tl.store(tokens_cnts_ptr + off_t + expert_id, token_cnt + 1)
def moe_align_block_size_triton(
topk_ids: torch.Tensor,
num_experts: int,
block_size: int,
sorted_token_ids: torch.Tensor,
expert_ids: torch.Tensor,
num_tokens_post_pad: torch.Tensor,
) -> None:
numel = topk_ids.numel()
grid = (num_experts,)
tokens_cnts = torch.zeros(
(num_experts + 1, num_experts), dtype=torch.int32, device=topk_ids.device
)
cumsum = torch.zeros((num_experts + 1,), dtype=torch.int32, device=topk_ids.device)
tokens_per_thread = ceil_div(numel, num_experts)
moe_align_block_size_stage1[grid](
topk_ids,
tokens_cnts,
num_experts,
numel,
tokens_per_thread,
)
moe_align_block_size_stage2[grid](
tokens_cnts,
num_experts,
)
moe_align_block_size_stage3[(1,)](
num_tokens_post_pad,
tokens_cnts,
cumsum,
num_experts,
block_size,
)
moe_align_block_size_stage4[grid](
topk_ids,
sorted_token_ids,
expert_ids,
tokens_cnts,
cumsum,
num_experts,
block_size,
numel,
tokens_per_thread,
)
@pytest.mark.parametrize(
"block_size,num_tokens,topk,num_experts,pad_sorted_token_ids",
list(
itertools.product(
[32, 64, 128, 256], # block_size
[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096], # num_tokens
[1, 2, 4, 8, 16, 32, 64], # topk
[64, 160, 256, 257, 260, 264], # num_experts
[True, False], # pad_sorted_token_ids
)
),
)
def test_moe_align_block_size_compare_implementations(
block_size, num_tokens, topk, num_experts, pad_sorted_token_ids
):
topk_ids = torch.argsort(torch.rand(num_tokens, num_experts, device="cuda"), dim=1)[
:, :topk
]
max_num_tokens_padded = topk_ids.numel() + (num_experts + 1) * (block_size - 1)
if topk_ids.numel() < num_experts + 1:
max_num_tokens_padded = topk_ids.numel() * block_size
sorted_ids_cuda = torch.empty(
(max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
)
if not pad_sorted_token_ids:
sorted_ids_cuda.fill_(topk_ids.numel())
max_num_m_blocks = max_num_tokens_padded // block_size
expert_ids_cuda = torch.zeros(
(max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
)
num_tokens_post_pad_cuda = torch.empty(
(1), dtype=torch.int32, device=topk_ids.device
)
cumsum_buffer = torch.empty(
num_experts + 2, dtype=torch.int32, device=topk_ids.device
)
sorted_ids_triton = torch.empty_like(sorted_ids_cuda)
sorted_ids_triton.fill_(topk_ids.numel())
expert_ids_triton = torch.zeros_like(expert_ids_cuda)
num_tokens_post_pad_triton = torch.empty_like(num_tokens_post_pad_cuda)
moe_align_block_size(
topk_ids,
num_experts + 1,
block_size,
sorted_ids_cuda,
expert_ids_cuda,
num_tokens_post_pad_cuda,
cumsum_buffer,
pad_sorted_token_ids,
)
moe_align_block_size_triton(
topk_ids,
num_experts + 1,
block_size,
sorted_ids_triton,
expert_ids_triton,
num_tokens_post_pad_triton,
)
assert torch.allclose(expert_ids_cuda, expert_ids_triton, atol=0, rtol=0), (
f"Expert IDs mismatch for block_size={block_size}, "
f"num_tokens={num_tokens}, topk={topk}\n"
f"CUDA expert_ids: {expert_ids_cuda}\n"
f"Triton expert_ids: {expert_ids_triton}"
)
assert torch.allclose(
num_tokens_post_pad_cuda, num_tokens_post_pad_triton, atol=0, rtol=0
), (
f"Num tokens post pad mismatch for block_size={block_size}, "
f"num_tokens={num_tokens}, topk={topk}\n"
f"CUDA num_tokens_post_pad: {num_tokens_post_pad_cuda}\n"
f"Triton num_tokens_post_pad: {num_tokens_post_pad_triton}"
)
# Select an expert to check
expert_idx = expert_ids_cuda.max().item()
# Get the first and last block id where expert_ids_cuda == expert_idx
matching_indices = torch.where(expert_ids_cuda == expert_idx)[0]
block_sorted_start = matching_indices[0].item() * block_size
block_sorted_end = min(
(matching_indices[-1].item() + 1) * block_size, num_tokens_post_pad_cuda.item()
)
selected_sorted_ids_cuda = sorted_ids_cuda[
block_sorted_start:block_sorted_end
].sort()[0]
selected_sorted_ids_triton = sorted_ids_triton[
block_sorted_start:block_sorted_end
].sort()[0]
assert torch.allclose(
selected_sorted_ids_cuda,
selected_sorted_ids_triton,
atol=0,
rtol=0,
), (
f"Sorted IDs mismatch for block_size={block_size}, "
f"num_tokens={num_tokens}, topk={topk}\n"
f"CUDA sorted_ids: {selected_sorted_ids_cuda}\n"
f"Triton sorted_ids: {selected_sorted_ids_triton}"
)
@pytest.mark.parametrize("m", [1, 33, 64, 222])
@pytest.mark.parametrize("topk", [2, 6])
@pytest.mark.parametrize("k", [128, 511, 1024])
@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
@pytest.mark.skipif(_is_hip, reason="Skip for AMD GPU")
def test_moe_sum(m: int, topk: int, k: int, dtype: torch.dtype):
input = torch.randn((m, topk, k), device="cuda", dtype=dtype)
actual = torch.empty((m, k), device="cuda", dtype=dtype)
expected = input.sum(dim=1)
moe_sum(input, actual)
torch.testing.assert_close(actual, expected, atol=2e-2, rtol=0)
if __name__ == "__main__":
pytest.main([__file__])
|