[code style] Clean dead triton kernel code in fused_moe and useless vllm_ops import (#8310)

python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py

@@ -53,9 +53,7 @@ elif _is_hip:
             from aiter import moe_sum
         except ImportError:
             raise ImportError("aiter is required when SGLANG_USE_AITER is set to True")
-else:
-    from vllm import _custom_ops as vllm_ops
-    from vllm._custom_ops import scaled_fp8_quant
+
 
 if _is_cuda or _is_hip:
     from sgl_kernel import moe_align_block_size as sgl_moe_align_block_size
@@ -63,9 +61,6 @@ if _is_cuda or _is_hip:
 
 logger = logging.getLogger(__name__)
 padding_size = 128 if bool(int(os.getenv("SGLANG_MOE_PADDING", "0"))) else 0
-enable_moe_align_block_size_triton = bool(
-    int(os.getenv("ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
-)
 
 
 @triton.jit
@@ -533,190 +528,6 @@ def fused_moe_kernel(
     tl.store(c_ptrs, accumulator, mask=c_mask)
 
 
-@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,
-    )
-
-
-@triton.jit
-def init_sorted_ids_and_cumsum_buffer_kernel(
-    sorted_ids_ptr,
-    cumsum_buffer_ptr,
-    max_num_tokens_padded,
-    topk_ids_numel,
-    num_experts: tl.constexpr,
-    BLOCK_SIZE: tl.constexpr,
-    ALIGNED_NUM_EXPERTS_P1: tl.constexpr,
-):
-    pid = tl.program_id(0)
-    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-
-    sorted_ids_blocks = tl.cdiv(max_num_tokens_padded, BLOCK_SIZE)
-
-    if pid < sorted_ids_blocks:
-        mask = offsets < max_num_tokens_padded
-        tl.store(
-            sorted_ids_ptr + offsets,
-            tl.full((BLOCK_SIZE,), topk_ids_numel, dtype=tl.int32),
-            mask=mask,
-        )
-    elif pid == sorted_ids_blocks:
-        offset_e = tl.arange(0, ALIGNED_NUM_EXPERTS_P1)
-        mask_e = offset_e < num_experts + 1
-        tl.store(
-            cumsum_buffer_ptr + offset_e,
-            tl.zeros((ALIGNED_NUM_EXPERTS_P1,), dtype=tl.int32),
-            mask=mask_e,
-        )
-
-
-def init_sorted_ids_and_cumsum_buffer(
-    max_num_tokens_padded: int, topk_ids_numel: int, num_experts: int, device="cuda"
-):
-    sorted_ids = torch.empty((max_num_tokens_padded,), dtype=torch.int32, device=device)
-    cumsum_buffer = torch.empty((num_experts + 1,), dtype=torch.int32, device=device)
-
-    BLOCK_SIZE = 1024
-    sorted_ids_blocks = triton.cdiv(max_num_tokens_padded, BLOCK_SIZE)
-    grid = (sorted_ids_blocks + 1,)
-
-    init_sorted_ids_and_cumsum_buffer_kernel[grid](
-        sorted_ids,
-        cumsum_buffer,
-        max_num_tokens_padded,
-        topk_ids_numel,
-        num_experts,
-        BLOCK_SIZE,
-        next_power_of_2(num_experts + 1),
-    )
-
-    return sorted_ids, cumsum_buffer
-
-
 def moe_align_block_size(
     topk_ids: torch.Tensor, block_size: int, num_experts: int
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -766,17 +577,7 @@ def moe_align_block_size(
         (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
     )
     num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)
-    if enable_moe_align_block_size_triton:
-        sorted_ids.fill_(topk_ids.numel())
-        moe_align_block_size_triton(
-            topk_ids,
-            num_experts,
-            block_size,
-            sorted_ids,
-            expert_ids,
-            num_tokens_post_pad,
-        )
-    else:
+
     cumsum_buffer = torch.empty(
         (num_experts + 1,), dtype=torch.int32, device=topk_ids.device
     )

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -28,15 +28,6 @@ if TYPE_CHECKING:
         CompressedTensorsConfig,
     )
 
-_is_cuda = is_cuda()
-_is_npu = is_npu()
-_is_cpu_amx_available = cpu_has_amx_support()
-_is_cpu = is_cpu()
-_is_hip = is_hip()
-
-if not (_is_cuda or _is_npu or (_is_cpu and _is_cpu_amx_available) or _is_hip):
-    from vllm import _custom_ops as vllm_ops
-    from vllm._custom_ops import scaled_fp8_quant
 
 try:
     import vllm
@@ -568,6 +559,8 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
                 requires_grad=False,
             )
 
+        from vllm import _custom_ops as vllm_ops
+
         marlin_w13_qweight = vllm_ops.gptq_marlin_moe_repack(
             layer.w13_weight_packed,
             layer.w13_g_idx_sort_indices,

python/sglang/srt/layers/quantization/utils.py

@@ -17,15 +17,6 @@ from sglang.srt.utils import cpu_has_amx_support, is_cpu, is_cuda, is_hip, is_np
 if TYPE_CHECKING:
     from sglang.srt.layers.quantization.base_config import QuantizationConfig
 
-_is_cuda = is_cuda()
-_is_npu = is_npu()
-_is_cpu_amx_available = cpu_has_amx_support()
-_is_cpu = is_cpu()
-_is_hip = is_hip()
-
-if not (_is_cuda or _is_npu or (_is_cpu and _is_cpu_amx_available) or _is_hip):
-    from vllm._custom_ops import scaled_fp8_quant
-
 
 def is_layer_skipped(
     prefix: str,