(2/n - prefill optimize)perf(lora): remove GPU-CPU sync barrier (.item()) in MoE LoRA path and remove duplicate code (#24246)

Co-authored-by: Cursor <cursoragent@cursor.com>

python/sglang/srt/lora/lora_moe_runners.py

@@ -34,7 +34,6 @@ from sglang.srt.utils import is_cuda, is_hip, is_xpu, next_power_of_2
 
 _is_cuda = is_cuda()
 _is_hip = is_hip()
-_is_hip = is_hip()
 _is_xpu = is_xpu()
 
 if _is_cuda or _is_hip or _is_xpu:
@@ -64,112 +63,6 @@ def _get_moe_lora_block_config(max_lora_rank: int) -> dict:
 _SPARSITY_FACTOR = 8
 
 
-def _naive_moe_lora_align_block_size(
-    topk_ids: torch.Tensor,
-    seg_indptr: torch.Tensor,
-    req_to_lora: torch.Tensor,
-    num_experts: int,
-    block_size_m: int,
-    max_loras: int,
-    max_num_tokens_padded: int,
-    max_num_m_blocks: int,
-    adapter_enabled: torch.Tensor,
-    device: torch.device,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """Construct LoRA token-expert alignment on CPU for small batches.
-
-    When the number of tokens is very small, the overhead of launching the
-    CUDA-based moe_lora_align_block_size kernel exceeds the actual
-    computation. This function builds the same data structures using simple
-    Python loops on CPU and transfers the result to GPU in one shot.
-    """
-    M, top_k = topk_ids.shape
-    num_valid_tokens = M * top_k
-
-    sorted_token_ids = torch.full(
-        (max_loras * max_num_tokens_padded,),
-        num_valid_tokens,
-        dtype=torch.int32,
-    )
-    expert_ids_out = torch.full((max_loras * max_num_m_blocks,), -1, dtype=torch.int32)
-    num_tokens_post_padded = torch.zeros(max_loras, dtype=torch.int32)
-
-    seg_indptr_list = seg_indptr.cpu().tolist()
-    req_to_lora_list = req_to_lora.cpu().tolist()
-    topk_ids_list = topk_ids.cpu().tolist()
-    adapter_enabled_list = adapter_enabled.cpu().tolist()
-
-    for lora_id in range(max_loras):
-        if not adapter_enabled_list[lora_id]:
-            continue
-
-        pairs: list[tuple[int, int]] = []
-        for seg_idx in range(len(seg_indptr_list) - 1):
-            if req_to_lora_list[seg_idx] != lora_id:
-                continue
-            start = seg_indptr_list[seg_idx]
-            end = seg_indptr_list[seg_idx + 1]
-            for m in range(start, end):
-                for k in range(top_k):
-                    pairs.append((topk_ids_list[m][k], m * top_k + k))
-
-        if not pairs:
-            continue
-
-        pairs.sort()
-
-        base_t = lora_id * max_num_tokens_padded
-        base_e = lora_id * max_num_m_blocks
-        pos = 0
-        block_idx = 0
-        i = 0
-        while i < len(pairs):
-            cur_expert = pairs[i][0]
-            group_start = pos
-            while i < len(pairs) and pairs[i][0] == cur_expert:
-                sorted_token_ids[base_t + pos] = pairs[i][1]
-                pos += 1
-                i += 1
-            group_len = pos - group_start
-            padded_len = ((group_len + block_size_m - 1) // block_size_m) * block_size_m
-            num_blocks = padded_len // block_size_m
-            for b in range(num_blocks):
-                expert_ids_out[base_e + block_idx + b] = cur_expert
-            block_idx += num_blocks
-            pos = group_start + padded_len
-
-        num_tokens_post_padded[lora_id] = pos
-
-    return (
-        sorted_token_ids.to(device),
-        expert_ids_out.to(device),
-        num_tokens_post_padded.to(device),
-    )
-
-
-def _get_moe_lora_block_config(max_lora_rank: int) -> dict:
-    """Compute rank-aware block sizes for MoE LoRA kernels.
-
-    Shrink: output dim is the rank -> cap BLOCK_SIZE_N to avoid waste.
-    Expand: input dim is the rank -> cap BLOCK_SIZE_K similarly.
-    """
-    if max_lora_rank <= 0:
-        rank_pow2 = 64
-    else:
-        rank_pow2 = next_power_of_2(max_lora_rank)
-
-    shrink_n = min(64, rank_pow2)
-    expand_k = max(16, min(64, rank_pow2))
-
-    return {
-        "shrink_block_size_n": shrink_n,
-        "expand_block_size_k": expand_k,
-    }
-
-
-_SPARSITY_FACTOR = 8
-
-
 def _naive_moe_lora_align_block_size(
     topk_ids: torch.Tensor,
     seg_indptr: torch.Tensor,
@@ -288,7 +181,6 @@ class LoRAInfo:
     num_experts: int
     experts_shared_outer_loras: bool = False
     cg_buffers: dict | None = None
-    cg_buffers: dict | None = None
 
     fully_sharded: bool = False
     tp_size: int = 1
@@ -447,18 +339,10 @@ def _add_lora_gate_up_delta(
         from sglang.srt.model_executor.cuda_graph_runner import get_capture_lora_variant
 
         # Record LoRA kernels for lora graph; skip for nolora graph.
-        has_active_lora = get_capture_lora_variant() != "nolora"
+        if get_capture_lora_variant() == "nolora":
-    else:
+            return
-        num_loras = len(lora_info.lora_ranks)
+
-        has_active_lora = (
+    if lora_info is None or lora_info.max_lora_rank == 0:
-            (
-                lora_info.adapter_enabled[:num_loras]
-                * (lora_info.lora_ranks > 0).to(lora_info.adapter_enabled.dtype)
-            )
-            .any()
-            .item()
-        )
-    if not has_active_lora or lora_info is None or lora_info.max_lora_rank == 0:
         return
 
     M, top_k, gate_up_dim = intermediate_cache.shape
@@ -565,11 +449,6 @@ def _add_lora_down_delta(
     if lora_info.experts_shared_outer_loras and not lora_info.lora_use_virtual_experts:
         down_lora_b = down_lora_b.expand(-1, lora_info.num_experts, -1, -1)
 
-    if lora_info.fully_sharded and lora_info.tp_size > 1:
-        shard_size = lora_info.hidden_size // lora_info.tp_size
-        offset = shard_size * lora_info.tp_rank
-    else:
-        offset = 0
     if lora_info.fully_sharded and lora_info.tp_size > 1:
         shard_size = lora_info.hidden_size // lora_info.tp_size
         offset = shard_size * lora_info.tp_rank