[Refactor] Remove moe_align_block_size_triton (#21335)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

benchmarks/kernels/benchmark_moe_align_block_size.py

@@ -5,9 +5,8 @@ import itertools
 
 import torch
 
-from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
-    moe_align_block_size_triton,
+    moe_align_block_size,
 )
 from vllm.triton_utils import triton
 
@@ -21,60 +20,6 @@ def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
     )
 
 
-def check_correctness(num_tokens, num_experts=256, block_size=256, topk=8):
-    """
-    Verifies vllm vs. Triton
-    """
-    topk_ids = get_topk_ids(num_tokens, num_experts, topk)
-
-    # 1. malloc space for triton and vllm
-    # malloc enough space (max_num_tokens_padded) for the sorted ids
-    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-    sorted_ids_triton = torch.empty(
-        (max_num_tokens_padded,), dtype=torch.int32, device="cuda"
-    )
-    expert_ids_triton = torch.empty(
-        (max_num_tokens_padded // block_size,), dtype=torch.int32, device="cuda"
-    )
-    num_tokens_post_pad_triton = torch.empty((1,), dtype=torch.int32, device="cuda")
-
-    sorted_ids_vllm = torch.empty_like(sorted_ids_triton)
-    expert_ids_vllm = torch.empty_like(expert_ids_triton)
-    num_tokens_post_pad_vllm = torch.empty_like(num_tokens_post_pad_triton)
-
-    # 2. run implementations
-    moe_align_block_size_triton(
-        topk_ids,
-        num_experts,
-        block_size,
-        sorted_ids_triton,
-        expert_ids_triton,
-        num_tokens_post_pad_triton,
-    )
-
-    ops.moe_align_block_size(
-        topk_ids,
-        num_experts,
-        block_size,
-        sorted_ids_vllm,
-        expert_ids_vllm,
-        num_tokens_post_pad_vllm,
-    )
-    print(f"✅ VLLM implementation works with {num_experts} experts!")
-
-    # 3. compare results
-    if torch.allclose(expert_ids_triton, expert_ids_vllm) and torch.allclose(
-        num_tokens_post_pad_triton, num_tokens_post_pad_vllm
-    ):
-        print("✅ Triton and VLLM implementations match.")
-    else:
-        print("❌ Triton and VLLM implementations DO NOT match.")
-        print("Triton expert_ids:", expert_ids_triton)
-        print("VLLM expert_ids:", expert_ids_vllm)
-        print("Triton num_tokens_post_pad:", num_tokens_post_pad_triton)
-        print("VLLM num_tokens_post_pad:", num_tokens_post_pad_vllm)
-
-
 # test configurations
 num_tokens_range = [1, 16, 256, 4096]
 num_experts_range = [16, 64, 224, 256, 280, 512]
@@ -87,8 +32,8 @@ configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range
         x_names=["num_tokens", "num_experts", "topk"],
         x_vals=configs,
         line_arg="provider",
-        line_vals=["vllm", "triton"],  # "triton"
+        line_vals=["vllm"],
-        line_names=["VLLM", "Triton"],  # "Triton"
+        line_names=["vLLM"],
         plot_name="moe-align-block-size-performance",
         args={},
     )
@@ -98,36 +43,11 @@ def benchmark(num_tokens, num_experts, topk, provider):
     block_size = 256
     topk_ids = get_topk_ids(num_tokens, num_experts, topk)
 
-    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-    sorted_ids = torch.empty((max_num_tokens_padded,), dtype=torch.int32, device="cuda")
-    max_num_m_blocks = max_num_tokens_padded // block_size
-    expert_ids = torch.empty((max_num_m_blocks,), dtype=torch.int32, device="cuda")
-    num_tokens_post_pad = torch.empty((1,), dtype=torch.int32, device="cuda")
-
     quantiles = [0.5, 0.2, 0.8]
 
     if provider == "vllm":
         ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: ops.moe_align_block_size(
+            lambda: moe_align_block_size(topk_ids, block_size, num_experts),
-                topk_ids,
-                num_experts,
-                block_size,
-                sorted_ids.clone(),
-                expert_ids.clone(),
-                num_tokens_post_pad.clone(),
-            ),
-            quantiles=quantiles,
-        )
-    elif provider == "triton":
-        ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: moe_align_block_size_triton(
-                topk_ids,
-                num_experts,
-                block_size,
-                sorted_ids.clone(),
-                expert_ids.clone(),
-                num_tokens_post_pad.clone(),
-            ),
             quantiles=quantiles,
         )
 
@@ -151,6 +71,4 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    print("Running correctness check...")
-    check_correctness(num_tokens=1024, num_experts=args.num_experts, topk=args.topk)
     benchmark.run(print_data=True, show_plots=True)

vllm/model_executor/layers/fused_moe/moe_align_block_size.py

@@ -5,144 +5,8 @@ from typing import Optional
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.triton_utils import tl, triton
+from vllm.triton_utils import triton
-from vllm.utils import cdiv, round_up
+from vllm.utils import round_up
-
-
-@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)
-
-
-# Triton implementation based on:
-# https://github.com/sgl-project/sglang/commit/ba5112ff691d791a9e38c6c71f59324a5fcb49d0
-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 = cdiv(numel, num_experts)
-    sorted_token_ids.fill_(numel)
-    expert_ids.zero_()
-
-    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,
-    )
 
 
 def moe_align_block_size(