1. fix call FusedMoE.select_experts failed

2. fix potential overflow and remove debug cruft with  tlrmchlsmth's review
3. add benchmark for performance

Signed-off-by: Caleb_Du <Caleb_Du@zju.edu.cn>

Author: CalebDu

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -0,0 +1,349 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+from typing import Any, TypedDict
+
+import ray
+import torch
+from transformers import AutoConfig
+
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+    _moe_permute, _moe_unpermute_and_reduce)
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
+from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+class BenchmarkConfig(TypedDict):
+    BLOCK_SIZE_M: int
+    BLOCK_SIZE_N: int
+    BLOCK_SIZE_K: int
+    GROUP_SIZE_M: int
+    num_warps: int
+    num_stages: int
+
+
+def benchmark_permute(num_tokens: int,
+                      num_experts: int,
+                      hidden_size: int,
+                      topk: int,
+                      dtype: torch.dtype,
+                      use_fp8_w8a8: bool,
+                      use_int8_w8a16: bool,
+                      num_iters: int = 100,
+                      use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    # output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    gating_output = torch.randn(num_iters,
+                                num_tokens,
+                                num_experts,
+                                dtype=torch.float32)
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare(i: int):
+        input_gating.copy_(gating_output[i])
+
+    def run():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+
+    # JIT compilation & warmup
+    run()
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run()
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        prepare(i)
+        torch.cuda.synchronize()
+
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+def benchmark_unpermute(num_tokens: int,
+                        num_experts: int,
+                        hidden_size: int,
+                        topk: int,
+                        dtype: torch.dtype,
+                        use_fp8_w8a8: bool,
+                        use_int8_w8a16: bool,
+                        num_iters: int = 100,
+                        use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+            # convert to fp16/bf16 as gemm output
+            return (permuted_hidden_states.to(dtype), first_token_off,
+                    inv_perm_idx, m_indices)
+        else:
+            (permuted_qhidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+            # convert to fp16/bf16 as gemm output
+            return (permuted_qhidden_states.to(dtype), a1q_scale,
+                    sorted_token_ids, expert_ids, inv_perm)
+
+    def run(input: tuple):
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = input
+            moe_unpermute(permuted_hidden_states, topk_weights, topk_ids,
+                          inv_perm_idx, first_token_off, topk, num_experts,
+                          num_experts)
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = input
+            _moe_unpermute_and_reduce(output_hidden_states,
+                                      permuted_hidden_states, inv_perm,
+                                      topk_weights)
+
+    # JIT compilation & warmup
+    input = prepare()
+    run(input)
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run(input)
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        torch.cuda.synchronize()
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+@ray.remote(num_gpus=1)
+class BenchmarkWorker:
+
+    def __init__(self, seed: int) -> None:
+        torch.set_default_device("cuda")
+        current_platform.seed_everything(seed)
+        self.seed = seed
+        # Get the device ID to allocate tensors and kernels
+        # on the respective GPU. This is required for Ray to work
+        # correctly with multi-GPU tuning on the ROCm platform.
+        self.device_id = int(ray.get_gpu_ids()[0])
+
+    def benchmark(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a16: bool,
+        use_customized_permute: bool = False,
+    ) -> tuple[dict[str, int], float]:
+        current_platform.seed_everything(self.seed)
+
+        permute_time = benchmark_permute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        unpermute_time = benchmark_unpermute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        return permute_time, unpermute_time
+
+
+def get_weight_block_size_safety(config, default_value=None):
+
+    quantization_config = getattr(config, 'quantization_config', {})
+    if isinstance(quantization_config, dict):
+        return quantization_config.get('weight_block_size', default_value)
+    return default_value
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    config = AutoConfig.from_pretrained(
+        args.model, trust_remote_code=args.trust_remote_code)
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+    elif (config.architectures[0] == "DeepseekV3ForCausalLM"
+          or config.architectures[0] == "DeepseekV2ForCausalLM"):
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+    elif config.architectures[0] in [
+            "Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"
+    ]:
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+
+    else:
+        # Support for llama4
+        config = config.get_text_config()
+        # Default: Mixtral.
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+
+    hidden_size = config.hidden_size
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
+    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
+    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    use_customized_permute = args.use_customized_permute
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
+            2048, 3072, 4096
+        ]
+    else:
+        batch_sizes = [args.batch_size]
+
+    ray.init()
+    num_gpus = int(ray.available_resources()["GPU"])
+    workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
+
+    def _distribute(method: str, inputs: list[Any]) -> list[Any]:
+        outputs = []
+        worker_idx = 0
+        for input_args in inputs:
+            worker = workers[worker_idx]
+            worker_method = getattr(worker, method)
+            output = worker_method.remote(*input_args)
+            outputs.append(output)
+            worker_idx = (worker_idx + 1) % num_gpus
+        return ray.get(outputs)
+
+    outputs = _distribute(
+        "benchmark", [(batch_size, E, hidden_size, topk, dtype, use_fp8_w8a8,
+                       use_int8_w8a16, use_customized_permute)
+                      for batch_size in batch_sizes])
+
+    for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
+        print(f"Batch size: {batch_size}")
+        print(f"Permute time: {permute:.2f} us")
+        print(f"Unpermute time: {unpermute:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument("--model",
+                        type=str,
+                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
+    parser.add_argument("--dtype",
+                        type=str,
+                        choices=["auto", "fp8_w8a8", "int8_w8a16"],
+                        default="auto")
+    parser.add_argument("--use-customized-permute", action="store_true")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+
+    main(args)

csrc/moe/moe_permute_unpermute_op.cu

@@ -113,9 +113,6 @@ void moe_unpermute(
   TORCH_CHECK(
       permuted_hidden_states.scalar_type() == hidden_states.scalar_type(),
       "topk_ids dtype must be same as src_row_id2dst_row_id_map");
-  // TORCH_CHECK(permuted_hidden_states.size(0) == hidden_states.size(0) * topk,
-  //             "permuted_hidden_states must be [n_token * topk, n_hidden],"
-  //             "hidden_states must be [n_token, n_hidden]");
   auto n_token = hidden_states.size(0);
   auto n_hidden = hidden_states.size(1);
   auto stream = at::cuda::getCurrentCUDAStream().stream();

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -112,7 +112,7 @@ void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
                        int num_experts, int num_experts_per_node, int k,
                        CubKeyValueSorter& sorter, void* sorter_ws,
                        cudaStream_t stream) {
-  int64_t const expanded_num_rows = k * num_rows;
+  int64_t const expanded_num_rows = static_cast<int64_t>(k) * num_rows;
   // We need to use the full num_experts because that is the sentinel value used
   // by topk for disabled experts
   sorter.updateNumExperts(num_experts);

vllm/model_executor/layers/fused_moe/layer.py

@@ -191,7 +191,7 @@ def forward_cuda(
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
-        topk_weights, topk_ids, token_expert_indices = FusedMoE.select_experts(
+        topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
             use_grouped_topk=use_grouped_topk,