sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

benchmark/kernels/deepep/deepep_utils.py

+# ADAPTED FROM https://github.com/deepseek-ai/DeepEP/blob/main/tests/utils.py
+
+import os
+import sys
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.distributed as dist
+
+
+def init_dist(local_rank: int, num_local_ranks: int, args):
+    ip = args.master_addr
+    port = args.master_port
+    num_nodes = args.nnodes
+    node_rank = args.node_rank
+    assert (num_local_ranks < 8 and num_nodes == 1) or num_local_ranks == 8
+
+    dist.init_process_group(
+        backend="nccl",
+        init_method=f"tcp://{ip}:{port}",
+        world_size=num_nodes * num_local_ranks,
+        rank=node_rank * num_local_ranks + local_rank,
+    )
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_default_device("cuda")
+    torch.cuda.set_device(local_rank)
+
+    return (
+        dist.get_rank(),
+        dist.get_world_size(),
+        dist.new_group(list(range(num_local_ranks * num_nodes))),
+    )
+
+
+def calc_diff(x: torch.Tensor, y: torch.Tensor):
+    x, y = x.double() + 1, y.double() + 1
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return (1 - sim).item()
+
+
+def per_token_cast_to_fp8(x: torch.Tensor):
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(
+        m, n
+    ), (x_amax / 448.0).view(m, -1)
+
+
+def per_token_cast_back(x_fp8: torch.Tensor, x_scales: torch.Tensor):
+    x_fp32 = x_fp8.to(torch.float32).view(x_fp8.size(0), -1, 128)
+    x_scales = x_scales.view(x_fp8.size(0), -1, 1)
+    return (x_fp32 * x_scales).view(x_fp8.shape).to(torch.bfloat16)
+
+
+def inplace_unique(x: torch.Tensor, num_slots: int):
+    assert x.dim() == 2
+    mask = x < 0
+    x_padded = x.masked_fill(mask, num_slots)
+    bin_count = torch.zeros((x.size(0), num_slots + 1), dtype=x.dtype, device=x.device)
+    bin_count.scatter_add_(1, x_padded, torch.ones_like(x_padded))
+    bin_count = bin_count[:, :num_slots]
+    sorted_bin_count, sorted_bin_idx = torch.sort(bin_count, dim=-1, descending=True)
+    sorted_bin_idx.masked_fill_(sorted_bin_count == 0, -1)
+    sorted_bin_idx = torch.sort(sorted_bin_idx, descending=True, dim=-1).values
+    x[:, :].fill_(-1)
+    valid_len = min(num_slots, x.size(1))
+    x[:, :valid_len] = sorted_bin_idx[:, :valid_len]
+
+
+def create_grouped_scores(
+    scores: torch.Tensor, group_idx: torch.Tensor, num_groups: int
+):
+    num_tokens, num_experts = scores.shape
+    scores = scores.view(num_tokens, num_groups, -1)
+    mask = torch.zeros((num_tokens, num_groups), dtype=torch.bool, device=scores.device)
+    mask = mask.scatter_(1, group_idx, True).unsqueeze(-1).expand_as(scores)
+    return (scores * mask).view(num_tokens, num_experts)
+
+
+def bench(fn, num_warmups: int = 20, num_tests: int = 30, post_fn=None):
+    # Flush L2 cache with 256 MB data
+    torch.cuda.synchronize()
+    cache = torch.empty(int(256e6 // 4), dtype=torch.int, device="cuda")
+
+    # Warmup
+    for _ in range(num_warmups):
+        fn()
+
+    # Flush L2
+    cache.zero_()
+
+    # Testing
+    start_events = [torch.cuda.Event(enable_timing=True) for _ in range(num_tests)]
+    end_events = [torch.cuda.Event(enable_timing=True) for _ in range(num_tests)]
+    for i in range(num_tests):
+        # Record
+        start_events[i].record()
+        fn()
+        end_events[i].record()
+        if post_fn is not None:
+            post_fn()
+    torch.cuda.synchronize()
+
+    times = np.array(
+        [s.elapsed_time(e) / 1e3 for s, e in zip(start_events, end_events)]
+    )[1:]
+    return np.average(times), np.min(times), np.max(times)
+
+
+class empty_suppress:
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *_):
+        pass
+
+
+class suppress_stdout_stderr:
+    def __enter__(self):
+        self.outnull_file = open(os.devnull, "w")
+        self.errnull_file = open(os.devnull, "w")
+
+        self.old_stdout_fileno_undup = sys.stdout.fileno()
+        self.old_stderr_fileno_undup = sys.stderr.fileno()
+
+        self.old_stdout_fileno = os.dup(sys.stdout.fileno())
+        self.old_stderr_fileno = os.dup(sys.stderr.fileno())
+
+        self.old_stdout = sys.stdout
+        self.old_stderr = sys.stderr
+
+        os.dup2(self.outnull_file.fileno(), self.old_stdout_fileno_undup)
+        os.dup2(self.errnull_file.fileno(), self.old_stderr_fileno_undup)
+
+        sys.stdout = self.outnull_file
+        sys.stderr = self.errnull_file
+        return self
+
+    def __exit__(self, *_):
+        sys.stdout = self.old_stdout
+        sys.stderr = self.old_stderr
+
+        os.dup2(self.old_stdout_fileno, self.old_stdout_fileno_undup)
+        os.dup2(self.old_stderr_fileno, self.old_stderr_fileno_undup)
+
+        os.close(self.old_stdout_fileno)
+        os.close(self.old_stderr_fileno)
+
+        self.outnull_file.close()
+        self.errnull_file.close()
+
+
+def bench_kineto(
+    fn,
+    kernel_names,
+    num_tests: int = 30,
+    suppress_kineto_output: bool = False,
+    trace_path: Optional[str] = None,
+    barrier_comm_profiling: bool = False,
+):
+    # Profile
+    suppress = suppress_stdout_stderr if suppress_kineto_output else empty_suppress
+    with suppress():
+        schedule = torch.profiler.schedule(wait=0, warmup=1, active=1, repeat=1)
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CUDA], schedule=schedule
+        ) as prof:
+            for i in range(2):
+                # NOTES: use a large kernel and a barrier to eliminate the unbalanced CPU launch overhead
+                if barrier_comm_profiling:
+                    lhs = torch.randn((8192, 8192), dtype=torch.float, device="cuda")
+                    rhs = torch.randn((8192, 8192), dtype=torch.float, device="cuda")
+                    lhs @ rhs
+                    dist.all_reduce(torch.ones(1, dtype=torch.float, device="cuda"))
+                for _ in range(num_tests):
+                    fn()
+                prof.step()
+
+    # Parse the profiling table
+    assert isinstance(kernel_names, str) or isinstance(kernel_names, tuple)
+    is_tupled = isinstance(kernel_names, tuple)
+    prof_lines = (
+        prof.key_averages()
+        .table(sort_by="cuda_time_total", max_name_column_width=100)
+        .split("\n")
+    )
+    kernel_names = (kernel_names,) if isinstance(kernel_names, str) else kernel_names
+    assert all([isinstance(name, str) for name in kernel_names])
+    for name in kernel_names:
+        assert (
+            sum([name in line for line in prof_lines]) == 1
+        ), f"Errors of the kernel {name} in the profiling table"
+
+    # Save chrome traces
+    if trace_path is not None:
+        prof.export_chrome_trace(trace_path)
+
+    # Return average kernel times
+    units = {"ms": 1e3, "us": 1e6}
+    kernel_times = []
+    for name in kernel_names:
+        for line in prof_lines:
+            if name in line:
+                time_str = line.split()[-2]
+                for unit, scale in units.items():
+                    if unit in time_str:
+                        kernel_times.append(float(time_str.replace(unit, "")) / scale)
+                        break
+                break
+    return tuple(kernel_times) if is_tupled else kernel_times[0]
+
+
+def hash_tensor(t: torch.Tensor):
+    return t.view(torch.int64).sum().item()

benchmark/kernels/deepep/tuning_deepep.py

+# MODIFIED FROM https://github.com/deepseek-ai/DeepEP/blob/main/tests/test_internode.py
+
+"""
+Example usage:
+python tuning_deepep.py --nnodes 4 --node-rank $MY_NODE_RANK --master-addr 1.2.3.4
+Then check `deepep_tuned.json`
+"""
+
+import argparse
+import json
+import time
+from copy import deepcopy
+from pathlib import Path
+
+# noinspection PyUnresolvedReferences
+import deep_ep
+import torch
+import torch.distributed as dist
+from deepep_utils import (
+    bench,
+    calc_diff,
+    create_grouped_scores,
+    init_dist,
+    inplace_unique,
+    per_token_cast_back,
+    per_token_cast_to_fp8,
+)
+
+
+def test_main(
+    num_sms: int,
+    local_rank: int,
+    num_local_ranks: int,
+    num_ranks: int,
+    num_nodes: int,
+    rank: int,
+    buffer: deep_ep.Buffer,
+    group: dist.ProcessGroup,
+    args,
+):
+    # Settings
+    num_tokens, hidden, num_topk_groups, num_topk, num_experts = (
+        4096,
+        7168,
+        min(num_nodes, 4),
+        8,
+        (256 // num_ranks) * num_ranks,
+    )
+    assert num_experts % num_ranks == 0 and num_local_ranks == 8
+    if local_rank == 0:
+        print(
+            f"[config] num_tokens={num_tokens}, hidden={hidden}, num_topk_groups={num_topk_groups}, num_topk={num_topk}",
+            flush=True,
+        )
+
+    # Random data
+    x = torch.ones((num_tokens, hidden), dtype=torch.bfloat16, device="cuda") * rank
+    x_pure_rand = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device="cuda")
+    x_e4m3 = per_token_cast_to_fp8(x)
+    scores = (
+        torch.randn((num_tokens, num_experts), dtype=torch.float32, device="cuda").abs()
+        + 1
+    )
+    group_scores = scores.view(num_tokens, num_nodes, -1).amax(dim=-1)
+    group_idx = torch.topk(
+        group_scores, k=num_topk_groups, dim=-1, sorted=False
+    ).indices
+    masked_scores = create_grouped_scores(scores, group_idx, num_nodes)
+    topk_idx = torch.topk(masked_scores, num_topk, dim=-1, largest=True, sorted=False)[
+        1
+    ]
+    topk_weights = (
+        torch.ones((num_tokens, num_topk), dtype=torch.float32, device="cuda") * rank
+    )
+    topk_weights_pure_rand = torch.randn(
+        (num_tokens, num_topk), dtype=torch.float32, device="cuda"
+    )
+    rank_idx = topk_idx // (num_experts // num_ranks)
+    rank_idx.masked_fill_(topk_idx == -1, -1)
+    inplace_unique(rank_idx, num_ranks)
+    rdma_rank_idx = rank_idx // num_local_ranks
+    rdma_rank_idx.masked_fill_(rank_idx == -1, -1)
+    inplace_unique(rdma_rank_idx, num_nodes)
+
+    # RDMA dispatch counts
+    rdma_idx = topk_idx // (num_experts // num_nodes)
+    rdma_idx.masked_fill_(topk_idx == -1, -1)
+    inplace_unique(rdma_idx, num_nodes)
+    num_rdma_token_sent = rdma_idx.ne(-1).sum().item()
+
+    # Expert meta
+    num_tokens_per_expert = torch.zeros((num_experts,), dtype=torch.int, device="cuda")
+    for i in range(num_experts):
+        num_tokens_per_expert[i] = (topk_idx == i).sum()
+    gbl_num_tokens_per_expert = num_tokens_per_expert.clone()
+    dist.all_reduce(gbl_num_tokens_per_expert, group=group)
+
+    # Rank layout meta
+    num_tokens_per_rank = torch.empty((num_ranks,), dtype=torch.int, device="cuda")
+    num_tokens_per_rdma_rank = torch.empty((num_nodes,), dtype=torch.int, device="cuda")
+    token_idx_in_rank = torch.full(
+        (num_ranks, num_tokens), -1, dtype=torch.long, device="cuda"
+    )
+    for i in range(num_ranks):
+        num_tokens_per_rank[i] = (rank_idx == i).sum()
+        token_sel = (rank_idx == i).max(dim=-1)[0]
+        count = token_sel.sum().item()
+        tokens = torch.sort(token_sel.to(torch.int), descending=True)[1]
+        tokens[:count] = torch.sort(tokens[:count])[0]
+        token_idx_in_rank[i][tokens[:count]] = torch.arange(
+            count, dtype=torch.long, device="cuda"
+        )
+    for i in range(num_nodes):
+        num_tokens_per_rdma_rank[i] = (rdma_rank_idx == i).sum()
+    token_idx_in_rank = token_idx_in_rank.T.contiguous().to(torch.int)
+    is_token_in_rank = token_idx_in_rank >= 0
+    gbl_num_tokens_per_rank = num_tokens_per_rank.clone()
+    dist.all_reduce(gbl_num_tokens_per_rank, group=group)
+
+    (
+        ref_num_tokens_per_rank,
+        ref_num_tokens_per_rdma_rank,
+        ref_num_tokens_per_expert,
+        ref_is_token_in_rank,
+        _,
+    ) = buffer.get_dispatch_layout(topk_idx, num_experts)
+    assert torch.allclose(ref_num_tokens_per_rank, num_tokens_per_rank)
+    assert torch.allclose(ref_num_tokens_per_rdma_rank, num_tokens_per_rdma_rank)
+    assert torch.allclose(ref_num_tokens_per_expert, num_tokens_per_expert)
+    assert torch.allclose(ref_is_token_in_rank, is_token_in_rank)
+    t = bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts))[0]
+    if local_rank == 0:
+        print(f"[layout] Kernel performance: {t * 1000:.3f} ms", flush=True)
+        print("", flush=True)
+    group.barrier()
+    time.sleep(1)
+
+    # Config
+    rdma_buffer_size, nvl_buffer_size = 128, (720 if num_ranks in (144, 160) else 512)
+    config = deep_ep.Config(num_sms, 8, nvl_buffer_size, 16, rdma_buffer_size)
+
+    # Test dispatch
+    # noinspection PyShadowingNames
+    def check_data(check_x, recv_gbl_rank_prefix_sum):
+        assert torch.allclose(check_x.amin(dim=1), check_x.amax(dim=1))
+        check_start = 0
+        for i in range(num_ranks):
+            check_end = recv_gbl_rank_prefix_sum[i].item()
+            assert (check_x[check_start:check_end, :].int() - i).sum().item() == 0
+            check_start = check_end
+
+    for previous_mode in (False, True):
+        for async_mode in (False, True):
+            for current_x in (x_pure_rand, x, x_e4m3):
+                for with_topk in (False, True):
+                    if local_rank == 0:
+                        print(
+                            f'[testing] Running with {"FP8" if isinstance(current_x, tuple) else "BF16"}, {"with" if with_topk else "without"} top-k (async={async_mode}, previous={previous_mode}) ...',
+                            flush=True,
+                            end="",
+                        )
+                    dispatch_args = {
+                        "x": current_x,
+                        "num_tokens_per_rank": num_tokens_per_rank,
+                        "num_tokens_per_rdma_rank": num_tokens_per_rdma_rank,
+                        "is_token_in_rank": is_token_in_rank,
+                        "num_tokens_per_expert": num_tokens_per_expert,
+                        "config": config,
+                        "async_finish": async_mode,
+                    }
+                    if with_topk:
+                        dispatch_args.update(
+                            {
+                                "topk_idx": topk_idx,
+                                "topk_weights": (
+                                    topk_weights_pure_rand
+                                    if current_x is x_pure_rand
+                                    else topk_weights
+                                ),
+                            }
+                        )
+                    if previous_mode:
+                        dispatch_args.update({"previous_event": buffer.capture()})
+                    (
+                        recv_x,
+                        recv_topk_idx,
+                        recv_topk_weights,
+                        recv_num_tokens_per_expert_list,
+                        handle,
+                        event,
+                    ) = buffer.dispatch(**dispatch_args)
+                    event.current_stream_wait() if async_mode else ()
+                    recv_x = (
+                        per_token_cast_back(*recv_x)
+                        if isinstance(recv_x, tuple)
+                        else recv_x
+                    )
+
+                    # Checks
+                    recv_gbl_rank_prefix_sum = handle[-4]
+                    assert gbl_num_tokens_per_rank[rank].item() == recv_x.size(
+                        0
+                    ), f"{gbl_num_tokens_per_rank[rank].item()} != {recv_x.size(0)}"
+                    assert (
+                        gbl_num_tokens_per_expert.view(num_ranks, -1)[rank].tolist()
+                        == recv_num_tokens_per_expert_list
+                    )
+                    if current_x is not x_pure_rand:
+                        check_data(recv_x, recv_gbl_rank_prefix_sum)
+                    if with_topk:
+                        # Check `topk_idx`
+                        assert (
+                            recv_topk_idx.eq(-1)
+                            | (
+                                (recv_topk_idx >= 0)
+                                & (recv_topk_idx < (num_experts // num_ranks))
+                            )
+                        ).sum().item() == recv_topk_idx.numel()
+                        for i, count in enumerate(recv_num_tokens_per_expert_list):
+                            assert recv_topk_idx.eq(i).sum().item() == count
+
+                        # Check `topk_weights`
+                        if current_x is not x_pure_rand:
+                            recv_topk_weights[recv_topk_idx.eq(-1)] = (
+                                recv_topk_weights.amax(dim=1, keepdim=True).expand_as(
+                                    recv_topk_weights
+                                )[recv_topk_idx.eq(-1)]
+                            )
+                            check_data(recv_topk_weights, recv_gbl_rank_prefix_sum)
+
+                    # Test cached dispatch (must without top-k staffs)
+                    if not with_topk:
+                        dispatch_args = {
+                            "x": current_x,
+                            "handle": handle,
+                            "config": config,
+                            "async_finish": async_mode,
+                        }
+                        if previous_mode:
+                            dispatch_args.update({"previous_event": buffer.capture()})
+                        recv_x, _, _, _, _, event = buffer.dispatch(**dispatch_args)
+                        event.current_stream_wait() if async_mode else ()
+                        recv_x = (
+                            per_token_cast_back(*recv_x)
+                            if isinstance(recv_x, tuple)
+                            else recv_x
+                        )
+                        if current_x is not x_pure_rand:
+                            check_data(recv_x, recv_gbl_rank_prefix_sum)
+
+                    # Test combine
+                    combine_args = {
+                        "x": recv_x,
+                        "handle": handle,
+                        "config": config,
+                        "async_finish": async_mode,
+                    }
+                    if with_topk:
+                        combine_args.update({"topk_weights": recv_topk_weights})
+                    if previous_mode:
+                        combine_args.update({"previous_event": buffer.capture()})
+                    combined_x, combined_topk_weights, event = buffer.combine(
+                        **combine_args
+                    )
+                    event.current_stream_wait() if async_mode else ()
+                    check_x = combined_x.float() / is_token_in_rank.sum(
+                        dim=1
+                    ).unsqueeze(1)
+                    ref_x = x_pure_rand if current_x is x_pure_rand else x
+                    assert calc_diff(check_x, ref_x) < 5e-6
+                    if with_topk:
+                        check_topk_weights = (
+                            combined_topk_weights
+                            if (current_x is x_pure_rand)
+                            else (
+                                combined_topk_weights
+                                / is_token_in_rank.sum(dim=1).unsqueeze(1)
+                            )
+                        )
+                        ref_topk_weights = (
+                            topk_weights_pure_rand
+                            if current_x is x_pure_rand
+                            else topk_weights
+                        )
+                        assert calc_diff(check_topk_weights, ref_topk_weights) < 1e-9
+
+                    # For later tuning
+                    dispatch_bf16_rdma_send_bytes = num_rdma_token_sent * hidden * 2
+                    dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2
+                    combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes
+                    combine_bf16_rdma_recv_bytes = dispatch_bf16_rdma_send_bytes
+
+                    if local_rank == 0:
+                        print(" passed", flush=True)
+    if local_rank == 0:
+        print("", flush=True)
+
+    output_data = {}
+
+    # Tune dispatch performance
+    best_dispatch_results = None
+    fp8_factor = (1 + 4 / 128) / 2
+    for current_x in (x_e4m3, x):
+        best_time, best_results = 1e10, None
+        rdma_send_bytes = (
+            (dispatch_bf16_rdma_send_bytes * fp8_factor)
+            if isinstance(current_x, tuple)
+            else dispatch_bf16_rdma_send_bytes
+        )
+        nvl_recv_bytes = (
+            (dispatch_bf16_nvl_recv_bytes * fp8_factor)
+            if isinstance(current_x, tuple)
+            else dispatch_bf16_nvl_recv_bytes
+        )
+        for nvl_chunk_size in range(4, 33, 4):
+            for rdma_chunk_size in range(4, 33, 4):
+                config_kwargs = {
+                    "num_sms": num_sms,
+                    "num_max_nvl_chunked_send_tokens": nvl_chunk_size,
+                    "num_max_nvl_chunked_recv_tokens": nvl_buffer_size,
+                    "num_max_rdma_chunked_send_tokens": rdma_chunk_size,
+                    "num_max_rdma_chunked_recv_tokens": rdma_buffer_size,
+                }
+                config = deep_ep.Config(**config_kwargs)
+                tune_args = {"x": current_x, "handle": handle, "config": config}
+                t = bench(lambda: buffer.dispatch(**tune_args))[0]
+                if t < best_time:
+                    best_time, best_results = t, (
+                        num_sms,
+                        nvl_chunk_size,
+                        rdma_chunk_size,
+                        config_kwargs,
+                    )
+                if local_rank == 0:
+                    print(
+                        f"[tuning] SMs {num_sms}, NVL chunk {nvl_chunk_size}, RDMA chunk {rdma_chunk_size}: {rdma_send_bytes / 1e9 / t:.2f} GB/s (RDMA), {nvl_recv_bytes / 1e9 / t:.2f} GB/s (NVL) ",
+                        flush=True,
+                    )
+        if local_rank == 0:
+            print(
+                f'[tuning] Best dispatch ({"FP8" if isinstance(current_x, tuple) else "BF16"}): SMs {best_results[0]}, NVL chunk {best_results[1]}, RDMA chunk {best_results[2]}: {rdma_send_bytes / 1e9 / best_time:.2f} GB/s (RDMA), {nvl_recv_bytes / 1e9 / best_time:.2f} GB/s (NVL)',
+                flush=True,
+            )
+            print("", flush=True)
+            is_fp8 = isinstance(current_x, tuple)
+            if is_fp8:
+                output_data["normal_dispatch"] = deepcopy(best_results[3])
+
+        if isinstance(current_x, tuple):
+            # Gather FP8 the best config from rank 0
+            best_dispatch_results = torch.tensor(
+                [best_results[0], best_results[1], best_results[2]],
+                dtype=torch.int32,
+                device="cuda",
+            )
+            all_best_fp8_results_list = [
+                torch.zeros_like(best_dispatch_results)
+                for _ in range(torch.distributed.get_world_size())
+            ]
+            dist.all_gather(
+                all_best_fp8_results_list, best_dispatch_results, group=group
+            )
+            best_dispatch_results = all_best_fp8_results_list[0].tolist()
+    dispatch_config = deep_ep.Config(
+        best_dispatch_results[0],
+        best_dispatch_results[1],
+        nvl_buffer_size,
+        best_dispatch_results[2],
+        rdma_buffer_size,
+    )
+
+    dispatch_args = {
+        "x": x,
+        "num_tokens_per_rank": num_tokens_per_rank,
+        "num_tokens_per_rdma_rank": num_tokens_per_rdma_rank,
+        "is_token_in_rank": is_token_in_rank,
+        "num_tokens_per_expert": num_tokens_per_expert,
+        "config": dispatch_config if dispatch_config is not None else config,
+    }
+    recv_x, _, _, _, handle, _ = buffer.dispatch(**dispatch_args)
+
+    # Tune combine performance
+    best_time, best_results = 1e10, None
+    for nvl_chunk_size in range(1, 5, 1):
+        for rdma_chunk_size in range(8, 33, 4):
+            config_kwargs = {
+                "num_sms": num_sms,
+                "num_max_nvl_chunked_send_tokens": nvl_chunk_size,
+                "num_max_nvl_chunked_recv_tokens": nvl_buffer_size,
+                "num_max_rdma_chunked_send_tokens": rdma_chunk_size,
+                "num_max_rdma_chunked_recv_tokens": rdma_buffer_size,
+            }
+            config = deep_ep.Config(**config_kwargs)
+            tune_args = {"x": recv_x, "handle": handle, "config": config}
+            t = bench(lambda: buffer.combine(**tune_args))[0]
+            if local_rank == 0:
+                print(
+                    f"[tuning] SMs {num_sms}, NVL chunk {nvl_chunk_size}, RDMA chunk {rdma_chunk_size}: {combine_bf16_rdma_recv_bytes / 1e9 / t:.2f} GB/s (RDMA), {combine_bf16_nvl_send_bytes / 1e9 / t:.2f} GB/s (NVL) ",
+                    flush=True,
+                )
+                if t < best_time:
+                    best_time, best_results = t, (
+                        num_sms,
+                        nvl_chunk_size,
+                        rdma_chunk_size,
+                        config_kwargs,
+                    )
+
+    if local_rank == 0:
+        print(
+            f"[tuning] Best combine: SMs {best_results[0]}, NVL chunk {best_results[1]}, RDMA chunk {best_results[2]}: {combine_bf16_rdma_recv_bytes / 1e9 / best_time:.2f} GB/s (RDMA), {combine_bf16_nvl_send_bytes / 1e9 / best_time:.2f} GB/s (NVL)",
+            flush=True,
+        )
+        print("", flush=True)
+        output_data["normal_combine"] = deepcopy(best_results[3])
+
+    if rank == 0 and local_rank == 0:
+        _write_output(args, output_data)
+
+
+def _write_output(args, output_data):
+    text = json.dumps(output_data, indent=4)
+    output_path = args.output_path
+    print(f"Write to {output_path} with {text}")
+    Path(output_path).write_text(text)
+
+
+# noinspection PyUnboundLocalVariable
+def test_loop(local_rank: int, num_local_ranks: int, args):
+    num_nodes = args.nnodes
+    rank, num_ranks, group = init_dist(local_rank, num_local_ranks, args)
+
+    num_sms = args.num_sms
+    num_qps_per_rank = num_sms // 2
+
+    buffer = deep_ep.Buffer(
+        group,
+        int(1e9),
+        int(1e9),
+        low_latency_mode=False,
+        num_qps_per_rank=num_qps_per_rank,
+    )
+    assert num_local_ranks == 8 and num_ranks > 8
+    torch.manual_seed(rank)
+
+    for i in (num_sms,):
+        test_main(
+            i,
+            local_rank,
+            num_local_ranks,
+            num_ranks,
+            num_nodes,
+            rank,
+            buffer,
+            group,
+            args,
+        )
+        if local_rank == 0:
+            print("", flush=True)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--num-sms", type=int, default=24)
+    parser.add_argument("--output-path", type=str, default="deepep_tuned.json")
+    parser.add_argument("--nnodes", type=int, default=1)
+    parser.add_argument("--node-rank", type=int, default=0)
+    parser.add_argument("--master-addr", type=str, default="127.0.0.1")
+    parser.add_argument("--master-port", type=int, default=8361)
+    args = parser.parse_args()
+    print(f"Start system with {args=}")
+
+    num_processes = 8
+    torch.multiprocessing.spawn(
+        test_loop, args=(num_processes, args), nprocs=num_processes
+    )

benchmark/kernels/deepseek/README.md

+## DeepSeek kernels benchmark
+
+
+### Prerequisites
+- You should install [DeepGemm](https://github.com/deepseek-ai/DeepGEMM) from source before run `benchmark_deepgemm_fp8_gemm.py` and `benchmark_deepgemm_fp8_group_gemm.py`.
+
+### Benchmark
+- `benchmark_deepgemm_fp8_gemm.py`
+    ```bash
+    python benchmark_deepgemm_fp8_gemm.py --run_correctness --tp_size 1
+    ```
+
+- `benchmark_deepgemm_fp8_group_gemm.py`
+    ```bash
+    python benchmark_deepgemm_fp8_group_gemm.py --run_correctness --tp_size 1
+    ```
+
+ - You can use the `--run_correctness` parameter to verify all kernels results's correctness.
+    - You can use the `--tp_size` parameter to benchmark all FP8 w8a8 block-wise matrix multiplications involved in DeepSeek V3/R1 under the current tensor parallelism (TP) setting. This benchmark compares DeepSeek's open-source [DeepGemm](https://github.com/deepseek-ai/DeepGEMM) implementation with SGLang's and VLLM Triton implementation.

benchmark/kernels/deepseek/benchmark_deepgemm_fp8_gemm.py

+from typing import Tuple
+
+import deep_gemm
+import tilelang
+import tilelang.language as T
+import torch
+import triton
+from deep_gemm import ceil_div, get_col_major_tma_aligned_tensor
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    w8a8_block_fp8_matmul as vllm_w8a8_block_fp8_matmul,
+)
+
+from sglang.srt.layers.quantization.fp8_kernel import (
+    w8a8_block_fp8_matmul_deepgemm as w8a8_block_fp8_matmul,
+)
+
+
+# Adapted from https://github.com/tile-ai/tilelang/blob/a8cfdce92795cb861c9033573534653ee040b5ed/examples/deepseek_deepgemm/example_deepgemm_fp8_2xAcc.py#L1
+def tl_gemm(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+):
+    assert in_dtype in [
+        "e4m3_float8",
+    ], "Currently only e4m3_float8 is supported"
+    assert out_dtype in [
+        "bfloat16",
+        "float16",
+    ], "Currently only bfloat16 and float16 are supported"
+
+    TILE_SIZE = (128, 128, 128)
+    block_M = TILE_SIZE[0]
+    block_N = TILE_SIZE[1]
+    block_K = TILE_SIZE[2]
+
+    A_shape = (M, K)
+    Scales_A_shape = (M, T.ceildiv(K, block_K))
+    B_shape = (N, K)
+    Scales_B_shape = (T.ceildiv(N, block_N), T.ceildiv(K, block_K))
+    A_shared_shape = (block_M, block_K)
+    B_shared_shape = (block_N, block_K)
+    C_shared_shape = (block_M, block_N)
+
+    @T.prim_func
+    def main(
+        A: T.Buffer(A_shape, in_dtype),
+        scales_a: T.Buffer(Scales_A_shape, "float32"),
+        B: T.Buffer(B_shape, in_dtype),
+        scales_b: T.Buffer(Scales_B_shape, "float32"),
+        C: T.Buffer((M, N), out_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (
+            bx,
+            by,
+        ):
+
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype)
+            B_shared = T.alloc_shared(B_shared_shape, in_dtype)
+            C_shared = T.alloc_shared(C_shared_shape, out_dtype)
+            Scale_C_shared = T.alloc_shared((block_M), "float32")
+            C_local = T.alloc_fragment(C_shared_shape, accum_dtype)
+            C_local_accum = T.alloc_fragment(C_shared_shape, accum_dtype)
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_local)
+            T.clear(C_local_accum)
+            K_iters = T.ceildiv(K, block_K)
+            for k in T.Pipelined(K_iters, num_stages=4):
+                # Load A into shared memory
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                # Load B into shared memory
+                T.copy(B[bx * block_N, k * block_K], B_shared)
+                # Load scale into shared memory
+                Scale_B = scales_b[bx, k]
+                for i in T.Parallel(block_M):
+                    Scale_C_shared[i] = scales_a[by * block_M + i, k] * Scale_B
+
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+                # Promote to enable 2xAcc
+                for i, j in T.Parallel(block_M, block_N):
+                    C_local_accum[i, j] += C_local[i, j] * Scale_C_shared[i]
+                T.clear(C_local)
+            # TMA store
+            T.copy(C_local_accum, C_shared)
+            T.copy(C_shared, C[by * block_M, bx * block_N])
+
+    return main
+
+
+def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(
+        m, n
+    ), (x_amax / 448.0).view(m, -1)
+
+
+def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros(
+        (ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device
+    )
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(
+        x_view.size(0), x_view.size(2)
+    )
+
+
+def fp8_gemm_deepgemm(
+    x_fp8: torch.Tensor,
+    x_scale: torch.Tensor,
+    y_fp8: torch.Tensor,
+    y_scale: torch.Tensor,
+    m: int,
+    n: int,
+    k: int,
+):
+    """DeepGEMM implementation of FP8 GEMM"""
+    out = torch.empty((m, n), device="cuda", dtype=torch.bfloat16)
+
+    # Run DeepGEMM kernel
+    deep_gemm.gemm_fp8_fp8_bf16_nt((x_fp8, x_scale), (y_fp8, y_scale), out)
+    return out
+
+
+def fp8_gemm_sglang(
+    x_fp8: torch.Tensor,
+    x_scale: torch.Tensor,
+    y_fp8: torch.Tensor,
+    y_scale: torch.Tensor,
+    m: int,
+    n: int,
+    k: int,
+):
+    """SGLang implementation of FP8 GEMM"""
+    block_size = [128, 128]  # Matches the block size in per_block_cast_to_fp8
+
+    # Run SGLang kernel
+    out = w8a8_block_fp8_matmul(
+        x_fp8, y_fp8, x_scale, y_scale, block_size, torch.bfloat16
+    )
+    return out
+
+
+def fp8_gemm_vllm(
+    x_fp8: torch.Tensor,
+    x_scale: torch.Tensor,
+    y_fp8: torch.Tensor,
+    y_scale: torch.Tensor,
+    m: int,
+    n: int,
+    k: int,
+):
+    """vLLM implementation of FP8 GEMM"""
+    block_size = [128, 128]  # Matches the block size in per_block_cast_to_fp8
+
+    # Run vLLM kernel
+    out = vllm_w8a8_block_fp8_matmul(
+        x_fp8, y_fp8, x_scale, y_scale, block_size, torch.bfloat16
+    )
+    return out
+
+
+def calculate_diff(m: int, n: int, k: int):
+    x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+    y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+
+    x_fp8, x_scale = per_token_cast_to_fp8(x.clone())
+    y_fp8, y_scale = per_block_cast_to_fp8(y.clone())
+    x_scale_col_major = get_col_major_tma_aligned_tensor(x_scale.clone())
+
+    out_deepgemm = fp8_gemm_deepgemm(
+        x_fp8.clone(),
+        x_scale_col_major.clone(),
+        y_fp8.clone(),
+        y_scale.clone(),
+        m,
+        n,
+        k,
+    )
+    out_sglang = fp8_gemm_sglang(
+        x_fp8.clone(), x_scale.clone(), y_fp8.clone(), y_scale.clone(), m, n, k
+    )
+
+    tilelang_func = tl_gemm(m, n, k, "e4m3_float8", "bfloat16", "float32")
+    tilelang_kernel = tilelang.compile(tilelang_func, out_idx=[-1])
+    out_tilelang = tilelang_kernel(
+        x_fp8.clone(), x_scale.clone(), y_fp8.clone(), y_scale.clone()
+    )
+
+    diff_sglang_deepgemm = torch.abs(out_deepgemm - out_sglang).mean().item()
+    diff_tilelang_deepgemm = torch.abs(out_deepgemm - out_tilelang).mean().item()
+    diff_tilelang_sglang = torch.abs(out_tilelang - out_sglang).mean().item()
+
+    print(f"Shape m={m}, n={n}, k={k}:")
+    print(f"DeepGEMM output: {out_deepgemm[0, 0:5]}")
+    print(f"SGLang output: {out_sglang[0, 0:5]}")
+    print(f"TileLang output: {out_tilelang[0, 0:5]}")
+    print(f"Mean absolute difference (SGLang-DeepGEMM): {diff_sglang_deepgemm}")
+    print(f"Mean absolute difference (TileLang-DeepGEMM): {diff_tilelang_deepgemm}")
+    print(f"Mean absolute difference (TileLang-SGLang): {diff_tilelang_sglang}")
+
+    sglang_deepgemm_match = torch.allclose(
+        out_deepgemm, out_sglang, atol=1e-2, rtol=1e-2
+    )
+    tilelang_deepgemm_match = torch.allclose(
+        out_deepgemm, out_tilelang, atol=1e-2, rtol=1e-2
+    )
+    tilelang_sglang_match = torch.allclose(
+        out_tilelang, out_sglang, atol=1e-2, rtol=1e-2
+    )
+
+    if sglang_deepgemm_match and tilelang_deepgemm_match and tilelang_sglang_match:
+        print("✅ All implementations match\n")
+    else:
+        print("❌ Some implementations differ:")
+        print(f"  - SGLang vs DeepGEMM: {'✅' if sglang_deepgemm_match else '❌'}")
+        print(f"  - TileLang vs DeepGEMM: {'✅' if tilelang_deepgemm_match else '❌'}")
+        print(f"  - TileLang vs SGLang: {'✅' if tilelang_sglang_match else '❌'}\n")
+
+
+def get_weight_shapes(tp_size):
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+
+    return weight_shapes
+
+
+def create_benchmark_configs(tp_size):
+    configs = []
+    weight_shapes = get_weight_shapes(tp_size)
+    batch_sizes = [8, 16, 32, 64, 128, 256, 1024, 2048, 4096]
+
+    for n, k in weight_shapes:
+        for m in batch_sizes:
+            configs.append((m, n, k, tp_size))
+
+    return configs
+
+
+def get_benchmark(tp_size):
+    all_configs = create_benchmark_configs(tp_size)
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["m", "n", "k", "tp_size"],
+            x_vals=[list(config) for config in all_configs],
+            line_arg="provider",
+            line_vals=["deepgemm", "sglang", "tilelang"],
+            line_names=["DeepGEMM", "SGLang", "TileLang"],
+            styles=[("blue", "-"), ("red", "-"), ("green", "-")],
+            ylabel="ms",
+            plot_name=f"fp8-gemm-performance-comparison-tp{tp_size}",
+            args={},
+        )
+    )
+    def benchmark(m, n, k, tp_size, provider):
+        print(f"Shape (m={m}, n={n}, k={k}, tp={tp_size}), Provider: {provider}")
+        x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+        y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+
+        # Preprocess data before benchmarking
+        x_fp8, x_scale = per_token_cast_to_fp8(x)
+        y_fp8, y_scale = per_block_cast_to_fp8(y)
+        x_scale_col_major = get_col_major_tma_aligned_tensor(x_scale.clone())
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "deepgemm":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_deepgemm(
+                    x_fp8.clone(),
+                    x_scale_col_major.clone(),
+                    y_fp8.clone(),
+                    y_scale.clone(),
+                    m,
+                    n,
+                    k,
+                ),
+                quantiles=quantiles,
+            )
+        elif provider == "sglang":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_sglang(
+                    x_fp8.clone(),
+                    x_scale.clone(),
+                    y_fp8.clone(),
+                    y_scale.clone(),
+                    m,
+                    n,
+                    k,
+                ),
+                quantiles=quantiles,
+            )
+        else:  # tilelang
+            tilelang_func = tl_gemm(m, n, k, "e4m3_float8", "bfloat16", "float32")
+            tilelang_kernel = tilelang.compile(tilelang_func, out_idx=[-1])
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: tilelang_kernel(
+                    x_fp8.clone(),
+                    x_scale.clone(),
+                    y_fp8.clone(),
+                    y_scale.clone(),
+                ),
+                quantiles=quantiles,
+            )
+
+        # Calculate TFLOPS
+        flops = 2 * m * n * k  # multiply-adds
+        tflops = flops / (ms * 1e-3) / 1e12
+
+        # Print shape-specific results with TFLOPS
+        print(f"Time: {ms*1000:.2f} ms, TFLOPS: {tflops:.2f}")
+        return ms * 1000, max_ms * 1000, min_ms * 1000  # convert to ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/fp8_gemm/",
+        help="Path to save fp8 gemm benchmark results",
+    )
+    parser.add_argument(
+        "--run_correctness",
+        action="store_true",
+        default=True,
+        help="Whether to run correctness test",
+    )
+    parser.add_argument(
+        "--tp_size",
+        type=int,
+        default=1,
+        help="Tensor parallelism size to benchmark (default: 1)",
+    )
+    args = parser.parse_args()
+
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+    torch.cuda.manual_seed(0)
+
+    # Enable TF32, adapted from https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_core.py#L148
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    # Run correctness tests on a few examples
+    if args.run_correctness:
+        print("Running correctness tests...")
+        calculate_diff(64, 512, 7168)  # Small test
+        calculate_diff(64, 7168, 16384)  # Medium test
+        calculate_diff(64, 18432, 7168)  # Large test
+
+    # Get the benchmark function with the specified tp_size
+    benchmark = get_benchmark(args.tp_size)
+
+    print(f"Running performance benchmark for TP size = {args.tp_size}...")
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmark/kernels/deepseek/benchmark_deepgemm_fp8_group_gemm.py

+from typing import Tuple
+
+import deep_gemm
+import torch
+import triton
+import triton.language as tl
+from deep_gemm import calc_diff, get_col_major_tma_aligned_tensor
+
+# Import shared functionality from the regular GEMM benchmark
+from sglang.benchmark.kernels.deepseek.benchmark_deepgemm_fp8_gemm import (
+    per_block_cast_to_fp8,
+    per_token_cast_to_fp8,
+)
+
+
+def construct_grouped_and_flat_fp8(
+    x: torch.Tensor, y: torch.Tensor, num_groups: int, is_masked: bool
+) -> Tuple[
+    Tuple[torch.Tensor, torch.Tensor],  # grouped x_fp8
+    Tuple[torch.Tensor, torch.Tensor],  # grouped y_fp8
+    Tuple[torch.Tensor, torch.Tensor],  # flat x_fp8
+    Tuple[torch.Tensor, torch.Tensor],  # flat y_fp8
+    torch.Tensor,  # output
+    torch.Tensor,  # reference output
+]:
+    # Verify input shapes
+    m, k = x.shape
+    n, k_y = y.shape
+    assert k == k_y, f"Incompatible shapes: x({m}, {k}), y({n}, {k_y})"
+    assert m % num_groups == 0, f"m({m}) must be divisible by num_groups({num_groups})"
+    assert m % 4 == 0, f"TMA alignment error: {m}"
+
+    # Reshape inputs for grouped processing
+    m_per_group = m // num_groups
+    x_grouped = x.view(num_groups, m_per_group, k)
+    y_grouped = y.unsqueeze(0).expand(num_groups, n, k)
+
+    # Initialize output tensors
+    out = torch.empty((num_groups, m_per_group, n), device="cuda", dtype=torch.bfloat16)
+    ref_out = torch.einsum("gmk,gnk->gmn", x_grouped, y_grouped)
+
+    # Quantize grouped tensors
+    x_fp8_grouped = (
+        torch.empty_like(x_grouped, dtype=torch.float8_e4m3fn),
+        torch.empty(
+            (num_groups, m_per_group, k // 128), device="cuda", dtype=torch.float
+        ),
+    )
+    y_fp8_grouped = (
+        torch.empty_like(y_grouped, dtype=torch.float8_e4m3fn),
+        torch.empty(
+            (num_groups, (n + 127) // 128, k // 128), device="cuda", dtype=torch.float
+        ),
+    )
+    for i in range(num_groups):
+        x_fp8_grouped[0][i], x_fp8_grouped[1][i] = per_token_cast_to_fp8(x_grouped[i])
+        y_fp8_grouped[0][i], y_fp8_grouped[1][i] = per_block_cast_to_fp8(y_grouped[i])
+
+    # Quantize flat tensors
+    x_fp8_flat = per_token_cast_to_fp8(x)
+    y_fp8_flat = per_block_cast_to_fp8(y)
+
+    # For non-masked input, merge the group and M dims in output
+    if not is_masked:
+        x_fp8_grouped = (
+            x_fp8_grouped[0].view(-1, k),
+            per_token_cast_to_fp8(x_grouped.view(-1, k))[1],
+        )
+        out, ref_out = out.view(-1, n), ref_out.view(-1, n)
+
+    # Transpose earlier for testing
+    x_fp8_grouped = (
+        x_fp8_grouped[0],
+        get_col_major_tma_aligned_tensor(x_fp8_grouped[1]),
+    )
+    x_fp8_flat = (x_fp8_flat[0], get_col_major_tma_aligned_tensor(x_fp8_flat[1]))
+
+    return x_fp8_grouped, y_fp8_grouped, x_fp8_flat, y_fp8_flat, out, ref_out
+
+
+# Since we don't have a group gemm kernel in SGLang/vLLM, we implemented a
+# custom kernel based on the Triton tutorial.
+# https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html
+@triton.jit
+def fp8_gemm_group_triton_kernel(
+    # Pointers to matrices
+    a_ptr,
+    b_ptr,
+    c_ptr,
+    # Pointers to scaling factors
+    a_scale_ptr,
+    b_scale_ptr,
+    # Matrix dimensions
+    M,
+    N,
+    K,
+    # The stride variables represent how much to increase the ptr by when moving by 1
+    # element in a particular dimension.
+    stride_am,
+    stride_ak,
+    stride_bk,
+    stride_bn,
+    stride_cm,
+    stride_cn,
+    # Strides for scaling factors
+    stride_a_scale_m,
+    stride_a_scale_k,
+    stride_b_scale_n,
+    stride_b_scale_k,
+    # Meta-parameters
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    GROUP_SIZE_M: tl.constexpr,
+):
+    """Kernel for computing the matmul C = A x B with FP8 inputs and scaling factors.
+    A has shape (M, K), B has shape (K, N) and C has shape (M, N)
+
+    Note: Block sizes must be multiples of 32 for optimal TMA performance.
+    """
+    # Map program ids to the block of C it should compute
+    pid_group = tl.program_id(axis=0)  # Group ID
+    pid_n = tl.program_id(axis=1)  # N dimension ID
+
+    # Compute the M block ID within this group
+    group_size_m = min(M - pid_group * GROUP_SIZE_M, GROUP_SIZE_M)
+    pid_m_within_group = tl.program_id(axis=2) % group_size_m
+    pid_m = pid_group * GROUP_SIZE_M + pid_m_within_group
+
+    # Create pointers for the first blocks of A and B
+    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
+    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
+    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
+
+    # Initialize accumulator
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+    # Main loop
+    for k_block in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        k_offset = k_block * BLOCK_SIZE_K
+
+        # Load the next block of A and B, with masks
+        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k_offset, other=0.0)
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k_offset, other=0.0)
+
+        # Calculate indices for scaling factors for this K block
+        a_scale_ptrs = a_scale_ptr + (
+            offs_am * stride_a_scale_m + k_block * stride_a_scale_k
+        )
+        b_scale_ptrs = b_scale_ptr + (
+            pid_n * stride_b_scale_n + k_block * stride_b_scale_k
+        )
+
+        # Perform matrix multiplication in FP8
+        res = tl.dot(a, b)
+
+        # Load scaling factors for the current block
+        a_scale = tl.load(a_scale_ptrs)[:, None]  # [BLOCK_SIZE_M, 1]
+        b_scale = tl.load(b_scale_ptrs)
+
+        # Apply scaling factors to the accumulated result
+        accumulator += res * a_scale * b_scale
+
+        # Advance pointers
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    # Convert to bfloat16 for output
+    c = accumulator.to(tl.bfloat16)
+
+    # Write back the result
+    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, c, mask=c_mask)
+
+
+def fp8_gemm_group_triton(a_tuple, b_tuple, c, num_groups):
+    """
+    Perform matrix multiplication with FP8 inputs and proper scaling.
+
+    Args:
+        a_tuple: Tuple of (quantized_tensor, scale_factors) for input A
+        b_tuple: Tuple of (quantized_tensor, scale_factors) for input B
+        c: Output tensor in BF16 format
+        num_groups: Number of groups for grouped GEMM
+
+    Returns:
+        Result tensor in BF16 format
+    """
+    # Unpack the tuples
+    a, a_scale = a_tuple
+    b, b_scale = b_tuple
+
+    M, K = a.shape
+    _, N = b.shape
+
+    # Configure block sizes - must be multiples of 32 for TMA alignment
+    BLOCK_SIZE_M = 128
+    BLOCK_SIZE_N = 128
+    BLOCK_SIZE_K = 128
+
+    # Calculate grid dimensions
+    num_pid_m = triton.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = triton.cdiv(N, BLOCK_SIZE_N)
+    num_groups_grid = triton.cdiv(num_pid_m, num_groups)
+
+    # 3D grid launch - (group, n_blocks, m_blocks_per_group)
+    grid = (num_groups_grid, num_pid_n, min(num_groups, num_pid_m))
+
+    fp8_gemm_group_triton_kernel[grid](
+        a,
+        b,
+        c,
+        a_scale,
+        b_scale,
+        M,
+        N,
+        K,
+        a.stride(0),
+        a.stride(1),
+        b.stride(0),
+        b.stride(1),
+        c.stride(0),
+        c.stride(1),
+        a_scale.stride(0),
+        1,  # Stride in the K dimension may be 1
+        b_scale.stride(0),
+        1 if b_scale.dim() > 1 else 0,
+        BLOCK_SIZE_M=BLOCK_SIZE_M,
+        BLOCK_SIZE_N=BLOCK_SIZE_N,
+        BLOCK_SIZE_K=BLOCK_SIZE_K,
+        GROUP_SIZE_M=num_groups,
+    )
+
+    return c
+
+
+def fp8_gemm_group_deepgemm(x_fp8_grouped, y_fp8_grouped, out, m_indices):
+    deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
+        x_fp8_grouped,
+        y_fp8_grouped,
+        out,
+        m_indices,
+    )
+    return out
+
+
+def calculate_diff(m: int, n: int, k: int, num_groups: int):
+    print(f"Shape (m={m}, n={n}, k={k}")
+    x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+    y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+    x_fp8_grouped, y_fp8_grouped, x_fp8_flat, y_fp8_flat, out, out_torch = (
+        construct_grouped_and_flat_fp8(x, y, num_groups, is_masked=False)
+    )
+    m_per_group = m // num_groups
+    out_deepgemm = out.clone()
+    m_indices = torch.arange(0, num_groups, device="cuda", dtype=torch.int)
+    m_indices = (
+        m_indices.unsqueeze(-1).expand(num_groups, m_per_group).contiguous().view(-1)
+    )
+
+    fp8_gemm_group_deepgemm(
+        x_fp8_grouped,
+        y_fp8_grouped,
+        out_deepgemm,
+        m_indices,
+    )
+    torch.cuda.synchronize()
+
+    # Prepare inputs for Triton
+    a, a_scale = x_fp8_flat
+    b, b_scale = y_fp8_flat
+    b = b.T.contiguous()
+    # Ensure scales are in the right format and contiguous
+    a_scale, b_scale = a_scale.contiguous(), b_scale.contiguous()
+    M, _ = a.shape
+    _, N = b.shape
+    c = torch.empty((M, N), device=a.device, dtype=torch.bfloat16)
+    out_triton = fp8_gemm_group_triton((a, a_scale), (b, b_scale), c, num_groups)
+    torch.cuda.synchronize()
+
+    diff_torch_deepgemm = torch.abs(out_torch - out_deepgemm).mean().item()
+    diff_torch_triton = torch.abs(out_torch - out_triton).mean().item()
+    diff_deepgemm_triton = torch.abs(out_deepgemm - out_triton).mean().item()
+
+    print(f"Shape m={m}, n={n}, k={k}:")
+    print(f"Torch output: {out_torch[0, 0:5]}")
+    print(f"DeepGEMM output: {out_deepgemm[0, 0:5]}")
+    print(f"Triton output: {out_triton[0, 0:5]}")
+    print(f"Mean absolute difference (Torch-DeepGEMM): {diff_torch_deepgemm}")
+    print(f"Mean absolute difference (Torch-Triton): {diff_torch_triton}")
+    print(f"Mean absolute difference (DeepGEMM-Triton): {diff_deepgemm_triton}")
+
+    deepgemm_torch_diff = calc_diff(out_deepgemm, out_torch)
+    triton_torch_diff = calc_diff(out_triton, out_torch)
+    deepgemm_triton_diff = calc_diff(out_deepgemm, out_triton)
+
+    DIFF_THRESHOLD = 0.001
+    all_match = (
+        deepgemm_torch_diff < DIFF_THRESHOLD
+        and triton_torch_diff < DIFF_THRESHOLD
+        and deepgemm_triton_diff < DIFF_THRESHOLD
+    )
+    if all_match:
+        print("✅ All implementations match\n")
+    else:
+        print("❌ Some implementations differ:")
+        print(
+            f"  - Torch vs DeepGEMM: {'✅' if deepgemm_torch_diff < DIFF_THRESHOLD else '❌'}"
+            f"  - Torch vs Triton: {'✅' if triton_torch_diff < DIFF_THRESHOLD else '❌'}"
+            f"  - DeepGEMM vs Triton: {'✅' if deepgemm_triton_diff < DIFF_THRESHOLD else '❌'}"
+        )
+
+
+def get_weight_shapes(tp_size):
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+
+    return weight_shapes
+
+
+def create_benchmark_configs(tp_size):
+    configs = []
+    weight_shapes = get_weight_shapes(tp_size)
+    batch_sizes = [2048, 4096]
+    group_sizes = [4, 8]
+    for n, k in weight_shapes:
+        for m in batch_sizes:
+            for num_groups in group_sizes:
+                configs.append((m, n, k, num_groups, tp_size))
+
+    return configs
+
+
+def get_benchmark(tp_size):
+    all_configs = create_benchmark_configs(tp_size)
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["m", "n", "k", "num_groups", "tp_size"],
+            x_vals=[config for config in all_configs],
+            line_arg="provider",
+            line_vals=["deepgemm", "triton"],
+            line_names=["DeepGEMM", "Triton"],
+            styles=[("blue", "-"), ("red", "-")],
+            ylabel="ms",
+            plot_name=f"fp8-group-gemm-performance-comparison-tp{tp_size}",
+            args={},
+        )
+    )
+    def benchmark(m, n, k, num_groups, tp_size, provider):
+        print(
+            f"Shape (m={m}, n={n}, k={k}, tp={tp_size}, num_groups={num_groups}, Provider: {provider}"
+        )
+        x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+        y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+        x_fp8_grouped, y_fp8_grouped, x_fp8_flat, y_fp8_flat, out, out_torch = (
+            construct_grouped_and_flat_fp8(x, y, num_groups, is_masked=False)
+        )
+        m_per_group = m // num_groups
+        m_indices = torch.arange(0, num_groups, device="cuda", dtype=torch.int)
+        m_indices = (
+            m_indices.unsqueeze(-1)
+            .expand(num_groups, m_per_group)
+            .contiguous()
+            .view(-1)
+        )
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "deepgemm":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_group_deepgemm(
+                    x_fp8_grouped,
+                    y_fp8_grouped,
+                    out,
+                    m_indices,
+                ),
+                quantiles=quantiles,
+            )
+        elif provider == "triton":
+            # Prepare inputs for Triton
+            # We did it outside of the lambda function to make it fair comparison like deepgemm
+            a, a_scale = x_fp8_flat
+            b, b_scale = y_fp8_flat
+            b = b.T.contiguous()
+            # Ensure scales are in the right format and contiguous
+            a_scale, b_scale = a_scale.contiguous(), b_scale.contiguous()
+            M, _ = a.shape
+            _, N = b.shape
+            c = torch.empty((M, N), device=a.device, dtype=torch.bfloat16)
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_group_triton(
+                    (a, a_scale),
+                    (b, b_scale),
+                    c,
+                    num_groups,
+                ),
+                quantiles=quantiles,
+            )
+
+        # Calculate TFLOPS
+        flops = 2 * m * n * k  # multiply-adds
+        tflops = flops / (ms * 1e-3) / 1e12
+
+        print(f"Time: {ms*1000:.2f} ms, TFLOPS: {tflops:.2f}")
+        return ms * 1000, max_ms * 1000, min_ms * 1000  # convert to ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/fp8_group_gemm/",
+        help="Path to save deepgemm fp8 group gemm benchmark results",
+    )
+    parser.add_argument(
+        "--run_correctness",
+        action="store_true",
+        help="Whether to run correctness test",
+    )
+    parser.add_argument(
+        "--tp_size",
+        type=int,
+        default=1,
+        help="Tensor parallelism size to benchmark (default: 1)",
+    )
+    args = parser.parse_args()
+
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+    torch.cuda.manual_seed(0)
+
+    # Enable TF32, adapted from https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_core.py#L148
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    # Run correctness tests on a few examples
+    if args.run_correctness:
+        print("Running correctness tests...")
+        calculate_diff(8192, 7168, 4096, 4)
+        calculate_diff(8192, 2048, 7168, 4)
+        calculate_diff(4096, 7168, 4096, 8)
+        calculate_diff(4096, 2048, 7168, 8)
+        calculate_diff(4096, 576, 7168, 8)
+
+    # Get the benchmark function with the specified tp_size
+    benchmark = get_benchmark(args.tp_size)
+
+    print(f"Running performance benchmark for TP size = {args.tp_size}...")
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmark/kernels/fbgemm/README.md

+## Benchmark FBGEMM Grouped GEMM
+
+Benchmark FBGEMM Grouped GEMM in both Triton and CUDA version and SGLang Triton Grouped GEMM, it will be used to compare the bandwidth of different implementations.
+
+### Requirements
+
+```shell
+pip install fbgemm-gpu-genai
+```
+
+### Usage
+
+```bash
+python3 benchmark/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8
+```
+
+For example, in H200, the Qwen2-57B-A14B-Instruct TP4 fp8w8a8 grouped gemm bandwidth result is as follows:
+
+```shell
+grouped-gemm-performance:
+   batch_size  FBGEMM Triton Grouped GEMM FP8  FBGEMM CUTLASS F8F8BF16 Rowwise  SGLang Grouped GEMM FP8
+0       256.0                     3704.841339                      3042.626402              2254.725030
+1       512.0                     3691.426346                      3029.065684              2269.504543
+2      1024.0                     3653.938629                      2258.471467              2358.319020
+3      2048.0                     3596.644313                      2271.611904              2476.895397
+4      4096.0                     3468.496435                      2231.283986              2179.473910
+```
+
+The theoretical peak bandwidth of H200 is 4.8 TB/s. Taking batch_size 256 as an example, the bandwidth of FBGEMM Triton Grouped GEMM FP8 is 3704.841339 GB/s, the bandwidth of FBGEMM CUTLASS F8F8BF16 Rowwise is 3042.626402 GB/s, and the bandwidth of SGLang Grouped GEMM FP8 is 2254.725030 GB/s. Therefore, FBGEMM Triton Grouped GEMM FP8 achieves 77.9% of H200's theoretical peak bandwidth, FBGEMM CUTLASS F8F8BF16 Rowwise achieves 63.4% of H200's theoretical peak bandwidth, and SGLang Grouped GEMM FP8 achieves 46.9% of H200's theoretical peak bandwidth.

benchmark/kernels/fbgemm/benchmark_fbgemm_grouped_gemm.py

+# python3 benchmark/fbgemm/benchmark_fbgemm_grouped_gemm.py --model Qwen/Qwen2-57B-A14B-Instruct --tp-size 4 --use-fp8-w8a8
+import argparse
+
+import torch
+import triton
+from fbgemm_gpu.experimental.gemm.triton_gemm.fp8_gemm import (
+    quantize_fp8_row,
+    triton_quantize_fp8_row,
+)
+from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import (
+    grouped_gemm as fbgemm_grouped_gemm,
+)
+from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import (
+    grouped_gemm_fp8_rowwise as fbgemm_grouped_gemm_fp8_rowwise,
+)
+from transformers import AutoConfig
+
+from sglang.srt.layers.moe.ep_moe.kernels import (
+    grouped_gemm_triton as sglang_grouped_gemm,
+)
+
+
+def get_model_config(model_name: str, tp_size: int):
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+    if config.architectures[0] == "DbrxForCausalLM":
+        num_groups = config.ffn_config.moe_num_experts
+        intermediate_size = config.ffn_config.ffn_hidden_size
+    elif config.architectures[0] == "JambaForCausalLM":
+        num_groups = config.num_experts
+        intermediate_size = config.intermediate_size
+    elif config.architectures[0] == "Qwen2MoeForCausalLM":
+        num_groups = config.num_experts
+        intermediate_size = config.moe_intermediate_size
+    elif config.architectures[0] == "Qwen3MoeForCausalLM":
+        num_groups = config.num_experts
+        intermediate_size = config.moe_intermediate_size
+    elif config.architectures[0] in [
+        "DeepseekV2ForCausalLM",
+        "DeepseekV3ForCausalLM",
+    ]:
+        num_groups = config.n_routed_experts
+        intermediate_size = config.moe_intermediate_size
+    elif config.architectures[0] == "Llama4ForConditionalGeneration":
+        num_groups = config.text_config.num_local_experts
+        intermediate_size = config.text_config.intermediate_size
+    elif config.architectures[0] in [
+        "Grok1ForCausalLM",
+        "Grok1ImgGen",
+        "Grok1AForCausalLM",
+    ]:
+        num_groups = config.num_local_experts
+        intermediate_size = config.moe_intermediate_size
+    else:
+        num_groups = config.num_local_experts
+        intermediate_size = config.intermediate_size
+
+    shape_configs = {
+        "num_groups": num_groups,
+        "hidden_size": config.hidden_size,
+        "intermediate_size": intermediate_size,
+        "dtype": config.torch_dtype,
+    }
+    print(f"{shape_configs=}")
+    return shape_configs
+
+
+def create_test_data(batch_size, num_groups, hidden_size, intermediate_size):
+    torch.manual_seed(42)
+
+    tokens_per_group = batch_size // num_groups
+    m_sizes = torch.full(
+        (num_groups,), tokens_per_group, dtype=torch.int32, device="cuda"
+    )
+
+    x = torch.randn(batch_size, hidden_size, dtype=torch.bfloat16, device="cuda")
+
+    base_weights = torch.randn(
+        num_groups, intermediate_size, hidden_size, dtype=torch.bfloat16, device="cuda"
+    )
+
+    w_fbgemm = base_weights.reshape(num_groups * intermediate_size, hidden_size)
+    w_sglang = base_weights
+
+    c_fbgemm = torch.empty(
+        batch_size, intermediate_size, dtype=torch.bfloat16, device="cuda"
+    )
+    c_sglang = torch.empty(
+        batch_size, intermediate_size, dtype=torch.bfloat16, device="cuda"
+    )
+
+    seg_indptr = torch.zeros(num_groups + 1, dtype=torch.int32, device="cuda")
+    for i in range(1, num_groups + 1):
+        seg_indptr[i] = seg_indptr[i - 1] + tokens_per_group
+
+    weight_indices = torch.arange(num_groups, dtype=torch.int32, device="cuda")
+
+    return (
+        x,
+        w_fbgemm,
+        w_sglang,
+        c_fbgemm,
+        c_sglang,
+        m_sizes,
+        seg_indptr,
+        weight_indices,
+    )
+
+
+def create_fp8_test_data(
+    batch_size, num_groups, hidden_size, intermediate_size, backend="triton"
+):
+    """
+    Create test data for FP8 grouped GEMM operations.
+
+    Args:
+        batch_size: Total batch size
+        num_groups: Number of groups
+        hidden_size: Hidden dimension size
+        intermediate_size: Intermediate dimension size
+        backend: "triton" for Triton GEMM, "cutlass" for CUTLASS GEMM
+
+    Returns:
+        For triton: (x_fp8, w_fp8, m_sizes, x_scale, w_scale)
+        For cutlass: (x, wq, w_scale, m_sizes)
+    """
+    torch.manual_seed(42)
+
+    tokens_per_group = batch_size // num_groups
+
+    # Create weight matrices for each group
+    w_list = []
+    for _ in range(num_groups):
+        w = torch.randn(
+            intermediate_size, hidden_size, dtype=torch.float16, device="cuda"
+        )
+        w_list.append(w)
+
+    # Quantize weights using quantize_fp8_row for each group
+    wq_list, w_scale_list = zip(*[quantize_fp8_row(w) for w in w_list])
+
+    if backend == "triton":
+        # Triton format: concatenated weights
+        w_fp8 = torch.concat(wq_list, dim=0).contiguous()
+        w_scale = torch.concat(w_scale_list, dim=0).contiguous()
+
+        # Create m_sizes as int32 for triton
+        m_sizes = torch.full(
+            (num_groups,), tokens_per_group, dtype=torch.int32, device="cuda"
+        )
+
+        # Create and quantize input
+        x_fp16 = torch.randn(
+            batch_size, hidden_size, dtype=torch.float16, device="cuda"
+        )
+        x_fp8, x_scale = triton_quantize_fp8_row(x_fp16)
+        x_scale = x_scale.view(batch_size, -1)
+
+        return x_fp8, w_fp8, m_sizes, x_scale, w_scale
+
+    elif backend == "cutlass":
+        # CUTLASS format: stacked weights
+        wq = torch.stack(wq_list, dim=0).contiguous()
+        w_scale = torch.stack(w_scale_list, dim=0).contiguous()
+
+        # Create m_sizes as int64 for cutlass
+        m_values = [tokens_per_group] * num_groups
+        m_sizes = torch.tensor(m_values).to(dtype=torch.int64, device="cuda")
+
+        # Create input data - separate for each group then concat
+        x_list = []
+        for _ in range(num_groups):
+            x = torch.randn(
+                tokens_per_group, hidden_size, dtype=torch.float16, device="cuda"
+            )
+            x_list.append(x)
+
+        # Concatenate inputs into single tensor
+        x = torch.concat(x_list, dim=0).contiguous()
+
+        return x, wq, w_scale, m_sizes
+
+    else:
+        raise ValueError(f"Unsupported backend: {backend}")
+
+
+def calculate_memory_bandwidth(m_sizes, hidden_size, intermediate_size, dtype):
+    """
+    Calculate memory bandwidth based on accessed expert weights.
+
+    Args:
+        m_sizes: Tensor containing batch sizes for each group
+        hidden_size: Hidden dimension size
+        intermediate_size: Intermediate dimension size
+        dtype: Data type of weights
+
+    Returns:
+        Memory size in bytes for accessed expert weights
+    """
+    # Count non-zero groups (active experts)
+    if hasattr(m_sizes, "cpu"):
+        active_experts = torch.count_nonzero(m_sizes).item()
+    else:
+        active_experts = sum(1 for m in m_sizes if m > 0)
+
+    # Calculate bytes per element based on dtype
+    if dtype in [torch.float16, torch.bfloat16]:
+        bytes_per_element = 2
+    elif dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+        bytes_per_element = 1
+    elif dtype == torch.float32:
+        bytes_per_element = 4
+    else:
+        # Default to 2 bytes for unknown dtypes
+        bytes_per_element = 2
+
+    # Memory per expert weight matrix
+    memory_per_expert = hidden_size * intermediate_size * bytes_per_element
+
+    # Total memory for active experts
+    total_memory_bytes = active_experts * memory_per_expert
+
+    return total_memory_bytes
+
+
+def get_benchmark_config(use_fp8_w8a8=False):
+    if use_fp8_w8a8:
+        return {
+            "line_vals": [
+                "fbgemm_triton_grouped_gemm_fp8",
+                "fbgemm_cutlass_f8f8bf16_rowwise",
+                "sglang_grouped_gemm",
+            ],
+            "line_names": [
+                "FBGEMM Triton Grouped GEMM FP8",
+                "FBGEMM CUTLASS F8F8BF16 Rowwise",
+                "SGLang Grouped GEMM FP8",
+            ],
+            "styles": [("blue", "-"), ("orange", "-"), ("red", "-")],
+        }
+    else:
+        return {
+            "line_vals": ["fbgemm_triton_grouped_gemm", "sglang_grouped_gemm"],
+            "line_names": [
+                "FBGEMM Triton Grouped GEMM BF16",
+                "SGLang Grouped GEMM BF16",
+            ],
+            "styles": [("blue", "-"), ("green", "-")],
+        }
+
+
+def run_benchmark(
+    model_config, use_fp8_w8a8=False, save_path="./benchmark_grouped_gemm/"
+):
+    config = get_benchmark_config(use_fp8_w8a8)
+
+    benchmark_config = triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[256, 512, 1024, 2048, 4096],
+        line_arg="provider",
+        line_vals=config["line_vals"],
+        line_names=config["line_names"],
+        styles=config["styles"],
+        ylabel="Bandwidth (GB/s)",
+        plot_name="grouped-gemm-performance",
+        args={},
+    )
+
+    @triton.testing.perf_report(benchmark_config)
+    def dynamic_benchmark(batch_size, provider, model_config, use_fp8_w8a8=False):
+        print(f"Benchmarking {provider} with batch_size={batch_size}")
+        torch.cuda.manual_seed_all(0)
+
+        num_groups = model_config["num_groups"]
+        hidden_size = model_config["hidden_size"]
+        intermediate_size = model_config["intermediate_size"]
+
+        if provider == "fbgemm_triton_grouped_gemm_fp8":
+            try:
+                test_data = create_fp8_test_data(
+                    batch_size,
+                    num_groups,
+                    hidden_size,
+                    intermediate_size,
+                    backend="triton",
+                )
+                x_fp8, w_fp8, m_sizes, x_scale, w_scale = test_data
+
+                # Calculate memory bandwidth
+                memory_bytes = calculate_memory_bandwidth(
+                    m_sizes, hidden_size, intermediate_size, torch.float8_e4m3fn
+                )
+
+                def run_func():
+                    return fbgemm_grouped_gemm_fp8_rowwise(
+                        x_fp8, w_fp8, m_sizes, x_scale, w_scale, use_fast_accum=True
+                    )
+
+            except Exception as e:
+                print(f"FP8 not supported, skipping: {e}")
+                return float("inf"), float("inf"), float("inf")
+
+        elif provider == "fbgemm_cutlass_f8f8bf16_rowwise":
+            try:
+                test_data = create_fp8_test_data(
+                    batch_size,
+                    num_groups,
+                    hidden_size,
+                    intermediate_size,
+                    backend="cutlass",
+                )
+                x, wq, w_scale, m_sizes = test_data
+
+                # Calculate memory bandwidth
+                memory_bytes = calculate_memory_bandwidth(
+                    m_sizes, hidden_size, intermediate_size, torch.float8_e4m3fn
+                )
+
+                # Quantize input using triton_quantize_fp8_row
+                xq, x_scale = triton_quantize_fp8_row(x)
+                x_scale = x_scale.view(batch_size, -1)
+
+                def run_func():
+                    return torch.ops.fbgemm.f8f8bf16_rowwise_grouped_stacked(
+                        xq, wq, x_scale, w_scale, m_sizes
+                    )
+
+            except Exception as e:
+                print(
+                    f"CUTLASS f8f8bf16_rowwise_grouped_stacked not supported, "
+                    f"skipping: {e}"
+                )
+                return float("inf"), float("inf"), float("inf")
+        else:
+            test_data = create_test_data(
+                batch_size, num_groups, hidden_size, intermediate_size
+            )
+            (
+                x,
+                w_fbgemm,
+                w_sglang,
+                c_fbgemm,
+                c_sglang,
+                m_sizes,
+                seg_indptr,
+                weight_indices,
+            ) = test_data
+
+            # Calculate memory bandwidth for BF16 operations
+            memory_bytes = calculate_memory_bandwidth(
+                m_sizes, hidden_size, intermediate_size, torch.bfloat16
+            )
+
+            if provider == "fbgemm_triton_grouped_gemm":
+
+                def run_func():
+                    return fbgemm_grouped_gemm(
+                        x, w_fbgemm, m_sizes, use_fast_accum=True
+                    )
+
+            else:
+
+                def run_func():
+                    return sglang_grouped_gemm(
+                        x,
+                        w_sglang,
+                        c_sglang,
+                        num_groups,
+                        weight_column_major=True,
+                        seg_indptr=seg_indptr,
+                        weight_indices=weight_indices,
+                        c_dtype=c_sglang.dtype,
+                    )
+
+        for _ in range(10):
+            try:
+                run_func()
+            except Exception as e:
+                print(f"Error during warmup for {provider}: {e}")
+                return float("inf"), float("inf"), float("inf")
+
+        torch.cuda.synchronize()
+
+        try:
+            quantiles = [0.5, 0.2, 0.8]
+            ms, min_ms, max_ms = triton.testing.do_bench(run_func, quantiles=quantiles)
+
+            # Convert time (ms) to bandwidth (GB/s)
+            # Bandwidth = Memory (bytes) / Time (seconds)
+            # Convert ms to seconds and bytes to GB (1e9)
+            gb_per_s = (memory_bytes / 1e9) / (ms / 1000)
+            # min bandwidth = max time, max bandwidth = min time
+            min_gb_per_s = (memory_bytes / 1e9) / (max_ms / 1000)
+            max_gb_per_s = (memory_bytes / 1e9) / (min_ms / 1000)
+
+            return gb_per_s, min_gb_per_s, max_gb_per_s
+        except Exception as e:
+            print(f"Error during benchmarking for {provider}: {e}")
+            return 0.0, 0.0, 0.0
+
+    dynamic_benchmark.run(
+        show_plots=True,
+        print_data=True,
+        save_path=save_path,
+        model_config=model_config,
+        use_fp8_w8a8=use_fp8_w8a8,
+    )
+
+
+def verify_correctness(model_config):
+    print("Verifying correctness...")
+    batch_size = 128
+    num_groups = model_config["num_groups"]
+    hidden_size = model_config["hidden_size"]
+    intermediate_size = model_config["intermediate_size"]
+
+    test_data = create_test_data(batch_size, num_groups, hidden_size, intermediate_size)
+    (
+        x,
+        w_fbgemm,
+        w_sglang,
+        c_fbgemm,
+        c_sglang,
+        m_sizes,
+        seg_indptr,
+        weight_indices,
+    ) = test_data
+
+    result_fbgemm = fbgemm_grouped_gemm(x, w_fbgemm, m_sizes, use_fast_accum=True)
+
+    result_sglang = sglang_grouped_gemm(
+        x,
+        w_sglang,
+        c_sglang,
+        num_groups,
+        weight_column_major=True,
+        seg_indptr=seg_indptr,
+        weight_indices=weight_indices,
+        c_dtype=c_sglang.dtype,
+    )
+
+    if torch.allclose(result_fbgemm, result_sglang, rtol=1e-3, atol=1e-3):
+        print("✓ BF16 Correctness verification passed!")
+    else:
+        max_diff = torch.max(torch.abs(result_fbgemm - result_sglang))
+        print(f"✗ BF16 Correctness verification failed! Max diff: {max_diff}")
+        return False
+
+    return True
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Benchmark FBGEMM vs SGLang Grouped GEMM"
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        help="Model name to get configuration from",
+    )
+    parser.add_argument(
+        "--tp-size", type=int, default=1, help="Tensor parallelism size"
+    )
+    parser.add_argument(
+        "--use-fp8-w8a8", action="store_true", help="Enable FP8 W8A8 benchmark"
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./benchmark_grouped_gemm/",
+        help="Path to save benchmark results",
+    )
+    parser.add_argument(
+        "--verify-correctness",
+        action="store_true",
+        help="Verify correctness before benchmarking",
+    )
+
+    args = parser.parse_args()
+
+    try:
+        model_config = get_model_config(args.model, args.tp_size)
+    except Exception as e:
+        print(f"Failed to get model config: {e}")
+        print("Using default configuration...")
+        model_config = {
+            "num_groups": 8,
+            "hidden_size": 4096,
+            "intermediate_size": 14336,
+            "dtype": torch.bfloat16,
+        }
+
+    print("Running benchmark with:")
+    print(f"  num_groups: {model_config['num_groups']}")
+    print(f"  hidden_size: {model_config['hidden_size']}")
+    print(f"  intermediate_size: {model_config['intermediate_size']}")
+    print(f"  use_fp8_w8a8: {args.use_fp8_w8a8}")
+
+    if args.verify_correctness:
+        if not verify_correctness(model_config):
+            print("Correctness verification failed. Exiting...")
+            return
+
+    try:
+        run_benchmark(
+            model_config=model_config,
+            use_fp8_w8a8=args.use_fp8_w8a8,
+            save_path=args.save_path,
+        )
+    except Exception as e:
+        print(f"Benchmark failed: {e}")
+
+
+if __name__ == "__main__":
+    main()

benchmark/kernels/flashinfer_allreduce_fusion/README.md

+# FlashInfer Fused AllReduce + RMSNorm Benchmark
+
+This benchmark script is modified from the [original implementation](https://github.com/vllm-project/vllm/blob/237e1fb887c7f5a579420fa0295097f24b006594/benchmarks/kernels/benchmark_fused_collective.py) by the vLLM community. It aims to compare the performance differences between FlashInfer fused operators in SGLang (trtllm_allreduce_fusion: AllReduce + Residual Add + RMSNorm + optional quantization) and conventional implementations (standard `tensor_model_parallel_all_reduce` + separate RMSNorm/quantization). Specifically, this script tests the timing performance of two implementation paths: 1) Standard AllReduce and RMSNorm executed separately; 2) FlashInfer's fused operator combining AllReduce, Residual Add, RMSNorm, and optional quantization operations.
+
+This benchmark script helps us tune the ipc workspace size of the `flashinfer_allreduce_residual_rmsnorm` operator in SGLang and prepare for applications with FP8/FP4 quantized fused operators.
+
+Script path: `benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py`
+
+## Feature Overview
+
+- Compare average execution time (ms) and calculate speedup ratios for the following paths:
+  - standard_allreduce_rmsnorm (Standard AllReduce + RMSNorm)
+  - flashinfer_fused_allreduce_rmsnorm (Fused AllReduce + RMSNorm), including oneshot and twoshot modes
+  - Optionally compare FP8/FP4 quantized fused paths with standard paths
+- Use CUDA Graph capture and batch replay to reduce measurement noise
+- Automatically select the faster "standard baseline" (native/compiled version) as the denominator for speedup calculation
+- Optionally export results in Markdown format
+
+## Runtime Environment and Prerequisites
+
+- At least 2 GPUs, and launch multi-process distributed training using `torchrun` (NCCL backend)
+- Properly install/compile sglang along with sgl-kernel and custom operators
+
+## Quick Start (Command Examples)
+
+The following examples use world_size=2. You can modify `--nproc_per_node` and parameters according to your machine:
+
+- Regular paths only (no quantization):
+```
+torchrun --nproc_per_node=2 \
+benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py \
+--no-quant --hidden-dim 1024 --seq-lens 512 1024 2048 4096 --trials 100
+```
+
+- FP8 quantization paths only:
+```
+torchrun --nproc_per_node=2 \
+benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py \
+--quant-fp8 --hidden-dim 1024 --seq-lens 512 1024 2048 4096 --trials 100
+```
+
+- FP4 quantization paths only:
+```
+torchrun --nproc_per_node=2 \
+benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py \
+--quant-fp4 --hidden-dim 1024 --seq-lens 512 1024 2048 4096 --trials 100
+```
+
+- Larger hidden dimensions:
+```
+torchrun --nproc_per_node=2 \
+benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py \
+--no-quant  --hidden-dim 4096 --seq-lens 512 1024 2048 4096 --trials 100
+```
+
+## Parameter Description
+- `--seq-lens`: List of sequence lengths to test (default: 128 512 1024 2048)
+- `--hidden-dim`: Hidden dimension (default: 8192)
+- `--dtypes`: Data type list, `float16|bfloat16|float32` (default: bfloat16)
+- `--no-residual`: Only test "no residual" scenarios (default tests both "with/without residual")
+- Mutually exclusive quantization options:
+  - `--no-quant`: No quantization testing
+  - `--quant-fp8`: Only FP8 quantization testing
+  - `--quant-fp4`: Only FP4 quantization testing
+  - `--quant-all`: Test all (default)
+- FlashInfer related:
+  - `--disable-oneshot`: Disable oneshot mode (default enables oneshot and tests twoshot simultaneously)
+- Runtime configuration:
+  - `--warmup`: Warmup count before graph capture and before graph replay (default 5)
+  - `--trials`: Benchmark iteration count (default 20; internally each `graph.replay()` will batch replay multiple times)
+  - `--output-file`: Save results as Markdown file (only rank0 takes effect)
+
+## Output Example
+
+Each configuration group prints a table showing average execution time and relative speedup ratios (baseline is the faster standard implementation). For example:
+```
+================================================================================
+Results: seq_len=1024, hidden_dim=1024
+dtype=torch.bfloat16, residual=yes, quant_mode=none
+================================================================================
+Operation                                          Time (ms)    Speedup
+--------------------------------------------------------------------------------
+standard_allreduce_rmsnorm                         0.024        0.98x
+standard_allreduce_rmsnorm_native_compiled         0.023        baseline
+flashinfer_fused_allreduce_rmsnorm_oneshot         0.011        2.19x
+flashinfer_fused_allreduce_rmsnorm_twoshot         0.041        0.57x
+```
+
+If `--output-file` is specified, all configurations will be summarized in Markdown tables in that file.
+
+## Important Notes and Recommendations
+
+- Distributed: The script uses `torchrun` environment variables to initialize distributed training and binds tensors/communication groups to the current rank's corresponding device.
+- World size: Requires `WORLD_SIZE > 1` to perform communication operator benchmarks. Otherwise, the script will error and prompt.
+- FlashInfer:
+  - If not installed or interfaces are missing, the script will only run standard paths and provide prompts in the logs.
+  - The fused operator internally uses "oneshot"/"twoshot" two trigger methods; oneshot is enabled by default and twoshot is tested simultaneously.
+- FP8/FP4:
+  - FP8 uses sglang's FP8 tools and dtype, with underlying platform selection of `e4m3`/`e4m3fnuz` etc.
+  - FP4 uses sgl-kernel's `scaled_fp4_quant`, requiring corresponding platform support.
+- CUDA Graph:
+  - Uses sglang's `graph_capture()` to prepare capture-ready state for communication, then uses `torch.cuda.graph` to capture kernels, reducing measurement jitter.

benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py

+# Modified from https://github.com/vllm-project/vllm/blob/237e1fb887c7f5a579420fa0295097f24b006594/benchmarks/kernels/benchmark_fused_collective.py
+
+"""
+Benchmark for FlashInfer fused collective operations vs standard operations.
+
+This benchmark compares:
+1. FlashInfer's trtllm_allreduce_fusion (fused allreduce + rmsnorm + optional quant)
+2. Standard tensor_model_parallel_all_reduce + separate rmsnorm/quant operations
+
+Usage with torchrun:
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --no-quant --hidden-dim 1024 --seq-len 512 1024 2048 4096 --trials 100
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --quant-fp8 --hidden-dim 1024 --seq-len 512 1024 2048 4096 --trials 100
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --quant-fp4 --hidden-dim 1024 --seq-len 512 1024 2048 4096 --trials 100
+
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --no-quant --hidden-dim 4096 --seq-len 512 1024 2048 4096 --trials 100
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --quant-fp8 --hidden-dim 4096 --seq-len 512 1024 2048 4096 --trials 100
+    torchrun --nproc_per_node=2 benchmark/kernels/flashinfer_allreduce_fusion/benchmark_fused_collective.py --quant-fp4 --hidden-dim 4096 --seq-len 512 1024 2048 4096 --trials 100
+"""
+
+import argparse
+import contextlib
+import itertools
+import logging
+import os
+import time
+from typing import Optional
+
+import torch  # type: ignore
+import torch.distributed as dist  # type: ignore
+
+from sglang.srt.distributed import get_tp_group, tensor_model_parallel_all_reduce
+from sglang.srt.distributed.parallel_state import (
+    cleanup_dist_env_and_memory,
+    graph_capture,
+    init_distributed_environment,
+    initialize_model_parallel,
+)
+from sglang.srt.layers.layernorm import RMSNorm  # noqa
+from sglang.srt.layers.quantization.fp8_kernel import fp8_dtype as SGLANG_FP8_DTYPE
+from sglang.srt.layers.quantization.fp8_kernel import static_quant_fp8
+
+try:
+    from sgl_kernel import fused_add_rmsnorm as SGL_FUSED_ADD_RMS_NORM
+    from sgl_kernel import rmsnorm as SGL_RMS_NORM
+    from sgl_kernel import scaled_fp4_quant as SGL_SCALED_FP4_QUANT
+except Exception:  # pragma: no cover - fallback on non-supported platforms
+    SGL_FUSED_ADD_RMS_NORM = None
+    SGL_RMS_NORM = None
+    SGL_SCALED_FP4_QUANT = None
+
+FP8_DTYPE = SGLANG_FP8_DTYPE
+
+logger = logging.getLogger(__name__)
+
+# Try to import FlashInfer
+try:
+    import flashinfer.comm as flashinfer_comm  # type: ignore
+
+    if not hasattr(flashinfer_comm, "trtllm_allreduce_fusion"):
+        flashinfer_comm = None
+        logger.warning(
+            "FlashInfer comm module found but missing trtllm_allreduce_fusion"
+        )
+except ImportError:
+    flashinfer_comm = None
+    logger.warning("FlashInfer not found, only benchmarking standard operations")
+
+# Constants
+MiB = 1024 * 1024
+
+# FlashInfer max sizes per world size
+# Enable 64MB for 2, 4, 8 world sizes to verify large input sizes
+# use --disable-oneshot to disable oneshot mode for very large input sizes
+_FI_MAX_SIZES = {
+    2: 64 * MiB,  # 64MB
+    4: 64 * MiB,  # 64MB
+    8: 64 * MiB,  # 64MB
+}
+
+# Global workspace tensor for FlashInfer
+_FI_WORKSPACE_TENSOR = None
+
+
+def setup_flashinfer_workspace(
+    world_size: int,
+    rank: int,
+    hidden_dim: int,
+    max_token_num: int,
+    use_fp32_lamport: bool = False,
+):
+    """Setup FlashInfer workspace for fused allreduce operations."""
+    global _FI_WORKSPACE_TENSOR
+
+    if flashinfer_comm is None:
+        return None, None
+
+    if world_size not in _FI_MAX_SIZES:
+        logger.warning("FlashInfer not supported for world size %s", world_size)
+        return None, None
+
+    try:
+        # Create IPC workspace
+        ipc_handles, workspace_tensor = (
+            flashinfer_comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
+                tp_rank=rank,
+                tp_size=world_size,
+                max_token_num=max_token_num,
+                hidden_dim=hidden_dim,
+                group=get_tp_group().device_group,
+                use_fp32_lamport=use_fp32_lamport,
+            )
+        )
+
+        _FI_WORKSPACE_TENSOR = workspace_tensor
+        return ipc_handles, workspace_tensor
+    except Exception as e:
+        logger.error("Failed to setup FlashInfer workspace: %s", e)
+        return None, None
+
+
+def cleanup_flashinfer_workspace(ipc_handles):
+    """Cleanup FlashInfer workspace."""
+    if flashinfer_comm is None or ipc_handles is None:
+        return
+
+    try:
+        group = get_tp_group().device_group
+        flashinfer_comm.trtllm_destroy_ipc_workspace_for_all_reduce(ipc_handles, group)
+    except Exception as e:
+        logger.error("Failed to cleanup FlashInfer workspace: %s", e)
+
+
+class FlashInferFusedAllReduceParams:
+    """Parameters for FlashInfer fused allreduce operations."""
+
+    def __init__(
+        self,
+        rank: int,
+        world_size: int,
+        use_fp32_lamport: bool = False,
+        max_token_num: int = 1024,
+    ):
+        self.rank = rank
+        self.world_size = world_size
+        self.use_fp32_lamport = use_fp32_lamport
+        self.trigger_completion_at_end = True
+        self.launch_with_pdl = True
+        self.fp32_acc = True
+        self.max_token_num = max_token_num
+
+    def get_trtllm_fused_allreduce_kwargs(self):
+        return {
+            "world_rank": self.rank,
+            "world_size": self.world_size,
+            "launch_with_pdl": self.launch_with_pdl,
+            "trigger_completion_at_end": self.trigger_completion_at_end,
+            "fp32_acc": self.fp32_acc,
+        }
+
+
+def flashinfer_fused_allreduce_rmsnorm(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    allreduce_params: "FlashInferFusedAllReduceParams",
+    use_oneshot: bool,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """FlashInfer fused allreduce + rmsnorm operation."""
+    if flashinfer_comm is None or _FI_WORKSPACE_TENSOR is None:
+        raise RuntimeError("FlashInfer not available or workspace not initialized")
+
+    if norm_out is None:
+        norm_out = input_tensor
+        residual_out = residual
+    else:
+        residual_out = input_tensor
+
+    flashinfer_comm.trtllm_allreduce_fusion(
+        allreduce_in=input_tensor,
+        token_num=input_tensor.shape[0],
+        residual_in=residual,
+        residual_out=residual_out,
+        norm_out=norm_out,
+        rms_gamma=rms_gamma,
+        rms_eps=rms_eps,
+        hidden_dim=input_tensor.shape[-1],
+        workspace_ptrs=_FI_WORKSPACE_TENSOR,
+        pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm,
+        allreduce_out=None,
+        quant_out=None,
+        scale_out=None,
+        layout_code=None,
+        scale_factor=None,
+        use_oneshot=use_oneshot,
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+    )
+
+
+def flashinfer_fused_allreduce_rmsnorm_fp8_quant(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    scale_factor: torch.Tensor,
+    allreduce_params: FlashInferFusedAllReduceParams,
+    use_oneshot: bool = True,
+    norm_out: Optional[torch.Tensor] = None,
+    quant_out: Optional[torch.Tensor] = None,
+):
+    """FlashInfer fused allreduce + rmsnorm + FP8 quantization."""
+    if flashinfer_comm is None or _FI_WORKSPACE_TENSOR is None:
+        raise RuntimeError("FlashInfer not available or workspace not initialized")
+
+    if norm_out is None:
+        norm_out = input_tensor
+        residual_out = residual
+    else:
+        residual_out = input_tensor
+
+    flashinfer_comm.trtllm_allreduce_fusion(
+        allreduce_in=input_tensor,
+        token_num=input_tensor.shape[0],
+        residual_in=residual,
+        residual_out=residual_out,
+        norm_out=norm_out,
+        rms_gamma=rms_gamma,
+        rms_eps=rms_eps,
+        hidden_dim=input_tensor.shape[-1],
+        workspace_ptrs=_FI_WORKSPACE_TENSOR,
+        pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP8Quant,
+        allreduce_out=None,
+        quant_out=quant_out,
+        scale_out=None,
+        layout_code=None,
+        scale_factor=scale_factor,
+        use_oneshot=use_oneshot,
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+    )
+
+
+def flashinfer_fused_allreduce_rmsnorm_fp4_quant(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    input_global_scale: torch.Tensor,
+    allreduce_params: FlashInferFusedAllReduceParams,
+    quant_out: torch.Tensor,
+    use_oneshot: bool,
+    output_scale: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """FlashInfer fused allreduce + rmsnorm + FP4 quantization."""
+    if flashinfer_comm is None or _FI_WORKSPACE_TENSOR is None:
+        raise RuntimeError("FlashInfer not available or workspace not initialized")
+
+    if norm_out is None:
+        norm_out = input_tensor
+        residual_out = residual
+    else:
+        residual_out = input_tensor
+
+    flashinfer_comm.trtllm_allreduce_fusion(
+        allreduce_in=input_tensor,
+        token_num=input_tensor.shape[0],
+        residual_in=residual,
+        residual_out=residual_out,
+        norm_out=norm_out,
+        rms_gamma=rms_gamma,
+        rms_eps=rms_eps,
+        hidden_dim=input_tensor.shape[-1],
+        workspace_ptrs=_FI_WORKSPACE_TENSOR,
+        pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP4Quant,
+        allreduce_out=None,
+        quant_out=quant_out,
+        scale_out=output_scale,
+        layout_code=None,
+        scale_factor=input_global_scale,
+        use_oneshot=use_oneshot,
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+    )
+
+
+def standard_allreduce_rmsnorm(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm operations."""
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+    # Then RMS norm
+    if residual is not None:
+        # Fused add + RMS norm (in-place on allreduce_out)
+        if SGL_FUSED_ADD_RMS_NORM is not None:
+            SGL_FUSED_ADD_RMS_NORM(allreduce_out, residual, rms_gamma, rms_eps)
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            rms.forward_native(allreduce_out, residual)
+    else:
+        # Just RMS norm
+        if SGL_RMS_NORM is not None:
+            _ = SGL_RMS_NORM(allreduce_out, rms_gamma, rms_eps)
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            _ = rms.forward_native(allreduce_out)
+
+
+def standard_allreduce_rmsnorm_fp8_quant(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    scale_factor: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+    quant_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm + FP8 quantization."""
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+
+    # Then RMS norm + static FP8 quantization
+    if residual is not None:
+        if SGL_FUSED_ADD_RMS_NORM is not None:
+            SGL_FUSED_ADD_RMS_NORM(allreduce_out, residual, rms_gamma, rms_eps)
+            quant_out, _ = static_quant_fp8(
+                allreduce_out, scale_factor, repeat_scale=False
+            )
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            normed, _ = rms.forward_native(allreduce_out, residual)
+            quant_out, _ = static_quant_fp8(normed, scale_factor, repeat_scale=False)
+        return quant_out, residual
+    else:
+        if SGL_RMS_NORM is not None:
+            normed = SGL_RMS_NORM(allreduce_out, rms_gamma, rms_eps)
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            normed = rms.forward_native(allreduce_out)
+        quant_out, _ = static_quant_fp8(normed, scale_factor, repeat_scale=False)
+        return quant_out
+
+
+def standard_allreduce_rmsnorm_fp4_quant(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rms_gamma: torch.Tensor,
+    rms_eps: float,
+    input_global_scale: torch.Tensor,
+    quant_out: torch.Tensor,
+    output_scale: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm + FP4 quantization."""
+
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+
+    # Then RMS norm
+    if residual is not None:
+        if SGL_FUSED_ADD_RMS_NORM is not None:
+            SGL_FUSED_ADD_RMS_NORM(allreduce_out, residual, rms_gamma, rms_eps)
+            quant_input = allreduce_out
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            quant_input, _ = rms.forward_native(allreduce_out, residual)
+        residual_out = residual
+    else:
+        if SGL_RMS_NORM is not None:
+            quant_input = SGL_RMS_NORM(allreduce_out, rms_gamma, rms_eps)
+        else:
+            rms = RMSNorm(allreduce_out.shape[-1], eps=rms_eps)
+            rms.weight.data = rms_gamma
+            quant_input = rms.forward_native(allreduce_out)
+        residual_out = allreduce_out
+
+    # Finally FP4 quantization
+    if SGL_SCALED_FP4_QUANT is None:
+        raise RuntimeError("scaled_fp4_quant is not available on this platform")
+    quant_res, output_scale_res = SGL_SCALED_FP4_QUANT(quant_input, input_global_scale)
+    if residual is not None:
+        return quant_res, residual_out, output_scale_res
+    else:
+        return quant_res, quant_input
+
+
+def standard_allreduce_rmsnorm_native(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm operations using native RMSNorm forward."""
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+    # Apply native RMSNorm
+    if residual is not None:
+        result = rmsnorm_layer.forward_native(allreduce_out, residual)
+        return result  # Returns (norm_out, residual_out)
+    else:
+        result = rmsnorm_layer.forward_native(allreduce_out)
+        return result  # Returns norm_out
+
+
+def standard_allreduce_rmsnorm_fp8_quant_native(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    scale_factor: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+    quant_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm + FP8 quantization using native implementations."""
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+
+    # Apply native RMSNorm
+    if residual is not None:
+        norm_out, residual_out = rmsnorm_layer.forward_native(allreduce_out, residual)
+    else:
+        norm_out = rmsnorm_layer.forward_native(allreduce_out)
+        residual_out = allreduce_out
+
+    # Apply native FP8 quantization
+    quant_out, _ = static_quant_fp8(norm_out, scale_factor, repeat_scale=False)
+
+    if residual is not None:
+        return quant_out, residual_out
+    else:
+        return quant_out
+
+
+def standard_allreduce_rmsnorm_fp4_quant_native(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    input_global_scale: torch.Tensor,
+    quant_out: torch.Tensor,
+    output_scale: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Standard allreduce + rmsnorm + FP4 quantization using native RMSNorm."""
+    # All-reduce first
+    allreduce_out = tensor_model_parallel_all_reduce(input_tensor)
+
+    # Apply native RMSNorm
+    if residual is not None:
+        norm_out, residual_out = rmsnorm_layer.forward_native(allreduce_out, residual)
+        quant_input = norm_out
+    else:
+        norm_out = rmsnorm_layer.forward_native(allreduce_out)
+        quant_input = norm_out
+        residual_out = allreduce_out
+
+    # Apply FP4 quantization (still using fused CUDA op as there's no native FP4)
+    if SGL_SCALED_FP4_QUANT is None:
+        raise RuntimeError("scaled_fp4_quant is not available on this platform")
+    quant_res, output_scale_res = SGL_SCALED_FP4_QUANT(quant_input, input_global_scale)
+
+    if residual is not None:
+        return quant_res, residual_out, output_scale_res
+    else:
+        return quant_res, norm_out
+
+
+# Compiled versions of native functions
+@torch.compile
+def standard_allreduce_rmsnorm_native_compiled(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Compiled version of standard allreduce + rmsnorm."""
+    return standard_allreduce_rmsnorm_native(
+        input_tensor, residual, rmsnorm_layer, norm_out
+    )
+
+
+@torch.compile
+def standard_allreduce_rmsnorm_fp8_quant_native_compiled(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    scale_factor: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+    quant_out: Optional[torch.Tensor] = None,
+):
+    """Compiled version of standard allreduce + rmsnorm + FP8 quantization."""
+    return standard_allreduce_rmsnorm_fp8_quant_native(
+        input_tensor,
+        residual,
+        rmsnorm_layer,
+        scale_factor,
+        norm_out,
+        quant_out,
+    )
+
+
+@torch.compile
+def standard_allreduce_rmsnorm_fp4_quant_native_compiled(
+    input_tensor: torch.Tensor,
+    residual: Optional[torch.Tensor],
+    rmsnorm_layer: RMSNorm,
+    input_global_scale: torch.Tensor,
+    quant_out: torch.Tensor,
+    output_scale: torch.Tensor,
+    norm_out: Optional[torch.Tensor] = None,
+):
+    """Compiled version of standard allreduce + rmsnorm + FP4 quantization."""
+    return standard_allreduce_rmsnorm_fp4_quant_native(
+        input_tensor,
+        residual,
+        rmsnorm_layer,
+        input_global_scale,
+        quant_out,
+        output_scale,
+        norm_out,
+    )
+
+
+def create_test_tensors(
+    seq_len: int, hidden_dim: int, dtype: torch.dtype, use_residual: bool = True
+):
+    """Create test tensors for benchmarking."""
+    input_tensor = torch.randn(seq_len, hidden_dim, dtype=dtype)
+    residual = (
+        torch.randn_like(input_tensor)
+        if use_residual
+        else torch.zeros_like(input_tensor)
+    )
+    rms_gamma = torch.ones(hidden_dim, dtype=dtype)
+    norm_out = None if use_residual else torch.empty_like(input_tensor)
+
+    # Quantization scales
+    scale_fp8 = torch.tensor(1.0, dtype=torch.float32)
+    scale_fp4 = torch.tensor(1.0, dtype=torch.float32)
+    quant_out_fp8 = torch.empty_like(input_tensor, dtype=FP8_DTYPE)
+    # Pre-allocate FP4 output tensors (to avoid allocation overhead in benchmarks)
+    fp4_quant_out = torch.empty((seq_len, hidden_dim // 2), dtype=torch.uint8)
+    fp4_output_scale = torch.empty((128, 4), dtype=torch.int32)
+
+    return (
+        input_tensor,
+        norm_out,
+        residual,
+        rms_gamma,
+        scale_fp8,
+        quant_out_fp8,
+        scale_fp4,
+        fp4_quant_out,
+        fp4_output_scale,
+    )
+
+
+def benchmark_operation(
+    operation_func, *args, warmup: int = 5, trials: int = 20, **kwargs
+):
+    """Benchmark a single operation using CUDA graphs."""
+    # Warmup before graph capture
+    for _ in range(warmup):
+        operation_func(*args, **kwargs)
+    torch.cuda.synchronize()
+
+    # Create CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    num_op_per_cudagraph = 10
+
+    # Use sglang's graph_capture to make tensor_model_parallel_all_reduce graph-safe
+    with graph_capture() as graph_capture_context:
+        with torch.cuda.graph(graph, stream=graph_capture_context.stream):
+            for _ in range(num_op_per_cudagraph):
+                operation_func(*args, **kwargs)
+
+    # Graph warmup
+    torch.cuda.synchronize()
+    for _ in range(warmup):
+        graph.replay()
+
+    # Benchmark with CUDA graph
+    torch.cuda.synchronize()
+    start_time = time.perf_counter()
+
+    for _ in range(trials // num_op_per_cudagraph):
+        # operation_func(*args, **kwargs)
+        graph.replay()
+
+    torch.cuda.synchronize()
+    end_time = time.perf_counter()
+
+    avg_time_ms = ((end_time - start_time) / trials) * 1000
+    return avg_time_ms
+
+
+def run_benchmarks(
+    seq_len: int,
+    hidden_dim: int,
+    dtype: torch.dtype,
+    use_residual: bool,
+    allreduce_params: Optional[FlashInferFusedAllReduceParams],
+    quant_mode: str = "all",
+    disable_oneshot: bool = False,
+):
+    """Run all benchmarks for given configuration.
+
+    Args:
+        quant_mode: "none", "fp8_only", "fp4_only", or "all"
+    """
+    (
+        input_tensor,
+        norm_out,
+        residual,
+        rms_gamma,
+        scale_fp8,
+        quant_out_fp8,
+        scale_fp4,
+        fp4_quant_out,
+        fp4_output_scale,
+    ) = create_test_tensors(seq_len, hidden_dim, dtype, use_residual)
+
+    rms_eps = 1e-6
+    results = {}
+
+    # Create RMSNorm once for native benchmarks
+    rmsnorm_layer = RMSNorm(hidden_dim, eps=rms_eps)
+    rmsnorm_layer.weight.data = rms_gamma
+
+    if quant_mode in ["all", "none"]:
+        # Standard AllReduce + RMSNorm
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm,
+                input_tensor,
+                norm_out=norm_out,
+                residual=residual,
+                rms_gamma=rms_gamma,
+                rms_eps=rms_eps,
+            )
+            results["standard_allreduce_rmsnorm"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm failed: %s", e)
+            results["standard_allreduce_rmsnorm"] = float("inf")
+
+        # Standard AllReduce + RMSNorm Native Compiled
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm_native_compiled,
+                input_tensor,
+                residual=residual,
+                rmsnorm_layer=rmsnorm_layer,
+                norm_out=norm_out,
+            )
+            results["standard_allreduce_rmsnorm_native_compiled"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm Native Compiled failed: %s", e)
+            results["standard_allreduce_rmsnorm_native_compiled"] = float("inf")
+
+        # FlashInfer Fused AllReduce + RMSNorm Oneshot
+        if flashinfer_comm is not None and allreduce_params is not None:
+            try:
+                if not disable_oneshot:
+                    time_ms = benchmark_operation(
+                        flashinfer_fused_allreduce_rmsnorm,
+                        input_tensor,
+                        residual=residual,
+                        norm_out=norm_out,
+                        rms_gamma=rms_gamma,
+                        rms_eps=rms_eps,
+                        allreduce_params=allreduce_params,
+                        use_oneshot=True,
+                    )
+                    results["flashinfer_fused_allreduce_rmsnorm_oneshot"] = time_ms
+            except Exception as e:
+                logger.error("FlashInfer Fused AllReduce+RMSNorm Oneshot failed: %s", e)
+                results["flashinfer_fused_allreduce_rmsnorm_oneshot"] = float("inf")
+
+            # FlashInfer Fused AllReduce + RMSNorm Two-shot
+            try:
+                time_ms = benchmark_operation(
+                    flashinfer_fused_allreduce_rmsnorm,
+                    input_tensor,
+                    residual=residual,
+                    norm_out=norm_out,
+                    rms_gamma=rms_gamma,
+                    rms_eps=rms_eps,
+                    allreduce_params=allreduce_params,
+                    use_oneshot=False,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_twoshot"] = time_ms
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm Two-shot failed: %s", e
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_twoshot"] = float("inf")
+
+    if quant_mode in ["all", "fp8_only"]:
+        # Standard AllReduce + RMSNorm + FP8 Quant
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm_fp8_quant,
+                input_tensor,
+                norm_out=norm_out,
+                residual=residual,
+                rms_gamma=rms_gamma,
+                rms_eps=rms_eps,
+                scale_factor=scale_fp8,
+                quant_out=quant_out_fp8,
+            )
+            results["standard_allreduce_rmsnorm_fp8_quant"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm+FP8 failed: %s", e)
+            results["standard_allreduce_rmsnorm_fp8_quant"] = float("inf")
+
+        # Standard AllReduce + RMSNorm + FP8 Quant Native Compiled
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm_fp8_quant_native_compiled,
+                input_tensor,
+                residual=residual,
+                rmsnorm_layer=rmsnorm_layer,
+                # quant_fp8_layer removed in sglang version; static_quant_fp8 is used within the function
+                scale_factor=scale_fp8,
+                norm_out=norm_out,
+                quant_out=quant_out_fp8,
+            )
+            results["standard_allreduce_rmsnorm_fp8_quant_native_compiled"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm+FP8 Native Compiled failed: %s", e)
+            results["standard_allreduce_rmsnorm_fp8_quant_native_compiled"] = float(
+                "inf"
+            )
+
+        # FlashInfer Fused AllReduce + RMSNorm + FP8 Quant Oneshot
+        if flashinfer_comm is not None and allreduce_params is not None:
+            try:
+                if not disable_oneshot:
+                    time_ms = benchmark_operation(
+                        flashinfer_fused_allreduce_rmsnorm_fp8_quant,
+                        input_tensor,
+                        norm_out=norm_out,
+                        residual=residual,
+                        rms_gamma=rms_gamma,
+                        rms_eps=rms_eps,
+                        scale_factor=scale_fp8,
+                        quant_out=quant_out_fp8,
+                        allreduce_params=allreduce_params,
+                        use_oneshot=True,
+                    )
+                    results["flashinfer_fused_allreduce_rmsnorm_fp8_quant_oneshot"] = (
+                        time_ms
+                    )
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm+FP8 Oneshot failed: %s",
+                    e,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp8_quant_oneshot"] = float(
+                    "inf"
+                )
+            # FlashInfer Fused AllReduce + RMSNorm + FP8 Quant Two-shot
+            try:
+                time_ms = benchmark_operation(
+                    flashinfer_fused_allreduce_rmsnorm_fp8_quant,
+                    input_tensor,
+                    norm_out=norm_out,
+                    residual=residual,
+                    rms_gamma=rms_gamma,
+                    rms_eps=rms_eps,
+                    scale_factor=scale_fp8,
+                    quant_out=quant_out_fp8,
+                    allreduce_params=allreduce_params,
+                    use_oneshot=False,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp8_quant_twoshot"] = (
+                    time_ms
+                )
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm+FP8 Two-shot failed: %s",
+                    e,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp8_quant_twoshot"] = float(
+                    "inf"
+                )
+
+    if quant_mode in ["all", "fp4_only"]:
+        # Standard AllReduce + RMSNorm + FP4 Quant
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm_fp4_quant,
+                input_tensor,
+                norm_out=norm_out,
+                residual=residual,
+                rms_gamma=rms_gamma,
+                rms_eps=rms_eps,
+                input_global_scale=scale_fp4,
+                quant_out=fp4_quant_out,
+                output_scale=fp4_output_scale,
+            )
+            results["standard_allreduce_rmsnorm_fp4_quant"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm+FP4 failed: %s", e)
+            results["standard_allreduce_rmsnorm_fp4_quant"] = float("inf")
+
+        # Standard AllReduce + RMSNorm + FP4 Quant Native Compiled
+        try:
+            time_ms = benchmark_operation(
+                standard_allreduce_rmsnorm_fp4_quant_native_compiled,
+                input_tensor,
+                residual=residual,
+                rmsnorm_layer=rmsnorm_layer,
+                input_global_scale=scale_fp4,
+                quant_out=fp4_quant_out,
+                output_scale=fp4_output_scale,
+                norm_out=norm_out,
+            )
+            results["standard_allreduce_rmsnorm_fp4_quant_native_compiled"] = time_ms
+        except Exception as e:
+            logger.error("Standard AllReduce+RMSNorm+FP4 Native Compiled failed: %s", e)
+            results["standard_allreduce_rmsnorm_fp4_quant_native_compiled"] = float(
+                "inf"
+            )
+
+        # FlashInfer Fused AllReduce + RMSNorm + FP4 Quant Oneshot
+        if flashinfer_comm is not None and allreduce_params is not None:
+            try:
+                if not disable_oneshot:
+                    time_ms = benchmark_operation(
+                        flashinfer_fused_allreduce_rmsnorm_fp4_quant,
+                        input_tensor,
+                        residual=residual,
+                        norm_out=norm_out,
+                        rms_gamma=rms_gamma,
+                        rms_eps=rms_eps,
+                        input_global_scale=scale_fp4,
+                        allreduce_params=allreduce_params,
+                        quant_out=fp4_quant_out,
+                        output_scale=fp4_output_scale,
+                        use_oneshot=True,
+                    )
+                    results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_oneshot"] = (
+                        time_ms
+                    )
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm+FP4 Oneshot failed: %s",
+                    e,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_oneshot"] = float(
+                    "inf"
+                )
+
+        # FlashInfer Fused AllReduce + RMSNorm + FP4 Quant Two-shot
+        if flashinfer_comm is not None and allreduce_params is not None:
+            try:
+                time_ms = benchmark_operation(
+                    flashinfer_fused_allreduce_rmsnorm_fp4_quant,
+                    input_tensor,
+                    residual=residual,
+                    norm_out=norm_out,
+                    rms_gamma=rms_gamma,
+                    rms_eps=rms_eps,
+                    input_global_scale=scale_fp4,
+                    allreduce_params=allreduce_params,
+                    quant_out=fp4_quant_out,
+                    output_scale=fp4_output_scale,
+                    use_oneshot=False,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_twoshot"] = (
+                    time_ms
+                )
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm+FP4 Two-shot failed: %s",
+                    e,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_twoshot"] = float(
+                    "inf"
+                )
+
+    return results
+
+
+def prepare_results_with_speedups(results_dict):
+    """Prepare results with speedup calculations based on dynamic baseline selection."""
+    prepared_results = []
+
+    # Determine the fastest baseline for each operation type
+    def get_fastest_baseline(op_name, results_dict):
+        """Get the fastest baseline between standard and native_compiled versions."""
+        if "fp8_quant" in op_name:
+            candidates = [
+                "standard_allreduce_rmsnorm_fp8_quant",
+                "standard_allreduce_rmsnorm_fp8_quant_native_compiled",
+            ]
+        elif "fp4_quant" in op_name:
+            candidates = [
+                "standard_allreduce_rmsnorm_fp4_quant",
+                "standard_allreduce_rmsnorm_fp4_quant_native_compiled",
+            ]
+        else:
+            candidates = [
+                "standard_allreduce_rmsnorm",
+                "standard_allreduce_rmsnorm_native_compiled",
+            ]
+
+        # Find the fastest among available candidates
+        fastest_time = float("inf")
+        fastest_baseline = None
+
+        for candidate in candidates:
+            if (
+                candidate in results_dict
+                and results_dict[candidate] != float("inf")
+                and results_dict[candidate] < fastest_time
+            ):
+                fastest_time = results_dict[candidate]
+                fastest_baseline = candidate
+
+        return fastest_baseline
+
+    # Create dynamic baseline mapping
+    dynamic_baseline_mapping = {}
+    for op_name in results_dict:
+        if (
+            op_name.startswith("flashinfer_")
+            or op_name.startswith("standard_")
+            and not op_name.endswith("_native_compiled")
+        ):
+            dynamic_baseline_mapping[op_name] = get_fastest_baseline(
+                op_name, results_dict
+            )
+
+    for op_name, time_ms in results_dict.items():
+        if time_ms == float("inf"):
+            speedup_str = "FAILED"
+            time_str = "FAILED"
+        else:
+            time_str = f"{time_ms:.3f}"
+            # Find the appropriate baseline for this operation
+            baseline_op = dynamic_baseline_mapping.get(op_name)
+            if baseline_op and baseline_op in results_dict:
+                baseline_time = results_dict[baseline_op]
+                if baseline_time != float("inf") and baseline_time > 0:
+                    speedup = baseline_time / time_ms
+                    speedup_str = f"{speedup:.2f}x"
+                else:
+                    speedup_str = "N/A"
+            else:
+                # For baseline operations, determine if this is the fastest baseline
+                if op_name.endswith("_native_compiled") or (
+                    op_name.startswith("standard_")
+                    and not op_name.endswith("_native_compiled")
+                ):
+                    fastest_baseline = get_fastest_baseline(op_name, results_dict)
+                    if fastest_baseline == op_name:
+                        speedup_str = "baseline"
+                    else:
+                        if fastest_baseline and fastest_baseline in results_dict:
+                            baseline_time = results_dict[fastest_baseline]
+                            if baseline_time != float("inf") and baseline_time > 0:
+                                speedup = baseline_time / time_ms
+                                speedup_str = f"{speedup:.2f}x"
+                            else:
+                                speedup_str = "N/A"
+                        else:
+                            speedup_str = "N/A"
+                else:
+                    speedup_str = "N/A"
+
+        prepared_results.append(
+            {
+                "operation": op_name,
+                "time_ms": time_ms,
+                "time_str": time_str,
+                "speedup_str": speedup_str,
+            }
+        )
+
+    return prepared_results
+
+
+def print_results(results_dict, seq_len, hidden_dim, dtype, use_residual, quant_mode):
+    """Print benchmark results in a formatted table."""
+    print(f"\n{'=' * 80}")
+    print(f"Results: seq_len={seq_len}, hidden_dim={hidden_dim}")
+    print(
+        f"dtype={dtype}, residual={'yes' if use_residual else 'no'}, "
+        f"quant_mode={quant_mode}"
+    )
+    print(f"{'=' * 80}")
+    print(f"{'Operation':<50} {'Time (ms)':<12} {'Speedup':<10}")
+    print(f"{'-' * 80}")
+
+    # Prepare results with speedup calculations
+    prepared_results = prepare_results_with_speedups(results_dict)
+
+    for result in prepared_results:
+        if result["time_ms"] == float("inf"):
+            time_display = result["time_str"]
+        else:
+            time_display = f"{result['time_ms']:.3f}"
+
+        print(
+            f"{result['operation']:<50} {time_display:<12} {result['speedup_str']:<10}"
+        )
+
+
+def format_results_markdown(
+    all_results: list[dict], world_size: int, args: argparse.Namespace
+) -> str:
+    """Format all benchmark results as markdown."""
+    markdown = f"""# FlashInfer Fused Collective Operations Benchmark Results
+
+**World Size:** {world_size}
+**Hidden Dimension:** {args.hidden_dim}
+**Warmup Iterations:** {args.warmup}
+**Benchmark Trials:** {args.trials}
+**Quantization Mode:** {all_results[0]["quant_mode"] if all_results else "N/A"}
+
+---
+
+"""
+
+    for result in all_results:
+        seq_len = result["seq_len"]
+        dtype = result["dtype"]
+        use_residual = result["use_residual"]
+        results_dict = result["results"]
+
+        residual_str = "with residual" if use_residual else "no residual"
+
+        markdown += f"""
+## Configuration: seq_len={seq_len}, dtype={dtype}, {residual_str}
+
+| Operation | Time (ms) | Speedup |
+|-----------|-----------|---------|
+"""
+
+        # Prepare results with speedup calculations
+        prepared_results = prepare_results_with_speedups(results_dict)
+
+        for result in prepared_results:
+            # Format operation name for better readability
+            formatted_op_name = result["operation"].replace("_", " ").title()
+            markdown += f"| {formatted_op_name} | {result['time_str']} |"
+            markdown += f"{result['speedup_str']} |\n"
+
+        markdown += "\n"
+
+    return markdown
+
+
+def save_results_to_file(
+    all_results: list[dict], world_size: int, args: argparse.Namespace, rank: int
+):
+    """Save benchmark results to markdown file (only on rank 0)."""
+    if rank != 0:
+        return
+
+    if not all_results:
+        logger.warning("No results to save")
+        return
+
+    output_path = args.output_file
+
+    try:
+        markdown_content = format_results_markdown(all_results, world_size, args)
+
+        with open(output_path, "w") as f:
+            f.write(markdown_content)
+
+    except Exception as e:
+        logger.error("Failed to save results to file: %s", e)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Benchmark fused collective operations"
+    )
+    parser.add_argument(
+        "--seq-lens",
+        type=int,
+        nargs="+",
+        default=[128, 512, 1024, 2048],
+        help="Sequence lengths to test",
+    )
+    parser.add_argument(
+        "--hidden-dim", type=int, default=8192, help="Hidden dimension size"
+    )
+    parser.add_argument(
+        "--dtypes",
+        type=str,
+        nargs="+",
+        default=["bfloat16"],
+        choices=["float16", "bfloat16", "float32"],
+        help="Data types to test",
+    )
+    parser.add_argument(
+        "--no-residual",
+        action="store_true",
+        help="Skip residual connection tests",
+    )
+
+    # Quantization mode options (mutually exclusive with --no-quant)
+    quant_group = parser.add_mutually_exclusive_group()
+    quant_group.add_argument(
+        "--no-quant", action="store_true", help="Skip all quantization tests"
+    )
+    quant_group.add_argument(
+        "--quant-fp8", action="store_true", help="Only run FP8 quantization tests"
+    )
+    quant_group.add_argument(
+        "--quant-fp4", action="store_true", help="Only run FP4 quantization tests"
+    )
+    quant_group.add_argument(
+        "--quant-all",
+        action="store_true",
+        help="Run all quantization tests (default)",
+    )
+
+    parser.add_argument(
+        "--disable-oneshot",
+        action="store_true",
+        help="Disable oneshot mode for FlashInfer operations",
+    )
+    parser.add_argument(
+        "--warmup", type=int, default=5, help="Number of warmup iterations"
+    )
+    parser.add_argument(
+        "--trials", type=int, default=20, help="Number of benchmark trials"
+    )
+    parser.add_argument(
+        "--output-file",
+        type=str,
+        help="""Output file path for markdown results
+                (default: benchmark_results_<timestamp>.md)
+        """,
+    )
+
+    args = parser.parse_args()
+
+    # Check if running with torchrun (required for collective operations)
+    if "RANK" not in os.environ or "WORLD_SIZE" not in os.environ:
+        raise RuntimeError(
+            "Must run with torchrun for distributed benchmarking. "
+            "Example: torchrun --nproc_per_node=2 benchmark_fused_collective.py"
+        )
+
+    # Initialize distributed environment
+    rank = int(os.environ["RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+    torch.set_default_device(device)
+
+    init_distributed_environment(
+        world_size=world_size,
+        rank=rank,
+        local_rank=rank,
+        backend="nccl",
+    )
+    initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+    # Validate world size (must be > 1 for collective operations)
+    if world_size <= 1:
+        raise ValueError(
+            "World size must be > 1 for collective operations benchmarking. "
+            f"Current world size: {world_size}. Use torchrun with --nproc_per_node > 1."
+        )
+
+    # Determine quantization mode
+    if args.no_quant:
+        quant_mode = "none"
+    elif args.quant_fp8:
+        quant_mode = "fp8_only"
+    elif args.quant_fp4:
+        quant_mode = "fp4_only"
+    else:  # args.quant_all or default
+        quant_mode = "all"
+
+    if rank == 0:
+        logger.info("Running benchmark with world_size=%s, rank=%s", world_size, rank)
+        logger.info("Quantization mode: %s", quant_mode)
+        if flashinfer_comm is not None:
+            oneshot_status = "enabled" if not args.disable_oneshot else "disabled"
+            logger.info(
+                "FlashInfer available - will benchmark fused operations (oneshot: %s)",
+                oneshot_status,
+            )
+        else:
+            logger.info(
+                "FlashInfer not available - only benchmarking standard operations"
+            )
+
+    # Convert dtype strings to torch dtypes
+    dtype_map = {
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+        "float32": torch.float32,
+    }
+    dtypes = [dtype_map[dt] for dt in args.dtypes]
+
+    # Test configurations
+    residual_options = [True] if not args.no_residual else [False]
+    if not args.no_residual:
+        residual_options.append(False)
+
+    configs = list(itertools.product(args.seq_lens, dtypes, residual_options))
+
+    # Setup FlashInfer workspace if available
+    ipc_handles = None
+    allreduce_params = None
+
+    if flashinfer_comm is not None:
+        # Use the largest hidden dimension for workspace setup
+        max_num_token = _FI_MAX_SIZES.get(world_size) // (
+            args.hidden_dim * world_size * 2
+        )
+
+        ipc_handles, workspace_tensor = setup_flashinfer_workspace(
+            world_size, rank, args.hidden_dim, max_num_token
+        )
+
+        if workspace_tensor is not None:
+            allreduce_params = FlashInferFusedAllReduceParams(
+                rank=rank,
+                world_size=world_size,
+                max_token_num=max_num_token,
+            )
+
+    # Collect all results for markdown export
+    all_results = []
+
+    try:
+        # Run benchmarks
+        for seq_len, dtype, use_residual in configs:
+            if rank == 0:
+                logger.info(
+                    "\nTesting:  seq_len=%s, hidden_dim=%s, dtype=%s, residual=%s",
+                    seq_len,
+                    args.hidden_dim,
+                    dtype,
+                    use_residual,
+                )
+
+            results = run_benchmarks(
+                seq_len,
+                args.hidden_dim,
+                dtype,
+                use_residual,
+                allreduce_params,
+                quant_mode=quant_mode,
+                disable_oneshot=args.disable_oneshot,
+            )
+
+            # Store results for markdown export
+            if rank == 0:
+                all_results.append(
+                    {
+                        "seq_len": seq_len,
+                        "hidden_dim": args.hidden_dim,
+                        "dtype": str(dtype).replace("torch.", ""),
+                        "use_residual": use_residual,
+                        "quant_mode": quant_mode,
+                        "results": results,
+                    }
+                )
+
+                print_results(
+                    results,
+                    seq_len,
+                    args.hidden_dim,
+                    dtype,
+                    use_residual,
+                    quant_mode,
+                )
+
+        # Save results to markdown file
+        if args.output_file and rank == 0:
+            save_results_to_file(all_results, world_size, args, rank)
+
+    finally:
+        # Cleanup
+        if ipc_handles is not None:
+            cleanup_flashinfer_workspace(ipc_handles)
+
+        with contextlib.suppress(Exception):
+            dist.barrier()
+        cleanup_dist_env_and_memory(shutdown_ray=False)
+
+
+if __name__ == "__main__":
+    main()

benchmark/kernels/fused_moe_triton/README.md

+## Tuning Triton MoE Kernels
+
+This directory contains benchmarking tools for MoE (Mixture of Experts) kernels.
+
+### Tuning Tool
+
+- `tuning_fused_moe_triton.py`: A tool for tuning the `fused_moe_triton` kernel. Adapted from [vllm's benchmark_moe.py](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py), with added support for various model architectures.
+
+Example usage:
+```bash
+# Tune Mixtral-8x7B with default settings
+python benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py \
+    --model mistralai/Mixtral-8x7B-Instruct-v0.1 \
+    --tune
+
+# Tune Qwen2-57B with FP8 and TP=4
+python benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py \
+    --model Qwen/Qwen2-57B-A14B-Instruct \
+    --tp-size 4 \
+    --dtype fp8_w8a8 \
+    --tune
+
+# Tune Qwen3-235B-A22B-FP8 and TP=4
+python benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py \
+    --model Qwen/Qwen3-235B-A22B-FP8 \
+    --tp-size 4 \
+    --dtype fp8_w8a8 \
+    --tune
+
+# Tune DeepSeek-V3 with FP8 and TP=8
+python benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py \
+    --model deepseek-ai/DeepSeek-V3-0324 \
+    --tp-size 8 \
+    --dtype fp8_w8a8 \
+    --tune
+
+# Tune DeepSeek-R1 with channel-wise INT8 and TP=16
+python benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py \
+    --model meituan/DeepSeek-R1-Channel-INT8 \
+    --tp-size 16 \
+    --dtype int8_w8a8 \
+    --tune
+```
+
+After tuning, a configuration file (e.g., `E=64,N=640,device_name=NVIDIA_GeForce_RTX_4090,dtype=fp8_w8a8.json`) will be generated in the current directory. You can move this file to `sglang/srt/layers/fused_moe_triton/configs/triton_version` dir to use it in `sglang`.
+
+### Performance Comparison Tool
+
+- `benchmark_vllm_vs_sglang_fused_moe_triton.py`: A tool for comparing the performance of fused MoE kernels between vllm and sglang implementations. Supports various model architectures and data types.
+
+Example usage:
+```bash
+# Compare with default settings (Mixtral model)
+python benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py
+
+# Compare with FP8 mode for Qwen2-57B
+python benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py \
+    --model Qwen/Qwen2-57B-A14B-Instruct \
+    --use-fp8-w8a8
+
+# Compare with custom TP size
+python benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py \
+    --model deepseek-ai/DeepSeek-V3-0324 \
+    --tp-size 8
+
+# Compare with custom TP size
+python benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py \
+    --model deepseek-ai/DeepSeek-V3-0324 \
+    --tp-size 8
+```
+
+The benchmark results will be saved as plots and data files in the specified output directory (default: `./configs/benchmark_ops/vllm_sglang_fused_moe/`).
+
+- `benchmark_torch_compile_fused_moe.py`: A tool for benchmarking the performance of the fused MoE kernel with `torch.compile` and original fused MoE kernel.
+
+Usage is the same as `benchmark_vllm_vs_sglang_fused_moe_triton.py`, note that `torch.compile` does not support `fp8_w8a8` and `int8_w8a8` fused_moe_kernel.

benchmark/kernels/fused_moe_triton/benchmark_sglang_fused_moe_triton.py

+# python3 benchmark/kernels/fused_moe_triton/sglang_fused_moe_triton.py --model /DeepSeek-V3/ --tp-size 8
+import argparse
+
+import torch
+import triton
+from transformers import AutoConfig
+
+from sglang.srt.distributed.parallel_state import (
+    destroy_distributed_environment,
+    destroy_model_parallel,
+    init_distributed_environment,
+    initialize_model_parallel,
+)
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe as fused_moe_sglang,
+)
+from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
+    triton_kernel_moe_forward,
+)
+from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
+from sglang.srt.layers.moe.topk import TopK, TopKConfig, select_experts
+
+
+def get_model_config(model_name: str, tp_size: int):
+    """Get model configuration parameters"""
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+    if config.architectures[0] == "Qwen2MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen3MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in [
+        "DeepseekV2ForCausalLM",
+        "DeepseekV3ForCausalLM",
+        "Glm4MoeForCausalLM",
+    ]:
+        E = (
+            config.n_routed_experts + 1
+            if config.architectures[0] in ["DeepseekV3ForCausalLM"]
+            else config.n_routed_experts
+        )
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    else:
+        # Default: Mixtral
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+
+    block_shape = None
+    if (
+        hasattr(config, "quantization_config")
+        and "weight_block_size" in config.quantization_config
+    ):
+        block_shape = config.quantization_config["weight_block_size"]
+        assert len(block_shape) == 2
+
+    shape_configs = {
+        "num_experts": E,
+        "topk": topk,
+        "hidden_size": config.hidden_size,
+        "shard_intermediate_size": shard_intermediate_size,
+        "dtype": config.torch_dtype,
+        "block_shape": block_shape,
+    }
+    print(f"{shape_configs=}")
+    return shape_configs
+
+
+def fused_moe_triton_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+):
+    topk_op = TopK(
+        top_k=topk,
+        renormalize=False,
+        use_grouped_topk=False,
+    )
+    topk_op.use_triton_kernels = True
+    triton_topk_output = topk_op.forward_cuda(
+        hidden_states=x,
+        router_logits=input_gating,
+    )
+
+    moe_runner_config = MoeRunnerConfig(
+        inplace=False,
+    )
+    return triton_kernel_moe_forward(
+        x,
+        w1,
+        w2,
+        triton_topk_output,
+        moe_runner_config,
+    )
+
+
+def fused_moe_sglang_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+    block_shape=None,
+):
+    topk_output = select_experts(
+        hidden_states=x,
+        router_logits=input_gating,
+        topk_config=TopKConfig(top_k=topk, renormalize=False),
+    )
+    return fused_moe_sglang(
+        x,
+        w1,
+        w2,
+        topk_output,
+        use_fp8_w8a8=use_fp8_w8a8,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        block_shape=block_shape,
+    )
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=list([128, 256, 512, 1024, 2048, 4096, 8192]),
+        line_arg="provider",
+        line_vals=[
+            "sglang_fused_moe_triton_v340",
+            "sglang_fused_moe_triton",
+        ],
+        line_names=[
+            "sglang_fused_moe_triton_v340",
+            "sglang_fused_moe_triton",
+        ],
+        styles=[
+            ("blue", "-"),
+            ("green", "-"),
+        ],
+        ylabel="Time (ms)",
+        plot_name="fused-moe-performance",
+        args={},
+    )
+)
+def benchmark(
+    batch_size,
+    provider,
+    model_config,
+    use_fp8_w8a8=False,
+    use_cuda_graph: bool = False,
+):
+    print(f"benchmark {provider} with batch_size={batch_size}")
+    torch.set_default_device("cuda")
+    torch.cuda.manual_seed_all(0)
+
+    num_tokens = batch_size
+    num_experts = model_config["num_experts"]
+    hidden_size = model_config["hidden_size"]
+    shard_intermediate_size = model_config["shard_intermediate_size"]
+    topk = model_config["topk"]
+    dtype = model_config["dtype"]
+    block_shape = model_config["block_shape"]
+
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+
+    w1 = torch.randn(num_experts, shard_intermediate_size, hidden_size, dtype=dtype)
+    w2 = torch.randn(
+        num_experts, hidden_size, shard_intermediate_size // 2, dtype=dtype
+    )
+
+    w1_tri = w1.clone()
+    w2_tri = w2.clone()
+    w1_tri = w1_tri.transpose(-2, -1).contiguous()
+    w2_tri = w2_tri.transpose(-2, -1).contiguous()
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    if provider == "sglang_fused_moe_triton_v340":
+        api_func = fused_moe_triton_api
+        api_kwargs = {
+            "x": x,
+            "w1": w1_tri,
+            "w2": w2_tri,
+            "input_gating": input_gating,
+            "topk": topk,
+        }
+    else:
+        api_func = fused_moe_sglang_api
+        api_kwargs = {
+            "x": x,
+            "w1": w1,
+            "w2": w2,
+            "input_gating": input_gating,
+            "topk": topk,
+            "use_fp8_w8a8": use_fp8_w8a8,
+            "block_shape": block_shape,
+        }
+
+    # Warmup
+    for _ in range(10):
+        _ = api_func(**api_kwargs)
+    torch.cuda.synchronize()
+
+    if use_cuda_graph:
+        stream = torch.cuda.Stream()
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph, stream=stream):
+            api_func(**api_kwargs)
+        torch.cuda.synchronize()
+
+        bench_lambda = lambda: graph.replay()
+    else:
+        bench_lambda = lambda: api_func(**api_kwargs)
+
+    quantiles = [0.5, 0.2, 0.8]
+    ms, min_ms, max_ms = triton.testing.do_bench(bench_lambda, quantiles=quantiles)
+    return ms, min_ms, max_ms
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument("--tp-size", type=int, default=2)
+    parser.add_argument("--use-fp8-w8a8", action="store_true")
+    parser.add_argument(
+        "--use-cuda-graph", action="store_true", help="Enable CUDA Graph capture/replay"
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/benchmark_ops/sglang_fused_moe/",
+    )
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+
+    try:
+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group(
+                backend="nccl" if torch.cuda.is_available() else "gloo",
+                init_method="tcp://127.0.0.1:23456",
+                world_size=1,
+                rank=0,
+            )
+
+        init_distributed_environment(
+            world_size=1,
+            rank=0,
+            distributed_init_method="tcp://127.0.0.1:23456",
+            local_rank=0,
+            backend="nccl" if torch.cuda.is_available() else "gloo",
+        )
+
+        initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+        )
+
+        model_config = get_model_config(args.model, args.tp_size)
+        benchmark.run(
+            show_plots=True,
+            print_data=True,
+            save_path=args.save_path,
+            model_config=model_config,
+            use_fp8_w8a8=args.use_fp8_w8a8,
+            use_cuda_graph=args.use_cuda_graph,
+        )
+    finally:
+        destroy_model_parallel()
+        destroy_distributed_environment()
+
+
+if __name__ == "__main__":
+    main()

benchmark/kernels/fused_moe_triton/benchmark_sum_scale.py

+import torch
+import triton
+import triton.language as tl
+from triton.testing import do_bench
+
+
+@triton.jit
+def _moe_sum_reduce_kernel(
+    input_ptr,
+    input_stride_0,
+    input_stride_1,
+    input_stride_2,
+    output_ptr,
+    output_stride_0,
+    output_stride_1,
+    token_num: int,
+    topk_num: int,
+    hidden_dim: int,
+    routed_scaling_factor: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DIM: tl.constexpr,
+    NUM_STAGE: tl.constexpr,
+):
+    input_stride_0 = tl.cast(input_stride_0, dtype=tl.int64)
+    input_stride_1 = tl.cast(input_stride_1, dtype=tl.int64)
+    output_stride_0 = tl.cast(output_stride_0, dtype=tl.int64)
+
+    token_block_id = tl.program_id(0)
+    dim_block_id = tl.program_id(1)
+
+    offs_token = token_block_id * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_dim = dim_block_id * BLOCK_DIM + tl.arange(0, BLOCK_DIM)
+
+    mask_token = offs_token < token_num
+    mask_dim = offs_dim < hidden_dim
+
+    base_ptrs = input_ptr + offs_token[:, None] * input_stride_0 + offs_dim[None, :]
+
+    accumulator = tl.zeros((BLOCK_M, BLOCK_DIM), dtype=tl.float32)
+
+    for i in tl.range(0, topk_num, num_stages=NUM_STAGE):
+        tile = tl.load(
+            base_ptrs + i * input_stride_1,
+            mask=mask_token[:, None] & mask_dim[None, :],
+            other=0.0,
+        )
+        accumulator += tile.to(tl.float32)
+    accumulator *= routed_scaling_factor
+
+    # -------- Write back --------
+    store_ptrs = output_ptr + offs_token[:, None] * output_stride_0 + offs_dim[None, :]
+    tl.store(
+        store_ptrs,
+        accumulator.to(input_ptr.dtype.element_ty),
+        mask=mask_token[:, None] & mask_dim[None, :],
+    )
+
+
+# _moe_sum_reduce_kernel kernel modified from https://github.com/ModelTC/lightllm/blob/main/lightllm/common/fused_moe/moe_sum_reduce.py
+def moe_sum_reduce(
+    input: torch.Tensor, output: torch.Tensor, routed_scaling_factor: float
+):
+    assert input.is_contiguous()
+    assert output.is_contiguous()
+
+    token_num, topk_num, hidden_dim = input.shape
+    assert output.shape[0] == token_num and output.shape[1] == hidden_dim
+
+    BLOCK_M = 1
+    BLOCK_DIM = 2048
+    NUM_STAGE = 1
+    num_warps = 16
+
+    grid = (
+        triton.cdiv(token_num, BLOCK_M),
+        triton.cdiv(hidden_dim, BLOCK_DIM),
+    )
+
+    _moe_sum_reduce_kernel[grid](
+        input,
+        *input.stride(),
+        output,
+        *output.stride(),
+        token_num=token_num,
+        topk_num=topk_num,
+        hidden_dim=hidden_dim,
+        routed_scaling_factor=routed_scaling_factor,
+        BLOCK_M=BLOCK_M,
+        BLOCK_DIM=BLOCK_DIM,
+        NUM_STAGE=NUM_STAGE,
+        num_warps=num_warps,
+    )
+    return
+
+
+def compute_sum_scaled_baseline(
+    x: torch.Tensor, out: torch.Tensor, routed_scaling_factor: float
+) -> torch.Tensor:
+    torch.sum(x, dim=1, out=out)
+    out.mul_(routed_scaling_factor)
+    return out
+
+
+@torch.compile
+def compute_sum_scaled_compiled(
+    x: torch.Tensor, out: torch.Tensor, routed_scaling_factor: float
+) -> torch.Tensor:
+    torch.sum(x * routed_scaling_factor, dim=1, out=out)
+    return out
+
+
+def get_benchmark():
+    num_tokens_range = [2**i for i in range(0, 13)]
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["num_tokens"],
+            x_vals=num_tokens_range,
+            line_arg="version",
+            line_vals=["baseline", "compiled", "triton"],
+            line_names=["Original", "TorchCompile", "TritonKernel"],
+            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name="sum_scaled_performance",
+            args={},
+        )
+    )
+    def benchmark(num_tokens, version):
+        topk = 9
+        hidden_size = 4096
+        dtype = torch.bfloat16
+        scaling_factor = 0.3
+
+        x = torch.randn(num_tokens, topk, hidden_size, dtype=dtype, device="cuda")
+        out = torch.empty(num_tokens, hidden_size, dtype=dtype, device="cuda")
+
+        # Warmup
+        for _ in range(3):
+            if version == "baseline":
+                compute_sum_scaled_baseline(x, out, scaling_factor)
+            elif version == "compiled":
+                compute_sum_scaled_compiled(x, out, scaling_factor)
+            else:
+                moe_sum_reduce(x, out, scaling_factor)
+
+        # Benchmark
+        quantiles = [0.5, 0.2, 0.8]
+        if version == "baseline":
+            ms, min_ms, max_ms = do_bench(
+                lambda: compute_sum_scaled_baseline(x, out, scaling_factor),
+                quantiles=quantiles,
+            )
+        elif version == "compiled":
+            ms, min_ms, max_ms = do_bench(
+                lambda: compute_sum_scaled_compiled(x, out, scaling_factor),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = do_bench(
+                lambda: moe_sum_reduce(x, out, scaling_factor), quantiles=quantiles
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+def verify_correctness(num_tokens=1024):
+    x = torch.randn(num_tokens, 9, 4096, device="cuda", dtype=torch.bfloat16)
+    scaling_factor = 0.3
+
+    out_baseline = torch.empty_like(x[:, 0])
+    compute_sum_scaled_baseline(x, out_baseline, scaling_factor)
+
+    out_compiled = torch.empty_like(out_baseline)
+    compute_sum_scaled_compiled(x, out_compiled, scaling_factor)
+
+    out_triton = torch.empty_like(out_baseline)
+    moe_sum_reduce(x, out_triton, scaling_factor)
+
+    if torch.allclose(
+        out_baseline, out_compiled, atol=1e-2, rtol=1e-2
+    ) and torch.allclose(out_baseline, out_triton, atol=1e-2, rtol=1e-2):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+        print(
+            f"Baseline vs Compiled: {(out_baseline - out_compiled).abs().max().item()}"
+        )
+        print(f"Baseline vs Triton: {(out_baseline - out_triton).abs().max().item()}")
+
+
+if __name__ == "__main__":
+    print("Running correctness verification...")
+    verify_correctness()
+
+    print("\nRunning performance benchmark...")
+    benchmark = get_benchmark()
+    benchmark.run(
+        print_data=True,
+        # save_path="./configs/benchmark_ops/sum_scaled/"
+    )

benchmark/kernels/fused_moe_triton/benchmark_torch_compile_fused_moe.py

+# python3 benchmark/kernels/fused_moe_triton/benchmark_torch_compile_fused_moe.py --model /DeepSeek-V3/ --tp-size 8 --use-fp8-w8a8
+import argparse
+
+import torch
+import triton
+from torch.nn import functional as F
+from transformers import AutoConfig
+
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe as fused_moe_triton,
+)
+from sglang.srt.model_executor.cuda_graph_runner import set_torch_compile_config
+
+
+def get_model_config(model_name: str, tp_size: int):
+    """Get model configuration parameters"""
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+        intermediate_size = config.ffn_config.ffn_hidden_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen2MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen3MoeForCausalLM":
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Llama4ForConditionalGeneration":
+        E = config.text_config.num_local_experts
+        topk = config.text_config.num_experts_per_tok
+        intermediate_size = config.text_config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in [
+        "Grok1ForCausalLM",
+        "Grok1ImgGen",
+        "Grok1AForCausalLM",
+    ]:
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    else:
+        # Default: Mixtral
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+
+    shape_configs = {
+        "num_experts": E,
+        "topk": topk,
+        "hidden_size": config.hidden_size,
+        "shard_intermediate_size": shard_intermediate_size,
+        "dtype": config.torch_dtype,
+    }
+    print(f"{shape_configs=}")
+    return shape_configs
+
+
+def fused_topk_native(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+):
+    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
+    M, _ = hidden_states.shape
+    topk_weights = torch.empty(
+        M, topk, dtype=torch.float32, device=hidden_states.device
+    )
+    topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
+    topk_weights = F.softmax(gating_output.float(), dim=-1)
+    topk_weights, topk_ids = torch.topk(topk_weights, topk, dim=-1)
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    return topk_weights, topk_ids
+
+
+@torch.compile(dynamic=False)
+def fused_moe_torch(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+) -> torch.Tensor:
+    assert not use_fp8_w8a8, "Fp8_w8a8 fused_moe is not supported for torch compile"
+
+    topk_weights, topk_ids = fused_topk_native(
+        hidden_states=x,
+        gating_output=input_gating,
+        topk=topk,
+        renormalize=True,
+    )
+    w13_weights = w1[topk_ids]
+    w1_weights, w3_weights = torch.chunk(w13_weights, 2, dim=2)
+    w2_weights = w2[topk_ids]
+    x1 = torch.einsum("ti,taoi -> tao", x, w1_weights)
+    x1 = F.silu(x1)
+    x3 = torch.einsum("ti, taoi -> tao", x, w3_weights)
+    expert_outs = torch.einsum("tao, taio -> tai", (x1 * x3), w2_weights)
+    return torch.einsum("tai,ta -> ti", expert_outs, topk_weights.to(expert_outs.dtype))
+
+
+def fused_moe_torch_compile(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+):
+    return fused_moe_torch(
+        x,
+        w1,
+        w2,
+        input_gating,
+        topk,
+        use_fp8_w8a8=use_fp8_w8a8,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+    )
+
+
+def fused_moe_sglang_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+):
+    return fused_moe_triton(
+        x,
+        w1,
+        w2,
+        input_gating,
+        topk,
+        renormalize=True,
+        inplace=True,
+        use_fp8_w8a8=use_fp8_w8a8,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+    )
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=list(range(1, 5)),
+        line_arg="provider",
+        line_vals=[
+            "fused_moe_triton",
+            "fused_moe_torch_compile",
+        ],
+        line_names=[
+            "fused_moe_triton",
+            "fused_moe_torch_compile",
+        ],
+        styles=[
+            ("blue", "-"),
+            ("green", "-"),
+        ],
+        ylabel="Time (ms)",
+        plot_name="fused-moe-performance",
+        args={},
+    )
+)
+def benchmark(batch_size, provider, model_config, use_fp8_w8a8=False):
+    print(f"benchmark {provider} with batch_size={batch_size}")
+    torch.set_default_device("cuda")
+    torch.cuda.manual_seed_all(0)
+    set_torch_compile_config()
+
+    num_tokens = batch_size
+    num_experts = model_config["num_experts"]
+    hidden_size = model_config["hidden_size"]
+    shard_intermediate_size = model_config["shard_intermediate_size"]
+    topk = model_config["topk"]
+    dtype = model_config["dtype"]
+
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+
+    if use_fp8_w8a8:
+        init_dtype = dtype
+        w1 = torch.randn(
+            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
+        )
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
+        )
+        w1 = w1.to(torch.float8_e4m3fn)
+        w2 = w2.to(torch.float8_e4m3fn)
+        w1_scale = torch.randn(num_experts, dtype=torch.float32)
+        w2_scale = torch.randn(num_experts, dtype=torch.float32)
+        a1_scale = torch.randn(1, dtype=torch.float32)
+        a2_scale = torch.randn(1, dtype=torch.float32)
+    else:
+        w1 = torch.randn(num_experts, shard_intermediate_size, hidden_size, dtype=dtype)
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=dtype
+        )
+        w1_scale = w2_scale = a1_scale = a2_scale = None
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    # Warmup
+    api_func = (
+        fused_moe_torch_compile
+        if provider == "fused_moe_torch_compile"
+        else fused_moe_sglang_api
+    )
+    for _ in range(10):
+        y = api_func(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+        )
+    torch.cuda.synchronize()
+
+    quantiles = [0.5, 0.2, 0.8]
+    ms, min_ms, max_ms = triton.testing.do_bench(
+        lambda: api_func(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+        )[0],
+        quantiles=quantiles,
+    )
+    return ms, min_ms, max_ms
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument("--tp-size", type=int, default=2)
+    parser.add_argument("--use-fp8-w8a8", action="store_true")
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/benchmark_ops/fused_moe_torch_compile/",
+    )
+    args = parser.parse_args()
+
+    model_config = get_model_config(args.model, args.tp_size)
+    benchmark.run(
+        show_plots=True,
+        print_data=True,
+        save_path=args.save_path,
+        model_config=model_config,
+        use_fp8_w8a8=args.use_fp8_w8a8,
+    )
+
+
+if __name__ == "__main__":
+    main()

benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py

+# python3 benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py --model /DeepSeek-V3/ --tp-size 8 --use-fp8-w8a8
+import argparse
+
+import torch
+import triton
+import vllm
+from transformers import AutoConfig
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_moe as fused_moe_vllm
+
+from sglang.srt.distributed.parallel_state import (
+    destroy_distributed_environment,
+    destroy_model_parallel,
+    init_distributed_environment,
+    initialize_model_parallel,
+)
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe as fused_moe_sglang,
+)
+
+
+def get_model_config(model_name: str, tp_size: int):
+    """Get model configuration parameters"""
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+        intermediate_size = config.ffn_config.ffn_hidden_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen2MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Qwen3MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in [
+        "DeepseekV2ForCausalLM",
+        "DeepseekV3ForCausalLM",
+        "Glm4MoeForCausalLM",
+    ]:
+        E = (
+            config.n_routed_experts + 1
+            if config.architectures[0] in ["DeepseekV3ForCausalLM"]
+            else config.n_routed_experts
+        )
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "Llama4ForConditionalGeneration":
+        E = config.text_config.num_local_experts
+        topk = config.text_config.num_experts_per_tok
+        intermediate_size = config.text_config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in [
+        "Grok1ForCausalLM",
+        "Grok1ImgGen",
+        "Grok1AForCausalLM",
+    ]:
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    else:
+        # Default: Mixtral
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+
+    vllm_version_num = (
+        vllm.__version_tuple__[0] * 100
+        + vllm.__version_tuple__[1] * 10
+        + vllm.__version_tuple__[2]
+    )
+    block_shape = None
+    if (
+        hasattr(config, "quantization_config")
+        and "weight_block_size" in config.quantization_config
+    ):
+        block_shape = config.quantization_config["weight_block_size"]
+        assert len(block_shape) == 2
+        assert (
+            vllm_version_num >= 66
+        ), "Block-wise quantized fp8 fused_moe is only supported for VLLM>=0.6.6.post1"
+
+    shape_configs = {
+        "num_experts": E,
+        "topk": topk,
+        "hidden_size": config.hidden_size,
+        "shard_intermediate_size": shard_intermediate_size,
+        "dtype": config.torch_dtype,
+        "block_shape": block_shape,
+    }
+    print(f"{shape_configs=}")
+    return shape_configs
+
+
+def fused_moe_vllm_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+    block_shape=None,
+):
+    if block_shape is not None:
+        return fused_moe_vllm(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            renormalize=True,
+            inplace=True,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            block_shape=block_shape,
+        )
+    else:
+        return fused_moe_vllm(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            renormalize=True,
+            inplace=True,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+        )
+
+
+def fused_moe_sglang_api(
+    x,
+    w1,
+    w2,
+    input_gating,
+    topk,
+    use_fp8_w8a8=False,
+    w1_scale=None,
+    w2_scale=None,
+    a1_scale=None,
+    a2_scale=None,
+    block_shape=None,
+):
+    return fused_moe_sglang(
+        x,
+        w1,
+        w2,
+        input_gating,
+        topk,
+        renormalize=True,
+        inplace=True,
+        use_fp8_w8a8=use_fp8_w8a8,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        block_shape=block_shape,
+    )
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=list(range(1, 513)),
+        line_arg="provider",
+        line_vals=[
+            "vllm_fused_moe_triton",
+            "sglang_fused_moe_triton",
+        ],
+        line_names=[
+            "vllm_fused_moe_triton",
+            "sglang_fused_moe_triton",
+        ],
+        styles=[
+            ("blue", "-"),
+            ("green", "-"),
+        ],
+        ylabel="Time (ms)",
+        plot_name="fused-moe-performance",
+        args={},
+    )
+)
+def benchmark(batch_size, provider, model_config, use_fp8_w8a8=False):
+    print(f"benchmark {provider} with batch_size={batch_size}")
+    torch.set_default_device("cuda")
+    torch.cuda.manual_seed_all(0)
+
+    num_tokens = batch_size
+    num_experts = model_config["num_experts"]
+    hidden_size = model_config["hidden_size"]
+    shard_intermediate_size = model_config["shard_intermediate_size"]
+    topk = model_config["topk"]
+    dtype = model_config["dtype"]
+    block_shape = model_config["block_shape"]
+
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    w1_scale = w2_scale = a1_scale = a2_scale = None
+
+    if use_fp8_w8a8:
+        init_dtype = dtype
+        w1 = torch.randn(
+            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
+        )
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
+        )
+        w1 = w1.to(torch.float8_e4m3fn)
+        w2 = w2.to(torch.float8_e4m3fn)
+
+        if block_shape is None:
+            w1_scale = torch.randn(num_experts, dtype=torch.float32)
+            w2_scale = torch.randn(num_experts, dtype=torch.float32)
+            a1_scale = torch.randn(1, dtype=torch.float32)
+            a2_scale = torch.randn(1, dtype=torch.float32)
+        else:
+            block_n, block_k = block_shape[0], block_shape[1]
+            n_tiles_w1 = (shard_intermediate_size + block_n - 1) // block_n
+            n_tiles_w2 = (hidden_size + block_n - 1) // block_n
+            k_tiles_w1 = (hidden_size + block_k - 1) // block_k
+            k_tiles_w2 = (shard_intermediate_size // 2 + block_k - 1) // block_k
+            w1_scale = torch.rand(
+                (num_experts, n_tiles_w1, k_tiles_w1), dtype=torch.float32
+            )
+            w2_scale = torch.rand(
+                (num_experts, n_tiles_w2, k_tiles_w2), dtype=torch.float32
+            )
+    else:
+        w1 = torch.randn(num_experts, shard_intermediate_size, hidden_size, dtype=dtype)
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=dtype
+        )
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    # Warmup
+    api_func = (
+        fused_moe_vllm_api
+        if provider == "vllm_fused_moe_triton"
+        else fused_moe_sglang_api
+    )
+    for _ in range(10):
+        y = api_func(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            block_shape=block_shape,
+        )
+    torch.cuda.synchronize()
+
+    quantiles = [0.5, 0.2, 0.8]
+    ms, min_ms, max_ms = triton.testing.do_bench(
+        lambda: api_func(
+            x,
+            w1,
+            w2,
+            input_gating,
+            topk,
+            use_fp8_w8a8=use_fp8_w8a8,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            block_shape=block_shape,
+        )[0],
+        quantiles=quantiles,
+    )
+    return ms, min_ms, max_ms
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument("--tp-size", type=int, default=2)
+    parser.add_argument("--use-fp8-w8a8", action="store_true")
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/benchmark_ops/vllm_sglang_fused_moe/",
+    )
+    args = parser.parse_args()
+
+    try:
+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group(
+                backend="nccl" if torch.cuda.is_available() else "gloo",
+                init_method="tcp://127.0.0.1:23456",
+                world_size=1,
+                rank=0,
+            )
+
+        init_distributed_environment(
+            world_size=1,
+            rank=0,
+            distributed_init_method="tcp://127.0.0.1:23456",
+            local_rank=0,
+            backend="nccl" if torch.cuda.is_available() else "gloo",
+        )
+
+        initialize_model_parallel(
+            tensor_model_parallel_size=1,
+            pipeline_model_parallel_size=1,
+        )
+
+        model_config = get_model_config(args.model, args.tp_size)
+        benchmark.run(
+            show_plots=True,
+            print_data=True,
+            save_path=args.save_path,
+            model_config=model_config,
+            use_fp8_w8a8=args.use_fp8_w8a8,
+        )
+    finally:
+        destroy_model_parallel()
+        destroy_distributed_environment()
+
+
+if __name__ == "__main__":
+    main()

benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py

+# Adapted from https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py
+import argparse
+import json
+import time
+from contextlib import nullcontext
+from datetime import datetime
+from typing import Any, Dict, List, Tuple, TypedDict
+
+import ray
+import torch
+import triton
+from ray.experimental.tqdm_ray import tqdm
+from transformers import AutoConfig
+
+from sglang.srt.layers.moe.fused_moe_triton import override_config
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe,
+    get_config_dtype_str,
+    get_config_file_name,
+    get_default_config,
+    get_moe_configs,
+)
+from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
+from sglang.srt.layers.moe.topk import TopKConfig, select_experts
+from sglang.srt.utils import is_hip
+
+_is_hip = is_hip()
+
+
+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_config(
+    config: BenchmarkConfig,
+    num_tokens: int,
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a8: bool,
+    use_int8_w8a16: bool,
+    block_shape: List[int] = None,
+    num_iters: int = 100,
+) -> float:
+    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    if use_int8_w8a16 or use_int8_w8a8:
+        w1 = torch.randint(
+            -127,
+            127,
+            (
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+            ),
+            dtype=torch.int8,
+        )
+        w2 = torch.randint(
+            -127,
+            127,
+            (
+                num_experts,
+                hidden_size,
+                shard_intermediate_size // 2,
+            ),
+            dtype=torch.int8,
+        )
+    else:
+        w1 = torch.randn(
+            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
+        )
+        w2 = torch.randn(
+            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
+        )
+    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)
+
+    w1_scale = None
+    w2_scale = None
+    a1_scale = None
+    a2_scale = None
+    if use_int8_w8a16:
+        w1_scale = torch.randn(
+            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
+        )
+        w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
+    if use_fp8_w8a8 or use_int8_w8a8:
+        if use_int8_w8a8 and block_shape is None:
+            w1_scale = torch.randn(
+                num_experts, shard_intermediate_size, dtype=torch.float32
+            )
+            w2_scale = torch.randn(num_experts, hidden_size, dtype=torch.float32)
+        elif block_shape is None:
+            w1_scale = torch.randn(num_experts, dtype=torch.float32)
+            w2_scale = torch.randn(num_experts, dtype=torch.float32)
+            a1_scale = torch.randn(1, dtype=torch.float32)
+            a2_scale = torch.randn(1, dtype=torch.float32)
+        else:
+            block_n, block_k = block_shape[0], block_shape[1]
+            n_tiles_w1 = (shard_intermediate_size + block_n - 1) // block_n
+            n_tiles_w2 = (hidden_size + block_n - 1) // block_n
+            k_tiles_w1 = (hidden_size + block_k - 1) // block_k
+            k_tiles_w2 = (shard_intermediate_size // 2 + block_k - 1) // block_k
+            w1_scale = torch.rand(
+                (num_experts, n_tiles_w1, k_tiles_w1), dtype=torch.float32
+            )
+            w2_scale = torch.rand(
+                (num_experts, n_tiles_w2, k_tiles_w2), dtype=torch.float32
+            )
+
+    if use_fp8_w8a8:
+        w1 = w1.to(torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn)
+        w2 = w2.to(torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn)
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+    topk_config = TopKConfig(
+        top_k=topk,
+        renormalize=True,
+    )
+    topk_output = select_experts(x, input_gating, topk_config)
+
+    def prepare(i: int):
+        input_gating = gating_output[i]
+        new_topk_output = select_experts(x, input_gating, topk_config)
+        topk_output.topk_weights.copy_(new_topk_output.topk_weights)
+        topk_output.topk_ids.copy_(new_topk_output.topk_ids)
+        topk_output.router_logits.copy_(new_topk_output.router_logits)
+
+    def run():
+        moe_runner_config = MoeRunnerConfig(
+            inplace=True,
+        )
+
+        with override_config(config):
+            fused_moe(
+                x,
+                w1,
+                w2,
+                topk_output,
+                moe_runner_config=moe_runner_config,
+                use_fp8_w8a8=use_fp8_w8a8,
+                use_int8_w8a8=use_int8_w8a8,
+                use_int8_w8a16=use_int8_w8a16,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a1_scale,
+                a2_scale=a2_scale,
+                block_shape=block_shape,
+            )
+
+    # 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 get_rocm_configs_compute_bound() -> List[Dict[str, int]]:
+    configs: List[BenchmarkConfig] = []
+    waves_per_eu_range = 0
+    for num_stages in [2]:
+        for block_m in [32, 64, 128, 256]:
+            for block_k in [32, 64, 128, 256]:
+                for block_n in [16, 32, 64, 128, 256]:
+                    for num_warps in [1, 2, 4, 8]:
+                        for group_size in [1, 4, 8, 16, 32]:
+                            configs.append(
+                                {
+                                    "BLOCK_SIZE_M": block_m,
+                                    "BLOCK_SIZE_N": block_n,
+                                    "BLOCK_SIZE_K": block_k,
+                                    "GROUP_SIZE_M": group_size,
+                                    "num_warps": num_warps,
+                                    "num_stages": num_stages,
+                                    "waves_per_eu": waves_per_eu_range,
+                                }
+                            )
+    return configs
+
+
+def get_configs_compute_bound() -> List[Dict[str, int]]:
+    # Reduced search space for faster tuning.
+    # TODO(woosuk): Increase the search space and use a performance model to
+    # prune the search space.
+    configs: List[BenchmarkConfig] = []
+    if _is_hip:
+        configs = get_rocm_configs_compute_bound()
+    else:
+        for num_stages in [2, 3, 4, 5]:
+            for block_m in [16, 32, 64, 128, 256]:
+                for block_k in [64, 128, 256]:
+                    for block_n in [32, 64, 128, 256]:
+                        for num_warps in [4, 8]:
+                            for group_size in [1, 16, 32, 64]:
+                                configs.append(
+                                    {
+                                        "BLOCK_SIZE_M": block_m,
+                                        "BLOCK_SIZE_N": block_n,
+                                        "BLOCK_SIZE_K": block_k,
+                                        "GROUP_SIZE_M": group_size,
+                                        "num_warps": num_warps,
+                                        "num_stages": num_stages,
+                                    }
+                                )
+    return configs
+
+
+@ray.remote(num_gpus=1)
+class BenchmarkWorker:
+
+    def __init__(self, seed: int) -> None:
+        torch.set_default_device("cuda")
+        torch.cuda.manual_seed_all(0)
+        self.seed = seed
+        # Get the device ID to allocate tensors and kernels
+        # on the respective GPU.
+        self.device_id = int(ray.get_gpu_ids()[0])
+
+    def benchmark(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        shard_intermediate_size: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a8: bool,
+        use_int8_w8a16: bool,
+        block_shape: List[int],
+    ) -> Tuple[Dict[str, int], float]:
+        torch.cuda.manual_seed_all(0)
+        dtype_str = get_config_dtype_str(
+            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+        )
+        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
+        # is the intermediate size after silu_and_mul.
+        block_n = block_shape[0] if block_shape else 0
+        block_k = block_shape[1] if block_shape else 0
+        op_config = get_moe_configs(
+            num_experts, shard_intermediate_size // 2, dtype_str, block_n, block_k
+        )
+        if op_config is None:
+            config = get_default_config(
+                num_tokens,
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+                topk,
+                dtype_str,
+                False,
+                block_shape,
+            )
+        else:
+            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
+        with torch.cuda.device(self.device_id) if is_hip() else nullcontext():
+            kernel_time = benchmark_config(
+                config,
+                num_tokens,
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+                topk,
+                dtype,
+                use_fp8_w8a8,
+                use_int8_w8a8,
+                use_int8_w8a16,
+                block_shape,
+            )
+        return config, kernel_time
+
+    def tune(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        shard_intermediate_size: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a8: bool,
+        use_int8_w8a16: bool,
+        block_shape: List[int],
+        search_space: List[Dict[str, int]],
+    ) -> Dict[str, int]:
+        best_config = None
+        best_time = float("inf")
+        with torch.cuda.device(self.device_id) if is_hip() else nullcontext():
+            for config in tqdm(search_space):
+                try:
+                    kernel_time = benchmark_config(
+                        config,
+                        num_tokens,
+                        num_experts,
+                        shard_intermediate_size,
+                        hidden_size,
+                        topk,
+                        dtype,
+                        use_fp8_w8a8,
+                        use_int8_w8a8,
+                        use_int8_w8a16,
+                        block_shape,
+                        num_iters=10,
+                    )
+                except (triton.runtime.autotuner.OutOfResources, RuntimeError):
+                    # Some configurations may be invalid and fail to compile.
+                    continue
+
+                if kernel_time < best_time:
+                    best_time = kernel_time
+                    best_config = config
+        now = datetime.now()
+        print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
+        assert best_config is not None
+        return best_config
+
+
+def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
+    return {
+        "BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
+        "BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
+        "BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
+        "GROUP_SIZE_M": config["GROUP_SIZE_M"],
+        "num_warps": config["num_warps"],
+        "num_stages": config["num_stages"],
+        **(
+            {"waves_per_eu": config["waves_per_eu"]} if "waves_per_eu" in config else {}
+        ),
+    }
+
+
+def save_configs(
+    configs: Dict[int, BenchmarkConfig],
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a8: bool,
+    use_int8_w8a16: bool,
+    block_shape: List[int],
+) -> None:
+    dtype_str = get_config_dtype_str(
+        dtype,
+        use_int8_w8a16=use_int8_w8a16,
+        use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a8=use_int8_w8a8,
+    )
+
+    # NOTE(woosuk): The current naming convention uses w2.shape[2], which
+    # is the intermediate size after silu_and_mul.
+    filename = get_config_file_name(
+        num_experts,
+        shard_intermediate_size // 2,
+        dtype_str,
+        block_shape,
+    )
+
+    print(f"Writing best config to {filename}...")
+    with open(filename, "w") as f:
+        json.dump(configs, f, indent=4)
+        f.write("\n")
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    config = AutoConfig.from_pretrained(args.model, trust_remote_code=True)
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+        intermediate_size = config.ffn_config.ffn_hidden_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in ["Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"]:
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+        E = (
+            config.n_routed_experts + (0 if args.disable_shared_experts_fusion else 1)
+            if config.architectures[0] in ["DeepseekV3ForCausalLM"]
+            else config.n_routed_experts
+        )
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] == "Llama4ForConditionalGeneration":
+        E = config.text_config.num_local_experts + (
+            0 if args.disable_shared_experts_fusion else 1
+        )
+        topk = config.text_config.num_experts_per_tok
+        intermediate_size = config.text_config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in [
+        "Grok1ForCausalLM",
+        "Grok1ImgGen",
+        "Grok1AForCausalLM",
+    ]:
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in ["Glm4MoeForCausalLM"]:
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    else:
+        # Default: Mixtral
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+
+    hidden_size = getattr(config, "hidden_size", None) or config.text_config.hidden_size
+    dtype = config.torch_dtype
+    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
+    use_int8_w8a8 = args.dtype == "int8_w8a8"
+    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    block_shape = None
+    if (
+        hasattr(config, "quantization_config")
+        and "weight_block_size" in config.quantization_config
+    ):
+        block_shape = config.quantization_config["weight_block_size"]
+        assert len(block_shape) == 2
+
+    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)
+
+    if args.tune:
+        search_space = get_configs_compute_bound()
+        if block_shape is not None:
+            block_n, block_k = block_shape[0], block_shape[1]
+            search_space = [
+                config
+                for config in search_space
+                if block_k % config["BLOCK_SIZE_K"] == 0
+            ]
+        print(f"Start tuning over {len(search_space)} configurations...")
+
+        start = time.perf_counter()
+        configs = _distribute(
+            "tune",
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a8,
+                    use_int8_w8a16,
+                    block_shape,
+                    search_space,
+                )
+                for batch_size in batch_sizes
+            ],
+        )
+        best_configs = {
+            M: sort_config(config) for M, config in zip(batch_sizes, configs)
+        }
+        save_configs(
+            best_configs,
+            E,
+            shard_intermediate_size,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a8,
+            use_int8_w8a16,
+            block_shape,
+        )
+        end = time.perf_counter()
+        print(f"Tuning took {end - start:.2f} seconds")
+    else:
+        outputs = _distribute(
+            "benchmark",
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a8,
+                    use_int8_w8a16,
+                    block_shape,
+                )
+                for batch_size in batch_sizes
+            ],
+        )
+
+        for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
+            print(f"Batch size: {batch_size}, config: {config}")
+            print(f"Kernel time: {kernel_time:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument("--tp-size", "--tp", type=int, default=2)
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        choices=["auto", "fp8_w8a8", "int8_w8a16", "int8_w8a8"],
+        default="auto",
+    )
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--tune", action="store_true")
+    parser.add_argument("--disable-shared-experts-fusion", action="store_true")
+    args = parser.parse_args()
+
+    main(args)

benchmark/kernels/minmax-text-01-lightning_attention/benchmark_lightning_attention_decode.py

+import itertools
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import triton
+import triton.language as tl
+from einops import rearrange
+from sgl_kernel import lightning_attention_decode as sgl_lightning_attention_decode
+
+
+@triton.jit
+def _decode_kernel(
+    Q,
+    K,
+    V,
+    KV,
+    Out,
+    S,
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n: tl.constexpr,
+    d: tl.constexpr,
+    d_original: tl.constexpr,
+    e: tl.constexpr,
+    e_original: tl.constexpr,
+):
+    off_bh = tl.program_id(0)
+    off_h = off_bh % h
+
+    qk_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    o_offset = off_bh * n * e
+    kv_offset = off_bh * d * e
+
+    s = tl.load(S + off_h)
+    ratio = tl.exp(-s)
+
+    d_idx = tl.arange(0, d)
+    e_idx = tl.arange(0, e)
+
+    # Create masks for original dimensions
+    d_mask = d_idx < d_original
+    e_mask = e_idx < e_original
+
+    # Load with masking
+    q = tl.load(Q + qk_offset + d_idx, mask=d_mask, other=0.0)
+    k = tl.load(K + qk_offset + d_idx, mask=d_mask, other=0.0)
+    v = tl.load(V + v_offset + e_idx, mask=e_mask, other=0.0)
+
+    # Load KV with 2D masking
+    kv = tl.load(
+        KV + kv_offset + d_idx[:, None] * e + e_idx[None, :],
+        mask=(d_mask[:, None] & e_mask[None, :]),
+        other=0.0,
+    )
+
+    # Compute outer product using element-wise operations
+    k_v_prod = k[:, None] * v[None, :]
+    kv = ratio * kv + k_v_prod
+
+    # Store KV with 2D masking
+    tl.store(
+        KV + kv_offset + d_idx[:, None] * e + e_idx[None, :],
+        kv.to(KV.dtype.element_ty),
+        mask=(d_mask[:, None] & e_mask[None, :]),
+    )
+
+    # Compute matrix-vector multiplication using element-wise operations and reduction
+    o = tl.sum(q[:, None] * kv, axis=0)
+
+    # Store output with masking
+    tl.store(Out + o_offset + e_idx, o.to(Out.dtype.element_ty), mask=e_mask)
+
+
+def lightning_attn_decode(q, k, v, kv, s):
+    """Triton implementation of Lightning Attention decode operation"""
+    b, h, n, d = q.shape
+    e = v.shape[-1]
+    assert n == 1, "Sequence length must be 1 in decode mode"
+
+    # Get padded dimensions (power of 2)
+    d_padded = next_power_of_2(d)
+    e_padded = next_power_of_2(e)
+
+    # Create output tensor (padded)
+    o_padded = torch.empty(b, h, n, e_padded, dtype=v.dtype, device=v.device)
+
+    # Create padded tensors without actually padding the data
+    q_padded = torch.empty(b, h, n, d_padded, dtype=q.dtype, device=q.device)
+    k_padded = torch.empty(b, h, n, d_padded, dtype=k.dtype, device=k.device)
+    v_padded = torch.empty(b, h, n, e_padded, dtype=v.dtype, device=v.device)
+    kv_padded = torch.empty(
+        b, h, d_padded, e_padded, dtype=torch.float32, device=kv.device
+    )
+
+    # Copy data to padded tensors
+    q_padded[..., :d] = q
+    k_padded[..., :d] = k
+    v_padded[..., :e] = v
+    kv_padded[..., :d, :e] = kv
+
+    # Launch kernel
+    grid = (b * h, 1)
+    _decode_kernel[grid](
+        q_padded,
+        k_padded,
+        v_padded,
+        kv_padded,
+        o_padded,
+        s,
+        b=b,
+        h=h,
+        n=n,
+        d=d_padded,
+        d_original=d,
+        e=e_padded,
+        e_original=e,
+    )
+
+    # Get unpadded outputs
+    o = o_padded[..., :e]
+    kv_out = kv_padded[..., :d, :e]
+
+    return o, kv_out
+
+
+def next_power_of_2(n):
+    return 2 ** (int(math.ceil(math.log(n, 2))))
+
+
+class MiniMaxText01LightningAttention(nn.Module):
+    def __init__(self, config=None, layer_idx: Optional[int] = None, **kwargs):
+        super().__init__()
+        if config is None:
+            config = type("Config", (), kwargs)
+
+        bias = False
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+
+        self.out_proj = nn.Linear(
+            self.head_dim * self.num_heads, self.hidden_size, bias=bias
+        )
+        self.act = get_activation_fn(config.hidden_act)
+        self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads)
+
+        self.qkv_proj = nn.Linear(
+            self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias
+        )
+        self.output_gate = nn.Linear(
+            self.hidden_size, self.head_dim * self.num_heads, bias=bias
+        )
+
+        # for inference only
+        self.offset = 0
+        self.layer_idx = layer_idx
+
+    def forward(
+        self,
+        hidden_states,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, h, n, m)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if (not self.training) and (not do_eval):
+            return self.inference(
+                hidden_states,
+                attn_mask,
+                output_attentions,
+                past_key_value,
+                use_cache,
+                slope_rate,
+            )
+
+    def inference(
+        self,
+        x,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, n)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,  # (h, 1, 1)
+    ):
+        # x: b n d
+        b, n, d = x.shape
+        # linear map
+        qkv = self.act(self.qkv_proj(x))
+        new_shape = qkv.size()[:-1] + (self.num_heads, -1)
+        qkv = qkv.view(*new_shape)
+        q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3)
+        q = q.transpose(1, 2)  # [b, n, h, d] -> [b, h, n, d]
+        k = k.transpose(1, 2)  # [b, n, h, d] -> [b, h, n, d]
+        v = v.transpose(1, 2)  # [b, n, h, d] -> [b, h, n, e]
+
+        self.offset += 1
+        ratio = torch.exp(-slope_rate)  # [h, 1, 1]
+
+        # decode mode
+        kv = past_key_value  # [b, h, d, e]
+        output = []
+        for i in range(n):
+            # kv: [b, h, d, e]
+            # ratio: [h, 1, 1]
+            # k: [b, h, n, d]
+            # v: [b, h, n, e]
+            # k[:, :, i : i + 1]: [b, h, 1, d]
+            # v[:, :, i : i + 1]: [b, h, 1, e]
+            # ratio * kv: [b, h, d, e]
+            # torch.einsum(
+            #     "... n d, ... n e -> ... d e",
+            #     k[:, :, i : i + 1],
+            #     v[:, :, i : i + 1],
+            # )
+            # [b, h, d, e] + [b, h, d, e] -> [b, h, d, e]
+            kv = ratio * kv + torch.einsum(
+                "... n d, ... n e -> ... d e",
+                k[:, :, i : i + 1],
+                v[:, :, i : i + 1],
+            )
+            # q[:, :, i : i + 1]: [b, h, 1, d]
+            # kv.to(q.dtype): [b, h, d, e]
+            # torch.einsum(
+            #     "... n e, ... e d -> ... n d", q[:, :, i : i + 1], kv.to(q.dtype)
+            # )
+            # [b, h, 1, d] * [b, h, d, e] -> [b, h, 1, e]
+            qkv = torch.einsum(
+                "... n e, ... e d -> ... n d", q[:, :, i : i + 1], kv.to(q.dtype)
+            )
+            output.append(qkv)
+        output = torch.cat(output, dim=-2)
+
+        # reshape
+        output = rearrange(output, "b h n d -> b n (h d)")
+        # normalize
+        output = self.norm(output)
+        # gate
+        output = F.sigmoid(self.output_gate(x)) * output
+        # outproj
+        output = self.out_proj(output)
+
+        attn_weights = None
+
+        return output, attn_weights, kv
+
+
+def get_activation_fn(activation):
+    if activation == "gelu":
+        return F.gelu
+    elif activation == "relu":
+        return F.relu
+    elif activation == "elu":
+        return F.elu
+    elif activation == "sigmoid":
+        return F.sigmoid
+    elif activation == "exp":
+
+        def f(x):
+            with torch.no_grad():
+                x_max = torch.max(x, dim=-1, keepdims=True).values
+            y = torch.exp(x - x_max)
+            return y
+
+        return f
+    elif activation == "leak":
+        return F.leaky_relu
+    elif activation == "1+elu":
+
+        def f(x):
+            return 1 + F.elu(x)
+
+        return f
+    elif activation == "2+elu":
+
+        def f(x):
+            return 2 + F.elu(x)
+
+        return f
+    elif activation == "silu" or activation == "swish":
+        return F.silu
+    elif activation == "sine":
+        return torch.sin
+    else:
+        return lambda x: x
+
+
+class MiniMaxText01RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniMaxText01RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+def test_lightning_attention_implementations(model_params):
+    torch.manual_seed(42)
+
+    batch_size = 64
+    seq_len = 1
+    dtype = torch.bfloat16
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    hidden_states = torch.randn(
+        batch_size, seq_len, model_params["hidden_size"], dtype=dtype, device=device
+    )
+
+    attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+    slope_rate = _build_slope_tensor(model_params["num_attention_heads"]).to(device)
+
+    model_attn = MiniMaxText01LightningAttention(**model_params).to(dtype).to(device)
+    model_attn.eval()
+
+    d = model_params["head_dim"]
+    past_kv = torch.randn(
+        batch_size,
+        model_params["num_attention_heads"],
+        d,
+        d,
+        device=device,
+    )
+    with torch.no_grad():
+        model_output, _, new_kv = model_attn.inference(
+            hidden_states,
+            attn_mask=attention_mask,
+            slope_rate=slope_rate,
+            past_key_value=past_kv,
+        )
+
+    qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+    new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+    qkv = qkv.view(*new_shape)
+    q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    past_kv = past_kv.contiguous()
+    slope_rate = slope_rate.contiguous()
+
+    # Test Triton implementation
+    triton_output, triton_new_kv = lightning_attn_decode(q, k, v, past_kv, slope_rate)
+    triton_output = triton_output.transpose(1, 2).contiguous()
+    triton_output = triton_output.view(batch_size, seq_len, -1)
+    triton_output = model_attn.norm(triton_output)
+    triton_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * triton_output
+    triton_output = model_attn.out_proj(triton_output)
+
+    # Test SGL implementation
+    sgl_output = torch.empty_like(v)
+    sgl_new_kv = torch.empty_like(past_kv)
+    sgl_lightning_attention_decode(q, k, v, past_kv, slope_rate, sgl_output, sgl_new_kv)
+
+    sgl_output = sgl_output.transpose(1, 2).contiguous()
+    sgl_output = sgl_output.view(batch_size, seq_len, -1)
+    sgl_output = model_attn.norm(sgl_output)
+    sgl_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * sgl_output
+    sgl_output = model_attn.out_proj(sgl_output)
+
+    # Verify Triton implementation results
+    torch.testing.assert_close(
+        model_output,
+        triton_output,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="Triton lightning attention implementation produces different output results",
+    )
+    torch.testing.assert_close(
+        new_kv,
+        triton_new_kv,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="Triton lightning attention implementation produces different kv results",
+    )
+
+    # Verify SGL implementation results
+    torch.testing.assert_close(
+        model_output,
+        sgl_output,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="SGL lightning attention implementation produces different output results",
+    )
+    torch.testing.assert_close(
+        new_kv,
+        sgl_new_kv,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="SGL lightning attention implementation produces different kv results",
+    )
+
+    print("✅ All implementations match")
+
+
+def _build_slope_tensor(n_attention_heads: int):
+    def get_slopes(n):
+        def get_slopes_power_of_2(n):
+            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)
+        else:
+            closest_power_of_2 = 2 ** math.floor(math.log2(n))
+            return (
+                get_slopes_power_of_2(closest_power_of_2)
+                + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+            )
+
+    slopes = torch.tensor(get_slopes(n_attention_heads)).reshape(
+        n_attention_heads, 1, 1
+    )
+    return slopes
+
+
+def get_benchmark():
+    batch_size_range = [i for i in range(1, 33)]  # max 32
+    seq_length_range = [1]  # decode mode sequence length is fixed to 1
+    configs = list(itertools.product(batch_size_range, seq_length_range))
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["Original", "Triton", "SGL"],
+            line_names=[
+                "Original PyTorch Implementation",
+                "Triton Implementation",
+                "SGL Implementation",
+            ],
+            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name="lightning-attention-decode-performance",
+            args={},
+        )
+    )
+    def benchmark(batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        device = torch.device("cuda")
+
+        params = {
+            "hidden_size": 6144,
+            "num_attention_heads": 64,
+            "head_dim": 96,
+            "hidden_act": "gelu",
+        }
+
+        hidden_states = torch.randn(
+            batch_size, seq_len, params["hidden_size"], dtype=dtype, device=device
+        )
+
+        attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+        slope_rate = _build_slope_tensor(params["num_attention_heads"]).to(device)
+        model_attn = MiniMaxText01LightningAttention(**params).to(dtype).to(device)
+        model_attn.eval()
+
+        d = params["head_dim"]
+        past_kv = torch.randn(
+            batch_size,
+            params["num_attention_heads"],
+            d,
+            d,
+            device=device,
+        )
+
+        quantiles = [0.5, 0.2, 0.8]
+        if provider == "Original":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: model_attn.inference(
+                    hidden_states,
+                    attn_mask=attention_mask,
+                    slope_rate=slope_rate,
+                    past_key_value=past_kv,
+                ),
+                quantiles=quantiles,
+            )
+        elif provider == "Triton":
+
+            def run_triton():
+                qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+                new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+                qkv = qkv.view(*new_shape)
+                q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+                q = q.transpose(1, 2)
+                k = k.transpose(1, 2)
+                v = v.transpose(1, 2)
+
+                output, new_kv = lightning_attn_decode(q, k, v, past_kv, slope_rate)
+                output = output.transpose(1, 2).contiguous()
+                output = output.view(batch_size, seq_len, -1)
+                output = model_attn.norm(output)
+                output = torch.sigmoid(model_attn.output_gate(hidden_states)) * output
+                return model_attn.out_proj(output)
+
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                run_triton,
+                quantiles=quantiles,
+            )
+        else:  # SGL
+
+            def run_sgl():
+                qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+                new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+                qkv = qkv.view(*new_shape)
+                q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+                q = q.transpose(1, 2).contiguous()
+                k = k.transpose(1, 2).contiguous()
+                v = v.transpose(1, 2).contiguous()
+
+                output = torch.empty_like(v)
+                new_kv = torch.empty_like(past_kv)
+                sgl_lightning_attention_decode(
+                    q, k, v, past_kv, slope_rate, output, new_kv
+                )
+
+                output = output.transpose(1, 2).contiguous()
+                output = output.view(batch_size, seq_len, -1)
+                output = model_attn.norm(output)
+                output = torch.sigmoid(model_attn.output_gate(hidden_states)) * output
+                return model_attn.out_proj(output)
+
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                run_sgl,
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/lightning_attention_decode/",
+        help="Path to save lightning attention decode benchmark results",
+    )
+    args = parser.parse_args()
+
+    params = {
+        "hidden_size": 6144,
+        "num_attention_heads": 64,
+        "head_dim": 96,
+        "hidden_act": "silu",
+    }
+    # Run correctness test first
+    # Adapted from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/config.json
+    test_lightning_attention_implementations(params)
+
+    # Run performance benchmark
+    benchmark = get_benchmark()
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmark/kernels/minmax-text-01-lightning_attention/benchmark_lightning_attention_prefill.py

+import itertools
+import math
+import os
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import triton
+import triton.language as tl
+from einops import rearrange
+
+
+# Adapted from https://github.com/OpenNLPLab/lightning-attention/blob/main/lightning_attn/ops/triton/lightning_attn2.py
+@triton.jit
+def _fwd_kernel(
+    Q,
+    K,
+    V,
+    Out,
+    S,  # log lambda
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n: tl.constexpr,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK: tl.constexpr,
+    BLOCK_MODEL: tl.constexpr,
+):
+    ##### get offset
+    off_bh = tl.program_id(0)
+    off_h = off_bh % h
+    off_e = tl.program_id(1)
+    qk_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    o_offset = off_bh * n * e
+    # channel offset
+    e_offset = off_e * BLOCK_MODEL
+
+    ##### get block ptr
+    Q_block_ptr = Q + qk_offset + tl.arange(0, d)[None, :]
+    K_trans_block_ptr = K + qk_offset + tl.arange(0, d)[:, None]
+    V_block_ptr = V + v_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
+    O_block_ptr = Out + o_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
+    S_block_ptr = S + off_h
+
+    ##### init diag decay(Lambda); q, k decay; kv
+    s = tl.load(S_block_ptr)
+    # q, k decay
+    off_block = tl.arange(
+        0, BLOCK
+    )  # Not bug, this is a bit different from algorithm 1, but is mathematically equivalent
+    q_decay = tl.exp(-s.to(tl.float32) * off_block[:, None])
+    k_trans_decay = tl.exp(-s.to(tl.float32) * (BLOCK - off_block[None, :]))
+    block_decay = tl.exp(-s.to(tl.float32) * BLOCK)
+    # diag decay
+    index = off_block[:, None] - off_block[None, :]
+    s_index = s * index
+    s_index = tl.where(index >= 0, -s_index, float("-inf"))
+    diag_decay = tl.exp(s_index)
+    kv = tl.zeros([d, BLOCK_MODEL], dtype=tl.float32)
+
+    ##### compute
+    for i in range(NUM_BLOCK):
+        # load
+        q = tl.load(
+            Q_block_ptr + off_block[:, None] * d, mask=off_block[:, None] < n, other=0.0
+        ).to(tl.float32)
+        k_trans = tl.load(
+            K_trans_block_ptr + off_block[None, :] * d,
+            mask=off_block[None, :] < n,
+            other=0.0,
+        ).to(tl.float32)
+        v = tl.load(
+            V_block_ptr + off_block[:, None] * e, mask=off_block[:, None] < n, other=0.0
+        ).to(tl.float32)
+
+        # compute
+        qk = tl.dot(q, k_trans) * diag_decay
+        o_intra = tl.dot(qk, v)
+        o_inter = tl.dot(q, kv) * q_decay
+        o = o_intra + o_inter
+
+        # save and update
+        tl.store(
+            O_block_ptr + off_block[:, None] * e,
+            o.to(O_block_ptr.dtype.element_ty),
+            mask=off_block[:, None] < n,
+        )
+        kv = block_decay * kv + tl.dot(k_trans * k_trans_decay, v)
+        off_block += BLOCK
+
+
+def lightning_attn2(q, k, v, s):
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    s = s.contiguous()
+
+    b, h, n, d = q.shape
+    e = v.shape[-1]
+
+    # Pad d to next power of 2
+    d_padded = next_power_of_2(d)
+    if d_padded != d:
+        q_padded = F.pad(q, (0, d_padded - d))
+        k_padded = F.pad(k, (0, d_padded - d))
+    else:
+        q_padded = q
+        k_padded = k
+
+    # Pad e to next power of 2
+    e_padded = next_power_of_2(e)
+    if e_padded != e:
+        v_padded = F.pad(v, (0, e_padded - e))
+    else:
+        v_padded = v
+
+    o_padded = torch.empty((b, h, n, e_padded), dtype=q.dtype, device=q.device)
+
+    BLOCK = 64
+    NUM_BLOCK = triton.cdiv(q.shape[2], BLOCK)
+    # parallel over channel
+    BLOCK_MODEL = min(triton.next_power_of_2(e_padded), 32)
+    grid = (b * h, triton.cdiv(e_padded, BLOCK_MODEL))
+
+    _fwd_kernel[grid](
+        q_padded,
+        k_padded,
+        v_padded,
+        o_padded,
+        s,
+        b,
+        h,
+        n,
+        d_padded,
+        e_padded,
+        BLOCK=BLOCK,
+        NUM_BLOCK=NUM_BLOCK,
+        BLOCK_MODEL=BLOCK_MODEL,
+    )
+
+    # Remove padding from output
+    if e_padded != e:
+        o = o_padded[..., :e]
+    else:
+        o = o_padded
+
+    return o
+
+
+def is_support(dim):
+    return 16 % dim
+
+
+def next_power_of_2(n):
+    return 2 ** (int(math.ceil(math.log(n, 2))))
+
+
+def lightning_attn_func(q, k, v, s):
+    b, h, n, d = q.shape
+    e = v.shape[-1]
+    assert is_support(d) and is_support(e)
+
+    # pad v's feature dim to power of 2
+    e_pad = next_power_of_2(e)
+    need_pad = e_pad != e
+    if need_pad:
+        v = F.pad(v, (0, e_pad - e))
+
+    if d > 128:
+        # split over head
+        if 64 % d:
+            m = 64
+        elif 32 % d:
+            m = 32
+        elif 16 % d:
+            m = 16
+        arr = [m * i for i in range(d // m + 1)]
+        if arr[-1] != d:
+            arr.append(d)
+        n = len(arr)
+        o = 0
+        for i in range(n - 1):
+            start = arr[i]
+            end = arr[i + 1]
+            q1 = q[..., start:end]
+            k1 = k[..., start:end]
+            o += lightning_attn2(q1, k1, v, s)
+    else:
+        o = lightning_attn2(q, k, v, s)
+
+    if need_pad:
+        o = o[:, :, :, :e]
+
+    return o
+
+
+debug = eval(os.environ.get("debug", default="False"))
+
+BLOCK = 256
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MiniMaxText01
+class MiniMaxText01RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniMaxText01RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
+def get_activation_fn(activation):
+    if debug:
+        logger.info(f"activation: {activation}")
+    if activation == "gelu":
+        return F.gelu
+    elif activation == "relu":
+        return F.relu
+    elif activation == "elu":
+        return F.elu
+    elif activation == "sigmoid":
+        return F.sigmoid
+    elif activation == "exp":
+
+        def f(x):
+            with torch.no_grad():
+                x_max = torch.max(x, dim=-1, keepdims=True).values
+            y = torch.exp(x - x_max)
+
+            return y
+
+        return f
+    elif activation == "leak":
+        return F.leaky_relu
+    elif activation == "1+elu":
+
+        def f(x):
+            return 1 + F.elu(x)
+
+        return f
+    elif activation == "2+elu":
+
+        def f(x):
+            return 2 + F.elu(x)
+
+        return f
+    elif activation == "silu" or activation == "swish":
+        return F.silu
+    elif activation == "sine":
+        return torch.sin
+    else:
+        logger.info(f"activation: does not support {activation}, use Identity!!!")
+        return lambda x: x
+
+
+# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
+class MiniMaxText01LightningAttention(nn.Module):
+    def __init__(self, config=None, layer_idx: Optional[int] = None, **kwargs):
+        super().__init__()
+        if config is None:
+            config = type("Config", (), kwargs)
+
+        bias = False
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+
+        self.out_proj = nn.Linear(
+            self.head_dim * self.num_heads, self.hidden_size, bias=bias
+        )
+        self.act = get_activation_fn(config.hidden_act)
+        self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads)
+
+        self.qkv_proj = nn.Linear(
+            self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias
+        )
+        self.output_gate = nn.Linear(
+            self.hidden_size, self.head_dim * self.num_heads, bias=bias
+        )
+
+        # for inference only
+        self.offset = 0
+        self.layer_idx = layer_idx
+
+    def forward(
+        self,
+        hidden_states,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, h, n, m)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if (not self.training) and (not do_eval):
+            return self.inference(
+                hidden_states,
+                attn_mask,
+                output_attentions,
+                past_key_value,
+                use_cache,
+                slope_rate,
+            )
+
+    def inference(
+        self,
+        x,
+        attn_mask: Optional[torch.Tensor] = None,  # (b, n)
+        output_attentions: bool = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: bool = False,
+        slope_rate: Optional[torch.Tensor] = None,  # (h, 1, 1)
+    ):
+        # x: b n d
+        b, n, d = x.shape
+        # linear map
+        qkv = self.act(self.qkv_proj(x))
+        new_shape = qkv.size()[:-1] + (self.num_heads, -1)
+        qkv = qkv.view(*new_shape)
+        q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        if past_key_value is None:
+            self.offset = q.shape[-2]
+        else:
+            self.offset += 1
+
+        # for align with metaseq
+        ratio = torch.exp(-slope_rate)
+
+        # only use for the first time
+        if past_key_value is None:
+            slope_rate = slope_rate.to(torch.float32)
+            if attn_mask is not None:
+                v = v.masked_fill(
+                    (1 - attn_mask).unsqueeze(1).unsqueeze(-1).to(torch.bool), 0
+                )
+            NUM_BLOCK = (n + BLOCK - 1) // BLOCK
+            b, h, n, d = q.shape
+            e = v.shape[-1]
+            # other
+            array = torch.arange(BLOCK).to(q) + 1
+            q_decay = torch.exp(-slope_rate * array.reshape(-1, 1))
+            k_decay = torch.exp(-slope_rate * (BLOCK - array.reshape(-1, 1)))
+            index = array[:, None] - array[None, :]
+            s_index = (
+                slope_rate
+                * index[
+                    None,
+                    None,
+                ]
+            )
+            s_index = torch.where(index >= 0, -s_index, float("-inf"))
+            diag_decay = torch.exp(s_index)
+
+            kv = torch.zeros(b, h, d, e).to(torch.float32).to(q.device)
+            output = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
+            for i in range(NUM_BLOCK):
+                si = i * BLOCK
+                ei = min(si + BLOCK, n)
+                m = ei - si
+                qi = q[:, :, si:ei].contiguous()
+                ki = k[:, :, si:ei].contiguous()
+                vi = v[:, :, si:ei].contiguous()
+                qkv_none_diag = torch.matmul(qi * q_decay[:, :m], kv).to(torch.float32)
+
+                # diag
+                qk = (
+                    torch.matmul(qi, ki.transpose(-1, -2)).to(torch.float32)
+                    * diag_decay[:, :, :m, :m]
+                )
+                qkv_diag = torch.matmul(qk, vi.to(torch.float32))
+                block_decay = torch.exp(-slope_rate * m)
+                output[:, :, si:ei] = qkv_none_diag + qkv_diag
+                kv = block_decay * kv + torch.matmul(
+                    (ki * k_decay[:, -m:]).transpose(-1, -2).to(vi.dtype), vi
+                )
+
+        else:
+            kv = past_key_value
+            output = []
+            for i in range(n):
+                kv = ratio * kv + torch.einsum(
+                    "... n d, ... n e -> ... d e",
+                    k[:, :, i : i + 1],
+                    v[:, :, i : i + 1],
+                )
+                qkv = torch.einsum(
+                    "... n e, ... e d -> ... n d", q[:, :, i : i + 1], kv.to(q.dtype)
+                )
+                output.append(qkv)
+            output = torch.cat(output, dim=-2)
+        # reshape
+        output = rearrange(output, "b h n d -> b n (h d)")
+        # normalize
+        output = self.norm(output)
+        # gate
+        output = F.sigmoid(self.output_gate(x)) * output
+        # outproj
+        output = self.out_proj(output)
+
+        attn_weights = None
+
+        return output, attn_weights, kv
+
+
+def _build_slope_tensor(n_attention_heads: int):
+    def get_slopes(n):
+        def get_slopes_power_of_2(n):
+            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(
+                n
+            )  # In the paper, we only train models that have 2^a heads for some a. This function has
+        else:  # some good properties that only occur when the input is a power of 2. To maintain that even
+            closest_power_of_2 = 2 ** math.floor(
+                math.log2(n)
+            )  # when the number of heads is not a power of 2, we use this workaround.
+            return (
+                get_slopes_power_of_2(closest_power_of_2)
+                + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+            )
+
+    # h, 1, 1
+    slopes = torch.tensor(get_slopes(n_attention_heads)).reshape(
+        n_attention_heads, 1, 1
+    )
+
+    return slopes
+
+
+def test_lightning_attention_implementations(model_params):
+    torch.manual_seed(42)
+
+    batch_size = 2
+    seq_len = 1024
+    dtype = torch.bfloat16
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    hidden_states = torch.randn(
+        batch_size, seq_len, model_params["hidden_size"], dtype=dtype, device=device
+    )
+
+    attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+    slope_rate = _build_slope_tensor(model_params["num_attention_heads"]).to(device)
+
+    model_attn = MiniMaxText01LightningAttention(**model_params).to(dtype).to(device)
+    model_attn.eval()
+
+    with torch.no_grad():
+        model_output, _, _ = model_attn.inference(
+            hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
+        )
+
+    qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+    new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+    qkv = qkv.view(*new_shape)
+    q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+
+    lib_output = lightning_attn_func(q, k, v, slope_rate)
+    lib_output = lib_output.transpose(1, 2).contiguous()
+    lib_output = lib_output.view(batch_size, seq_len, -1)
+    lib_output = model_attn.norm(lib_output)
+    lib_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
+    lib_output = model_attn.out_proj(lib_output)
+
+    torch.testing.assert_close(
+        model_output,
+        lib_output,
+        rtol=1e-3,
+        atol=1e-2,
+        msg="Lightning attention implementations produce different results",
+    )
+
+    print("✅ Two implementations match")
+
+
+def get_benchmark():
+    batch_size_range = [2**i for i in range(0, 7)]  # max 64
+    seq_length_range = [256, 512, 1024, 2048, 4096]  # max 4096
+    configs = list(itertools.product(batch_size_range, seq_length_range))
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["MiniMax-Text-01", "OpenNLPLab"],
+            line_names=[
+                "MiniMax-Text-01 Model Implementation",
+                "OpenNLPLab Library Implementation",
+            ],
+            styles=[("blue", "-"), ("green", "-")],
+            ylabel="us",
+            plot_name="lightning-attention-prefill-performance",
+            args={},
+        )
+    )
+    def benchmark(batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        device = torch.device("cuda")
+
+        params = {
+            "hidden_size": 6144,
+            "num_attention_heads": 64,
+            "head_dim": 96,
+            "hidden_act": "gelu",
+        }
+
+        hidden_states = torch.randn(
+            batch_size, seq_len, params["hidden_size"], dtype=dtype, device=device
+        )
+
+        attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
+
+        slope_rate = _build_slope_tensor(params["num_attention_heads"]).to(device)
+        model_attn = MiniMaxText01LightningAttention(**params).to(dtype).to(device)
+        model_attn.eval()
+
+        quantiles = [0.5, 0.2, 0.8]
+        if provider == "MiniMax-Text-01":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: model_attn.inference(
+                    hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
+                ),
+                quantiles=quantiles,
+            )
+        else:
+
+            def run_lib():
+                qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
+                new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
+                qkv = qkv.view(*new_shape)
+                q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
+                q = q.transpose(1, 2)
+                k = k.transpose(1, 2)
+                v = v.transpose(1, 2)
+
+                lib_output = lightning_attn_func(q, k, v, slope_rate)
+                lib_output = lib_output.transpose(1, 2).contiguous()
+                lib_output = lib_output.view(batch_size, seq_len, -1)
+                lib_output = model_attn.norm(lib_output)
+                lib_output = (
+                    torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
+                )
+                return model_attn.out_proj(lib_output)
+
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                run_lib,
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/lightning_attention_prefill/",
+        help="Path to save lightning attention prefill benchmark results",
+    )
+    args = parser.parse_args()
+
+    # Run correctness test first
+    # Adapted from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/config.json
+    params = {
+        "hidden_size": 6144,
+        "num_attention_heads": 64,
+        "head_dim": 96,
+        "hidden_act": "silu",
+    }
+    test_lightning_attention_implementations(params)
+
+    # Run performance benchmark
+    benchmark = get_benchmark()
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmark/kernels/quantization/bench_fp4_quant.py

+import argparse
+import itertools
+
+import torch
+import triton
+from sgl_kernel import scaled_fp4_grouped_quant, silu_and_mul_scaled_fp4_grouped_quant
+from sgl_kernel.elementwise import silu_and_mul
+
+from sglang.srt.layers.moe.ep_moe.kernels import silu_and_mul_masked_post_quant_fwd
+from sglang.srt.layers.quantization import deep_gemm_wrapper
+
+
+def _test_accuracy_once(E, M, K, input_dtype, device):
+    x = torch.randn(E, M, K, device=device, dtype=input_dtype)
+    glb_scales = torch.ones((E,), dtype=torch.float32, device=device)
+    masks = torch.full((E,), M, dtype=torch.int32, device=device)
+    out, blk_scales = silu_and_mul_scaled_fp4_grouped_quant(x, glb_scales, masks)
+    out1, blk_scales1 = scaled_fp4_grouped_quant(
+        silu_and_mul(x),
+        glb_scales,
+        masks,
+    )
+
+    torch.testing.assert_close(out, out1)
+    torch.testing.assert_close(blk_scales, blk_scales1)
+    print(f"E: {E}, M: {M}, K: {K}, type: {input_dtype} OK")
+
+
+NUM_RANKS = 48
+M_PER_RANKs = [128, 256, 512, 1024]
+Ms = [M_PER_RANK * NUM_RANKS for M_PER_RANK in M_PER_RANKs]
+Ks = [2048, 4096, 7168]
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["M", "K"],
+        x_vals=list(itertools.product(Ms, Ks)),
+        x_log=False,
+        line_arg="provider",
+        line_vals=["triton_fp8", "cuda_unfused_fp4", "cuda_fused_fp4"],
+        line_names=["triton_fp8", "cuda_unfused_fp4", "cuda_fused_fp4"],
+        styles=[("blue", "-"), ("orange", "-"), ("green", "-")],
+        ylabel="ms",
+        plot_name="fp4 quant",
+        args={},
+    )
+)
+def benchmark(M, K, provider):
+    E = 6
+    device = "cuda"
+    x = torch.randn(E, M, K, device=device, dtype=torch.bfloat16)
+    glb_scales = torch.ones((E,), dtype=torch.float32, device=device)
+    masks = torch.randint(1, 4096, (E,), dtype=torch.int32, device=device)
+    fp8_out = torch.empty(
+        (
+            x.shape[0],
+            x.shape[1],
+            x.shape[2] // 2,
+        ),
+        device=x.device,
+        dtype=torch.float8_e4m3fn,
+    )
+    scale_block_size = 128
+    fp8_scales = torch.empty(
+        (
+            x.shape[0],
+            x.shape[1],
+            x.shape[2] // 2 // scale_block_size,
+        ),
+        device=x.device,
+        dtype=torch.float32,
+    )
+
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == "triton_fp8":
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: silu_and_mul_masked_post_quant_fwd(
+                x,
+                fp8_out,
+                fp8_scales,
+                scale_block_size,
+                masks,
+                scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+            ),
+            quantiles=quantiles,
+        )
+    if provider == "cuda_unfused_fp4":
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: scaled_fp4_grouped_quant(
+                silu_and_mul(x),
+                glb_scales,
+                masks,
+            ),
+            quantiles=quantiles,
+        )
+    if provider == "cuda_fused_fp4":
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: silu_and_mul_scaled_fp4_grouped_quant(
+                x,
+                glb_scales,
+                masks,
+            ),
+            quantiles=quantiles,
+        )
+
+    return ms, min_ms, max_ms
+
+
+def test_accuracy():
+    E = 6
+    N_RANKS = 48
+    Ms = [128, 256, 512, 1024]
+    Ks = [2048, 4096, 7168]
+    input_dtype = torch.bfloat16
+    for M in Ms:
+        for K in Ks:
+            _test_accuracy_once(E, N_RANKS * M, K, input_dtype, "cuda")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./bench_fp4_quant_res",
+        help="Path to save fp4 quant benchmark results",
+    )
+    args = parser.parse_args()
+
+    test_accuracy()
+
+    benchmark.run(print_data=True, show_plots=True, save_path=args.save_path)

benchmark/kernels/quantization/bench_int8_quant.py

+import argparse
+
+import torch
+import triton
+from vllm._custom_ops import scaled_int8_quant as vllm_scaled_int8_quant
+
+from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
+
+
+@torch.compile(backend="inductor")
+def torch_int8_quant(x):
+    int8_max = torch.iinfo(torch.int8).max
+
+    abs_max = x.abs().max(dim=-1, keepdim=True).values
+    scales = abs_max.to(torch.float32) / float(int8_max)
+
+    q_x = (x / scales).round().to(torch.int8)
+
+    return q_x, scales
+
+
+def _test_accuracy_once(M, K, input_dtype, device):
+    x = torch.randn(M, K, dtype=input_dtype, device=device) * 5000
+    out, scales, _ = vllm_scaled_int8_quant(x, symmetric=True)
+    out1, scales1 = per_token_quant_int8(x)
+    out2, scales2 = torch_int8_quant(x)
+    torch.testing.assert_close(out, out2, atol=1, rtol=0)
+    torch.testing.assert_close(out, out1, atol=1, rtol=0)
+    torch.testing.assert_close(scales, scales2)
+    torch.testing.assert_close(scales1, scales2)
+    print(f"M: {M}, K: {K}, type: {input_dtype} OK")
+
+
+def test_accuracy():
+    Ms = [1, 13, 128, 1024, 2048, 4096]
+    Ks = [512, 1024, 2048, 8192]
+    input_dtypes = [torch.float16, torch.bfloat16]
+    for M in Ms:
+        for K in Ks:
+            for input_dtype in input_dtypes:
+                _test_accuracy_once(M, K, input_dtype, "cuda")
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size"],
+        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048],
+        x_log=False,
+        line_arg="provider",
+        line_vals=["vllm op", "triton", "torch.compile"],
+        line_names=["vllm op", "triton", "torch.compile"],
+        styles=[("blue", "-"), ("orange", "-"), ("red", "-")],
+        ylabel="ms",
+        plot_name="int8 per token quant",
+        args={},
+    )
+)
+def benchmark(batch_size, provider):
+    M, K = batch_size, 16384
+    x = torch.randn(M, K, dtype=torch.float16, device="cuda") * 1000
+
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == "vllm op":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: vllm_scaled_int8_quant(x, symmetric=True),
+            quantiles=quantiles,
+        )
+    if provider == "triton":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: per_token_quant_int8(x),
+            quantiles=quantiles,
+        )
+    if provider == "torch.compile":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: torch_int8_quant(x),
+            quantiles=quantiles,
+        )
+
+    return ms, min_ms, max_ms
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./bench_int8_quant_res",
+        help="Path to save int8 quant benchmark results",
+    )
+    args = parser.parse_args()
+
+    test_accuracy()
+
+    benchmark.run(print_data=True, show_plots=True, save_path=args.save_path)

benchmark/kernels/quantization/tuning_block_wise_kernel.py

+# Copyright 2025 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import argparse
+import json
+import multiprocessing as mp
+import os
+import time
+from datetime import datetime
+from typing import Any, Dict, List
+
+import torch
+import triton
+from tqdm import tqdm
+
+mp.set_start_method("spawn", force=True)
+
+from sglang.srt.layers.quantization.fp8_kernel import (
+    _w8a8_block_fp8_matmul,
+    _w8a8_block_fp8_matmul_unrolledx4,
+)
+from sglang.srt.layers.quantization.int8_kernel import _w8a8_block_int8_matmul
+from sglang.srt.utils import get_device_core_count, get_device_name, is_hip
+
+_is_hip = is_hip()
+
+DTYPE_MAP = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+}
+
+
+def w8a8_block_matmul(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    block_size: List[int],
+    config: Dict[str, Any],
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    """This function performs matrix multiplication with block-wise quantization.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+
+    Args:
+        A: The input tensor, e.g., activation.
+        B: The input tensor, e.g., weight.
+        As: The per-token-group quantization scale for `A`.
+        Bs: The per-block quantization scale for `B`.
+        block_size: The block size for per-block quantization. It should be 2-dim, e.g., [128, 128].
+        output_dytpe: The dtype of the returned tensor.
+
+    Returns:
+        torch.Tensor: The result of matmul.
+    """
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+
+    assert A.shape[-1] == B.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
+    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
+    M = A.numel() // A.shape[-1]
+
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    N, K = B.shape
+    assert triton.cdiv(N, block_n) == Bs.shape[0]
+    assert triton.cdiv(K, block_k) == Bs.shape[1]
+
+    C_shape = A.shape[:-1] + (N,)
+    C = A.new_empty(C_shape, dtype=output_dtype)
+
+    def grid(META):
+        return (
+            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        )
+
+    # Use manually unrolledx4 kernel on AMD GPU when the grid size is small.
+    # Empirical testing shows the sweet spot lies when it's less than the # of
+    # compute units available on the device.
+    num_workgroups = triton.cdiv(M, config["BLOCK_SIZE_M"]) * triton.cdiv(
+        N, config["BLOCK_SIZE_N"]
+    )
+
+    if A.dtype == torch.float8_e4m3fnuz or A.dtype == torch.float8_e4m3fn:
+        kernel = (
+            _w8a8_block_fp8_matmul_unrolledx4
+            if (_is_hip == True and num_workgroups <= get_device_core_count())
+            else _w8a8_block_fp8_matmul
+        )
+    else:
+        kernel = _w8a8_block_int8_matmul
+
+    kernel[grid](
+        A,
+        B,
+        C,
+        As,
+        Bs,
+        M,
+        N,
+        K,
+        block_n,
+        block_k,
+        A.stride(-2),
+        A.stride(-1),
+        B.stride(1),
+        B.stride(0),
+        C.stride(-2),
+        C.stride(-1),
+        As.stride(-2),
+        As.stride(-1),
+        Bs.stride(1),
+        Bs.stride(0),
+        **config,
+    )
+
+    return C
+
+
+def get_rocm_configs_compute_bound():
+    configs = []
+    waves_per_eu_range = 0
+    for num_stages in [2]:
+        for block_m in [32, 64, 128, 256]:
+            for block_k in [32, 64, 128, 256]:
+                for block_n in [16, 32, 64, 128, 256]:
+                    for num_warps in [4, 8]:
+                        for group_size in [1, 4, 8, 16, 32]:
+                            configs.append(
+                                {
+                                    "BLOCK_SIZE_M": block_m,
+                                    "BLOCK_SIZE_N": block_n,
+                                    "BLOCK_SIZE_K": block_k,
+                                    "GROUP_SIZE_M": group_size,
+                                    "num_warps": num_warps,
+                                    "num_stages": num_stages,
+                                    "waves_per_eu": waves_per_eu_range,
+                                }
+                            )
+    return configs
+
+
+def get_configs_compute_bound():
+    configs = []
+    if _is_hip:
+        configs = get_rocm_configs_compute_bound()
+    else:
+        for num_stages in [2, 3, 4, 5]:
+            for block_m in [16, 32, 64, 128, 256]:
+                for block_k in [64, 128]:
+                    for block_n in [32, 64, 128, 256]:
+                        for num_warps in [4, 8]:
+                            for group_size in [1, 16, 32, 64]:
+                                configs.append(
+                                    {
+                                        "BLOCK_SIZE_M": block_m,
+                                        "BLOCK_SIZE_N": block_n,
+                                        "BLOCK_SIZE_K": block_k,
+                                        "GROUP_SIZE_M": group_size,
+                                        "num_warps": num_warps,
+                                        "num_stages": num_stages,
+                                    }
+                                )
+    return configs
+
+
+def get_weight_shapes(tp_size):
+    # NOTE(HandH1998): The weight shapes only works for DeepSeek-V3. Modify them, if you tune for another different model.
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+    return weight_shapes
+
+
+def benchmark_config(
+    A, B, As, Bs, block_size, config, out_dtype=torch.float16, num_iters=10
+):
+    def run():
+        w8a8_block_matmul(A, B, As, Bs, block_size, config, out_dtype)
+
+    torch.cuda.synchronize()
+    # JIT complication & warmup
+    for _ in range(5):
+        run()
+    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()
+        run()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    return avg
+
+
+def tune(M, N, K, block_size, out_dtype, search_space, input_type):
+    factor_for_scale = 1e-2
+
+    if input_type == "fp8":
+        fp8_info = torch.finfo(
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
+        )
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        A_fp32 = (
+            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
+        )
+        A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
+        )
+
+        B_fp32 = (
+            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
+        )
+        B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
+        )
+    else:
+        int8_info = torch.iinfo(torch.int8)
+        int8_max, int8_min = int8_info.max, int8_info.min
+
+        A_fp32 = (
+            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * int8_max
+        )
+        A = A_fp32.clamp(min=int8_min, max=int8_max).to(torch.int8)
+
+        B_fp32 = (
+            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * int8_max
+        )
+        B = B_fp32.clamp(min=int8_min, max=int8_max).to(torch.int8)
+
+    block_n, block_k = block_size[0], block_size[1]
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+
+    As = torch.rand(M, k_tiles, dtype=torch.float32, device="cuda") * factor_for_scale
+    Bs = (
+        torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda")
+        * factor_for_scale
+    )
+
+    best_config = None
+    best_time = float("inf")
+    for config in tqdm(search_space):
+        try:
+            kernel_time = benchmark_config(
+                A,
+                B,
+                As,
+                Bs,
+                block_size,
+                config,
+                out_dtype,
+                num_iters=10,
+            )
+        except triton.runtime.autotuner.OutOfResources:
+            # Some configurations may be invalid and fail to compile.
+            continue
+
+        if kernel_time < best_time:
+            best_time = kernel_time
+            best_config = config
+    now = datetime.now()
+    print(f"{now.ctime()}] Completed tuning for batch_size={M}")
+    assert best_config is not None
+    return best_config
+
+
+def save_configs(
+    N,
+    K,
+    block_n,
+    block_k,
+    configs,
+    save_path,
+    input_type="fp8",
+) -> None:
+    os.makedirs(save_path, exist_ok=True)
+    device_name = get_device_name().replace(" ", "_")
+    json_file_name = f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,block_shape=[{block_n}, {block_k}].json"
+
+    config_file_path = os.path.join(save_path, json_file_name)
+    print(f"Writing best config to {config_file_path}...")
+
+    with open(config_file_path, "w") as f:
+        json.dump(configs, f, indent=4)
+        f.write("\n")
+
+
+def get_available_gpu_count():
+    """Get the number of available GPUs."""
+    return torch.cuda.device_count()
+
+
+def tune_on_gpu(args_dict):
+    """Run tuning on a specific GPU."""
+    gpu_id = args_dict["gpu_id"]
+    batch_sizes = args_dict["batch_sizes"]
+    weight_shapes = args_dict["weight_shapes"]
+    args = args_dict["args"]
+
+    torch.cuda.set_device(gpu_id)
+    print(f"Starting tuning on GPU {gpu_id} with batch sizes {batch_sizes}")
+
+    block_n = args.block_n
+    block_k = args.block_k
+    out_dtype = DTYPE_MAP[args.out_dtype]
+    save_path = args.save_path
+    input_type = args.input_type
+
+    search_space = get_configs_compute_bound()
+    search_space = [
+        config for config in search_space if block_k % config["BLOCK_SIZE_K"] == 0
+    ]
+
+    start = time.perf_counter()
+    results = {}
+    for shape in tqdm(weight_shapes, desc=f"GPU {gpu_id} - Shapes"):
+        N, K = shape[0], shape[1]
+        print(f"[GPU {gpu_id}] Tune for weight shape of `N: {N}, K: {K}`")
+        benchmark_results = [
+            tune(
+                batch_size,
+                N,
+                K,
+                [block_n, block_k],
+                out_dtype,
+                search_space,
+                input_type,
+            )
+            for batch_size in tqdm(batch_sizes, desc=f"GPU {gpu_id} - Batch sizes")
+        ]
+        best_configs = {M: config for M, config in zip(batch_sizes, benchmark_results)}
+        save_configs(N, K, block_n, block_k, best_configs, save_path, input_type)
+
+    end = time.perf_counter()
+    print(f"Tuning on GPU {gpu_id} took {end - start:.2f} seconds")
+
+
+def distribute_batch_sizes(batch_sizes, num_gpus):
+    """Distribute batch sizes across available GPUs."""
+    batches_per_gpu = []
+    for i in range(num_gpus):
+        start_idx = i * len(batch_sizes) // num_gpus
+        end_idx = (i + 1) * len(batch_sizes) // num_gpus
+        batches_per_gpu.append(batch_sizes[start_idx:end_idx])
+    return batches_per_gpu
+
+
+def main(args):
+    print(args)
+
+    num_gpus = get_available_gpu_count()
+    if num_gpus == 0:
+        raise RuntimeError("No GPU available for tuning")
+    print(f"Found {num_gpus} GPUs for parallel tuning")
+
+    torch.cuda.init()
+
+    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]
+        num_gpus = 1  # If only one batch size, use only one GPU
+
+    weight_shapes = get_weight_shapes(args.tp_size)
+
+    batches_per_gpu = distribute_batch_sizes(batch_sizes, num_gpus)
+
+    process_args = []
+    for gpu_id in range(num_gpus):
+        process_args.append(
+            {
+                "gpu_id": gpu_id,
+                "batch_sizes": batches_per_gpu[gpu_id],
+                "weight_shapes": weight_shapes,  # Each GPU processes all weight shapes
+                "args": args,
+            }
+        )
+
+    ctx = mp.get_context("spawn")
+    with ctx.Pool(num_gpus) as pool:
+        pool.map(tune_on_gpu, process_args)
+
+    print("Multi-GPU tuning completed")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--tp-size", "-tp", type=int, default=8)
+    parser.add_argument(
+        "--input-type", type=str, choices=["fp8", "int8"], default="fp8"
+    )
+    parser.add_argument(
+        "--out-dtype",
+        type=str,
+        choices=["float32", "float16", "bfloat16", "half"],
+        default="float16",
+    )
+    parser.add_argument("--block-n", type=int, default=128)
+    parser.add_argument("--block-k", type=int, default=128)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument(
+        "--save-path", type=str, default="python/sglang/srt/layers/quantization/configs"
+    )
+    args = parser.parse_args()
+
+    main(args)