[fused_router][pytorch] Optimize naive topk path and add perf benchmark

tests/pytorch/test_fused_router_perf.py

@@ -0,0 +1,371 @@
+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+import os
+from typing import Callable, Optional, Tuple
+
+import pytest
+import torch
+
+from transformer_engine.pytorch.router import (
+    fused_compute_score_for_moe_aux_loss,
+    fused_moe_aux_loss,
+    fused_topk_with_score_function,
+)
+
+
+SEED = 42
+
+
+def _set_seed() -> None:
+    torch.manual_seed(SEED)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(SEED)
+
+
+pytestmark = pytest.mark.skipif(
+    not torch.cuda.is_available() or os.getenv("TE_RUN_PERF_TESTS", "0") != "1",
+    reason=(
+        "Benchmark test - run with: TE_RUN_PERF_TESTS=1 pytest"
+        " tests/pytorch/test_fused_router_perf.py"
+    ),
+)
+
+
+def _benchmark_cuda_kernel(fn: Callable[[], object], warmup: int = 20, iters: int = 100) -> float:
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    for _ in range(warmup):
+        fn()
+    torch.cuda.synchronize()
+
+    start_event.record()
+    for _ in range(iters):
+        fn()
+    end_event.record()
+    torch.cuda.synchronize()
+
+    return start_event.elapsed_time(end_event) / iters
+
+
+def group_limited_topk(
+    scores: torch.Tensor,
+    topk: int,
+    num_tokens: int,
+    num_experts: int,
+    num_groups: int,
+    group_topk: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    group_scores = (
+        scores.view(num_tokens, num_groups, -1).topk(topk // group_topk, dim=-1)[0].sum(dim=-1)
+    )
+    group_idx = torch.topk(group_scores, k=group_topk, dim=-1, sorted=False)[1]
+    group_mask = torch.zeros_like(group_scores)
+    group_mask.scatter_(1, group_idx, 1)
+
+    score_mask = (
+        group_mask.unsqueeze(-1)
+        .expand(num_tokens, num_groups, num_experts // num_groups)
+        .reshape(num_tokens, -1)
+    )
+    masked_scores = scores.masked_fill(~score_mask.bool(), float("-inf"))
+    probs, top_indices = torch.topk(masked_scores, k=topk, dim=-1)
+    return probs, top_indices
+
+
+def topk_softmax_sigmoid_pytorch(
+    logits: torch.Tensor,
+    topk: int,
+    use_pre_softmax: bool = False,
+    num_groups: Optional[int] = None,
+    group_topk: Optional[int] = None,
+    scaling_factor: Optional[float] = None,
+    score_function: str = "softmax",
+    expert_bias: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    num_tokens, num_experts = logits.shape
+
+    def compute_topk(scores, topk_value, num_groups_value=None, group_topk_value=None):
+        if group_topk_value:
+            assert num_groups_value is not None
+            return group_limited_topk(
+                scores=scores,
+                topk=topk_value,
+                num_tokens=num_tokens,
+                num_experts=num_experts,
+                num_groups=num_groups_value,
+                group_topk=group_topk_value,
+            )
+        return torch.topk(scores, k=topk_value, dim=1)
+
+    if score_function == "softmax":
+        if use_pre_softmax:
+            scores = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits)
+            probs, top_indices = compute_topk(scores, topk, num_groups, group_topk)
+        else:
+            scores, top_indices = compute_topk(logits, topk, num_groups, group_topk)
+            probs = torch.softmax(scores, dim=-1, dtype=torch.float32).type_as(logits)
+    elif score_function == "sigmoid":
+        scores = torch.sigmoid(logits.float()).type_as(logits)
+        if expert_bias is not None:
+            scores_for_routing = scores + expert_bias
+            _, top_indices = compute_topk(scores_for_routing, topk, num_groups, group_topk)
+            scores = torch.gather(scores, dim=1, index=top_indices).type_as(logits)
+        else:
+            scores, top_indices = compute_topk(scores, topk, num_groups, group_topk)
+        probs = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) if topk > 1 else scores
+    else:
+        raise ValueError(f"Invalid score_function: {score_function}")
+
+    if scaling_factor:
+        probs = probs * scaling_factor
+
+    topk_masked_gates = torch.zeros_like(logits).scatter(1, top_indices, probs)
+    topk_map = torch.zeros_like(logits).int().scatter(1, top_indices, 1).bool()
+    return topk_masked_gates, topk_map
+
+
+def compute_scores_for_aux_loss_pytorch(
+    logits: torch.Tensor, topk: int, score_function: str
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    if score_function == "softmax":
+        scores = torch.softmax(logits, dim=-1, dtype=torch.float32)
+    elif score_function == "sigmoid":
+        scores = torch.sigmoid(logits)
+        scores = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) if topk > 1 else scores
+    else:
+        raise ValueError(f"Invalid score_function: {score_function}")
+
+    _, top_indices = torch.topk(scores, k=topk, dim=1)
+    routing_map = torch.zeros_like(logits).int().scatter(1, top_indices, 1).bool()
+    return routing_map, scores
+
+
+def aux_loss_pytorch(
+    probs: torch.Tensor,
+    tokens_per_expert: torch.Tensor,
+    total_num_tokens: int,
+    topk: int,
+    num_experts: int,
+    moe_aux_loss_coeff: float,
+) -> torch.Tensor:
+    aggregated_probs_per_expert = probs.sum(dim=0)
+    return torch.sum(aggregated_probs_per_expert * tokens_per_expert) * (
+        num_experts * moe_aux_loss_coeff / (topk * total_num_tokens * total_num_tokens)
+    )
+
+
+def _make_router_logits(
+    dtype: torch.dtype, num_tokens: int, num_experts: int, score_function: str
+) -> torch.Tensor:
+    if score_function == "sigmoid":
+        offset = torch.arange(-num_tokens // 2, num_tokens // 2, dtype=dtype, device="cuda") * 1e-4
+        logits = (
+            torch.arange(-num_experts // 2, num_experts // 2, device="cuda", dtype=dtype) * 1e-2
+        )
+        return logits.unsqueeze(0).repeat(num_tokens, 1) + offset.unsqueeze(1)
+
+    logits = (
+        torch.arange(
+            -num_tokens * num_experts // 2,
+            num_tokens * num_experts // 2,
+            device="cuda",
+            dtype=dtype,
+        )
+        * 1e-4
+    )
+    return logits.view(num_tokens, num_experts)
+
+
+def _make_router_bias(num_experts: int) -> torch.Tensor:
+    bias = torch.arange(num_experts, device="cuda", dtype=torch.float32) * 0.1
+    return torch.flip(bias, dims=[0])
+
+
+def _print_perf_result(case_name: str, torch_ms: float, fused_ms: float) -> None:
+    speedup = torch_ms / fused_ms
+    print(f"{case_name}: torch={torch_ms:.6f} ms, fused={fused_ms:.6f} ms, speedup={speedup:.4f}x")
+
+
+@pytest.mark.parametrize(
+    "score_function,use_pre_softmax,enable_bias",
+    [("softmax", False, False), ("sigmoid", False, True)],
+    ids=["softmax", "sigmoid_with_bias"],
+)
+def test_fused_topk_router_perf_against_torch(
+    score_function, use_pre_softmax, enable_bias, record_property
+):
+    _set_seed()
+
+    dtype = torch.float32
+    num_tokens = 4096
+    num_experts = 192
+    topk = 8
+    num_groups = 8
+    group_topk = 4
+    scaling_factor = 1.2
+
+    logits = _make_router_logits(dtype, num_tokens, num_experts, score_function)
+    expert_bias = _make_router_bias(num_experts) if enable_bias else None
+
+    torch_probs, torch_map = topk_softmax_sigmoid_pytorch(
+        logits=logits,
+        topk=topk,
+        use_pre_softmax=use_pre_softmax,
+        num_groups=num_groups,
+        group_topk=group_topk,
+        scaling_factor=scaling_factor,
+        score_function=score_function,
+        expert_bias=expert_bias,
+    )
+    fused_probs, fused_map = fused_topk_with_score_function(
+        logits=logits,
+        topk=topk,
+        use_pre_softmax=use_pre_softmax,
+        num_groups=num_groups,
+        group_topk=group_topk,
+        scaling_factor=scaling_factor,
+        score_function=score_function,
+        expert_bias=expert_bias,
+    )
+
+    torch_ms = _benchmark_cuda_kernel(
+        lambda: topk_softmax_sigmoid_pytorch(
+            logits=logits,
+            topk=topk,
+            use_pre_softmax=use_pre_softmax,
+            num_groups=num_groups,
+            group_topk=group_topk,
+            scaling_factor=scaling_factor,
+            score_function=score_function,
+            expert_bias=expert_bias,
+        )
+    )
+    fused_ms = _benchmark_cuda_kernel(
+        lambda: fused_topk_with_score_function(
+            logits=logits,
+            topk=topk,
+            use_pre_softmax=use_pre_softmax,
+            num_groups=num_groups,
+            group_topk=group_topk,
+            scaling_factor=scaling_factor,
+            score_function=score_function,
+            expert_bias=expert_bias,
+        )
+    )
+
+    record_property("torch_ms", round(torch_ms, 6))
+    record_property("fused_ms", round(fused_ms, 6))
+    record_property("speedup", round(torch_ms / fused_ms, 6))
+    _print_perf_result(f"topk_router[{score_function}]", torch_ms, fused_ms)
+
+    torch.testing.assert_close(torch_probs, fused_probs)
+    torch.testing.assert_close(torch_map, fused_map)
+
+
+@pytest.mark.parametrize("score_function", ["softmax", "sigmoid"])
+def test_fused_scores_for_aux_loss_perf_against_torch(score_function, record_property):
+    _set_seed()
+
+    dtype = torch.float32
+    num_tokens = 8192
+    num_experts = 128
+    topk = 8
+    logits = _make_router_logits(dtype, num_tokens, num_experts, score_function)
+
+    torch_map, torch_scores = compute_scores_for_aux_loss_pytorch(
+        logits=logits,
+        topk=topk,
+        score_function=score_function,
+    )
+    fused_map, fused_scores = fused_compute_score_for_moe_aux_loss(
+        logits=logits,
+        topk=topk,
+        score_function=score_function,
+    )
+
+    torch_ms = _benchmark_cuda_kernel(
+        lambda: compute_scores_for_aux_loss_pytorch(
+            logits=logits,
+            topk=topk,
+            score_function=score_function,
+        )
+    )
+    fused_ms = _benchmark_cuda_kernel(
+        lambda: fused_compute_score_for_moe_aux_loss(
+            logits=logits,
+            topk=topk,
+            score_function=score_function,
+        )
+    )
+
+    record_property("torch_ms", round(torch_ms, 6))
+    record_property("fused_ms", round(fused_ms, 6))
+    record_property("speedup", round(torch_ms / fused_ms, 6))
+    _print_perf_result(f"scores_for_aux_loss[{score_function}]", torch_ms, fused_ms)
+
+    torch.testing.assert_close(torch_scores, fused_scores)
+    torch.testing.assert_close(torch_map, fused_map)
+
+
+def test_fused_moe_aux_loss_perf_against_torch(record_property):
+    _set_seed()
+
+    dtype = torch.float32
+    num_tokens = 8192
+    num_experts = 128
+    topk = 4
+    coeff = 0.01
+
+    offset = torch.arange(-num_tokens // 2, num_tokens // 2, dtype=dtype, device="cuda") * 1e-4
+    probs = torch.arange(-num_experts // 2, num_experts // 2, device="cuda", dtype=dtype) * 1e-2
+    probs = probs.unsqueeze(0).repeat(num_tokens, 1) + offset.unsqueeze(1)
+    probs = probs.view(num_tokens, num_experts)
+    tokens_per_expert = torch.randint(1, 1000, (num_experts,), device="cuda", dtype=torch.int32)
+
+    torch_loss = aux_loss_pytorch(
+        probs=probs,
+        tokens_per_expert=tokens_per_expert,
+        total_num_tokens=num_tokens,
+        topk=topk,
+        num_experts=num_experts,
+        moe_aux_loss_coeff=coeff,
+    )
+    fused_loss = fused_moe_aux_loss(
+        probs=probs,
+        tokens_per_expert=tokens_per_expert,
+        total_num_tokens=num_tokens,
+        num_experts=num_experts,
+        topk=topk,
+        coeff=coeff,
+    )
+
+    torch_ms = _benchmark_cuda_kernel(
+        lambda: aux_loss_pytorch(
+            probs=probs,
+            tokens_per_expert=tokens_per_expert,
+            total_num_tokens=num_tokens,
+            topk=topk,
+            num_experts=num_experts,
+            moe_aux_loss_coeff=coeff,
+        )
+    )
+    fused_ms = _benchmark_cuda_kernel(
+        lambda: fused_moe_aux_loss(
+            probs=probs,
+            tokens_per_expert=tokens_per_expert,
+            total_num_tokens=num_tokens,
+            num_experts=num_experts,
+            topk=topk,
+            coeff=coeff,
+        )
+    )
+
+    record_property("torch_ms", round(torch_ms, 6))
+    record_property("fused_ms", round(fused_ms, 6))
+    record_property("speedup", round(torch_ms / fused_ms, 6))
+    _print_perf_result("moe_aux_loss", torch_ms, fused_ms)
+
+    torch.testing.assert_close(torch_loss, fused_loss)

transformer_engine/common/fused_router/fused_score_for_moe_aux_loss.cu

@@ -270,11 +270,7 @@ __global__ void fused_score_for_moe_aux_loss_backward_kernel(const CompType *int
       for (int i = lane_id; i < num_experts; i += kThreadsPerWarp) {
         local_sum_Output_x_Grad += local_grad[i] * act_output[i];
       }
-      // Warp reduce the sum
-      for (int s = 16; s > 0; s /= 2) {
-        local_sum_Output_x_Grad += __shfl_xor_sync(0xffffffff, local_sum_Output_x_Grad, s);
-      }
-      CompType sum_Output_x_Grad = local_sum_Output_x_Grad;
+      CompType sum_Output_x_Grad = warp_reduce_sum_float(local_sum_Output_x_Grad);
       // In-place update
       for (int i = lane_id; i < num_experts; i += kThreadsPerWarp) {
         local_grad[i] = local_grad[i] / (sum_fwd_input + epsilon) -