87bd91892f
Signed-off-by: Yongye Zhu <zyy1102000@gmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Tests that triton_kernel_moe_forward correctly applies expert_map
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remapping when expert parallelism (EP) is enabled.
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Previously, legacy_routing was always used and it produced routing data
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with global expert IDs that didn't correspond to local weight indices,
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causing illegal memory access with EP. The fix splits routing: when
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expert_map is provided, topk selection is performed first, expert_map is
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applied to remap global→local IDs, and make_routing_data builds routing
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structures from the local IDs.
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"""
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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class TestTritonMoeForwardExpertMap:
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"""Test that triton_kernel_moe_forward applies expert_map remapping
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when expert_map is provided (EP active)."""
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@pytest.mark.parametrize("expert_map_present", [False, True])
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def test_routing_path_selection(self, expert_map_present):
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"""Verify that the EP-aware routing path is taken when expert_map
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is present, and the legacy_routing path is taken otherwise."""
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# This is a structural test: we mock the routing functions to
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# verify the correct path is exercised.
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mock_expert_map = (
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torch.tensor([0, -1, 1, -1], device=device) if expert_map_present else None
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)
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with (
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patch(
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"vllm.model_executor.layers.fused_moe."
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"gpt_oss_triton_kernels_moe.legacy_routing"
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) as mock_legacy,
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patch("triton_kernels.topk.topk") as mock_topk,
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patch(
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"vllm.model_executor.layers.fused_moe."
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"gpt_oss_triton_kernels_moe.make_routing_data"
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) as mock_make_routing,
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patch(
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"vllm.model_executor.layers.fused_moe."
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"gpt_oss_triton_kernels_moe.triton_kernel_fused_experts"
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) as mock_fused_experts,
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):
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from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import ( # noqa: E501
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triton_kernel_moe_forward,
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)
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# Set up return values
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mock_routing_data = MagicMock()
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mock_gather = MagicMock()
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mock_scatter = MagicMock()
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if expert_map_present:
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sparse_result = MagicMock()
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sparse_result.indx = torch.tensor([[0, 2]], dtype=torch.int32)
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sparse_result.vals = torch.tensor([[0.6, 0.4]])
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mock_topk.return_value = sparse_result
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mock_make_routing.return_value = (
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mock_routing_data,
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mock_gather,
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mock_scatter,
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)
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else:
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mock_legacy.return_value = (
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mock_routing_data,
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mock_gather,
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mock_scatter,
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)
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mock_fused_experts.return_value = torch.zeros((1, 8), device=device)
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hidden = torch.randn((1, 8), device=device)
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w1 = torch.randn((2, 8, 16), device=device)
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w2 = torch.randn((2, 8, 8), device=device)
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logits = torch.randn((1, 4), device=device)
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triton_kernel_moe_forward(
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hidden_states=hidden,
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w1=w1,
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w2=w2,
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gating_output=logits,
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topk=2,
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renormalize=True,
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expert_map=mock_expert_map,
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)
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if expert_map_present:
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# EP path: should use topk + make_routing_data, NOT
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# legacy_routing
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mock_topk.assert_called_once()
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mock_make_routing.assert_called_once()
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mock_legacy.assert_not_called()
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# expert_map should be None in the fused_experts call
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# (already applied)
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call_kwargs = mock_fused_experts.call_args
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assert call_kwargs[1].get("expert_map") is None or (
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len(call_kwargs[0]) > 0
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)
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else:
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# Non-EP path: should use legacy_routing
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mock_legacy.assert_called_once()
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mock_topk.assert_not_called()
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mock_make_routing.assert_not_called()
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