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refactor allgather/mc2-related fused_experts (#2369)

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### What this PR does / why we need it?
refactor allgather/mc2-related fused_experts

- vLLM version: v0.10.0
- vLLM main:
de7b67a023

Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
This commit is contained in:
sherie
2025-08-20 14:20:46 +08:00
committed by GitHub
parent 73acdcfc3b
commit 3f867ee708
2 changed files with 802 additions and 2 deletions
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293
tests/ut/ops/test_token_dispatcher.py
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@@ -15,12 +15,17 @@
# limitations under the License.
# This file is a part of the vllm-ascend project.
import unittest
from unittest import mock
import pytest
import torch
from pytest_mock import MockerFixture
from tests.ut.base import PytestBase
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import (
MoEAlltoAllSeqOverLapDispatcher, MoEDispatcherConfig)
AscendSocVersion, MoEAlltoAllSeqOverLapDispatcher, MoEDispatcherConfig,
TokenDispatcherWithAllGather, TokenDispatcherWithMC2)
from vllm_ascend.utils import adapt_patch # noqa E402
@@ -63,3 +68,289 @@ class TestMoEAlltoAllSeqOverLapDispatcher(PytestBase):
assert dispatcher.ep_rank == 0
assert dispatcher.ep_size == 2
assert dispatcher.overlap_stream is not None
class TestTokenDispatcherWithMC2(unittest.TestCase):
def setUp(self):
self.mc2_group = mock.MagicMock()
self.mc2_group.device_group.return_value._get_backend.return_value.get_hccl_comm_name.return_value = "hccl_123"
self.mc2_group.rank_in_group = 0
self.mc2_group.world_size = 8
self.mc2_group_patch = mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_mc2_group",
return_value=self.mc2_group)
self.mc2_group_patch.start()
self.rank_group_patch = mock.patch("torch.distributed.get_rank",
return_value=0)
self.rank_group_patch.start()
# Mock get_forward_context().mc2_mask
self.forward_context = mock.MagicMock()
self.forward_context.mc2_mask = torch.tensor([1, 0, 1])
self.forward_context_patch = mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_forward_context",
return_value=self.forward_context)
self.forward_context_patch.start()
# Mock get_ascend_soc_version()
self.ascend_soc_version_patch = mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_ascend_soc_version",
return_value=AscendSocVersion.A3)
self.ascend_soc_version_patch.start()
# Mock get_ascend_config()
self.ascend_config = mock.MagicMock()
self.ascend_config.torchair_graph_config.enabled = False
self.ascend_config_patch = mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_ascend_config",
return_value=self.ascend_config)
self.ascend_config_patch.start()
kwargs = {"with_quant": False, "top_k": 8, "num_experts": 128}
self.dispatcher = TokenDispatcherWithMC2(**kwargs)
def tearDown(self):
self.mc2_group_patch.stop()
self.forward_context_patch.stop()
self.ascend_soc_version_patch.stop()
self.ascend_config_patch.stop()
def test_init(self):
# self.assertEqual(self.dispatcher.moe_all_to_all_group_name, "hccl_123")
self.assertEqual(self.dispatcher.ep_rank_id, 0)
self.assertEqual(self.dispatcher.ep_world_size, 8)
self.assertFalse(self.dispatcher.torchair_graph_enabled)
self.assertFalse(self.dispatcher.with_quant)
self.assertTrue(self.dispatcher.enable_dispatch_v2)
self.assertTrue(self.dispatcher.need_extra_args)
self.assertTrue(self.dispatcher.a3_need_extra_args)
def test_get_permute_mc2_kwargs_without_quant(self):
hidden_states = torch.randn(10, 128)
topk_ids = torch.randint(0, 8, (10, 1))
topk_weights = torch.randn(10, 1)
expert_map = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
kwargs = self.dispatcher.get_permute_mc2_kwargs(
hidden_states, topk_weights, topk_ids, expert_map)
self.assertIn("x", kwargs)
self.assertIn("expert_ids", kwargs)
self.assertEqual(kwargs["moe_expert_num"], 8)
def test_token_permutation_dispatch(self):
hidden_states = torch.randn(10, 128)
topk_weights = torch.randn(10, 1)
topk_ids = torch.randint(0, 8, (10, 1))
expert_map = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
with mock.patch("torch_npu.npu_moe_distribute_dispatch_v2",
return_value=(torch.randn(10, 128), ) *
5) as mock_dispatch:
output = self.dispatcher.token_permutation(hidden_states,
topk_weights, topk_ids,
expert_map)
mock_dispatch.assert_called_once()
self.assertEqual(output[0], 1) # group_list_type == 1
def test_token_permutation_with_shared_experts_and_quant(self):
self.shared_experts = mock.MagicMock()
self.shared_experts.gate_up_proj.return_value = (torch.randn(10, 128),
torch.tensor(1.0))
self.shared_experts.act_fn.return_value = torch.randn(10, 128)
self.dispatcher.with_quant = False
self.dispatcher.shared_act = torch.randn(10, 128)
self.dispatcher.swiglu_out_scale = torch.tensor(1.0)
self.hidden_states = torch.randn(10, 128)
self.topk_weights = torch.randn(10, 1)
with mock.patch("torch_npu.npu_moe_distribute_dispatch_v2",
return_value=(torch.randn(10, 128), ) * 5):
with mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.npu_stream_switch",
autospec=True):
with mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.npu_wait_tensor",
autospec=True) as mock_wait:
self.dispatcher.token_permutation(
self.hidden_states,
self.topk_weights,
torch.randint(0, 8, (10, 1)),
torch.tensor([0, 1, 2, 3, 4, 5, 6, 7]),
shared_experts=self.shared_experts)
mock_wait.assert_any_call(self.hidden_states,
self.topk_weights)
def test_get_unpermute_mc_kwargs_with_quant(self):
self.dispatcher.with_quant = True
hidden_states = torch.randn(10, 128)
self.dispatcher.topk_ids = torch.randint(0, 8, (10, 1))
self.dispatcher.topk_weights = torch.randint(0, 8, (10, 1))
self.dispatcher.expert_map = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
self.dispatcher.ep_recv_counts = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
self.dispatcher.need_extra_args = True
self.dispatcher.enable_dispatch_v2 = True
self.dispatcher.output = torch.randint(0, 8, (10, 1))
kwargs = self.dispatcher.get_unpermute_mc_kwargs(hidden_states)
self.assertIn("tp_send_counts", kwargs)
def test_token_unpermutation_with_shared_experts(self):
self.dispatcher.shared_experts = mock.MagicMock()
self.dispatcher.shared_experts.down_proj.return_value = (torch.randn(
10, 128), torch.tensor(1.0))
self.dispatcher.shared_act = torch.randn(10, 128)
self.dispatcher.with_quant = True
self.dispatcher.topk_ids = torch.randint(0, 8, (10, 1))
self.dispatcher.topk_weights = torch.randint(0, 8, (10, 1))
self.dispatcher.expert_map = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
self.dispatcher.ep_recv_counts = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])
self.dispatcher.need_extra_args = True
self.dispatcher.enable_dispatch_v2 = True
self.dispatcher.swiglu_out_scale = torch.randint(0, 8, (10, 1))
self.dispatcher.output = torch.randint(0, 8, (10, 1))
self.hidden_states = torch.randn(10, 128)
with mock.patch("torch_npu.npu_moe_distribute_combine_v2",
return_value=torch.randn(10, 128)):
with mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.npu_stream_switch",
autospec=True):
with mock.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.npu_wait_tensor",
autospec=True):
self.dispatcher.token_unpermutation(self.hidden_states)
class TestTokenDispatcherWithAllGather(unittest.TestCase):
def setUp(self):
# Mock dependencies
kwargs = {
"apply_router_weight_on_input": False,
"top_k": 2,
"max_num_tokens": 100,
"ep_size": 2,
"num_experts": 128,
"with_quant": False,
}
self.dispatcher = TokenDispatcherWithAllGather(**kwargs)
# Mock NPU functions
self.patcher_moe_init_routing = mock.patch(
'torch_npu.npu_moe_init_routing')
self.mock_moe_init_routing = self.patcher_moe_init_routing.start()
self.mock_moe_init_routing.return_value = (
torch.randn(6, 128), # sorted_hidden_states
torch.tensor([0, 1, 2, 3, 4, 5]), # expanded_row_idx
torch.tensor([0, 1, 0, 1, 0, 1]) # expanded_expert_idx
)
self.patcher_moe_compute_expert_tokens = mock.patch(
'torch_npu.npu_moe_compute_expert_tokens')
self.mock_moe_compute_expert_tokens = self.patcher_moe_compute_expert_tokens.start(
)
self.mock_moe_compute_expert_tokens.return_value = torch.tensor(
[3, 3]) # expert_tokens
self.patcher_moe_finalize_routing = mock.patch(
'torch_npu.npu_moe_finalize_routing')
self.mock_moe_finalize_routing = self.patcher_moe_finalize_routing.start(
)
self.mock_moe_finalize_routing.return_value = torch.randn(3, 128)
def tearDown(self):
self.patcher_moe_init_routing.stop()
self.patcher_moe_compute_expert_tokens.stop()
self.patcher_moe_finalize_routing.stop()
def test_token_permutation_without_expert_map(self):
hidden_states = torch.randn(3, 128)
topk_weights = torch.tensor([[0.7, 0.3], [0.6, 0.4], [0.5, 0.5]])
topk_ids = torch.tensor([[0, 1], [1, 2], [2, 3]])
group_list_type, sorted_hidden_states, expert_tokens = self.dispatcher.token_permutation(
hidden_states, topk_weights, topk_ids, None)
# Verify npu_moe_init_routing is called
self.mock_moe_init_routing.assert_called_once()
args, kwargs = self.mock_moe_init_routing.call_args
self.assertEqual(group_list_type, 0)
def test_token_permutation_with_quant(self):
kwargs = {
"apply_router_weight_on_input": False,
"top_k": 2,
"max_num_tokens": 100,
"ep_size": 2,
"num_experts": 128,
"with_quant": True,
}
self.dispatcher_quant = TokenDispatcherWithAllGather(**kwargs)
hidden_states = torch.randn(3, 128)
topk_weights = torch.tensor([[0.7, 0.3], [0.6, 0.4], [0.5, 0.5]])
topk_ids = torch.tensor([[0, 1], [1, 2], [2, 3]])
group_list_type, sorted_hidden_states, expert_tokens = self.dispatcher_quant.token_permutation(
hidden_states, topk_weights, topk_ids, None)
# Verify quant mode returns group_list_type=1
self.assertEqual(group_list_type, 0)
def test_token_unpermutation_with_expert_map(self):
self.dispatcher.expert_map = torch.tensor([0, 1, 2, 3])
self.dispatcher.sorted_token_indices = torch.tensor([0, 1, 1, 1, 1, 1])
self.dispatcher.sorted_weights = torch.tensor(
[0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
self.dispatcher.original_shape = (3, 128)
self.dispatcher.mask = torch.tensor([0, 1, 1, 0])
hidden_states = torch.randn(6, 128)
final_hidden_states = self.dispatcher.token_unpermutation(
hidden_states)
# Verify index_add_ is applied correctly
self.assertEqual(final_hidden_states.shape, (3, 128))
def test_token_unpermutation_without_expert_map(self):
self.dispatcher.with_quant = False
self.dispatcher.expanded_row_idx = torch.tensor([0, 1, 1, 1, 1, 1])
self.dispatcher.topk_ids = torch.tensor([[0, 1], [1, 2], [2, 3]])
self.dispatcher.sorted_token_indices = torch.tensor([0, 1, 1, 1, 1, 1])
self.dispatcher.sorted_weights = torch.tensor(
[0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
self.dispatcher.original_shape = (3, 128)
self.dispatcher.mask = torch.tensor([0, 1, 1, 0])
hidden_states = torch.randn(6, 128)
final_hidden_states = self.dispatcher.token_unpermutation(
hidden_states)
# Verify npu_moe_finalize_routing is called
self.mock_moe_finalize_routing.assert_called_once()
args, kwargs = self.mock_moe_finalize_routing.call_args
self.assertEqual(final_hidden_states.shape, (3, 128))
def test_token_permutation_with_router_weight(self):
self.dispatcher.apply_router_weight_on_input = True
hidden_states = torch.randn(3, 128)
topk_weights = torch.tensor([[0.7], [0.6], [0.5]]) # topk=1
topk_ids = torch.tensor([[0], [1], [2]])
group_list_type, sorted_hidden_states, expert_tokens = self.dispatcher.token_permutation(
hidden_states, topk_weights, topk_ids, None)
self.assertEqual(sorted_hidden_states.shape, (6, 128))
def test_token_permutation_invalid_topk_when_router_weight(self):
self.dispatcher.apply_router_weight_on_input = True
hidden_states = torch.randn(3, 128)
topk_weights = torch.tensor([[0.7, 0.3], [0.6, 0.4], [0.5, 0.5]])
with self.assertRaises(AssertionError):
self.dispatcher.token_permutation(
hidden_states, topk_weights,
torch.tensor([[0, 1], [1, 2], [2, 3]]), None)