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xc-llm-ascend/tests/ut/ops/test_token_dispatcher.py
Clorist33 4f0dddc9ee [Bugfix] bugfix for moe_mlp in vllm-ascend/v0.11.0-dev (#4885) 
### What this PR does / why we need it?
This PR fixes a bug in the moe_mlp module by correcting the arguments
passed to the torch_npu.npu_dequant_swiglu_quant function.It properly
converts group_list from a cumulative sum to counts for the group_index
parameter.

### Does this PR introduce _any_ user-facing change?
No


- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/main

---------

Signed-off-by: tanqingshan (A)  <50050625@china.huawei.com>
Signed-off-by: tanqingshan (A) <50050625@china.huawei.com>
Co-authored-by: tanqingshan (A) <50050625@china.huawei.com>
Co-authored-by: Mercykid-bash <ruanche0218@gmail.com>
2025-12-12 14:51:47 +08:00

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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM 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.
# This file is a part of the vllm-ascend project.
from unittest.mock import MagicMock, PropertyMock, patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.ops.moe.token_dispatcher import ( # isort: skip
AscendSocVersion, TokenDispatcherWithAll2AllV,
TokenDispatcherWithAllGather, TokenDispatcherWithMC2)
class TestTokenDispatcherWithMC2(TestBase):
def setUp(self):
self.mc2_group = 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 = patch(
"vllm_ascend.ops.moe.token_dispatcher.get_mc2_group",
return_value=self.mc2_group)
self.mc2_group_patch.start()
self.rank_group_patch = patch("torch.distributed.get_rank",
return_value=0)
self.rank_group_patch.start()
# Mock get_forward_context().mc2_mask
self.forward_context = MagicMock()
self.forward_context.mc2_mask = torch.tensor([1, 0, 1])
self.forward_context_patch = patch(
"vllm.forward_context.get_forward_context",
return_value=self.forward_context)
self.forward_context_patch.start()
# Mock get_ascend_soc_version()
self.ascend_soc_version_patch = patch(
"vllm_ascend.ops.moe.token_dispatcher.get_ascend_soc_version",
return_value=AscendSocVersion.A3)
self.ascend_soc_version_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()
def test_init(self):
self.assertEqual(self.dispatcher.ep_rank_id, 0)
self.assertEqual(self.dispatcher.ep_world_size, 8)
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_dispatch_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_dispatch_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 patch("torch_npu.npu_moe_distribute_dispatch_v2",
return_value=(torch.randn(10, 128), ) * 5 +
(None, None)) as mock_dispatch:
output = self.dispatcher.token_dispatch(hidden_states,
topk_weights, topk_ids,
expert_map)
mock_dispatch.assert_called_once()
self.assertEqual(output["group_list_type"],
0) # group_list_type == 0
def test_token_dispatch_with_shared_experts_and_quant(self):
self.shared_experts = 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 patch("torch_npu.npu_moe_distribute_dispatch_v2",
return_value=(torch.randn(10, 128), ) * 5 + (None, None)):
self.dispatcher.token_dispatch(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)
def test_get_combine_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))
self.dispatcher.moe_expert_num = len(self.dispatcher.expert_map)
kwargs = self.dispatcher.get_combine_mc_kwargs(hidden_states)
self.assertIn("tp_send_counts", kwargs)
def test_token_combine_with_shared_experts(self):
self.dispatcher.shared_experts = 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.dispatcher.moe_expert_num = len(self.dispatcher.expert_map)
self.hidden_states = torch.randn(10, 128)
with patch("torch_npu.npu_moe_distribute_combine_v2",
return_value=torch.randn(10, 128)):
self.dispatcher.token_combine(self.hidden_states)
class TestTokenDispatcherWithAllGather(TestBase):
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_npu_moe_init_routing_v2 = patch(
'torch_npu.npu_moe_init_routing_v2')
self.mock_npu_moe_init_routing_v2 = self.patcher_npu_moe_init_routing_v2.start(
)
self.mock_npu_moe_init_routing_v2.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
torch.tensor([0, 1, 0, 1, 0, 1]))
self.patcher_npu_moe_token_unpermute = patch(
'torch_npu.npu_moe_token_unpermute')
self.mock_npu_moe_token_unpermute = self.patcher_npu_moe_token_unpermute.start(
)
self.mock_npu_moe_token_unpermute.return_value = torch.randn(6, 128)
def tearDown(self):
self.patcher_npu_moe_init_routing_v2.stop()
self.patcher_npu_moe_token_unpermute.stop()
def test_token_dispatch_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]])
results = self.dispatcher.token_dispatch(hidden_states, topk_weights,
topk_ids, None)
# Verify npu_moe_init_routing is called
self.mock_npu_moe_init_routing_v2.assert_called_once()
args, kwargs = self.mock_npu_moe_init_routing_v2.call_args
self.assertEqual(results["group_list_type"], 1)
def test_token_dispatch_with_expert_map(self):
self.dispatcher.expert_map = torch.tensor([0, 1, 2, 3])
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]])
results = self.dispatcher.token_dispatch(hidden_states, topk_weights,
topk_ids, None)
# Verify npu_moe_init_routing is called
self.mock_npu_moe_init_routing_v2.assert_called_once()
args, kwargs = self.mock_npu_moe_init_routing_v2.call_args
self.assertEqual(results["group_list_type"], 1)
def test_token_dispatch_without_quant(self):
kwargs = {
"apply_router_weight_on_input": False,
"top_k": 2,
"max_num_tokens": 100,
"ep_size": 2,
"num_experts": 128,
}
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]])
results = self.dispatcher_quant.token_dispatch(hidden_states,
topk_weights, topk_ids,
None)
self.assertEqual(results["group_list_type"], 1)
def test_token_dispatch_with_quant(self):
kwargs = {
"apply_router_weight_on_input": False,
"top_k": 2,
"max_num_tokens": 100,
"ep_size": 2,
"num_experts": 128,
}
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]])
results = self.dispatcher_quant.token_dispatch(hidden_states,
topk_weights,
topk_ids,
None,
with_quant=True)
self.assertIsNotNone(results["hidden_states"])
self.assertIsNotNone(results["group_list"])
self.assertIsNotNone(results["dynamic_scale"])
self.assertEqual(results["group_list_type"], 1)
def test_token_combine_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.expanded_row_idx = 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_combine(hidden_states)
self.assertEqual(final_hidden_states.shape, (6, 128))
def test_token_combine_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_combine(hidden_states)
# Verify npu_moe_finalize_routing is called
self.mock_npu_moe_token_unpermute.assert_called_once()
args, kwargs = self.mock_npu_moe_token_unpermute.call_args
self.assertEqual(final_hidden_states.shape, (6, 128))
def test_token_dispatch_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]])
results = self.dispatcher.token_dispatch(hidden_states, topk_weights,
topk_ids, None)
self.assertEqual(results["hidden_states"].shape, (6, 128))
class TestTokenDispatcherWithAll2AllV(TestBase):
def setUp(self):
# Patch properties
patcher1 = patch.object(TokenDispatcherWithAll2AllV,
'ep_group',
new_callable=PropertyMock,
return_value=MagicMock())
patcher2 = patch.object(TokenDispatcherWithAll2AllV,
'ep_rank',
new_callable=PropertyMock,
return_value=0)
patcher3 = patch.object(TokenDispatcherWithAll2AllV,
'ep_size',
new_callable=PropertyMock,
return_value=2)
self.addCleanup(patcher1.stop)
self.addCleanup(patcher2.stop)
self.addCleanup(patcher3.stop)
self.mock_ep_group_prop = patcher1.start()
self.mock_ep_rank_prop = patcher2.start()
self.mock_ep_size_prop = patcher3.start()
# Mock torch_npu.npu_moe_token_permute
patcher4 = patch('torch_npu.npu_moe_token_permute')
self.mock_npu_moe_token_permute = patcher4.start()
self.addCleanup(patcher4.stop)
self.mock_npu_moe_token_permute.return_value = (torch.randn(16, 16),
torch.arange(16))
# Mock torch_npu.npu_moe_token_unpermute
patcher5 = patch('torch_npu.npu_moe_token_unpermute')
self.mock_npu_moe_token_unpermute = patcher5.start()
self.addCleanup(patcher5.stop)
self.mock_npu_moe_token_unpermute.return_value = torch.randn(8, 16)
# Mock async_all_to_all
patcher6 = patch('vllm_ascend.ops.moe.comm_utils.async_all_to_all')
self.mock_async_all_to_all = patcher6.start()
self.addCleanup(patcher6.stop)
self.mock_async_all_to_all.return_value = (None, torch.randn(16, 16),
MagicMock())
# Mock gather_from_sequence_parallel_region
patcher7 = patch(
'vllm_ascend.ops.moe.token_dispatcher.gather_from_sequence_parallel_region'
)
self.mock_gather_from_sequence_parallel_region = patcher7.start()
self.addCleanup(patcher7.stop)
self.mock_gather_from_sequence_parallel_region.return_value = torch.tensor(
[[2, 2, 2, 2], [2, 2, 2, 2]], dtype=torch.int64)
# Mock torch.histc
patcher8 = patch('torch.histc')
self.mock_histc = patcher8.start()
self.addCleanup(patcher8.stop)
self.mock_histc.return_value = torch.tensor([2, 2, 2, 2],
dtype=torch.int64)
# Mock torch.npu.current_device
patcher9 = patch('torch.npu.current_device')
self.mock_current_device = patcher9.start()
self.addCleanup(patcher9.stop)
self.mock_current_device.return_value = 'cpu'
# Mock torch_npu.npu_dynamic_quant
patcher10 = patch('torch_npu.npu_dynamic_quant')
self.mock_npu_dynamic_quant = patcher10.start()
self.addCleanup(patcher10.stop)
self.mock_npu_dynamic_quant.return_value = (torch.randn(16, 16),
torch.randn(16))
# Mock torch_npu.npu_moe_init_routing_v2
patcher11 = patch('torch_npu.npu_moe_init_routing_v2')
self.mock_npu_moe_init_routing_v2 = patcher11.start()
self.addCleanup(patcher11.stop)
self.mock_npu_moe_init_routing_v2.return_value = (torch.randn(
16, 16), torch.arange(16), None, torch.randn(16))
# Mock torch.repeat_interleave
patcher12 = patch('torch.repeat_interleave')
self.mock_repeat_interleave = patcher12.start()
self.addCleanup(patcher12.stop)
self.mock_repeat_interleave.return_value = torch.arange(16)
self.dispatcher = TokenDispatcherWithAll2AllV(top_k=2,
num_experts=4,
num_local_experts=2,
with_quant=False)
def test_token_dispatch(self):
hidden_states = torch.randn(8, 16)
topk_weights = torch.rand(8, 4)
topk_ids = torch.randint(0, 4, (8, 2)).long()
expert_map = torch.tensor([0, 1, 2, 3])
self.dispatcher.expert_ids_per_ep_rank = torch.tensor(
[0, 1], dtype=torch.int32)
self.dispatcher.local_expert_indices = [0, 1]
result = self.dispatcher.token_dispatch(hidden_states=hidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])
self.assertEqual(result["group_list_type"], 1)
def test_token_combine(self):
self.dispatcher.hidden_shape = (8, 16)
self.dispatcher.hidden_shape_before_permute = (8, 16)
self.dispatcher.reversed_local_input_permutation_mapping = torch.arange(
8)
self.dispatcher.topk_weights = torch.rand(8, 4)
self.dispatcher.input_splits = [4, 4]
self.dispatcher.output_splits = [4, 4]
self.dispatcher.reversed_global_input_permutation_mapping = torch.arange(
16)
self.dispatcher.expert_ids_per_ep_rank = torch.tensor(
[0, 1], dtype=torch.int32)
self.dispatcher.local_expert_indices = [0, 1]
self.dispatcher.num_global_tokens_per_local_expert = torch.tensor(
[[2, 2], [2, 2]], dtype=torch.int64)
expert_output = torch.randn(16, 16)
output = self.dispatcher.token_combine(expert_output)
self.assertIsNotNone(output)
self.assertEqual(output.shape, (8, 16))
def test_token_dispatch_with_quant(self):
self.dispatcher = TokenDispatcherWithAll2AllV(top_k=2,
num_experts=4,
num_local_experts=2)
hidden_states = torch.randn(8, 16)
topk_weights = torch.rand(8, 4)
topk_ids = torch.randint(0, 4, (8, 2)).long()
expert_map = torch.tensor([0, 1, 2, 3])
self.dispatcher.expert_ids_per_ep_rank = torch.tensor(
[0, 1], dtype=torch.int32)
self.dispatcher.local_expert_indices = [0, 1]
result = self.dispatcher.token_dispatch(hidden_states=hidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map,
with_quant=True)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])
self.assertIsNotNone(result["dynamic_scale"])
self.assertEqual(result["group_list_type"], 1)
def test_token_dispatch_with_quant_no_active_tokens(self):
self.dispatcher = TokenDispatcherWithAll2AllV(top_k=2,
num_experts=4,
num_local_experts=2)
self.mock_repeat_interleave.return_value = torch.tensor(
[], dtype=torch.long)
hidden_states = torch.randn(8, 16)
topk_weights = torch.rand(8, 4)
topk_ids = torch.randint(0, 4, (8, 2)).long()
expert_map = torch.tensor([0, 1, 2, 3])
self.dispatcher.expert_ids_per_ep_rank = torch.tensor(
[0, 1], dtype=torch.int32)
self.dispatcher.local_expert_indices = [0, 1]
result = self.dispatcher.token_dispatch(hidden_states=hidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map,
with_quant=True)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])
self.assertIsNotNone(result["dynamic_scale"])
self.assertEqual(result["group_list_type"], 1)
def test_token_dispatch_with_log2phy(self):
hidden_states = torch.randn(8, 16)
topk_weights = torch.rand(8, 4)
topk_ids = torch.randint(0, 4, (8, 2)).long()
expert_map = torch.tensor([0, 1, 2, 3])
log2phy = torch.tensor([1, 0, 3, 2])
self.dispatcher.expert_ids_per_ep_rank = torch.tensor(
[0, 1], dtype=torch.int32)
self.dispatcher.local_expert_indices = [0, 1]
result = self.dispatcher.token_dispatch(hidden_states=hidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map,
log2phy=log2phy)
self.assertIsNotNone(result["hidden_states"])
self.assertIsNotNone(result["group_list"])
self.assertEqual(result["group_list_type"], 1)
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