b350edae9a
refactor test_expert_load_balancer to keep the ut code style This PR also fixed the break change from https://github.com/vllm-project/vllm/pull/16188/files#diff-e2942ece30a5c580437694ffb964bfc664b510c59244c08e5921b8f5cefb4280 This is just a quick fix. We'll support embedding on V1 later Closes: https://github.com/vllm-project/vllm-ascend/issues/1299 Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
148 lines
5.1 KiB
Python
148 lines
5.1 KiB
Python
# fused moe ops test will hit the infer_schema error, we need add the patch
|
|
# here to make the test pass.
|
|
import vllm_ascend.patch.worker.patch_common.patch_utils # type: ignore[import] # isort: skip # noqa
|
|
|
|
import json
|
|
import unittest
|
|
from typing import List, TypedDict
|
|
from unittest import mock
|
|
|
|
import torch
|
|
|
|
from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
|
|
|
|
|
|
class Device(TypedDict):
|
|
device_id: int
|
|
device_expert: List[int]
|
|
|
|
|
|
class Layer(TypedDict):
|
|
layer_id: int
|
|
device_count: int
|
|
device_list: List[Device]
|
|
|
|
|
|
class MockData(TypedDict):
|
|
moe_layer_count: int
|
|
layer_list: List[Layer]
|
|
|
|
|
|
MOCK_DATA: MockData = {
|
|
"moe_layer_count":
|
|
1,
|
|
"layer_list": [{
|
|
"layer_id":
|
|
0,
|
|
"device_count":
|
|
2,
|
|
"device_list": [{
|
|
"device_id": 0,
|
|
"device_expert": [7, 2, 0, 3, 5]
|
|
}, {
|
|
"device_id": 1,
|
|
"device_expert": [6, 1, 4, 7, 2]
|
|
}]
|
|
}]
|
|
}
|
|
|
|
|
|
class TestExpertLoadBalancer(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
json_file = "expert_map.json"
|
|
with open(json_file, 'w') as f:
|
|
json.dump(MOCK_DATA, f)
|
|
|
|
self.expert_load_balancer = ExpertLoadBalancer(json_file,
|
|
global_expert_num=8)
|
|
|
|
def test_init(self):
|
|
|
|
self.assertIsInstance(self.expert_load_balancer.expert_map_tensor,
|
|
torch.Tensor)
|
|
self.assertEqual(self.expert_load_balancer.layers_num,
|
|
MOCK_DATA["moe_layer_count"])
|
|
self.assertEqual(self.expert_load_balancer.ranks_num,
|
|
MOCK_DATA["layer_list"][0]["device_count"])
|
|
|
|
def test_generate_index_dicts(self):
|
|
tensor_2d = torch.tensor([[7, 2, 0, 3, 5], [6, 1, 4, 7, 2]])
|
|
result = self.expert_load_balancer.generate_index_dicts(tensor_2d)
|
|
expected_result = [{
|
|
7: 0,
|
|
2: 1,
|
|
0: 2,
|
|
3: 3,
|
|
5: 4
|
|
}, {
|
|
6: 5,
|
|
1: 6,
|
|
4: 7,
|
|
7: 8,
|
|
2: 9
|
|
}]
|
|
self.assertEqual(result, expected_result)
|
|
|
|
def test_generate_expert_placement_map(self):
|
|
expert_placement_map = self.expert_load_balancer.generate_expert_placement_map(
|
|
)
|
|
self.assertEqual(expert_placement_map.shape,
|
|
(self.expert_load_balancer.layers_num,
|
|
self.expert_load_balancer.ranks_num, 8))
|
|
self.assertTrue(torch.all(expert_placement_map >= -1))
|
|
|
|
def test_generate_log2phy_expert_map(self):
|
|
layer_id = 0
|
|
log2phy_map = self.expert_load_balancer.generate_log2phy_expert_map(
|
|
layer_id)
|
|
self.assertEqual(log2phy_map.shape,
|
|
(self.expert_load_balancer.ranks_num, 8))
|
|
self.assertTrue(torch.all(log2phy_map >= -1))
|
|
|
|
@mock.patch("torch_npu.npu._lazy_init")
|
|
@mock.patch("torch.npu.current_device", return_value="cpu")
|
|
def test_get_rank_placement_map(self, mock_current_device, mock_lazy_init):
|
|
layer_id = 0
|
|
rank_id = 0
|
|
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
|
|
layer_id, rank_id)
|
|
self.assertEqual(rank_local_expert_num, 5)
|
|
expected_tensor = torch.tensor([2, -1, 1, 3, -1, 4, -1, 0],
|
|
dtype=torch.int32).to(
|
|
rank_expert_map.device)
|
|
self.assertTrue(rank_expert_map.equal(expected_tensor))
|
|
|
|
rank_id = 1
|
|
rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
|
|
layer_id, rank_id)
|
|
expected_tensor = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3],
|
|
dtype=torch.int32).to(
|
|
rank_expert_map.device)
|
|
self.assertTrue(rank_expert_map.equal(expected_tensor))
|
|
|
|
def test_get_rank_log2phy_map(self):
|
|
layer_id = 0
|
|
rank_id = 0
|
|
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
|
|
layer_id, rank_id)
|
|
expected_tensor = torch.tensor([2, 6, 1, 3, 7, 4, 5, 0],
|
|
dtype=torch.int32).to(
|
|
log2phy_map.device)
|
|
self.assertTrue(log2phy_map.equal(expected_tensor))
|
|
|
|
rank_id = 1
|
|
log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
|
|
layer_id, rank_id)
|
|
expected_tensor = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8],
|
|
dtype=torch.int32).to(
|
|
log2phy_map.device)
|
|
self.assertTrue(log2phy_map.equal(expected_tensor))
|
|
|
|
def test_get_global_redundant_expert_num(self):
|
|
redundant_expert_num = self.expert_load_balancer.get_global_redundant_expert_num(
|
|
)
|
|
expected_redundant_expert_num = len(MOCK_DATA["layer_list"][0]["device_list"][0]["device_expert"]) * \
|
|
MOCK_DATA["layer_list"][0]["device_count"] - 8
|
|
self.assertEqual(redundant_expert_num, expected_redundant_expert_num)
|