0c1d239df4
### What this PR does / why we need it? Use Base test and cleanup all manaul patch code - Cleanup EPLB config to avoid tmp test file - Use BaseTest with global cache - Add license - Add a doc to setup unit test in local env ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
142 lines
5.3 KiB
Python
142 lines
5.3 KiB
Python
#
|
|
# 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.
|
|
#
|
|
|
|
import json
|
|
import os
|
|
from typing import List, TypedDict
|
|
from unittest import mock
|
|
|
|
import torch
|
|
|
|
from tests.ut.base import TestBase
|
|
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]
|
|
|
|
|
|
class TestExpertLoadBalancer(TestBase):
|
|
|
|
def setUp(self):
|
|
_TEST_DIR = os.path.dirname(__file__)
|
|
json_file = _TEST_DIR + "/expert_map.json"
|
|
with open(json_file, 'r') as f:
|
|
self.expert_map: MockData = json.load(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,
|
|
self.expert_map["moe_layer_count"])
|
|
self.assertEqual(self.expert_load_balancer.ranks_num,
|
|
self.expert_map["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(self.expert_map["layer_list"][0]["device_list"][0]["device_expert"]) * \
|
|
self.expert_map["layer_list"][0]["device_count"] - 8
|
|
self.assertEqual(redundant_expert_num, expected_redundant_expert_num)
|