Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_phimoe.py 3.4 KB
Newer Older
Wenxiang's avatar
[Model] Adding support for MSFT Phi-3.5-MoE (#7729)  
Wenxiang committed
1
2
3
4
5
6
7
8
9 
"""Compare the outputs of HF and vLLM for moe models using greedy sampling.

Run `pytest tests/models/test_phimoe.py`.
"""
import pytest
import torch

from vllm.utils import is_cpu
Cyrus Leung's avatar
[CI/Build] Reorganize models tests (#7820)  
Cyrus Leung committed
10 
from ...utils import check_logprobs_close
Wenxiang's avatar
[Model] Adding support for MSFT Phi-3.5-MoE (#7729)  
Wenxiang committed
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111 

MODELS = [
    "microsoft/Phi-3.5-MoE-instruct",
]


def test_phimoe_routing_function():
    from vllm.model_executor.models.phimoe import phimoe_routing_function
    test_case = {
        0: {
            "hidden_states":
            torch.tensor([1, 2, 3, 4, 5, 6, 7, 8],
                         dtype=torch.float32,
                         requires_grad=False).view(4, 2),
            "gating_output":
            torch.tensor([0.1, 0.2, 0.3, 0.4],
                         dtype=torch.float32,
                         requires_grad=False),
            "topk":
            2,
            "renormalize":
            False,
        },
        1: {
            "hidden_states":
            torch.tensor([1, 2, 3, 4, 5, 6, 7, 8],
                         dtype=torch.float32,
                         requires_grad=False).view(4, 2),
            "gating_output":
            torch.tensor([0.4, 0.2, 0.3, 0.4],
                         dtype=torch.float32,
                         requires_grad=False),
            "topk":
            2,
            "renormalize":
            False,
        }
    }

    ground_truth = {
        0: {
            "topk_weights":
            torch.tensor([1., 1.], dtype=torch.float32, requires_grad=False),
            "topk_ids":
            torch.tensor([3, 2], dtype=torch.long, requires_grad=False),
        },
        1: {
            "topk_weights":
            torch.tensor([0.5, 1.], dtype=torch.float32, requires_grad=False),
            "topk_ids":
            torch.tensor([0, 3], dtype=torch.long, requires_grad=False),
        }
    }

    for test_id in test_case:
        topk_weights, topk_ids = phimoe_routing_function(**test_case[test_id])
        assert torch.allclose(topk_weights,
                              ground_truth[test_id]["topk_weights"])
        assert torch.equal(topk_ids, ground_truth[test_id]["topk_ids"])


def get_gpu_memory():
    try:
        props = torch.cuda.get_device_properties(torch.cuda.current_device())
        gpu_memory = props.total_memory / (1024**3)
        return gpu_memory
    except Exception:
        return 0


@pytest.mark.skipif(condition=is_cpu(),
                    reason="This test takes a lot time to run on CPU, "
                    "and vllm CI's disk space is not enough for this model.")
@pytest.mark.skipif(condition=get_gpu_memory() < 100,
                    reason="Skip this test if GPU memory is insufficient.")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    with hf_runner(model, dtype=dtype) as hf_model:
        hf_outputs = hf_model.generate_greedy_logprobs_limit(
            example_prompts, max_tokens, num_logprobs)

    with vllm_runner(model, dtype=dtype) as vllm_model:
        vllm_outputs = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs)
    check_logprobs_close(
        outputs_0_lst=hf_outputs,
        outputs_1_lst=vllm_outputs,
        name_0="hf",
        name_1="vllm",
    )