File size: 3,913 Bytes
df66f6e
 
2a5f9fb
 
df66f6e
bbf76cb
 
a49f289
2a5f9fb
976f398
 
2a5f9fb
b5474e9
2a5f9fb
 
 
 
 
3bdbd04
2a5f9fb
976f398
 
 
 
 
c58b612
bbf76cb
c58b612
 
f50141c
c58b612
f50141c
 
 
 
c58b612
9d22eee
 
 
 
 
976f398
2a5f9fb
 
 
 
 
 
 
 
 
 
 
a49f289
027390b
 
2a5f9fb
 
 
 
 
 
 
 
 
a49f289
2a5f9fb
 
 
 
 
 
 
 
 
 
 
a49f289
2a5f9fb
 
a49f289
3bdbd04
c58b612
e1d8ae4
a49f289
 
2a5f9fb
 
976f398
69f0225
976f398
 
2a5f9fb
 
 
e1d8ae4
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
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
112
113
114
115
116
117
118
import json
import os
from datetime import datetime, timezone

from src.display.formatting import styled_error, styled_message, styled_warning
from src.display.utils import Version, VllmVersion
from src.envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN
from src.submission.check_validity import already_submitted_models, check_model_card, is_model_on_hub

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None


def add_new_eval(
    model: str,
    revision: str,
    precision: str,
    model_type: str,
    add_special_tokens: str,
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    current_version = Version.v1_4_1.value.name
    current_vllm_version = VllmVersion.current.value.name

    # バージョン情報を含めた重複チェック
    submission_id = f"{model}_{precision}_{add_special_tokens}_{current_version}_{current_vllm_version}"
    if submission_id in REQUESTED_MODELS:
        return styled_warning(
            f"This model has already been evaluated with llm-jp-eval version {current_version} "
            f"and vllm version {current_vllm_version}"
        )

    user_name = ""
    model_path = model
    if "/" in model:
        user_name = model.split("/")[0]
        model_path = model.split("/")[1]

    precision = precision.split(" ")[0]
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    if model_type is None or model_type == "":
        return styled_error("Please select a model type.")

    # Does the model actually exist?
    if revision == "":
        revision = "main"

    # Is the model on the hub?
    model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
    if not model_on_hub:
        return styled_error(f'Model "{model}" {error}')

    # Is the model info correctly filled?
    try:
        model_info = API.model_info(repo_id=model, revision=revision)
    except Exception:
        return styled_error("Could not get your model information. Please fill it up properly.")

    # Were the model card and license filled?
    try:
        _ = model_info.cardData["license"]
    except Exception:
        return styled_error("Please select a license for your model")

    modelcard_OK, error_msg = check_model_card(model)
    if not modelcard_OK:
        return styled_error(error_msg)

    # Seems good, creating the eval
    print("Adding new eval")

    eval_entry = {
        "model_type": model_type,
        "model": model,
        "precision": precision,
        "revision": revision,
        "add_special_tokens": add_special_tokens,
        "llm_jp_eval_version": current_version,
        "vllm_version": current_vllm_version,
        "status": "PENDING",
        "submitted_time": current_time,
    }

    # Check for duplicate submission
    if f"{model}_{precision}_{add_special_tokens}" in REQUESTED_MODELS:
        return styled_warning("This model has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = (
        f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{add_special_tokens}_{current_vllm_version}.json"
    )

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    print("Uploading eval file")
    API.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to eval queue",
    )

    # Remove the local file
    os.remove(out_path)

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
    )