File size: 6,201 Bytes
df66f6e
 
2a5f9fb
 
0c7ef71
 
df66f6e
0c7ef71
2a5f9fb
 
 
df66f6e
 
 
 
2a5f9fb
 
976f398
 
2a5f9fb
 
 
 
 
 
 
 
 
 
976f398
 
 
 
 
9d22eee
 
 
 
 
976f398
2a5f9fb
 
 
 
 
 
 
9d22eee
 
 
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
7302987
2a5f9fb
 
 
 
0c7ef71
2a5f9fb
 
0c7ef71
 
 
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7ef71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
0c7ef71
 
2a5f9fb
 
 
 
0c7ef71
 
 
 
 
2a5f9fb
 
0c7ef71
 
2a5f9fb
 
976f398
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c7ef71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
import json
import os
from datetime import datetime, timezone

from huggingface_hub import ModelCard

from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_PATH, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_REPO, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
from src.submission.check_validity import (
    already_submitted_models,
    check_model_card,
    get_model_size,
    is_model_on_hub,
    user_submission_permission,
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def add_new_eval(
    model: str,
    base_model: str,
    revision: str,
    precision: str,
    private: bool,
    weight_type: str,
    model_type: 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)

    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.")

    # Is the user rate limited?
    if user_name != "":
        user_can_submit, error_msg = user_submission_permission(
            user_name, USERS_TO_SUBMISSION_DATES, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
        )
        if not user_can_submit:
            return styled_error(error_msg)

    # Did the model authors forbid its submission to the leaderboard?
    if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS:
        return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")

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

    # Is the model on the hub?
    if weight_type in ["Delta", "Adapter"]:
        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=H4_TOKEN, test_tokenizer=True)
        if not base_model_on_hub:
            return styled_error(f'Base model "{base_model}" {error}')

    if not weight_type == "Adapter":
        model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True)
        if not model_on_hub:
            return styled_error(f'Model "{model}" {error}')
        architecture = "?"
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)


    # 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.")

    model_size = get_model_size(model_info=model_info, precision=precision)

    # Were the model card and license filled?
    try:
        license = 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)
    
    # Storing the model tags
    tags = []

    model_card = ModelCard.load(model)
    is_merge_from_metadata = "merge" in model_card.data.tags if model_card.data.tags else False
    merge_keywords = ["mergekit", "merged model", "merge model", "merging"]
    # If the model is a merge but not saying it in the metadata, we flag it
    is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in merge_keywords)
    if is_merge_from_model_card:
        tags.append("merge")
        if not is_merge_from_metadata:
            tags.append("flagged:undisclosed_merge")
    if "moe" in model_card.data.tags:
        tags.append("moe")


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

    eval_entry = {
        "model": model,
        "base_model": base_model,
        "revision": revision,
        "private": private,
        "precision": precision,
        "params": model_size,
        "architectures": architecture,
        "weight_type": weight_type,
        "status": "PENDING",
        "submitted_time": current_time,
        "model_type": model_type,
        "job_id": -1,
        "job_start_time": None,
    }

    supplementary_info = {
        "likes": model_info.likes,
        "license": license,
        "still_on_hub": True,
        "tags": tags,
    }

    # Check for duplicate submission
    if f"{model}_{revision}_{precision}" 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_{private}_{precision}_{weight_type}.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",
    )

    with open(DYNAMIC_INFO_FILE_PATH) as f:
        all_supplementary_info = json.load(f)

    all_supplementary_info[model] = supplementary_info
    with open(DYNAMIC_INFO_FILE_PATH, "w") as f:
        json.dump(all_supplementary_info, f, indent=2)

    API.upload_file(
        path_or_fileobj=DYNAMIC_INFO_FILE_PATH,
        path_in_repo=DYNAMIC_INFO_FILE_PATH.split("/")[-1],
        repo_id=DYNAMIC_INFO_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to dynamic info 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."
    )