File size: 20,114 Bytes
6b87e28
bcd77eb
beb2b32
bf23d2b
9346f1c
beb2b32
b4ba8b7
 
bcd77eb
b2895a3
2a5f9fb
beb2b32
 
 
2a5f9fb
8c49cb6
 
 
 
 
 
976f398
df66f6e
 
 
 
 
 
 
0a3530a
9d22eee
0a3530a
beb2b32
0a3530a
 
 
 
b4ba8b7
 
0a3530a
 
beb2b32
 
b4ba8b7
df66f6e
beb2b32
 
 
b4ba8b7
beb2b32
 
 
 
 
 
 
 
8131376
 
beb2b32
 
 
09f61d7
051fb0a
09f61d7
 
 
 
 
 
051fb0a
 
 
 
beb2b32
 
 
 
 
 
 
 
 
 
 
10f9b3c
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aee9960
8131376
beb2b32
8131376
09f61d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8131376
 
 
09f61d7
beb2b32
 
8131376
beb2b32
 
 
 
 
 
051fb0a
 
 
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
efed7dc
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
711437e
beb2b32
 
711437e
beb2b32
 
 
 
 
 
 
 
 
 
 
 
a39f12a
beb2b32
 
 
 
 
 
 
 
 
 
 
a39f12a
beb2b32
 
 
 
 
 
 
 
 
 
 
 
a39f12a
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
711437e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8cb7546
d16cee2
 
 
 
 
67109fc
d16cee2
adb0416
 
d16cee2
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09f61d7
beb2b32
8131376
051fb0a
09f61d7
 
8131376
 
09f61d7
 
beb2b32
 
 
 
 
 
 
 
 
 
 
 
 
8131376
beb2b32
 
 
b2895a3
 
051fb0a
 
b2895a3
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
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
import logging
import time
import schedule
import datetime
import gradio as gr
from threading import Thread
import datasets
from huggingface_hub import snapshot_download, WebhooksServer, WebhookPayload, RepoCard
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
from apscheduler.schedulers.background import BackgroundScheduler

# Start ephemeral Spaces on PRs (see config in README.md)
from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    Precision,
    WeightType,
    fields,
    EvalQueueColumn
)
from src.envs import (
    API,
    EVAL_REQUESTS_PATH,
    AGGREGATED_REPO,
    HF_TOKEN,
    QUEUE_REPO,
    REPO_ID,
    VOTES_REPO,
    VOTES_PATH,
    HF_HOME,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.voting.vote_system import VoteManager, run_scheduler

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Start ephemeral Spaces on PRs (see config in README.md)
from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci

# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set.
# This controls whether a full initialization should be performed.
DO_FULL_INIT = True # os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"
NEW_DATA_ON_LEADERBOARD = True
LEADERBOARD_DF = None

def restart_space():
    logging.info(f"Restarting space with repo ID: {REPO_ID}")
    try:
        # Check if new data is pending and download if necessary
        if NEW_DATA_ON_LEADERBOARD:
            logging.info("Fetching latest leaderboard data before restart.")
            get_latest_data_leaderboard()

        # Now restart the space
        API.restart_space(repo_id=REPO_ID, token=HF_TOKEN)
        logging.info("Space restarted successfully.")
    except Exception as e:
        logging.error(f"Failed to restart space: {e}")

def time_diff_wrapper(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        diff = end_time - start_time
        logging.info(f"Time taken for {func.__name__}: {diff} seconds")
        return result

    return wrapper


@time_diff_wrapper
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5):
    """Download dataset with exponential backoff retries."""
    attempt = 0
    while attempt < max_attempts:
        try:
            logging.info(f"Downloading {repo_id} to {local_dir}")
            snapshot_download(
                repo_id=repo_id,
                local_dir=local_dir,
                repo_type=repo_type,
                tqdm_class=None,
                etag_timeout=30,
                max_workers=8,
            )
            logging.info("Download successful")
            return
        except Exception as e:
            wait_time = backoff_factor**attempt
            logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s")
            time.sleep(wait_time)
            attempt += 1
    raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts")

def get_latest_data_leaderboard(leaderboard_initial_df=None):
    global NEW_DATA_ON_LEADERBOARD
    global LEADERBOARD_DF
    if NEW_DATA_ON_LEADERBOARD:
        logging.info("Leaderboard updated at reload!")
        try:
            leaderboard_dataset = datasets.load_dataset(
                AGGREGATED_REPO,
                "default",
                split="train",
                cache_dir=HF_HOME,
                download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,  # Always download fresh data
                verification_mode="no_checks"
            )
            LEADERBOARD_DF = get_leaderboard_df(
                leaderboard_dataset=leaderboard_dataset,
                cols=COLS,
                benchmark_cols=BENCHMARK_COLS,
            )
            logging.info("Leaderboard dataset successfully downloaded.")
        except Exception as e:
            logging.error(f"Failed to download leaderboard dataset: {e}")
            return

        # Reset the flag after successful download
        NEW_DATA_ON_LEADERBOARD = False
    else:
        LEADERBOARD_DF = leaderboard_initial_df
        logging.info("Using cached leaderboard dataset.")
    return LEADERBOARD_DF


def get_latest_data_queue():
    eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
    return eval_queue_dfs

def init_space():
    """Initializes the application space, loading only necessary data."""
    global NEW_DATA_ON_LEADERBOARD
    NEW_DATA_ON_LEADERBOARD = True  # Ensure new data is always pulled on restart

    if DO_FULL_INIT:
        # These downloads only occur on full initialization
        try:
            download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
            download_dataset(VOTES_REPO, VOTES_PATH)
        except Exception:
            restart_space()

    # Always redownload the leaderboard DataFrame
    global LEADERBOARD_DF
    LEADERBOARD_DF = get_latest_data_leaderboard()

    # Evaluation queue DataFrame retrieval is independent of initialization detail level
    eval_queue_dfs = get_latest_data_queue()

    return LEADERBOARD_DF, eval_queue_dfs

# Initialize VoteManager
vote_manager = VoteManager(VOTES_PATH, EVAL_REQUESTS_PATH, VOTES_REPO)


# Schedule the upload_votes method to run every 15 minutes
schedule.every(15).minutes.do(vote_manager.upload_votes)

# Start the scheduler in a separate thread
scheduler_thread = Thread(target=run_scheduler, args=(vote_manager,), daemon=True)
scheduler_thread.start()

# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable.
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag.
LEADERBOARD_DF, eval_queue_dfs = init_space()
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs


# Function to check if a user is logged in
def check_login(profile: gr.OAuthProfile | None) -> bool:
    if profile is None:
        return False
    return True

def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.dummy],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.fullname.name, AutoEvalColumn.license.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
            ColumnFilter(
                AutoEvalColumn.params.name,
                type="slider",
                min=0.01,
                max=150,
                label="Select the number of parameters (B)",
            ),
            ColumnFilter(
                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
            ),
            ColumnFilter(
                AutoEvalColumn.merged.name, type="boolean", label="Merge/MoErge", default=True
            ),
            ColumnFilter(AutoEvalColumn.moe.name, type="boolean", label="MoE", default=False),
            ColumnFilter(AutoEvalColumn.not_flagged.name, type="boolean", label="Flagged", default=True),
            ColumnFilter(AutoEvalColumn.maintainers_highlight.name, type="boolean", label="Show only maintainer's highlight", default=False),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

main_block = gr.Blocks(css=custom_css)
with main_block:
    with gr.Row(elem_id="header-row"):
        gr.HTML(TITLE)
    
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("πŸ… LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)

        with gr.TabItem("πŸš€ Submit ", elem_id="llm-benchmark-tab-table", id=5):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown("# βœ‰οΈβœ¨ Submit your model here!", elem_classes="markdown-text")
                login_button = gr.LoginButton(elem_id="oauth-button")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name")
                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="latest")
                    with gr.Row():
                        model_type = gr.Dropdown(
                            choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
                            label="Model type",
                            multiselect=False,
                            value=ModelType.FT.to_str(" : "),
                            interactive=True,
                        )
                        chat_template_toggle = gr.Checkbox(
                            label="Use chat template", 
                            value=False,
                            info="Is your model a chat model?",
                        )

                with gr.Column():
                    precision = gr.Dropdown(
                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value.name for i in WeightType],
                        label="Weights type",
                        multiselect=False,
                        value=WeightType.Original.value.name,
                        interactive=True,
                    )
                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)", interactive=False)
            
            with gr.Column():
                with gr.Accordion(
                    f"βœ… Finished Evaluations ({len(finished_eval_queue_df)})",
                    open=False,
                    ):
                        with gr.Row():
                            finished_eval_table = gr.components.Dataframe(
                                value=finished_eval_queue_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                                interactive=False,
                            )
                with gr.Accordion(
                    f"πŸ”„ Running Evaluation Queue ({len(running_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        running_eval_table = gr.components.Dataframe(
                            value=running_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                            interactive=False,
                        )

                with gr.Accordion(
                    f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        pending_eval_table = gr.components.Dataframe(
                            value=pending_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                            interactive=False,
                        )

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()

            # The chat template checkbox update function
            def update_chat_checkbox(model_type_value):
                return ModelType.from_str(model_type_value) == ModelType.chat
            
            model_type.change(
                fn=update_chat_checkbox,
                inputs=[model_type],  # Pass the current checkbox value
                outputs=chat_template_toggle,
            )

            # The base_model_name_textbox interactivity and value reset function
            def update_base_model_name_textbox(weight_type_value):
                # Convert the dropdown value back to the corresponding WeightType Enum
                weight_type_enum = WeightType[weight_type_value]
                
                # Determine if the textbox should be interactive
                interactive = weight_type_enum in [WeightType.Adapter, WeightType.Delta]
                
                # Reset the value if weight type is "Original"
                reset_value = "" if not interactive else None

                return gr.update(interactive=interactive, value=reset_value)
            
            weight_type.change(
                fn=update_base_model_name_textbox,
                inputs=[weight_type],
                outputs=[base_model_name_textbox],
            )

            submit_button.click(
                add_new_eval,
                [
                    model_name_textbox,
                    base_model_name_textbox,
                    revision_name_textbox,
                    precision,
                    weight_type,
                    model_type,
                    chat_template_toggle,
                ],
                submission_result,
            )

        # Ensure  the values in 'pending_eval_queue_df' are correct and ready for the DataFrame component
        with gr.TabItem("πŸ†™ Model Vote"):
            with gr.Row():
                gr.Markdown(
                    "## Vote for the models which should be evaluated first! \nYou'll need to sign in with the button above first. All votes are recorded.", 
                    elem_classes="markdown-text"
                )
                login_button = gr.LoginButton(elem_id="oauth-button")


            with gr.Row():
                pending_models = pending_eval_queue_df[EvalQueueColumn.model_name.name].to_list()

                with gr.Column():
                    selected_model = gr.Dropdown(
                        choices=pending_models,
                        label="Models",
                        multiselect=False,
                        value="str",
                        interactive=True,
                    )

                    vote_button = gr.Button("Vote", variant="primary")

            with gr.Row():
                with gr.Accordion(
                    f"Available models pending ({len(pending_eval_queue_df)})",
                    open=True,
                ):
                    with gr.Row():
                        pending_eval_table_votes = gr.components.Dataframe(
                            value=vote_manager.create_request_vote_df(
                                pending_eval_queue_df
                            ),
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                            interactive=False
                        )

            # Set the click event for the vote button
            vote_button.click(
                vote_manager.add_vote,
                inputs=[selected_model, pending_eval_table],
                outputs=[pending_eval_table_votes]
            )


    with gr.Row():
        with gr.Accordion("πŸ“™ Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )

    main_block.load(fn=get_latest_data_leaderboard, inputs=[leaderboard], outputs=[leaderboard])
    leaderboard.change(fn=get_latest_data_queue, inputs=None, outputs=[finished_eval_table, running_eval_table, pending_eval_table])
    pending_eval_table.change(fn=vote_manager.create_request_vote_df, inputs=[pending_eval_table], outputs=[pending_eval_table_votes])

main_block.queue(default_concurrency_limit=40)


def enable_space_ci_and_return_server(ui: gr.Blocks) -> WebhooksServer:
    # Taken from https://huggingface.co/spaces/Wauplin/gradio-space-ci/blob/075119aee75ab5e7150bf0814eec91c83482e790/src/gradio_space_ci/webhook.py#L61
    # Compared to original, this one do not monkeypatch Gradio which allows us to define more webhooks.
    # ht to Lucain!
    if SPACE_ID is None:
        print("Not in a Space: Space CI disabled.")
        return WebhooksServer(ui=main_block)

    if IS_EPHEMERAL_SPACE:
        print("In an ephemeral Space: Space CI disabled.")
        return WebhooksServer(ui=main_block)

    card = RepoCard.load(repo_id_or_path=SPACE_ID, repo_type="space")
    config = card.data.get("space_ci", {})
    print(f"Enabling Space CI with config from README: {config}")

    return configure_space_ci(
        blocks=ui,
        trusted_authors=config.get("trusted_authors"),
        private=config.get("private", "auto"),
        variables=config.get("variables", "auto"),
        secrets=config.get("secrets"),
        hardware=config.get("hardware"),
        storage=config.get("storage"),
    )

# Create webhooks server (with CI url if in Space and not ephemeral)
webhooks_server = enable_space_ci_and_return_server(ui=main_block)

# Add webhooks
@webhooks_server.add_webhook
def update_leaderboard(payload: WebhookPayload) -> None:
    """Redownloads the leaderboard dataset each time it updates"""
    if payload.repo.type == "dataset" and payload.event.action == "update":
        global NEW_DATA_ON_LEADERBOARD
        logging.info("New data detected, downloading updated leaderboard dataset.")
        
        # Mark the flag for new data
        NEW_DATA_ON_LEADERBOARD = True

        # Now actually download the latest data immediately
        get_latest_data_leaderboard()

# The below code is not used at the moment, as we can manage the queue file locally
LAST_UPDATE_QUEUE = datetime.datetime.now()
@webhooks_server.add_webhook    
def update_queue(payload: WebhookPayload) -> None:
    """Redownloads the queue dataset each time it updates"""
    if payload.repo.type == "dataset" and payload.event.action == "update":
        current_time = datetime.datetime.now()
        global LAST_UPDATE_QUEUE
        if current_time - LAST_UPDATE_QUEUE > datetime.timedelta(minutes=10):
            print("Would have updated the queue")
            # We only redownload is last update was more than 10 minutes ago, as the queue is 
            # updated regularly and heavy to download
            download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
            LAST_UPDATE_QUEUE = datetime.datetime.now()

webhooks_server.launch()

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", hours=1)  # Restart every 1h
logging.info("Scheduler initialized to restart space every 1 hour.")
scheduler.start()