File size: 8,631 Bytes
d489aeb
 
894c4b4
 
 
a88d51c
894c4b4
 
 
 
 
 
 
669da77
 
 
 
894c4b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
669da77
 
c8ea768
 
 
 
 
 
 
68d5bd5
 
c8ea768
 
 
 
 
894c4b4
 
669da77
 
894c4b4
 
 
c8ea768
894c4b4
 
 
669da77
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6411ad7
669da77
 
 
 
 
 
 
 
 
c8ea768
669da77
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85b25b4
 
 
f6e5d38
 
669da77
ca9ece0
669da77
 
 
 
 
 
 
 
9fbeaa1
 
669da77
a88d51c
 
 
669da77
a88d51c
669da77
 
9fbeaa1
5a2355a
 
c8ea768
5a2355a
 
 
669da77
5a2355a
c8ea768
5a2355a
 
 
669da77
 
 
 
 
 
 
 
 
894c4b4
 
 
 
 
 
f6e5d38
 
894c4b4
 
 
669da77
894c4b4
 
 
 
c8ea768
894c4b4
 
 
85b25b4
 
 
 
669da77
894c4b4
c8ea768
894c4b4
 
 
669da77
894c4b4
 
 
85b25b4
669da77
 
 
 
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
#!/usr/bin/env python

import os
import json

import random
from datetime import datetime

from huggingface_hub import snapshot_download

from src.backend.run_eval_suite import run_evaluation
from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
from src.backend.sort_queue import sort_models_by_priority
from src.backend.envs import Tasks, EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PATH_BACKEND, DEVICE, LIMIT, Task

from src.backend.manage_requests import EvalRequest
from src.leaderboard.read_evals import EvalResult

from src.envs import QUEUE_REPO, RESULTS_REPO, API

import logging
import pprint

logging.getLogger("openai").setLevel(logging.WARNING)

logging.basicConfig(level=logging.ERROR)
pp = pprint.PrettyPrinter(width=80)

PENDING_STATUS = "PENDING"
RUNNING_STATUS = "RUNNING"
FINISHED_STATUS = "FINISHED"
FAILED_STATUS = "FAILED"

TASKS_HARNESS = [task.value for task in Tasks]


def my_snapshot_download(repo_id, revision, local_dir, repo_type, max_workers):
    for i in range(10):
        try:
            snapshot_download(repo_id=repo_id, revision=revision, local_dir=local_dir, repo_type=repo_type, max_workers=max_workers)
            return
        except Exception:
            import time
            time.sleep(60)
    return


my_snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)


def sanity_checks():
    print(f'Device: {DEVICE}')

    # pull the eval dataset from the hub and parse any eval requests
    # check completed evals and set them to finished
    my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
    check_completed_evals(api=API, checked_status=RUNNING_STATUS, completed_status=FINISHED_STATUS,
                          failed_status=FAILED_STATUS, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND,
                          hf_repo_results=RESULTS_REPO, local_dir_results=EVAL_RESULTS_PATH_BACKEND)
    return


def request_to_result_name(request: EvalRequest) -> str:
    # Request: EvalRequest(model='meta-llama/Llama-2-13b-hf', private=False, status='FINISHED',
    # json_filepath='./eval-queue-bk/meta-llama/Llama-2-13b-hf_eval_request_False_False_False.json',
    # weight_type='Original', model_type='pretrained', precision='float32', base_model='', revision='main',
    # submitted_time='2023-09-09T10:52:17Z', likes=389, params=13.016, license='?')
    #
    # EvalResult(eval_name='meta-llama_Llama-2-13b-hf_float32', full_model='meta-llama/Llama-2-13b-hf',
    # org='meta-llama', model='Llama-2-13b-hf', revision='main',
    # results={'nq_open': 33.739612188365655, 'triviaqa': 74.12505572893447},
    # precision=<Precision.float32: ModelDetails(name='float32', symbol='')>,
    # model_type=<ModelType.PT: ModelDetails(name='pretrained', symbol='🟢')>,
    # weight_type=<WeightType.Original: ModelDetails(name='Original', symbol='')>,
    # architecture='LlamaForCausalLM', license='?', likes=389, num_params=13.016, date='2023-09-09T10:52:17Z', still_on_hub=True)
    #
    org_and_model = request.model.split("/", 1)
    if len(org_and_model) == 1:
        model = org_and_model[0]
        res = f"{model}_{request.precision}"
    else:
        org = org_and_model[0]
        model = org_and_model[1]
        res = f"{org}_{model}_{request.precision}"
    return res


def process_evaluation(task: Task, eval_request: EvalRequest) -> dict:
    results = run_evaluation(eval_request=eval_request, task_names=[task.benchmark], num_fewshot=task.num_fewshot,
                             batch_size=1, device=DEVICE, use_cache=None, limit=LIMIT)

    dumped = json.dumps(results, indent=2)
    print(dumped)

    output_path = os.path.join(EVAL_RESULTS_PATH_BACKEND, *eval_request.model.split("/"), f"results_{datetime.now()}.json")
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    with open(output_path, "w") as f:
        f.write(dumped)

    my_snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
    API.upload_file(path_or_fileobj=output_path, path_in_repo=f"{eval_request.model}/results_{datetime.now()}.json",
                    repo_id=RESULTS_REPO, repo_type="dataset")
    return results


def process_finished_requests() -> bool:
    sanity_checks()

    current_finished_status = [FINISHED_STATUS]

    # Get all eval request that are FINISHED, if you want to run other evals, change this parameter
    eval_requests: list[EvalRequest] = get_eval_requests(job_status=current_finished_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
    # Sort the evals by priority (first submitted first run)
    eval_requests: list[EvalRequest] = sort_models_by_priority(api=API, models=eval_requests)

    # XXX
    # eval_requests = [r for r in eval_requests if 'bloom-560m' in r.model]

    random.shuffle(eval_requests)

    from src.leaderboard.read_evals import get_raw_eval_results
    eval_results: list[EvalResult] = get_raw_eval_results(EVAL_RESULTS_PATH_BACKEND, EVAL_REQUESTS_PATH_BACKEND, True)

    result_name_to_request = {request_to_result_name(r): r for r in eval_requests}
    result_name_to_result = {r.eval_name: r for r in eval_results}

    for eval_request in eval_requests:
        result_name: str = request_to_result_name(eval_request)

        # Check the corresponding result
        from typing import Optional
        eval_result: Optional[EvalResult] = result_name_to_result[result_name] if result_name in result_name_to_result else None

        task_lst = TASKS_HARNESS.copy()
        random.shuffle(task_lst)

        # Iterate over tasks and, if we do not have results for a task, run the relevant evaluations
        for task in task_lst:
            task_name = task.benchmark

            if eval_result is None or task_name not in eval_result.results:
                eval_request: EvalRequest = result_name_to_request[result_name]

                my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
                set_eval_request(api=API, eval_request=eval_request, set_to_status=RUNNING_STATUS, hf_repo=QUEUE_REPO,
                                 local_dir=EVAL_REQUESTS_PATH_BACKEND)

                results = process_evaluation(task, eval_request)

                my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
                set_eval_request(api=API, eval_request=eval_request, set_to_status=FINISHED_STATUS, hf_repo=QUEUE_REPO,
                                 local_dir=EVAL_REQUESTS_PATH_BACKEND)

                return True

    return False


def process_pending_requests() -> bool:
    sanity_checks()

    current_pending_status = [PENDING_STATUS]

    # Get all eval request that are PENDING, if you want to run other evals, change this parameter
    eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
    # Sort the evals by priority (first submitted first run)
    eval_requests = sort_models_by_priority(api=API, models=eval_requests)

    random.shuffle(eval_requests)

    print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")

    if len(eval_requests) == 0:
        return False

    eval_request = eval_requests[0]
    pp.pprint(eval_request)

    my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
    set_eval_request(api=API, eval_request=eval_request, set_to_status=RUNNING_STATUS, hf_repo=QUEUE_REPO,
                     local_dir=EVAL_REQUESTS_PATH_BACKEND)

    task_lst = TASKS_HARNESS.copy()
    random.shuffle(task_lst)

    for task in task_lst:
        results = process_evaluation(task, eval_request)

    my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
    set_eval_request(api=API, eval_request=eval_request, set_to_status=FINISHED_STATUS, hf_repo=QUEUE_REPO,
                     local_dir=EVAL_REQUESTS_PATH_BACKEND)

    return True


if __name__ == "__main__":
    # res = False
    res = process_pending_requests()

    if res is False:
        res = process_finished_requests()