File size: 6,485 Bytes
894c4b4
 
 
 
 
 
 
 
 
 
669da77
 
 
 
894c4b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
669da77
 
894c4b4
 
 
 
669da77
 
894c4b4
 
 
 
 
 
669da77
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
894c4b4
 
 
 
 
 
 
 
 
669da77
894c4b4
 
 
 
 
 
 
6c79b12
669da77
894c4b4
 
 
 
669da77
894c4b4
 
 
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
import os
import json

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]

snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
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
    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, no_cache=True, 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)

    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)

    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)

    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
        eval_result: EvalResult = result_name_to_result[result_name]

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

            if task_name not in eval_result.results:
                results = process_evaluation(task, eval_request)
                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)

    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)

    set_eval_request(api=API, eval_request=eval_request, set_to_status=RUNNING_STATUS, hf_repo=QUEUE_REPO,
                     local_dir=EVAL_REQUESTS_PATH_BACKEND)

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

    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 = process_pending_requests()

    if res is False:
        res = process_finished_requests()