File size: 10,322 Bytes
5a5a36e
 
 
259a967
5a5a36e
 
 
 
fa9f3ea
5a5a36e
 
 
 
 
cf7af95
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
49e6356
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b10d6d4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5a5a36e
 
653f44e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bd64e6e
 
653f44e
 
 
 
cf7af95
 
653f44e
5a5a36e
 
 
 
 
 
 
 
653f44e
 
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
653f44e
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58fe595
 
5a5a36e
ac138f8
 
 
 
 
 
 
 
 
6e4a981
 
 
 
 
 
 
 
 
 
 
ac138f8
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
fa9f3ea
 
 
 
715b290
fa9f3ea
 
 
 
2a6cc68
 
 
 
 
fa9f3ea
 
259a967
 
fa9f3ea
 
 
f89f4fd
 
fa9f3ea
 
5a5a36e
 
228e920
5a5a36e
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
import json
import os
from datetime import datetime, timezone
import time

from huggingface_hub import ModelCard, snapshot_download

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, REPO, GIT_REQUESTS_PATH, GIT_STATUS_PATH, GLOBAL_COND
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,
    get_quantized_model_parameters_memory,
    is_model_on_hub,
    is_gguf_on_hub,
    user_submission_permission,
    get_model_tags
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def add_new_eval(
    model: str,
    revision: str,
    private: bool,
    compute_dtype: str="float16",
    precision: str="4bit",
    weight_dtype: str="int4",
    gguf_ftype: str="*Q4_0.gguf",
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(GIT_STATUS_PATH)

    quant_type = None
    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")

    # 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:
        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"

    architecture = "?"
    downloads = 0
    created_at = ""
    gguf_on_hub, error, gguf_files, new_gguf_ftype = is_gguf_on_hub(repo_id=model, filename=gguf_ftype)
    if new_gguf_ftype is not None:
        gguf_ftype = new_gguf_ftype

    model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True)

    # Is the model on the hub?
    if (not model_on_hub or model_config is None) and (not gguf_on_hub or gguf_files is None):
        return styled_error(f'Model "{model}" {error}')

    if model_config is not None:
        architectures = getattr(model_config, "architectures", None)
        if architectures:
            architecture = ";".join(architectures)
        downloads = getattr(model_config, 'downloads', 0)
        created_at = getattr(model_config, 'created_at', '')
        quantization_config = getattr(model_config, 'quantization_config', None)

    if gguf_files is not None:
        architectures = ""
        downloads = 0
        created_at = ""
        quantization_config = None
        quant_type = "llama.cpp"


    # 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:
        if model_info.cardData is None:
            license = "unknown"
        else:
            license = model_info.cardData.get("license", "unknown")
    except Exception:
        return styled_error("Please select a license for your model")

    modelcard_OK, error_msg, model_card = check_model_card(model)

    # maybe don't have model card
    """
    if not modelcard_OK:
        return styled_error(error_msg)
    """

    tags = get_model_tags(model_card, model)

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

    script = "ITREX"
    hardware = "cpu"
    precision = "4bit"
    if quantization_config is not None:
        quant_method = quantization_config.get("quant_method", None)
        if "bnb_4bit_quant_type" in quantization_config:
            quant_method = "bitsandbytes"
            quant_type = "bitsandbytes"
            hardware = "gpu"
            if quantization_config.get("load_in_4bit", True):
                precision = "4bit"
            if quantization_config.get("load_in_8bit", True):
                precision = "8bit"
        if quant_method == "gptq":
            hardware = "cpu"
            quant_type = "GPTQ"
            precision = f"{quantization_config.get('bits', '4bit')}bit"
        if quant_method == "awq":
            hardware = "gpu"
            quant_type = "AWQ"
            precision = f"{quantization_config.get('bits', '4bit')}bit"
        if quant_method == "aqlm":
            hardware = "gpu"
            quant_type = "AQLM"
            nbits_per_codebook = quantization_config.get('nbits_per_codebook')
            num_codebooks = quantization_config.get('num_codebooks')
            in_group_size = quantization_config.get('in_group_size')
            bits = int(nbits_per_codebook * num_codebooks / in_group_size)
            precision = f"{bits}bit"

    if precision == "4bit":
        weight_dtype = "int4"
    elif precision == "3bit":
        weight_dtype = "int3"
    elif precision == "2bit":
        weight_dtype = "int2"

    if quant_type is None or quant_type == "":
        # return styled_error("Please select a quantization model like GPTQ, AWQ etc.")
        # for eval fp32/fp16/bf16
        quant_type = None

    if quant_type is None:
        weight_dtype = str(getattr(model_config, "torch_dtype", "float16"))
        if weight_dtype in ["torch.float16", "float16"]:
            weight_dtype = "float16"
            precision = "16bit"
        elif weight_dtype in ["torch.bfloat16", "bfloat16"]:
            weight_dtype = "bfloat16"
            precision = "16bit"
        elif weight_dtype in ["torch.float32", "float32"]:
            weight_dtype = "float32"
            precision = "32bit"
        else:
            weight_dtype = "float32"
            precision = "32bit"
        model_type = "original"
        model_params, model_size = get_model_size(model_info=model_info, precision=precision)
    else:
        model_params, model_size = get_quantized_model_parameters_memory(model_info,
            quant_method=quant_type.lower(),
            bits=precision)
        model_type = "quantization"

    if quant_type == "llama.cpp":
        hardware = "cpu"
        script = "llama_cpp"
        tags = "llama.cpp"
    else:
        hardware = "gpu"

    if compute_dtype == "?":
        compute_dtype = "float16"

    eval_entry = {
        "model": model,
        "revision": revision,
        "private": private,
        "params": model_size,
        "architectures": architecture,
        "quant_type": quant_type,
        "precision": precision,
        "model_params": model_params,
        "model_size": model_size,
        "precision": precision,
        "weight_dtype": weight_dtype,
        "compute_dtype": compute_dtype,
        "gguf_ftype": gguf_ftype,
        "hardware": hardware,
        "status": "Pending",
        "submitted_time": current_time,
        "model_type": model_type,
        "job_id": -1,
        "job_start_time": None,
        "scripts": script
    }

    supplementary_info = {
        "likes": model_info.likes,
        "license": license,
        "still_on_hub": True,
        "tags": tags,
        "downloads": downloads,
        "created_at": created_at
    }
    print(eval_entry)

    # ToDo: need open
    # Check for duplicate submission
    if f"{model}_{revision}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}" in REQUESTED_MODELS:
        return styled_warning("This model has been already submitted.")

    print("Creating huggingface/dataset 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}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json"

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

    print("Uploading eval file")
    try:
        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",
        )
    except Exception as e:
        print(str(e))
        print("upload error........")

    print("Creating git eval file")
    OUT_DIR = f"{GIT_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    req_out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json"
    req_git_path = "/".join(req_out_path.split('/')[1:])

    print("Creating status file")
    OUT_DIR = f"{GIT_STATUS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    sta_out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json"
    sta_git_path = "/".join(sta_out_path.split('/')[1:])

    print("Uploading eval file")
    try:
        print("git-push get lock..............")
        GLOBAL_COND.acquire()
        branch = REPO.active_branch.name
        REPO.remotes.origin.pull(branch)

        REPO.index.remove("requests", False, r=True)

        with open(req_out_path, "w") as f:
            f.write(json.dumps(eval_entry, indent=4))
        with open(sta_out_path, "w") as f:
            f.write(json.dumps(eval_entry, indent=4))

        REPO.index.add([req_git_path, sta_git_path])
        commit = REPO.index.commit(f"Add {model} to eval requests/status.")
        REPO.remotes.origin.push(branch)
        time.sleep(10)

        print("git-push release lock..............")
        GLOBAL_COND.release()
    except Exception as e:
        print(str(e))
        print("git-push error........")
        GLOBAL_COND.release()

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to 3 hours for the model to show in the PENDING list."
    )