File size: 31,074 Bytes
eeb7ca1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
import os
import sys
from functools import partial
from typing import List, Union
import fire
import numpy as np

from loaders import get_loaders, get_tokenizer
from prompter import generate_prompt, prompt_types, PromptType
from utils import get_githash, copy_code
import torch


def log(*args, **kwargs):
    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
        if 'flush' not in kwargs:
            kwargs['flush'] = True
        print(*args, **kwargs)


# supported by huggingface evaluate
supported_metrics = ['bleu', 'rouge', 'sacrebleu', 'meteor']


def train(
        save_code: bool = False,
        run_id: int = None,

        base_model: str = 'h2oai/h2ogpt-oig-oasst1-512-6_9b',
        # base_model: str = 'h2oai/h2ogpt-oasst1-512-12b',
        # base_model: str = 'h2oai/h2ogpt-oasst1-512-20b',
        # base_model: str = 'EleutherAI/gpt-neox-20b',
        # base_model: str = 'EleutherAI/pythia-12b-deduped',
        # base_model: str = 'togethercomputer/GPT-NeoXT-Chat-Base-20B',
        # base_model: str = 'decapoda-research/llama-7b-hf',
        # base_model: str = 'decapoda-research/llama-13b-hf',
        # base_model: str = 'decapoda-research/llama-30b-hf',
        # base_model: str = 'EleutherAI/gpt-j-6B',

        # only needed if base_model is self-exported HF state without tokenizer
        tokenizer_base_model: str = None,
        # tokenizer_base_model: str = 'EleutherAI/gpt-neox-20b',

        data_path: str = "h2oai/openassistant_oasst1_h2ogpt",
        data_col_dict: dict = None,
        # data_path: str = "./dai_docs.train.json",
        prompt_type: Union[str, int] = "plain",  # "plain", "instruct", "quality", "human_bot", "dai_faq"

        valid_path: str = None,
        # valid_path: str = "./dai_docs.valid.json",

        # data_mix_in_path: str = "laion/OIG",  # way too big, medium quality
        data_mix_in_path: str = "0-hero/OIG-small-chip2",  # high quality, 50 MB, good enough for now
        data_mix_in_factor: float = 0.0,  # >1: more mix-in data, <1: more of data_path data
        data_mix_in_col_dict: dict = {'user': 'instruction', 'chip2': 'output'},
        data_mix_in_prompt_type: str = "instruct",  # just instruction->output, same as instruct

        output_dir: str = None,

        # LoRA checkpoint continuation
        lora_weights: str = "",

        # batching training hyperparams
        batch_size: int = 128,
        micro_batch_size: int = 4,
        gradient_checkpointing=False,  # unnecessary with gradient accumulation enabled
        fp16=True,
        train_8bit=False,
        train_4bit=False,

        # general training hyperparams
        num_epochs: float = 1,
        learning_rate: float = 3e-4,

        # validation settings
        val_set_size: int = None,
        val_metrics: List[str] = [],
        eval_steps: int = None,  # to control eval steps via steps
        eval_epochs: float = None,  # to control eval steps via epochs

        # lora hyperparams
        lora_r: int = 8,
        lora_alpha: int = 16,
        lora_dropout: float = 0.05,
        lora_target_modules: List[str] = None,
        llama_type: bool = None,
        llama_flash_attn: bool = False,

        # llm hyperparams
        train_on_inputs: bool = True,  # if False, masks out inputs in loss
        group_by_length: bool = False,  # if True, faster, but produces an odd training loss curve
        resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
        cutoff_len: int = 512,  # larger values use more memory
        drop_truncations: bool = False,  # if True, drop any truncated long sequences

        # torch training params
        ddp: bool = True,  # set to False if OOM with True, for multi-GPU model parallelism
        local_files_only: bool = False,  # else will download new versions, normally unwanted
        resume_download: bool = True,
        use_auth_token: Union[str, bool] = False,  # True requires CLI did huggingface-cli login before running
        warmup_steps: int = 100,
        logging_steps: int = 1,
        save_steps: int = None,  # must be round multiple of eval_steps
        save_total_limit: int = 3,
        add_eos_token: bool = False,
):
    if llama_flash_attn:
        # Need to call this before importing transformers.
        from llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
        replace_llama_attn_with_flash_attn()

    # allow set token directly
    use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)

    prompt_type = str(prompt_type)  # migration from integers
    assert prompt_type in prompt_types

    world_size = int(os.getenv("WORLD_SIZE", 1))
    local_rank = int(os.getenv("LOCAL_RANK", 0))
    rank = int(os.getenv("RANK", 0))
    print(f"local_rank: {local_rank}")
    print(f"global rank: {rank}")

    gpus = max(world_size, torch.cuda.device_count())
    run_id = run_id or 0
    if not data_path:
        raise ValueError("No data_path provided")
    if not output_dir:
        output_dir = f"{base_model.split('/')[-1]}.{data_path.replace('/', '')}.{num_epochs}_epochs.{get_githash() or 'nogit'}.{run_id}"
        if os.path.exists(output_dir) and not resume_from_checkpoint:
            raise FileExistsError(
                f"output_dir {output_dir} based on run_id {run_id} already exists. Please pick a different run_id.")
    else:
        if os.path.exists(output_dir) and not resume_from_checkpoint:
            raise FileExistsError(
                f"output_dir {output_dir} already exists. Please pick a different output_dir, or specify a run_id instead.")
    device_map = "auto"

    if save_code:
        copy_code(run_id)
    if tokenizer_base_model is None:
        tokenizer_base_model = base_model
    if llama_type is None:
        llama_type = "llama" in base_model.lower()
    if llama_type and llama_flash_attn:
        import pkg_resources
        try:
            pkg_resources.get_distribution('flash_attn')
            can_do_flash_attn = True
        except (pkg_resources.DistributionNotFound, pkg_resources.ContextualVersionConflict):
            can_do_flash_attn = False

        if not can_do_flash_attn:
            raise RuntimeError("""Flash attention not installed.
            NOTE: for current pytorch 2.0, flash attention requires installing cuda 11.7 via https://developer.nvidia.com/cuda-11-7-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=runfile_local and then when running, to avoid installing driver, docs, samples, just install toolkit.  Then when pip installing flash attention do:

            CUDA_HOME=/usr/local/cuda-11.7 pip install flash-attn""")
    assert (
        base_model
    ), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
    gradient_accumulation_steps = batch_size // micro_batch_size
    assert gradient_accumulation_steps >= world_size, "must increase batch_size for multi-GPU"

    device_map = "auto"

    locals_dict = locals()
    locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()])
    log(f"Training model with params:\n{locals_print}")
    log("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), get_githash()))

    max_memory = None
    if gpus > 1:
        if ddp:
            log("Distributed: data parallel")
            device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
            gradient_accumulation_steps = gradient_accumulation_steps // world_size
        else:
            free_in_GB = int(min(torch.cuda.mem_get_info()) / 1024 ** 3)
            max_memory = f"{free_in_GB - 2}GB"
            max_memory = {i: max_memory for i in range(gpus)}
            log("world_size: %d" % world_size)
            log("num_gpus: %d" % gpus)
            log("max mem: %s" % max_memory)

    model_loader, tokenizer_loader = get_loaders(llama_type=llama_type, model_name=base_model, reward_type=False)

    model = model_loader.from_pretrained(
        base_model,
        load_in_8bit=train_8bit,
        load_in_4bit=train_4bit,
        device_map=device_map,
        torch_dtype=torch.float16,
        max_memory=max_memory,
        local_files_only=local_files_only,
        trust_remote_code=True,
        resume_download=resume_download,
        use_auth_token=use_auth_token,
    )
    if gpus > 1:
        if not ddp:
            log("model parallel")
            model.is_parallelizable = True
            model.model_parallel = True

    tokenizer = get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token)

    if train_8bit or train_4bit:
        from peft import (
            prepare_model_for_kbit_training,
        )

        model = prepare_model_for_kbit_training(model)

    from peft import LoraConfig, get_peft_model, set_peft_model_state_dict
    try:
        from peft import utils
        lora_mappings = utils.TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.copy()
    except AttributeError:
        from peft import mapping
        lora_mappings = mapping.TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.copy()
    lora_mappings['distilgpt2'] = ["c_attn"]

    if lora_weights:

        from peft import PeftModel
        model = PeftModel.from_pretrained(
            model,
            lora_weights,
            torch_dtype=torch.float16,
            device_map=device_map,
            local_files_only=local_files_only,
            resume_download=resume_download,
            use_auth_token=use_auth_token,
        )
    elif lora_r > 0:
        if lora_target_modules is None:
            base_model_lower = base_model.lower()
            if base_model_lower in lora_mappings:
                lora_target_modules_cand = [lora_mappings[base_model_lower]]
            else:
                lora_target_modules_cand = [["query_key_value"], ["q_proj", "v_proj"]]
        else:
            lora_target_modules_cand = [lora_target_modules]

        for lora_target_modules in lora_target_modules_cand:
            try:
                config = LoraConfig(
                    r=lora_r,
                    lora_alpha=lora_alpha,
                    target_modules=lora_target_modules,
                    lora_dropout=lora_dropout,
                    bias="none",
                    task_type="CAUSAL_LM",
                )
                model = get_peft_model(model, config)
                break
            except ValueError as e:
                if "Target modules" in str(e) and "not found" in str(e):
                    continue
                else:
                    raise
        from peft import PeftModel
        assert isinstance(model, PeftModel), "LoRA failed. Please provide --lora_target_modules explicitly."
    if resume_from_checkpoint:
        # Check the available weights and load them
        checkpoint_name = os.path.join(
            resume_from_checkpoint, "pytorch_model.bin"
        )  # Full checkpoint
        if not os.path.exists(checkpoint_name):
            checkpoint_name = os.path.join(
                resume_from_checkpoint, "adapter_model.bin"
            )  # only LoRA model - LoRA config above has to fit
            resume_from_checkpoint = False  # So the trainer won't try loading its state
        # The two files above have a different name depending on how they were saved, but are actually the same.
        if os.path.exists(checkpoint_name):
            log(f"Restarting from {checkpoint_name}")
            adapters_weights = torch.load(checkpoint_name)
            set_peft_model_state_dict(model, adapters_weights)
        else:
            log(f"Checkpoint {checkpoint_name} not found")

    print(model)
    try:
        # only for PeftModel
        model.print_trainable_parameters()  # Be more transparent about the % of trainable params.
    except:
        pass

    metrics = {}
    for name in supported_metrics:
        if name in val_metrics:
            import evaluate  # Causes hang for 'python generate.py' on dual 4090 if imported early, 100% reproducible
            metrics[name] = evaluate.load(name)
    log("Using Validation Metrics: %s" % str(list(metrics.keys())))
    log("Supported Metrics: %s" % supported_metrics)

    if val_set_size is None:
        if len(metrics) == 0:
            val_set_size = 1000
        else:
            val_set_size = 100
        log("Auto set val_set_size %s" % val_set_size)
    elif val_set_size < 1.0 and val_set_size != 0:
        raise RuntimeError("Fractional validation size not supported.")

    from datasets import load_dataset, concatenate_datasets
    if valid_path:
        data = load_dataset("json", data_files={"train": data_path, "valid": valid_path})
    else:
        if "json" in data_path:
            data = load_dataset("json", data_files={"train": data_path})
        else:
            data = load_dataset(data_path)
            data = data.rename_columns(data_col_dict or {})

    valid_data = None
    train_data_mix_in = None
    valid_data_mix_in = None

    if data_mix_in_path and data_mix_in_factor > 0:
        # get mix-in training/validation data - to keep model "sane"
        num_rows = data["train"].num_rows
        log("Loading mix-in dataset: %s" % data_mix_in_path)
        if "json" in data_mix_in_path:
            data_mix_in = load_dataset("json", data_files={"train": data_mix_in_path})["train"]
        else:
            data_mix_in = load_dataset(data_mix_in_path)["train"]  # can be large
        data_mix_in = data_mix_in.rename_columns(data_mix_in_col_dict or {})
        mix_in_rows = int(num_rows * data_mix_in_factor)

        if mix_in_rows > data_mix_in.num_rows:
            # duplicate rows if mix-in is smaller than required
            log("Duplicating mixin to compensate for its size for training size and mixin fraction")
            data_mix_in = concatenate_datasets([data_mix_in] * int(np.ceil(mix_in_rows / data_mix_in.num_rows)))

        # only get as much as we need to balance
        valid_size = min(data_mix_in.num_rows // 2, val_set_size or 0)
        train_size = max(1, min(data_mix_in.num_rows - valid_size, mix_in_rows))
        mixin_small = data_mix_in.train_test_split(
            test_size=train_size + valid_size,
            shuffle=True, seed=np.random.randint(10000),
        )["test"]
        if valid_size:
            mixin_train_test = mixin_small.train_test_split(
                test_size=valid_size, shuffle=False,
            )
            train_data_mix_in = mixin_train_test["train"]
            valid_data_mix_in = mixin_train_test["test"]
        else:
            train_data_mix_in = mixin_small

        if "prompt_type" not in train_data_mix_in.column_names:
            train_data_mix_in = train_data_mix_in.add_column(
                "prompt_type",
                [data_mix_in_prompt_type] * train_data_mix_in.num_rows,
            )
            log("Added prompt type %s to mix-in training data" % data_mix_in_prompt_type)
        if valid_data_mix_in and "prompt_type" not in valid_data_mix_in.column_names:
            valid_data_mix_in = valid_data_mix_in.add_column(
                "prompt_type",
                [data_mix_in_prompt_type] * valid_data_mix_in.num_rows,
            )
            log("Added prompt type %s to mix-in validation data" % data_mix_in_prompt_type)
        log("Created mix-in data:\nTrain %s\nValid %s" % (train_data_mix_in, valid_data_mix_in))

    # get our own training/validation data - for fine-tuning
    if val_set_size > 0 and not valid_path and not data_mix_in_path:
        # create valid split from train
        train_val = data["train"].train_test_split(
            test_size=val_set_size, shuffle=True, seed=42
        )
        train_data = train_val["train"]
        valid_data = train_val["test"]
    else:
        train_data = data["train"]
        if valid_path:
            # use given valid split, has priority over data_mix_in_path
            valid_data = data["valid"]
    if "prompt_type" not in train_data.column_names:
        train_data = train_data.add_column(
            "prompt_type",
            [prompt_type] * train_data.num_rows,
        )
        log("Added prompt type %s to training data" % prompt_type)
    if valid_data and "prompt_type" not in valid_data.column_names:
        valid_data = valid_data.add_column(
            "prompt_type",
            [prompt_type] * valid_data.num_rows,
        )
        log("Added prompt type %s to validation data" % prompt_type)

    assert train_data is not None

    generate_and_tokenize_prompt_fun = partial(generate_and_tokenize_prompt, prompt_type=prompt_type,
                                               train_on_inputs=train_on_inputs, add_eos_token=add_eos_token,
                                               cutoff_len=cutoff_len, tokenizer=tokenizer)

    # shuffle and tokenize data
    if train_data_mix_in:
        train_data = concatenate_datasets([train_data, train_data_mix_in])
    log("Tokenizing %s training rows" % train_data.num_rows)
    train_data = train_data.shuffle().map(generate_and_tokenize_prompt_fun,
                                          num_proc=os.cpu_count() // torch.cuda.device_count())
    if drop_truncations:
        log("avoid keeping truncated cases to avoid contaminating model with truncation cases.  Original size: %s" % train_data.num_rows)
        prune_long_sequences_func = partial(prune_long_sequences, cutoff_len=cutoff_len)
        train_data = train_data.filter(prune_long_sequences_func, num_proc=os.cpu_count() // torch.cuda.device_count())
        log("avoid keeping truncated cases to avoid contaminating model with truncation cases.  New size: %s" % train_data.num_rows)
    train_set_size = len(train_data)

    if valid_data and valid_data_mix_in:
        valid_data = concatenate_datasets([valid_data, valid_data_mix_in])
    elif valid_data_mix_in:
        valid_data = valid_data_mix_in

    if valid_data:
        log("Tokenizing %s validation rows" % valid_data.num_rows)
        valid_data = valid_data.shuffle().map(generate_and_tokenize_prompt_fun,
                                              num_proc=os.cpu_count() // torch.cuda.device_count())
        val_set_size = len(valid_data)
    else:
        val_set_size = 0
    log("Final fine-tuning data:\nTrain %s\nValid %s" % (train_data, valid_data))
    sample_row_dict = train_data[:1]
    del sample_row_dict['input_ids']
    del sample_row_dict['attention_mask']
    del sample_row_dict['labels']
    log("Sample input: %s" % sample_row_dict)

    try:
        import neptune
        from transformers.integrations import NeptuneCallback

        neptune_run = neptune.init_run(
            source_files=[],
        )
        log("Connected to Neptune.")
    except ImportError:
        neptune_run = None
        log("Please pip install neptune for tracking.")
    except neptune.exceptions.NeptuneMissingApiTokenException:
        neptune_run = None
        os.environ["NEPTUNE_MODE"] = 'debug'
        log("No neptune configured, set NEPTUNE_API_TOKEN env var.")

    if neptune_run:
        neptune_callback = NeptuneCallback(run=neptune_run)
        callbacks = [neptune_callback]
    else:
        from transformers.integrations import TensorBoardCallback, is_tensorboard_available
        if is_tensorboard_available:
            # tensorboard --logdir=runs/
            from torch.utils.tensorboard import SummaryWriter
            tb_writer = SummaryWriter()
            callbacks = [TensorBoardCallback(tb_writer=tb_writer)]
        else:
            callbacks = []

    expected_steps = (train_set_size * num_epochs) // batch_size
    if eval_steps is None and eval_epochs is None:
        # 20 evaluations for a run
        eval_steps = max(1, int(expected_steps / 20))
        log("Auto set eval_steps to %s out of %s total training steps" % (eval_steps, expected_steps))
    elif eval_steps is None and eval_epochs is not None:
        eval_steps = max(1, int(expected_steps * eval_epochs / num_epochs))
        log("Auto converted eval_epochs=%s to eval_steps %s"
            " out of %s total training steps" % (eval_epochs, eval_steps, expected_steps))
    if save_steps is None:
        save_steps = eval_steps
        log("Auto step save_steps to %s" % save_steps)
    elif save_steps > eval_steps:
        # save steps must be round multiple of eval_steps
        save_steps0 = save_steps
        save_steps = max(1, (save_steps // eval_steps)) * eval_steps
        if save_steps0 != save_steps:
            log("Auto converted save_steps from %s to %s" % (save_steps0, save_steps))

    def compute_metrics(eval_preds):
        # e.g. see: https://huggingface.co/docs/transformers/v4.25.1/en/tasks/translation#evaluate
        inputs = eval_preds.inputs
        label_ids = eval_preds.label_ids
        predictions = eval_preds.predictions

        # inputs = np.where(inputs != -100, inputs, tokenizer.pad_token_id)
        # decoded_inputs = tokenizer.batch_decode(inputs, skip_special_tokens=True)
        # decoded_inputs = [pred.strip() for pred in decoded_inputs]

        label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id)
        # tokenizer behavior like generate time
        decoded_labels = tokenizer.batch_decode(label_ids, skip_special_tokens=True,
                                                clean_up_tokenization_spaces=True)
        decoded_labels = [pred.strip() for pred in decoded_labels]

        predictions = np.argmax(predictions, -1)
        predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
        # tokenizer behavior like generate time
        decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True,
                                                     clean_up_tokenization_spaces=True)
        decoded_predictions = [pred.strip() for pred in decoded_predictions]

        result = {}
        for metric in metrics.values():
            result1 = metric.compute(predictions=decoded_predictions, references=decoded_labels)
            # get rid of lists, for precision etc., for now
            numeric_results = {k: v for k, v in result1.items() if isinstance(v, (int, float))}
            result.update(numeric_results)
        return result

    # the callback that computes metrics of interest
    if val_metrics:
        trainer_kwargs = dict(compute_metrics=compute_metrics)
    else:
        trainer_kwargs = dict()

    import transformers
    trainer = transformers.Trainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_data,
        eval_dataset=valid_data,
        # FIXME: might need Seq2SeqTrainingArguments for some models
        args=transformers.TrainingArguments(
            per_device_train_batch_size=micro_batch_size,
            per_device_eval_batch_size=1,
            eval_accumulation_steps=10,
            # predict_with_generate=True,  # SEQ2SEQ only
            include_inputs_for_metrics=True,
            gradient_accumulation_steps=gradient_accumulation_steps,
            warmup_steps=warmup_steps,
            num_train_epochs=num_epochs,
            learning_rate=learning_rate,
            gradient_checkpointing=gradient_checkpointing,
            fp16=fp16,
            # cosnider 8-bit adam: https://huggingface.co/docs/transformers/v4.18.0/en/performance#8bit-adam
            optim="adamw_torch",  # consider "adafactor" to save memory
            logging_steps=logging_steps,
            logging_strategy="steps",
            evaluation_strategy="steps" if val_set_size > 0 else "no",
            save_strategy="steps",
            eval_steps=eval_steps if val_set_size > 0 else None,
            save_steps=save_steps,
            output_dir=output_dir,
            save_total_limit=save_total_limit,
            load_best_model_at_end=True if val_set_size > 0 else False,
            ddp_find_unused_parameters=False if ddp else None,
            group_by_length=group_by_length,
            # fsdp="shard_grad_op auto_wrap" if gpus > 1 and not ddp else None,
            # fsdp_min_num_params=20000 if gpus > 1 and not ddp else None,
            report_to='tensorboard' if not neptune_run else 'neptune',
        ),
        data_collator=transformers.DataCollatorForSeq2Seq(
            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
        ),
        callbacks=callbacks,
        **trainer_kwargs,
    )
    model.config.use_cache = False

    old_state_dict = model.state_dict
    from peft import get_peft_model_state_dict

    model.state_dict = (
        lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
    ).__get__(model, type(model))

    if torch.__version__ >= "2" and sys.platform != "win32":
        model = torch.compile(model)
        # WIP (not generally replacing layers until pytorch 2.1)
        if not llama_flash_attn:
            torch.backends.cuda.enable_flash_sdp(True)

    if gpus > 1 and not ddp:
        assert trainer.is_model_parallel
    else:
        assert not trainer.is_model_parallel
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

    model.save_pretrained(output_dir)

    log("\n If there's a warning about missing keys above, please disregard :)")


def tokenize(prompt, tokenizer, cutoff_len, add_eos_token=False):
    # there's probably a way to do this with the tokenizer settings
    # but again, gotta move fast
    result = tokenizer(
        prompt,
        truncation=True,
        max_length=cutoff_len,
        padding=False,
        return_tensors=None,
    )
    if (
            result["input_ids"][-1] != tokenizer.eos_token_id
            and len(result["input_ids"]) < cutoff_len
            and add_eos_token
    ):
        result["input_ids"].append(tokenizer.eos_token_id)
        result["attention_mask"].append(1)

    result["labels"] = result["input_ids"].copy()

    return result


def prune_long_sequences(data_point, cutoff_len=None):
    """
    Prune if too long for tokenizer, so truncation doesn't lead training to learn from truncated language
    :param data_point:
    :param cutoff_len:
    :return:
    """
    assert cutoff_len is not None
    return len(data_point['input_ids']) < cutoff_len


def generate_and_tokenize_prompt(data_point, prompt_type=None, train_on_inputs=False, add_eos_token=False,
                                 cutoff_len=None, tokenizer=None):
    assert prompt_type is not None
    assert cutoff_len is not None
    assert tokenizer is not None
    prompt_dict = ''  # only for custom prompt_type
    assert prompt_type != PromptType.custom.name, "custom not setup for finetune"
    full_prompt, _, _, _ = generate_prompt(data_point, prompt_type, prompt_dict, False, False)
    tokenized_full_prompt = tokenize(full_prompt, tokenizer, cutoff_len, add_eos_token=add_eos_token)
    if not train_on_inputs:
        user_prompt, _, _, _ = generate_prompt({**data_point, "output": ""}, prompt_type, prompt_dict, False, False)
        tokenized_user_prompt = tokenize(user_prompt, tokenizer, cutoff_len, add_eos_token=add_eos_token)
        user_prompt_len = len(tokenized_user_prompt["input_ids"])
        if add_eos_token:
            user_prompt_len -= 1

        # ignore_index=-100 ensures torch/tf don't include padding token id in CrossEntropyLoss
        tokenized_full_prompt["labels"] = [
                                              -100
                                          ] * user_prompt_len + tokenized_full_prompt["labels"][
                                                                user_prompt_len:
                                                                ]  # could be sped up, probably
    return tokenized_full_prompt


def test_debug():
    fire.Fire(train)


if __name__ == "__main__":
    CONFIG = "NCCL_P2P_LEVEL=LOC WORLD_SIZE=5 torchrun --nnodes=5 --master_addr=10.10.10.2 --master_port=1111 --nproc_per_node=1"
    CMD = "finetune.py --data_path=config.json --num_epochs=1 --base_model=decapoda-research/llama-13b-hf"
    log(f"""
    Example runs on 4 GPUs:
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='decapoda-research/llama-7b-hf' --data_path=data/config.json --run_id=0 &> 0.log
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='decapoda-research/llama-30b-hf' --data_path=data/config.json --batch_size=16 --micro_batch_size=1 --run_id=1 --save_code=True &> 1.log
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='EleutherAI/gpt-j-6B' --data_path=data/config.json --run_id=2 &> 2.log
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='EleutherAI/gpt-neox-20b' --data_path=data/config.json --run_id=8 --batch_size=16 --micro_batch_size=4 &> 8.log
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --data_path=data/config.json --prompt_type='dai_faq' --run_id=13 --batch_size=16 --micro_batch_size=4 --num_epochs=100 --val_set_size=0 data_mix_in_path='' &> 13.log
    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 finetune.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --data_path=data/config.json --run_id=28 --batch_size=16 --micro_batch_size=4 --num_epochs=8 --val_set_size=0 --data_mix_in_factor=0.1 --data_mix_in_prompt_type='human_bot' --save_code=True --cutoff_len=512  &> 28.log

    All metrics:
    CUDA_VISIBLE_DEVICES= finetune.py --data_mix_in_factor=0 --eval_steps=100 --warmup_steps=2 --val_set_size=100 --val_metrics="['bleu', 'rouge', 'sacrebleu', 'meteor']"

    # Fine-tune 20B on 24GB GPUs across 3 nodes with 3+2+2 GPUs
    rippa>
NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1,2" torchrun --node_rank 0 --nproc_per_node=3 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank0
    ova>
NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1" torchrun --node_rank 1 --nproc_per_node=2 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank1
    timemachine>
NCCL_P2P_LEVEL=LOC WORLD_SIZE=7 CUDA_VISIBLE_DEVICES="0,1" torchrun --node_rank 2 --nproc_per_node=2 --master_port=1234 --nnodes=3 --master_addr=10.10.10.2 finetune.py --data_path=merged_shuffled_OIG_87f6a1e788.json --micro_batch_size=1 --batch_size=7 --cutoff_len=512 --run_id=17 &>log.17.rank2

    """, flush=True)

    if os.environ.get("LOCAL_RANK") is None:
        # then not using torchrun, so can't do distributed, ensure CVD set
        assert os.environ.get(
            "CUDA_VISIBLE_DEVICES") is not None, "Run python script using: torchrun finetune.py OR set CUDA_VISIBLE_DEVICES to single GPU"

    fire.Fire(train)