mirror of
https://github.com/SakanaAI/doc-to-lora.git
synced 2026-07-23 17:01:04 +02:00
585 lines
20 KiB
Python
Executable file
585 lines
20 KiB
Python
Executable file
import logging
|
|
import os
|
|
import random
|
|
import string
|
|
import time
|
|
from multiprocess import set_start_method
|
|
from math import ceil
|
|
from collections import defaultdict
|
|
from copy import copy, deepcopy
|
|
from functools import partial
|
|
from importlib.resources import read_binary
|
|
from typing import Callable, Optional
|
|
|
|
import numpy as np
|
|
import torch
|
|
import wandb
|
|
import yaml
|
|
|
|
from ctx_to_lora.data_utils import (
|
|
DS_LEN,
|
|
convert_ctx_prompt_response_to_messages,
|
|
get_preprocessing_fn,
|
|
get_sft_prompt_formatting_fn,
|
|
get_tokenized_dataset,
|
|
tokenize_chat_messages,
|
|
tokenize_ctx_text,
|
|
)
|
|
from datasets import (
|
|
concatenate_datasets,
|
|
interleave_datasets,
|
|
disable_caching,
|
|
load_dataset,
|
|
IterableDataset,
|
|
)
|
|
from ctx_to_lora.model_loading import (
|
|
get_lora_config,
|
|
get_model_and_tokenizer,
|
|
get_tokenizer,
|
|
)
|
|
from ctx_to_lora.modeling_utils import (
|
|
EarlyExit,
|
|
HyperLoRA,
|
|
ModulatedPretrainedModel,
|
|
get_hypernet_config,
|
|
)
|
|
from rouge_score import rouge_scorer
|
|
from ctx_to_lora.training_utils import TRAINING_TASK, train_model
|
|
from transformers import (
|
|
AutoModelForCausalLM,
|
|
AutoTokenizer,
|
|
AutoConfig,
|
|
PretrainedConfig,
|
|
DataCollatorForSeq2Seq,
|
|
EvalPrediction,
|
|
HfArgumentParser,
|
|
set_seed,
|
|
)
|
|
from peft import PeftModel
|
|
from transformers.utils import is_liger_kernel_available
|
|
from transformers.data import DataCollatorWithFlattening
|
|
from torch.utils.data import DataLoader
|
|
from ctx_to_lora.utils import (
|
|
extract_cli_args,
|
|
get_base_model,
|
|
get_run_name,
|
|
log_num_train_params,
|
|
save_yaml,
|
|
setup_logging,
|
|
validate_args,
|
|
)
|
|
|
|
from ctx_to_lora.configs import (
|
|
ArgumentParser,
|
|
CtxTrainingArguments,
|
|
TrainingArguments,
|
|
DataArguments,
|
|
ExperimentSetup,
|
|
LoRAArguments,
|
|
ModelArguments,
|
|
HypernetArguments,
|
|
AggregatorArguments,
|
|
CtxEncoderArguments,
|
|
)
|
|
|
|
logger = logging.getLogger()
|
|
|
|
LOCAL_RANK = int(os.getenv("LOCAL_RANK", "0"))
|
|
|
|
|
|
def compute_per_token_acc(shift_logits, shift_labels, valid_masks):
|
|
indices = torch.where(valid_masks)
|
|
# acc = (shift_logits.argmax(-1) == shift_labels)[indices].float().mean().item()
|
|
# return {"per_token_acc": acc}
|
|
acc = (shift_logits.argmax(-1) == shift_labels)[indices].float()
|
|
return {
|
|
"per_token_accs": acc.flatten().tolist(),
|
|
"n_per_token_accs": valid_masks.sum().item(),
|
|
}
|
|
|
|
|
|
def compute_prefix_matching(shift_logits, shift_labels, valid_masks):
|
|
lengths = valid_masks.sum(dim=1)
|
|
|
|
is_wrong = (shift_logits.argmax(-1) != shift_labels) * valid_masks
|
|
is_correct = (shift_logits.argmax(-1) == shift_labels) * valid_masks
|
|
# NOTE: not reliable for multi-turn conversations
|
|
# ie, all tokens in the following user's turn will be correct
|
|
# still monotonically correlate with perf though
|
|
wrong_pos = torch.argmax(is_wrong, dim=1) - torch.argmax(valid_masks, dim=1)
|
|
perf = wrong_pos / lengths
|
|
|
|
# if all tokens are correct, set to 1
|
|
perf = torch.where(is_correct.sum(dim=1) == lengths, 1, perf)
|
|
# return {"prefix_matching": perf.mean().item()}
|
|
return {
|
|
"prefix_matchings": perf.tolist(),
|
|
"n_prefix_matchings": valid_masks.shape[0],
|
|
}
|
|
|
|
|
|
@torch.no_grad()
|
|
def compute_perplexity(shift_logits, shift_labels, valid_masks):
|
|
loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
|
|
loss = loss_fct(shift_logits.transpose(1, 2), shift_labels)
|
|
loss = (loss * valid_masks).sum(dim=1) / valid_masks.sum(dim=1)
|
|
# perplexity = torch.exp(loss).mean().item()
|
|
# return {"perplexity": perplexity}
|
|
preplexities = torch.exp(loss)
|
|
return {
|
|
"perplexities": preplexities.tolist(),
|
|
"n_perplexities": valid_masks.shape[0],
|
|
}
|
|
|
|
|
|
class Evaluator:
|
|
def __init__(self, metric_fns: list[Callable]):
|
|
self.metric_fns = metric_fns
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.accum_metrics = defaultdict(list)
|
|
self.count = defaultdict(list)
|
|
|
|
def update(self, shift_logits, shift_labels, valid_masks):
|
|
for metric_fn in self.metric_fns:
|
|
metric = metric_fn(shift_logits, shift_labels, valid_masks)
|
|
for k, v in metric.items():
|
|
if k.startswith("n_"):
|
|
self.count[k[2:]].append(v)
|
|
else:
|
|
self.accum_metrics[k] += v
|
|
|
|
def compute(self):
|
|
# Get result across entire eval set
|
|
result = {
|
|
k: np.sum(v) / np.sum(self.count[k]) for k, v in self.accum_metrics.items()
|
|
}
|
|
# Reset batch statistics
|
|
self.reset()
|
|
return result
|
|
|
|
|
|
@torch.no_grad()
|
|
def compute_metrics(
|
|
eval_pred: EvalPrediction,
|
|
compute_result: bool,
|
|
evaluator: Evaluator,
|
|
) -> Optional[dict]:
|
|
logits, labels = eval_pred.predictions, eval_pred.label_ids
|
|
shift_logits = logits[..., :-1, :]
|
|
shift_labels = labels[..., 1:]
|
|
valid_masks = torch.where(shift_labels != -100, 1, 0)
|
|
evaluator.update(shift_logits, shift_labels, valid_masks)
|
|
if compute_result:
|
|
return evaluator.compute()
|
|
|
|
|
|
# def compute_metrics(eval_pred: EvalPrediction) -> dict:
|
|
# """
|
|
# Custom metrics function for the trainer
|
|
# Args:
|
|
# eval_pred: tuple of predictions and labels
|
|
# Returns:
|
|
# dictionary containing metric names (str) and values (Any)
|
|
# """
|
|
# # compute per token accuracy
|
|
# # logits, labels = eval_pred.predictions, eval_pred.label_ids
|
|
# # shift_logits = logits[..., :-1, :]
|
|
# pred_ids, labels = eval_pred.predictions, eval_pred.label_ids
|
|
# shift_pred_ids = pred_ids[..., :-1]
|
|
# shift_labels = labels[..., 1:]
|
|
# valid_masks = np.where(shift_labels != -100, 1, 0)
|
|
|
|
# per_token_acc = compute_per_token_acc(shift_pred_ids, shift_labels, valid_masks)
|
|
# prefix_matching = compute_prefix_matching(shift_pred_ids, shift_labels, valid_masks)
|
|
# # entropy = compute_entropy(shift_logits, shift_labels, valid_masks)
|
|
# return dict(
|
|
# **per_token_acc,
|
|
# **prefix_matching,
|
|
# # **entropy,
|
|
# num_valid_tokens=valid_masks.sum(),
|
|
# num_samples=valid_masks.shape[0],
|
|
# )
|
|
|
|
|
|
# # https://discuss.huggingface.co/t/cuda-out-of-memory-when-using-trainer-with-compute-metrics/2941/13
|
|
# def preprocess_logits_for_metrics(logits, labels):
|
|
# """
|
|
# Original Trainer may have a memory leak.
|
|
# This is a workaround to avoid storing too many tensors that are not needed.
|
|
# """
|
|
# pred_ids = torch.argmax(logits, dim=-1)
|
|
# return pred_ids
|
|
|
|
|
|
def main():
|
|
############ Argument parsing
|
|
parser = ArgumentParser(
|
|
(
|
|
DataArguments,
|
|
CtxTrainingArguments,
|
|
ModelArguments,
|
|
LoRAArguments,
|
|
TrainingArguments,
|
|
HypernetArguments,
|
|
AggregatorArguments,
|
|
CtxEncoderArguments,
|
|
)
|
|
)
|
|
(
|
|
data_args,
|
|
ctx_args,
|
|
model_args,
|
|
lora_args,
|
|
training_args,
|
|
hypernet_args,
|
|
aggregator_args,
|
|
ctx_encoder_args,
|
|
) = parser.parse()
|
|
|
|
# there shouldn't be overlap between args
|
|
validate_args(
|
|
[
|
|
data_args,
|
|
ctx_args,
|
|
model_args,
|
|
lora_args,
|
|
training_args,
|
|
hypernet_args,
|
|
aggregator_args,
|
|
ctx_encoder_args,
|
|
]
|
|
)
|
|
|
|
set_seed(training_args.seed)
|
|
checkpoint_dir = training_args.resume_from_checkpoint
|
|
if checkpoint_dir and not os.path.isdir(checkpoint_dir):
|
|
raise NotADirectoryError(f"Checkpoint{checkpoint_dir} is not a directory")
|
|
|
|
# should be the same across processes
|
|
# still possible to have a name crash though
|
|
# logging_dir is just "runs/DATE_TIME_HOSTNAME"
|
|
slurm_job_id = f"_{os.getenv('SLURM_JOB_ID')}" if os.getenv("SLURM_JOB_ID") else ""
|
|
run_name = (
|
|
get_run_name(seed_str=training_args.logging_dir.strip("runs/") + slurm_job_id)
|
|
if not checkpoint_dir
|
|
else checkpoint_dir.strip("/").split("/")[-2]
|
|
)
|
|
|
|
output_dir = f"train_outputs/runs/{run_name}"
|
|
setup_logging(output_dir, debug=os.getenv("DEBUG", False))
|
|
logger.debug(f"CMD: {' '.join(os.sys.argv)}")
|
|
cli_args = extract_cli_args(os.sys.argv)
|
|
save_yaml(cli_args, f"{output_dir}/cli_args.yaml")
|
|
if "config" in cli_args:
|
|
config_name = os.path.basename(cli_args["config"]).split(".yaml")[0]
|
|
os.environ["WANDB_TAGS"] = config_name
|
|
|
|
run_name = os.path.basename(output_dir)
|
|
training_args.run_name = run_name
|
|
training_args.output_dir = output_dir
|
|
training_args.logging_dir = output_dir
|
|
|
|
if (
|
|
training_args.lr_scheduler_type == "cosine_with_min_lr"
|
|
and training_args.lr_scheduler_kwargs is None
|
|
):
|
|
training_args.lr_scheduler_kwargs = {"min_lr": 1e-7}
|
|
args = {
|
|
**vars(deepcopy(data_args)),
|
|
**vars(deepcopy(ctx_args)),
|
|
**vars(deepcopy(model_args)),
|
|
**vars(deepcopy(lora_args)),
|
|
**vars(deepcopy(training_args)),
|
|
**vars(deepcopy(hypernet_args)),
|
|
**vars(deepcopy(aggregator_args)),
|
|
**vars(deepcopy(ctx_encoder_args)),
|
|
}
|
|
args["deepspeed_plugin"] = None
|
|
logger.debug(f"args: {args}")
|
|
save_yaml(args, f"{output_dir}/args.yaml")
|
|
|
|
############ Model setup
|
|
|
|
if not ctx_args.from_pretrained_checkpoint:
|
|
model_name = model_args.model_name_or_path
|
|
model, tokenizer = get_model_and_tokenizer(
|
|
**vars(model_args),
|
|
train=True,
|
|
requires_grad=ctx_args.exp_setup == ExperimentSetup.FULL_FINETUNE,
|
|
peft_config=get_lora_config(model_name, **vars(lora_args)),
|
|
)
|
|
ctx_name = ctx_encoder_args.ctx_encoder_model_name_or_path
|
|
if ctx_name is not None:
|
|
ctx_encoder_model_config = AutoConfig.from_pretrained(
|
|
ctx_name, trust_remote_code=True
|
|
)
|
|
if "Llama" in ctx_name and "Vision" in ctx_name:
|
|
ctx_encoder_model_config = ctx_encoder_model_config.text_config
|
|
ctx_tokenizer = get_tokenizer(ctx_name, train=True)
|
|
else:
|
|
ctx_name = model.base_model.config.name_or_path
|
|
ctx_encoder_model_config = model.config
|
|
ctx_tokenizer = tokenizer
|
|
|
|
if ctx_args.exp_setup == ExperimentSetup.HYPER_LORA:
|
|
# TODO: handle only extra_modules case (no target_modules)
|
|
logger.info("Using HyperLoRA")
|
|
if not ctx_args.from_pretrained_checkpoint:
|
|
hypernet_config = get_hypernet_config(
|
|
model, ctx_encoder_model_config, hypernet_args, aggregator_args
|
|
)
|
|
if ctx_encoder_args.layer_idx is None:
|
|
ctx_encoder_args.layer_idx = (
|
|
ctx_encoder_model_config.num_hidden_layers // 4
|
|
)
|
|
logger.info(
|
|
f"Using the first {ctx_encoder_args.layer_idx} layers"
|
|
" as the context encoder"
|
|
)
|
|
model = ModulatedPretrainedModel(
|
|
model,
|
|
hypernet_config,
|
|
ctx_encoder_args,
|
|
ctx_args.use_kl_loss,
|
|
)
|
|
training_args.gen_lora_l1_reg_coef = ctx_args.gen_lora_l1_reg_coef
|
|
else:
|
|
logger.info(
|
|
f"Loading from checkpoint: {ctx_args.from_pretrained_checkpoint}"
|
|
)
|
|
model = ModulatedPretrainedModel.from_state_dict(
|
|
torch.load(ctx_args.from_pretrained_checkpoint),
|
|
train=True,
|
|
use_flash_attn=model_args.use_flash_attn,
|
|
)
|
|
tokenizer = get_tokenizer(model.base_model.config.name_or_path, train=True)
|
|
ctx_name = model.ctx_encoder_args.ctx_encoder_model_name_or_path
|
|
if ctx_name is None:
|
|
ctx_name = model.base_model.config.name_or_path
|
|
ctx_tokenizer = get_tokenizer(ctx_name, train=True)
|
|
if len([p for p in model.ctx_encoder.parameters() if p.requires_grad]):
|
|
raise ValueError("ctx_encoder contains trainable parameters")
|
|
if len([p for p in model.base_model.parameters() if p.requires_grad]):
|
|
raise ValueError("base model contains trainable parameters")
|
|
else:
|
|
# activate LoRA
|
|
base_model_config = AutoConfig.from_pretrained(
|
|
model_args.model_name_or_path, trust_remote_code=True
|
|
)
|
|
base_model_config.save_pretrained(output_dir)
|
|
logger.info("Using LoRA")
|
|
model.set_adapter("default")
|
|
|
|
model.train()
|
|
logger.debug(model)
|
|
log_num_train_params(model)
|
|
|
|
############ Dataset setup
|
|
|
|
logger.info("Loading dataset...")
|
|
|
|
add_ctx_to_chat = not isinstance(model, ModulatedPretrainedModel)
|
|
tokenizer_kwargs = {"max_length": ctx_args.max_base_len}
|
|
ctx_tokenizer_kwargs = {"max_length": ctx_args.max_ctx_len} # not used for now
|
|
|
|
_get_tokenized_dataset = partial(
|
|
get_tokenized_dataset,
|
|
tokenizer=tokenizer,
|
|
tokenizer_kwargs=tokenizer_kwargs,
|
|
ctx_tokenizer=ctx_tokenizer,
|
|
ctx_tokenizer_kwargs=ctx_tokenizer_kwargs,
|
|
add_ctx_to_chat=add_ctx_to_chat,
|
|
add_repeat_prompt=ctx_args.add_repeat_prompt,
|
|
add_negative_prompt=ctx_args.add_negative_prompt,
|
|
use_kl_loss=ctx_args.use_kl_loss,
|
|
set_format=None if ctx_args.use_sequence_packing else "pt",
|
|
# streaming=data_args.streaming,
|
|
)
|
|
tokenized_ds = {}
|
|
for split, ds_names in zip(
|
|
["train", "validation", "test"],
|
|
[data_args.train_ds_names, data_args.val_ds_names, data_args.test_ds_names],
|
|
):
|
|
if not ds_names:
|
|
continue
|
|
streaming = (split == "train") and data_args.streaming
|
|
tokenized_ds[split] = {
|
|
os.path.basename(ds_name): _get_tokenized_dataset(
|
|
ds_name, split, streaming=streaming
|
|
)
|
|
for ds_name in ds_names
|
|
}
|
|
|
|
train_ds = tokenized_ds["train"]
|
|
logging.info(f"train_ds: {train_ds}")
|
|
val_ds = dict()
|
|
if "validation" in tokenized_ds:
|
|
n_val_samples = data_args.max_val_samples_per_ds
|
|
for ds_name, ds in tokenized_ds["validation"].items():
|
|
if ds is None:
|
|
# take some samples from train_ds
|
|
ds = train_ds[ds_name].take(n_val_samples)
|
|
train_ds[ds_name] = train_ds[ds_name].skip(n_val_samples)
|
|
|
|
val_ds[ds_name] = ds
|
|
val_indices = np.random.permutation(len(ds))[:n_val_samples]
|
|
val_ds[ds_name] = val_ds[ds_name].select(val_indices)
|
|
|
|
max_steps = ceil(
|
|
sum([DS_LEN[ds] for ds in train_ds])
|
|
* training_args.num_train_epochs
|
|
/ training_args.per_device_train_batch_size
|
|
/ training_args.gradient_accumulation_steps
|
|
/ training_args.world_size
|
|
)
|
|
training_args.max_steps = max_steps
|
|
|
|
# interleaving streaming datasets
|
|
# simplify the probs for smaller datasets
|
|
# slightly upsample those datasets
|
|
probs = [
|
|
0.01 if "fw_qa" not in ds_name else 1 + 0.01 - 0.01 * len(train_ds)
|
|
for ds_name in train_ds
|
|
]
|
|
train_ds = interleave_datasets(
|
|
list(train_ds.values()),
|
|
probabilities=probs,
|
|
stopping_strategy="all_exhausted",
|
|
seed=training_args.seed,
|
|
)
|
|
|
|
logger.info(f"train_ds: {train_ds}")
|
|
logger.info(f"val_ds: {val_ds}")
|
|
flattener = DataCollatorWithFlattening()
|
|
|
|
def train_packed_collator(inp_list):
|
|
# no padding
|
|
packed_inputs = flattener(inp_list, return_tensors="pt")
|
|
if "ctx_ids" in inp_list[0]:
|
|
ctx_ids = [{"input_ids": example["ctx_ids"]} for example in inp_list]
|
|
packed_ctx = flattener(ctx_ids, return_tensors="pt")
|
|
packed_inputs["ctx_ids"] = packed_ctx["input_ids"]
|
|
packed_inputs["ctx_position_ids"] = packed_ctx["position_ids"]
|
|
return packed_inputs
|
|
|
|
def train_collator(inp_list, tokenizer):
|
|
# input is a list of tokenized sequences
|
|
padding_kwargs = dict(
|
|
padding=True,
|
|
padding_side="right",
|
|
pad_to_multiple_of=8,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
ctx_ids = None
|
|
if "ctx_ids" in inp_list[0]:
|
|
# have to be manual since it has [ctx_len, features] shape
|
|
# pad to the longest ctx_len in the batch
|
|
# which can have a different length from the input_ids, attn_mask, labels
|
|
|
|
ctx_ids = [example.pop("ctx_ids") for example in inp_list]
|
|
ctx_ids = torch.nn.utils.rnn.pad_sequence(
|
|
ctx_ids,
|
|
batch_first=True,
|
|
padding_value=0,
|
|
)
|
|
# exotic keys won't be padded, so we need to pad them as well
|
|
ctx_attn_mask = [example.pop("ctx_attn_mask") for example in inp_list]
|
|
ctx_attn_mask = torch.nn.utils.rnn.pad_sequence(
|
|
ctx_attn_mask,
|
|
batch_first=True,
|
|
padding_value=0,
|
|
)
|
|
|
|
labels = [x.pop("labels") for x in inp_list]
|
|
padded_seq = tokenizer.pad(inp_list, **padding_kwargs)
|
|
|
|
# hacky explicit padding since the labels are not padded by default
|
|
labels = tokenizer.pad({"input_ids": labels}, **padding_kwargs)["input_ids"]
|
|
labels = torch.where(padded_seq["attention_mask"] == 0, -100, labels)
|
|
out = {**padded_seq, "labels": labels}
|
|
if ctx_ids is not None:
|
|
out["ctx_ids"] = ctx_ids
|
|
out["ctx_attn_mask"] = ctx_attn_mask
|
|
|
|
return out
|
|
|
|
train_collator = (
|
|
train_packed_collator
|
|
if ctx_args.use_sequence_packing
|
|
else partial(train_collator, tokenizer=tokenizer)
|
|
)
|
|
|
|
# TODO: use SFTTrainer instead? https://huggingface.co/docs/trl/en/sft_trainer
|
|
# TODO: use packing with SFTTrainer
|
|
|
|
# HACK [local patch]: deepspeed model loading problem (for resume training)
|
|
# see https://github.com/microsoft/DeepSpeed/pull/6626/files
|
|
# /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora/lib/python3.10/site-packages/deepspeed/runtime/engine.py
|
|
if training_args.use_liger_kernel and is_liger_kernel_available():
|
|
from liger_kernel.transformers import _apply_liger_kernel_to_instance
|
|
|
|
if isinstance(model, ModulatedPretrainedModel):
|
|
logger.info("Applying liger-kernel to ModulatedPretrainedModel")
|
|
if isinstance(model.base_model, PeftModel):
|
|
_apply_liger_kernel_to_instance(model=model.base_model.base_model.model)
|
|
else:
|
|
_apply_liger_kernel_to_instance(model=model.base_model.model)
|
|
if ctx_name is not None:
|
|
logger.info("Applying liger-kernel to ctx_encoder_model")
|
|
_apply_liger_kernel_to_instance(model=model.ctx_encoder.base_model)
|
|
elif isinstance(model, PeftModel):
|
|
logger.info("Applying liger-kernel to PeftModel")
|
|
_apply_liger_kernel_to_instance(model=model.base_model.model)
|
|
|
|
if LOCAL_RANK == 0:
|
|
wandb.init(
|
|
project=os.getenv("WANDB_PROJECT"),
|
|
name=run_name,
|
|
group=run_name,
|
|
config=args,
|
|
tags=os.getenv("WANDB_TAGS").split(","),
|
|
notes=ctx_args.notes,
|
|
resume="allow",
|
|
)
|
|
else:
|
|
wandb.init(mode="disabled")
|
|
|
|
train_model(
|
|
model,
|
|
# tokenizer,
|
|
training_args,
|
|
train_ds,
|
|
val_ds,
|
|
train_collator,
|
|
# partial(generation_collator, tokenizer=tokenizer),
|
|
compute_metrics=partial(
|
|
compute_metrics,
|
|
evaluator=Evaluator(
|
|
[compute_per_token_acc, compute_prefix_matching, compute_perplexity]
|
|
),
|
|
),
|
|
# max_new_tokens=ctx_args.max_new_tokens,
|
|
# gen_per_device_eval_batch_size=ctx_args.gen_per_device_eval_batch_size,
|
|
)
|
|
logger.info(f"Training run finished and saved to {output_dir}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "true"
|
|
os.environ["WANDB_PROJECT"] = "ctx_to_lora"
|
|
os.environ["WANDB_WATCH"] = "" # "all"
|
|
os.environ["WANDB_CONSOLE"] = "off"
|
|
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
|
|
# os.environ["KMP_AFFINITY"] = "disabled" # fixing iterable dataset stuck
|
|
# os.environ["OMP_NUM_THREADS"] = "16"
|
|
# os.environ["HF_DATASETS_IN_MEMORY_MAX_SIZE"] = "137438953472" # 128 TB
|
|
if os.getenv("DEBUG", False):
|
|
disable_caching()
|
|
# randomly sleep to avoid run_name collision
|
|
# time.sleep(random.random() * 13)
|
|
# set_start_method("spawn", force=True)
|
|
main()
|