mirror of
https://github.com/CodedotAl/gpt-code-clippy.git
synced 2024-09-11 06:55:47 +03:00
825 lines
35 KiB
Python
Executable File
825 lines
35 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
# Copyright 2021 The HuggingFace Team All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
|
|
|
|
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
|
|
https://huggingface.co/models?filter=causal-lm
|
|
"""
|
|
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
|
|
|
|
from ast import Str
|
|
import logging
|
|
import math
|
|
import os
|
|
import sys
|
|
import time
|
|
from dataclasses import dataclass, field
|
|
from pathlib import Path
|
|
from typing import Callable, Optional
|
|
import json
|
|
import shutil
|
|
from collections import defaultdict
|
|
from flax import training
|
|
import numpy as np
|
|
import datasets
|
|
from datasets import Dataset, load_dataset
|
|
from tqdm import tqdm
|
|
|
|
import jax
|
|
import jax.profiler
|
|
import jax.numpy as jnp
|
|
import optax
|
|
import transformers
|
|
from flax import jax_utils, traverse_util
|
|
from flax.jax_utils import unreplicate
|
|
from flax.training import train_state
|
|
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
|
|
from flax.training.checkpoints import save_checkpoint, restore_checkpoint
|
|
from flax.serialization import to_bytes, from_bytes
|
|
from transformers import (
|
|
CONFIG_MAPPING,
|
|
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
|
|
AutoConfig,
|
|
AutoTokenizer,
|
|
FlaxAutoModelForCausalLM,
|
|
HfArgumentParser,
|
|
TrainingArguments,
|
|
is_tensorboard_available,
|
|
)
|
|
from transformers.testing_utils import CaptureLogger
|
|
|
|
from importlib.util import find_spec
|
|
from utils import PrefetchDataloader, make_batch
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
|
|
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
|
|
|
|
|
@dataclass
|
|
class ModelArguments:
|
|
"""
|
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
|
"""
|
|
|
|
model_name_or_path: Optional[str] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "The model checkpoint for weights initialization."
|
|
"Don't set if you want to train a model from scratch."
|
|
},
|
|
)
|
|
model_type: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
|
)
|
|
config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
|
)
|
|
tokenizer_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
|
)
|
|
cache_dir: Optional[str] = field(
|
|
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
|
)
|
|
use_fast_tokenizer: bool = field(
|
|
default=True,
|
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
|
)
|
|
dtype: Optional[str] = field(
|
|
default="float32",
|
|
metadata={
|
|
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
|
},
|
|
)
|
|
save_optimizer: Optional[bool] = field(
|
|
default=True,
|
|
metadata={"help": "Whether to store full train state including optimizer."},
|
|
)
|
|
repo_path_or_name: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "Path to the modelhub repo directory"},
|
|
)
|
|
repo_url: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "URL of the modelhub repo"},
|
|
)
|
|
decay_steps: int = field(default=None, metadata={"help":"Number of steps from peak to final learning rate"})
|
|
|
|
@dataclass
|
|
class DataTrainingArguments:
|
|
"""
|
|
Arguments pertaining to what data we are going to input our model for training and eval.
|
|
"""
|
|
|
|
dataset_name: Optional[str] = field(
|
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
|
)
|
|
dataset_config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
|
)
|
|
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
|
validation_file: Optional[str] = field(
|
|
default=None,
|
|
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
|
)
|
|
data_dir: Optional[str] = field(default=None, metadata={"help": "Path to data directory."})
|
|
max_train_samples: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
|
"value if set."
|
|
},
|
|
)
|
|
max_eval_samples: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
|
"value if set."
|
|
},
|
|
)
|
|
overwrite_cache: bool = field(
|
|
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
|
)
|
|
validation_split_percentage: Optional[int] = field(
|
|
default=5,
|
|
metadata={
|
|
"help": "The percentage of the train set used as validation set in case there's no validation split"
|
|
},
|
|
)
|
|
block_size: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "Optional input sequence length after tokenization. "
|
|
"The training dataset will be truncated in block of this size for training. "
|
|
"Default to the model max input length for single sentence inputs (take into account special tokens)."
|
|
},
|
|
)
|
|
overwrite_cache: bool = field(
|
|
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
|
)
|
|
preprocessing_num_workers: Optional[int] = field(
|
|
default=None,
|
|
metadata={"help": "The number of processes to use for the preprocessing."},
|
|
)
|
|
text_column_name: Optional[str] = field(
|
|
default='text',
|
|
metadata={"help": "Column containing main text data."},
|
|
)
|
|
shuffle_buffer_size: int = field(
|
|
default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
|
|
)
|
|
num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
|
|
num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
|
|
prefetch_buffer: int = field(default=8, metadata={"help": "The number of batches to prefetch for loading"})
|
|
|
|
def __post_init__(self):
|
|
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
|
|
raise ValueError("Need either a dataset name or a training/validation file.")
|
|
else:
|
|
if self.train_file is not None:
|
|
extension = self.train_file.split(".")[-1]
|
|
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
|
|
if self.validation_file is not None:
|
|
extension = self.validation_file.split(".")[-1]
|
|
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
|
|
|
|
|
|
class TrainState(train_state.TrainState):
|
|
dropout_rng: jnp.ndarray
|
|
|
|
def replicate(self):
|
|
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
|
|
|
# the below functions are not used now, probably to be removed
|
|
def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
|
|
num_samples = len(samples_idx)
|
|
samples_to_remove = num_samples % batch_size
|
|
|
|
if samples_to_remove != 0:
|
|
samples_idx = samples_idx[:-samples_to_remove]
|
|
sections_split = num_samples // batch_size
|
|
batch_idx = np.split(samples_idx, sections_split)
|
|
return batch_idx
|
|
|
|
|
|
def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
|
|
"""
|
|
The training iterator is advanced so that after groupifying the samples,
|
|
`num_samples` of length `max_seq_length` are returned.
|
|
"""
|
|
num_total_tokens = max_seq_length * num_samples
|
|
samples = defaultdict(list)
|
|
|
|
i = 0
|
|
while i < num_total_tokens:
|
|
tokenized_samples = next(train_iterator)
|
|
i += len(tokenized_samples["input_ids"])
|
|
|
|
# concatenate tokenized samples to list
|
|
samples = {k: samples[k] + tokenized_samples[k] for k in tokenized_samples.keys()}
|
|
|
|
# Concatenated tokens are split to lists of length `max_seq_length`.
|
|
# Note that remainedr of % max_seq_length are thrown away.
|
|
def group_texts(examples):
|
|
result = {
|
|
k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
|
|
for k, t in examples.items()
|
|
}
|
|
return result
|
|
|
|
grouped_samples = group_texts(samples)
|
|
return grouped_samples
|
|
|
|
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
|
"""
|
|
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
|
Shuffle batches if `shuffle` is `True`.
|
|
"""
|
|
steps_per_epoch = len(dataset) // batch_size
|
|
|
|
if shuffle:
|
|
batch_idx = jax.random.permutation(rng, len(dataset))
|
|
else:
|
|
batch_idx = jnp.arange(len(dataset))
|
|
|
|
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
|
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
|
|
|
for idx in batch_idx:
|
|
batch = dataset[idx]
|
|
batch = {k: jnp.array(v) for k, v in batch.items()}
|
|
|
|
batch = shard(batch)
|
|
|
|
yield batch
|
|
|
|
|
|
def write_train_metric(summary_writer, train_metrics, train_time, step):
|
|
summary_writer.scalar("train_time", train_time, step)
|
|
|
|
train_metrics = get_metrics(train_metrics)
|
|
for key, vals in train_metrics.items():
|
|
tag = f"train_{key}"
|
|
for i, val in enumerate(vals):
|
|
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
|
|
|
|
|
def write_eval_metric(summary_writer, eval_metrics, step):
|
|
for metric_name, value in eval_metrics.items():
|
|
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
|
|
|
|
|
def create_learning_rate_fn(
|
|
num_train_steps: int, train_batch_size: int, num_warmup_steps: int, learning_rate: float
|
|
) -> Callable[[int], jnp.array]:
|
|
"""Returns a linear warmup, linear_decay learning rate function."""
|
|
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
|
decay_fn = optax.linear_schedule(
|
|
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
|
)
|
|
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
|
return schedule_fn
|
|
def gpt3_schedule(warmup_steps,
|
|
total_steps,
|
|
peak_lr,
|
|
end_lr):
|
|
def sch(step):
|
|
warmup_pct = jnp.clip(step, 0, warmup_steps) / warmup_steps
|
|
anneal_pct = jnp.clip(step - warmup_steps, 0, total_steps) / total_steps
|
|
|
|
return warmup_pct * peak_lr - (peak_lr - end_lr) * (1 - jnp.cos(jnp.pi * anneal_pct)) / 2
|
|
|
|
return sch
|
|
|
|
# utils
|
|
def mb_item(x):
|
|
return x.item() if hasattr(x, "item") else x
|
|
|
|
#checkpoint functions
|
|
def save_model_checkpoint(model, save_dir, state, with_opt:bool=True, push_to_hub:bool=False):
|
|
"""
|
|
If `push_to_hub` is True, will save to `save_dir`. Otherwise will save to `save_dir/ckpt-{step}`.
|
|
"""
|
|
state = jax_utils.unreplicate(state)
|
|
logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
|
|
if not push_to_hub:
|
|
save_dir = f"{save_dir}/ckpt-{mb_item(state.step)-1}"
|
|
model.save_pretrained(
|
|
save_dir,
|
|
params=state.params,
|
|
push_to_hub=push_to_hub,
|
|
commit_message=f"Saving weights and logs at step {mb_item(state.step)-1}",
|
|
)
|
|
if with_opt:
|
|
with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
|
|
f.write(to_bytes(state.opt_state))
|
|
with open(os.path.join(save_dir, "training_state.json"), "w") as f:
|
|
json.dump({"step": state.step.item()}, f)
|
|
logger.info("checkpoint saved")
|
|
|
|
def restore_model_checkpoint(save_dir, state):
|
|
logger.info(f"RESTORING CHECKPOINT FROM {save_dir}...")
|
|
with open(os.path.join(save_dir, "flax_model.msgpack"), "rb") as f:
|
|
params = from_bytes(state.params, f.read())
|
|
|
|
with open(os.path.join(save_dir, "opt_state.msgpack"), "rb") as f:
|
|
opt_state = from_bytes(state.opt_state, f.read())
|
|
|
|
with open(os.path.join(save_dir, "training_state.json"), "r") as f:
|
|
training_state = json.load(f)
|
|
step = training_state["step"]
|
|
|
|
logger.info("checkpoint restored")
|
|
return state.replace(step=step, params=params, opt_state=opt_state), step
|
|
|
|
def rotate_checkpoints(ckpt_dir:str, save_total_limit:int):
|
|
"Removes older checkpoints so that `save_total_limit` checkpoints are kept"
|
|
# TODO: what to remove is decided using step number only, we might want to improve that
|
|
ckpts = [str(x) for x in Path(ckpt_dir).glob("ckpt-*")]
|
|
# sort checkpoints by step
|
|
ckpts_sorted = sorted(ckpts, key=lambda x: int(x.split('-')[-1]))
|
|
ckpts_to_delete = ckpts_sorted[:-save_total_limit]
|
|
for ckpt in ckpts_to_delete:
|
|
logger.info(f"Deleting older checkpoint [{ckpt}] due to save_total_limit ({save_total_limit})")
|
|
shutil.rmtree(ckpt)
|
|
|
|
def main():
|
|
# See all possible arguments in src/transformers/training_args.py
|
|
# or by passing the --help flag to this script.
|
|
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
|
|
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
|
# If we pass only one argument to the script and it's the path to a json file,
|
|
# let's parse it to get our arguments.
|
|
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
|
else:
|
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
|
|
|
if (
|
|
os.path.exists(training_args.output_dir)
|
|
and os.listdir(training_args.output_dir)
|
|
and training_args.do_train
|
|
and not training_args.overwrite_output_dir
|
|
):
|
|
raise ValueError(
|
|
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
|
"Use --overwrite_output_dir to overcome."
|
|
)
|
|
|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
# Setup logging, we only want one process per machine to log things on the screen.
|
|
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
|
if jax.process_index() == 0:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
|
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
|
logger.info(f"Training/evaluation parameters {training_args}")
|
|
|
|
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
|
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
|
# (the dataset will be downloaded automatically from the datasets Hub).
|
|
#
|
|
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
|
# 'text' is found. You can easily tweak this behavior (see below).
|
|
#
|
|
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
|
# download the dataset.
|
|
if data_args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
train_dataset = load_dataset(
|
|
data_args.dataset_name,
|
|
data_dir=data_args.data_dir,
|
|
cache_dir=model_args.cache_dir,
|
|
streaming=True,
|
|
split="train"
|
|
)
|
|
eval_dataset = load_dataset(
|
|
data_args.dataset_name,
|
|
data_dir=data_args.data_dir,
|
|
cache_dir=model_args.cache_dir,
|
|
streaming=True,
|
|
split="validation"
|
|
)
|
|
|
|
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
|
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
|
|
|
# Load pretrained model and tokenizer
|
|
|
|
# Distributed training:
|
|
# The .from_pretrained methods guarantee that only one local process can concurrently
|
|
# download model & vocab.
|
|
if model_args.config_name:
|
|
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
|
elif model_args.model_name_or_path:
|
|
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
|
else:
|
|
config = CONFIG_MAPPING[model_args.model_type]()
|
|
logger.warning("You are instantiating a new config instance from scratch.")
|
|
|
|
if model_args.tokenizer_name:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
|
)
|
|
elif model_args.model_name_or_path:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
|
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
|
)
|
|
|
|
if model_args.model_name_or_path:
|
|
model = FlaxAutoModelForCausalLM.from_pretrained(
|
|
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
|
)
|
|
else:
|
|
model = FlaxAutoModelForCausalLM.from_config(
|
|
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
|
)
|
|
|
|
# Preprocessing the datasets.
|
|
# First we tokenize all the texts.
|
|
# column_names = eval_dataset.column_names
|
|
text_column_name = data_args.text_column_name # if data_args.text_column_name in column_names else column_names[0]
|
|
|
|
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
|
|
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
|
|
|
|
def tokenize_function(examples):
|
|
with CaptureLogger(tok_logger) as cl:
|
|
output = tokenizer(examples[text_column_name])
|
|
# clm input could be much much longer than block_size
|
|
if "Token indices sequence length is longer than the" in cl.out:
|
|
tok_logger.warning(
|
|
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
|
|
)
|
|
return output
|
|
|
|
tokenized_dataset = train_dataset.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
)
|
|
tokenized_eval_dataset = eval_dataset.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
# remove_columns=column_names,
|
|
# num_proc=data_args.preprocessing_num_workers,
|
|
# load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
|
|
if data_args.block_size is None:
|
|
block_size = tokenizer.model_max_length
|
|
if block_size > config.max_position_embeddings:
|
|
logger.warning(
|
|
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
|
"Picking 1024 instead. You can change that default value by passing --block_size xxx."
|
|
)
|
|
block_size = 1024
|
|
else:
|
|
if data_args.block_size > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
|
|
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
|
|
)
|
|
block_size = min(data_args.block_size, tokenizer.model_max_length)
|
|
|
|
# # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
|
def group_texts(examples):
|
|
# Concatenate all texts.
|
|
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
|
|
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
|
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
|
# customize this part to your needs.
|
|
total_length = (total_length // block_size) * block_size
|
|
# Split by chunks of max_len.
|
|
result = {
|
|
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
|
for k, t in concatenated_examples.items()
|
|
}
|
|
result["labels"] = result["input_ids"].copy()
|
|
return result
|
|
|
|
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
|
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
|
# to preprocess.
|
|
#
|
|
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
|
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
|
|
|
shuffle_seed = training_args.seed
|
|
# if training_args.do_train:
|
|
# if "train" not in tokenized_dataset:
|
|
# raise ValueError("--do_train requires a train dataset")
|
|
# train_dataset = tokenized_dataset
|
|
# if data_args.max_train_samples is not None:
|
|
# train_dataset = train_dataset.take(range(data_args.max_train_samples))
|
|
# train_dataset = train_dataset.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
|
|
# train_iter = iter(train_dataset)
|
|
|
|
|
|
train_loader = PrefetchDataloader(
|
|
tokenized_dataset,
|
|
training_args.max_steps * training_args.gradient_accumulation_steps,
|
|
int(training_args.per_device_train_batch_size) * jax.device_count(),
|
|
block_size,
|
|
prefetch_buffer=data_args.prefetch_buffer,
|
|
seed=shuffle_seed
|
|
)
|
|
# evaluation data is not in streaming mode
|
|
# if training_args.do_eval:
|
|
# eval_dataset = tokenized_eval_dataset.map(
|
|
# group_texts,
|
|
# batched=True,
|
|
# num_proc=data_args.preprocessing_num_workers,
|
|
# load_from_cache_file=not data_args.overwrite_cache,
|
|
# )
|
|
# if data_args.max_eval_samples is not None:
|
|
# eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
|
|
|
# Enable tensorboard only on the master node
|
|
has_tensorboard = is_tensorboard_available()
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
try:
|
|
from flax.metrics.tensorboard import SummaryWriter
|
|
|
|
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
|
except ImportError as ie:
|
|
has_tensorboard = False
|
|
logger.warning(
|
|
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
|
)
|
|
else:
|
|
logger.warning(
|
|
"Unable to display metrics through TensorBoard because the package is not installed: "
|
|
"Please run pip install tensorboard to enable."
|
|
)
|
|
|
|
# enable wandb tracking
|
|
has_wandb = find_spec("wandb") is not None
|
|
if jax.process_index() == 0 and has_wandb and ("wandb" in training_args.report_to):
|
|
try:
|
|
import wandb
|
|
wandb.init(
|
|
name=training_args.run_name,
|
|
entity="wandb",
|
|
project="hf-flax-gpt-neo-copilot",
|
|
sync_tensorboard=True
|
|
)
|
|
wandb.config.update(training_args)
|
|
wandb.config.update(model_args)
|
|
wandb.config.update(data_args)
|
|
except ImportError as e:
|
|
print(e)
|
|
has_wandb = False
|
|
|
|
|
|
# Initialize our training
|
|
rng = jax.random.PRNGKey(training_args.seed)
|
|
rng, dropout_rng = jax.random.split(rng)
|
|
|
|
# Store some constant
|
|
num_epochs = int(training_args.num_train_epochs)
|
|
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() * training_args.gradient_accumulation_steps
|
|
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
|
total_train_steps = training_args.max_steps * training_args.gradient_accumulation_steps
|
|
|
|
# Create learning rate schedule
|
|
gpt3_schedule_fn = gpt3_schedule(
|
|
training_args.warmup_steps,
|
|
model_args.decay_steps,
|
|
training_args.learning_rate,
|
|
training_args.learning_rate / 10.
|
|
)
|
|
|
|
# We use Optax's "masking" functionality to not apply weight decay
|
|
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
|
# mask boolean with the same structure as the parameters.
|
|
# The mask is True for parameters that should be decayed.
|
|
# Note that this mask is specifically adapted for FlaxGPT2.
|
|
# For other models, one should correct the layer norm parameter naming
|
|
# accordingly.
|
|
def decay_mask_fn(params):
|
|
flat_params = traverse_util.flatten_dict(params)
|
|
flat_mask = {
|
|
path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
|
|
for path in flat_params
|
|
}
|
|
return traverse_util.unflatten_dict(flat_mask)
|
|
|
|
# create optimizer
|
|
if training_args.adafactor:
|
|
# We use the default parameters here to initialize adafactor,
|
|
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
|
|
optimizer = optax.adafactor(
|
|
learning_rate=gpt3_schedule_fn,
|
|
)
|
|
else:
|
|
optimizer = optax.adamw(
|
|
learning_rate=gpt3_schedule_fn,
|
|
b1=training_args.adam_beta1,
|
|
b2=training_args.adam_beta2,
|
|
eps=training_args.adam_epsilon,
|
|
weight_decay=training_args.weight_decay,
|
|
mask=decay_mask_fn,
|
|
)
|
|
if training_args.gradient_accumulation_steps > 1:
|
|
optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
|
|
grad_accum_steps = training_args.gradient_accumulation_steps
|
|
|
|
# Setup train state
|
|
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
|
|
|
|
if training_args.resume_from_checkpoint:
|
|
state = restore_checkpoint(training_args.resume_from_checkpoint, state)
|
|
resume_step = mb_item(state.step)
|
|
else:
|
|
resume_step = 0
|
|
|
|
def loss_fn(logits, labels):
|
|
shift_logits = logits[..., :-1, :]
|
|
shift_labels = labels[..., 1:]
|
|
loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
|
|
return loss.mean()
|
|
|
|
# Define gradient update step fn
|
|
def train_step(state, batch):
|
|
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
|
|
|
def compute_loss(params):
|
|
labels = batch.pop("labels")
|
|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
|
loss = loss_fn(logits, labels)
|
|
return loss
|
|
|
|
grad_fn = jax.value_and_grad(compute_loss)
|
|
loss, grad = grad_fn(state.params)
|
|
grad = jax.lax.pmean(grad, "batch")
|
|
|
|
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
|
|
|
metrics = {"loss": loss, "learning_rate": gpt3_schedule_fn(state.step // grad_accum_steps)}
|
|
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
|
|
|
return new_state, metrics
|
|
|
|
# Define eval fn
|
|
def eval_step(params, batch):
|
|
labels = batch.pop("labels")
|
|
logits = model(**batch, params=params, train=False)[0]
|
|
loss = loss_fn(logits, labels)
|
|
|
|
# summarize metrics
|
|
metrics = {"loss": loss}
|
|
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
|
return metrics
|
|
|
|
# Create parallel version of the train and eval step
|
|
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
|
|
p_eval_step = jax.pmap(eval_step, "batch")
|
|
|
|
# Replicate the train state on each device
|
|
state = state.replicate()
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed and grad_accum) = {train_batch_size}")
|
|
logger.info(f" Total optimization steps = {training_args.max_steps}")
|
|
|
|
if not training_args.skip_memory_metrics:
|
|
server = jax.profiler.start_server(9999)
|
|
|
|
train_time = 0
|
|
train_metrics = []
|
|
# TODO: figure out training duration
|
|
steps = tqdm(range(training_args.max_steps), position=0, initial=resume_step)
|
|
for step in range(total_train_steps):
|
|
# ======================== Training ================================
|
|
train_start = time.time()
|
|
rng, input_rng = jax.random.split(rng)
|
|
|
|
cur_step = step
|
|
# skip to the step from which we are resuming
|
|
if cur_step < resume_step:
|
|
continue
|
|
|
|
# using advance_iter_and_group_samples seem to make training slower
|
|
# samples = advance_iter_and_group_samples(iter(tokenized_dataset), int(training_args.per_device_train_batch_size) * jax.device_count(), block_size)
|
|
# batch = shard(make_batch(samples))
|
|
batch = shard(next(train_loader))
|
|
# logger.info(f"{batch['input_ids'].shape}")
|
|
state, train_metric = p_train_step(state, batch)
|
|
train_metrics.append(train_metric)
|
|
if step % grad_accum_steps == 0:
|
|
steps.update(1)
|
|
|
|
if cur_step % (training_args.logging_steps * grad_accum_steps)== 0 and cur_step > 0:
|
|
# Save metrics
|
|
train_metric = unreplicate(train_metric)
|
|
train_time += time.time() - train_start
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
|
if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
|
|
# TODO: add accumulation of metrics
|
|
_metrics = {k if k=="learning_rate" else f"train_{k}":mb_item(v.mean()) for k, v in train_metric.items()}
|
|
wandb.log({"training_step":cur_step, **_metrics}, commit=True)
|
|
|
|
steps.write(
|
|
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
|
|
)
|
|
|
|
train_metrics = []
|
|
|
|
if cur_step % (training_args.eval_steps * grad_accum_steps) == 0 and cur_step > 0 and training_args.do_eval:
|
|
# ======================== Evaluating ==============================
|
|
eval_metrics = []
|
|
eval_steps = data_args.max_eval_samples # len(eval_dataset) // eval_batch_size
|
|
# eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
|
eval_loader = PrefetchDataloader(
|
|
tokenized_eval_dataset,
|
|
eval_steps,
|
|
eval_batch_size,
|
|
block_size,
|
|
prefetch_buffer=data_args.prefetch_buffer,
|
|
shuffle=False,
|
|
)
|
|
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
|
# Model forward
|
|
batch = shard(next(eval_loader))
|
|
metrics = p_eval_step(state.params, batch)
|
|
eval_metrics.append(metrics)
|
|
|
|
# normalize eval metrics
|
|
eval_metrics = get_metrics(eval_metrics)
|
|
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
|
|
|
try:
|
|
eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
|
|
except OverflowError:
|
|
eval_metrics["perplexity"] = float("inf")
|
|
# TODO: this needs to be closed properly
|
|
eval_loader.terminate()
|
|
# Print metrics and update progress bar
|
|
desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
|
|
steps.write(desc)
|
|
steps.desc = desc
|
|
|
|
# Save metrics
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
# cur_step = epoch * (len(train_dataset) // train_batch_size)
|
|
write_eval_metric(summary_writer, eval_metrics, cur_step)
|
|
if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
|
|
_metrics = {f"eval_{k}":mb_item(v) for k, v in eval_metrics.items()}
|
|
wandb.log({"eval_step":cur_step, **_metrics})
|
|
|
|
if cur_step % (training_args.save_steps * grad_accum_steps) == 0 and cur_step > 0:
|
|
# save checkpoint after each epoch and push checkpoint to the hub
|
|
if jax.process_index() == 0:
|
|
save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
|
|
push_to_hub=training_args.push_to_hub)
|
|
if model_args.save_optimizer:
|
|
# this saves full state including optimizer
|
|
save_checkpoint(training_args.output_dir, jax_utils.unreplicate(state), cur_step, keep=training_args.save_total_limit, overwrite=True)
|
|
if training_args.save_total_limit is not None:
|
|
rotate_checkpoints(training_args.output_dir, training_args.save_total_limit)
|
|
|
|
train_loader.terminate()
|
|
# save model after training is over
|
|
save_checkpoint(model, training_args.output_dir, state, with_opt=False,
|
|
push_to_hub=training_args.push_to_hub)
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|