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Add filter script
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training/run_clm_streaming_filter_flax.py
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778
training/run_clm_streaming_filter_flax.py
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=causal-lm
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"""
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# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
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from ast import Str
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import logging
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import math
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import os
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import sys
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import time
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Callable, Optional
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import json
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import shutil
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from flax import training
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import numpy as np
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import datasets
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from datasets import load_dataset
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from tqdm import tqdm
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import jax
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import jax.profiler
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import jax.numpy as jnp
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import optax
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import transformers
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import flax
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from flax import jax_utils, traverse_util
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from flax.jax_utils import unreplicate
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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from flax.training.checkpoints import save_checkpoint, restore_checkpoint
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from flax.serialization import to_bytes, from_bytes
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoTokenizer,
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FlaxAutoModelForCausalLM,
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HfArgumentParser,
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TrainingArguments,
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is_tensorboard_available,
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)
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from transformers.testing_utils import CaptureLogger
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from importlib.util import find_spec
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from utils import PrefetchDataloaderWithFilter, make_batch
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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dtype: Optional[str] = field(
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default="float32",
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metadata={
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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},
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)
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save_optimizer: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to store full train state including optimizer."},
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)
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repo_path_or_name: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the modelhub repo directory"},
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)
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repo_url: Optional[str] = field(
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default=None,
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metadata={"help": "URL of the modelhub repo"},
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)
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decay_steps: int = field(default=None, metadata={"help":"Number of steps from peak to final learning rate"})
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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data_dir: Optional[str] = field(default=None, metadata={"help": "Path to data directory."})
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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validation_split_percentage: Optional[int] = field(
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default=5,
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metadata={
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"help": "The percentage of the train set used as validation set in case there's no validation split"
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},
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)
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block_size: Optional[int] = field(
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default=None,
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metadata={
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"help": "Optional input sequence length after tokenization. "
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"The training dataset will be truncated in block of this size for training. "
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"Default to the model max input length for single sentence inputs (take into account special tokens)."
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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text_column_name: Optional[str] = field(
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default='text',
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metadata={"help": "Column containing main text data."},
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)
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shuffle_buffer_size: int = field(
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default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
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)
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num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
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num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
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prefetch_buffer: int = field(default=8, metadata={"help": "The number of batches to prefetch for loading"})
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
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class TrainState(train_state.TrainState):
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dropout_rng: jnp.ndarray
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def replicate(self):
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return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
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def write_train_metric(summary_writer, train_metrics, train_time, step):
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summary_writer.scalar("train_time", train_time, step)
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train_metrics = get_metrics(train_metrics)
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for key, vals in train_metrics.items():
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tag = f"train_{key}"
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for i, val in enumerate(vals):
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summary_writer.scalar(tag, val, step - len(vals) + i + 1)
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def write_eval_metric(summary_writer, eval_metrics, step):
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for metric_name, value in eval_metrics.items():
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summary_writer.scalar(f"eval_{metric_name}", value, step)
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def create_learning_rate_fn(
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num_train_steps: int, train_batch_size: int, num_warmup_steps: int, learning_rate: float
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) -> Callable[[int], jnp.array]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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def gpt3_schedule(warmup_steps,
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total_steps,
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peak_lr,
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end_lr):
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def sch(step):
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warmup_pct = jnp.clip(step, 0, warmup_steps) / warmup_steps
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anneal_pct = jnp.clip(step - warmup_steps, 0, total_steps) / total_steps
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return warmup_pct * peak_lr - (peak_lr - end_lr) * (1 - jnp.cos(jnp.pi * anneal_pct)) / 2
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return sch
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# utils
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def mb_item(x):
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return x.item() if hasattr(x, "item") else x
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#checkpoint functions
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def save_model_checkpoint(model, save_dir, state, with_opt:bool=True, push_to_hub:bool=False):
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"""
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If `push_to_hub` is True, will save to `save_dir`. Otherwise will save to `save_dir/ckpt-{step}`.
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"""
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state = jax_utils.unreplicate(state)
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logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
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if not push_to_hub:
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save_dir = f"{save_dir}/ckpt-{mb_item(state.step)-1}"
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model.save_pretrained(
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save_dir,
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params=state.params,
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push_to_hub=push_to_hub,
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commit_message=f"Saving weights and logs at step {mb_item(state.step)-1}",
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)
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if with_opt:
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with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
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f.write(to_bytes(state.opt_state))
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with open(os.path.join(save_dir, "training_state.json"), "w") as f:
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json.dump({"step": state.step.item()}, f)
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logger.info("checkpoint saved")
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# this is added to make resuming from checkpoint to work with adafactor
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# to be removed when issue is fixed
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# notice that adafactor state is perturbed by fake_update
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def _zeros_tree_like(inp_tree):
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return jax.tree_map(jnp.zeros_like, inp_tree)
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def fake_update(state):
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fake_updates = _zeros_tree_like(state.params)
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_, new_inner_opt_state = state.tx.inner_opt.update(fake_updates, state.opt_state.inner_opt_state, state.params)
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opt_state = state.opt_state
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new_opt_state = optax.MultiStepsState(mini_step=opt_state.mini_step,
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gradient_step=opt_state.gradient_step,
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inner_opt_state=new_inner_opt_state,
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acc_grads=opt_state.acc_grads)
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return state.replace(opt_state=new_opt_state)
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def reinstantiate_states(opt_state):
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new_state = []
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for state in opt_state:
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if isinstance(state, list):
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new_state.append(reinstantiate_states(state))
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else:
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cls = getattr(optax, type(state).__name__)
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new_state.append(cls(**{k:getattr(state, k) for k in state._fields}))
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return new_state
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def restore_model_checkpoint(save_dir, state):
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logger.info(f"RESTORING CHECKPOINT FROM {save_dir}...")
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with open(os.path.join(save_dir, "flax_model.msgpack"), "rb") as f:
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params = from_bytes(state.params, f.read())
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with open(os.path.join(save_dir, "opt_state.msgpack"), "rb") as f:
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opt_state = from_bytes(state.opt_state, f.read())
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with open(os.path.join(save_dir, "training_state.json"), "r") as f:
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training_state = json.load(f)
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step = training_state["step"]
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logger.info("checkpoint restored")
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# reinstantiate inner opt state to avoid type conflict
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if hasattr(opt_state, "inner_opt_state"):
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print("restoring state ofmultisteps optimizer")
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inner_opt_state = reinstantiate_states(opt_state.inner_opt_state)
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ms_state_dict = {k:getattr(state.opt_state, k) for k in state.opt_state._fields}
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ms_state_dict["inner_opt_state"] = inner_opt_state
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opt_state = optax.MultiStepsState(**ms_state_dict)
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return state.replace(step=step, params=params, opt_state=opt_state)
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def rotate_checkpoints(ckpt_dir:str, save_total_limit:int):
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"Removes older checkpoints so that `save_total_limit` checkpoints are kept"
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# TODO: what to remove is decided using step number only, we might want to improve that
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ckpts = [str(x) for x in Path(ckpt_dir).glob("ckpt-*")]
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# sort checkpoints by step
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ckpts_sorted = sorted(ckpts, key=lambda x: int(x.split('-')[-1]))
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ckpts_to_delete = ckpts_sorted[:-save_total_limit]
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for ckpt in ckpts_to_delete:
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logger.info(f"Deleting older checkpoint [{ckpt}] due to save_total_limit ({save_total_limit})")
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shutil.rmtree(ckpt)
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty."
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"Use --overwrite_output_dir to overcome."
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)
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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# Setup logging, we only want one process per machine to log things on the screen.
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logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
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if jax.process_index() == 0:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# Set the verbosity to info of the Transformers logger (on main process only):
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logger.info(f"Training/evaluation parameters {training_args}")
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantees that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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train_dataset = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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data_dir=data_args.data_dir,
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cache_dir=model_args.cache_dir,
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streaming=True,
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split="train"
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)
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eval_dataset = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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data_dir=data_args.data_dir,
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cache_dir=model_args.cache_dir,
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streaming=True,
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split="validation"
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Load pretrained model and tokenizer
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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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)
|
||||
|
||||
# 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
|
||||
|
||||
train_loader = PrefetchDataloaderWithFilter(
|
||||
tokenized_dataset,
|
||||
training_args.max_steps * training_args.gradient_accumulation_steps,
|
||||
int(training_args.per_device_train_batch_size) * jax.device_count(),
|
||||
block_size,
|
||||
shuffle_buffer=10000,
|
||||
prefetch_buffer=data_args.prefetch_buffer,
|
||||
seed=training_args.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
|
||||
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,
|
||||
)
|
||||
optimizer = optax.chain(
|
||||
optax.clip_by_global_norm(1),
|
||||
optimizer
|
||||
)
|
||||
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_model_checkpoint(training_args.resume_from_checkpoint, state)
|
||||
resume_step = mb_item(state.step)
|
||||
if training_args.adafactor:
|
||||
state = fake_update(state)
|
||||
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//grad_accum_steps)
|
||||
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//grad_accum_steps, **_metrics}, commit=True)
|
||||
|
||||
steps.write(
|
||||
f"Step... ({cur_step // grad_accum_steps} | 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 = PrefetchDataloaderWithFilter(
|
||||
tokenized_eval_dataset,
|
||||
eval_steps,
|
||||
eval_batch_size,
|
||||
block_size,
|
||||
prefetch_buffer=data_args.prefetch_buffer,
|
||||
shuffle=False,
|
||||
)
|
||||
eval_pbar = tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False)
|
||||
for _ in eval_pbar:
|
||||
# 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//grad_accum_steps} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
|
||||
eval_pbar.write(desc)
|
||||
eval_pbar.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//grad_accum_steps)
|
||||
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//grad_accum_steps, **_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=model_args.save_optimizer,
|
||||
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_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
|
||||
push_to_hub=training_args.push_to_hub)
|
||||
|
||||
logger.info("***Training comleted")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
Loading…
Reference in New Issue
Block a user