feat: fine-tuning scripts and instructions
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@ -7,3 +7,5 @@
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/data/*
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!/data/.keep
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/training/data/*
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!/training/data/.keep
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14
README.md
14
README.md
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@ -80,4 +80,16 @@ Soldier: To the jail with you *hit court wizard*
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## Fine-tuning a model
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To-do. I haven't documented this yet.
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Due to hardware limitations (read: lack of GPUs with massive amounts of VRAM), I need to make use of ColossalAI's optimizations to be able to fine-tune models. However, their example code for fine-tuning OPT lacks some important stuff. Notably: metric logging (so we can know what is going on) and checkpoint saving/loading.
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I've gone ahead and, using [their example scripts](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt) as a starting point, made a slightly adjusted version that's actually usable for real-world scenarios. All that stuff is inside the [training folder](/training/).
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If you don't want to mess with anything, all you need to do is put the built data file at `/training/data/train.json` and invoke [finetune.bash](/training/finetune.bash). To see metrics, you can use Tensorboard by visiting http://localhost:6006 after starting the server like this:
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```bash
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tensorboard serve --port 6006 --logdir training/checkpoints/runs`
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```
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## Running inference on the fine-tuned model
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To-do: write this up.
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import torch.distributed as dist
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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class barrier_context():
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"""
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This context manager is used to allow one process to execute while blocking all
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other processes in the same process group. This is often useful when downloading is required
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as we only want to download in one process to prevent file corruption.
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Args:
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executor_rank (int): the process rank to execute without blocking, all other processes will be blocked
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parallel_mode (ParallelMode): the parallel mode corresponding to a process group
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Usage:
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with barrier_context():
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dataset = CIFAR10(root='./data', download=True)
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"""
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def __init__(self, executor_rank: int = 0, parallel_mode: ParallelMode = ParallelMode.GLOBAL):
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# the class name is lowercase by convention
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current_rank = gpc.get_local_rank(parallel_mode=parallel_mode)
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self.should_block = current_rank != executor_rank
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self.group = gpc.get_group(parallel_mode=parallel_mode)
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def __enter__(self):
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if self.should_block:
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dist.barrier(group=self.group)
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def __exit__(self, exc_type, exc_value, exc_traceback):
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if not self.should_block:
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dist.barrier(group=self.group)
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@ -0,0 +1,625 @@
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. 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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Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...)
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on a text file or a dataset without using HuggingFace Trainer.
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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=text-generation
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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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import math
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import os
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import time
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from itertools import chain
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import datasets
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import torch
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import torch.distributed as dist
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from accelerate.utils import set_seed
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from context import barrier_context
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from datasets import load_dataset
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from packaging import version
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from torch.utils.data import DataLoader
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from tqdm.auto import tqdm
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import colossalai
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import transformers
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.logging import disable_existing_loggers, get_dist_logger
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.tensor import ProcessGroup
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from colossalai.utils import get_current_device, get_dataloader, save_checkpoint
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from transformers import (
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CONFIG_MAPPING,
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MODEL_MAPPING,
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AutoConfig,
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AutoTokenizer,
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GPT2Tokenizer,
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OPTForCausalLM,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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)
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from transformers.utils.versions import require_version
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
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MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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def get_time_stamp():
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torch.cuda.synchronize()
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return time.time()
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def parse_args():
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parser = colossalai.get_default_parser()
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help="The name of the dataset to use (via the datasets library).",
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The configuration name of the dataset to use (via the datasets library).",
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)
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parser.add_argument("--train_file",
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type=str,
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default=None,
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help="A csv or a json file containing the training data.")
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parser.add_argument("--validation_file",
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type=str,
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default=None,
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help="A csv or a json file containing the validation data.")
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parser.add_argument(
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"--validation_split_percentage",
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default=5,
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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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parser.add_argument(
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"--model_name_or_path",
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type=str,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=True,
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)
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parser.add_argument(
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"--config_name",
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type=str,
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default=None,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--use_slow_tokenizer",
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action="store_true",
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help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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)
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
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)
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parser.add_argument("--num_warmup_steps",
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type=int,
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default=0,
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help="Number of steps for the warmup in the lr scheduler.")
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--model_type",
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type=str,
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default=None,
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help="Model type to use if training from scratch.",
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choices=MODEL_TYPES,
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)
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parser.add_argument(
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"--block_size",
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type=int,
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default=None,
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help=("Optional input sequence length after tokenization. The training dataset will be truncated in block of"
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" this size for training. Default to the model max input length for single sentence inputs (take into"
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" account special tokens)."),
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)
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parser.add_argument(
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"--preprocessing_num_workers",
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type=int,
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default=None,
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help="The number of processes to use for the preprocessing.",
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)
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parser.add_argument("--overwrite_cache",
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type=bool,
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default=False,
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help="Overwrite the cached training and evaluation sets")
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parser.add_argument("--no_keep_linebreaks",
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action="store_true",
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help="Do not keep line breaks when using TXT files.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_model_id",
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type=str,
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help="The name of the repository to keep in sync with the local `output_dir`.")
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parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default=None,
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help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help="If the training should continue from a checkpoint folder.",
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)
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parser.add_argument(
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"--comment", type=str, help="Experiment comment for the Tensorboard writer."
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)
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# NOTE(11b): These last two are useless.
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to enable experiment trackers for logging.",
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="all",
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help=('The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
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' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
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"Only applicable when `--with_tracking` is passed."),
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)
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parser.add_argument("--mem_cap", type=int, default=0, help="use mem cap")
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parser.add_argument("--init_in_cpu", action='store_true', default=False, help="init training model in cpu")
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args = parser.parse_args()
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# Sanity checks
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if args.dataset_name is None and args.train_file is None and args.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 args.train_file is not None:
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extension = args.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
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if args.validation_file is not None:
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extension = args.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
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if args.push_to_hub:
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assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
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return args
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def colo_memory_cap(size_in_GB):
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from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device
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cuda_capacity = colo_device_memory_capacity(get_current_device())
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if size_in_GB * (1024**3) < cuda_capacity:
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colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity)
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print("Using {} GB of GPU memory".format(size_in_GB))
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def main():
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args = parse_args()
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disable_existing_loggers()
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colossalai.launch_from_torch(config=dict())
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logger = get_dist_logger()
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is_main_process = dist.get_rank() == 0
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if is_main_process:
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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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if args.mem_cap > 0:
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colo_memory_cap(args.mem_cap)
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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logger.info(f"Rank {dist.get_rank()}: random seed is set to {args.seed}")
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# Handle the repository creation
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with barrier_context():
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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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 guarantee that only one local process can concurrently
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# download the dataset.
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logger.info("Start preparing dataset", ranks=[0])
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if args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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args.dataset_name,
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args.dataset_config_name,
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split=f"train[:{args.validation_split_percentage}%]",
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)
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raw_datasets["train"] = load_dataset(
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args.dataset_name,
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args.dataset_config_name,
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split=f"train[{args.validation_split_percentage}%:]",
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)
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else:
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data_files = {}
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dataset_args = {}
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if args.train_file is not None:
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data_files["train"] = args.train_file
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if args.validation_file is not None:
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data_files["validation"] = args.validation_file
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extension = args.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
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raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{args.validation_split_percentage}%]",
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**dataset_args,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{args.validation_split_percentage}%:]",
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**dataset_args,
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)
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logger.info("Dataset is prepared", ranks=[0])
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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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#
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# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if args.config_name:
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config = AutoConfig.from_pretrained(args.config_name)
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elif args.model_name_or_path:
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config = AutoConfig.from_pretrained(args.model_name_or_path)
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else:
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config = CONFIG_MAPPING[args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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logger.info("Model config has been created", ranks=[0])
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if args.model_name_or_path == 'facebook/opt-13b':
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tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
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else:
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print(f'load model from {args.model_name_or_path}')
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
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logger.info(f"{tokenizer.__class__.__name__} has been created", ranks=[0])
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if args.init_in_cpu:
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init_dev = torch.device('cpu')
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else:
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init_dev = get_current_device()
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# build model
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if args.model_name_or_path is None or args.model_name_or_path == 'facebook/opt-13b':
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# currently, there has a bug in pretrained opt-13b
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# we can not import it until huggingface fix it
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logger.info("Train a new model from scratch", ranks=[0])
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with ColoInitContext(device=init_dev):
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model = OPTForCausalLM(config)
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else:
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logger.info("Finetune a pre-trained model", ranks=[0])
|
||||
with ColoInitContext(device=init_dev):
|
||||
model = OPTForCausalLM.from_pretrained(args.model_name_or_path,
|
||||
from_tf=bool(".ckpt" in args.model_name_or_path),
|
||||
config=config,
|
||||
local_files_only=False)
|
||||
|
||||
# enable graident checkpointing
|
||||
model.gradient_checkpointing_enable()
|
||||
|
||||
PLACEMENT_POLICY = 'auto'
|
||||
cai_version = colossalai.__version__
|
||||
logger.info(f'using Colossal-AI version {cai_version}')
|
||||
if version.parse(cai_version) > version.parse("0.1.10"):
|
||||
from colossalai.nn.parallel import GeminiDDP
|
||||
model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True)
|
||||
elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
|
||||
from colossalai.gemini import ChunkManager, GeminiManager
|
||||
pg = ProcessGroup()
|
||||
chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
|
||||
chunk_manager = ChunkManager(chunk_size,
|
||||
pg,
|
||||
enable_distributed_storage=True,
|
||||
init_device=GeminiManager.get_default_device(PLACEMENT_POLICY))
|
||||
gemini_manager = GeminiManager(PLACEMENT_POLICY, chunk_manager)
|
||||
model = ZeroDDP(model, gemini_manager)
|
||||
|
||||
logger.info(f'{model.__class__.__name__} has been created', ranks=[0])
|
||||
|
||||
if args.resume_from_checkpoint is not None:
|
||||
logger.info(f"Resuming from checkpoint {args.resume_from_checkpoint}", ranks=[0])
|
||||
colossalai.utils.load_checkpoint(args.resume_from_checkpoint, model)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
column_names = raw_datasets["train"].column_names
|
||||
text_column_name = "text" if "text" in column_names else column_names[0]
|
||||
|
||||
def tokenize_function(examples):
|
||||
return tokenizer(examples[text_column_name])
|
||||
|
||||
with barrier_context(executor_rank=0, parallel_mode=ParallelMode.DATA):
|
||||
tokenized_datasets = raw_datasets.map(
|
||||
tokenize_function,
|
||||
batched=True,
|
||||
num_proc=args.preprocessing_num_workers,
|
||||
remove_columns=column_names,
|
||||
load_from_cache_file=not args.overwrite_cache,
|
||||
desc="Running tokenizer on dataset",
|
||||
)
|
||||
|
||||
if args.block_size is None:
|
||||
block_size = tokenizer.model_max_length
|
||||
if block_size > 1024:
|
||||
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 args.block_size > tokenizer.model_max_length:
|
||||
logger.warning(f"The block_size passed ({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(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: list(chain(*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.
|
||||
if total_length >= block_size:
|
||||
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
|
||||
|
||||
with barrier_context(executor_rank=0, parallel_mode=ParallelMode.DATA):
|
||||
lm_datasets = tokenized_datasets.map(
|
||||
group_texts,
|
||||
batched=True,
|
||||
num_proc=args.preprocessing_num_workers,
|
||||
load_from_cache_file=not args.overwrite_cache,
|
||||
desc=f"Grouping texts in chunks of {block_size}",
|
||||
)
|
||||
|
||||
train_dataset = lm_datasets["train"]
|
||||
eval_dataset = lm_datasets["validation"]
|
||||
|
||||
# Log a few random samples from the training set:
|
||||
# for index in random.sample(range(len(train_dataset)), 3):
|
||||
# logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
||||
|
||||
# DataLoaders creation:
|
||||
train_dataloader = get_dataloader(train_dataset,
|
||||
shuffle=True,
|
||||
add_sampler=True,
|
||||
collate_fn=default_data_collator,
|
||||
batch_size=args.per_device_train_batch_size)
|
||||
eval_dataloader = DataLoader(eval_dataset,
|
||||
collate_fn=default_data_collator,
|
||||
batch_size=args.per_device_eval_batch_size)
|
||||
logger.info("Dataloaders have been created", ranks=[0])
|
||||
|
||||
# Optimizer
|
||||
# Split weights in two groups, one with weight decay and the other not.
|
||||
no_decay = ["bias", "LayerNorm.weight"]
|
||||
optimizer_grouped_parameters = [
|
||||
{
|
||||
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
||||
"weight_decay": args.weight_decay,
|
||||
},
|
||||
{
|
||||
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
||||
"weight_decay": 0.0,
|
||||
},
|
||||
]
|
||||
|
||||
optimizer = HybridAdam(optimizer_grouped_parameters, lr=args.learning_rate)
|
||||
optimizer = ZeroOptimizer(optimizer, model, initial_scale=2**14)
|
||||
|
||||
# Scheduler and math around the number of training steps.
|
||||
overrode_max_train_steps = False
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
if args.max_train_steps is None:
|
||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
||||
overrode_max_train_steps = True
|
||||
|
||||
lr_scheduler = get_scheduler(
|
||||
name=args.lr_scheduler_type,
|
||||
optimizer=optimizer,
|
||||
num_warmup_steps=args.num_warmup_steps,
|
||||
num_training_steps=args.max_train_steps,
|
||||
)
|
||||
|
||||
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
if overrode_max_train_steps:
|
||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
||||
# Afterwards we recalculate our number of training epochs
|
||||
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
|
||||
# Train!
|
||||
total_batch_size = args.per_device_train_batch_size * gpc.get_world_size(ParallelMode.DATA)
|
||||
|
||||
logger.info("***** Running training *****", ranks=[0])
|
||||
logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
|
||||
logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
|
||||
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}", ranks=[0])
|
||||
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
|
||||
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}", ranks=[0])
|
||||
logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
|
||||
|
||||
# Only show the progress bar once on each machine.
|
||||
writer = torch.utils.tensorboard.SummaryWriter(log_dir=f"{args.output_dir}/runs", comment=args.comment)
|
||||
progress_bar = tqdm(range(args.max_train_steps), disable=not is_main_process)
|
||||
completed_steps = 0
|
||||
starting_epoch = 0
|
||||
global_step = 0
|
||||
|
||||
for epoch in range(starting_epoch, args.num_train_epochs):
|
||||
|
||||
if completed_steps >= args.max_train_steps:
|
||||
break
|
||||
|
||||
model.train()
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
batch = {k: v.cuda() for k, v in batch.items()}
|
||||
outputs = model(use_cache=False, **batch) # Caching is incompatible with gradient checkpointing.
|
||||
loss = outputs['loss']
|
||||
optimizer.backward(loss)
|
||||
|
||||
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad()
|
||||
progress_bar.update(1)
|
||||
completed_steps += 1
|
||||
|
||||
global_step += 1
|
||||
logger.info("Global step {} finished".format(global_step + 1), ranks=[0])
|
||||
|
||||
try:
|
||||
train_perplexity = math.exp(loss)
|
||||
except OverflowError:
|
||||
train_perplexity = float("inf")
|
||||
writer.add_scalar("Train/Perplexity (Step)", train_perplexity, step)
|
||||
writer.add_scalar("Train/Loss (Step)", loss, step)
|
||||
|
||||
if args.output_dir is not None and args.checkpointing_steps is not None:
|
||||
if args.checkpointing_steps != "epoch" and completed_steps % int(args.checkpointing_steps) == 0:
|
||||
checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt'
|
||||
logger.info(f" Saving iter checkpoint...", ranks=[0])
|
||||
save_checkpoint(checkpoint_path, epoch, model)
|
||||
logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0])
|
||||
|
||||
if completed_steps >= args.max_train_steps:
|
||||
break
|
||||
|
||||
model.eval()
|
||||
losses = []
|
||||
for step, batch in enumerate(eval_dataloader):
|
||||
with torch.no_grad():
|
||||
batch = {k: v.cuda() for k, v in batch.items()}
|
||||
outputs = model(**batch)
|
||||
|
||||
loss = outputs['loss'].unsqueeze(0)
|
||||
losses.append(loss)
|
||||
|
||||
losses = torch.cat(losses)
|
||||
losses = losses[:len(eval_dataset)]
|
||||
try:
|
||||
eval_loss = torch.mean(losses)
|
||||
perplexity = math.exp(eval_loss)
|
||||
except OverflowError:
|
||||
perplexity = float("inf")
|
||||
|
||||
logger.info(f"Epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0])
|
||||
writer.add_scalar("Eval/Loss (Epoch)", eval_loss, epoch)
|
||||
writer.add_scalar("Eval/Perplexity (Epoch)", perplexity, epoch)
|
||||
|
||||
if args.output_dir is not None and args.checkpointing_steps == "epoch":
|
||||
checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt'
|
||||
logger.info(f" Saving epoch checkpoint...", ranks=[0])
|
||||
save_checkpoint(checkpoint_path, epoch, model)
|
||||
logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0])
|
||||
|
||||
if args.output_dir is not None:
|
||||
checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt'
|
||||
logger.info(f" Saving final checkpoint...", ranks=[0])
|
||||
save_checkpoint(checkpoint_path, epoch, model)
|
||||
logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0])
|
||||
|
||||
logger.info("Training finished", ranks=[0])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
#!/usr/bin/env bash
|
||||
set -x
|
||||
|
||||
export BATCH_SIZE=4
|
||||
export MODEL="facebook/opt-350m"
|
||||
export NUMBER_OF_GPUS=1
|
||||
export OUTPUT_DIR="checkpoints"
|
||||
LOG_NAME=$(date "+%Y-%m-%d_%H-%M-%S")
|
||||
|
||||
# Set HuggingFace Datasets to offline mode by default: since we're using local
|
||||
# JSON files, hitting their servers means something went wrong. If you're doing
|
||||
# something else, adjust this accordingly.
|
||||
export HF_DATASETS_OFFLINE=1
|
||||
|
||||
# HuggingFace transformers should be allowed to hit their servers though, to
|
||||
# download pre-trained models during the first execution for example.
|
||||
# export TRANSFORMERS_OFFLINE=1
|
||||
|
||||
mkdir -p "$OUTPUT_DIR/logs"
|
||||
mkdir -p "$OUTPUT_DIR/runs"
|
||||
|
||||
torchrun \
|
||||
--nproc_per_node ${NUMBER_OF_GPUS} \
|
||||
--master_port 19198 \
|
||||
./colossalai/run_clm.py \
|
||||
--train_file "./data/train.json" \
|
||||
--learning_rate "2e-5" \
|
||||
--checkpointing_steps 64 \
|
||||
--mem_cap 0 \
|
||||
--model_name_or_path "$MODEL" \
|
||||
--output_dir "$OUTPUT_DIR" \
|
||||
--per_device_eval_batch_size "$BATCH_SIZE" \
|
||||
--per_device_train_batch_size "$BATCH_SIZE" "$@" \
|
||||
2>&1 | tee "$OUTPUT_DIR/logs/$LOG_NAME.log"
|
||||
Loading…
Reference in New Issue