679 lines
29 KiB
Python
679 lines
29 KiB
Python
#!/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 datetime
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import math
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import os
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import signal
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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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AutoModelForCausalLM,
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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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# Explanation: "AutoModelForCausalLM" will instantiate the proper subclass after
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# ColossalAI has attempted to do a bunch of meta-programming trickery, so it
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# crashes due to missing attributes. To work around that, we need to import the
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# subclass - even if we don't use it - so ColossalAI properly patches the inner
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# modules.
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from transformers import (
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BloomForCausalLM,
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OPTForCausalLM,
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GPTNeoXForCausalLM,
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)
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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 = AutoModelForCausalLM(config)
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else:
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logger.info("Finetune a pre-trained model", ranks=[0])
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with ColoInitContext(device=init_dev):
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model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path,
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from_tf=bool(".ckpt" in args.model_name_or_path),
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config=config,
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local_files_only=False)
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# enable graident checkpointing
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model.gradient_checkpointing_enable()
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PLACEMENT_POLICY = 'auto'
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cai_version = colossalai.__version__
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logger.info(f'using Colossal-AI version {cai_version}')
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if version.parse(cai_version) > version.parse("0.1.10"):
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from colossalai.nn.parallel import GeminiDDP
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model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True)
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elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
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from colossalai.gemini import ChunkManager, GeminiManager
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pg = ProcessGroup()
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chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
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chunk_manager = ChunkManager(chunk_size,
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pg,
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enable_distributed_storage=True,
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init_device=GeminiManager.get_default_device(PLACEMENT_POLICY))
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gemini_manager = GeminiManager(PLACEMENT_POLICY, chunk_manager)
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model = ZeroDDP(model, gemini_manager)
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logger.info(f'{model.__class__.__name__} has been created', ranks=[0])
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# Preprocessing the datasets.
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# First we tokenize all the texts.
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column_names = raw_datasets["train"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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def tokenize_function(examples):
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return tokenizer(examples[text_column_name])
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with barrier_context(executor_rank=0, parallel_mode=ParallelMode.DATA):
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tokenized_datasets = raw_datasets.map(
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tokenize_function,
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batched=True,
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num_proc=args.preprocessing_num_workers,
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remove_columns=column_names,
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load_from_cache_file=not args.overwrite_cache,
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desc="Running tokenizer on dataset",
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)
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if args.block_size is None:
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block_size = tokenizer.model_max_length
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if block_size > 1024:
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logger.warning(
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f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
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"Picking 1024 instead. You can change that default value by passing --block_size xxx.")
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block_size = 1024
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else:
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if args.block_size > tokenizer.model_max_length:
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logger.warning(f"The block_size passed ({args.block_size}) is larger than the maximum length for the model"
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f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}.")
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block_size = min(args.block_size, tokenizer.model_max_length)
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# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
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def group_texts(examples):
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# Concatenate all texts.
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concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
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total_length = len(concatenated_examples[list(examples.keys())[0]])
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# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
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# customize this part to your needs.
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if total_length >= block_size:
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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,
|
|
)
|
|
|
|
if args.resume_from_checkpoint is not None:
|
|
# FIXME(11b): Implement this properly. Need to save/restore all the other
|
|
# state as well (optimizer, LR scheduler, dataloader position via step counter...)
|
|
logger.info(f"Resuming from checkpoint {args.resume_from_checkpoint}", ranks=[0])
|
|
colossalai.utils.load_checkpoint(args.resume_from_checkpoint, model, optimizer, lr_scheduler)
|
|
|
|
# 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])
|
|
|
|
now = datetime.datetime.now()
|
|
run_name = now.strftime("%Y-%m-%dT_%H-%M-%S%z")
|
|
writer = torch.utils.tensorboard.SummaryWriter(log_dir=f"{args.output_dir}/runs/{run_name}", comment=args.comment)
|
|
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not is_main_process)
|
|
completed_steps = 0
|
|
starting_epoch = 0
|
|
global_step = 0
|
|
|
|
# FIXME(11b): One needs to manually update this when resuming from a
|
|
# checkpoint. Not ideal.
|
|
step_from_checkpoint = 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):
|
|
if step < step_from_checkpoint:
|
|
completed_steps += 1
|
|
global_step += 1
|
|
continue
|
|
|
|
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, global_step)
|
|
writer.add_scalar("Train/Loss (Step)", loss, global_step)
|
|
writer.add_scalar("Train/Learning Rate (Step)", lr_scheduler.get_last_lr()[-1], global_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, optimizer, lr_scheduler)
|
|
logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0])
|
|
|
|
if completed_steps % (int(args.checkpointing_steps) * 8) == 0:
|
|
# Evaluate every X checkpoints.
|
|
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"Step {global_step}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0])
|
|
model.train()
|
|
|
|
if completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
# Evaluate per epoch.
|
|
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])
|
|
# TODO(11b): This messes up the intra-epoch graphs. Apparently I need to
|
|
# read up on the Tensorboard docs to do this properly. Ignoring for now.
|
|
# writer.add_scalar("Eval/Loss (Global Step)", eval_loss, completed_steps)
|
|
# writer.add_scalar("Eval/Perplexity (Global Step)", perplexity, completed_steps)
|
|
|
|
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, optimizer, lr_scheduler)
|
|
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, optimizer, lr_scheduler)
|
|
logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0])
|
|
|
|
logger.info("Training finished", ranks=[0])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|