mirror of https://github.com/hpcaitech/ColossalAI
Jiarui Fang
2 years ago
committed by
GitHub
10 changed files with 234 additions and 684 deletions
@ -1,6 +0,0 @@
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from colossalai.zero.shard_utils import TensorShardStrategy |
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zero = dict(model_config=dict(shard_strategy=TensorShardStrategy(), |
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tensor_placement_policy="auto", |
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reuse_fp16_shard=True), |
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optimizer_config=dict(gpu_margin_mem_ratio=0.8, initial_scale=16384)) |
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@ -1,32 +0,0 @@
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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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@ -1,6 +0,0 @@
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colossalai |
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torch >= 1.8.1 |
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datasets >= 1.8.0 |
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sentencepiece != 0.1.92 |
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protobuf |
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accelerate == 0.13.2 |
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@ -1,596 +0,0 @@
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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 |
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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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"--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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|
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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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|
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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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|
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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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|
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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]) |
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with ColoInitContext(device=init_dev): |
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model = OPTForCausalLM.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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|
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# enable graident checkpointing |
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model.gradient_checkpointing_enable() |
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|
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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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|
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logger.info(f'{model.__class__.__name__} has been created', ranks=[0]) |
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|
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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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|
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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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|
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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. |
||||
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. |
||||
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(**batch) |
||||
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]) |
||||
|
||||
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]) |
||||
|
||||
if args.output_dir is not None: |
||||
model_state = model.state_dict() |
||||
if is_main_process: |
||||
torch.save(model_state, args.output_dir + '/epoch_{}_model.pth'.format(completed_steps)) |
||||
dist.barrier() |
||||
# load_state = torch.load(args.output_dir + '/epoch_{}_model.pth'.format(completed_steps)) |
||||
# model.load_state_dict(load_state, strict=False) |
||||
|
||||
logger.info("Training finished", ranks=[0]) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
main() |
||||
@ -1,22 +0,0 @@
|
||||
set -x |
||||
export BS=${1:-16} |
||||
export MEMCAP=${2:-0} |
||||
export MODEL=${3:-"125m"} |
||||
export GPUNUM=${4:-1} |
||||
|
||||
# make directory for logs |
||||
mkdir -p ./logs |
||||
|
||||
export MODLE_PATH="facebook/opt-${MODEL}" |
||||
|
||||
# HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 |
||||
torchrun \ |
||||
--nproc_per_node ${GPUNUM} \ |
||||
--master_port 19198 \ |
||||
run_clm.py \ |
||||
--dataset_name wikitext \ |
||||
--dataset_config_name wikitext-2-raw-v1 \ |
||||
--output_dir $PWD \ |
||||
--mem_cap ${MEMCAP} \ |
||||
--model_name_or_path ${MODLE_PATH} \ |
||||
--per_device_train_batch_size ${BS} 2>&1 | tee ./logs/colo_${MODEL}_bs_${BS}_cap_${MEMCAP}_gpu_${GPUNUM}.log |
||||
@ -0,0 +1,20 @@
|
||||
set -x |
||||
export BS=${BS:-16} |
||||
export MEMCAP=${MEMCAP:-0} |
||||
# Acceptable values include `125m`, `350m`, `1.3b`, `2.7b`, `6.7`, `13b`, `30b`, `66b`. For `175b` |
||||
export MODEL=${MODEL:-"125m"} |
||||
export GPUNUM=${GPUNUM:-1} |
||||
|
||||
# make directory for logs |
||||
mkdir -p ./logs |
||||
|
||||
export MODLE_PATH="facebook/opt-${MODEL}" |
||||
|
||||
# HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 |
||||
torchrun \ |
||||
--nproc_per_node ${GPUNUM} \ |
||||
--master_port 19198 \ |
||||
train_gemini_opt.py \ |
||||
--mem_cap ${MEMCAP} \ |
||||
--model_name_or_path ${MODLE_PATH} \ |
||||
--batch_size ${BS} 2>&1 | tee ./logs/colo_${MODEL}_bs_${BS}_cap_${MEMCAP}_gpu_${GPUNUM}.log |
||||
@ -0,0 +1,211 @@
|
||||
#!/usr/bin/env python |
||||
# coding=utf-8 |
||||
# Copyright 2021 The HuggingFace Inc. team. All rights reserved. |
||||
# |
||||
# Licensed under the Apache License, Version 2.0 (the "License"); |
||||
# you may not use this file except in compliance with the License. |
||||
# You may obtain a copy of the License at |
||||
# |
||||
# http://www.apache.org/licenses/LICENSE-2.0 |
||||
# |
||||
# Unless required by applicable law or agreed to in writing, software |
||||
# distributed under the License is distributed on an "AS IS" BASIS, |
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
||||
# See the License for the specific language governing permissions and |
||||
# limitations under the License. |
||||
""" |
||||
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) |
||||
on a text file or a dataset without using HuggingFace Trainer. |
||||
|
||||
Here is the full list of checkpoints on the hub that can be fine-tuned by this script: |
||||
https://huggingface.co/models?filter=text-generation |
||||
""" |
||||
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. |
||||
|
||||
import time |
||||
from functools import partial |
||||
|
||||
import datasets |
||||
import torch |
||||
import torch.distributed as dist |
||||
import transformers |
||||
from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, OPTForCausalLM |
||||
from transformers.utils.versions import require_version |
||||
|
||||
import colossalai |
||||
from colossalai.logging import disable_existing_loggers, get_dist_logger |
||||
from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer |
||||
from colossalai.nn.parallel import GeminiDDP |
||||
from colossalai.utils import get_current_device |
||||
from colossalai.utils.model.colo_init_context import ColoInitContext |
||||
|
||||
|
||||
def get_data(batch_size, seq_len, vocab_size): |
||||
input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device()) |
||||
attention_mask = torch.ones_like(input_ids) |
||||
return input_ids, attention_mask |
||||
|
||||
|
||||
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") |
||||
|
||||
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) |
||||
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) |
||||
|
||||
|
||||
def get_time_stamp(): |
||||
torch.cuda.synchronize() |
||||
return time.time() |
||||
|
||||
|
||||
def get_tflops(model_numel, batch_size, seq_len, step_time): |
||||
return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) |
||||
|
||||
|
||||
def parse_args(): |
||||
parser = colossalai.get_default_parser() |
||||
parser.add_argument( |
||||
"--model_name_or_path", |
||||
type=str, |
||||
help="Path to pretrained model or model identifier from huggingface.co/models.", |
||||
required=True, |
||||
) |
||||
parser.add_argument( |
||||
"--config_name", |
||||
type=str, |
||||
default=None, |
||||
help="Pretrained config name or path if not the same as model_name", |
||||
) |
||||
parser.add_argument( |
||||
"--batch_size", |
||||
type=int, |
||||
default=8, |
||||
help="Batch size (per dp group) for the training dataloader.", |
||||
) |
||||
parser.add_argument( |
||||
"--learning_rate", |
||||
type=float, |
||||
default=5e-5, |
||||
help="Initial learning rate (after the potential warmup period) to use.", |
||||
) |
||||
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") |
||||
parser.add_argument( |
||||
"--max_train_steps", |
||||
type=int, |
||||
default=20, |
||||
help="Total number of training steps to perform.", |
||||
) |
||||
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") |
||||
parser.add_argument( |
||||
"--model_type", |
||||
type=str, |
||||
default=None, |
||||
help="Model type to use if training from scratch.", |
||||
choices=MODEL_TYPES, |
||||
) |
||||
parser.add_argument("--mem_cap", type=int, default=0, help="use mem cap") |
||||
parser.add_argument("--init_in_cpu", action='store_true', default=False, help="init training model in cpu") |
||||
args = parser.parse_args() |
||||
|
||||
return args |
||||
|
||||
|
||||
def colo_memory_cap(size_in_GB): |
||||
from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device |
||||
cuda_capacity = colo_device_memory_capacity(get_current_device()) |
||||
if size_in_GB * (1024**3) < cuda_capacity: |
||||
colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity) |
||||
print("Using {} GB of GPU memory".format(size_in_GB)) |
||||
|
||||
|
||||
def main(): |
||||
args = parse_args() |
||||
disable_existing_loggers() |
||||
colossalai.launch_from_torch({}) |
||||
logger = get_dist_logger() |
||||
is_main_process = dist.get_rank() == 0 |
||||
|
||||
if is_main_process: |
||||
datasets.utils.logging.set_verbosity_warning() |
||||
transformers.utils.logging.set_verbosity_info() |
||||
else: |
||||
datasets.utils.logging.set_verbosity_error() |
||||
transformers.utils.logging.set_verbosity_error() |
||||
|
||||
if args.mem_cap > 0: |
||||
colo_memory_cap(args.mem_cap) |
||||
|
||||
# If passed along, set the training seed now. |
||||
if args.seed is not None: |
||||
torch.mannul_seed(args.seed) |
||||
logger.info(f"Rank {dist.get_rank()}: random seed is set to {args.seed}") |
||||
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at |
||||
# https://huggingface.co/docs/datasets/loading_datasets.html. |
||||
|
||||
# Load pretrained model |
||||
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently |
||||
# download model & vocab. |
||||
if args.config_name: |
||||
config = AutoConfig.from_pretrained(args.config_name) |
||||
elif args.model_name_or_path: |
||||
config = AutoConfig.from_pretrained(args.model_name_or_path) |
||||
else: |
||||
config = CONFIG_MAPPING[args.model_type]() |
||||
logger.warning("You are instantiating a new config instance from scratch.") |
||||
logger.info("Model config has been created", ranks=[0]) |
||||
|
||||
if args.init_in_cpu: |
||||
init_dev = torch.device('cpu') |
||||
else: |
||||
init_dev = get_current_device() |
||||
|
||||
# build model |
||||
if args.model_name_or_path is None or args.model_name_or_path == 'facebook/opt-13b': |
||||
# currently, there has a bug in pretrained opt-13b |
||||
# we can not import it until huggingface fix it |
||||
logger.info("Train a new model from scratch", ranks=[0]) |
||||
with ColoInitContext(device=init_dev, dtype=torch.half): |
||||
model = OPTForCausalLM(config) |
||||
else: |
||||
logger.info("Finetune a pre-trained model", ranks=[0]) |
||||
with ColoInitContext(device=init_dev, dtype=torch.half): |
||||
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() |
||||
|
||||
numel = sum([p.numel() for p in model.parameters()]) |
||||
PLACEMENT_POLICY = 'cpu' |
||||
model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True) |
||||
optimizer = GeminiAdamOptimizer(model, lr=args.learning_rate, initial_scale=2**14, gpu_margin_mem_ratio=0.0) |
||||
|
||||
SEQ_LEN = 1024 |
||||
VOCAB_SIZE = 50257 |
||||
|
||||
get_tflops_func = partial(get_tflops, numel, args.batch_size, SEQ_LEN) |
||||
|
||||
model.train() |
||||
for step in range(args.max_train_steps): |
||||
st_time = time.time() |
||||
input_ids, attn_mask = get_data(args.batch_size, SEQ_LEN, VOCAB_SIZE) |
||||
|
||||
outputs = model(input_ids=input_ids, attention_mask=attn_mask, labels=input_ids, use_cache=False) |
||||
loss = outputs['loss'] |
||||
optimizer.backward(loss) |
||||
|
||||
optimizer.step() |
||||
optimizer.zero_grad() |
||||
torch.cuda.synchronize() |
||||
step_time = time.time() - st_time |
||||
step_tflops = get_tflops_func(step_time) |
||||
|
||||
logger.info("step {} finished, Tflops {}".format(step, step_tflops), ranks=[0]) |
||||
|
||||
logger.info("Training finished", ranks=[0]) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
main() |
||||
Loading…
Reference in new issue