mirror of https://github.com/hpcaitech/ColossalAI
[example] simplify opt example (#2344)
Jiarui Fang
GitHub
parent
7080a8edb0
commit
35e22be2f6
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@ -5,7 +5,6 @@ from time import time
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import psutil
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import psutil
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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from model_zoo import model_builder
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from packaging import version
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from packaging import version
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.nn.parallel import DistributedDataParallel as DDP
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from utils import get_data, get_tflops
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from utils import get_data, get_tflops
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@ -16,6 +15,7 @@ from colossalai.nn.parallel import ZeroDDP
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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
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from colossalai.utils import get_current_device
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from colossalai.utils import get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from model_zoo import model_builder
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CAI_VERSION = colossalai.__version__
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CAI_VERSION = colossalai.__version__
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@ -29,24 +29,5 @@ We adapt the OPT training code to ColossalAI by leveraging Gemini and ZeRO DDP.
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You can launch training by using the following bash script
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You can launch training by using the following bash script
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```bash
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```bash
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bash ./run_clm.sh <batch-size-per-gpu> <mem-cap> <model> <gpu-num>
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bash ./run_gemini.sh
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```
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```
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- batch-size-per-gpu: number of samples fed to each GPU, default is 16
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- mem-cap: limit memory usage within a value in GB, default is 0 (no limit)
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- model: the size of the OPT model, default is `6.7b`. Acceptable values include `125m`, `350m`, `1.3b`, `2.7b`, `6.7`, `13b`, `30b`, `66b`. For `175b`, you can request
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the pretrained weights from [OPT weight downloading page](https://github.com/facebookresearch/metaseq/tree/main/projects/OPT).
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- gpu-num: the number of GPUs to use, default is 1.
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## Remarkable Performance
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On a single GPU, Colossal-AI’s automatic strategy provides remarkable performance gains from the ZeRO Offloading strategy by Microsoft DeepSpeed.
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Users can experience up to a 40% speedup, at a variety of model scales. However, when using a traditional deep learning training framework like PyTorch, a single GPU can no longer support the training of models at such a scale.
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT.png" width=1000/>
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</p>
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Adopting the distributed training strategy with 8 GPUs is as simple as adding a `-nprocs 8` to the training command of Colossal-AI!
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More details about behind the scenes can be found on the corresponding [blog](https://medium.com/@yangyou_berkeley/colossal-ai-seamlessly-accelerates-large-models-at-low-costs-with-hugging-face-4d1a887e500d),
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and a detailed tutorial will be added in [Documentation](https://www.colossalai.org/docs/get_started/installation) very soon.
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@ -14,7 +14,7 @@ do
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pkill -9 torchrun
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pkill -9 torchrun
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pkill -9 python
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pkill -9 python
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bash ./run_clm.sh $BS $MEMCAP $MODEL $GPUNUM
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env BS=$BS MEM_CAP=$MEMCAP MODEL=$MODEL GPUNUM=$GPUNUM bash ./run_gemini.sh
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done
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done
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done
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done
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done
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done
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@ -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
|
|
||||||
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(config=dict())
|
|
||||||
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:
|
|
||||||
set_seed(args.seed)
|
|
||||||
logger.info(f"Rank {dist.get_rank()}: random seed is set to {args.seed}")
|
|
||||||
|
|
||||||
# Handle the repository creation
|
|
||||||
with barrier_context():
|
|
||||||
if args.output_dir is not None:
|
|
||||||
os.makedirs(args.output_dir, exist_ok=True)
|
|
||||||
|
|
||||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
|
||||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
|
||||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
|
||||||
#
|
|
||||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
|
||||||
# 'text' is found. You can easily tweak this behavior (see below).
|
|
||||||
#
|
|
||||||
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
|
||||||
# download the dataset.
|
|
||||||
logger.info("Start preparing dataset", ranks=[0])
|
|
||||||
if args.dataset_name is not None:
|
|
||||||
# Downloading and loading a dataset from the hub.
|
|
||||||
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
|
|
||||||
if "validation" not in raw_datasets.keys():
|
|
||||||
raw_datasets["validation"] = load_dataset(
|
|
||||||
args.dataset_name,
|
|
||||||
args.dataset_config_name,
|
|
||||||
split=f"train[:{args.validation_split_percentage}%]",
|
|
||||||
)
|
|
||||||
raw_datasets["train"] = load_dataset(
|
|
||||||
args.dataset_name,
|
|
||||||
args.dataset_config_name,
|
|
||||||
split=f"train[{args.validation_split_percentage}%:]",
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
data_files = {}
|
|
||||||
dataset_args = {}
|
|
||||||
if args.train_file is not None:
|
|
||||||
data_files["train"] = args.train_file
|
|
||||||
if args.validation_file is not None:
|
|
||||||
data_files["validation"] = args.validation_file
|
|
||||||
extension = args.train_file.split(".")[-1]
|
|
||||||
if extension == "txt":
|
|
||||||
extension = "text"
|
|
||||||
dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
|
|
||||||
raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
|
|
||||||
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
|
|
||||||
if "validation" not in raw_datasets.keys():
|
|
||||||
raw_datasets["validation"] = load_dataset(
|
|
||||||
extension,
|
|
||||||
data_files=data_files,
|
|
||||||
split=f"train[:{args.validation_split_percentage}%]",
|
|
||||||
**dataset_args,
|
|
||||||
)
|
|
||||||
raw_datasets["train"] = load_dataset(
|
|
||||||
extension,
|
|
||||||
data_files=data_files,
|
|
||||||
split=f"train[{args.validation_split_percentage}%:]",
|
|
||||||
**dataset_args,
|
|
||||||
)
|
|
||||||
logger.info("Dataset is prepared", ranks=[0])
|
|
||||||
|
|
||||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
|
||||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
|
||||||
|
|
||||||
# Load pretrained model and tokenizer
|
|
||||||
#
|
|
||||||
# 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.model_name_or_path == 'facebook/opt-13b':
|
|
||||||
tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
|
|
||||||
else:
|
|
||||||
print(f'load model from {args.model_name_or_path}')
|
|
||||||
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
|
|
||||||
logger.info(f"{tokenizer.__class__.__name__} 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):
|
|
||||||
model = OPTForCausalLM(config)
|
|
||||||
else:
|
|
||||||
logger.info("Finetune a pre-trained model", ranks=[0])
|
|
||||||
with ColoInitContext(device=init_dev):
|
|
||||||
model = OPTForCausalLM.from_pretrained(args.model_name_or_path,
|
|
||||||
from_tf=bool(".ckpt" in args.model_name_or_path),
|
|
||||||
config=config,
|
|
||||||
local_files_only=False)
|
|
||||||
|
|
||||||
# enable graident checkpointing
|
|
||||||
model.gradient_checkpointing_enable()
|
|
||||||
|
|
||||||
PLACEMENT_POLICY = 'auto'
|
|
||||||
cai_version = colossalai.__version__
|
|
||||||
logger.info(f'using Colossal-AI version {cai_version}')
|
|
||||||
if version.parse(cai_version) > version.parse("0.1.10"):
|
|
||||||
from colossalai.nn.parallel import GeminiDDP
|
|
||||||
model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True)
|
|
||||||
elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
|
|
||||||
from colossalai.gemini import ChunkManager, GeminiManager
|
|
||||||
pg = ProcessGroup()
|
|
||||||
chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
|
|
||||||
chunk_manager = ChunkManager(chunk_size,
|
|
||||||
pg,
|
|
||||||
enable_distributed_storage=True,
|
|
||||||
init_device=GeminiManager.get_default_device(PLACEMENT_POLICY))
|
|
||||||
gemini_manager = GeminiManager(PLACEMENT_POLICY, chunk_manager)
|
|
||||||
model = ZeroDDP(model, gemini_manager)
|
|
||||||
|
|
||||||
logger.info(f'{model.__class__.__name__} has been created', ranks=[0])
|
|
||||||
|
|
||||||
# Preprocessing the datasets.
|
|
||||||
# First we tokenize all the texts.
|
|
||||||
column_names = raw_datasets["train"].column_names
|
|
||||||
text_column_name = "text" if "text" in column_names else column_names[0]
|
|
||||||
|
|
||||||
def tokenize_function(examples):
|
|
||||||
return tokenizer(examples[text_column_name])
|
|
||||||
|
|
||||||
with barrier_context(executor_rank=0, parallel_mode=ParallelMode.DATA):
|
|
||||||
tokenized_datasets = raw_datasets.map(
|
|
||||||
tokenize_function,
|
|
||||||
batched=True,
|
|
||||||
num_proc=args.preprocessing_num_workers,
|
|
||||||
remove_columns=column_names,
|
|
||||||
load_from_cache_file=not args.overwrite_cache,
|
|
||||||
desc="Running tokenizer on dataset",
|
|
||||||
)
|
|
||||||
|
|
||||||
if args.block_size is None:
|
|
||||||
block_size = tokenizer.model_max_length
|
|
||||||
if block_size > 1024:
|
|
||||||
logger.warning(
|
|
||||||
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
|
||||||
"Picking 1024 instead. You can change that default value by passing --block_size xxx.")
|
|
||||||
block_size = 1024
|
|
||||||
else:
|
|
||||||
if args.block_size > tokenizer.model_max_length:
|
|
||||||
logger.warning(f"The block_size passed ({args.block_size}) is larger than the maximum length for the model"
|
|
||||||
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}.")
|
|
||||||
block_size = min(args.block_size, tokenizer.model_max_length)
|
|
||||||
|
|
||||||
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
|
||||||
def group_texts(examples):
|
|
||||||
# Concatenate all texts.
|
|
||||||
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
|
|
||||||
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
|
||||||
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
|
||||||
# customize this part to your needs.
|
|
||||||
if total_length >= block_size:
|
|
||||||
total_length = (total_length // block_size) * block_size
|
|
||||||
# Split by chunks of max_len.
|
|
||||||
result = {
|
|
||||||
k: [t[i:i + block_size] for i in range(0, total_length, block_size)
|
|
||||||
] for k, t in concatenated_examples.items()
|
|
||||||
}
|
|
||||||
result["labels"] = result["input_ids"].copy()
|
|
||||||
return result
|
|
||||||
|
|
||||||
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
|
||||||
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
|
||||||
# to preprocess.
|
|
||||||
#
|
|
||||||
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
|
||||||
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
|
||||||
|
|
||||||
with barrier_context(executor_rank=0, parallel_mode=ParallelMode.DATA):
|
|
||||||
lm_datasets = tokenized_datasets.map(
|
|
||||||
group_texts,
|
|
||||||
batched=True,
|
|
||||||
num_proc=args.preprocessing_num_workers,
|
|
||||||
load_from_cache_file=not args.overwrite_cache,
|
|
||||||
desc=f"Grouping texts in chunks of {block_size}",
|
|
||||||
)
|
|
||||||
|
|
||||||
train_dataset = lm_datasets["train"]
|
|
||||||
eval_dataset = lm_datasets["validation"]
|
|
||||||
|
|
||||||
# Log a few random samples from the training set:
|
|
||||||
# for index in random.sample(range(len(train_dataset)), 3):
|
|
||||||
# logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
||||||
|
|
||||||
# DataLoaders creation:
|
|
||||||
train_dataloader = get_dataloader(train_dataset,
|
|
||||||
shuffle=True,
|
|
||||||
add_sampler=True,
|
|
||||||
collate_fn=default_data_collator,
|
|
||||||
batch_size=args.per_device_train_batch_size)
|
|
||||||
eval_dataloader = DataLoader(eval_dataset,
|
|
||||||
collate_fn=default_data_collator,
|
|
||||||
batch_size=args.per_device_eval_batch_size)
|
|
||||||
logger.info("Dataloaders have been created", ranks=[0])
|
|
||||||
|
|
||||||
# Optimizer
|
|
||||||
# Split weights in two groups, one with weight decay and the other not.
|
|
||||||
no_decay = ["bias", "LayerNorm.weight"]
|
|
||||||
optimizer_grouped_parameters = [
|
|
||||||
{
|
|
||||||
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
|
||||||
"weight_decay": args.weight_decay,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
|
||||||
"weight_decay": 0.0,
|
|
||||||
},
|
|
||||||
]
|
|
||||||
|
|
||||||
optimizer = HybridAdam(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
||||||
optimizer = ZeroOptimizer(optimizer, model, initial_scale=2**14)
|
|
||||||
|
|
||||||
# Scheduler and math around the number of training steps.
|
|
||||||
overrode_max_train_steps = False
|
|
||||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
||||||
if args.max_train_steps is None:
|
|
||||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
||||||
overrode_max_train_steps = True
|
|
||||||
|
|
||||||
lr_scheduler = get_scheduler(
|
|
||||||
name=args.lr_scheduler_type,
|
|
||||||
optimizer=optimizer,
|
|
||||||
num_warmup_steps=args.num_warmup_steps,
|
|
||||||
num_training_steps=args.max_train_steps,
|
|
||||||
)
|
|
||||||
|
|
||||||
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
||||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
||||||
if overrode_max_train_steps:
|
|
||||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
||||||
# Afterwards we recalculate our number of training epochs
|
|
||||||
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
||||||
|
|
||||||
# Train!
|
|
||||||
total_batch_size = args.per_device_train_batch_size * gpc.get_world_size(ParallelMode.DATA)
|
|
||||||
|
|
||||||
logger.info("***** Running training *****", ranks=[0])
|
|
||||||
logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
|
|
||||||
logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
|
|
||||||
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}", ranks=[0])
|
|
||||||
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
|
|
||||||
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}", ranks=[0])
|
|
||||||
logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
|
|
||||||
|
|
||||||
# Only show the progress bar once on each machine.
|
|
||||||
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