diff --git a/training/colossalai/context.py b/training/colossalai/context.py deleted file mode 100644 index 95f0abf..0000000 --- a/training/colossalai/context.py +++ /dev/null @@ -1,32 +0,0 @@ -import torch.distributed as dist - -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc - - -class barrier_context(): - """ - This context manager is used to allow one process to execute while blocking all - other processes in the same process group. This is often useful when downloading is required - as we only want to download in one process to prevent file corruption. - Args: - executor_rank (int): the process rank to execute without blocking, all other processes will be blocked - parallel_mode (ParallelMode): the parallel mode corresponding to a process group - Usage: - with barrier_context(): - dataset = CIFAR10(root='./data', download=True) - """ - - def __init__(self, executor_rank: int = 0, parallel_mode: ParallelMode = ParallelMode.GLOBAL): - # the class name is lowercase by convention - current_rank = gpc.get_local_rank(parallel_mode=parallel_mode) - self.should_block = current_rank != executor_rank - self.group = gpc.get_group(parallel_mode=parallel_mode) - - def __enter__(self): - if self.should_block: - dist.barrier(group=self.group) - - def __exit__(self, exc_type, exc_value, exc_traceback): - if not self.should_block: - dist.barrier(group=self.group) diff --git a/training/colossalai/run_clm.py b/training/colossalai/run_clm.py deleted file mode 100644 index 735ed87..0000000 --- a/training/colossalai/run_clm.py +++ /dev/null @@ -1,686 +0,0 @@ -#!/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 datetime -import math -import os -import re -import signal -import time -from itertools import chain - -import datasets -import torch -import torch.distributed as dist -from accelerate.utils import set_seed -from context import barrier_context -from datasets import load_dataset -from packaging import version -from torch.utils.data import DataLoader -from tqdm.auto import tqdm - -import colossalai -import transformers -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP -from colossalai.tensor import ProcessGroup -from colossalai.utils import get_current_device, get_dataloader, save_checkpoint -from colossalai.utils.model.colo_init_context import ColoInitContext -from transformers import ( - CONFIG_MAPPING, - MODEL_MAPPING, - AutoConfig, - AutoTokenizer, - GPT2Tokenizer, - AutoModelForCausalLM, - SchedulerType, - default_data_collator, - get_scheduler, -) -from transformers.utils.versions import require_version - -# Explanation: "AutoModelForCausalLM" will instantiate the proper subclass after -# ColossalAI has attempted to do a bunch of meta-programming trickery, so it -# crashes due to missing attributes. To work around that, we need to import the -# subclass - even if we don't use it - so ColossalAI properly patches the inner -# modules. -from transformers import ( - BloomForCausalLM, - OPTForCausalLM, - GPTNeoXForCausalLM, -) - -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 parse_args(): - parser = colossalai.get_default_parser() - parser.add_argument( - "--dataset_name", - type=str, - default=None, - help="The name of the dataset to use (via the datasets library).", - ) - parser.add_argument( - "--dataset_config_name", - type=str, - default=None, - help="The configuration name of the dataset to use (via the datasets library).", - ) - parser.add_argument("--train_file", - type=str, - default=None, - help="A csv or a json file containing the training data.") - parser.add_argument("--validation_file", - type=str, - default=None, - help="A csv or a json file containing the validation data.") - parser.add_argument( - "--validation_split_percentage", - default=5, - help="The percentage of the train set used as validation set in case there's no validation split", - ) - 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( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--use_slow_tokenizer", - action="store_true", - help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", - ) - parser.add_argument( - "--per_device_train_batch_size", - type=int, - default=8, - help="Batch size (per device) for the training dataloader.", - ) - parser.add_argument( - "--per_device_eval_batch_size", - type=int, - default=8, - help="Batch size (per device) for the evaluation 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("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--lr_scheduler_type", - type=SchedulerType, - default="linear", - help="The scheduler type to use.", - choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], - ) - parser.add_argument("--num_warmup_steps", - type=int, - default=0, - help="Number of steps for the warmup in the lr scheduler.") - parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") - 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( - "--block_size", - type=int, - default=None, - help=("Optional input sequence length after tokenization. The training dataset will be truncated in block of" - " this size for training. Default to the model max input length for single sentence inputs (take into" - " account special tokens)."), - ) - parser.add_argument( - "--preprocessing_num_workers", - type=int, - default=None, - help="The number of processes to use for the preprocessing.", - ) - parser.add_argument("--overwrite_cache", - type=bool, - default=False, - help="Overwrite the cached training and evaluation sets") - parser.add_argument("--no_keep_linebreaks", - action="store_true", - help="Do not keep line breaks when using TXT files.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_model_id", - type=str, - help="The name of the repository to keep in sync with the local `output_dir`.") - parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--checkpointing_steps", - type=str, - default=None, - help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", - ) - parser.add_argument("-r", - "--resume_from_checkpoint", - type=str, - default=None, - help="If the training should continue from a checkpoint folder.", - ) - parser.add_argument( - "--comment", type=str, help="Experiment comment for the Tensorboard writer." - ) - # NOTE(11b): These last two are useless. - parser.add_argument( - "--with_tracking", - action="store_true", - help="Whether to enable experiment trackers for logging.", - ) - parser.add_argument( - "--report_to", - type=str, - default="all", - help=('The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' - ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.' - "Only applicable when `--with_tracking` is passed."), - ) - - 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() - - # Sanity checks - if args.dataset_name is None and args.train_file is None and args.validation_file is None: - raise ValueError("Need either a dataset name or a training/validation file.") - else: - if args.train_file is not None: - extension = args.train_file.split(".")[-1] - assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." - if args.validation_file is not None: - extension = args.validation_file.split(".")[-1] - assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." - - if args.push_to_hub: - assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." - - 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(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 = AutoModelForCausalLM(config) - else: - logger.info("Finetune a pre-trained model", ranks=[0]) - with ColoInitContext(device=init_dev): - model = AutoModelForCausalLM.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, - ) - - if args.resume_from_checkpoint is not None: - # FIXME(11b): Implement this properly. Need to save/restore all the other - # state as well (optimizer, LR scheduler, dataloader position via step counter...) - logger.info(f"Resuming from checkpoint {args.resume_from_checkpoint}", ranks=[0]) - colossalai.utils.load_checkpoint(args.resume_from_checkpoint, model, optimizer, lr_scheduler) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # Train! - total_batch_size = args.per_device_train_batch_size * gpc.get_world_size(ParallelMode.DATA) - - logger.info("***** Running training *****", ranks=[0]) - logger.info(f" Num examples = {len(train_dataset)}", ranks=[0]) - logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0]) - logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}", ranks=[0]) - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0]) - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}", ranks=[0]) - logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0]) - - now = datetime.datetime.now() - run_name = now.strftime("%Y-%m-%dT_%H-%M-%S%z") - writer = torch.utils.tensorboard.SummaryWriter(log_dir=f"{args.output_dir}/runs/{run_name}", comment=args.comment) - - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not is_main_process) - completed_steps = 0 - starting_epoch = 0 - global_step = 0 - - step_from_checkpoint = 0 - if args.resume_from_checkpoint is not None: - step_from_checkpoint = int(re.findall(r"epoch_\d+_step_(\d+).pt", args.resume_from_checkpoint)[0]) - - for epoch in range(starting_epoch, args.num_train_epochs): - - if completed_steps >= args.max_train_steps: - break - - model.train() - for step, batch in enumerate(train_dataloader): - if step < step_from_checkpoint: - completed_steps += 1 - global_step += 1 - progress_bar.update(1) - progress_bar.refresh() - - # Apparently ColossalAI's checkpoint utilities don't work - # correctly for saving/restore the LR scheduler? So we "step" it - # manually here. - lr_scheduler.step() - continue - - batch = {k: v.cuda() for k, v in batch.items()} - outputs = model(use_cache=False, **batch) # Caching is incompatible with gradient checkpointing. - loss = outputs['loss'] - optimizer.backward(loss) - - if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - progress_bar.update(1) - completed_steps += 1 - - global_step += 1 - logger.info("Global step {} finished".format(global_step + 1), ranks=[0]) - - try: - train_perplexity = math.exp(loss) - except OverflowError: - train_perplexity = float("inf") - writer.add_scalar("Train/Perplexity (Step)", train_perplexity, global_step) - writer.add_scalar("Train/Loss (Step)", loss, global_step) - writer.add_scalar("Train/Learning Rate (Step)", lr_scheduler.get_last_lr()[-1], global_step) - - if args.output_dir is not None and args.checkpointing_steps is not None: - if args.checkpointing_steps != "epoch" and completed_steps % int(args.checkpointing_steps) == 0: - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving iter checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - if completed_steps % (int(args.checkpointing_steps) * 8) == 0: - # Evaluate every X checkpoints. - model.eval() - losses = [] - for step, batch in enumerate(eval_dataloader): - with torch.no_grad(): - batch = {k: v.cuda() for k, v in batch.items()} - outputs = model(**batch) - - loss = outputs['loss'].unsqueeze(0) - losses.append(loss) - - losses = torch.cat(losses) - losses = losses[:len(eval_dataset)] - try: - eval_loss = torch.mean(losses) - perplexity = math.exp(eval_loss) - except OverflowError: - perplexity = float("inf") - logger.info(f"Step {global_step}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0]) - model.train() - - if completed_steps >= args.max_train_steps: - break - - # Evaluate per epoch. - model.eval() - losses = [] - for step, batch in enumerate(eval_dataloader): - with torch.no_grad(): - batch = {k: v.cuda() for k, v in batch.items()} - outputs = model(**batch) - - loss = outputs['loss'].unsqueeze(0) - losses.append(loss) - - losses = torch.cat(losses) - losses = losses[:len(eval_dataset)] - try: - eval_loss = torch.mean(losses) - perplexity = math.exp(eval_loss) - except OverflowError: - perplexity = float("inf") - - logger.info(f"Epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0]) - # TODO(11b): This messes up the intra-epoch graphs. Apparently I need to - # read up on the Tensorboard docs to do this properly. Ignoring for now. - # writer.add_scalar("Eval/Loss (Global Step)", eval_loss, completed_steps) - # writer.add_scalar("Eval/Perplexity (Global Step)", perplexity, completed_steps) - - if args.output_dir is not None and args.checkpointing_steps == "epoch": - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving epoch checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - if args.output_dir is not None: - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving final checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - logger.info("Training finished", ranks=[0]) - - -if __name__ == "__main__": - main() diff --git a/training/colossalai/run_sft.py b/training/colossalai/run_sft.py deleted file mode 100644 index c9ad7f0..0000000 --- a/training/colossalai/run_sft.py +++ /dev/null @@ -1,733 +0,0 @@ -#!/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 datetime -import math -import os -import signal -import time -from itertools import chain - -import datasets -import torch -import torch.distributed as dist -from accelerate.utils import set_seed -from context import barrier_context -from datasets import load_dataset -from packaging import version -from torch.utils.data import DataLoader -from tqdm.auto import tqdm - -import colossalai -import transformers -from colossalai.context import ParallelMode -from colossalai.core import global_context as gpc -from colossalai.logging import disable_existing_loggers, get_dist_logger -from colossalai.nn.optimizer import HybridAdam -from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer -from colossalai.nn.parallel import ZeroDDP -from colossalai.tensor import ProcessGroup -from colossalai.utils import get_current_device, get_dataloader, save_checkpoint -from colossalai.utils.model.colo_init_context import ColoInitContext -from transformers import ( - CONFIG_MAPPING, - MODEL_MAPPING, - AutoConfig, - AutoTokenizer, - GPT2Tokenizer, - AutoModelForCausalLM, - SchedulerType, - default_data_collator, - get_scheduler, -) -from transformers.utils.versions import require_version - -# Explanation: "AutoModelForCausalLM" will instantiate the proper subclass after -# ColossalAI has attempted to do a bunch of meta-programming trickery, so it -# crashes due to missing attributes. To work around that, we need to import the -# subclass - even if we don't use it - so ColossalAI properly patches the inner -# modules. -from transformers import ( - BloomForCausalLM, - OPTForCausalLM, - GPTNeoXForCausalLM, -) - -import re - -# haru SFT stuff -from harubaru_convogpt.dataset import SFTDataset -from harubaru_convogpt.sft import sft_forward - -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 parse_args(): - parser = colossalai.get_default_parser() - parser.add_argument( - "--dataset_name", - type=str, - default=None, - help="The name of the dataset to use (via the datasets library).", - ) - parser.add_argument( - "--dataset_config_name", - type=str, - default=None, - help="The configuration name of the dataset to use (via the datasets library).", - ) - parser.add_argument("--train_file", - type=str, - default=None, - help="A csv or a json file containing the training data.") - parser.add_argument("--validation_file", - type=str, - default=None, - help="A csv or a json file containing the validation data.") - parser.add_argument( - "--validation_split_percentage", - default=5, - help="The percentage of the train set used as validation set in case there's no validation split", - ) - 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( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--use_slow_tokenizer", - action="store_true", - help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", - ) - parser.add_argument( - "--per_device_train_batch_size", - type=int, - default=8, - help="Batch size (per device) for the training dataloader.", - ) - parser.add_argument( - "--per_device_eval_batch_size", - type=int, - default=8, - help="Batch size (per device) for the evaluation 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("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--lr_scheduler_type", - type=SchedulerType, - default="linear", - help="The scheduler type to use.", - choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], - ) - parser.add_argument("--num_warmup_steps", - type=int, - default=0, - help="Number of steps for the warmup in the lr scheduler.") - parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") - 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( - "--block_size", - type=int, - default=None, - help=("Optional input sequence length after tokenization. The training dataset will be truncated in block of" - " this size for training. Default to the model max input length for single sentence inputs (take into" - " account special tokens)."), - ) - parser.add_argument( - "--preprocessing_num_workers", - type=int, - default=None, - help="The number of processes to use for the preprocessing.", - ) - parser.add_argument("--overwrite_cache", - type=bool, - default=False, - help="Overwrite the cached training and evaluation sets") - parser.add_argument("--no_keep_linebreaks", - action="store_true", - help="Do not keep line breaks when using TXT files.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_model_id", - type=str, - help="The name of the repository to keep in sync with the local `output_dir`.") - parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--checkpointing_steps", - type=str, - default=None, - help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", - ) - parser.add_argument("-r", - "--resume_from_checkpoint", - type=str, - default=None, - help="If the training should continue from a checkpoint folder.", - ) - parser.add_argument( - "--comment", type=str, help="Experiment comment for the Tensorboard writer." - ) - # NOTE(11b): These last two are useless. - parser.add_argument( - "--with_tracking", - action="store_true", - help="Whether to enable experiment trackers for logging.", - ) - parser.add_argument( - "--report_to", - type=str, - default="all", - help=('The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' - ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.' - "Only applicable when `--with_tracking` is passed."), - ) - - 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() - - # Sanity checks - if args.dataset_name is None and args.train_file is None and args.validation_file is None: - raise ValueError("Need either a dataset name or a training/validation file.") - else: - if args.train_file is not None: - extension = args.train_file.split(".")[-1] - assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file." - if args.validation_file is not None: - extension = args.validation_file.split(".")[-1] - assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file." - - if args.push_to_hub: - assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." - - 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(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 = AutoModelForCausalLM(config) - else: - logger.info("Finetune a pre-trained model", ranks=[0]) - with ColoInitContext(device=init_dev): - model = AutoModelForCausalLM.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"] - - tokenizer.pad_token = tokenizer.eos_token - # tokenizer.add_special_tokens({'pad_token': '[PAD]'}) - - train_dataset = SFTDataset(args.train_file, tokenizer) - eval_dataset = SFTDataset(args.validation_file, tokenizer) - - # 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]}.") - - def collate_fn(batches): - input_ids = [ - batch["input_ids"].squeeze(0) for batch in batches - ] - # padded_tokens = {"input_ids": input_ids} - padded_tokens = tokenizer.pad( - {"input_ids": input_ids}, return_tensors="pt", padding=True - ) - start_positions = torch.stack( - [batch["start_positions"] for batch in batches] - ) - end_positions = torch.stack( - [batch["end_positions"] for batch in batches] - ) - return { - "input_ids": padded_tokens["input_ids"], - "attention_mask": padded_tokens["attention_mask"], - "start_positions": start_positions, - "end_positions": end_positions, - } - - # DataLoaders creation: - train_dataloader = get_dataloader(train_dataset, - shuffle=True, - add_sampler=True, - collate_fn=collate_fn, - batch_size=args.per_device_train_batch_size) - eval_dataloader = DataLoader(eval_dataset, - collate_fn=collate_fn, - batch_size=args.per_device_eval_batch_size) - logger.info("Dataloaders have been created", ranks=[0]) - - # Optimizer - # Split weights in two groups, one with weight decay and the other not. - no_decay = ["bias", "LayerNorm.weight"] - optimizer_grouped_parameters = [ - { - "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], - "weight_decay": args.weight_decay, - }, - { - "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], - "weight_decay": 0.0, - }, - ] - - optimizer = HybridAdam(optimizer_grouped_parameters, lr=args.learning_rate) - optimizer = ZeroOptimizer(optimizer, model, initial_scale=2**14) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - name=args.lr_scheduler_type, - optimizer=optimizer, - num_warmup_steps=args.num_warmup_steps, - num_training_steps=args.max_train_steps, - ) - - if args.resume_from_checkpoint is not None: - # FIXME(11b): Implement this properly. Need to save/restore all the other - # state as well (optimizer, LR scheduler, dataloader position via step counter...) - logger.info(f"Resuming from checkpoint {args.resume_from_checkpoint}", ranks=[0]) - colossalai.utils.load_checkpoint(args.resume_from_checkpoint, model, optimizer, lr_scheduler) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # Train! - total_batch_size = args.per_device_train_batch_size * gpc.get_world_size(ParallelMode.DATA) - - logger.info("***** Running training *****", ranks=[0]) - logger.info(f" Num examples = {len(train_dataset)}", ranks=[0]) - logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0]) - logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}", ranks=[0]) - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0]) - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}", ranks=[0]) - logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0]) - - now = datetime.datetime.now() - run_name = now.strftime("%Y-%m-%dT_%H-%M-%S%z") - writer = torch.utils.tensorboard.SummaryWriter(log_dir=f"{args.output_dir}/runs/{run_name}", comment=args.comment) - - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(args.max_train_steps), disable=not is_main_process) - completed_steps = 0 - starting_epoch = 0 - global_step = 0 - - step_from_checkpoint = 0 - if args.resume_from_checkpoint is not None: - step_from_checkpoint = int(re.findall(r"epoch_\d+_step_(\d+).pt", args.resume_from_checkpoint)[0]) - - # Add supervised finetuning forward method to model - model.sft_forward = sft_forward.__get__(model) - - for epoch in range(starting_epoch, args.num_train_epochs): - - if completed_steps >= args.max_train_steps: - break - - model.train() - for step, batch in enumerate(train_dataloader): - if step < step_from_checkpoint: - completed_steps += 1 - global_step += 1 - progress_bar.update(1) - - # Apparently ColossalAI's checkpoint utilities don't work - # correctly for saving/restore the LR scheduler? So we "step" it - # manually here. - lr_scheduler.step() - continue - - batch = {k: v.cuda() for k, v in batch.items()} - # outputs = model.sft_forward(use_cache=False, **batch) # Caching is incompatible with gradient checkpointing. - outputs = model.sft_forward( - input_ids=batch["input_ids"], - attention_mask=batch["attention_mask"], - start_positions=batch["start_positions"], - end_positions=batch["end_positions"], - ) - loss = outputs['loss'] - optimizer.backward(loss) - - if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - progress_bar.update(1) - completed_steps += 1 - - global_step += 1 - logger.info("Global step {} finished".format(global_step + 1), ranks=[0]) - - try: - train_perplexity = math.exp(loss) - except OverflowError: - train_perplexity = float("inf") - writer.add_scalar("Train/Perplexity (Step)", train_perplexity, global_step) - writer.add_scalar("Train/Loss (Step)", loss, global_step) - writer.add_scalar("Train/Learning Rate (Step)", lr_scheduler.get_last_lr()[-1], global_step) - - if args.output_dir is not None and args.checkpointing_steps is not None: - if args.checkpointing_steps != "epoch" and completed_steps % int(args.checkpointing_steps) == 0: - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving iter checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - if True and completed_steps % (int(args.checkpointing_steps) * 8) == 0: - # Evaluate every X checkpoints. - model.eval() - losses = [] - for step, batch in enumerate(eval_dataloader): - with torch.no_grad(): - batch = {k: v.cuda() for k, v in batch.items()} - outputs = model.sft_forward(**batch) - - loss = outputs['loss'].unsqueeze(0) - losses.append(loss) - - losses = torch.cat(losses) - losses = losses[:len(eval_dataset)] - try: - eval_loss = torch.mean(losses) - perplexity = math.exp(eval_loss) - except OverflowError: - perplexity = float("inf") - logger.info(f"Step {global_step}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0]) - model.train() - - if completed_steps >= args.max_train_steps: - break - - # Evaluate per epoch. - if False: - model.eval() - losses = [] - for step, batch in enumerate(eval_dataloader): - with torch.no_grad(): - batch = {k: v.cuda() for k, v in batch.items()} - outputs = model(**batch) - - loss = outputs['loss'].unsqueeze(0) - losses.append(loss) - - losses = torch.cat(losses) - losses = losses[:len(eval_dataset)] - try: - eval_loss = torch.mean(losses) - perplexity = math.exp(eval_loss) - except OverflowError: - perplexity = float("inf") - - logger.info(f"Epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}", ranks=[0]) - # TODO(11b): This messes up the intra-epoch graphs. Apparently I need to - # read up on the Tensorboard docs to do this properly. Ignoring for now. - # writer.add_scalar("Eval/Loss (Global Step)", eval_loss, completed_steps) - # writer.add_scalar("Eval/Perplexity (Global Step)", perplexity, completed_steps) - - if args.output_dir is not None and args.checkpointing_steps == "epoch": - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving epoch checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - if args.output_dir is not None: - checkpoint_path = f'{args.output_dir}/epoch_{epoch}_step_{completed_steps}.pt' - logger.info(f" Saving final checkpoint...", ranks=[0]) - save_checkpoint(checkpoint_path, epoch, model, optimizer, lr_scheduler) - logger.info(f" Saved checkpoint to {checkpoint_path}!", ranks=[0]) - - logger.info("Training finished", ranks=[0]) - - -if __name__ == "__main__": - main() diff --git a/training/convert_to_hf.py b/training/convert_to_hf.py deleted file mode 100644 index c9984a0..0000000 --- a/training/convert_to_hf.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python3 - -# Utility to convert ColossalAI checkpoints to a HuggingFace pre-trained model. - -import argparse -import logging - -import transformers -import torch - -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO) - - -def main() -> None: - args = _parse_args_from_argv() - model = _build_model(args) - - output_dir = args.output_dir - logger.info("Saving pre-trained HF model to `%s`...", output_dir) - model.save_pretrained(output_dir) - - -def _parse_args_from_argv() -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument( - "-m", - "--model-name", - default="EleutherAI/pythia-1.3b-deduped", - help="HuggingFace Transformers base model name.", - ) - parser.add_argument( - "-c", - "--checkpoint", - help="Fine-tune checkpoint to load into the base model.", - required=True, - ) - parser.add_argument( - "-o", - "--output-dir", - help="Name of the output folder to save the pre-trained HF model to.", - required=True, - ) - - return parser.parse_args() - - -def _build_model(args: argparse.Namespace) -> transformers.AutoModelForCausalLM: - logger.info(f"Loading checkpoint from `{args.checkpoint}`") - state_dict = torch.load(args.checkpoint, map_location="cuda").pop("model") - - logger.info(f"Loading the `{args.model_name}` model") - model = transformers.AutoModelForCausalLM.from_pretrained( - args.model_name, state_dict=state_dict) - model.eval().half() # .to("cuda") - - return model - - -if __name__ == "__main__": - main() diff --git a/training/data/.keep b/training/data/.keep deleted file mode 100644 index e69de29..0000000 diff --git a/training/finetune.bash b/training/finetune.bash deleted file mode 100755 index 7738d02..0000000 --- a/training/finetune.bash +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env bash -set -x - -export BATCH_SIZE=4 -export MODEL="facebook/opt-350m" -export NUMBER_OF_GPUS=1 -export OUTPUT_DIR="checkpoints" -LOG_NAME=$(date "+%Y-%m-%d_%H-%M-%S") - -# Set HuggingFace Datasets to offline mode by default: since we're using local -# JSON files, hitting their servers means something went wrong. If you're doing -# something else, adjust this accordingly. -export HF_DATASETS_OFFLINE=1 - -# HuggingFace transformers should be allowed to hit their servers though, to -# download pre-trained models during the first execution for example. -# export TRANSFORMERS_OFFLINE=1 - -mkdir -p "$OUTPUT_DIR/logs" -mkdir -p "$OUTPUT_DIR/runs" - -torchrun \ - --nproc_per_node ${NUMBER_OF_GPUS} \ - --master_port 19198 \ - ./colossalai/run_clm.py \ - --train_file "./data/train.json" \ - --learning_rate "2e-5" \ - --checkpointing_steps 64 \ - --mem_cap 0 \ - --model_name_or_path "$MODEL" \ - --output_dir "$OUTPUT_DIR" \ - --per_device_eval_batch_size "$BATCH_SIZE" \ - --per_device_train_batch_size "$BATCH_SIZE" "$@" \ - 2>&1 | tee "$OUTPUT_DIR/logs/$LOG_NAME.log" diff --git a/training/harubaru_convogpt/dataset.py b/training/harubaru_convogpt/dataset.py deleted file mode 100644 index e63b653..0000000 --- a/training/harubaru_convogpt/dataset.py +++ /dev/null @@ -1,137 +0,0 @@ -import os -import struct -import torch -import argparse -import numpy as np -import transformers -import json -from typing import Tuple - -def decode(in_file: str, out_file: str, tokenizer: transformers.AutoTokenizer) -> int: - mem = np.memmap(in_file, mode="r", dtype="uint16") - tokens = len(mem) - with open(out_file, "a") as f: - for token in mem: - f.write(tokenizer.decode([token])) - return tokens - -def encode(in_file: str, out_file: str, tokenizer: transformers.AutoTokenizer) -> int: - with open(in_file, "r", encoding="utf-8") as f: - text = f.read() - tokens = tokenizer.encode(text) - with open(out_file, "wb") as f: - for token in tokens: - f.write(np.uint16(token)) - return len(tokens) - -class TokenizedDataset(torch.utils.data.Dataset): - """ - Consumes a flat binary file containing 16-bit token serialization, aligned - along `context_length` chunks. - """ - - def __init__(self, path: str, context_length: int = 2048): - file_stat = os.stat(path) - self.file = open(path, 'rb') - self.length = int(file_stat.st_size / 2 / context_length) - self.formatstr = '%sH' % context_length - self.context_length = context_length - length_mb = os.stat(path).st_size / 1024.0 / 1024.0 - num_tokens = self.length * context_length - print(f"DATASET: {path}") - print(f"DATASET SIZE: {length_mb:,.2f}mb, {num_tokens:,} tokens, " - f"{self.length:,} contexts") - - def __len__(self) -> int: - return self.length - - def load(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: - self.seek(idx) - input_ids = torch.tensor( - struct.unpack(self.formatstr, - self.file.read(self.context_length * 2))) - mask = torch.zeros(self.context_length) - return input_ids, mask - - def seek(self, idx): - self.file.seek(self.context_length * idx * 2) - - def __getitem__(self, idx) -> Tuple[torch.Tensor, torch.Tensor]: - return self.load(idx) - -class FeedbackDataset(torch.utils.data.Dataset): - def __init__(self, feedback_file: str, tokenizer: transformers.AutoTokenizer, max_length: int = 512): - self.tokenizer = tokenizer - self.max_length = max_length - self.feedback_file = feedback_file - - with open(feedback_file) as f: - self.feedback = [json.loads(line) for line in f] - - def __len__(self): - return len(self.feedback) - - def __getitem__(self, idx): - feedback = self.feedback[idx] - feedback_input = '\n'.join(feedback["input"].split("\n")[-2:]) - feedback_str = f'{feedback_input} {feedback["output"].lstrip().rstrip()}' - seq = self.tokenizer( - feedback_str, - padding="max_length", - truncation=True, - return_tensors="pt" - ) - reward = torch.tensor([feedback["reward"]]).unsqueeze(0) - return seq, reward - -# sft file example -# { -# "input": "Anonymous: Hi, how are you?\nGPT:", -# "output": " I'm good, how are you?\n", -# "reward": 0.0 -# } -import tqdm -class SFTDataset(torch.utils.data.Dataset): - def __init__(self, sft_file: str, tokenizer: transformers.AutoTokenizer, max_length: int = 2048): - self.tokenizer = tokenizer - self.max_length = max_length - self.sft_file = sft_file - - with open(sft_file) as f: - self.sft = [json.loads(line) for line in f] - - # iterate over sft, removing any that have a reward of 0 - self.sft = [sft for sft in self.sft if sft["reward"] != 0.0] - - # iterate over sft, removing any that have too many tokens - for feedback in tqdm.tqdm(self.sft, desc="Validating SFT"): - inputs = feedback["input"] + f' {feedback["output"].lstrip().rstrip()}\n' - if len(self.tokenizer(inputs).input_ids) > self.max_length: - self.sft.remove(feedback) - print(f"Removed {feedback['output']} due to length") - - def __len__(self): - return len(self.sft) - - def __getitem__(self, idx): - sft = self.sft[idx] - sft_input_tokens = self.tokenizer(sft["input"], return_tensors="pt").input_ids - sft_output_tokens = self.tokenizer(f' {sft["output"].lstrip().rstrip()}\n', return_tensors="pt").input_ids - input_ids = torch.cat([sft_input_tokens, sft_output_tokens], dim=-1) - start_positions = torch.tensor([len(sft_input_tokens[0])]) - end_positions = torch.tensor([len(sft_input_tokens[0]) + len(sft_output_tokens[0]) - 1]) - return { - "input_ids": input_ids, - "start_positions": start_positions, - "end_positions": end_positions, - } - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Dataset Creator') - parser.add_argument('--in_file', type=str, help='input file to use', required=True) - parser.add_argument('--out_file', type=str, help='output file to use', required=True) - parser.add_argument('--model', type=str, help='model tokenizer to use', required=True) - args = parser.parse_args() - - encode(args.in_file, args.out_file, transformers.AutoTokenizer.from_pretrained(args.model)) diff --git a/training/harubaru_convogpt/sft.py b/training/harubaru_convogpt/sft.py deleted file mode 100644 index 9c4e909..0000000 --- a/training/harubaru_convogpt/sft.py +++ /dev/null @@ -1,292 +0,0 @@ -import os -import torch -import accelerate -import tqdm -import time -import argparse -# import wandb - -from .dataset import TokenizedDataset, FeedbackDataset, SFTDataset - -from transformers import AutoModelForCausalLM, AutoTokenizer -from transformers.modeling_outputs import CausalLMOutput - -from typing import Union, Optional - -# Supervised Finetuning: Compute loss between model output and target using start_positions and end_positions -def sft_forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, -) -> Union[torch.Tensor, CausalLMOutput]: - try: - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - except AttributeError: - return_dict = True - - ''' - outputs = self.transformer( - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - logits = self.lm_head(sequence_output) - ''' - outputs = self( - input_ids, - attention_mask=attention_mask, - use_cache=False, - # token_type_ids=token_type_ids, - # position_ids=position_ids, - # head_mask=head_mask, - # inputs_embeds=inputs_embeds, - # output_attentions=output_attentions, - # output_hidden_states=output_hidden_states, - # return_dict=return_dict, - ) - - logits = outputs["logits"] - - answer_logits = logits[:, start_positions[0]:end_positions[0]+1] - answer_input_ids = input_ids[:, start_positions[0]:end_positions[0]+1] - - # compute loss for prompt and answer - loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-1) - shift_answer_logits = answer_logits[..., :-1, :].contiguous() - shift_answer_labels = answer_input_ids[..., 1:].contiguous() - answer_loss = loss_fct(shift_answer_logits.view(-1, answer_logits.size(-1)), shift_answer_labels.view(-1)) - - loss = answer_loss - - if not return_dict: - output = (loss,) + outputs[2:] - return ((loss,) + outputs[2:]) if return_dict else output - - return CausalLMOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - -class SFT_Trainer: - def __init__( - self, - accelerator: accelerate.Accelerator, - model: AutoModelForCausalLM, - tokenizer: AutoTokenizer, - train_dataloader: torch.utils.data.DataLoader, - optimizer: torch.optim.Optimizer, - weight_dtype: torch.dtype, - args: argparse.Namespace, - ) -> None: - self.accelerator = accelerator - self.model = model - self.tokenizer = tokenizer - self.train_dataloader = train_dataloader - self.optimizer = optimizer - self.weight_dtype = weight_dtype - self.args = args - - if accelerator.is_main_process: - self.progress_bar = tqdm.tqdm( - total=self.args.epochs*len(train_dataloader), - desc="Total Steps", - leave=False, - ) - - self.run = wandb.init( - project="convogpt-sftlm", - name=f'{self.args.model}-{self.args.epochs}-{self.args.batch_size}-{self.args.learning_rate}--{int(time.time())}', - config=self.args, - ) - - self.global_step = 0 - - def save_model(self) -> None: - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process: - path = f'{self.args.output_dir}/{self.run.name}' - os.makedirs(path, exist_ok=True) - unwrapped_model = self.accelerator.unwrap_model(self.model) - unwrapped_model.save_pretrained(path, save_function=self.accelerator.save) - - def step(self, batch: dict) -> None: - with self.accelerator.accumulate(self.model): - input_ids = batch['input_ids'] - attention_mask = batch['attention_mask'] - start_positions = batch['start_positions'] - end_positions = batch['end_positions'] - - try: - outputs = sft_forward( - self.model, - input_ids=input_ids, - attention_mask=attention_mask, - start_positions=start_positions, - end_positions=end_positions, - ) - - loss = outputs.loss - self.accelerator.backward(loss) - if self.accelerator.sync_gradients: - self.accelerator.clip_grad_norm_(self.model.parameters(), 1.0) - self.optimizer.step() - self.optimizer.zero_grad() - except RuntimeError as e: - print(f"RuntimeError: {e}") - print(f"input_ids: {input_ids}") - print(f"attention_mask: {attention_mask}") - print(f"start_positions: {start_positions}") - print(f"end_positions: {end_positions}") - print('Skipping batch...') - loss = torch.tensor(float('nan'), device=self.accelerator.device) - - return { - "train/loss": loss.detach().item(), - } - - def train(self) -> None: - self.model.train() - for epoch in range(self.args.epochs): - for _, batch in enumerate(self.train_dataloader): - step_start = time.perf_counter() - - #print(f"####\n{self.tokenizer.decode(batch['input_ids'][0])}\n#{batch['start_positions'][0]}:{batch['end_positions'][0]}\n####") - - metrics = self.step(batch) - - step_end = time.perf_counter() - - if self.accelerator.is_main_process: - rank_samples_per_second = self.args.batch_size / (step_end - step_start) - world_samples_per_second = rank_samples_per_second * self.accelerator.num_processes - - metrics.update({ - "perf/rank_samples_per_second": rank_samples_per_second, - "perf/world_samples_per_second": world_samples_per_second, - "train/epoch": epoch, - "train/step": self.global_step, - "train/samples_seen": self.global_step * self.args.batch_size, - }) - - self.global_step += 1 - - self.progress_bar.update(1) - self.progress_bar.set_postfix(**metrics) - - self.run.log(metrics, step=self.global_step) - - if self.global_step % self.args.save_steps == 0: - self.save_model() - self.accelerator.wait_for_everyone() - self.save_model() - -def main() -> None: - - parser = argparse.ArgumentParser(description="Supervised GPT finetuning") - parser.add_argument("--model", type=str, default="hakurei/gpt-j-random-tinier", help="Model name") - parser.add_argument("--dataset", type=str, default="train.jsonl", help="Training file") - parser.add_argument("--output_dir", type=str, default="output", help="Output directory") - parser.add_argument("--epochs", type=int, default=1, help="Number of epochs") - parser.add_argument("--batch_size", type=int, default=1, help="Batch size") - parser.add_argument("--save_steps", type=int, default=1000, help="Save model every x steps") - parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate") - args = parser.parse_args() - - accelerator = accelerate.Accelerator() - accelerate.utils.set_seed(42) - - tokenizer = AutoTokenizer.from_pretrained(args.model) - tokenizer.pad_token = tokenizer.eos_token - - def collate_fn(batches): - input_ids = [ - batch["input_ids"].squeeze(0) for batch in batches - ] - padded_tokens = tokenizer.pad( - {"input_ids": input_ids}, return_tensors="pt", padding=True - ) - start_positions = torch.stack( - [batch["start_positions"] for batch in batches] - ) - end_positions = torch.stack( - [batch["end_positions"] for batch in batches] - ) - return { - "input_ids": padded_tokens["input_ids"], - "attention_mask": padded_tokens["attention_mask"], - "start_positions": start_positions, - "end_positions": end_positions, - } - - train_dataset = SFTDataset(args.dataset, tokenizer) - - train_dataloader = torch.utils.data.DataLoader( - train_dataset, - batch_size=args.batch_size, - shuffle=True, - collate_fn=collate_fn, - ) - - model = AutoModelForCausalLM.from_pretrained(args.model) - optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate) - - model, optimizer, train_dataloader = accelerator.prepare( - model, optimizer, train_dataloader - ) - - trainer = SFT_Trainer( - accelerator=accelerator, - model=model, - tokenizer=tokenizer, - train_dataloader=train_dataloader, - optimizer=optimizer, - weight_dtype=None, - args=args, - ) - - trainer.train() - -if __name__ == '__main__': - """ - # Load model and tokenizer - model = AutoModelForCausalLM.from_pretrained('distilgpt2') - tokenizer = AutoTokenizer.from_pretrained('distilgpt2') - - # Add supervised finetuning forward method to model - model.forward = sft_forward.__get__(model) - - # Create input tensors - question = 'What is the capital of France?' - answer = 'The capital of France is Paris.' - question_tokens = tokenizer.encode(question, return_tensors='pt') - answer_tokens = tokenizer.encode(answer, return_tensors='pt') - input_ids = torch.cat([question_tokens, answer_tokens], dim=-1) - - start_positions = torch.tensor([len(question_tokens[0])]) - end_positions = torch.tensor([len(question_tokens[0]) + len(answer_tokens[0]) - 1]) - - # Compute loss - loss = model(input_ids, start_positions=start_positions, end_positions=end_positions).loss - print(loss) - """ - main() diff --git a/training/inference.py b/training/inference.py deleted file mode 100755 index a44a1f5..0000000 --- a/training/inference.py +++ /dev/null @@ -1,286 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import logging -import typing as t -import re - -import torch -import transformers -import gradio as gr - -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.DEBUG) - -# TODO(11b): Type these functions up properly. - - -def main() -> None: - '''Script entrypoint.''' - args = _parse_args_from_argv() - # TODO(11b): We don't have the bot name at this point, since it's dynamic - # on the UI, so we can't build `bad_word_ids` as perfectly as I'd like. See - # if we can improve this later. - model, tokenizer = _build_model_and_tokenizer_for(args, bot_name="") - ui = _build_gradio_ui_for(model, tokenizer) - ui.launch(server_port=3000, share=False) - - -def _parse_args_from_argv() -> argparse.Namespace: - '''Parses arguments coming in from the command line.''' - parser = argparse.ArgumentParser() - parser.add_argument( - "-m", - "--model-name", - default="facebook/opt-350m", - help="HuggingFace Transformers model name.", - ) - parser.add_argument( - "-c", - "--checkpoint", - help="Fine-tune checkpoint to load into the base model. Optional.", - ) - - return parser.parse_args() - - -def _build_blacklist_for(bot_name: str) -> list[str]: - ''' - Builds a blacklist for the given bot name. - - This is used to stop the model from invoking modules when we haven't - prompted it to. - ''' - - # NOTE(11b): This should _ideally_ be shared with the actual implementations - # inside the package's .core.consts, but for simplicity's sake I'm - # re-implementing here (so there's no need to install the package just to - # run inference). - pdm_prefix = f"{bot_name}'s Persona: " - - # Not sure why, but the pre-trained OPT likes to generate these and it leaks - # out to the fine-tuned models as well. - bad_opt_generations = ["___", "____", "_____"] - - # And Pythia likes to do this. - bad_pythia_generations = ["...."] - - return [pdm_prefix, *bad_opt_generations, *bad_pythia_generations] - - -def _build_model_and_tokenizer_for(args: argparse.Namespace, - bot_name: str) -> t.Tuple[t.Any, t.Any]: - '''Sets up the model and accompanying tokenizer.''' - logger.info(f"Loading tokenizer for {args.model_name}") - tokenizer = transformers.AutoTokenizer.from_pretrained(args.model_name) - - state_dict = None - if args.checkpoint is not None: - logger.info(f"Loading checkpoint from {args.checkpoint}") - - # NOTE(11b): `.pop("model")` is specific to checkpoints saved by - # the ColossalAI helper. If using a regular HF Transformers checkpoint, - # comment that out. - state_dict = torch.load(args.checkpoint, - map_location="cuda").pop("model") - - tokenizer_kwargs = {"add_special_tokens": False} - if "facebook/opt-" in args.model_name: - tokenizer_kwargs["add_prefix_space"] = True - - bad_words_ids = [ - tokenizer(bad_word, **tokenizer_kwargs).input_ids - for bad_word in _build_blacklist_for(bot_name) - ] - - logger.info(f"Loading the {args.model_name} model") - model = transformers.AutoModelForCausalLM.from_pretrained( - args.model_name, state_dict=state_dict, bad_words_ids=bad_words_ids) - model.eval().half().to("cuda") - - logger.info("Model and tokenizer are ready") - return model, tokenizer - - -def _run_raw_inference(model: t.Any, tokenizer: t.Any, prompt: str, - user_message: str) -> str: - '''Runs raw inference on the model, and returns just the generated text.''' - - # First, sampling-based generation. - input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to("cuda") - logits = model.generate( - input_ids, - do_sample=True, - max_new_tokens=32, - top_k=50, - top_p=0.90, - ) - output = tokenizer.decode(logits[0], skip_special_tokens=True) - - # Then, contrastive search. - input_ids = tokenizer(output, return_tensors="pt").input_ids.to("cuda") - logits = model.generate(input_ids, - max_new_tokens=96, - penalty_alpha=0.6, - top_k=6) - - # Then, we trim out the input prompt from the generated output. - output = tokenizer.decode(logits[0], skip_special_tokens=True) - if (idx := prompt.rfind(user_message)) != -1: - trimmed_output = output[idx + len(user_message):].strip() - return trimmed_output - else: - raise ValueError("Couldn't find user message in the prompt. What?") - - -BAD_CHARS_FOR_REGEX_REGEX = re.compile(r"[-\/\\^$*+?.()|[\]{}]") - - -def _sanitize_string_for_use_in_a_regex(string: str) -> str: - '''Sanitizes `string` so it can be used inside of a regexp.''' - return BAD_CHARS_FOR_REGEX_REGEX.sub(r"\\\g<0>", string) - - -def _parse_messages_from_str(string: str, names: list[str]) -> list[str]: - ''' - Given a big string containing raw chat history, this function attempts to - parse it out into a list where each item is an individual message. - ''' - sanitized_names = [ - _sanitize_string_for_use_in_a_regex(name) for name in names - ] - - speaker_regex = re.compile(rf"^({'|'.join(sanitized_names)}): ", - re.MULTILINE) - - message_start_indexes = [] - for match in speaker_regex.finditer(string): - message_start_indexes.append(match.start()) - - if len(message_start_indexes) < 2: - # Single message in the string. - return [string.strip()] - - prev_start_idx = message_start_indexes[0] - messages = [] - - for start_idx in message_start_indexes[1:]: - message = string[prev_start_idx:start_idx].strip() - messages.append(message) - prev_start_idx = start_idx - - return messages - - -def _serialize_chat_history(history: list[str]) -> str: - '''Given a structured chat history object, collapses it down to a string.''' - return "\n".join(history) - - -def _gr_run_inference(model: t.Any, tokenizer: t.Any, context: str, - history: list[str], character_name: str, - user_message: str) -> t.Tuple[list[str], str]: - ''' - With `context` and `history` prompt-engineered into the model's input, feed - it `user_message` and return everything the Gradio UI expects. - ''' - - # TODO(11b): Lots of assumptions to fix here. We need to make sure - # everything fits, we need to use "You" from the `.core.consts` module, etc. - prompt = "\n".join( - [context, "", *history, f"You: {user_message}", f"{character_name}: "]) - - output = _run_raw_inference(model, tokenizer, prompt, user_message).strip() - logger.debug("_run_raw_inference returned `%s` after .strip()", output) - - # If there's enough space, the model will likely generate more than just its - # own message, so we need to trim that out and just remove the first - # generated message. - generated_messages = _parse_messages_from_str(output, - ["You", character_name]) - logger.debug("Generated messages is `%s`", generated_messages) - bot_message = generated_messages[0] - - logger.info("Generated message: `%s`", bot_message) - - history.append(f"You: {user_message}") - history.append(bot_message) - serialized_history = _serialize_chat_history(history) - return history, serialized_history - - -def _gr_regenerate_last_output(model: t.Any, tokenizer: t.Any, context: str, - history: list[str], character_name: str, - user_message: str) -> t.Tuple[list[str], str]: - history_without_last_message = history[:-2] - return _gr_run_inference(model, tokenizer, context, - history_without_last_message, character_name, - user_message) - - -def _gr_undo(history: list[str]) -> t.Tuple[list[str], str]: - updated_history = history[:-2] - return updated_history, _serialize_chat_history(updated_history) - - -def _build_gradio_ui_for(model: t.Any, tokenizer: t.Any) -> t.Any: - ''' - Builds a Gradio UI to interact with the model. Big thanks to TearGosling for - the initial version of this. - ''' - with gr.Blocks() as interface: - history = gr.State([]) - - with gr.Row(): - with gr.Column(): - user_message = gr.Textbox( - label="Input", - placeholder="Say something here", - interactive=True, - ) - character_name = gr.Textbox( - label="Name of character", - placeholder="Insert the name of your character here", - ) - context = gr.Textbox( - label="Long context", - lines=4, - placeholder= - "Insert the context of your character here, such as personality and scenario. Think of this as akin to CAI's short and long description put together.", - interactive=True, - ) - history_text = gr.Textbox( - label="Output", - lines=4, - placeholder="Your conversation will show up here!", - interactive=False, - ) - - with gr.Row(): - submit_btn = gr.Button("Submit input") - submit_fn = lambda context, history, character_name, user_message: _gr_run_inference( - model, tokenizer, context, history, character_name, user_message - ) - submit_btn.click( - fn=submit_fn, - inputs=[context, history, character_name, user_message], - outputs=[history, history_text]) - - regenerate_btn = gr.Button("Regenerate last output") - regenerate_fn = lambda context, history, character_name, user_message: _gr_regenerate_last_output( - model, tokenizer, context, history, character_name, user_message - ) - regenerate_btn.click( - fn=regenerate_fn, - inputs=[context, history, character_name, user_message], - outputs=[history, history_text]) - - undo_btn = gr.Button("Undo last exchange") - undo_btn.click(fn=_gr_undo, - inputs=[history], - outputs=[history, history_text]) - - return interface - - -if __name__ == "__main__": - main() diff --git a/training/supervised-finetune.bash b/training/supervised-finetune.bash deleted file mode 100644 index 79104c1..0000000 --- a/training/supervised-finetune.bash +++ /dev/null @@ -1,39 +0,0 @@ -#!/usr/bin/env bash -set -x - -export BATCH_SIZE=2 -export MODEL="EleutherAI/pythia-1.3b-deduped" -export NUMBER_OF_GPUS=1 -export OUTPUT_DIR="checkpoints" -LOG_NAME=$(date "+%Y-%m-%d_%H-%M-%S") - -# Set HuggingFace Datasets to offline mode by default: since we're using local -# JSON files, hitting their servers means something went wrong. If you're doing -# something else, adjust this accordingly. -export HF_DATASETS_OFFLINE=1 - -# HuggingFace transformers should be allowed to hit their servers though, to -# download pre-trained models during the first execution for example. -# export TRANSFORMERS_OFFLINE=1 - -mkdir -p "$OUTPUT_DIR/logs" -mkdir -p "$OUTPUT_DIR/runs" - -torchrun \ - --nproc_per_node ${NUMBER_OF_GPUS} \ - --master_port 19198 \ - ./colossalai/run_sft.py \ - --train_file "./data/train.json" \ - --validation_file "./data/eval.json" \ - --learning_rate "5.0e-5" \ - --checkpointing_steps 64 \ - --block_size 1024 \ - --mem_cap 0 \ - --lr_scheduler_type "cosine" \ - --num_warmup_steps 100 \ - --model_name_or_path "$MODEL" \ - --output_dir "$OUTPUT_DIR" \ - --num_train_epochs 1 \ - --per_device_eval_batch_size "$BATCH_SIZE" \ - --per_device_train_batch_size "$BATCH_SIZE" "$@" \ - 2>&1 | tee "$OUTPUT_DIR/logs/$LOG_NAME.log"