From a1650af4a4146073ff74477b1410fa797cd1ebb4 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 26 Jun 2025 13:55:50 -0400 Subject: [PATCH 01/18] Add accelerate example (wip) Signed-off-by: Fabrice Normandin --- .../distributed/accelerate_example/.gitignore | 3 + .../distributed/accelerate_example/README.md | 0 .../accelerate_example/accelerate_launch.sh | 6 + .../distributed/accelerate_example/index.rst | 53 + .../distributed/accelerate_example/job.sh | 12 + .../distributed/accelerate_example/main.py | 528 +++++++ .../accelerate_example/pyproject.toml | 50 + .../distributed/accelerate_example/uv.lock | 1326 +++++++++++++++++ docs/examples/distributed/index.rst | 1 + 9 files changed, 1979 insertions(+) create mode 100644 docs/examples/distributed/accelerate_example/.gitignore create mode 100644 docs/examples/distributed/accelerate_example/README.md create mode 100755 docs/examples/distributed/accelerate_example/accelerate_launch.sh create mode 100644 docs/examples/distributed/accelerate_example/index.rst create mode 100755 docs/examples/distributed/accelerate_example/job.sh create mode 100644 docs/examples/distributed/accelerate_example/main.py create mode 100644 docs/examples/distributed/accelerate_example/pyproject.toml create mode 100644 docs/examples/distributed/accelerate_example/uv.lock diff --git a/docs/examples/distributed/accelerate_example/.gitignore b/docs/examples/distributed/accelerate_example/.gitignore new file mode 100644 index 00000000..5a56a176 --- /dev/null +++ b/docs/examples/distributed/accelerate_example/.gitignore @@ -0,0 +1,3 @@ +.venv +.python-version +checkpoints \ No newline at end of file diff --git a/docs/examples/distributed/accelerate_example/README.md b/docs/examples/distributed/accelerate_example/README.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/examples/distributed/accelerate_example/accelerate_launch.sh b/docs/examples/distributed/accelerate_example/accelerate_launch.sh new file mode 100755 index 00000000..a3248b1e --- /dev/null +++ b/docs/examples/distributed/accelerate_example/accelerate_launch.sh @@ -0,0 +1,6 @@ +#!/bin/bash +exec accelerate launch --multi_gpu \ + --num_machines $SLURM_NNODES \ + --machine_rank $SLURM_PROCID \ + --num_processes $(( ${SLURM_NNODES:-1} * ${SLURM_GPUS_ON_NODE:-4} )) \ + "$@" \ No newline at end of file diff --git a/docs/examples/distributed/accelerate_example/index.rst b/docs/examples/distributed/accelerate_example/index.rst new file mode 100644 index 00000000..9c94b349 --- /dev/null +++ b/docs/examples/distributed/accelerate_example/index.rst @@ -0,0 +1,53 @@ +HuggingFace Accelerate example +============================== + +Prerequisites: + +* :doc:`/examples/frameworks/pytorch_setup/index` +* :doc:`/examples/distributed/single_gpu/index` +* :doc:`/examples/distributed/multi_gpu/index` +* :doc:`/examples/distributed/multi_node/index` + +Other interesting resources: + +* ``_ +* ``_ +* ``_ + + +Click here to see `the code for this example +`_ + +**job.sh** + +.. literalinclude:: job.sh + :language: bash + +**main.py** + +.. literalinclude:: main.py + :language: python + +**pyproject.toml** + +.. literalinclude:: pyproject.toml + :language: toml + +**Running this example** + +1. Install UV from https://docs.astral.sh/uv + +2. On SLURM clusters where you do not have internet access on compute nodes, you need to first create the virtual environment: + +.. code-block:: bash + + $ salloc --gpus=1 --cpus-per-task=4 --mem=16G # Get an interactive job + $ module load httproxy/1.0 # if on a compute node, use this to get some internet access + $ uv sync + + +3. Launch the job: + +.. code-block:: bash + + $ sbatch job.sh diff --git a/docs/examples/distributed/accelerate_example/job.sh b/docs/examples/distributed/accelerate_example/job.sh new file mode 100755 index 00000000..83f7bbd2 --- /dev/null +++ b/docs/examples/distributed/accelerate_example/job.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH --nodes=1 +#SBATCH --gres=gpu:4 +#SBATCH --cpus-per-task=48 +#SBATCH --mem=0 + +export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) +# Get a unique port for this job based on the job ID +export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) + +srun ./accelerate_launch.sh --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ + uv run python main.py "$@" diff --git a/docs/examples/distributed/accelerate_example/main.py b/docs/examples/distributed/accelerate_example/main.py new file mode 100644 index 00000000..f593609e --- /dev/null +++ b/docs/examples/distributed/accelerate_example/main.py @@ -0,0 +1,528 @@ +"""HuggingFace Example from https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py + +Differences with the reference example: +- Uses the slurm job ID + +""" + +# 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. +import argparse +import dataclasses +import os +from pathlib import Path +import pickle +import shutil +import sys +from typing import Literal +import tqdm +from transformers.tokenization_utils_base import BatchEncoding +from transformers.modeling_utils import PreTrainedModel +import evaluate +import simple_parsing +import torch +from accelerate import ( + Accelerator, + DataLoaderConfiguration, + DistributedType, +) + +from accelerate.utils import set_seed, ProjectConfiguration +from datasets import load_dataset +from torch.optim import AdamW +from torch.utils.data import DataLoader +from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + PretrainedBartModel, +) +from transformers.optimization import get_linear_schedule_with_warmup +from accelerate.logging import get_logger +######################################################################## +# This is a fully working simple example to use Accelerate, +# specifically showcasing the checkpointing capability, +# and builds off the `nlp_example.py` script. +# +# This example trains a Bert base model on GLUE MRPC +# in any of the following settings (with the same script): +# - single CPU or single GPU +# - multi GPUS (using PyTorch distributed mode) +# - (multi) TPUs +# - fp16 (mixed-precision) or fp32 (normal precision) +# +# To help focus on the differences in the code, building `DataLoaders` +# was refactored into its own function. +# New additions from the base script can be found quickly by +# looking for the # New Code # tags +# +# To run it in each of these various modes, follow the instructions +# in the readme for examples: +# https://github.com/huggingface/accelerate/tree/main/examples +# +######################################################################## + +MAX_GPU_BATCH_SIZE = 16 +EVAL_BATCH_SIZE = 32 +logger = get_logger(__name__) + + +@dataclasses.dataclass +class Args: + mixed_precision: Literal["no", "fp16", "bf16", "fp8"] = "no" + """"Whether to use mixed precision. + + Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10 and an Nvidia Ampere GPU. + """ + + cpu: bool = False + """If passed, will train on the CPU.""" + + checkpointing_steps: Literal["epoch"] | str | None = "epoch" + """Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" + + output_dir: Path = ( + (Path(os.environ["SCRATCH"]) / os.environ["SLURM_JOB_ID"]) + if "SLURM_JOB_ID" in os.environ + else Path("./checkpoints") + ) + """Optional save directory where all checkpoint folders will be stored.""" + + resume_from_checkpoint: str | None = None + """If the training should continue from a checkpoint folder.""" + + use_stateful_dataloader: bool = False + """Whether the dataloader should be a resumable stateful dataloader.""" + + lr: float = 2e-5 + """ Learning rate for the optimizer.""" + + num_epochs: int = 3 + """ Number of epochs to train for in total.""" + + seed: int = 42 + """ Random seed for initialization and reproducibility.""" + + batch_size: int = 16 + """Batch size for training.""" + + with_tracking: bool = False + """Whether to load in all available experiment trackers from the environment and use them for logging.""" + + +def get_dataloaders(accelerator: Accelerator, batch_size: int = 16): + """ + Creates a set of `DataLoader`s for the `glue` dataset, + using "bert-base-cased" as the tokenizer. + + Args: + accelerator (`Accelerator`): + An `Accelerator` object + batch_size (`int`, *optional*): + The batch size for the train and validation DataLoaders. + """ + tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + datasets = load_dataset("glue", "mrpc") + + def tokenize_function(examples): + # max_length=None => use the model max length (it's actually the default) + outputs = tokenizer( + examples["sentence1"], + examples["sentence2"], + truncation=True, + max_length=None, + ) + return outputs + + # Apply the method we just defined to all the examples in all the splits of the dataset + # starting with the main process first: + with accelerator.main_process_first(): + tokenized_datasets = datasets.map( + tokenize_function, + batched=True, + remove_columns=["idx", "sentence1", "sentence2"], + ) + + # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the + # transformers library + tokenized_datasets = tokenized_datasets.rename_column("label", "labels") + + def collate_fn(examples): + # On TPU it's best to pad everything to the same length or training will be very slow. + max_length = ( + 128 if accelerator.distributed_type == DistributedType.XLA else None + ) + # When using mixed precision we want round multiples of 8/16 + if accelerator.mixed_precision == "fp8": + pad_to_multiple_of = 16 + elif accelerator.mixed_precision != "no": + pad_to_multiple_of = 8 + else: + pad_to_multiple_of = None + + return tokenizer.pad( + examples, + padding="longest", + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors="pt", + ) + + # Instantiate dataloaders. + train_dataloader = DataLoader( + tokenized_datasets["train"], + shuffle=True, + collate_fn=collate_fn, + batch_size=batch_size, + ) + eval_dataloader = DataLoader( + tokenized_datasets["validation"], + shuffle=False, + collate_fn=collate_fn, + batch_size=EVAL_BATCH_SIZE, + ) + + return train_dataloader, eval_dataloader + + +# For testing only +if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": + from accelerate.test_utils.training import mocked_dataloaders + + get_dataloaders = mocked_dataloaders # noqa: F811 + + +def training_function(args: Args): + config = args + # For testing only + if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": + config = dataclasses.replace(config, num_epochs=2) + args = dataclasses.replace(args, output_dir=args.output_dir.resolve()) + # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs + lr = config.lr + num_epochs = config.num_epochs + seed = config.seed + batch_size = config.batch_size + + # Initialize accelerator + dataloader_config = DataLoaderConfiguration( + use_stateful_dataloader=args.use_stateful_dataloader + ) + + checkpoint_dir: Path | None = max( + [ + f + for f in args.output_dir.glob("*_*") + if f.is_dir() and not f.name.endswith(".tmp") + ], + key=lambda f: int(f.stem.rpartition("_")[2]), + default=None, + ) + + # If the batch size is too big for the GPU, we can use gradient accumulation + gradient_accumulation_steps = 1 + if batch_size > MAX_GPU_BATCH_SIZE: + gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE + batch_size = MAX_GPU_BATCH_SIZE + + accelerator = Accelerator( + cpu=args.cpu, + mixed_precision=args.mixed_precision, + dataloader_config=dataloader_config, + gradient_accumulation_steps=gradient_accumulation_steps, + project_dir=str(args.output_dir.resolve()), + ) + + # New Code # + # Parse out whether we are saving every epoch or after a certain number of batches + checkpointing_steps: Literal["epoch"] | int | None + if args.checkpointing_steps == "epoch": + checkpointing_steps = args.checkpointing_steps + elif isinstance(args.checkpointing_steps, str): + checkpointing_steps = int(args.checkpointing_steps) + elif args.checkpointing_steps is None: + # No checkpointing. + checkpointing_steps = None + else: + raise ValueError( + f"Argument `checkpointing_steps` must be either a number or `epoch`, not `{args.checkpointing_steps}`" + ) + + set_seed(seed) + + train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size) + metric = evaluate.load("glue", "mrpc") + + # Instantiate the model (we build the model here so that the seed also control new weights initialization) + model: PreTrainedModel = AutoModelForSequenceClassification.from_pretrained( + "bert-base-cased", return_dict=True + ) + + # We could avoid this line since the accelerator is set with `device_placement=True` (default value). + # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer + # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). + model = model.to(accelerator.device) + + # Instantiate optimizer + optimizer = AdamW(params=model.parameters(), lr=lr) + + # Instantiate scheduler + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=100, + num_training_steps=(len(train_dataloader) * num_epochs) + // gradient_accumulation_steps, + ) + + # Prepare everything + # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the + # prepare method. + model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = prepare( + accelerator, model, optimizer, train_dataloader, eval_dataloader, lr_scheduler + ) + + # New Code # + # We need to keep track of how many total steps we have iterated over + overall_step = 0 + # We also need to keep track of the stating epoch so files are named properly + starting_epoch = 0 + + skip_first_batches: int | None = None + + def _get_checkpoint_dir(step: int | None = None, epoch: int | None = None): + assert (step is not None) ^ (epoch is not None), "Use either `step` or `epoch`." + return args.output_dir / ( + f"step_{step}" if step is not None else f"epoch_{epoch}" + ) + + if checkpoint_dir: + _int_in_filename = int(checkpoint_dir.stem.rpartition("_")[2]) + if args.checkpointing_steps == "epoch": + # epoch_0 --> NO training done (initial weights). + # epoch_1 --> training done for 1 epoch. + starting_epoch = _int_in_filename + overall_step = starting_epoch * len(train_dataloader) + print(f"Resuming training at epoch {starting_epoch} from {checkpoint_dir}") + else: + # step_0 --> NO training done (initial weights). + # step_1 --> 1 training step done. + overall_step = _int_in_filename + starting_epoch = overall_step // len(train_dataloader) + if not args.use_stateful_dataloader: + skip_first_batches = overall_step % len(train_dataloader) + print(f"Resuming training at step {overall_step} in {checkpoint_dir}.") + + # We need to load the checkpoint back in before training here with `load_state` + # The total number of epochs is adjusted based on where the state is being loaded from, + # as we assume continuation of the same training script + accelerator.load_state(input_dir=str(checkpoint_dir)) + elif checkpointing_steps is not None: + # Save the initial state. + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `step_{overall_step}` or `epoch_{epoch}` depending on + # `args.checkpoint_steps`. + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and + # "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + checkpoint_dir = ( + _get_checkpoint_dir(epoch=0) + if checkpointing_steps == "epoch" + else _get_checkpoint_dir(step=0) + ) + save_state(accelerator, checkpoint_dir) + print(f"Saved initial state in {checkpoint_dir}") + + # Now we train the model + for epoch in tqdm.tqdm( + range(starting_epoch, num_epochs), + desc="Training", + unit="Epochs", + position=0, + disable=not (sys.stdout.isatty() and accelerator.is_main_process), + ): + model.train() + # New Code # + epoch_start_step = 0 + if epoch == starting_epoch and skip_first_batches: + # We need to skip steps until we reach the resumed step only if we are not using a stateful dataloader + assert not args.use_stateful_dataloader + logger.info(f"Skipping first {skip_first_batches} batches") + active_dataloader = accelerator.skip_first_batches( + train_dataloader, skip_first_batches + ) + epoch_start_step = skip_first_batches + else: + # After the first iteration though, we need to go back to the original dataloader + active_dataloader = train_dataloader + + for batch_index, batch in enumerate( + tqdm.tqdm( + active_dataloader, + desc=f"Train epoch {epoch}", + unit="samples", + unit_scale=batch_size, # to see samples/s in pbar + position=1, + disable=not (sys.stdout.isatty() and accelerator.is_main_process), + ), + start=epoch_start_step, + ): + assert isinstance(batch, BatchEncoding) + + # We could avoid this line since we set the accelerator with `device_placement=True`. + batch = batch.to(accelerator.device) + with accelerator.accumulate(model): + outputs = model(**batch) + loss = outputs.loss + loss = loss / gradient_accumulation_steps + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + overall_step += 1 + # New Code # + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `step_{overall_step}` + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + if ( + isinstance(checkpointing_steps, int) + and overall_step % checkpointing_steps == 0 + ): + checkpoint_dir = _get_checkpoint_dir(step=overall_step) + accelerator.save_state(str(checkpoint_dir)) + logger.info(f"Saved checkpoint in {checkpoint_dir}") + model.eval() + for batch_index, batch in enumerate(eval_dataloader): + assert isinstance(batch, BatchEncoding) + # We could avoid this line since we set the accelerator with `device_placement=True` (the default). + batch = batch.to(accelerator.device) + with torch.no_grad(): + outputs = model(**batch) + predictions = outputs.logits.argmax(dim=-1) + predictions, references = accelerator.gather_for_metrics( + (predictions, batch["labels"]) + ) + metric.add_batch( + predictions=predictions, + references=references, + ) + eval_metric = metric.compute() + assert eval_metric is not None + # Use accelerator.print to print only on the main process. + accelerator.print(f"epoch {epoch}:", eval_metric) + accelerator.log(eval_metric, step=overall_step) + # New Code # + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `epoch_{epoch}` + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + if checkpointing_steps == "epoch": + # Need to increment epoch here, since "epoch_1" means one epoch is done. + checkpoint_dir = _get_checkpoint_dir(epoch=epoch + 1) + assert not checkpoint_dir.exists() + accelerator.save_state(str(checkpoint_dir)) + + # Need to save a new epoch: + if isinstance(checkpointing_steps, int) and overall_step % checkpointing_steps == 0: + checkpoint_dir = _get_checkpoint_dir(step=overall_step) + assert not checkpoint_dir.exists() + accelerator.save_state(str(checkpoint_dir)) + logger.info(f"Saved final checkpoint in {checkpoint_dir}") + + accelerator.end_training() + + +def prepare[*Ts](accelerator: Accelerator, *args: *Ts) -> tuple[*Ts]: + """A wrapper around `accelerator.prepare` that preserves the type of the inputs.""" + return accelerator.prepare(*args) + + +def save_state( + accelerator: Accelerator, + checkpoint_dir: str | Path, +): + """Small convenience wrapper around `accelerator.save_state` with some tweaks. + + - Saves the state in a temporary directory with the suffix `.tmp`, and renames at the end. + (This is useful to avoid issues when the program is interrupted while saving a checkpoint). + + """ + checkpoint_dir = Path(checkpoint_dir) + if checkpoint_dir.exists(): + raise RuntimeError(f"Checkpoint directory {checkpoint_dir} already exists!") + temp_checkpoint_dir = checkpoint_dir.with_suffix(".tmp") + if temp_checkpoint_dir.exists(): + logger.warning( + f"Temporary checkpoint directory {checkpoint_dir} already exists (from previous attempt at checkpointing)." + ) + if accelerator.is_main_process: + shutil.rmtree(temp_checkpoint_dir) + + temp_checkpoint_dir.parent.mkdir(parents=True, exist_ok=True) + accelerator.save_state(str(temp_checkpoint_dir)) + temp_checkpoint_dir.rename(checkpoint_dir) + logger.info(f"Saved state in {checkpoint_dir}") + + +def parse_args() -> Args: + return simple_parsing.parse(Args) + + +def _parse_args(): + parser = argparse.ArgumentParser(description="Simple example of training script.") + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16", "fp8"], + help="Whether to use mixed precision. Choose" + "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." + "and an Nvidia Ampere GPU.", + ) + parser.add_argument( + "--cpu", action="store_true", help="If passed, will train on the CPU." + ) + 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( + "--output_dir", + type=str, + default=".", + help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + help="If the training should continue from a checkpoint folder.", + ) + parser.add_argument( + "--use_stateful_dataloader", + action="store_true", + help="If the dataloader should be a resumable stateful dataloader.", + ) + return parser.parse_args() + + +def main(): + args = parse_args() + # config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16} + training_function(args) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/distributed/accelerate_example/pyproject.toml b/docs/examples/distributed/accelerate_example/pyproject.toml new file mode 100644 index 00000000..9a05e1ca --- /dev/null +++ b/docs/examples/distributed/accelerate_example/pyproject.toml @@ -0,0 +1,50 @@ +[project] +name = "accelerate_example" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "accelerate>=1.7.0", + "datasets>=3.6.0", + "evaluate>=0.4.4", + "scikit-learn>=1.7.0", + "simple-parsing>=0.1.7", + "transformers>=4.52.4", +] + + +[tool.uv] +python-preference = "system" + +## From https://docs.astral.sh/uv/reference/settings/#index-strategy: +## "Only use results from the first index that returns a match for a given package name." +## In other words: only get the package from PyPI if there isn't a version of it in the DRAC wheelhouse. +# index-strategy = "first-index" + +## "Search for every package name across all indexes, exhausting the versions from the first index before +## moving on to the next" +## In other words: Only get the package from PyPI if the requested version is higher than the version +## in the DRAC wheelhouse. +# index-strategy = "unsafe-first-match" + +## "Search for every package name across all indexes, preferring the "best" version found. +## If a package version is in multiple indexes, only look at the entry for the first index." +## In other words: Consider all versions of the package DRAC + PyPI, and use the version that best matches +## the requested version. 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Fabrice Normandin --- .../accelerate_example/.gitignore | 0 .../accelerate_example/README.md | 0 .../LLMs/accelerate_example/README.rst | 700 ++++++++++++++++++ .../accelerate_example/accelerate_launch.sh | 2 + .../accelerate_example/index.rst | 2 +- docs/examples/LLMs/accelerate_example/job.sh | 22 + .../accelerate_example/main.py | 59 +- .../accelerate_example/pyproject.toml | 0 .../accelerate_example/uv.lock | 0 docs/examples/LLMs/index.rst | 12 + .../accelerate_example/accelerate_launch.sh | 6 - .../distributed/accelerate_example/job.sh | 12 - docs/examples/distributed/index.rst | 1 - 13 files changed, 788 insertions(+), 28 deletions(-) rename docs/examples/{distributed => LLMs}/accelerate_example/.gitignore (100%) rename docs/examples/{distributed => LLMs}/accelerate_example/README.md (100%) create mode 100644 docs/examples/LLMs/accelerate_example/README.rst create mode 100755 docs/examples/LLMs/accelerate_example/accelerate_launch.sh rename docs/examples/{distributed => LLMs}/accelerate_example/index.rst (97%) create mode 100755 docs/examples/LLMs/accelerate_example/job.sh rename docs/examples/{distributed => LLMs}/accelerate_example/main.py (90%) rename docs/examples/{distributed => LLMs}/accelerate_example/pyproject.toml (100%) rename docs/examples/{distributed => LLMs}/accelerate_example/uv.lock (100%) create mode 100644 docs/examples/LLMs/index.rst delete mode 100755 docs/examples/distributed/accelerate_example/accelerate_launch.sh delete mode 100755 docs/examples/distributed/accelerate_example/job.sh diff --git a/docs/examples/distributed/accelerate_example/.gitignore b/docs/examples/LLMs/accelerate_example/.gitignore similarity index 100% rename from docs/examples/distributed/accelerate_example/.gitignore rename to docs/examples/LLMs/accelerate_example/.gitignore diff --git a/docs/examples/distributed/accelerate_example/README.md b/docs/examples/LLMs/accelerate_example/README.md similarity index 100% rename from docs/examples/distributed/accelerate_example/README.md rename to docs/examples/LLMs/accelerate_example/README.md diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst new file mode 100644 index 00000000..eec14306 --- /dev/null +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -0,0 +1,700 @@ +.. NOTE: This file is auto-generated from examples/LLMs/accelerate_example/index.rst +.. This is done so this file can be easily viewed from the GitHub UI. +.. **DO NOT EDIT** + +HuggingFace Accelerate example +============================== + +Prerequisites: + +* `examples/frameworks/pytorch_setup `_ +* `examples/distributed/single_gpu `_ +* `examples/distributed/multi_gpu `_ +* `examples/distributed/multi_node `_ + +Other interesting resources: + +* ``_ +* ``_ +* ``_ + + +Click here to see `the code for this example +`_ + +**job.sh** + +.. code:: bash + + #!/bin/bash + #SBATCH --ntasks=2 + #SBATCH --ntasks-per-node=1 + #SBATCH --gpus-per-task=1 + #SBATCH --cpus-per-task=4 + #SBATCH --mem-per-gpu=16GB + + export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) + # Get a unique port for this job based on the job ID + export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) + + # TODO: Make this work in a multi-node setting with code checkpointing + # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. + srun --nodes=1 --ntasks-per-node=1 uv sync --offline + + # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable + srun uv run --offline bash -c "accelerate launch \ + --machine_rank \$SLURM_NODEID + --multi_gpu \ + --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ + --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ + main.py $@" + +**main.py** + +.. code:: python + + """HuggingFace Example from https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py + + Differences with the reference example: + - Uses the slurm job ID + + """ + + # 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. + import argparse + import dataclasses + import os + from pathlib import Path + import pickle + import shutil + import sys + from typing import Literal + import tqdm + from transformers.tokenization_utils_base import BatchEncoding + from transformers.modeling_utils import PreTrainedModel + import evaluate + import simple_parsing + import torch + from accelerate import ( + Accelerator, + DataLoaderConfiguration, + DistributedType, + ) + from datasets.dataset_dict import DatasetDict + from accelerate.utils import set_seed, ProjectConfiguration, InitProcessGroupKwargs + from datasets import load_dataset + from torch.optim import AdamW + from torch.utils.data import DataLoader + from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + PretrainedBartModel, + ) + from transformers.optimization import get_linear_schedule_with_warmup + from accelerate.logging import get_logger + ######################################################################## + # This is a fully working simple example to use Accelerate, + # specifically showcasing the checkpointing capability, + # and builds off the `nlp_example.py` script. + # + # This example trains a Bert base model on GLUE MRPC + # in any of the following settings (with the same script): + # - single CPU or single GPU + # - multi GPUS (using PyTorch distributed mode) + # - (multi) TPUs + # - fp16 (mixed-precision) or fp32 (normal precision) + # + # To help focus on the differences in the code, building `DataLoaders` + # was refactored into its own function. + # New additions from the base script can be found quickly by + # looking for the # New Code # tags + # + # To run it in each of these various modes, follow the instructions + # in the readme for examples: + # https://github.com/huggingface/accelerate/tree/main/examples + # + ######################################################################## + + MAX_GPU_BATCH_SIZE = 16 + EVAL_BATCH_SIZE = 32 + logger = get_logger(__name__) + + PREVIOUS_JOB_ID: int | None = None + if _slurm_job_dependency := os.environ.get("SLURM_JOB_DEPENDENCY"): + assert _slurm_job_dependency.startswith("afterok:"), _slurm_job_dependency + job_or_jobs: list[int] = list( + map(int, _slurm_job_dependency.removeprefix("afterok:").split(":")) + ) + # IDEA: Do something with this, for instance, load the dataset or checkpoints from the previous job. + # Currently, since we're not changing anything about the dataset preparation, it gets cached in the HF cache, + # so there's little need for this atm. + + + @dataclasses.dataclass + class Args: + mixed_precision: Literal["no", "fp16", "bf16", "fp8"] = "no" + """"Whether to use mixed precision. + + Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10 and an Nvidia Ampere GPU. + """ + + cpu: bool = False + """If passed, will train on the CPU.""" + + checkpointing_steps: Literal["epoch"] | str | None = "epoch" + """Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" + + output_dir: Path = ( + ( + Path(os.environ["SCRATCH"]) / str(PREVIOUS_JOB_ID) + if PREVIOUS_JOB_ID + else os.environ["SLURM_JOB_ID"] + ) + if "SLURM_JOB_ID" in os.environ + else Path("./checkpoints") + ) + """Optional save directory where all checkpoint folders will be stored.""" + + resume_from_checkpoint: str | None = None + """If the training should continue from a checkpoint folder.""" + + use_stateful_dataloader: bool = False + """Whether the dataloader should be a resumable stateful dataloader.""" + + lr: float = 2e-5 + """ Learning rate for the optimizer.""" + + num_epochs: int = 3 + """ Number of epochs to train for in total.""" + + seed: int = 42 + """ Random seed for initialization and reproducibility.""" + + batch_size: int = 16 + """Batch size for training.""" + + with_tracking: bool = False + """Whether to load in all available experiment trackers from the environment and use them for logging.""" + + only_prepare_dataset: bool = False + """ When set, return immediately after the dataset is done being prepared, without training. + + This can be useful on SLURM clusters so that a cpu-only job can be used to first prepare the dataset + before a GPU job is run. + """ + + + def get_dataloaders(accelerator: Accelerator, batch_size: int = 16): + """ + Creates a set of `DataLoader`s for the `glue` dataset, + using "bert-base-cased" as the tokenizer. + + Args: + accelerator (`Accelerator`): + An `Accelerator` object + batch_size (`int`, *optional*): + The batch size for the train and validation DataLoaders. + """ + tokenizer_name = "bert-base-cased" + dataset_name = "glue" + dataset_task = "mrpc" + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + datasets = load_dataset(dataset_name, dataset_task) + + def tokenize_function(examples): + # max_length=None => use the model max length (it's actually the default) + outputs = tokenizer( + examples["sentence1"], + examples["sentence2"], + truncation=True, + max_length=None, + ) + return outputs + + # Apply the method we just defined to all the examples in all the splits of the dataset + # starting with the main process first: + + with accelerator.main_process_first(): + assert isinstance(datasets, DatasetDict) + tokenized_datasets = datasets.map( + tokenize_function, + batched=True, + remove_columns=["idx", "sentence1", "sentence2"], + load_from_cache_file=True, + cache_file_names={ + k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" + for k in datasets + }, + # keep_in_memory=True, + ) + # tokenized_datasets.save_to_disk() + + # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the + # transformers library + tokenized_datasets = tokenized_datasets.rename_column("label", "labels") + + def collate_fn(examples): + # On TPU it's best to pad everything to the same length or training will be very slow. + max_length = ( + 128 if accelerator.distributed_type == DistributedType.XLA else None + ) + # When using mixed precision we want round multiples of 8/16 + if accelerator.mixed_precision == "fp8": + pad_to_multiple_of = 16 + elif accelerator.mixed_precision != "no": + pad_to_multiple_of = 8 + else: + pad_to_multiple_of = None + + return tokenizer.pad( + examples, + padding="longest", + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors="pt", + ) + + # Instantiate dataloaders. + train_dataloader = DataLoader( + tokenized_datasets["train"], + shuffle=True, + collate_fn=collate_fn, + batch_size=batch_size, + ) + eval_dataloader = DataLoader( + tokenized_datasets["validation"], + shuffle=False, + collate_fn=collate_fn, + batch_size=EVAL_BATCH_SIZE, + ) + + return train_dataloader, eval_dataloader + + + # For testing only + if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": + from accelerate.test_utils.training import mocked_dataloaders + + get_dataloaders = mocked_dataloaders # noqa: F811 + + + def training_function(args: Args): + config = args + # For testing only + if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": + config = dataclasses.replace(config, num_epochs=2) + args = dataclasses.replace(args, output_dir=args.output_dir.resolve()) + # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs + lr = config.lr + num_epochs = config.num_epochs + seed = config.seed + batch_size = config.batch_size + + # Initialize accelerator + dataloader_config = DataLoaderConfiguration( + use_stateful_dataloader=args.use_stateful_dataloader + ) + + checkpoint_dir: Path | None = max( + [ + f + for f in args.output_dir.glob("*_*") + if f.is_dir() and not f.name.endswith(".tmp") + ], + key=lambda f: int(f.stem.rpartition("_")[2]), + default=None, + ) + + # If the batch size is too big for the GPU, we can use gradient accumulation + gradient_accumulation_steps = 1 + if batch_size > MAX_GPU_BATCH_SIZE: + gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE + batch_size = MAX_GPU_BATCH_SIZE + + # kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=800), + # backend="nccl") + accelerator = Accelerator( + cpu=args.cpu, + mixed_precision=args.mixed_precision, + dataloader_config=dataloader_config, + gradient_accumulation_steps=gradient_accumulation_steps, + project_dir=str(args.output_dir.resolve()), + ) + + # New Code # + # Parse out whether we are saving every epoch or after a certain number of batches + checkpointing_steps: Literal["epoch"] | int | None + if args.checkpointing_steps == "epoch": + checkpointing_steps = args.checkpointing_steps + elif isinstance(args.checkpointing_steps, str): + checkpointing_steps = int(args.checkpointing_steps) + elif args.checkpointing_steps is None: + # No checkpointing. + checkpointing_steps = None + else: + raise ValueError( + f"Argument `checkpointing_steps` must be either a number or `epoch`, not `{args.checkpointing_steps}`" + ) + + set_seed(seed) + + train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size) + if args.only_prepare_dataset: + accelerator.print( + f"Done preparing the dataset, exiting without training (since {args.only_prepare_dataset=})" + ) + return + + metric = evaluate.load("glue", "mrpc") + + # Instantiate the model (we build the model here so that the seed also control new weights initialization) + model: PreTrainedModel = AutoModelForSequenceClassification.from_pretrained( + "bert-base-cased", return_dict=True + ) + + # We could avoid this line since the accelerator is set with `device_placement=True` (default value). + # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer + # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). + model = model.to(accelerator.device) + + # Instantiate optimizer + optimizer = AdamW(params=model.parameters(), lr=lr) + + # Instantiate scheduler + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=100, + num_training_steps=(len(train_dataloader) * num_epochs) + // gradient_accumulation_steps, + ) + + # Prepare everything + # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the + # prepare method. + model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = prepare( + accelerator, model, optimizer, train_dataloader, eval_dataloader, lr_scheduler + ) + + # New Code # + # We need to keep track of how many total steps we have iterated over + overall_step = 0 + # We also need to keep track of the stating epoch so files are named properly + starting_epoch = 0 + + skip_first_batches: int | None = None + + def _get_checkpoint_dir(step: int | None = None, epoch: int | None = None): + assert (step is not None) ^ (epoch is not None), "Use either `step` or `epoch`." + return args.output_dir / ( + f"step_{step}" if step is not None else f"epoch_{epoch}" + ) + + if checkpoint_dir: + _int_in_filename = int(checkpoint_dir.stem.rpartition("_")[2]) + if args.checkpointing_steps == "epoch": + # epoch_0 --> NO training done (initial weights). + # epoch_1 --> training done for 1 epoch. + starting_epoch = _int_in_filename + overall_step = starting_epoch * len(train_dataloader) + print(f"Resuming training at epoch {starting_epoch} from {checkpoint_dir}") + else: + # step_0 --> NO training done (initial weights). + # step_1 --> 1 training step done. + overall_step = _int_in_filename + starting_epoch = overall_step // len(train_dataloader) + if not args.use_stateful_dataloader: + skip_first_batches = overall_step % len(train_dataloader) + print(f"Resuming training at step {overall_step} in {checkpoint_dir}.") + + # We need to load the checkpoint back in before training here with `load_state` + # The total number of epochs is adjusted based on where the state is being loaded from, + # as we assume continuation of the same training script + accelerator.load_state(input_dir=str(checkpoint_dir)) + elif checkpointing_steps is not None: + # Save the initial state. + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `step_{overall_step}` or `epoch_{epoch}` depending on + # `args.checkpoint_steps`. + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and + # "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + checkpoint_dir = ( + _get_checkpoint_dir(epoch=0) + if checkpointing_steps == "epoch" + else _get_checkpoint_dir(step=0) + ) + save_state(accelerator, checkpoint_dir) + print(f"Saved initial state in {checkpoint_dir}") + + # Now we train the model + for epoch in tqdm.tqdm( + range(starting_epoch, num_epochs), + desc="Training", + unit="Epochs", + position=0, + disable=not (sys.stdout.isatty() and accelerator.is_main_process), + ): + model.train() + # New Code # + epoch_start_step = 0 + if epoch == starting_epoch and skip_first_batches: + # We need to skip steps until we reach the resumed step only if we are not using a stateful dataloader + assert not args.use_stateful_dataloader + logger.info(f"Skipping first {skip_first_batches} batches") + active_dataloader = accelerator.skip_first_batches( + train_dataloader, skip_first_batches + ) + epoch_start_step = skip_first_batches + else: + # After the first iteration though, we need to go back to the original dataloader + active_dataloader = train_dataloader + + for batch_index, batch in enumerate( + tqdm.tqdm( + active_dataloader, + desc=f"Train epoch {epoch}", + unit="samples", + unit_scale=batch_size, # to see samples/s in pbar + position=1, + disable=not (sys.stdout.isatty() and accelerator.is_main_process), + ), + start=epoch_start_step, + ): + assert isinstance(batch, BatchEncoding) + + # We could avoid this line since we set the accelerator with `device_placement=True`. + batch = batch.to(accelerator.device) + with accelerator.accumulate(model): + outputs = model(**batch) + loss = outputs.loss + loss = loss / gradient_accumulation_steps + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + overall_step += 1 + # New Code # + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `step_{overall_step}` + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + if ( + isinstance(checkpointing_steps, int) + and overall_step % checkpointing_steps == 0 + ): + checkpoint_dir = _get_checkpoint_dir(step=overall_step) + save_state(accelerator, checkpoint_dir) + logger.info(f"Saved checkpoint in {checkpoint_dir}") + model.eval() + for batch_index, batch in enumerate(eval_dataloader): + assert isinstance(batch, BatchEncoding) + # We could avoid this line since we set the accelerator with `device_placement=True` (the default). + batch = batch.to(accelerator.device) + with torch.no_grad(): + outputs = model(**batch) + predictions = outputs.logits.argmax(dim=-1) + predictions, references = accelerator.gather_for_metrics( + (predictions, batch["labels"]) + ) + metric.add_batch( + predictions=predictions, + references=references, + ) + eval_metric = metric.compute() + assert eval_metric is not None + # Use accelerator.print to print only on the main process. + accelerator.print(f"epoch {epoch}:", eval_metric) + accelerator.log(eval_metric, step=overall_step) + # New Code # + # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state` + # These are saved to folders named `epoch_{epoch}` + # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl" + # If mixed precision was used, will also save a "scalar.bin" file + if checkpointing_steps == "epoch": + # Need to increment epoch here, since "epoch_1" means one epoch is done. + checkpoint_dir = _get_checkpoint_dir(epoch=epoch + 1) + assert not checkpoint_dir.exists() + save_state(accelerator, checkpoint_dir) + + # Need to save a new epoch: + if isinstance(checkpointing_steps, int) and overall_step % checkpointing_steps == 0: + checkpoint_dir = _get_checkpoint_dir(step=overall_step) + assert not checkpoint_dir.exists() + save_state(accelerator, checkpoint_dir) + logger.info(f"Saved final checkpoint in {checkpoint_dir}") + + accelerator.end_training() + + + def prepare[*Ts](accelerator: Accelerator, *args: *Ts) -> tuple[*Ts]: + """A wrapper around `accelerator.prepare` that preserves the type of the inputs.""" + return accelerator.prepare(*args) + + + def save_state( + accelerator: Accelerator, + checkpoint_dir: str | Path, + ): + """Small convenience wrapper around `accelerator.save_state` with some tweaks. + + - Saves the state in a temporary directory with the suffix `.tmp`, and renames at the end. + (This is useful to avoid issues when the program is interrupted while saving a checkpoint). + + """ + if not accelerator.is_main_process: + return + checkpoint_dir = Path(checkpoint_dir) + if checkpoint_dir.exists(): + raise RuntimeError(f"Checkpoint directory {checkpoint_dir} already exists!") + temp_checkpoint_dir = checkpoint_dir.with_suffix(".tmp") + if temp_checkpoint_dir.exists(): + logger.warning( + f"Temporary checkpoint directory {checkpoint_dir} already exists (from previous attempt at checkpointing)." + ) + if accelerator.is_main_process: + shutil.rmtree(temp_checkpoint_dir) + + temp_checkpoint_dir.parent.mkdir(parents=True, exist_ok=True) + accelerator.save_state(str(temp_checkpoint_dir)) + temp_checkpoint_dir.rename(checkpoint_dir) + logger.info(f"Saved state in {checkpoint_dir}") + + + def parse_args() -> Args: + return simple_parsing.parse(Args) + + + def _parse_args(): + parser = argparse.ArgumentParser(description="Simple example of training script.") + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16", "fp8"], + help="Whether to use mixed precision. Choose" + "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." + "and an Nvidia Ampere GPU.", + ) + parser.add_argument( + "--cpu", action="store_true", help="If passed, will train on the CPU." + ) + 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( + "--output_dir", + type=str, + default=".", + help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + help="If the training should continue from a checkpoint folder.", + ) + parser.add_argument( + "--use_stateful_dataloader", + action="store_true", + help="If the dataloader should be a resumable stateful dataloader.", + ) + return parser.parse_args() + + + def main(): + args = parse_args() + # config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16} + training_function(args) + + + if __name__ == "__main__": + main() + +**pyproject.toml** + +.. code:: toml + + [project] + name = "accelerate_example" + version = "0.1.0" + description = "Add your description here" + readme = "README.md" + requires-python = ">=3.12" + dependencies = [ + "accelerate>=1.7.0", + "datasets>=3.6.0", + "evaluate>=0.4.4", + "scikit-learn>=1.7.0", + "simple-parsing>=0.1.7", + "transformers>=4.52.4", + ] + + + [tool.uv] + python-preference = "system" + + ## From https://docs.astral.sh/uv/reference/settings/#index-strategy: + ## "Only use results from the first index that returns a match for a given package name." + ## In other words: only get the package from PyPI if there isn't a version of it in the DRAC wheelhouse. + # index-strategy = "first-index" + + ## "Search for every package name across all indexes, exhausting the versions from the first index before + ## moving on to the next" + ## In other words: Only get the package from PyPI if the requested version is higher than the version + ## in the DRAC wheelhouse. + # index-strategy = "unsafe-first-match" + + ## "Search for every package name across all indexes, preferring the "best" version found. + ## If a package version is in multiple indexes, only look at the entry for the first index." + ## In other words: Consider all versions of the package DRAC + PyPI, and use the version that best matches + ## the requested version. In a tie, choose the DRAC wheel. + index-strategy = "unsafe-best-match" + + [[tool.uv.index]] + name = "drac-gentoo2023-x86-64-v3" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/x86-64-v3" + format = "flat" + + [[tool.uv.index]] + name = "drac-gentoo2023-generic" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/generic" + format = "flat" + + [[tool.uv.index]] + name = "drac-generic" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/generic" + format = "flat" + +**Running this example** + +1. Install UV from https://docs.astral.sh/uv + +2. On SLURM clusters where you do not have internet access on compute nodes, you need to first create the virtual environment: + +.. code-block:: bash + + $ salloc --gpus=1 --cpus-per-task=4 --mem=16G # Get an interactive job + $ module load httproxy/1.0 # if on a compute node, use this to get some internet access + $ uv sync + + +3. Launch the job: + +.. code-block:: bash + + $ sbatch job.sh diff --git a/docs/examples/LLMs/accelerate_example/accelerate_launch.sh b/docs/examples/LLMs/accelerate_example/accelerate_launch.sh new file mode 100755 index 00000000..b25ae32a --- /dev/null +++ b/docs/examples/LLMs/accelerate_example/accelerate_launch.sh @@ -0,0 +1,2 @@ +#!/bin/bash +exec accelerate launch --machine_rank $SLURM_NODEID "$@" diff --git a/docs/examples/distributed/accelerate_example/index.rst b/docs/examples/LLMs/accelerate_example/index.rst similarity index 97% rename from docs/examples/distributed/accelerate_example/index.rst rename to docs/examples/LLMs/accelerate_example/index.rst index 9c94b349..2c5d307d 100644 --- a/docs/examples/distributed/accelerate_example/index.rst +++ b/docs/examples/LLMs/accelerate_example/index.rst @@ -16,7 +16,7 @@ Other interesting resources: Click here to see `the code for this example -`_ +`_ **job.sh** diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh new file mode 100755 index 00000000..eaa64605 --- /dev/null +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -0,0 +1,22 @@ +#!/bin/bash +#SBATCH --ntasks=2 +#SBATCH --ntasks-per-node=1 +#SBATCH --gpus-per-task=1 +#SBATCH --cpus-per-task=4 +#SBATCH --mem-per-gpu=16GB + +export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) +# Get a unique port for this job based on the job ID +export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) + +# TODO: Make this work in a multi-node setting with code checkpointing +# (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. +srun --nodes=1 --ntasks-per-node=1 uv sync --offline + +# Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable +srun uv run --offline bash -c "accelerate launch \ + --machine_rank \$SLURM_NODEID + --multi_gpu \ + --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ + --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ + main.py $@" diff --git a/docs/examples/distributed/accelerate_example/main.py b/docs/examples/LLMs/accelerate_example/main.py similarity index 90% rename from docs/examples/distributed/accelerate_example/main.py rename to docs/examples/LLMs/accelerate_example/main.py index f593609e..54f10e24 100644 --- a/docs/examples/distributed/accelerate_example/main.py +++ b/docs/examples/LLMs/accelerate_example/main.py @@ -37,8 +37,8 @@ DataLoaderConfiguration, DistributedType, ) - -from accelerate.utils import set_seed, ProjectConfiguration +from datasets.dataset_dict import DatasetDict +from accelerate.utils import set_seed, ProjectConfiguration, InitProcessGroupKwargs from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader @@ -76,6 +76,16 @@ EVAL_BATCH_SIZE = 32 logger = get_logger(__name__) +PREVIOUS_JOB_ID: int | None = None +if _slurm_job_dependency := os.environ.get("SLURM_JOB_DEPENDENCY"): + assert _slurm_job_dependency.startswith("afterok:"), _slurm_job_dependency + job_or_jobs: list[int] = list( + map(int, _slurm_job_dependency.removeprefix("afterok:").split(":")) + ) + # IDEA: Do something with this, for instance, load the dataset or checkpoints from the previous job. + # Currently, since we're not changing anything about the dataset preparation, it gets cached in the HF cache, + # so there's little need for this atm. + @dataclasses.dataclass class Args: @@ -92,7 +102,11 @@ class Args: """Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" output_dir: Path = ( - (Path(os.environ["SCRATCH"]) / os.environ["SLURM_JOB_ID"]) + ( + Path(os.environ["SCRATCH"]) / str(PREVIOUS_JOB_ID) + if PREVIOUS_JOB_ID + else os.environ["SLURM_JOB_ID"] + ) if "SLURM_JOB_ID" in os.environ else Path("./checkpoints") ) @@ -119,6 +133,13 @@ class Args: with_tracking: bool = False """Whether to load in all available experiment trackers from the environment and use them for logging.""" + only_prepare_dataset: bool = False + """ When set, return immediately after the dataset is done being prepared, without training. + + This can be useful on SLURM clusters so that a cpu-only job can be used to first prepare the dataset + before a GPU job is run. + """ + def get_dataloaders(accelerator: Accelerator, batch_size: int = 16): """ @@ -131,8 +152,11 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16): batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. """ - tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") - datasets = load_dataset("glue", "mrpc") + tokenizer_name = "bert-base-cased" + dataset_name = "glue" + dataset_task = "mrpc" + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + datasets = load_dataset(dataset_name, dataset_task) def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) @@ -146,12 +170,21 @@ def tokenize_function(examples): # Apply the method we just defined to all the examples in all the splits of the dataset # starting with the main process first: + with accelerator.main_process_first(): + assert isinstance(datasets, DatasetDict) tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], + load_from_cache_file=True, + cache_file_names={ + k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" + for k in datasets + }, + # keep_in_memory=True, ) + # tokenized_datasets.save_to_disk() # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library @@ -235,6 +268,8 @@ def training_function(args: Args): gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE batch_size = MAX_GPU_BATCH_SIZE + # kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=800), + # backend="nccl") accelerator = Accelerator( cpu=args.cpu, mixed_precision=args.mixed_precision, @@ -261,6 +296,12 @@ def training_function(args: Args): set_seed(seed) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size) + if args.only_prepare_dataset: + accelerator.print( + f"Done preparing the dataset, exiting without training (since {args.only_prepare_dataset=})" + ) + return + metric = evaluate.load("glue", "mrpc") # Instantiate the model (we build the model here so that the seed also control new weights initialization) @@ -399,7 +440,7 @@ def _get_checkpoint_dir(step: int | None = None, epoch: int | None = None): and overall_step % checkpointing_steps == 0 ): checkpoint_dir = _get_checkpoint_dir(step=overall_step) - accelerator.save_state(str(checkpoint_dir)) + save_state(accelerator, checkpoint_dir) logger.info(f"Saved checkpoint in {checkpoint_dir}") model.eval() for batch_index, batch in enumerate(eval_dataloader): @@ -430,13 +471,13 @@ def _get_checkpoint_dir(step: int | None = None, epoch: int | None = None): # Need to increment epoch here, since "epoch_1" means one epoch is done. checkpoint_dir = _get_checkpoint_dir(epoch=epoch + 1) assert not checkpoint_dir.exists() - accelerator.save_state(str(checkpoint_dir)) + save_state(accelerator, checkpoint_dir) # Need to save a new epoch: if isinstance(checkpointing_steps, int) and overall_step % checkpointing_steps == 0: checkpoint_dir = _get_checkpoint_dir(step=overall_step) assert not checkpoint_dir.exists() - accelerator.save_state(str(checkpoint_dir)) + save_state(accelerator, checkpoint_dir) logger.info(f"Saved final checkpoint in {checkpoint_dir}") accelerator.end_training() @@ -457,6 +498,8 @@ def save_state( (This is useful to avoid issues when the program is interrupted while saving a checkpoint). """ + if not accelerator.is_main_process: + return checkpoint_dir = Path(checkpoint_dir) if checkpoint_dir.exists(): raise RuntimeError(f"Checkpoint directory {checkpoint_dir} already exists!") diff --git a/docs/examples/distributed/accelerate_example/pyproject.toml b/docs/examples/LLMs/accelerate_example/pyproject.toml similarity index 100% rename from docs/examples/distributed/accelerate_example/pyproject.toml rename to docs/examples/LLMs/accelerate_example/pyproject.toml diff --git a/docs/examples/distributed/accelerate_example/uv.lock b/docs/examples/LLMs/accelerate_example/uv.lock similarity index 100% rename from docs/examples/distributed/accelerate_example/uv.lock rename to docs/examples/LLMs/accelerate_example/uv.lock diff --git a/docs/examples/LLMs/index.rst b/docs/examples/LLMs/index.rst new file mode 100644 index 00000000..cec10c81 --- /dev/null +++ b/docs/examples/LLMs/index.rst @@ -0,0 +1,12 @@ +******************** +LLM-related Examples +******************** + +.. include:: ../header.rst + +.. toctree:: + :maxdepth: 1 + :glob: + :numbered: + + accelerate_example/index \ No newline at end of file diff --git a/docs/examples/distributed/accelerate_example/accelerate_launch.sh b/docs/examples/distributed/accelerate_example/accelerate_launch.sh deleted file mode 100755 index a3248b1e..00000000 --- a/docs/examples/distributed/accelerate_example/accelerate_launch.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -exec accelerate launch --multi_gpu \ - --num_machines $SLURM_NNODES \ - --machine_rank $SLURM_PROCID \ - --num_processes $(( ${SLURM_NNODES:-1} * ${SLURM_GPUS_ON_NODE:-4} )) \ - "$@" \ No newline at end of file diff --git a/docs/examples/distributed/accelerate_example/job.sh b/docs/examples/distributed/accelerate_example/job.sh deleted file mode 100755 index 83f7bbd2..00000000 --- a/docs/examples/distributed/accelerate_example/job.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash -#SBATCH --nodes=1 -#SBATCH --gres=gpu:4 -#SBATCH --cpus-per-task=48 -#SBATCH --mem=0 - -export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -# Get a unique port for this job based on the job ID -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) - -srun ./accelerate_launch.sh --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ - uv run python main.py "$@" diff --git a/docs/examples/distributed/index.rst b/docs/examples/distributed/index.rst index 2ca02b50..d12b1ac1 100644 --- a/docs/examples/distributed/index.rst +++ b/docs/examples/distributed/index.rst @@ -12,4 +12,3 @@ Distributed Training single_gpu/index multi_gpu/index multi_node/index - accelerate_example/index From 4704957d8981438363ca768bc099019bd26aa478 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 26 Jun 2025 17:16:42 -0400 Subject: [PATCH 03/18] Fix bug in srun command Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 2 +- docs/examples/LLMs/accelerate_example/job.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index eec14306..21d5eb0b 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -43,7 +43,7 @@ Click here to see `the code for this example # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun uv run --offline bash -c "accelerate launch \ - --machine_rank \$SLURM_NODEID + --machine_rank \$SLURM_NODEID \ --multi_gpu \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index eaa64605..7fd4ae88 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -15,7 +15,7 @@ srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun uv run --offline bash -c "accelerate launch \ - --machine_rank \$SLURM_NODEID + --machine_rank \$SLURM_NODEID \ --multi_gpu \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ From 4cc96be5b7a90b5ff1557f5b66b9773dd850e381 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Wed, 2 Jul 2025 09:20:03 -0400 Subject: [PATCH 04/18] Turn off checkpoint to .tmp then rename Signed-off-by: Fabrice Normandin --- .../LLMs/accelerate_example/README.rst | 21 ++++++++++--------- docs/examples/LLMs/accelerate_example/job.sh | 3 +-- docs/examples/LLMs/accelerate_example/main.py | 18 +++++++++------- 3 files changed, 22 insertions(+), 20 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 21d5eb0b..3f69b8fa 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -35,7 +35,7 @@ Click here to see `the code for this example export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) # Get a unique port for this job based on the job ID - export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) + export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. @@ -44,7 +44,6 @@ Click here to see `the code for this example # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun uv run --offline bash -c "accelerate launch \ --machine_rank \$SLURM_NODEID \ - --multi_gpu \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ main.py $@" @@ -233,10 +232,10 @@ Click here to see `the code for this example batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=True, - cache_file_names={ - k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" - for k in datasets - }, + # cache_file_names={ + # k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" + # for k in datasets + # }, # keep_in_memory=True, ) # tokenized_datasets.save_to_disk() @@ -563,10 +562,12 @@ Click here to see `the code for this example logger.warning( f"Temporary checkpoint directory {checkpoint_dir} already exists (from previous attempt at checkpointing)." ) - if accelerator.is_main_process: - shutil.rmtree(temp_checkpoint_dir) - - temp_checkpoint_dir.parent.mkdir(parents=True, exist_ok=True) + shutil.rmtree(temp_checkpoint_dir) + # TODO: Can't actually do this .tmp and rename, because `save_state` apparently does something + # asynchronously in a subprocess, and by the time it writes, the parent directory doesn't exist + # anymore, resulting in an error. + temp_checkpoint_dir = checkpoint_dir + temp_checkpoint_dir.mkdir(parents=True, exist_ok=False) accelerator.save_state(str(temp_checkpoint_dir)) temp_checkpoint_dir.rename(checkpoint_dir) logger.info(f"Saved state in {checkpoint_dir}") diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index 7fd4ae88..b4da030e 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -7,7 +7,7 @@ export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) # Get a unique port for this job based on the job ID -export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4)) +export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. @@ -16,7 +16,6 @@ srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun uv run --offline bash -c "accelerate launch \ --machine_rank \$SLURM_NODEID \ - --multi_gpu \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ main.py $@" diff --git a/docs/examples/LLMs/accelerate_example/main.py b/docs/examples/LLMs/accelerate_example/main.py index 54f10e24..c058f147 100644 --- a/docs/examples/LLMs/accelerate_example/main.py +++ b/docs/examples/LLMs/accelerate_example/main.py @@ -178,10 +178,10 @@ def tokenize_function(examples): batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=True, - cache_file_names={ - k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" - for k in datasets - }, + # cache_file_names={ + # k: f"{dataset_name}_{dataset_task}_tokenized_{tokenizer_name}_{k}.arrow" + # for k in datasets + # }, # keep_in_memory=True, ) # tokenized_datasets.save_to_disk() @@ -508,10 +508,12 @@ def save_state( logger.warning( f"Temporary checkpoint directory {checkpoint_dir} already exists (from previous attempt at checkpointing)." ) - if accelerator.is_main_process: - shutil.rmtree(temp_checkpoint_dir) - - temp_checkpoint_dir.parent.mkdir(parents=True, exist_ok=True) + shutil.rmtree(temp_checkpoint_dir) + # TODO: Can't actually do this .tmp and rename, because `save_state` apparently does something + # asynchronously in a subprocess, and by the time it writes, the parent directory doesn't exist + # anymore, resulting in an error. + temp_checkpoint_dir = checkpoint_dir + temp_checkpoint_dir.mkdir(parents=True, exist_ok=False) accelerator.save_state(str(temp_checkpoint_dir)) temp_checkpoint_dir.rename(checkpoint_dir) logger.info(f"Saved state in {checkpoint_dir}") From 4ae857852b9b406b3fed842d6432b52c2ea7088f Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:23:25 -0400 Subject: [PATCH 05/18] Add safe_sbatch, try code checkpoint + env on /tmp Signed-off-by: Fabrice Normandin --- .../LLMs/accelerate_example/README.rst | 18 ++++++++++++++++-- docs/examples/LLMs/accelerate_example/job.sh | 18 ++++++++++++++++-- .../LLMs/accelerate_example/safe_sbatch.sh | 19 +++++++++++++++++++ 3 files changed, 51 insertions(+), 4 deletions(-) create mode 100755 docs/examples/LLMs/accelerate_example/safe_sbatch.sh diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 3f69b8fa..b6a7fb71 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -37,12 +37,26 @@ Click here to see `the code for this example # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) + # https://stackoverflow.com/a/1371283/6388696 + project_dirname=${PWD##*/} + + # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. - srun --nodes=1 --ntasks-per-node=1 uv sync --offline + + # Expect this variable to be set, or raise an error with a message + GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} + + srun --ntasks-per-node=1 bash -c "\ + git clone \$SLURM_SUBMIT_DIR \$SLURM_TMPDIR/$project_dirname && \ + cd \$SLURM_TMPDIR/$project_dirname && \ + git checkout $GIT_COMMIT && \ + uv sync --offline" + # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable - srun uv run --offline bash -c "accelerate launch \ + srun bash -c "\ + uv run --directory \$SLURM_TMPDIR/$project_dirname --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index b4da030e..4b4c40f1 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -9,12 +9,26 @@ export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) +# https://stackoverflow.com/a/1371283/6388696 +project_dirname=${PWD##*/} + + # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. -srun --nodes=1 --ntasks-per-node=1 uv sync --offline + +# Expect this variable to be set, or raise an error with a message +GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} + +srun --ntasks-per-node=1 bash -c "\ + git clone \$SLURM_SUBMIT_DIR \$SLURM_TMPDIR/$project_dirname && \ + cd \$SLURM_TMPDIR/$project_dirname && \ + git checkout $GIT_COMMIT && \ + uv sync --offline" +# srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable -srun uv run --offline bash -c "accelerate launch \ +srun bash -c "\ + uv run --directory \$SLURM_TMPDIR/$project_dirname --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ diff --git a/docs/examples/LLMs/accelerate_example/safe_sbatch.sh b/docs/examples/LLMs/accelerate_example/safe_sbatch.sh new file mode 100755 index 00000000..5e6e8f81 --- /dev/null +++ b/docs/examples/LLMs/accelerate_example/safe_sbatch.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -eof pipefail +git_status=`git status --porcelain` +# idea: Could add command-line arguments to control whether to add all changes and commit before sbatch. +if [[ ! -z $git_status ]]; then + echo "Your working directory is dirty! Please add and commit changes before continuing." + exit 1 +fi; +# This environment variable will be available in the job script. +# It should be used to checkout the repo at this commit (in a different directory than the original). +# For example: +# ``` +# git clone "$repo" "$dest" +# echo "Checking out commit $GIT_COMMIT" +# cd "$dest" +# git checkout $GIT_COMMIT +# ``` +export GIT_COMMIT=`git rev-parse HEAD` +exec sbatch "$@" From 3c7c62bdffb8f26f400d0e5b0c390bc84951e688 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:24:26 -0400 Subject: [PATCH 06/18] Rename safe_sbatch.sh to safe_sbatch Signed-off-by: Fabrice Normandin --- .../LLMs/accelerate_example/{safe_sbatch.sh => safe_sbatch} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/examples/LLMs/accelerate_example/{safe_sbatch.sh => safe_sbatch} (100%) diff --git a/docs/examples/LLMs/accelerate_example/safe_sbatch.sh b/docs/examples/LLMs/accelerate_example/safe_sbatch similarity index 100% rename from docs/examples/LLMs/accelerate_example/safe_sbatch.sh rename to docs/examples/LLMs/accelerate_example/safe_sbatch From 449cf2d4be81168c71f929f28e32020f8284694c Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:34:14 -0400 Subject: [PATCH 07/18] Take into account submitting from a project subdir Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 11 +++++++++-- docs/examples/LLMs/accelerate_example/job.sh | 11 +++++++++-- 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index b6a7fb71..220103e1 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -48,15 +48,22 @@ Click here to see `the code for this example # Expect this variable to be set, or raise an error with a message GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} + # This assumes that we're inside the project when we submit the job. + # Need to know where to go after cloning the repo in /tmp. + project_root=$(git rev-parse --show-toplevel) + project_dirname=$(basename $project_root) + submit_dir_relative_to_project=$(realpath --relative-to=$project_root $SLURM_SUBMIT_DIR) + srun --ntasks-per-node=1 bash -c "\ - git clone \$SLURM_SUBMIT_DIR \$SLURM_TMPDIR/$project_dirname && \ + git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ uv sync --offline" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory \$SLURM_TMPDIR/$project_dirname --offline accelerate launch \ + uv run --project \$SLURM_TMPDIR/$project_dirname --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ + --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index 4b4c40f1..401f0aa2 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -20,15 +20,22 @@ project_dirname=${PWD##*/} # Expect this variable to be set, or raise an error with a message GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} +# This assumes that we're inside the project when we submit the job. +# Need to know where to go after cloning the repo in /tmp. +project_root=$(git rev-parse --show-toplevel) +project_dirname=$(basename $project_root) +submit_dir_relative_to_project=$(realpath --relative-to=$project_root $SLURM_SUBMIT_DIR) + srun --ntasks-per-node=1 bash -c "\ - git clone \$SLURM_SUBMIT_DIR \$SLURM_TMPDIR/$project_dirname && \ + git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ uv sync --offline" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory \$SLURM_TMPDIR/$project_dirname --offline accelerate launch \ + uv run --project \$SLURM_TMPDIR/$project_dirname --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ + --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ From 2ae0e44d9fb8ac4a48dfa34554ccfde73bfd4149 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:37:24 -0400 Subject: [PATCH 08/18] Run the UV commands in the same relative subdir Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 9 ++------- docs/examples/LLMs/accelerate_example/job.sh | 9 ++------- 2 files changed, 4 insertions(+), 14 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 220103e1..a491550a 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -37,14 +37,9 @@ Click here to see `the code for this example # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) - # https://stackoverflow.com/a/1371283/6388696 - project_dirname=${PWD##*/} - - # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. - # Expect this variable to be set, or raise an error with a message GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} @@ -58,11 +53,11 @@ Click here to see `the code for this example git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ - uv sync --offline" + uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --project \$SLURM_TMPDIR/$project_dirname --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index 401f0aa2..719a462a 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -9,14 +9,9 @@ export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) -# https://stackoverflow.com/a/1371283/6388696 -project_dirname=${PWD##*/} - - # TODO: Make this work in a multi-node setting with code checkpointing # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. - # Expect this variable to be set, or raise an error with a message GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} @@ -30,11 +25,11 @@ srun --ntasks-per-node=1 bash -c "\ git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ - uv sync --offline" + uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --project \$SLURM_TMPDIR/$project_dirname --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ --offline accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ From 10b04048ab4951555e79614d5a1f5ec5960ef71a Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:41:03 -0400 Subject: [PATCH 09/18] Fix submit_dir_relative_to_project Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 8 +++----- docs/examples/LLMs/accelerate_example/job.sh | 8 +++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index a491550a..7e3f1825 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -37,17 +37,15 @@ Click here to see `the code for this example # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) - # TODO: Make this work in a multi-node setting with code checkpointing - # (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. - - # Expect this variable to be set, or raise an error with a message + # Expect this variable to be set by the `safe_sbatch` submission script or similar. + # Otherwise, raises an error with a message. GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} # This assumes that we're inside the project when we submit the job. # Need to know where to go after cloning the repo in /tmp. project_root=$(git rev-parse --show-toplevel) project_dirname=$(basename $project_root) - submit_dir_relative_to_project=$(realpath --relative-to=$project_root $SLURM_SUBMIT_DIR) + submit_dir_relative_to_project=$(realpath --relative-to=$(dirname $project_root) $SLURM_SUBMIT_DIR) srun --ntasks-per-node=1 bash -c "\ git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index 719a462a..8e151fa8 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -9,17 +9,15 @@ export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) # Get a unique port for this job based on the job ID export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOB_ID | tail -c 4)) -# TODO: Make this work in a multi-node setting with code checkpointing -# (clone repo to $SLURM_TMPDIR and run things from there), without --nodes=1 so it runs on each node. - -# Expect this variable to be set, or raise an error with a message +# Expect this variable to be set by the `safe_sbatch` submission script or similar. +# Otherwise, raises an error with a message. GIT_COMMIT=${GIT_COMMIT:?"GIT_COMMIT must be set to the commit hash you want to run this job on. Use 'safe_sbatch' instead of 'sbatch' to submit this job."} # This assumes that we're inside the project when we submit the job. # Need to know where to go after cloning the repo in /tmp. project_root=$(git rev-parse --show-toplevel) project_dirname=$(basename $project_root) -submit_dir_relative_to_project=$(realpath --relative-to=$project_root $SLURM_SUBMIT_DIR) +submit_dir_relative_to_project=$(realpath --relative-to=$(dirname $project_root) $SLURM_SUBMIT_DIR) srun --ntasks-per-node=1 bash -c "\ git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ From 1e20247443665e841051a35137cd707c1af5c5f3 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:43:25 -0400 Subject: [PATCH 10/18] Fix formatting a bit Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 4 ++-- docs/examples/LLMs/accelerate_example/job.sh | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 7e3f1825..8489e7c9 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -55,8 +55,8 @@ Click here to see `the code for this example # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ - --offline accelerate launch \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline \ + accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index 8e151fa8..e809e0d3 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -27,8 +27,8 @@ srun --ntasks-per-node=1 bash -c "\ # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ - --offline accelerate launch \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline \ + accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ From 454f775e06409b096e46b45cca97fa0f65eda3d9 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 3 Jul 2025 17:51:16 -0400 Subject: [PATCH 11/18] Don't use offline mode by default Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.rst | 8 ++++++-- docs/examples/LLMs/accelerate_example/job.sh | 8 ++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 8489e7c9..81dcba76 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -47,15 +47,19 @@ Click here to see `the code for this example project_dirname=$(basename $project_root) submit_dir_relative_to_project=$(realpath --relative-to=$(dirname $project_root) $SLURM_SUBMIT_DIR) + # Use this on DRAC clusters where compute nodes don't have internet access. + # NOTE: make sure to run `uv sync` once before submitting the jobs. + # export UV_OFFLINE=1 + srun --ntasks-per-node=1 bash -c "\ git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ - uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline" + uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ diff --git a/docs/examples/LLMs/accelerate_example/job.sh b/docs/examples/LLMs/accelerate_example/job.sh index e809e0d3..a866531e 100755 --- a/docs/examples/LLMs/accelerate_example/job.sh +++ b/docs/examples/LLMs/accelerate_example/job.sh @@ -19,15 +19,19 @@ project_root=$(git rev-parse --show-toplevel) project_dirname=$(basename $project_root) submit_dir_relative_to_project=$(realpath --relative-to=$(dirname $project_root) $SLURM_SUBMIT_DIR) +# Use this on DRAC clusters where compute nodes don't have internet access. +# NOTE: make sure to run `uv sync` once before submitting the jobs. +# export UV_OFFLINE=1 + srun --ntasks-per-node=1 bash -c "\ git clone $project_root \$SLURM_TMPDIR/$project_dirname && \ cd \$SLURM_TMPDIR/$project_dirname && \ git checkout $GIT_COMMIT && \ - uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline" + uv sync --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project" # srun --nodes=1 --ntasks-per-node=1 uv sync --offline # Note: it is important to escape `$SLURM_PROCID` since we want the srun on each node to evaluate this variable srun bash -c "\ - uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project --offline \ + uv run --directory=\$SLURM_TMPDIR/$submit_dir_relative_to_project \ accelerate launch \ --machine_rank \$SLURM_NODEID \ --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT \ From 7bc1c6fb22aeef36b3077992287676a51349a789 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Mon, 21 Jul 2025 11:45:49 -0400 Subject: [PATCH 12/18] Fix linting errors Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/main.py | 4 ++-- docs/examples/LLMs/index.rst | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/main.py b/docs/examples/LLMs/accelerate_example/main.py index c058f147..933c5200 100644 --- a/docs/examples/LLMs/accelerate_example/main.py +++ b/docs/examples/LLMs/accelerate_example/main.py @@ -91,7 +91,7 @@ class Args: mixed_precision: Literal["no", "fp16", "bf16", "fp8"] = "no" """"Whether to use mixed precision. - + Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10 and an Nvidia Ampere GPU. """ @@ -135,7 +135,7 @@ class Args: only_prepare_dataset: bool = False """ When set, return immediately after the dataset is done being prepared, without training. - + This can be useful on SLURM clusters so that a cpu-only job can be used to first prepare the dataset before a GPU job is run. """ diff --git a/docs/examples/LLMs/index.rst b/docs/examples/LLMs/index.rst index cec10c81..70d5f918 100644 --- a/docs/examples/LLMs/index.rst +++ b/docs/examples/LLMs/index.rst @@ -9,4 +9,4 @@ LLM-related Examples :glob: :numbered: - accelerate_example/index \ No newline at end of file + accelerate_example/index From 01a165cb1b7a6736d2ecc67454acaa5847279e87 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Mon, 21 Jul 2025 11:47:01 -0400 Subject: [PATCH 13/18] Remove the unused `accelerate_launch.sh` script Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/accelerate_launch.sh | 2 -- 1 file changed, 2 deletions(-) delete mode 100755 docs/examples/LLMs/accelerate_example/accelerate_launch.sh diff --git a/docs/examples/LLMs/accelerate_example/accelerate_launch.sh b/docs/examples/LLMs/accelerate_example/accelerate_launch.sh deleted file mode 100755 index b25ae32a..00000000 --- a/docs/examples/LLMs/accelerate_example/accelerate_launch.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -exec accelerate launch --machine_rank $SLURM_NODEID "$@" From ff313028e79cfb43570da224d8a1cd41042c7375 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Mon, 21 Jul 2025 11:47:35 -0400 Subject: [PATCH 14/18] Remove empty README.md Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docs/examples/LLMs/accelerate_example/README.md diff --git a/docs/examples/LLMs/accelerate_example/README.md b/docs/examples/LLMs/accelerate_example/README.md deleted file mode 100644 index e69de29b..00000000 From a6c4936cf7528c41320f879342fe32e7ca6219bf Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Mon, 21 Jul 2025 11:53:49 -0400 Subject: [PATCH 15/18] Add missing explanation for `safe_sbatch` script Signed-off-by: Fabrice Normandin --- .../LLMs/accelerate_example/README.rst | 130 +++++++++++------- .../LLMs/accelerate_example/index.rst | 23 +++- 2 files changed, 101 insertions(+), 52 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 81dcba76..49316a85 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -66,6 +66,61 @@ Click here to see `the code for this example --num_machines $SLURM_NNODES --num_processes $SLURM_NTASKS \ main.py $@" +**pyproject.toml** + +.. code:: toml + + [project] + name = "accelerate_example" + version = "0.1.0" + description = "Add your description here" + readme = "README.md" + requires-python = ">=3.12" + dependencies = [ + "accelerate>=1.7.0", + "datasets>=3.6.0", + "evaluate>=0.4.4", + "scikit-learn>=1.7.0", + "simple-parsing>=0.1.7", + "transformers>=4.52.4", + ] + + + [tool.uv] + python-preference = "system" + + ## From https://docs.astral.sh/uv/reference/settings/#index-strategy: + ## "Only use results from the first index that returns a match for a given package name." + ## In other words: only get the package from PyPI if there isn't a version of it in the DRAC wheelhouse. + # index-strategy = "first-index" + + ## "Search for every package name across all indexes, exhausting the versions from the first index before + ## moving on to the next" + ## In other words: Only get the package from PyPI if the requested version is higher than the version + ## in the DRAC wheelhouse. + # index-strategy = "unsafe-first-match" + + ## "Search for every package name across all indexes, preferring the "best" version found. + ## If a package version is in multiple indexes, only look at the entry for the first index." + ## In other words: Consider all versions of the package DRAC + PyPI, and use the version that best matches + ## the requested version. In a tie, choose the DRAC wheel. + index-strategy = "unsafe-best-match" + + [[tool.uv.index]] + name = "drac-gentoo2023-x86-64-v3" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/x86-64-v3" + format = "flat" + + [[tool.uv.index]] + name = "drac-gentoo2023-generic" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/generic" + format = "flat" + + [[tool.uv.index]] + name = "drac-generic" + url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/generic" + format = "flat" + **main.py** .. code:: python @@ -644,60 +699,39 @@ Click here to see `the code for this example if __name__ == "__main__": main() -**pyproject.toml** - -.. code:: toml - - [project] - name = "accelerate_example" - version = "0.1.0" - description = "Add your description here" - readme = "README.md" - requires-python = ">=3.12" - dependencies = [ - "accelerate>=1.7.0", - "datasets>=3.6.0", - "evaluate>=0.4.4", - "scikit-learn>=1.7.0", - "simple-parsing>=0.1.7", - "transformers>=4.52.4", - ] - - [tool.uv] - python-preference = "system" +**safe_sbatch** - ## From https://docs.astral.sh/uv/reference/settings/#index-strategy: - ## "Only use results from the first index that returns a match for a given package name." - ## In other words: only get the package from PyPI if there isn't a version of it in the DRAC wheelhouse. - # index-strategy = "first-index" +This example implements code checkpointing, so that jobs are executed with the code +as it was at the time of job submission, even if the code is updated later. +This is done by submitting the job with the `safe_sbatch` script instead of `sbatch`. +Compared to `sbatch`, `safe_sbatch` will prevent submitting jobs if there are uncommitted +changes in the code repository. - ## "Search for every package name across all indexes, exhausting the versions from the first index before - ## moving on to the next" - ## In other words: Only get the package from PyPI if the requested version is higher than the version - ## in the DRAC wheelhouse. - # index-strategy = "unsafe-first-match" - ## "Search for every package name across all indexes, preferring the "best" version found. - ## If a package version is in multiple indexes, only look at the entry for the first index." - ## In other words: Consider all versions of the package DRAC + PyPI, and use the version that best matches - ## the requested version. In a tie, choose the DRAC wheel. - index-strategy = "unsafe-best-match" +.. code:: bash - [[tool.uv.index]] - name = "drac-gentoo2023-x86-64-v3" - url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/x86-64-v3" - format = "flat" + #!/bin/bash + set -eof pipefail + git_status=`git status --porcelain` + # idea: Could add command-line arguments to control whether to add all changes and commit before sbatch. + if [[ ! -z $git_status ]]; then + echo "Your working directory is dirty! Please add and commit changes before continuing." + exit 1 + fi; + # This environment variable will be available in the job script. + # It should be used to checkout the repo at this commit (in a different directory than the original). + # For example: + # ``` + # git clone "$repo" "$dest" + # echo "Checking out commit $GIT_COMMIT" + # cd "$dest" + # git checkout $GIT_COMMIT + # ``` + export GIT_COMMIT=`git rev-parse HEAD` + exec sbatch "$@" - [[tool.uv.index]] - name = "drac-gentoo2023-generic" - url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/gentoo2023/generic" - format = "flat" - [[tool.uv.index]] - name = "drac-generic" - url = "/cvmfs/soft.computecanada.ca/custom/python/wheelhouse/generic" - format = "flat" **Running this example** @@ -712,7 +746,7 @@ Click here to see `the code for this example $ uv sync -3. Launch the job: +3. Launch the job, either with `sbatch` or the provided `safe_sbatch` script: .. code-block:: bash diff --git a/docs/examples/LLMs/accelerate_example/index.rst b/docs/examples/LLMs/accelerate_example/index.rst index 2c5d307d..e481476b 100644 --- a/docs/examples/LLMs/accelerate_example/index.rst +++ b/docs/examples/LLMs/accelerate_example/index.rst @@ -23,15 +23,30 @@ Click here to see `the code for this example .. literalinclude:: job.sh :language: bash +**pyproject.toml** + +.. literalinclude:: pyproject.toml + :language: toml + **main.py** .. literalinclude:: main.py :language: python -**pyproject.toml** -.. literalinclude:: pyproject.toml - :language: toml +**safe_sbatch** + +This example implements code checkpointing, so that jobs are executed with the code +as it was at the time of job submission, even if the code is updated later. +This is done by submitting the job with the `safe_sbatch` script instead of `sbatch`. +Compared to `sbatch`, `safe_sbatch` will prevent submitting jobs if there are uncommitted +changes in the code repository. + + +.. literalinclude:: safe_sbatch + :language: bash + + **Running this example** @@ -46,7 +61,7 @@ Click here to see `the code for this example $ uv sync -3. Launch the job: +3. Launch the job, either with `sbatch` or the provided `safe_sbatch` script: .. code-block:: bash From 3c4e932e16faa992861b34ffbb7904aacbefe39d Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Tue, 29 Jul 2025 13:17:51 -0400 Subject: [PATCH 16/18] Add missing section link for LLM-related examples Signed-off-by: Fabrice Normandin --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 67292e37..a6b0fdfc 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -46,6 +46,7 @@ recommend you start by checking out the :ref:`short quick start guide examples/frameworks/index examples/distributed/index examples/good_practices/index + examples/LLMs/index 🔗 Research Project Template From d73a88eabb4752d28d9d80b2aad986292954af72 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Tue, 29 Jul 2025 13:23:05 -0400 Subject: [PATCH 17/18] Update header of main.py Signed-off-by: Fabrice Normandin --- .../LLMs/accelerate_example/README.rst | 45 +++++++++---------- docs/examples/LLMs/accelerate_example/main.py | 45 +++++++++---------- 2 files changed, 42 insertions(+), 48 deletions(-) diff --git a/docs/examples/LLMs/accelerate_example/README.rst b/docs/examples/LLMs/accelerate_example/README.rst index 49316a85..50678d0a 100644 --- a/docs/examples/LLMs/accelerate_example/README.rst +++ b/docs/examples/LLMs/accelerate_example/README.rst @@ -128,8 +128,26 @@ Click here to see `the code for this example """HuggingFace Example from https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py Differences with the reference example: - - Uses the slurm job ID - + - Supports checkpointing using the slurm job ID as part of the checkpoint directory + + This is a fully working simple example to use Accelerate, + specifically showcasing the checkpointing capability, + and builds off the `nlp_example.py` script. + This example trains a Bert base model on GLUE MRPC + in any of the following settings (with the same script): + - single CPU or single GPU + - multi GPUS (using PyTorch distributed mode) + - (multi) TPUs + - fp16 (mixed-precision) or fp32 (normal precision) + + To help focus on the differences in the code, building `DataLoaders` + was refactored into its own function. + New additions from the base script can be found quickly by + looking for the # New Code # tags + + To run it in each of these various modes, follow the instructions + in the readme for examples: + https://github.com/huggingface/accelerate/tree/main/examples """ # Copyright 2021 The HuggingFace Inc. team. All rights reserved. @@ -176,28 +194,7 @@ Click here to see `the code for this example ) from transformers.optimization import get_linear_schedule_with_warmup from accelerate.logging import get_logger - ######################################################################## - # This is a fully working simple example to use Accelerate, - # specifically showcasing the checkpointing capability, - # and builds off the `nlp_example.py` script. - # - # This example trains a Bert base model on GLUE MRPC - # in any of the following settings (with the same script): - # - single CPU or single GPU - # - multi GPUS (using PyTorch distributed mode) - # - (multi) TPUs - # - fp16 (mixed-precision) or fp32 (normal precision) - # - # To help focus on the differences in the code, building `DataLoaders` - # was refactored into its own function. - # New additions from the base script can be found quickly by - # looking for the # New Code # tags - # - # To run it in each of these various modes, follow the instructions - # in the readme for examples: - # https://github.com/huggingface/accelerate/tree/main/examples - # - ######################################################################## + MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 diff --git a/docs/examples/LLMs/accelerate_example/main.py b/docs/examples/LLMs/accelerate_example/main.py index 933c5200..9470b54a 100644 --- a/docs/examples/LLMs/accelerate_example/main.py +++ b/docs/examples/LLMs/accelerate_example/main.py @@ -1,8 +1,26 @@ """HuggingFace Example from https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py Differences with the reference example: -- Uses the slurm job ID - +- Supports checkpointing using the slurm job ID as part of the checkpoint directory + +This is a fully working simple example to use Accelerate, +specifically showcasing the checkpointing capability, +and builds off the `nlp_example.py` script. +This example trains a Bert base model on GLUE MRPC +in any of the following settings (with the same script): + - single CPU or single GPU + - multi GPUS (using PyTorch distributed mode) + - (multi) TPUs + - fp16 (mixed-precision) or fp32 (normal precision) + +To help focus on the differences in the code, building `DataLoaders` +was refactored into its own function. +New additions from the base script can be found quickly by +looking for the # New Code # tags + +To run it in each of these various modes, follow the instructions +in the readme for examples: +https://github.com/huggingface/accelerate/tree/main/examples """ # Copyright 2021 The HuggingFace Inc. team. All rights reserved. @@ -49,28 +67,7 @@ ) from transformers.optimization import get_linear_schedule_with_warmup from accelerate.logging import get_logger -######################################################################## -# This is a fully working simple example to use Accelerate, -# specifically showcasing the checkpointing capability, -# and builds off the `nlp_example.py` script. -# -# This example trains a Bert base model on GLUE MRPC -# in any of the following settings (with the same script): -# - single CPU or single GPU -# - multi GPUS (using PyTorch distributed mode) -# - (multi) TPUs -# - fp16 (mixed-precision) or fp32 (normal precision) -# -# To help focus on the differences in the code, building `DataLoaders` -# was refactored into its own function. -# New additions from the base script can be found quickly by -# looking for the # New Code # tags -# -# To run it in each of these various modes, follow the instructions -# in the readme for examples: -# https://github.com/huggingface/accelerate/tree/main/examples -# -######################################################################## + MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 From 00ef6dd5475eb115b1185b7ca549b34666ed7ce6 Mon Sep 17 00:00:00 2001 From: Fabrice Normandin Date: Thu, 28 Aug 2025 15:20:31 -0400 Subject: [PATCH 18/18] Make accelerate example a uv workspace member Signed-off-by: Fabrice Normandin --- docs/examples/LLMs/accelerate_example/uv.lock | 1326 ----------------- pyproject.toml | 1 + uv.lock | 151 +- 3 files changed, 8 insertions(+), 1470 deletions(-) delete mode 100644 docs/examples/LLMs/accelerate_example/uv.lock diff --git a/docs/examples/LLMs/accelerate_example/uv.lock b/docs/examples/LLMs/accelerate_example/uv.lock deleted file mode 100644 index 9ca6102e..00000000 --- a/docs/examples/LLMs/accelerate_example/uv.lock +++ /dev/null @@ -1,1326 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.12" - 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