From 0e2d2319e33c19481d1dc3e0ccc759a0e6a4149d Mon Sep 17 00:00:00 2001 From: Mehant Kammakomati Date: Sat, 14 Dec 2024 01:27:11 +0530 Subject: [PATCH] fix: add pad token when not present Signed-off-by: Mehant Kammakomati --- .../scripts/external_deps/test_performance.py | 2 + src/accelerate/test_utils/scripts/test_tp.py | 252 ++++++++++++++++++ 2 files changed, 254 insertions(+) create mode 100644 src/accelerate/test_utils/scripts/test_tp.py diff --git a/src/accelerate/test_utils/scripts/external_deps/test_performance.py b/src/accelerate/test_utils/scripts/external_deps/test_performance.py index 57fb1a01884..a087e6820a5 100644 --- a/src/accelerate/test_utils/scripts/external_deps/test_performance.py +++ b/src/accelerate/test_utils/scripts/external_deps/test_performance.py @@ -43,6 +43,8 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16, model_name: model_name (`str`, *optional*): """ tokenizer = AutoTokenizer.from_pretrained(model_name) + if not tokenizer.pad_token: + tokenizer.pad_token_id = 0 datasets = load_dataset("glue", "mrpc") def tokenize_function(examples): diff --git a/src/accelerate/test_utils/scripts/test_tp.py b/src/accelerate/test_utils/scripts/test_tp.py new file mode 100644 index 00000000000..e35ed563123 --- /dev/null +++ b/src/accelerate/test_utils/scripts/test_tp.py @@ -0,0 +1,252 @@ +# Copyright 2022 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 json +import os +from pathlib import Path + +import evaluate +import torch +from datasets import load_dataset +from torch.optim import AdamW +from torch.utils.data import DataLoader +from transformers import AutoModelForCausalLM, AutoTokenizer, get_linear_schedule_with_warmup, set_seed + +from accelerate import Accelerator, DistributedType +from accelerate.utils.deepspeed import DummyOptim, DummyScheduler + + +MAX_GPU_BATCH_SIZE = 16 +EVAL_BATCH_SIZE = 32 + + +def get_dataloaders(accelerator: Accelerator, batch_size: int = 16, model_name: str = "bert-base-cased"): + """ + Creates a set of `DataLoader`s for the `glue` dataset. + + Args: + accelerator (`Accelerator`): + An `Accelerator` object + batch_size (`int`, *optional*): + The batch size for the train and validation DataLoaders. + model_name (`str`, *optional*): + """ + tokenizer = AutoTokenizer.from_pretrained(model_name) + 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 + tokenized_datasets = datasets.map( + tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=False + ) + + # 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. + if accelerator.distributed_type == DistributedType.XLA: + return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") + return tokenizer.pad(examples, padding="longest", 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 + + +def training_function(config, args): + # Initialize accelerator + accelerator = Accelerator() + + # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs + lr = config["lr"] + num_epochs = int(config["num_epochs"]) + seed = int(config["seed"]) + batch_size = int(config["batch_size"]) + model_name = args.model_name_or_path + + set_seed(seed) + train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size, model_name) + + # Instantiate the model (we build the model here so that the seed also control new weights initialization) + model = AutoModelForCausalLM.from_pretrained(model_name, return_dict=True) + + # Instantiate optimizer + optimizer_cls = ( + AdamW + if accelerator.state.deepspeed_plugin is None + or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config + else DummyOptim + ) + optimizer = optimizer_cls(params=model.parameters(), lr=lr) + + max_training_steps = len(train_dataloader) * num_epochs + + # Instantiate scheduler + linear_decay_scheduler = False + if ( + accelerator.state.deepspeed_plugin is None + or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config + ): + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=max_training_steps, + ) + linear_decay_scheduler = True + else: + lr_scheduler = DummyScheduler(optimizer, total_num_steps=max_training_steps, warmup_num_steps=0) + + # 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 = accelerator.prepare( + model, optimizer, train_dataloader, eval_dataloader, lr_scheduler + ) + + # We also need to keep track of the stating epoch so files are named properly + starting_epoch = 0 + + # Now we train the model + metric = evaluate.load("glue", "mrpc") + best_performance = 0 + performance_metric = {} + expected_lr_after_first_optim_step = lr * ( + 1 - 1 / (max_training_steps / accelerator.num_processes / accelerator.gradient_accumulation_steps) + ) + lr_scheduler_check_completed = False + for epoch in range(starting_epoch, num_epochs): + model.train() + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(model): + outputs = model(**batch) + loss = outputs.loss + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # assert the learning rate after first optimizer step + if ( + accelerator.sync_gradients + and not lr_scheduler_check_completed + and linear_decay_scheduler + and accelerator.state.mixed_precision == "no" + ): + assert ( + lr_scheduler.get_last_lr()[0] == expected_lr_after_first_optim_step + ), f"Wrong lr found at second step, expected {expected_lr_after_first_optim_step}, got {lr_scheduler.get_last_lr()[0]}" + lr_scheduler_check_completed = True + + model.eval() + samples_seen = 0 + for step, batch in enumerate(eval_dataloader): + # We could avoid this line since we set the accelerator with `device_placement=True`. + batch.to(accelerator.device) + with torch.no_grad(): + outputs = model(**batch) + predictions = outputs.logits.argmax(dim=-1) + # It is slightly faster to call this once, than multiple times + predictions, references = accelerator.gather( + (predictions, batch["labels"]) + ) # If we are in a multiprocess environment, the last batch has duplicates + if accelerator.use_distributed: + if step == len(eval_dataloader) - 1: + predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] + references = references[: len(eval_dataloader.dataset) - samples_seen] + else: + samples_seen += references.shape[0] + metric.add_batch( + predictions=predictions, + references=references, + ) + + eval_metric = metric.compute() + # Use accelerator.print to print only on the main process. + accelerator.print(f"epoch {epoch}:", eval_metric) + performance_metric[f"epoch-{epoch}"] = eval_metric["accuracy"] + + if best_performance < eval_metric["accuracy"]: + best_performance = eval_metric["accuracy"] + + # check that the LR is 0 + if linear_decay_scheduler and accelerator.state.mixed_precision == "no": + assert ( + lr_scheduler.get_last_lr()[0] == 0 + ), f"Wrong lr found at last step, expected 0, got {lr_scheduler.get_last_lr()[0]}" + + if args.performance_lower_bound is not None: + assert ( + args.performance_lower_bound <= best_performance + ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}" + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: + json.dump(performance_metric, f) + + # Finally try saving the model + accelerator.save_model(model, args.output_dir) + accelerator.wait_for_everyone() + assert Path( + args.output_dir, "model.safetensors" + ).exists(), "Model was not saved when calling `Accelerator.save_model`" + accelerator.end_training() + + +def main(): + parser = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.") + parser.add_argument( + "--model_name_or_path", + type=str, + default="bert-base-cased", + help="Path to pretrained model or model identifier from huggingface.co/models.", + required=False, + ) + 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( + "--performance_lower_bound", + type=float, + default=None, + help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", + ) + parser.add_argument( + "--num_epochs", + type=int, + default=3, + help="Number of train epochs.", + ) + args = parser.parse_args() + config = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16} + training_function(config, args) + + +if __name__ == "__main__": + main()