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https://github.com/huggingface/accelerate.git
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5002e56704
* Setup 2023 tooling for quality * Result of styling * Simplify inits and remove isort and flake8 from doc * Puts back isort skip flag
382 lines
16 KiB
Python
382 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import gc
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import os
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import evaluate
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import torch
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
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from accelerate import Accelerator, DistributedType
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########################################################################
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# This is a fully working simple example to use Accelerate
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#
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# This example trains a Bert base model on GLUE MRPC
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# in any of the following settings (with the same script):
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# - single CPU or single GPU
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# - multi GPUS (using PyTorch distributed mode)
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# - (multi) TPUs
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# - fp16 (mixed-precision) or fp32 (normal precision)
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# - FSDP
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#
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# This example also demonstrates the checkpointing and sharding capabilities
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#
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# To run it in each of these various modes, follow the instructions
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# in the readme for examples:
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# https://github.com/huggingface/accelerate/tree/main/examples
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#
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########################################################################
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MAX_GPU_BATCH_SIZE = 16
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EVAL_BATCH_SIZE = 32
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# New Code #
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# Converting Bytes to Megabytes
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def b2mb(x):
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return int(x / 2**20)
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# New Code #
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# This context manager is used to track the peak memory usage of the process
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class TorchTracemalloc:
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def __enter__(self):
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
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self.begin = torch.cuda.memory_allocated()
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return self
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def __exit__(self, *exc):
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gc.collect()
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torch.cuda.empty_cache()
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self.end = torch.cuda.memory_allocated()
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self.peak = torch.cuda.max_memory_allocated()
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self.used = b2mb(self.end - self.begin)
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self.peaked = b2mb(self.peak - self.begin)
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# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
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# For testing only
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if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
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from accelerate.test_utils.training import mocked_dataloaders
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get_dataloaders = mocked_dataloaders # noqa: F811
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def training_function(config, args):
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# For testing only
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if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
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config["num_epochs"] = 2
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# Initialize accelerator
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if args.with_tracking:
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accelerator = Accelerator(
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cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="wandb", logging_dir=args.logging_dir
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)
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else:
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accelerator = Accelerator()
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accelerator.print(accelerator.distributed_type)
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if hasattr(args.checkpointing_steps, "isdigit"):
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if args.checkpointing_steps == "epoch":
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checkpointing_steps = args.checkpointing_steps
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elif args.checkpointing_steps.isdigit():
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checkpointing_steps = int(args.checkpointing_steps)
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else:
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raise ValueError(
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f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."
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)
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else:
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checkpointing_steps = None
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# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
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lr = config["lr"]
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num_epochs = int(config["num_epochs"])
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seed = int(config["seed"])
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batch_size = int(config["batch_size"])
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# We need to initialize the trackers we use, and also store our configuration
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if args.with_tracking:
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experiment_config = vars(args)
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accelerator.init_trackers("fsdp_glue_no_trainer", experiment_config)
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
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datasets = load_dataset("glue", "mrpc")
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metric = evaluate.load("glue", "mrpc")
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def tokenize_function(examples):
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# max_length=None => use the model max length (it's actually the default)
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outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
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return outputs
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# Apply the method we just defined to all the examples in all the splits of the dataset
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# starting with the main process first:
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with accelerator.main_process_first():
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tokenized_datasets = datasets.map(
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tokenize_function,
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batched=True,
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remove_columns=["idx", "sentence1", "sentence2"],
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)
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# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
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# transformers library
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tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
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# If the batch size is too big we use gradient accumulation
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gradient_accumulation_steps = 1
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if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
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gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
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batch_size = MAX_GPU_BATCH_SIZE
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def collate_fn(examples):
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# On TPU it's best to pad everything to the same length or training will be very slow.
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if accelerator.distributed_type == DistributedType.TPU:
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return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
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return tokenizer.pad(examples, padding="longest", return_tensors="pt")
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# Instantiate dataloaders.
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train_dataloader = DataLoader(
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tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
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)
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eval_dataloader = DataLoader(
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tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
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)
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set_seed(seed)
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# Instantiate the model (we build the model here so that the seed also control new weights initialization)
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model = AutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, return_dict=True)
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# New Code #
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# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
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model = accelerator.prepare(model)
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accelerator.print(model)
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# Instantiate optimizer
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# New Code #
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# For FSDP feature, at present it doesn't support multiple parameter groups,
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# so we need to create a single parameter group for the whole model
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optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr, weight_decay=2e-4)
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# Instantiate scheduler
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lr_scheduler = get_linear_schedule_with_warmup(
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optimizer=optimizer,
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num_warmup_steps=10,
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num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
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)
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# New Code #
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# For FSDP feature, prepare everything except the model as we have already prepared the model
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# before creating the optimizer
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# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
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# prepare method.
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optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
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optimizer, train_dataloader, eval_dataloader, lr_scheduler
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)
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overall_step = 0
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# Potentially load in the weights and states from a previous save
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if args.resume_from_checkpoint:
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if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
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accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
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accelerator.load_state(args.resume_from_checkpoint)
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path = os.path.basename(args.resume_from_checkpoint)
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else:
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# Get the most recent checkpoint
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dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
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dirs.sort(key=os.path.getctime)
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path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
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# Extract `epoch_{i}` or `step_{i}`
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training_difference = os.path.splitext(path)[0]
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if "epoch" in training_difference:
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num_epochs -= int(training_difference.replace("epoch_", ""))
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resume_step = None
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else:
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resume_step = int(training_difference.replace("step_", ""))
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num_epochs -= resume_step // len(train_dataloader)
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# If resuming by step, we also need to know exactly how far into the DataLoader we went
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resume_step = (num_epochs * len(train_dataloader)) - resume_step
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# Now we train the model
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for epoch in range(num_epochs):
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# New Code #
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# context manager to track the peak memory usage during the training epoch
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with TorchTracemalloc() as tracemalloc:
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model.train()
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if args.with_tracking:
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total_loss = 0
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for step, batch in enumerate(train_dataloader):
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# We need to skip steps until we reach the resumed step
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if args.resume_from_checkpoint and epoch == 0:
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if resume_step is not None and step < resume_step:
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pass
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch.to(accelerator.device)
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outputs = model(**batch)
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loss = outputs.loss
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loss = loss / gradient_accumulation_steps
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# We keep track of the loss at each epoch
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if args.with_tracking:
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total_loss += loss.detach().float()
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accelerator.backward(loss)
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if step % gradient_accumulation_steps == 0:
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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# accelerator.print(lr_scheduler.get_lr())
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overall_step += 1
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if isinstance(checkpointing_steps, int):
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output_dir = f"step_{overall_step}"
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if overall_step % checkpointing_steps == 0:
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if args.output_dir is not None:
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output_dir = os.path.join(args.output_dir, output_dir)
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accelerator.save_state(output_dir)
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# New Code #
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# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
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accelerator.print("Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
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accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
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accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
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accelerator.print(
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"Total Peak Memory consumed during the train (max): {}".format(
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tracemalloc.peaked + b2mb(tracemalloc.begin)
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)
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)
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# Logging the peak memory usage of the GPU to the tracker
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if args.with_tracking:
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accelerator.log(
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{
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"train_total_peak_memory": tracemalloc.peaked + b2mb(tracemalloc.begin),
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},
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step=epoch,
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)
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# New Code #
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# context manager to track the peak memory usage during the evaluation
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with TorchTracemalloc() as tracemalloc:
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model.eval()
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for step, batch in enumerate(eval_dataloader):
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch.to(accelerator.device)
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with torch.no_grad():
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outputs = model(**batch)
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predictions = outputs.logits.argmax(dim=-1)
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predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
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metric.add_batch(
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predictions=predictions,
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references=references,
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)
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eval_metric = metric.compute()
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# Use accelerator.print to print only on the main process.
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accelerator.print(f"epoch {epoch}:", eval_metric)
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if args.with_tracking:
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accelerator.log(
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{
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"accuracy": eval_metric["accuracy"],
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"f1": eval_metric["f1"],
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"train_loss": total_loss.item() / len(train_dataloader),
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},
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step=epoch,
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)
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if checkpointing_steps == "epoch":
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output_dir = f"epoch_{epoch}"
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if args.output_dir is not None:
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output_dir = os.path.join(args.output_dir, output_dir)
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accelerator.save_state(output_dir)
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# New Code #
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# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
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accelerator.print("Memory before entering the eval : {}".format(b2mb(tracemalloc.begin)))
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accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
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accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
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accelerator.print(
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"Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + b2mb(tracemalloc.begin))
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)
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# Logging the peak memory usage of the GPU to the tracker
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if args.with_tracking:
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accelerator.log(
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{
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"eval_total_peak_memory": tracemalloc.peaked + b2mb(tracemalloc.begin),
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},
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step=epoch,
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)
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if args.with_tracking:
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accelerator.end_training()
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def main():
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parser = argparse.ArgumentParser(description="Simple example of training script.")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help="Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU.",
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)
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parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default=None,
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help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help="If the training should continue from a checkpoint folder.",
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)
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to load in all available experiment trackers from the environment and use them for logging.",
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default=".",
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help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help="Location on where to store experiment tracking logs`",
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)
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parser.add_argument(
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"--model_name_or_path",
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type=str,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=True,
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)
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args = parser.parse_args()
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config = {"lr": 2e-5, "num_epochs": 3, "seed": 1, "batch_size": 16}
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training_function(config, args)
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if __name__ == "__main__":
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main()
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