Core scripts work 1:1

scripts/core_example_multigpu.py

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+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import evaluate
+import torch
+from datasets import load_dataset
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup
+
+from accelerate import Accelerator, DistributedType
+from accelerate.utils import set_seed
+
+import transformers
+
+transformers.logging.set_verbosity_error()
+
+import os
+from torch.nn.parallel import DistributedDataParallel
+import torch.distributed as torch_distributed
+
+
+
+def get_dataloaders(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):
+        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
+        return outputs
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+    def collate_fn(examples):
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            max_length=None,
+            pad_to_multiple_of=8,
+            return_tensors="pt",
+        )
+
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=32,
+        drop_last=False,
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def training_function():
+    torch_distributed.init_process_group(backend="nccl")
+    num_processes = torch_distributed.get_world_size()
+    process_index = torch_distributed.get_rank()
+    local_process_index = int(os.environ.get("LOCAL_RANK", -1))
+    device = torch.device("cuda", local_process_index)
+    torch.cuda.set_device(device)
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42}
+    seed = int(config["seed"])
+    batch_size = 32 # Check if this needs to be 32?
+    config["batch_size"] = batch_size
+    metric = evaluate.load("glue", "mrpc")
+
+    set_seed(seed, device_specific=False)
+    train_dataloader, eval_dataloader = get_dataloaders(batch_size)
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True).to(device)
+    model = DistributedDataParallel(
+        model, device_ids=[local_process_index], output_device=local_process_index
+    )
+
+    optimizer = AdamW(params=model.parameters(), lr=config["lr"])
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=(len(train_dataloader) * config["num_epochs"]),
+    )
+
+    current_step = 0
+    for epoch in range(config["num_epochs"]):
+        model.train()
+        total_loss = 0
+        for _, batch in enumerate(train_dataloader):
+            batch = batch.to(device)
+            outputs = model(**batch)
+            loss = outputs.loss
+            total_loss += loss.detach().cpu().float()
+            current_step += 1
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+        model.eval()
+        for step, batch in enumerate(eval_dataloader):
+            # We could avoid this line since we set the accelerator with `device_placement=True`.
+            batch = batch.to(device)
+            with torch.no_grad():
+                outputs = model(**batch)
+            predictions = outputs.logits.argmax(dim=-1)
+            metric.add_batch(
+                predictions=predictions,
+                references=batch["labels"],
+            )
+
+        eval_metric = metric.compute()
+        if process_index == 0:
+            print(
+                f"epoch {epoch}: {eval_metric}\n"
+                f"train_loss: {total_loss.item()/len(train_dataloader)}"
+            )
+
+
+def main():
+    training_function()
+
+
+if __name__ == "__main__":
+    main()

scripts/core_example_single_gpu.py

@@ -0,0 +1,138 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import evaluate
+import torch
+from datasets import load_dataset
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup
+
+from accelerate import Accelerator, DistributedType
+from accelerate.utils import set_seed
+
+import transformers
+
+transformers.logging.set_verbosity_error()
+
+
+
+def get_dataloaders(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):
+        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
+        return outputs
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+    def collate_fn(examples):
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            max_length=None,
+            pad_to_multiple_of=8,
+            return_tensors="pt",
+        )
+
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=32,
+        drop_last=False,
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def training_function():
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42}
+    seed = int(config["seed"])
+    batch_size = 32
+    config["batch_size"] = batch_size
+    metric = evaluate.load("glue", "mrpc")
+
+    set_seed(seed, device_specific=False)
+    train_dataloader, eval_dataloader = get_dataloaders(batch_size)
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
+    model.cuda()
+
+    optimizer = AdamW(params=model.parameters(), lr=config["lr"])
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=(len(train_dataloader) * config["num_epochs"]),
+    )
+
+    current_step = 0
+    for epoch in range(config["num_epochs"]):
+        model.train()
+        total_loss = 0
+        for _, batch in enumerate(train_dataloader):
+            batch = batch.to("cuda")
+            outputs = model(**batch)
+            loss = outputs.loss
+            total_loss += loss.detach().cpu().float()
+            current_step += 1
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+        model.eval()
+        for step, batch in enumerate(eval_dataloader):
+            # We could avoid this line since we set the accelerator with `device_placement=True`.
+            batch = batch.to("cuda")
+            with torch.no_grad():
+                outputs = model(**batch)
+            predictions = outputs.logits.argmax(dim=-1)
+            metric.add_batch(
+                predictions=predictions,
+                references=batch["labels"],
+            )
+
+        eval_metric = metric.compute()
+        
+        # Use accelerator.print to print only on the main process.
+        print(f"epoch {epoch}:", eval_metric)
+        print("train_loss: ", total_loss.item() / len(train_dataloader))
+
+
+def main():
+    training_function()
+
+
+if __name__ == "__main__":
+    main()

scripts/nlp_example.py

@@ -0,0 +1,184 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import evaluate
+import torch
+from datasets import load_dataset
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup
+
+from accelerate import Accelerator, DistributedType
+from accelerate.utils import set_seed
+
+
+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.TPU else None
+        # When using mixed precision we want round multiples of 8/16
+        if 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, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=32,
+        drop_last=(accelerator.mixed_precision == "fp8"),
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def training_function(config):
+    # Initialize accelerator
+    accelerator = Accelerator(
+        mixed_precision="fp16",
+        log_with="aim",
+        project_dir="aim_logs"
+    )
+    # 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 = 16 if accelerator.num_processes > 1 else 32
+    config["batch_size"] = batch_size
+    metric = evaluate.load("glue", "mrpc")
+
+    set_seed(seed, device_specific=True)
+    train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
+    lr = lr * accelerator.num_processes
+
+    optimizer = AdamW(params=model.parameters(), lr=lr)
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=(len(train_dataloader) * num_epochs),
+    )
+
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    )
+
+    accelerator.init_trackers(f'{accelerator.num_processes}_gpus', config)
+
+    current_step = 0
+    for epoch in range(num_epochs):
+        model.train()
+        total_loss = 0
+        for _, batch in enumerate(train_dataloader):
+            lr = lr_scheduler.get_lr()
+            outputs = model(**batch)
+            loss = outputs.loss
+            batch_loss = accelerator.gather(loss).detach().mean().cpu().float()
+            total_loss += batch_loss
+            current_step += 1
+            accelerator.log(
+                {
+                    "batch_loss":batch_loss,
+                    "learning_rate":lr,
+                }, 
+                step=current_step, 
+                log_kwargs={"aim":{"epoch":epoch}}
+                )
+            accelerator.backward(loss)
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+            current_step += 1
+
+        model.eval()
+        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)
+            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
+            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)
+
+        accelerator.log(
+            {
+                "accuracy": eval_metric["accuracy"],
+                "f1": eval_metric["f1"],
+                "train_loss": total_loss.item() / len(train_dataloader),
+            },
+            log_kwargs = {"aim":{"epoch":epoch}}
+        )
+    accelerator.end_training()
+
+
+def main():
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42}
+    training_function(config)
+
+
+if __name__ == "__main__":
+    main()