Add the auto_find_batch_size capability from Accelerate into Trainer

.circleci/config.yml

@@ -84,6 +84,7 @@ jobs:
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install tensorflow_probability
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -122,6 +123,7 @@ jobs:
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install tensorflow_probability
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -154,6 +156,7 @@ jobs:
             - run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -191,6 +194,7 @@ jobs:
             - run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -222,6 +226,7 @@ jobs:
             - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -258,6 +263,7 @@ jobs:
             - run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
             - run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.11.0+cpu.html
             - run: pip install https://github.com/kpu/kenlm/archive/master.zip
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:

setup.py

@@ -96,6 +96,7 @@
 # 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
 _deps = [
     "Pillow",
+    "accelerate>=0.7.1",
     "black~=22.0",
     "codecarbon==1.2.0",
     "cookiecutter==1.7.3",

src/transformers/dependency_versions_table.py

@@ -3,6 +3,7 @@
 # 2. run `make deps_table_update``
 deps = {
     "Pillow": "Pillow",
+    "accelerate": "accelerate>=0.7.1",
     "black": "black~=22.0",
     "codecarbon": "codecarbon==1.2.0",
     "cookiecutter": "cookiecutter==1.7.3",

src/transformers/testing_utils.py

@@ -40,6 +40,7 @@
     is_wandb_available,
 )
 from .utils import (
+    is_accelerate_available,
     is_apex_available,
     is_bitsandbytes_available,
     is_detectron2_available,
@@ -238,6 +239,13 @@ def require_git_lfs(test_case):
     return unittest.skipUnless(_run_git_lfs_tests, "test of git lfs workflow")(test_case)
 
 
+def require_accelerate(test_case):
+    """
+    Decorator marking a test that requires accelerate. These tests are skipped when accelerate isn't installed.
+    """
+    return unittest.skipUnless(is_accelerate_available(), "test requires accelerate")(test_case)
+
+
 def require_rjieba(test_case):
     """
     Decorator marking a test that requires rjieba. These tests are skipped when rjieba isn't installed.

src/transformers/trainer.py

@@ -115,6 +115,7 @@
     default_compute_objective,
     default_hp_space,
     denumpify_detensorize,
+    find_executable_batch_size,
     get_last_checkpoint,
     has_length,
     number_of_arguments,
@@ -548,6 +549,9 @@ def __init__(
         self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
         self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)
 
+        # Internal variables to keep track of the original batch size
+        self._train_batch_size = args.train_batch_size
+
         # very last
         self._memory_tracker.stop_and_update_metrics()
 
@@ -718,7 +722,7 @@ def get_train_dataloader(self) -> DataLoader:
             if self.args.world_size > 1:
                 train_dataset = IterableDatasetShard(
                     train_dataset,
-                    batch_size=self.args.train_batch_size,
+                    batch_size=self._train_batch_size,
                     drop_last=self.args.dataloader_drop_last,
                     num_processes=self.args.world_size,
                     process_index=self.args.process_index,
@@ -736,7 +740,7 @@ def get_train_dataloader(self) -> DataLoader:
 
         return DataLoader(
             train_dataset,
-            batch_size=self.args.train_batch_size,
+            batch_size=self._train_batch_size,
             sampler=train_sampler,
             collate_fn=self.data_collator,
             drop_last=self.args.dataloader_drop_last,
@@ -1267,6 +1271,20 @@ def train(
                 self._move_model_to_device(self.model, args.device)
             self.model_wrapped = self.model
 
+        inner_training_loop = find_executable_batch_size(
+            self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
+        )
+        return inner_training_loop(
+            args=args,
+            resume_from_checkpoint=resume_from_checkpoint,
+            trial=trial,
+            ignore_keys_for_eval=ignore_keys_for_eval,
+        )
+
+    def _inner_training_loop(
+        self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
+    ):
+        self._train_batch_size = batch_size
         # Data loader and number of training steps
         train_dataloader = self.get_train_dataloader()
 

src/transformers/trainer_utils.py

@@ -36,6 +36,7 @@
     is_torch_available,
     is_torch_cuda_available,
     is_torch_tpu_available,
+    requires_backends,
 )
 
 
@@ -355,6 +356,7 @@ class TrainerMemoryTracker:
     stages = {
         "__init__": "init",
         "train": "train",
+        "_inner_training_loop": "train",
         "evaluate": "eval",
         "predict": "test",
     }
@@ -584,6 +586,37 @@ class ShardedDDPOption(ExplicitEnum):
     AUTO_WRAP = "auto_wrap"
 
 
+def find_executable_batch_size(
+    function: callable = None, starting_batch_size: int = 128, auto_find_batch_size: bool = False
+):
+    """
+    Args:
+    A basic decorator that will try to execute `function`. If it fails from exceptions related to out-of-memory or
+    CUDNN, the batch size is cut in half and passed to `function` `function` must take in a `batch_size` parameter as
+    its first argument.
+        function (`callable`, *optional*)
+            A function to wrap
+        starting_batch_size (`int`, *optional*)
+            The batch size to try and fit into memory
+        auto_find_batch_size (`bool`, *optional*)
+            If False, will just execute `function`
+    """
+    if function is None:
+        return functools.partial(
+            find_executable_batch_size,
+            starting_batch_size=starting_batch_size,
+            auto_find_batch_size=auto_find_batch_size,
+        )
+
+    if auto_find_batch_size:
+        requires_backends(find_executable_batch_size, "accelerate")
+        import accelerate.memory_utils as mem_utils
+
+        return mem_utils.find_executable_batch_size(function=function, starting_batch_size=starting_batch_size)
+
+    return functools.partial(function, batch_size=starting_batch_size)
+
+
 class FSDPOption(ExplicitEnum):
     FULL_SHARD = "full_shard"
     SHARD_GRAD_OP = "shard_grad_op"

src/transformers/training_args.py

@@ -443,6 +443,9 @@ class TrainingArguments:
         include_inputs_for_metrics (`bool`, *optional*, defaults to `False`):
             Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics
             that need inputs, predictions and references for scoring calculation in Metric class.
+        auto_find_batch_size (`bool`, *optional*, defaults to `False`)
+            Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding
+            CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
     """
 
     output_dir: str = field(
@@ -803,6 +806,13 @@ class TrainingArguments:
         metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer"},
     )
 
+    auto_find_batch_size: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to automatically decrease the batch size in half and rerun the training loop again each time a CUDA Out-of-Memory was reached"
+        },
+    )
+
     def __post_init__(self):
         # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then).
         # This needs to happen before any call to self.device or self.n_gpu.

src/transformers/utils/__init__.py

@@ -85,6 +85,7 @@
     DummyObject,
     OptionalDependencyNotAvailable,
     _LazyModule,
+    is_accelerate_available,
     is_apex_available,
     is_bitsandbytes_available,
     is_coloredlogs_available,

src/transformers/utils/import_utils.py

@@ -428,6 +428,10 @@ def is_protobuf_available():
     return importlib.util.find_spec("google.protobuf") is not None
 
 
+def is_accelerate_available():
+    return importlib.util.find_spec("accelerate") is not None
+
+
 def is_tokenizers_available():
     return importlib.util.find_spec("tokenizers") is not None
 
@@ -725,6 +729,12 @@ def wrapper(*args, **kwargs):
 `pip install pyctcdecode`
 """
 
+# docstyle-ignore
+ACCELERATE_IMPORT_ERROR = """
+{0} requires the accelerate library but it was not found in your environment. You can install it with pip:
+`pip install accelerate`
+"""
+
 
 BACKENDS_MAPPING = OrderedDict(
     [
@@ -750,6 +760,7 @@ def wrapper(*args, **kwargs):
         ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
         ("vision", (is_vision_available, VISION_IMPORT_ERROR)),
         ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
+        ("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)),
     ]
 )
 

tests/trainer/test_trainer.py

@@ -21,6 +21,7 @@
 import random
 import re
 import subprocess
+import sys
 import tempfile
 import time
 import unittest
@@ -58,6 +59,7 @@
     require_torch_bf16,
     require_torch_gpu,
     require_torch_multi_gpu,
+    require_torch_non_multi_gpu,
     require_torch_tf32,
     require_torch_up_to_2_gpus,
     require_wandb,
@@ -1075,6 +1077,41 @@ def test_resume_training_with_randomness(self):
             self.assertAlmostEqual(a, a1, delta=1e-8)
             self.assertAlmostEqual(b, b1, delta=1e-8)
 
+    @slow
+    @require_torch_non_multi_gpu
+    def test_auto_batch_size_finder(self):
+
+        if torch.cuda.is_available():
+            torch.backends.cudnn.deterministic = True
+
+        SRC_DIR = os.path.abspath(
+            os.path.join(os.path.dirname(__file__), "..", "..", "examples", "pytorch", "text-classification")
+        )
+        sys.path.append(SRC_DIR)
+        import run_glue
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            testargs = f"""
+                run_glue.py
+                --model_name_or_path distilbert-base-uncased
+                --task_name mrpc
+                --do_train
+                --do_eval
+                --max_seq_len 128
+                --per_device_train_batch_size 4096
+                --learning_rate 2e-5
+                --num_train_epochs 1
+                --output_dir {tmpdir}
+                --auto_find_batch_size 0
+                """.split()
+            with self.assertRaises(RuntimeError):
+                with patch.object(sys, "argv", testargs):
+                    run_glue.main()
+
+        testargs[-1] = "1"
+        with patch.object(sys, "argv", testargs):
+            run_glue.main()
+
     # regression for this issue: https://github.com/huggingface/transformers/issues/12970
     def test_training_with_resume_from_checkpoint_false(self):
         train_dataset = RegressionDataset(length=128)

tests/trainer/test_trainer_utils.py

@@ -18,7 +18,8 @@
 
 import numpy as np
 
-from transformers.testing_utils import require_torch
+from transformers.testing_utils import require_accelerate, require_torch
+from transformers.trainer_utils import find_executable_batch_size
 from transformers.utils import is_torch_available
 
 
@@ -420,3 +421,39 @@ def test_shard_sampler(self):
 
             self.check_shard_sampler(dataset, 4, drop_last=True, num_processes=3)
             self.check_shard_sampler(dataset, 4, drop_last=False, num_processes=3)
+
+    @require_accelerate
+    def test_executable_batch_size(self):
+        batch_sizes = []
+
+        @find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=True)
+        def mock_training_loop_function(batch_size):
+            nonlocal batch_sizes
+            batch_sizes.append(batch_size)
+            if batch_size > 16:
+                raise RuntimeError("CUDA out of memory.")
+
+        mock_training_loop_function()
+        self.assertEqual(batch_sizes, [64, 32, 16])
+
+    @require_accelerate
+    def test_executable_batch_size_no_search(self):
+        batch_sizes = []
+
+        @find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=False)
+        def mock_training_loop_function(batch_size):
+            nonlocal batch_sizes
+            batch_sizes.append(batch_size)
+
+        mock_training_loop_function()
+        self.assertEqual(batch_sizes, [64])
+
+    @require_accelerate
+    def test_executable_batch_size_with_error(self):
+        @find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=False)
+        def mock_training_loop_function(batch_size):
+            raise RuntimeError("CUDA out of memory.")
+
+        with self.assertRaises(RuntimeError) as cm:
+            mock_training_loop_function()
+            self.assertEqual("CUDA out of memory", cm.args[0])