Clean up old Accelerate checks (#24279)

* Clean up old Accelerate checks

* Put back imports

setup.py

@@ -98,7 +98,7 @@ if stale_egg_info.exists():
 # 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
 _deps = [
     "Pillow",
-    "accelerate>=0.20.2",
+    "accelerate>=0.20.3",
     "av==9.2.0",  # Latest version of PyAV (10.0.0) has issues with audio stream.
     "beautifulsoup4",
     "black~=23.1",

src/transformers/dependency_versions_table.py

@@ -3,7 +3,7 @@
 # 2. run `make deps_table_update``
 deps = {
     "Pillow": "Pillow",
-    "accelerate": "accelerate>=0.20.2",
+    "accelerate": "accelerate>=0.20.3",
     "av": "av==9.2.0",
     "beautifulsoup4": "beautifulsoup4",
     "black": "black~=23.1",

src/transformers/modeling_utils.py

@@ -82,27 +82,17 @@ XLA_USE_BF16 = os.environ.get("XLA_USE_BF16", "0").upper()
 XLA_DOWNCAST_BF16 = os.environ.get("XLA_DOWNCAST_BF16", "0").upper()
 
 if is_accelerate_available():
-    from accelerate import __version__ as accelerate_version
     from accelerate import dispatch_model, infer_auto_device_map, init_empty_weights
     from accelerate.utils import (
+        check_tied_parameters_on_same_device,
         find_tied_parameters,
+        get_balanced_memory,
         load_offloaded_weights,
         offload_weight,
         save_offload_index,
         set_module_tensor_to_device,
     )
 
-    if version.parse(accelerate_version) > version.parse("0.11.0"):
-        from accelerate.utils import get_balanced_memory
-    else:
-        get_balanced_memory = None
-    if version.parse(accelerate_version) > version.parse("0.19.0"):
-        from accelerate.utils import check_tied_parameters_on_same_device
-    else:
-        check_tied_parameters_on_same_device = None
-else:
-    find_tied_parameters = None
-
 if is_safetensors_available():
     from safetensors import safe_open
     from safetensors.torch import load_file as safe_load_file
@@ -2792,8 +2782,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                     "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
                     "'sequential'."
                 )
-            elif device_map in ["balanced", "balanced_low_0"] and get_balanced_memory is None:
-                raise ValueError(f"`device_map={device_map}` requires a source install of Accelerate.")
 
             kwargs = {"no_split_module_classes": no_split_modules}
             if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
@@ -2803,7 +2791,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                     "This model has some weights that should be kept in higher precision, you need to upgrade "
                     "`accelerate` to properly deal with them (`pip install --upgrade accelerate`)."
                 )
-            if device_map != "sequential" and get_balanced_memory is not None:
+            if device_map != "sequential":
                 max_memory = get_balanced_memory(
                     model,
                     dtype=target_dtype,
@@ -2838,7 +2826,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             model.tie_weights()
             tied_params = find_tied_parameters(model)
             # check if we don't have tied param in different devices
-            if check_tied_parameters_on_same_device is not None:
             check_tied_parameters_on_same_device(tied_params, device_map)
 
         if from_tf:
@@ -3031,7 +3018,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         missing_keys = list(set(expected_keys) - set(loaded_keys))
         unexpected_keys = list(set(loaded_keys) - set(expected_keys))
 
-        if find_tied_parameters is not None:
+        if is_accelerate_available():
             tied_params = find_tied_parameters(model)
         else:
             tied_params = []

src/transformers/trainer.py

@@ -32,8 +32,6 @@ from collections.abc import Mapping
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
-from tqdm.auto import tqdm
-
 
 # Integrations must be imported before ML frameworks:
 # isort: off
@@ -206,14 +204,9 @@ if is_peft_available():
     from peft import PeftModel
 
 
-skip_first_batches = None
 if is_accelerate_available():
+    from accelerate import Accelerator, skip_first_batches
     from accelerate import __version__ as accelerate_version
-
-    if version.parse(accelerate_version) >= version.parse("0.16"):
-        from accelerate import skip_first_batches
-
-    from accelerate import Accelerator
     from accelerate.utils import DistributedDataParallelKwargs
 
     if version.parse(accelerate_version) > version.parse("0.20.3"):
@@ -322,6 +315,7 @@ class Trainer:
 
     """
 
+    # Those are used as methods of the Trainer in examples.
     from .trainer_pt_utils import _get_learning_rate, log_metrics, metrics_format, save_metrics, save_state
 
     def __init__(
@@ -1714,22 +1708,10 @@ class Trainer:
             logger.info(f"  Continuing training from epoch {epochs_trained}")
             logger.info(f"  Continuing training from global step {self.state.global_step}")
             if not args.ignore_data_skip:
-                if skip_first_batches is None:
-                    logger.info(
-                        f"  Will skip the first {epochs_trained} epochs then the first"
-                        f" {steps_trained_in_current_epoch} batches in the first epoch. If this takes a lot of time,"
-                        " you can install the latest version of Accelerate with `pip install -U accelerate`.You can"
-                        " also add the `--ignore_data_skip` flag to your launch command, but you will resume the"
-                        " training on data already seen by your model."
-                    )
-                else:
                 logger.info(
                     f"  Will skip the first {epochs_trained} epochs then the first"
                     f" {steps_trained_in_current_epoch} batches in the first epoch."
                 )
-                if self.is_local_process_zero() and not args.disable_tqdm and skip_first_batches is None:
-                    steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
-                    steps_trained_progress_bar.set_description("Skipping the first batches")
 
         # Update the references
         self.callback_handler.model = self.model
@@ -1787,7 +1769,7 @@ class Trainer:
 
             rng_to_sync = False
             steps_skipped = 0
-            if skip_first_batches is not None and steps_trained_in_current_epoch > 0:
+            if steps_trained_in_current_epoch > 0:
                 epoch_iterator = skip_first_batches(epoch_iterator, steps_trained_in_current_epoch)
                 steps_skipped = steps_trained_in_current_epoch
                 steps_trained_in_current_epoch = 0