Clean up diffs in Trainer/TFTrainer (#5417)

* Cleanup and unify Trainer/TFTrainer

* Forgot to adapt TFTrainingArgs

* In tf scripts n_gpu -> n_replicas

* Update src/transformers/training_args.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments

* Formatting

* Fix typo
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

examples/multiple-choice/run_tf_multiple_choice.py

@@ -108,7 +108,10 @@ def main():
         level=logging.INFO,
     )
     logger.warning(
-        "device: %s, n_gpu: %s, 16-bits training: %s", training_args.device, training_args.n_gpu, training_args.fp16,
+        "device: %s, n_replicas: %s, 16-bits training: %s",
+        training_args.device,
+        training_args.n_replicas,
+        training_args.fp16,
     )
     logger.info("Training/evaluation parameters %s", training_args)
 

examples/question-answering/run_tf_squad.py

@@ -137,9 +137,9 @@ def main():
         level=logging.INFO,
     )
     logger.info(
-        "n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.n_gpu,
-        bool(training_args.n_gpu > 1),
+        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
+        training_args.n_replicas,
+        bool(training_args.n_replicas > 1),
         training_args.fp16,
     )
     logger.info("Training/evaluation parameters %s", training_args)

examples/text-classification/run_tf_glue.py

@@ -131,9 +131,9 @@ def main():
         level=logging.INFO,
     )
     logger.info(
-        "n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.n_gpu,
-        bool(training_args.n_gpu > 1),
+        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
+        training_args.n_replicas,
+        bool(training_args.n_replicas > 1),
         training_args.fp16,
     )
     logger.info("Training/evaluation parameters %s", training_args)

examples/token-classification/run_tf_ner.py

@@ -109,9 +109,9 @@ def main():
         level=logging.INFO,
     )
     logger.info(
-        "n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.n_gpu,
-        bool(training_args.n_gpu > 1),
+        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
+        training_args.n_replicas,
+        bool(training_args.n_replicas > 1),
         training_args.fp16,
     )
     logger.info("Training/evaluation parameters %s", training_args)

src/transformers/__init__.py

@@ -155,7 +155,7 @@ from .tokenization_xlm_roberta import XLMRobertaTokenizer
 from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
 
 # Trainer
-from .trainer_utils import EvalPrediction
+from .trainer_utils import EvalPrediction, set_seed
 from .training_args import TrainingArguments
 from .training_args_tf import TFTrainingArguments
 
@@ -397,7 +397,7 @@ if is_torch_available():
     )
 
     # Trainer
-    from .trainer import Trainer, set_seed, torch_distributed_zero_first
+    from .trainer import Trainer, torch_distributed_zero_first
     from .data.data_collator import default_data_collator, DataCollator, DataCollatorForLanguageModeling
     from .data.datasets import GlueDataset, TextDataset, LineByLineTextDataset, GlueDataTrainingArguments
 

src/transformers/trainer.py

 import logging
 import math
 import os
-import random
 import re
 import shutil
 import warnings
@@ -23,7 +22,14 @@ from .data.data_collator import DataCollator, default_data_collator
 from .file_utils import is_apex_available, is_torch_tpu_available
 from .modeling_utils import PreTrainedModel
 from .optimization import AdamW, get_linear_schedule_with_warmup
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, TrainOutput, is_wandb_available
+from .trainer_utils import (
+    PREFIX_CHECKPOINT_DIR,
+    EvalPrediction,
+    PredictionOutput,
+    TrainOutput,
+    is_wandb_available,
+    set_seed,
+)
 from .training_args import TrainingArguments
 
 
@@ -60,20 +66,6 @@ if is_wandb_available():
 logger = logging.getLogger(__name__)
 
 
-def set_seed(seed: int):
-    """
-    Helper function for reproducible behavior to set the seed in ``random``, ``numpy`` and ``torch``.
-
-    Args:
-        seed (:obj:`int`): The seed to set.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    # ^^ safe to call this function even if cuda is not available
-
-
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
     """
@@ -541,8 +533,8 @@ class Trainer:
 
                         self._log(logs)
 
-                        if self.args.evaluate_during_training:
-                            self.evaluate()
+                    if self.args.evaluate_during_training and self.global_step % self.args.eval_steps == 0:
+                        self.evaluate()
 
                     if self.args.save_steps > 0 and self.global_step % self.args.save_steps == 0:
                         # In all cases (even distributed/parallel), self.model is always a reference
@@ -573,7 +565,7 @@ class Trainer:
             if self.args.max_steps > 0 and self.global_step > self.args.max_steps:
                 train_iterator.close()
                 break
-            if self.args.tpu_metrics_debug:
+            if self.args.tpu_metrics_debug or self.args.debug:
                 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                 xm.master_print(met.metrics_report())
 
@@ -754,7 +746,7 @@ class Trainer:
 
         self._log(output.metrics)
 
-        if self.args.tpu_metrics_debug:
+        if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report())
 

src/transformers/trainer_tf.py

@@ -3,7 +3,6 @@
 import logging
 import math
 import os
-import random
 from typing import Callable, Dict, Optional, Tuple
 
 import numpy as np
@@ -11,7 +10,7 @@ import tensorflow as tf
 
 from .modeling_tf_utils import TFPreTrainedModel
 from .optimization_tf import GradientAccumulator, create_optimizer
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, is_wandb_available
+from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, is_wandb_available, set_seed
 from .training_args_tf import TFTrainingArguments
 
 
@@ -22,12 +21,6 @@ if is_wandb_available():
 logger = logging.getLogger(__name__)
 
 
-def set_seed(seed: int):
-    random.seed(seed)
-    np.random.seed(seed)
-    tf.random.set_seed(seed)
-
-
 class TFTrainer:
     """
     TFTrainer is a simple but feature-complete training and eval loop for TensorFlow,
@@ -256,7 +249,7 @@ class TFTrainer:
                 if isinstance(labels, tuple):
                     labels = labels[0]
 
-                if self.args.n_gpu > 1:
+                if self.args.n_replicas > 1:
                     for val in logits.values:
                         if preds is None:
                             preds = val.numpy()
@@ -542,7 +535,7 @@ class TFTrainer:
         loss, logits = outputs[:2]
         if self.args.past_index >= 0:
             self._past = outputs[self.args.past_index]
-        loss += sum(self.model.losses) * (1.0 / self.args.n_gpu)
+        loss += sum(self.model.losses) * (1.0 / self.args.n_replicas)
 
         return loss, logits
 

src/transformers/trainer_utils.py

 import os
+import random
 from typing import Dict, NamedTuple, Optional
 
 import numpy as np
 
+from .file_utils import is_tf_available, is_torch_available
+
 
 try:
     import wandb
@@ -21,6 +24,28 @@ def is_wandb_available():
     return _has_wandb
 
 
+def set_seed(seed: int):
+    """
+    Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf``
+    (if installed).
+
+    Args:
+        seed (:obj:`int`): The seed to set.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    if is_torch_available():
+        import torch
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        # ^^ safe to call this function even if cuda is not available
+    if is_tf_available():
+        import tensorflow as tf
+
+        tf.random.set_seed(seed)
+
+
 class EvalPrediction(NamedTuple):
     """
     Evaluation output (always contains labels), to be used to compute metrics.

src/transformers/training_args.py

@@ -97,11 +97,13 @@ class TrainingArguments:
             During distributed training, the rank of the process.
         tpu_num_cores (:obj:`int`, `optional`):
             When training on TPU, the mumber of TPU cores (automatically passed by launcher script).
-        tpu_metrics_debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
+        debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
             When training on TPU, whether to print debug metrics or not.
         dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
             or not.
+        eval_steps (:obj:`int`, `optional`, defaults to 1000):
+            Number of update steps between two evaluations.
         past_index (:obj:`int`, `optional`, defaults to -1):
             Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
             make use of the past hidden states for their predictions. If this argument is set to a positive int, the
@@ -202,11 +204,16 @@ class TrainingArguments:
     tpu_num_cores: Optional[int] = field(
         default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"}
     )
-    tpu_metrics_debug: bool = field(default=False, metadata={"help": "TPU: Whether to print debug metrics"})
+    tpu_metrics_debug: bool = field(
+        default=False,
+        metadata={"help": "Deprecated, the use of `--debug` is preferred. TPU: Whether to print debug metrics"},
+    )
+    debug: bool = field(default=False, metadata={"help": "Whether to print debug metrics on TPU"})
 
     dataloader_drop_last: bool = field(
         default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
     )
+    eval_steps: int = field(default=1000, metadata={"help": "Run an evaluation every X steps."})
 
     past_index: int = field(
         default=-1,

src/transformers/training_args_tf.py

 import logging
+import warnings
 from dataclasses import dataclass, field
 from typing import Tuple
 
@@ -80,11 +81,13 @@ class TFTrainingArguments(TrainingArguments):
             During distributed training, the rank of the process.
         tpu_num_cores (:obj:`int`, `optional`):
             When training on TPU, the mumber of TPU cores (automatically passed by launcher script).
-        tpu_metrics_debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            When training on TPU, whether to print debug metrics or not.
+        debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Wheter to activate the trace to record computation graphs and profiling information or not.
         dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
             or not.
+        eval_steps (:obj:`int`, `optional`, defaults to 1000):
+            Number of update steps before two evaluations.
         past_index (:obj:`int`, `optional`, defaults to -1):
             Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
             make use of the past hidden states for their predictions. If this argument is set to a positive int, the
@@ -92,19 +95,11 @@ class TFTrainingArguments(TrainingArguments):
             at the next training step under the keyword argument ``mems``.
         tpu_name (:obj:`str`, `optional`):
             The name of the TPU the process is running on.
-        eval_steps (:obj:`int`, `optional`, defaults to 1000):
-            Number of update steps before two evaluations.
-        debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Wheter to activate the trace to record computation graphs and profiling information or not.
     """
 
     tpu_name: str = field(
         default=None, metadata={"help": "Name of TPU"},
     )
-    eval_steps: int = field(default=1000, metadata={"help": "Run an evaluation every X steps."})
-    debug: bool = field(
-        default=False, metadata={"help": "Activate the trace to record computation graphs and profiling information"}
-    )
 
     @cached_property
     @tf_required
@@ -148,10 +143,48 @@ class TFTrainingArguments(TrainingArguments):
         """
         return self._setup_strategy
 
+    @property
+    @tf_required
+    def n_replicas(self) -> int:
+        """
+        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
+        """
+        return self._setup_strategy.num_replicas_in_sync
+
+    @property
+    def train_batch_size(self) -> int:
+        """
+        The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training).
+        """
+        if self.per_gpu_train_batch_size:
+            logger.warning(
+                "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
+                "version. Using `--per_device_train_batch_size` is preferred."
+            )
+        per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
+        return per_device_batch_size * max(1, self.n_replicas)
+
+    @property
+    def eval_batch_size(self) -> int:
+        """
+        The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training).
+        """
+        if self.per_gpu_eval_batch_size:
+            logger.warning(
+                "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
+                "version. Using `--per_device_eval_batch_size` is preferred."
+            )
+        per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
+        return per_device_batch_size * max(1, self.n_replicas)
+
     @property
     @tf_required
     def n_gpu(self) -> int:
         """
-        The number of replicas (GPUs or TPU cores) used in this training.
+        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
         """
+        warnings.warn(
+            "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
+            FutureWarning,
+        )
         return self._setup_strategy.num_replicas_in_sync