Distributed eval: SequentialDistributedSampler + gather all results (#4243)

* Distributed eval: SequentialDistributedSampler + gather all results

* For consistency only write to disk from world_master

Close https://github.com/huggingface/transformers/issues/4272

* Working distributed eval

* Hook into scripts

* Fix #3721 again

* TPU.mesh_reduce: stay in tensor space

Thanks @jysohn23

* Just a small comment

* whitespace

* torch.hub: pip install packaging

* Add test scenarii

.github/workflows/github-torch-hub.yml

@@ -21,7 +21,7 @@ jobs:
     - name: Install dependencies
       run: |
         pip install torch
-        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses
+        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging
 
     - name: Torch hub list
       run: |

examples/language-modeling/run_language_modeling.py

@@ -251,7 +251,7 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval and training_args.local_rank in [-1, 0]:
+    if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
         eval_output = trainer.evaluate()
@@ -260,6 +260,7 @@ def main():
         result = {"perplexity": perplexity}
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
+        if trainer.is_world_master():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
                 for key in sorted(result.keys()):

examples/multiple-choice/run_multiple_choice.py

@@ -202,12 +202,13 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval and training_args.local_rank in [-1, 0]:
+    if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
         result = trainer.evaluate()
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
+        if trainer.is_world_master():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
                 for key, value in result.items():

examples/text-classification/run_glue.py

@@ -166,7 +166,7 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval and training_args.local_rank in [-1, 0]:
+    if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
         # Loop to handle MNLI double evaluation (matched, mis-matched)
@@ -181,6 +181,7 @@ def main():
             output_eval_file = os.path.join(
                 training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
             )
+            if trainer.is_world_master():
                 with open(output_eval_file, "w") as writer:
                     logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
                     for key, value in result.items():

examples/token-classification/run_ner.py

@@ -235,12 +235,13 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval and training_args.local_rank in [-1, 0]:
+    if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
         result = trainer.evaluate()
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
+        if trainer.is_world_master():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
                 for key, value in result.items():
@@ -250,7 +251,7 @@ def main():
             results.update(result)
 
     # Predict
-    if training_args.do_predict and training_args.local_rank in [-1, 0]:
+    if training_args.do_predict:
         test_dataset = NerDataset(
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
@@ -265,6 +266,7 @@ def main():
         preds_list, _ = align_predictions(predictions, label_ids)
 
         output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
+        if trainer.is_world_master():
             with open(output_test_results_file, "w") as writer:
                 for key, value in metrics.items():
                     logger.info("  %s = %s", key, value)
@@ -272,6 +274,7 @@ def main():
 
         # Save predictions
         output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
+        if trainer.is_world_master():
             with open(output_test_predictions_file, "w") as writer:
                 with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
                     example_id = 0
@@ -284,7 +287,9 @@ def main():
                             output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
                             writer.write(output_line)
                         else:
-                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
+                            logger.warning(
+                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
+                            )
 
     return results
 

src/transformers/trainer.py

 import json
 import logging
+import math
 import os
 import random
 import re
@@ -15,7 +16,7 @@ from torch import nn
 from torch.utils.data.dataloader import DataLoader
 from torch.utils.data.dataset import Dataset
 from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data.sampler import RandomSampler
+from torch.utils.data.sampler import RandomSampler, Sampler, SequentialSampler
 from tqdm.auto import tqdm, trange
 
 from .data.data_collator import DataCollator, DefaultDataCollator
@@ -90,7 +91,7 @@ def set_seed(seed: int):
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
     """
-    Decorator to make all processes in distributed training wait for the first one (locally) to do something.
+    Decorator to make all processes in distributed training wait for each local_master to do something.
     """
     if local_rank not in [-1, 0]:
         torch.distributed.barrier()
@@ -99,6 +100,50 @@ def torch_distributed_zero_first(local_rank: int):
         torch.distributed.barrier()
 
 
+class SequentialDistributedSampler(Sampler):
+    """
+    Distributed Sampler that subsamples indicies sequentially,
+    making it easier to collate all results at the end.
+
+    Even though we only use this sampler for eval and predict (no training),
+    which means that the model params won't have to be synced (i.e. will not hang
+    for synchronization even if varied number of forward passes), we still add extra
+    samples to the sampler to make it evenly divisible (like in `DistributedSampler`)
+    to make it easy to `gather` or `reduce` resulting tensors at the end of the loop.
+    """
+
+    def __init__(self, dataset, num_replicas=None, rank=None):
+        if num_replicas is None:
+            if not torch.distributed.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = torch.distributed.get_world_size()
+        if rank is None:
+            if not torch.distributed.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = torch.distributed.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self):
+        indices = list(range(len(self.dataset)))
+
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank * self.num_samples : (self.rank + 1) * self.num_samples]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+
 def get_tpu_sampler(dataset: Dataset):
     if xm.xrt_world_size() <= 1:
         return RandomSampler(dataset)
@@ -156,7 +201,7 @@ class Trainer:
         self.optimizers = optimizers
         if tb_writer is not None:
             self.tb_writer = tb_writer
-        elif is_tensorboard_available() and self.args.local_rank in [-1, 0]:
+        elif is_tensorboard_available() and self.is_world_master():
             self.tb_writer = SummaryWriter(log_dir=self.args.logging_dir)
         if not is_tensorboard_available():
             logger.warning(
@@ -171,7 +216,7 @@ class Trainer:
             )
         set_seed(self.args.seed)
         # Create output directory if needed
-        if self.is_local_master():
+        if self.is_world_master():
             os.makedirs(self.args.output_dir, exist_ok=True)
         if is_tpu_available():
             # Set an xla_device flag on the model's config.
@@ -208,13 +253,19 @@ class Trainer:
 
         eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
 
-        sampler = get_tpu_sampler(eval_dataset) if is_tpu_available() else None
+        if is_tpu_available():
+            sampler = SequentialDistributedSampler(
+                eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
+            )
+        elif self.args.local_rank != -1:
+            sampler = SequentialDistributedSampler(eval_dataset)
+        else:
+            sampler = SequentialSampler(eval_dataset)
 
         data_loader = DataLoader(
             eval_dataset,
             sampler=sampler,
             batch_size=self.args.eval_batch_size,
-            shuffle=False,
             collate_fn=self.data_collator.collate_batch,
         )
 
@@ -225,13 +276,19 @@ class Trainer:
 
     def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
         # We use the same batch_size as for eval.
-        sampler = get_tpu_sampler(test_dataset) if is_tpu_available() else None
+        if is_tpu_available():
+            sampler = SequentialDistributedSampler(
+                test_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
+            )
+        elif self.args.local_rank != -1:
+            sampler = SequentialDistributedSampler(test_dataset)
+        else:
+            sampler = SequentialSampler(test_dataset)
 
         data_loader = DataLoader(
             test_dataset,
             sampler=sampler,
             batch_size=self.args.eval_batch_size,
-            shuffle=False,
             collate_fn=self.data_collator.collate_batch,
         )
 
@@ -405,6 +462,9 @@ class Trainer:
             epochs_trained, int(num_train_epochs), desc="Epoch", disable=not self.is_local_master()
         )
         for epoch in train_iterator:
+            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
+                train_dataloader.sampler.set_epoch(epoch)
+
             epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=not self.is_local_master())
             for step, inputs in enumerate(epoch_iterator):
 
@@ -435,20 +495,17 @@ class Trainer:
                     self.global_step += 1
                     self.epoch = epoch + (step + 1) / len(epoch_iterator)
 
-                    if self.is_local_master():
                     if (self.args.logging_steps > 0 and self.global_step % self.args.logging_steps == 0) or (
                         self.global_step == 1 and self.args.logging_first_step
                     ):
                         logs: Dict[str, float] = {}
                         logs["loss"] = (tr_loss - logging_loss) / self.args.logging_steps
-                            # maintaining backward compatibility.
-                            # could use "scheduler.get_last_lr()[0]" instead for pytorch >= 1.4.0
+                        # backward compatibility for pytorch schedulers
                         logs["learning_rate"] = (
                             scheduler.get_last_lr()[0]
                             if version.parse(torch.__version__) >= version.parse("1.4")
                             else scheduler.get_lr()[0]
                         )
-
                         logging_loss = tr_loss
 
                         self._log(logs)
@@ -456,6 +513,7 @@ class Trainer:
                         if self.args.evaluate_during_training:
                             self.evaluate()
 
+                    if self.is_world_master():
                         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
                             # to the model we want to save.
@@ -548,7 +606,7 @@ class Trainer:
         Saving best-practices: if you use default names for the model,
         you can reload it using from_pretrained().
 
-        Will only save from the master process.
+        Will only save from the world_master process (unless in TPUs).
         """
 
         if is_tpu_available():
@@ -667,12 +725,15 @@ class Trainer:
 
         prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else self.prediction_loss_only
 
+        model = self.model
+        model.to(self.args.device)
         # multi-gpu eval
-        if self.args.n_gpu > 1 and not isinstance(self.model, torch.nn.DataParallel):
-            model = torch.nn.DataParallel(self.model)
+        if self.args.n_gpu > 1:
+            model = torch.nn.DataParallel(model)
         else:
             model = self.model
-        model.to(self.args.device)
+        # Note: in torch.distributed mode, there's no point in wrapping the model
+        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.
 
         if is_tpu_available():
             batch_size = dataloader._loader._loader.batch_size
@@ -682,8 +743,8 @@ class Trainer:
         logger.info("  Num examples = %d", self.num_examples(dataloader))
         logger.info("  Batch size = %d", batch_size)
         eval_losses: List[float] = []
-        preds: np.ndarray = None
-        label_ids: np.ndarray = None
+        preds: torch.Tensor = None
+        label_ids: torch.Tensor = None
         model.eval()
 
         for inputs in tqdm(dataloader, desc=description):
@@ -702,19 +763,33 @@ class Trainer:
 
             if not prediction_loss_only:
                 if preds is None:
-                    preds = logits.detach().cpu().numpy()
+                    preds = logits.detach()
                 else:
-                    preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
+                    preds = torch.cat((preds, logits.detach()), dim=0)
                 if inputs.get("labels") is not None:
                     if label_ids is None:
-                        label_ids = inputs["labels"].detach().cpu().numpy()
+                        label_ids = inputs["labels"].detach()
                     else:
-                        label_ids = np.append(label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
+                        label_ids = torch.cat((label_ids, inputs["labels"].detach()), dim=0)
 
-        if is_tpu_available() and preds is not None and label_ids is not None:
+        if self.args.local_rank != -1:
+            # In distributed mode, concatenate all results from all nodes:
+            if preds is not None:
+                preds = self.distributed_concat(preds, num_total_examples=self.num_examples(dataloader))
+            if label_ids is not None:
+                label_ids = self.distributed_concat(label_ids, num_total_examples=self.num_examples(dataloader))
+        elif is_tpu_available():
             # tpu-comment: Get all predictions and labels from all worker shards of eval dataset
-            preds = xm.mesh_reduce("eval_preds", preds, np.concatenate)
-            label_ids = xm.mesh_reduce("eval_out_label_ids", label_ids, np.concatenate)
+            if preds is not None:
+                preds = xm.mesh_reduce("eval_preds", preds, torch.cat)
+            if label_ids is not None:
+                label_ids = xm.mesh_reduce("eval_label_ids", label_ids, torch.cat)
+
+        # Finally, turn the aggregated tensors into numpy arrays.
+        if preds is not None:
+            preds = preds.cpu().numpy()
+        if label_ids is not None:
+            label_ids = label_ids.cpu().numpy()
 
         if self.compute_metrics is not None and preds is not None and label_ids is not None:
             metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
@@ -729,3 +804,15 @@ class Trainer:
                 metrics[f"eval_{key}"] = metrics.pop(key)
 
         return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics)
+
+    def distributed_concat(self, tensor: torch.Tensor, num_total_examples: int) -> torch.Tensor:
+        assert self.args.local_rank != -1
+
+        output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())]
+        torch.distributed.all_gather(output_tensors, tensor)
+
+        concat = torch.cat(output_tensors, dim=0)
+
+        # truncate the dummy elements added by SequentialDistributedSampler
+        output = concat[:num_total_examples]
+        return output

tests/test_trainer_distributed.py

+# This test is meant to be run in torch.distributed,
+# on a machine with multiple GPUs, in the following way:
+#
+#   python -m torch.distributed.launch --nproc_per_node 2 ./tests/test_trainer_distributed.py
+#
+# Replace 2 with the number of GPUs you have.
+#
+# You can also run it as a standalone file to test identical behavior in nn.DataParallel:
+#   python ./tests/test_trainer_distributed.py
+# and in single-GPU mode:
+#   CUDA_VISIBLE_DEVICES=0 python ./tests/test_trainer_distributed.py
+#
+
+
+import logging
+import sys
+from typing import Dict
+
+from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
+
+
+logger = logging.getLogger(__name__)
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+    from torch.utils.data.dataset import Dataset
+
+    from transformers import DataCollator, Trainer
+
+    class DummyDataset(Dataset):
+        def __init__(self, length: int = 101):
+            self.length = length
+
+        def __len__(self):
+            return self.length
+
+        def __getitem__(self, i) -> int:
+            return i
+
+    class DummyDataCollator(DataCollator):
+        def collate_batch(self, features):
+            return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}
+
+    class DummyModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            # Add some (unused) params otherwise DDP will complain.
+            self.fc = nn.Linear(120, 80)
+
+        def forward(self, input_ids, labels=None):
+            if labels is not None:
+                return torch.tensor(0.0, device=input_ids.device), input_ids
+            else:
+                return input_ids
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser((TrainingArguments,))
+    training_args = parser.parse_args_into_dataclasses(sys.argv + ["--output_dir", "./examples"])[0]
+
+    logging.basicConfig(level=logging.INFO)
+    logger.warning(
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s",
+        training_args.local_rank,
+        training_args.device,
+        training_args.n_gpu,
+        training_args.local_rank != -1,
+    )
+
+    # Essentially, what we want to verify in the distributed case is
+    # that we get all samples back, in the right order.
+    # (this is crucial for prediction for instance)
+    for dataset_length in [101, 40, 7]:
+        dataset = DummyDataset(dataset_length)
+
+        def compute_metrics(p: EvalPrediction) -> Dict:
+            sequential = list(range(len(dataset)))
+            success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
+            return {"success": success}
+
+        trainer = Trainer(
+            model=DummyModel(),
+            args=training_args,
+            data_collator=DummyDataCollator(),
+            eval_dataset=dataset,
+            compute_metrics=compute_metrics,
+        )
+        metrics = trainer.evaluate()
+        logger.info(metrics)
+        if metrics["eval_success"] is not True:
+            logger.error(metrics)
+            exit(1)
+
+        p = trainer.predict(dataset)
+        logger.info(p.metrics)
+        if p.metrics["eval_success"] is not True:
+            logger.error(p.metrics)
+            exit(1)
+
+    logger.info("🔥 All distributed tests successful")