Replacement of 20 asserts with exceptions (#24757)

* initial replacements of asserts with errors/exceptions

* replace assert with exception in generation, align and bart

* reset formatting change

* reset another formatting issue

* Apply suggestion

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* don't touch this file

* change to 'is not False'

* fix type

---------

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -242,10 +242,12 @@ class DataTrainingArguments:
         else:
             if self.train_file is not None:
                 extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+                if extension not in ["csv", "json", "txt"]:
+                    raise ValueError("train_file` should be a csv, json or text file.")
             if self.validation_file is not None:
                 extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+                if extension not in ["csv", "json", "txt"]:
+                    raise ValueError("`validation_file` should be a csv, json or text file.")
 
 
 @flax.struct.dataclass

examples/flax/language-modeling/run_clm_flax.py

@@ -251,10 +251,12 @@ class DataTrainingArguments:
         else:
             if self.train_file is not None:
                 extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+                if extension not in ["csv", "json", "txt"]:
+                    raise ValueError("train_file` should be a csv, json or text file.")
             if self.validation_file is not None:
                 extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+                if extension not in ["csv", "json", "txt"]:
+                    raise ValueError("`validation_file` should be a csv, json or text file.")
 
 
 class TrainState(train_state.TrainState):

src/transformers/benchmark/benchmark_args_utils.py

@@ -147,11 +147,12 @@ class BenchmarkArguments:
         return json.dumps(dataclasses.asdict(self), indent=2)
 
     @property
-    def model_names(self):
-        assert len(self.models) > 0, (
-            "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
-            " bert-base-cased` or `args.models = ['bert-base-cased']."
-        )
+    def model_names(self) -> List[str]:
+        if len(self.models) <= 0:
+            raise ValueError(
+                "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
+                " bert-base-cased` or `args.models = ['bert-base-cased']."
+            )
         return self.models
 
     @property

src/transformers/benchmark/benchmark_tf.py

@@ -60,9 +60,10 @@ def run_with_tf_optimizations(do_eager_mode: bool, use_xla: bool):
             return func(*args, **kwargs)
 
         if do_eager_mode is True:
-            assert (
-                use_xla is False
-            ), "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."
+            if use_xla is not False:
+                raise ValueError(
+                    "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."
+                )
             return run_in_eager_mode
         else:
             return run_in_graph_mode
@@ -88,13 +89,15 @@ class TensorFlowBenchmark(Benchmark):
     def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
         # initialize GPU on separate process
         strategy = self.args.strategy
-        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+        if strategy is None:
+            raise ValueError("A device strategy has to be initialized before using TensorFlow.")
         _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
         return self._measure_speed(_inference)
 
     def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
         strategy = self.args.strategy
-        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+        if strategy is None:
+            raise ValueError("A device strategy has to be initialized before using TensorFlow.")
         _train = self._prepare_train_func(model_name, batch_size, sequence_length)
         return self._measure_speed(_train)
 
@@ -105,7 +108,8 @@ class TensorFlowBenchmark(Benchmark):
         if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
         strategy = self.args.strategy
-        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+        if strategy is None:
+            raise ValueError("A device strategy has to be initialized before using TensorFlow.")
         _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
         return self._measure_memory(_inference)
 
@@ -115,7 +119,8 @@ class TensorFlowBenchmark(Benchmark):
         if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
         strategy = self.args.strategy
-        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
+        if strategy is None:
+            raise ValueError("A device strategy has to be initialized before using TensorFlow.")
 
         _train = self._prepare_train_func(model_name, batch_size, sequence_length)
         return self._measure_memory(_train)
@@ -164,9 +169,8 @@ class TensorFlowBenchmark(Benchmark):
     def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]:
         config = self.config_dict[model_name]
 
-        assert (
-            self.args.eager_mode is False
-        ), "Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."
+        if self.args.eager_mode is not False:
+            raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.")
 
         if self.args.fp16:
             raise NotImplementedError("Mixed precision is currently not supported.")
@@ -240,10 +244,11 @@ class TensorFlowBenchmark(Benchmark):
         with self.args.strategy.scope():
             try:
                 if self.args.trace_memory_line_by_line:
-                    assert self.args.eager_mode, (
-                        "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
-                        " consumption line by line."
-                    )
+                    if not self.args.eager_mode:
+                        raise ValueError(
+                            "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
+                            " consumption line by line."
+                        )
                     trace = start_memory_tracing("transformers")
 
                 if self.args.is_tpu:

src/transformers/benchmark/benchmark_utils.py

@@ -890,7 +890,8 @@ class Benchmark(ABC):
             return
         self.print_fn("Saving results to csv.")
         with open(filename, mode="w") as csv_file:
-            assert len(self.args.model_names) > 0, f"At least 1 model should be defined, but got {self.model_names}"
+            if len(self.args.model_names) <= 0:
+                raise ValueError(f"At least 1 model should be defined, but got {self.model_names}")
 
             fieldnames = ["model", "batch_size", "sequence_length"]
             writer = csv.DictWriter(csv_file, fieldnames=fieldnames + ["result"])

src/transformers/data/metrics/__init__.py

@@ -90,7 +90,8 @@ def glue_compute_metrics(task_name, preds, labels):
 def xnli_compute_metrics(task_name, preds, labels):
     warnings.warn(DEPRECATION_WARNING, FutureWarning)
     requires_backends(xnli_compute_metrics, "sklearn")
-    assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
+    if len(preds) != len(labels):
+        raise ValueError(f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}")
     if task_name == "xnli":
         return {"acc": simple_accuracy(preds, labels)}
     else:

src/transformers/generation/beam_search.py

@@ -380,7 +380,8 @@ class BeamSearchScorer(BeamScorer):
 
         # shorter batches are padded if needed
         if sent_lengths.min().item() != sent_lengths.max().item():
-            assert pad_token_id is not None, "`pad_token_id` has to be defined"
+            if pad_token_id is None:
+                raise ValueError("`pad_token_id` has to be defined")
             decoded.fill_(pad_token_id)
 
         if indices is not None:
@@ -855,7 +856,8 @@ class ConstrainedBeamSearchScorer(BeamScorer):
         decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
         # shorter batches are padded if needed
         if sent_lengths.min().item() != sent_lengths.max().item():
-            assert pad_token_id is not None, "`pad_token_id` has to be defined"
+            if pad_token_id is None:
+                raise ValueError("`pad_token_id` has to be defined")
             decoded.fill_(pad_token_id)
 
         # fill with hypotheses and eos_token_id if the latter fits in

src/transformers/models/align/convert_align_tf_to_hf.py

@@ -346,8 +346,10 @@ def convert_align_checkpoint(checkpoint_path, pytorch_dump_folder_path, save_mod
     text_features = tf.nn.l2_normalize(text_features, axis=-1)
 
     # Check whether original and HF model outputs match  -> np.allclose
-    assert np.allclose(image_features, hf_image_features, atol=1e-3), "The predicted image features are not the same."
-    assert np.allclose(text_features, hf_text_features, atol=1e-3), "The predicted text features are not the same."
+    if not np.allclose(image_features, hf_image_features, atol=1e-3):
+        raise ValueError("The predicted image features are not the same.")
+    if not np.allclose(text_features, hf_text_features, atol=1e-3):
+        raise ValueError("The predicted text features are not the same.")
     print("Model outputs match!")
 
     if save_model:

src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py

@@ -101,7 +101,10 @@ def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkp
     config = BartConfig.from_pretrained(hf_checkpoint_name)
     tokens = bart.encode(SAMPLE_TEXT).unsqueeze(0)
     tokens2 = BartTokenizer.from_pretrained(hf_checkpoint_name).encode(SAMPLE_TEXT, return_tensors="pt").unsqueeze(0)
-    assert torch.eq(tokens, tokens2).all()
+    if not torch.eq(tokens, tokens2).all():
+        raise ValueError(
+            f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokens2}"
+        )
 
     if checkpoint_path == "bart.large.mnli":
         state_dict = bart.state_dict()
@@ -130,8 +133,12 @@ def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkp
             new_model_outputs = model.model(tokens)[0]
 
     # Check results
-    assert fairseq_output.shape == new_model_outputs.shape
-    assert (fairseq_output == new_model_outputs).all().item()
+    if fairseq_output.shape != new_model_outputs.shape:
+        raise ValueError(
+            f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"
+        )
+    if (fairseq_output != new_model_outputs).any().item():
+        raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`")
     Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
     model.save_pretrained(pytorch_dump_folder_path)