fix benchmark non standard model (#5801)

src/transformers/benchmark/benchmark.py

@@ -88,7 +88,7 @@ class PyTorchBenchmark(Benchmark):
         if self.args.torchscript:
             config.torchscript = True
 
-        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
+        has_model_class_in_config = hasattr(config, "architectures") and len(config.architectures) > 0
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 model_class = config.architectures[0]
@@ -138,7 +138,7 @@ class PyTorchBenchmark(Benchmark):
     def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]:
         config = self.config_dict[model_name]
 
-        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
+        has_model_class_in_config = hasattr(config, "architectures") and len(config.architectures) > 0
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 model_class = config.architectures[0]

src/transformers/benchmark/benchmark_tf.py

@@ -132,7 +132,7 @@ class TensorFlowBenchmark(Benchmark):
         if self.args.fp16:
             raise NotImplementedError("Mixed precision is currently not supported.")
 
-        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
+        has_model_class_in_config = hasattr(config, "architectures") and len(config.architectures) > 0
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 model_class = "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
@@ -172,7 +172,7 @@ class TensorFlowBenchmark(Benchmark):
         if self.args.fp16:
             raise NotImplementedError("Mixed precision is currently not supported.")
 
-        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
+        has_model_class_in_config = hasattr(config, "architectures") and len(config.architectures) > 0
         if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                 model_class = "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model