choice for empty eval

model/supervised_finetuning/trainer.py

@@ -234,7 +234,6 @@ if __name__ == "__main__":
         report_to="wandb" if training_conf.log_wandb else None,
     )
 
-    assert len(evals) > 0
     if training_conf.log_wandb and not training_conf.deepspeed or training_conf.local_rank == 0:
         import wandb
 

model/supervised_finetuning/utils.py

@@ -61,13 +61,17 @@ class PerDatasetSampler(Sampler):
     @classmethod
     def build_sampler_from_config(cls, training_conf, datasets):
         dataset_sizes = [len(x) for x in datasets]
-        fractions = get_dataset_fractions(training_conf.datasets, dataset_sizes)
+        fractions = get_dataset_fractions(training_conf.datasets, dataset_sizes, verbose=training_conf.verbose)
         dataset_size_per_epoch = [int(size * frac) for size, frac in zip(dataset_sizes, fractions)]
         return cls(dataset_sizes, dataset_size_per_epoch)
 
 
-def get_dataset_fractions(conf, dataset_sizes):
+def get_dataset_fractions(conf, dataset_sizes, verbose=False):
     """Calculate fraction of each dataset to use per epoch when subsampling"""
+
+    if verbose:
+        print("Creating sampler for datasets:")
+
     fractions = []
     for i, data_config in enumerate(conf):
         dataset_name, _ = get_dataset_name_and_kwargs_from_data_config(data_config)
@@ -81,9 +85,12 @@ def get_dataset_fractions(conf, dataset_sizes):
                     raise ValueError(f"Please specify a size smaller than number of examples: {dataset_sizes[i]:,.0f}")
                 fractions.append(data_config[dataset_name]["size"] / dataset_sizes[i])
             else:
-                raise ValueError("Please specify either fraction or size in config.yaml. See README for instructions.")
+                fractions.append(1)
         else:
             fractions.append(1)
+
+        if verbose:
+            print(f"Dataset: {dataset_name} fraction chosen: {fractions[-1]:.2f}")
     return fractions
 
 
@@ -222,11 +229,11 @@ def get_model(conf, tokenizer):
 
 def get_dataset_name_and_kwargs_from_data_config(data_config):
     if isinstance(data_config, dict):
-        kwargs = data_config
+        name = list(data_config.keys())[0]
+        kwargs = data_config[name]
         # remove 'fraction' or 'size' from kwargs
         kwargs.pop("fraction", None)
         kwargs.pop("size", None)
-        name = list(data_config.keys())[0]
         return name, kwargs
     else:
         return data_config, {}
@@ -239,7 +246,9 @@ def get_dataset(conf, tokenizer):
         dataset_name, kwargs = get_dataset_name_and_kwargs_from_data_config(data_config)
         train, val = get_one_dataset(conf, dataset_name, **kwargs)
         train_datasets.append(train)
-        evals[dataset_name] = Subset(val, list(range(min(len(val), conf.eval_size)))) if conf.eval_size else val
+
+        if val is not None:
+            evals[dataset_name] = Subset(val, list(range(min(len(val), conf.eval_size)))) if conf.eval_size else val
 
     train = ConcatDataset(train_datasets)