raise error with non-existence image prompts

torchchat/generate.py

@@ -24,15 +24,6 @@
 
 from PIL import Image
 
-# torchtune model definition dependencies
-from torchtune.data import Message, padded_collate_tiled_images_and_mask
-
-from torchtune.generation import sample as tune_sample
-from torchtune.models.llama3 import llama3_tokenizer
-
-from torchtune.models.llama3_2_vision._model_builders import llama3_2_vision_transform
-from torchtune.training import set_default_dtype
-
 from torchchat.cli.builder import (
     _initialize_model,
     _initialize_tokenizer,
@@ -43,6 +34,15 @@
 from torchchat.utils.build_utils import device_sync, set_precision
 from torchchat.utils.device_info import get_device_info
 
+# torchtune model definition dependencies
+from torchtune.data import Message, padded_collate_tiled_images_and_mask
+
+from torchtune.generation import sample as tune_sample
+from torchtune.models.llama3 import llama3_tokenizer
+
+from torchtune.models.llama3_2_vision._model_builders import llama3_2_vision_transform
+from torchtune.training import set_default_dtype
+
 
 class _ChatFormatter(ABC):
     def __init__(self, tokenizer):
@@ -179,8 +179,15 @@ def from_args(cls, args):
 
         # Validate that all image prompts exist before expensive model load
         if image_prompts := getattr(args, "image_prompts", None):
-            if not all(os.path.exists(image_prompt) for image_prompt in image_prompts):
-                raise RuntimeError(f"Image prompt {image_prompt} does not exist")
+            non_existent_image_prompts = [
+                image_prompt
+                for image_prompt in image_prompts
+                if (not os.path.exists(image_prompt))
+            ]
+            if len(non_existent_image_prompts):
+                raise RuntimeError(
+                    f"Image prompt {non_existent_image_prompts} does not exist"
+                )
 
         return cls(
             prompt=getattr(args, "prompt", ""),
@@ -938,7 +945,8 @@ def chat(
                     TransformerCrossAttentionLayer,
                     TransformerSelfAttentionLayer,
                 )
-                decoder = self.model.model.decoder 
+
+                decoder = self.model.model.decoder
                 for m in reversed(list(decoder.modules())):
                     if isinstance(m, TransformerSelfAttentionLayer) or isinstance(
                         m, TransformerCrossAttentionLayer
@@ -984,7 +992,10 @@ def chat(
         # `is_torchtune_model` is a misnomer since it doesn't capture all
         # torchtune models (i.e. Flamingo)
         # See Issue: https://github.com/pytorch/torchchat/issues/1273
-        elif not generator_args.is_torchtune_model and self.model.config.model_type != ModelType.Flamingo:
+        elif (
+            not generator_args.is_torchtune_model
+            and self.model.config.model_type != ModelType.Flamingo
+        ):
             max_seq_length = min(
                 encoded.size(0) + generator_args.max_new_tokens,
                 (