fix llava (#1149)

llava/generate.py

@@ -79,10 +79,10 @@ def load_image(image_source):
 def prepare_inputs(processor, image, prompt):
     if isinstance(image, str):
         image = load_image(image)
-    inputs = processor(prompt, image, return_tensors="np")
+    inputs = processor(image, prompt, return_tensors="np")
     pixel_values = mx.array(inputs["pixel_values"])
     input_ids = mx.array(inputs["input_ids"])
-    return input_ids, pixel_values
+    return pixel_values, input_ids
 
 
 def load_model(model_path, tokenizer_config={}):
@@ -126,8 +126,7 @@ def main():
     processor, model = load_model(args.model, tokenizer_config)
 
     prompt = codecs.decode(args.prompt, "unicode_escape")
-
-    input_ids, pixel_values = prepare_inputs(processor, args.image, prompt)
+    pixel_values, input_ids = prepare_inputs(processor, args.image, prompt)
 
     print(prompt)
     generated_text = generate_text(

llava/llava.py

@@ -104,31 +104,21 @@ class LlavaModel(nn.Module):
         self, image_features, inputs_embeds, input_ids
     ):
         image_token_index = self.config.image_token_index
-        num_images, num_image_patches, embed_dim = image_features.shape
+        batch_size, num_image_patches, embed_dim = image_features.shape
 
         # Positions of <image> tokens in input_ids, assuming batch size is 1
-        image_positions = np.where(input_ids[0] == image_token_index)[0].tolist()
+        image_positions = mx.array(
+            np.where(input_ids[0] == image_token_index)[0], mx.uint32
+        )
 
-        if len(image_positions) != num_images:
+        if len(image_positions) != num_image_patches:
             raise ValueError(
                 f"The number of image tokens ({len(image_positions)}) does not "
-                f" match the number of image inputs ({num_images})."
+                f" match the number of image patches ({num_image_patches})."
             )
 
-        text_segments = []
-        start_idx = 0
-
-        for position in image_positions:
-            text_segments.append(inputs_embeds[:, start_idx:position])
-            start_idx = position + 1
-
-        image_embeddings = mx.split(image_features, image_features.shape[0])
-        final_embeddings = [v for p in zip(text_segments, image_embeddings) for v in p]
-        final_embeddings += [inputs_embeds[:, start_idx:]]
-
-        # Create a final embedding of shape
-        # (1, num_image_patches*num_images + sequence_len, embed_dim)
-        return mx.concatenate(final_embeddings, axis=1)
+        inputs_embeds[0, image_positions] = image_features
+        return inputs_embeds
 
     def __call__(self, input_ids: mx.array, pixel_values: mx.array, cache=None):
         input_embddings = self.get_input_embeddings(input_ids, pixel_values)

llms/mlx_lm/generate.py

@@ -1,6 +1,7 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import argparse
+import codecs
 import json
 import sys
 
@@ -188,6 +189,8 @@ def main():
     elif using_cache:
         tokenizer.chat_template = metadata["chat_template"]
 
+    prompt = codecs.decode(args.prompt, "unicode_escape")
+
     if not args.ignore_chat_template and (
         hasattr(tokenizer, "apply_chat_template")
         and tokenizer.chat_template is not None
@@ -199,7 +202,7 @@ def main():
         messages.append(
             {
                 "role": "user",
-                "content": sys.stdin.read() if args.prompt == "-" else args.prompt,
+                "content": sys.stdin.read() if prompt == "-" else prompt,
             }
         )
         prompt = tokenizer.apply_chat_template(
@@ -216,8 +219,6 @@ def main():
                 add_generation_prompt=True,
             )
             prompt = prompt[test_prompt.index("<query>") :]
-    else:
-        prompt = args.prompt
 
     sampler = make_sampler(args.temp, args.top_p, args.min_p, args.min_tokens_to_keep)
     response = generate(