cleaning and fixes

server/text_generation_server/models/custom_modeling/idefics_modeling.py

@@ -1040,7 +1040,7 @@ class IdeficsModel(IdeficsPreTrainedModel):
             image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
             image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
         else:
-            no_images = True
+            no_images = False
             num_images = pixel_values.shape[1]
             image_seq_len = image_hidden_states.shape[1] // num_images
 

server/text_generation_server/models/idefics_causal_lm.py

@@ -24,35 +24,6 @@ from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sam
 tracer = trace.get_tracer(__name__)
 
 
-# UTILS
-def base64_to_pil(encoded_image):
-    decoded_image = base64.b64decode(encoded_image)
-    pil_image = Image.open(BytesIO(decoded_image))
-    return pil_image
-
-def im_markdown_to_pil(im_markdown_str):
-    pattern = r'<img src="data:image/png;base64,([^"]+)" />'
-    match = re.search(pattern, im_markdown_str)
-    img_b64_str = match.group(1)
-    return base64_to_pil(img_b64_str)
-
-def split_str_on_im_markdown(string_with_potential_im_markdown):
-    """
-    Extract from a string (typically the user prompt string) the potentional images saved as a base64 representation
-    inside a markdown.
-    """
-    pattern = r'<img src="data:image/png;base64,([^"]+)" />'
-    parts = re.split(pattern, string_with_potential_im_markdown)
-    result = []
-    for i, part in enumerate(parts):
-        if i % 2 == 0:
-            result.append(part)
-        else:
-            img_tag = f'<img src="data:image/png;base64,{part.strip()}" />'
-            result.append(img_tag)
-    return result
-
-
 @dataclass
 class IdeficsCausalLMBatch(Batch):
     batch_id: int
@@ -126,8 +97,8 @@ class IdeficsCausalLMBatch(Batch):
                 r.stopping_parameters, tokenizer
             )
             stopping_criterias.append(stopping_criteria)
-            max_truncation = max(max_truncation, r.truncate) #TODO: understand that
-            max_decode_tokens += stopping_criteria.max_new_tokens # TODO: I think it is just the maximum of tokens to generate in the WHOLE batch
+            max_truncation = max(max_truncation, r.truncate)
+            max_decode_tokens += stopping_criteria.max_new_tokens
             padding_right_offset = max(
                 padding_right_offset, stopping_criteria.max_new_tokens
             )
@@ -145,16 +116,6 @@ class IdeficsCausalLMBatch(Batch):
                 )
             else:
                 raise ValueError("Unsupported type of input")
-            # I initially wanted to send the images in string base64 but they are too big to send in a consistent way...
-            # So resorting to uploading the image to a server and pulling them back
-            # splitted_inp = split_str_on_im_markdown(inp)
-            # prompts.append(
-            #     [
-            #         im_markdown_to_pil(s) if s.startswith('<img src="data:image/png;base64,') else s
-            #         for s in splitted_inp
-            #         if s != ""
-            #     ]
-            # )
 
         # The processor replaces the call to tokenizer, and
         # a/ takes care of fetching images from the URL
@@ -288,6 +249,10 @@ class IdeficsCausalLMBatch(Batch):
             + new_padding_right_offset,
             :
         ]
+        if self.image_hidden_states is None:
+            image_hidden_states = None
+        else:
+            image_hidden_states = self.image_hidden_states[keep_indices]
 
         # Ensure that past_key_values tensors can be updated in-place
         if type(self.past_key_values[0]) == tuple:
@@ -315,6 +280,7 @@ class IdeficsCausalLMBatch(Batch):
         self.requests_idx_mapping = requests_idx_mapping
         self.input_ids = input_ids
         self.pixel_values = pixel_values
+        self.image_hidden_states = image_hidden_states
         self.position_ids = position_ids
         self.all_input_ids = all_input_ids
         self.input_lengths = input_lengths
@@ -675,7 +641,7 @@ class IdeficsCausalLM(Model):
             # this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
             # token need to attend to the encoder hidden states (i.e. the vision encoder)
             # Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
-            image_attention_mask = batch.image_attention_mask[:, -batch.padding_right_offset].unsqueeze(1) #TODO: verify that index. i have a doubt whether there is +1 hanging around
+            image_attention_mask = batch.image_attention_mask[:, -(batch.padding_right_offset+1)].unsqueeze(1)
         else:
             image_attention_mask = batch.image_attention_mask[:, : -batch.padding_right_offset]