fix paligemma inference

src/llamafactory/chat/hf_engine.py

@@ -8,6 +8,7 @@ import torch
 from transformers import GenerationConfig, TextIteratorStreamer
 
 from ..data import get_template_and_fix_tokenizer
+from ..extras.constants import IMAGE_TOKEN
 from ..extras.misc import get_logits_processor
 from ..model import load_model, load_tokenizer
 from .base_engine import BaseEngine, Response
@@ -55,14 +56,28 @@ class HuggingfaceEngine(BaseEngine):
         image: Optional["NDArray"] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Tuple[Dict[str, Any], int]:
-        if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+        if (
-            messages[0]["content"] = "<image>" + messages[0]["content"]
+            processor is not None
+            and image is not None
+            and not hasattr(processor, "image_seq_length")
+            and IMAGE_TOKEN not in messages[0]["content"]
+        ):  # llava case
+            messages[0]["content"] = IMAGE_TOKEN + messages[0]["content"]
 
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         system = system or generating_args["default_system"]
+        pixel_values = None
         prompt_ids, _ = template.encode_oneturn(
             tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
         )
+        if processor is not None and image is not None:  # add image features
+            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            batch_feature = image_processor(image, return_tensors="pt")
+            pixel_values = batch_feature.to(model.device)["pixel_values"]  # shape (B, C, H, W)
+            if hasattr(processor, "image_seq_length"):  # paligemma case
+                image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
+                prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
+
         prompt_length = len(prompt_ids)
         inputs = torch.tensor([prompt_ids], device=model.device)
 
@@ -122,10 +137,8 @@ class HuggingfaceEngine(BaseEngine):
             logits_processor=get_logits_processor(),
         )
 
-        if processor is not None and image is not None:
+        if pixel_values is not None:
-            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            gen_kwargs["pixel_values"] = pixel_values
-            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
-            gen_kwargs["pixel_values"] = pixel_values.to(model.device)
 
         return gen_kwargs, prompt_length
 

src/llamafactory/chat/vllm_engine.py

@@ -2,6 +2,7 @@ import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union
 
 from ..data import get_template_and_fix_tokenizer
+from ..extras.constants import IMAGE_TOKEN
 from ..extras.logging import get_logger
 from ..extras.misc import get_device_count, infer_optim_dtype
 from ..extras.packages import is_vllm_available
@@ -17,7 +18,6 @@ if is_vllm_available():
 
 
 if TYPE_CHECKING:
-    import torch
     from numpy.typing import NDArray
     from transformers.image_processing_utils import BaseImageProcessor
 
@@ -67,7 +67,7 @@ class VllmEngine(BaseEngine):
             patch_size = config.vision_config.patch_size
             self.image_feature_size = (image_size // patch_size) ** 2
             engine_args["image_input_type"] = "pixel_values"
-            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
+            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
             engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
             engine_args["image_feature_size"] = self.image_feature_size
             if getattr(config, "is_yi_vl_derived_model", None):
@@ -92,14 +92,28 @@ class VllmEngine(BaseEngine):
         **input_kwargs,
     ) -> AsyncIterator["RequestOutput"]:
         request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-        if self.processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+
-            messages[0]["content"] = "<image>" * self.image_feature_size + messages[0]["content"]
+        if (
+            self.processor is not None
+            and image is not None
+            and not hasattr(self.processor, "image_seq_length")
+            and IMAGE_TOKEN not in messages[0]["content"]
+        ):  # llava case
+            messages[0]["content"] = IMAGE_TOKEN * self.image_feature_size + messages[0]["content"]
 
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         system = system or self.generating_args["default_system"]
         prompt_ids, _ = self.template.encode_oneturn(
             tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
         )
+
+        if self.processor is not None and image is not None:  # add image features
+            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
+            pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
+            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
+        else:
+            multi_modal_data = None
+
         prompt_length = len(prompt_ids)
 
         use_beam_search: bool = self.generating_args["num_beams"] > 1
@@ -144,13 +158,6 @@ class VllmEngine(BaseEngine):
             skip_special_tokens=True,
         )
 
-        if self.processor is not None and image is not None:
-            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
-            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
-            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
-        else:
-            multi_modal_data = None
-
         result_generator = self.model.generate(
             prompt=None,
             sampling_params=sampling_params,

src/llamafactory/data/template.py

@@ -290,10 +290,10 @@ def _convert_slots_to_jinja(slots: "SLOTS", tokenizer: "PreTrainedTokenizer", pl
                 slot_items.append(placeholder)
                 if slot_pieces[1]:
                     slot_items.append("'" + _jinja_escape(slot_pieces[1]) + "'")
-        elif isinstance(slot, set):
+        elif isinstance(slot, set):  # do not use {{ eos_token }} since it may be replaced
-            if "bos_token" in slot:
+            if "bos_token" in slot and tokenizer.bos_token_id is not None:
                 slot_items.append("'" + tokenizer.bos_token + "'")
-            elif "eos_token" in slot:  # do not use {{ eos_token }} since it may be replaced
+            elif "eos_token" in slot and tokenizer.eos_token_id is not None:
                 slot_items.append("'" + tokenizer.eos_token + "'")
         elif isinstance(slot, dict):
             raise ValueError("Dict is not supported.")
@@ -325,9 +325,11 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
         jinja_template += "{% if loop.index0 == 0 and system_message is defined %}"
         jinja_template += "{% set content = " + system_message + " + message['content'] %}"
         jinja_template += "{% endif %}"
+
     jinja_template += "{% if message['role'] == 'user' %}"
     user_message = _convert_slots_to_jinja(template.format_user.apply(), tokenizer)
     jinja_template += "{{ " + user_message + " }}"
+
     jinja_template += "{% elif message['role'] == 'assistant' %}"
     assistant_message = _convert_slots_to_jinja(
         template.format_assistant.apply() + template.format_separator.apply(), tokenizer
@@ -614,6 +616,9 @@ _register_template(
     name="empty",
     format_user=StringFormatter(slots=["{{content}}"]),
     format_assistant=StringFormatter(slots=["{{content}}"]),
+    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    efficient_eos=True,
+    force_system=True,
 )
 
 

src/llamafactory/extras/constants.py

@@ -22,6 +22,8 @@ FILEEXT2TYPE = {
 
 IGNORE_INDEX = -100
 
+IMAGE_TOKEN = "<image>"
+
 LAYERNORM_NAMES = {"norm", "ln"}
 
 METHODS = ["full", "freeze", "lora"]