Implement proper batch inference for HuggingFaceTransformersVlmModel

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

docling/datamodel/vlm_model_specs.py

@@ -27,6 +27,7 @@ SMOLDOCLING_MLX = InlineVlmOptions(
     supported_devices=[AcceleratorDevice.MPS],
     scale=2.0,
     temperature=0.0,
+    stop_strings=["</doctag>", "<end_of_utterance>"],
 )
 
 SMOLDOCLING_TRANSFORMERS = InlineVlmOptions(
@@ -42,6 +43,7 @@ SMOLDOCLING_TRANSFORMERS = InlineVlmOptions(
     ],
     scale=2.0,
     temperature=0.0,
+    stop_strings=["</doctag>", "<end_of_utterance>"],
 )
 
 SMOLDOCLING_VLLM = InlineVlmOptions(
@@ -56,8 +58,55 @@ SMOLDOCLING_VLLM = InlineVlmOptions(
     ],
     scale=2.0,
     temperature=0.0,
+    stop_strings=["</doctag>", "<end_of_utterance>"],
 )
 
+# SmolVLM-500-Instruct
+SMOLVLM500_TRANSFORMERS = InlineVlmOptions(
+    repo_id="HuggingFaceTB/SmolVLM-500M-Instruct",
+    prompt="Transcribe this image to plain text.",
+    response_format=ResponseFormat.DOCTAGS,
+    inference_framework=InferenceFramework.TRANSFORMERS,
+    transformers_model_type=TransformersModelType.AUTOMODEL_IMAGETEXTTOTEXT,
+    supported_devices=[
+        AcceleratorDevice.CPU,
+        AcceleratorDevice.CUDA,
+        AcceleratorDevice.MPS,
+    ],
+    scale=2.0,
+    temperature=0.0,
+)
+
+# SmolVLM-500-Instruct
+SMOLVLM500_MLX = InlineVlmOptions(
+    repo_id="moot20/SmolVLM-500M-Instruct-MLX",
+    prompt="Transcribe this image to plain text.",
+    response_format=ResponseFormat.DOCTAGS,
+    inference_framework=InferenceFramework.MLX,
+    transformers_model_type=TransformersModelType.AUTOMODEL_IMAGETEXTTOTEXT,
+    supported_devices=[
+        AcceleratorDevice.MPS,
+    ],
+    scale=2.0,
+    temperature=0.0,
+)
+
+SMOLVLM500_VLLM = InlineVlmOptions(
+    repo_id="HuggingFaceTB/SmolVLM-500M-Instruct",
+    prompt="Transcribe this image to plain text.",
+    response_format=ResponseFormat.DOCTAGS,
+    inference_framework=InferenceFramework.VLLM,
+    transformers_model_type=TransformersModelType.AUTOMODEL_IMAGETEXTTOTEXT,
+    supported_devices=[
+        AcceleratorDevice.CPU,
+        AcceleratorDevice.CUDA,
+        AcceleratorDevice.MPS,
+    ],
+    scale=2.0,
+    temperature=0.0,
+)
+
+
 # GraniteVision
 GRANITE_VISION_TRANSFORMERS = InlineVlmOptions(
     repo_id="ibm-granite/granite-vision-3.2-2b",

docling/models/readingorder_model.py

@@ -316,7 +316,7 @@ class ReadingOrderModel:
             current_list = None
 
             new_item = out_doc.add_text(
-                label=DocItemLabel.FORMULA, text="", orig=cap_text, prov=prov
+                label=DocItemLabel.FORMULA, text=cap_text, orig=cap_text, prov=prov
             )
         else:
             current_list = None

docling/models/vlm_models_inline/hf_transformers_model.py

@@ -7,6 +7,7 @@ from typing import Any, Optional, Union
 
 import numpy as np
 from PIL.Image import Image
+from transformers import StoppingCriteriaList, StopStringCriteria
 
 from docling.datamodel.accelerator_options import (
     AcceleratorOptions,
@@ -227,109 +228,119 @@ class HuggingFaceTransformersVlmModel(BaseVlmPageModel, HuggingFaceModelDownload
         image_batch: Iterable[Union[Image, np.ndarray]],
         prompt: Union[str, list[str]],
     ) -> Iterable[VlmPrediction]:
-        """Process raw images without page metadata in a single batched inference call.
-
-        Args:
-            image_batch: Iterable of PIL Images or numpy arrays
-            prompt: Either:
-                - str: Single prompt used for all images
-                - list[str]: List of prompts (one per image, must match image count)
-
-        Raises:
-            ValueError: If prompt list length doesn't match image count.
         """
+        Batched inference for Hugging Face Image-Text-to-Text VLMs (e.g., SmolDocling / SmolVLM).
+        - Lets the processor handle all padding & batching for text+images.
+        - Trims generated sequences per row using attention_mask (no pad-id fallbacks).
+        - Keeps your formulate_prompt() exactly as-is.
+        """
+        import numpy as np
+        import torch
+        from PIL import Image as PILImage
+
+        # -- Normalize images to RGB PIL (SmolDocling & friends accept PIL/np via processor)
         pil_images: list[Image] = []
-
         for img in image_batch:
-            # Convert numpy array to PIL Image if needed
             if isinstance(img, np.ndarray):
-                if img.ndim == 3 and img.shape[2] in [3, 4]:
-                    from PIL import Image as PILImage
-
+                if img.ndim == 3 and img.shape[2] in (3, 4):
                     pil_img = PILImage.fromarray(img.astype(np.uint8))
                 elif img.ndim == 2:
-                    from PIL import Image as PILImage
-
                     pil_img = PILImage.fromarray(img.astype(np.uint8), mode="L")
                 else:
                     raise ValueError(f"Unsupported numpy array shape: {img.shape}")
             else:
                 pil_img = img
-
-            # Ensure image is in RGB mode (handles RGBA, L, etc.)
             if pil_img.mode != "RGB":
                 pil_img = pil_img.convert("RGB")
-
             pil_images.append(pil_img)
 
-        if len(pil_images) == 0:
+        if not pil_images:
             return
 
-        # Handle prompt parameter
+        # -- Normalize prompts (1 per image)
         if isinstance(prompt, str):
-            # Single prompt for all images
             user_prompts = [prompt] * len(pil_images)
-        elif isinstance(prompt, list):
-            # List of prompts (one per image)
+        else:
             if len(prompt) != len(pil_images):
                 raise ValueError(
                     f"Number of prompts ({len(prompt)}) must match number of images ({len(pil_images)})"
                 )
             user_prompts = prompt
-        else:
-            raise ValueError(f"prompt must be str or list[str], got {type(prompt)}")
 
-        # Format prompts individually
-        prompts: list[str] = [
-            self.formulate_prompt(user_prompt) for user_prompt in user_prompts
-        ]
+        # Use your prompt formatter verbatim
+        prompts: list[str] = [self.formulate_prompt(p) for p in user_prompts]
 
+        # -- Processor performs BOTH text+image preprocessing + batch padding (recommended)
         inputs = self.processor(
-            text=prompts, images=pil_images, return_tensors="pt", padding=True
-        ).to(self.device)
+            text=prompts,
+            images=pil_images,
+            return_tensors="pt",
+            padding=True,  # pad across batch for both text and vision
+            # no truncation by default; match SmolDocling examples
+        )
+        inputs = {
+            k: (v.to(self.device) if hasattr(v, "to") else v) for k, v in inputs.items()
+        }
+
+        # -- Optional stopping criteria
+        stopping_criteria = None
+        if self.vlm_options.stop_strings:
+            stopping_criteria = StoppingCriteriaList(
+                [
+                    StopStringCriteria(
+                        stop_strings=self.vlm_options.stop_strings,
+                        tokenizer=self.processor.tokenizer,
+                    )
+                ]
+            )
+
+        # -- Generate (Image-Text-to-Text class expects these inputs from processor)
+        gen_kwargs = {
+            **inputs,
+            "max_new_tokens": self.max_new_tokens,
+            "use_cache": self.use_cache,
+            "generation_config": self.generation_config,
+            "temperature": self.temperature,
+            **self.vlm_options.extra_generation_config,
+        }
+        if stopping_criteria is not None:
+            gen_kwargs["stopping_criteria"] = stopping_criteria
 
         start_time = time.time()
-        generated_ids = self.vlm_model.generate(
-            **inputs,
-            max_new_tokens=self.max_new_tokens,
-            use_cache=self.use_cache,
-            # temperature=self.temperature,
-            generation_config=self.generation_config,
-            **self.vlm_options.extra_generation_config,
-        )
+        with torch.no_grad():
+            generated_ids = self.vlm_model.generate(**gen_kwargs)
         generation_time = time.time() - start_time
 
-        # Determine per-sample prompt lengths
-        try:
-            attention_mask = inputs["attention_mask"]
-            input_lengths: list[int] = attention_mask.sum(dim=1).tolist()
-        except KeyError:
-            tokenizer = (
-                self.processor.tokenizer
-            )  # Expect tokenizer to be present when text is provided
-            pad_token_id = tokenizer.pad_token_id
-            if pad_token_id is not None:
-                input_lengths = (
-                    (inputs["input_ids"] != pad_token_id).sum(dim=1).tolist()
-                )
-            else:
-                # Fallback: assume uniform prompt length (least accurate but preserves execution)
-                uniform_len = int(inputs["input_ids"].shape[1])
-                input_lengths = [uniform_len] * int(inputs["input_ids"].shape[0])
+        # -- Trim per sample using attention_mask (robust for batched prompts)
+        if "attention_mask" not in inputs:
+            raise RuntimeError(
+                "Processor did not return 'attention_mask'. Ensure padding=True and text tokenization are enabled."
+            )
+        input_lengths = inputs["attention_mask"].sum(dim=1).tolist()
 
         trimmed_sequences: list[list[int]] = [
             generated_ids[i, int(input_lengths[i]) :].tolist()
             for i in range(generated_ids.shape[0])
         ]
-        decoded_texts: list[str] = self.processor.batch_decode(
+
+        # -- Decode with the processor/tokenizer (skip specials, keep DocTags as text)
+        decode_fn = getattr(self.processor, "batch_decode", None)
+        if decode_fn is None and getattr(self.processor, "tokenizer", None) is not None:
+            decode_fn = self.processor.tokenizer.batch_decode
+        if decode_fn is None:
+            raise RuntimeError(
+                "Neither processor.batch_decode nor tokenizer.batch_decode is available."
+            )
+
+        decoded_texts: list[str] = decode_fn(
             trimmed_sequences, skip_special_tokens=True
         )
 
-        # Logging tokens count for the first sample as a representative metric
+        # -- Optional logging
         if generated_ids.shape[0] > 0:
-            num_tokens = int(generated_ids[0].shape[0])
             _log.debug(
-                f"Generated {num_tokens} tokens in time {generation_time:.2f} seconds."
+                f"Generated {int(generated_ids[0].shape[0])} tokens in {generation_time:.2f}s "
+                f"for batch size {generated_ids.shape[0]}."
             )
 
         for text in decoded_texts:

docling/models/vlm_models_inline/mlx_model.py

@@ -142,8 +142,6 @@ class HuggingFaceMlxModel(BaseVlmPageModel, HuggingFaceModelDownloadMixin):
         Raises:
             ValueError: If prompt list length doesn't match image count.
         """
-        from mlx_vlm import generate
-
         # Convert image batch to list for length validation
         image_list = list(image_batch)
 
@@ -194,33 +192,67 @@ class HuggingFaceMlxModel(BaseVlmPageModel, HuggingFaceModelDownloadMixin):
                     self.processor, self.config, user_prompt, num_images=1
                 )
 
-                # Generate text from the image - MLX can accept PIL Images directly despite type annotations
+                # Stream generate with stop strings support
                 start_time = time.time()
-                generated_result = generate(
+                _log.debug("start generating ...")
+
+                tokens: list[VlmPredictionToken] = []
+                output = ""
+
+                # Use stream_generate for proper stop string handling
+                for token in self.stream_generate(
                     self.vlm_model,
                     self.processor,
                     formatted_prompt,
-                    image=image,  # Pass PIL Image directly - much more efficient than disk I/O
+                    [image],  # MLX stream_generate expects list of images
+                    max_tokens=self.max_tokens,
                     verbose=False,
                     temp=self.temperature,
-                    max_tokens=self.max_tokens,
-                )
+                ):
+                    # Collect token information
+                    if len(token.logprobs.shape) == 1:
+                        tokens.append(
+                            VlmPredictionToken(
+                                text=token.text,
+                                token=token.token,
+                                logprob=token.logprobs[token.token],
+                            )
+                        )
+                    elif (
+                        len(token.logprobs.shape) == 2 and token.logprobs.shape[0] == 1
+                    ):
+                        tokens.append(
+                            VlmPredictionToken(
+                                text=token.text,
+                                token=token.token,
+                                logprob=token.logprobs[0, token.token],
+                            )
+                        )
+                    else:
+                        _log.warning(
+                            f"incompatible shape for logprobs: {token.logprobs.shape}"
+                        )
+
+                    output += token.text
+
+                    # Check for any configured stop strings
+                    if self.vlm_options.stop_strings:
+                        if any(
+                            stop_str in output
+                            for stop_str in self.vlm_options.stop_strings
+                        ):
+                            _log.debug("Stopping generation due to stop string match")
+                            break
+
                 generation_time = time.time() - start_time
 
-                # MLX generate returns a tuple (text, info_dict), extract just the text
-                if isinstance(generated_result, tuple):
-                    generated_text = generated_result[0]
-                    _log.debug(
-                        f"MLX generate returned tuple with additional info: {generated_result[1] if len(generated_result) > 1 else 'N/A'}"
-                    )
-                else:
-                    generated_text = generated_result
+                _log.debug(
+                    f"{generation_time:.2f} seconds for {len(tokens)} tokens ({len(tokens) / generation_time:.1f} tokens/sec)."
+                )
 
-                _log.debug(f"Generated text in {generation_time:.2f}s.")
                 yield VlmPrediction(
-                    text=generated_text,
+                    text=output,
                     generation_time=generation_time,
-                    # MLX generate doesn't expose tokens directly, so we leave it empty
-                    generated_tokens=[],
+                    generated_tokens=tokens,
                 )
             _log.debug("MLX model: Released global lock")

docling/pipeline/threaded_multistage_vlm_pipeline.py

@@ -57,6 +57,8 @@ from docling.datamodel.vlm_model_specs import (
     DOLPHIN_TRANSFORMERS,
     SMOLDOCLING_MLX,
     SMOLDOCLING_TRANSFORMERS,
+    SMOLVLM500_MLX,
+    SMOLVLM500_TRANSFORMERS,
 )
 from docling.models.layout_model import LayoutModel
 from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
@@ -159,21 +161,23 @@ class ThreadedMultiStageVlmPipelineOptions(PaginatedPipelineOptions):
         # text_opts = DOLPHIN_TRANSFORMERS.model_copy()
         # text_opts.prompt = "<s>Read text in the image. <Answer/>"
 
-        base_model = SMOLDOCLING_TRANSFORMERS
-
-        formula_opts = base_model.model_copy()
-        formula_opts.prompt = "Convert formula to latex."
-        formula_opts.response_format = ResponseFormat.OTSL
-
-        code_opts = base_model.model_copy()
-        code_opts.prompt = "Convert code to text."
-        code_opts.response_format = ResponseFormat.OTSL
+        base_model = SMOLVLM500_TRANSFORMERS
 
         text_opts = base_model.model_copy()
-        text_opts.prompt = "Convert this page to docling."
-        text_opts.response_format = ResponseFormat.OTSL
+        # text_opts.prompt = "Convert this page to docling."
+        text_opts.prompt = "What does this say?"
+        text_opts.response_format = ResponseFormat.PLAINTEXT
 
-        table_opts = base_model.model_copy()
+        formula_opts = base_model.model_copy()
+        # formula_opts.prompt = "Convert formula to latex."
+        formula_opts.prompt = "What does this say?"
+        formula_opts.response_format = ResponseFormat.PLAINTEXT
+
+        code_opts = SMOLDOCLING_TRANSFORMERS.model_copy()
+        code_opts.prompt = "Convert code to text."
+        code_opts.response_format = ResponseFormat.DOCTAGS
+
+        table_opts = SMOLDOCLING_TRANSFORMERS.model_copy()
         table_opts.prompt = "Convert this table to OTSL."
         table_opts.response_format = ResponseFormat.OTSL
 

docling/pipeline/vlm_pipeline.py

@@ -128,7 +128,9 @@ class VlmPipeline(PaginatedPipeline):
             page._backend = conv_res.input._backend.load_page(page.page_no)  # type: ignore
             if page._backend is not None and page._backend.is_valid():
                 page.size = page._backend.get_size()
-                page.parsed_page = page._backend.get_segmented_page()
+
+                if self.force_backend_text:
+                    page.parsed_page = page._backend.get_segmented_page()
 
         return page