feat(SmolDocling): Support MLX acceleration in VLM pipeline (#1199)
* Initial implementation to support MLX for VLM pipeline and SmolDocling Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * mlx_model unit Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Add CLI choices for VLM pipeline and model Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Initial implementation to support MLX for VLM pipeline and SmolDocling Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * mlx_model unit Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Add CLI choices for VLM pipeline and model Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Updated minimal vlm pipeline example Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * make vlm_pipeline python3.9 compatible Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixed extract_text_from_backend definition Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated README Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated example Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated documentation Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * corrections in the documentation Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Consmetic changes Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Co-authored-by: Maksym Lysak <mly@zurich.ibm.com> Co-authored-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Maxim Lysak
GitHub
parent
b454aa1551
commit
1c26769785
20
README.md
20
README.md
@ -35,7 +35,7 @@ Docling simplifies document processing, parsing diverse formats — including ad
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* 🔒 Local execution capabilities for sensitive data and air-gapped environments
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* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
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* 🔍 Extensive OCR support for scanned PDFs and images
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* 🥚 Support of Visual Language Models ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview))
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* 🥚 Support of Visual Language Models ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview)) 🆕
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* 💻 Simple and convenient CLI
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### Coming soon
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@ -57,7 +57,7 @@ More [detailed installation instructions](https://docling-project.github.io/docl
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## Getting started
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To convert individual documents, use `convert()`, for example:
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To convert individual documents with python, use `convert()`, for example:
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```python
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from docling.document_converter import DocumentConverter
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@ -71,6 +71,22 @@ print(result.document.export_to_markdown()) # output: "## Docling Technical Rep
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More [advanced usage options](https://docling-project.github.io/docling/usage/) are available in
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the docs.
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## CLI
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Docling has a built-in CLI to run conversions.
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```bash
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docling https://arxiv.org/pdf/2206.01062
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```
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You can also use 🥚[SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) and other VLMs via Docling CLI:
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```bash
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docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
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```
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This will use MLX acceleration on supported Apple Silicon hardware.
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Read more [here](https://docling-project.github.io/docling/usage/)
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## Documentation
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Check out Docling's [documentation](https://docling-project.github.io/docling/), for details on
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@ -32,13 +32,21 @@ from docling.datamodel.pipeline_options import (
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AcceleratorOptions,
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EasyOcrOptions,
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OcrOptions,
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PaginatedPipelineOptions,
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PdfBackend,
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PdfPipeline,
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PdfPipelineOptions,
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TableFormerMode,
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VlmModelType,
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VlmPipelineOptions,
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granite_vision_vlm_conversion_options,
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smoldocling_vlm_conversion_options,
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smoldocling_vlm_mlx_conversion_options,
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)
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from docling.datamodel.settings import settings
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from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption
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from docling.models.factories import get_ocr_factory
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from docling.pipeline.vlm_pipeline import VlmPipeline
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warnings.filterwarnings(action="ignore", category=UserWarning, module="pydantic|torch")
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warnings.filterwarnings(action="ignore", category=FutureWarning, module="easyocr")
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@ -200,6 +208,14 @@ def convert(
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help="Image export mode for the document (only in case of JSON, Markdown or HTML). With `placeholder`, only the position of the image is marked in the output. In `embedded` mode, the image is embedded as base64 encoded string. In `referenced` mode, the image is exported in PNG format and referenced from the main exported document.",
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),
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] = ImageRefMode.EMBEDDED,
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pipeline: Annotated[
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PdfPipeline,
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typer.Option(..., help="Choose the pipeline to process PDF or image files."),
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] = PdfPipeline.STANDARD,
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vlm_model: Annotated[
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VlmModelType,
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typer.Option(..., help="Choose the VLM model to use with PDF or image files."),
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] = VlmModelType.SMOLDOCLING,
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ocr: Annotated[
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bool,
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typer.Option(
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@ -420,6 +436,9 @@ def convert(
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ocr_options.lang = ocr_lang_list
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accelerator_options = AcceleratorOptions(num_threads=num_threads, device=device)
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pipeline_options: PaginatedPipelineOptions
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if pipeline == PdfPipeline.STANDARD:
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pipeline_options = PdfPipelineOptions(
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allow_external_plugins=allow_external_plugins,
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enable_remote_services=enable_remote_services,
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@ -445,9 +464,6 @@ def convert(
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)
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pipeline_options.images_scale = 2
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if artifacts_path is not None:
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pipeline_options.artifacts_path = artifacts_path
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backend: Type[PdfDocumentBackend]
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if pdf_backend == PdfBackend.DLPARSE_V1:
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backend = DoclingParseDocumentBackend
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@ -464,6 +480,33 @@ def convert(
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pipeline_options=pipeline_options,
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backend=backend, # pdf_backend
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)
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elif pipeline == PdfPipeline.VLM:
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pipeline_options = VlmPipelineOptions()
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if vlm_model == VlmModelType.GRANITE_VISION:
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pipeline_options.vlm_options = granite_vision_vlm_conversion_options
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elif vlm_model == VlmModelType.SMOLDOCLING:
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pipeline_options.vlm_options = smoldocling_vlm_conversion_options
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if sys.platform == "darwin":
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try:
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import mlx_vlm
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pipeline_options.vlm_options = (
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smoldocling_vlm_mlx_conversion_options
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)
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except ImportError:
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_log.warning(
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"To run SmolDocling faster, please install mlx-vlm:\n"
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"pip install mlx-vlm"
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)
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pdf_format_option = PdfFormatOption(
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pipeline_cls=VlmPipeline, pipeline_options=pipeline_options
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)
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if artifacts_path is not None:
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pipeline_options.artifacts_path = artifacts_path
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format_options: Dict[InputFormat, FormatOption] = {
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InputFormat.PDF: pdf_format_option,
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InputFormat.IMAGE: pdf_format_option,
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@ -263,6 +263,11 @@ class ResponseFormat(str, Enum):
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MARKDOWN = "markdown"
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class InferenceFramework(str, Enum):
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MLX = "mlx"
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TRANSFORMERS = "transformers"
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class HuggingFaceVlmOptions(BaseVlmOptions):
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kind: Literal["hf_model_options"] = "hf_model_options"
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@ -271,6 +276,7 @@ class HuggingFaceVlmOptions(BaseVlmOptions):
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llm_int8_threshold: float = 6.0
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quantized: bool = False
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inference_framework: InferenceFramework
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response_format: ResponseFormat
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@property
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@ -278,10 +284,19 @@ class HuggingFaceVlmOptions(BaseVlmOptions):
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return self.repo_id.replace("/", "--")
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smoldocling_vlm_mlx_conversion_options = HuggingFaceVlmOptions(
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repo_id="ds4sd/SmolDocling-256M-preview-mlx-bf16",
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prompt="Convert this page to docling.",
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response_format=ResponseFormat.DOCTAGS,
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inference_framework=InferenceFramework.MLX,
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)
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smoldocling_vlm_conversion_options = HuggingFaceVlmOptions(
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repo_id="ds4sd/SmolDocling-256M-preview",
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prompt="Convert this page to docling.",
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response_format=ResponseFormat.DOCTAGS,
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inference_framework=InferenceFramework.TRANSFORMERS,
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)
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granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
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@ -289,9 +304,15 @@ granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
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# prompt="OCR the full page to markdown.",
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prompt="OCR this image.",
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response_format=ResponseFormat.MARKDOWN,
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inference_framework=InferenceFramework.TRANSFORMERS,
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)
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class VlmModelType(str, Enum):
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SMOLDOCLING = "smoldocling"
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GRANITE_VISION = "granite_vision"
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# Define an enum for the backend options
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class PdfBackend(str, Enum):
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"""Enum of valid PDF backends."""
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@ -327,13 +348,14 @@ class PipelineOptions(BaseModel):
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class PaginatedPipelineOptions(PipelineOptions):
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artifacts_path: Optional[Union[Path, str]] = None
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images_scale: float = 1.0
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generate_page_images: bool = False
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generate_picture_images: bool = False
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class VlmPipelineOptions(PaginatedPipelineOptions):
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artifacts_path: Optional[Union[Path, str]] = None
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generate_page_images: bool = True
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force_backend_text: bool = (
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@ -346,7 +368,6 @@ class VlmPipelineOptions(PaginatedPipelineOptions):
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class PdfPipelineOptions(PaginatedPipelineOptions):
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"""Options for the PDF pipeline."""
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artifacts_path: Optional[Union[Path, str]] = None
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do_table_structure: bool = True # True: perform table structure extraction
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do_ocr: bool = True # True: perform OCR, replace programmatic PDF text
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do_code_enrichment: bool = False # True: perform code OCR
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@ -377,3 +398,8 @@ class PdfPipelineOptions(PaginatedPipelineOptions):
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)
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generate_parsed_pages: bool = False
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class PdfPipeline(str, Enum):
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STANDARD = "standard"
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VLM = "vlm"
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137
docling/models/hf_mlx_model.py
Normal file
137
docling/models/hf_mlx_model.py
Normal file
@ -0,0 +1,137 @@
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import logging
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import time
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from pathlib import Path
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from typing import Iterable, List, Optional
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from docling.datamodel.base_models import Page, VlmPrediction
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import (
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AcceleratorDevice,
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AcceleratorOptions,
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HuggingFaceVlmOptions,
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)
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from docling.datamodel.settings import settings
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from docling.models.base_model import BasePageModel
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from docling.utils.accelerator_utils import decide_device
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from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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class HuggingFaceMlxModel(BasePageModel):
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def __init__(
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self,
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enabled: bool,
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artifacts_path: Optional[Path],
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accelerator_options: AcceleratorOptions,
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vlm_options: HuggingFaceVlmOptions,
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):
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self.enabled = enabled
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self.vlm_options = vlm_options
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if self.enabled:
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try:
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from mlx_vlm import generate, load # type: ignore
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from mlx_vlm.prompt_utils import apply_chat_template # type: ignore
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from mlx_vlm.utils import load_config, stream_generate # type: ignore
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except ImportError:
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raise ImportError(
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"mlx-vlm is not installed. Please install it via `pip install mlx-vlm` to use MLX VLM models."
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)
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repo_cache_folder = vlm_options.repo_id.replace("/", "--")
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self.apply_chat_template = apply_chat_template
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self.stream_generate = stream_generate
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# PARAMETERS:
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if artifacts_path is None:
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artifacts_path = self.download_models(self.vlm_options.repo_id)
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elif (artifacts_path / repo_cache_folder).exists():
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artifacts_path = artifacts_path / repo_cache_folder
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self.param_question = vlm_options.prompt # "Perform Layout Analysis."
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## Load the model
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self.vlm_model, self.processor = load(artifacts_path)
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self.config = load_config(artifacts_path)
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@staticmethod
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def download_models(
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repo_id: str,
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local_dir: Optional[Path] = None,
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force: bool = False,
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progress: bool = False,
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) -> Path:
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from huggingface_hub import snapshot_download
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from huggingface_hub.utils import disable_progress_bars
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if not progress:
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disable_progress_bars()
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download_path = snapshot_download(
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repo_id=repo_id,
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force_download=force,
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local_dir=local_dir,
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# revision="v0.0.1",
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)
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return Path(download_path)
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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) -> Iterable[Page]:
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for page in page_batch:
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assert page._backend is not None
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if not page._backend.is_valid():
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yield page
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else:
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with TimeRecorder(conv_res, "vlm"):
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assert page.size is not None
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hi_res_image = page.get_image(scale=2.0) # 144dpi
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# hi_res_image = page.get_image(scale=1.0) # 72dpi
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if hi_res_image is not None:
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im_width, im_height = hi_res_image.size
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# populate page_tags with predicted doc tags
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page_tags = ""
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if hi_res_image:
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if hi_res_image.mode != "RGB":
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hi_res_image = hi_res_image.convert("RGB")
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prompt = self.apply_chat_template(
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self.processor, self.config, self.param_question, num_images=1
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)
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start_time = time.time()
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# Call model to generate:
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output = ""
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for token in self.stream_generate(
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self.vlm_model,
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self.processor,
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prompt,
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[hi_res_image],
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max_tokens=4096,
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verbose=False,
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):
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output += token.text
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if "</doctag>" in token.text:
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break
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generation_time = time.time() - start_time
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page_tags = output
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# inference_time = time.time() - start_time
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# tokens_per_second = num_tokens / generation_time
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# print("")
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# print(f"Page Inference Time: {inference_time:.2f} seconds")
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# print(f"Total tokens on page: {num_tokens:.2f}")
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# print(f"Tokens/sec: {tokens_per_second:.2f}")
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# print("")
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page.predictions.vlm_response = VlmPrediction(text=page_tags)
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yield page
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@ -14,8 +14,13 @@ from docling.backend.md_backend import MarkdownDocumentBackend
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from docling.backend.pdf_backend import PdfDocumentBackend
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from docling.datamodel.base_models import InputFormat, Page
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from docling.datamodel.document import ConversionResult, InputDocument
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from docling.datamodel.pipeline_options import ResponseFormat, VlmPipelineOptions
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from docling.datamodel.pipeline_options import (
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InferenceFramework,
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ResponseFormat,
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VlmPipelineOptions,
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)
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from docling.datamodel.settings import settings
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from docling.models.hf_mlx_model import HuggingFaceMlxModel
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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from docling.pipeline.base_pipeline import PaginatedPipeline
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from docling.utils.profiling import ProfilingScope, TimeRecorder
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@ -29,12 +34,6 @@ class VlmPipeline(PaginatedPipeline):
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super().__init__(pipeline_options)
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self.keep_backend = True
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warnings.warn(
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"The VlmPipeline is currently experimental and may change in upcoming versions without notice.",
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category=UserWarning,
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stacklevel=2,
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)
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self.pipeline_options: VlmPipelineOptions
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artifacts_path: Optional[Path] = None
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@ -58,6 +57,19 @@ class VlmPipeline(PaginatedPipeline):
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self.keep_images = self.pipeline_options.generate_page_images
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if (
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self.pipeline_options.vlm_options.inference_framework
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== InferenceFramework.MLX
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):
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self.build_pipe = [
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HuggingFaceMlxModel(
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enabled=True, # must be always enabled for this pipeline to make sense.
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artifacts_path=artifacts_path,
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accelerator_options=pipeline_options.accelerator_options,
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vlm_options=self.pipeline_options.vlm_options,
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),
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]
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else:
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self.build_pipe = [
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HuggingFaceVlmModel(
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enabled=True, # must be always enabled for this pipeline to make sense.
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@ -79,7 +91,9 @@ class VlmPipeline(PaginatedPipeline):
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return page
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def extract_text_from_backend(self, page: Page, bbox: BoundingBox | None) -> str:
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def extract_text_from_backend(
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self, page: Page, bbox: Union[BoundingBox, None]
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) -> str:
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# Convert bounding box normalized to 0-100 into page coordinates for cropping
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text = ""
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if bbox:
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@ -10,13 +10,15 @@ from docling.datamodel.pipeline_options import (
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VlmPipelineOptions,
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granite_vision_vlm_conversion_options,
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smoldocling_vlm_conversion_options,
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smoldocling_vlm_mlx_conversion_options,
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)
|
||||
from docling.datamodel.settings import settings
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
from docling.pipeline.vlm_pipeline import VlmPipeline
|
||||
|
||||
sources = [
|
||||
"tests/data/2305.03393v1-pg9-img.png",
|
||||
# "tests/data/2305.03393v1-pg9-img.png",
|
||||
"tests/data/pdf/2305.03393v1-pg9.pdf",
|
||||
]
|
||||
|
||||
## Use experimental VlmPipeline
|
||||
@ -29,7 +31,10 @@ pipeline_options.force_backend_text = False
|
||||
# pipeline_options.accelerator_options.cuda_use_flash_attention2 = True
|
||||
|
||||
## Pick a VLM model. We choose SmolDocling-256M by default
|
||||
pipeline_options.vlm_options = smoldocling_vlm_conversion_options
|
||||
# pipeline_options.vlm_options = smoldocling_vlm_conversion_options
|
||||
|
||||
## Pick a VLM model. Fast Apple Silicon friendly implementation for SmolDocling-256M via MLX
|
||||
pipeline_options.vlm_options = smoldocling_vlm_mlx_conversion_options
|
||||
|
||||
## Alternative VLM models:
|
||||
# pipeline_options.vlm_options = granite_vision_vlm_conversion_options
|
||||
@ -63,9 +68,6 @@ for source in sources:
|
||||
|
||||
res = converter.convert(source)
|
||||
|
||||
print("------------------------------------------------")
|
||||
print("MD:")
|
||||
print("------------------------------------------------")
|
||||
print("")
|
||||
print(res.document.export_to_markdown())
|
||||
|
||||
@ -83,8 +85,17 @@ for source in sources:
|
||||
with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
|
||||
fp.write(json.dumps(res.document.export_to_dict()))
|
||||
|
||||
pg_num = res.document.num_pages()
|
||||
res.document.save_as_json(
|
||||
out_path / f"{res.input.file.stem}.md",
|
||||
image_mode=ImageRefMode.PLACEHOLDER,
|
||||
)
|
||||
|
||||
res.document.save_as_markdown(
|
||||
out_path / f"{res.input.file.stem}.md",
|
||||
image_mode=ImageRefMode.PLACEHOLDER,
|
||||
)
|
||||
|
||||
pg_num = res.document.num_pages()
|
||||
print("")
|
||||
inference_time = time.time() - start_time
|
||||
print(
|
||||
|
||||
@ -26,7 +26,7 @@ Docling simplifies document processing, parsing diverse formats — including ad
|
||||
* 🔒 Local execution capabilities for sensitive data and air-gapped environments
|
||||
* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
|
||||
* 🔍 Extensive OCR support for scanned PDFs and images
|
||||
* 🥚 Support of Visual Language Models ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview))
|
||||
* 🥚 Support of Visual Language Models ([SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview)) 🆕🔥
|
||||
* 💻 Simple and convenient CLI
|
||||
|
||||
### Coming soon
|
||||
|
||||
@ -17,10 +17,15 @@ print(result.document.export_to_markdown()) # output: "### Docling Technical Re
|
||||
|
||||
You can also use Docling directly from your command line to convert individual files —be it local or by URL— or whole directories.
|
||||
|
||||
A simple example would look like this:
|
||||
```console
|
||||
docling https://arxiv.org/pdf/2206.01062
|
||||
```
|
||||
You can also use 🥚[SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) and other VLMs via Docling CLI:
|
||||
```bash
|
||||
docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
|
||||
```
|
||||
This will use MLX acceleration on supported Apple Silicon hardware.
|
||||
|
||||
|
||||
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](../reference/cli.md).
|
||||
|
||||
|
||||
@ -192,6 +192,7 @@ module = [
|
||||
"docling_ibm_models.*",
|
||||
"easyocr.*",
|
||||
"ocrmac.*",
|
||||
"mlx_vlm.*",
|
||||
"lxml.*",
|
||||
"huggingface_hub.*",
|
||||
"transformers.*",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user