Initial implementation to support MLX for VLM pipeline and SmolDocling

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

docling/datamodel/pipeline_options.py

@@ -263,6 +263,11 @@ class ResponseFormat(str, Enum):
     MARKDOWN = "markdown"
 
 
+class InferenceFramework(str, Enum):
+    MLX = "mlx"
+    TRANSFORMERS = "transformers"
+
+
 class HuggingFaceVlmOptions(BaseVlmOptions):
     kind: Literal["hf_model_options"] = "hf_model_options"
 
@@ -271,6 +276,7 @@ class HuggingFaceVlmOptions(BaseVlmOptions):
     llm_int8_threshold: float = 6.0
     quantized: bool = False
 
+    inference_framework: InferenceFramework
     response_format: ResponseFormat
 
     @property
@@ -278,10 +284,19 @@ class HuggingFaceVlmOptions(BaseVlmOptions):
         return self.repo_id.replace("/", "--")
 
 
+smoldocling_vlm_mlx_conversion_options = HuggingFaceVlmOptions(
+    repo_id="ds4sd/SmolDocling-256M-preview-mlx-bf16",
+    prompt="Convert this page to docling.",
+    response_format=ResponseFormat.DOCTAGS,
+    inference_framework=InferenceFramework.MLX,
+)
+
+
 smoldocling_vlm_conversion_options = HuggingFaceVlmOptions(
     repo_id="ds4sd/SmolDocling-256M-preview",
     prompt="Convert this page to docling.",
     response_format=ResponseFormat.DOCTAGS,
+    inference_framework=InferenceFramework.TRANSFORMERS,
 )
 
 granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
@@ -289,6 +304,7 @@ granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
     # prompt="OCR the full page to markdown.",
     prompt="OCR this image.",
     response_format=ResponseFormat.MARKDOWN,
+    inference_framework=InferenceFramework.TRANSFORMERS,
 )
 
 

docling/pipeline/vlm_pipeline.py

@@ -14,8 +14,13 @@ from docling.backend.md_backend import MarkdownDocumentBackend
 from docling.backend.pdf_backend import PdfDocumentBackend
 from docling.datamodel.base_models import InputFormat, Page
 from docling.datamodel.document import ConversionResult, InputDocument
-from docling.datamodel.pipeline_options import ResponseFormat, VlmPipelineOptions
+from docling.datamodel.pipeline_options import (
+    InferenceFramework,
+    ResponseFormat,
+    VlmPipelineOptions,
+)
 from docling.datamodel.settings import settings
+from docling.models.hf_mlx_model import HuggingFaceMlxModel
 from docling.models.hf_vlm_model import HuggingFaceVlmModel
 from docling.pipeline.base_pipeline import PaginatedPipeline
 from docling.utils.profiling import ProfilingScope, TimeRecorder
@@ -58,14 +63,27 @@ class VlmPipeline(PaginatedPipeline):
 
         self.keep_images = self.pipeline_options.generate_page_images
 
-        self.build_pipe = [
-            HuggingFaceVlmModel(
-                enabled=True,  # must be always enabled for this pipeline to make sense.
-                artifacts_path=artifacts_path,
-                accelerator_options=pipeline_options.accelerator_options,
-                vlm_options=self.pipeline_options.vlm_options,
-            ),
-        ]
+        if (
+            self.pipeline_options.vlm_options.inference_framework
+            == InferenceFramework.MLX
+        ):
+            self.build_pipe = [
+                HuggingFaceMlxModel(
+                    enabled=True,  # must be always enabled for this pipeline to make sense.
+                    artifacts_path=artifacts_path,
+                    accelerator_options=pipeline_options.accelerator_options,
+                    vlm_options=self.pipeline_options.vlm_options,
+                ),
+            ]
+        else:
+            self.build_pipe = [
+                HuggingFaceVlmModel(
+                    enabled=True,  # must be always enabled for this pipeline to make sense.
+                    artifacts_path=artifacts_path,
+                    accelerator_options=pipeline_options.accelerator_options,
+                    vlm_options=self.pipeline_options.vlm_options,
+                ),
+            ]
 
         self.enrichment_pipe = [
             # Other models working on `NodeItem` elements in the DoclingDocument

docs/examples/minimal_vlm_pipeline.py

@@ -10,6 +10,7 @@ from docling.datamodel.pipeline_options import (
     VlmPipelineOptions,
     granite_vision_vlm_conversion_options,
     smoldocling_vlm_conversion_options,
+    smoldocling_vlm_mlx_conversion_options,
 )
 from docling.datamodel.settings import settings
 from docling.document_converter import DocumentConverter, PdfFormatOption
@@ -29,7 +30,10 @@ 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