Docling/docling/utils/accelerator_utils.py
Peter W. J. Staar cfdf4cea25
feat: new vlm-models support (#1570)
* feat: adding new vlm-models support

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the transformers

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* got microsoft/Phi-4-multimodal-instruct to work

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* working on vlm's

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* refactoring the VLM part

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* all working, now serious refacgtoring necessary

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* refactoring the download_model

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* added the formulate_prompt

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* pixtral 12b runs via MLX and native transformers

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* added the VlmPredictionToken

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* refactoring minimal_vlm_pipeline

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the MyPy

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* added pipeline_model_specializations file

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* need to get Phi4 working again ...

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* finalising last points for vlms support

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the pipeline for Phi4

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* streamlining all code

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* reformatted the code

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixing the tests

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* added the html backend to the VLM pipeline

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the static load_from_doctags

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* restore stable imports

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* use AutoModelForVision2Seq for Pixtral and review example (including rename)

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* remove unused value

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* refactor instances of VLM models

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* skip compare example in CI

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* use lowercase and uppercase only

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* add new minimal_vlm example and refactor pipeline_options_vlm_model for cleaner import

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* rename pipeline_vlm_model_spec

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* move more argument to options and simplify model init

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* add supported_devices

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* remove not-needed function

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* exclude minimal_vlm

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* missing file

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* add message for transformers version

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* rename to specs

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* use module import and remove MLX from non-darwin

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* remove hf_vlm_model and add extra_generation_args

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* use single HF VLM model class

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* remove torch type

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* add docs for vision models

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

---------

Signed-off-by: Peter Staar <taa@zurich.ibm.com>
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
Co-authored-by: Michele Dolfi <dol@zurich.ibm.com>
2025-06-02 17:01:06 +02:00

84 lines
2.8 KiB
Python

import logging
from typing import List, Optional
import torch
from docling.datamodel.accelerator_options import AcceleratorDevice
_log = logging.getLogger(__name__)
def decide_device(
accelerator_device: str, supported_devices: Optional[List[AcceleratorDevice]] = None
) -> str:
r"""
Resolve the device based on the acceleration options and the available devices in the system.
Rules:
1. AUTO: Check for the best available device on the system.
2. User-defined: Check if the device actually exists, otherwise fall-back to CPU
"""
device = "cpu"
has_cuda = torch.backends.cuda.is_built() and torch.cuda.is_available()
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
if supported_devices is not None:
if has_cuda and AcceleratorDevice.CUDA not in supported_devices:
_log.info(
f"Removing CUDA from available devices because it is not in {supported_devices=}"
)
has_cuda = False
if has_mps and AcceleratorDevice.MPS not in supported_devices:
_log.info(
f"Removing MPS from available devices because it is not in {supported_devices=}"
)
has_mps = False
if accelerator_device == AcceleratorDevice.AUTO.value: # Handle 'auto'
if has_cuda:
device = "cuda:0"
elif has_mps:
device = "mps"
elif accelerator_device.startswith("cuda"):
if has_cuda:
# if cuda device index specified extract device id
parts = accelerator_device.split(":")
if len(parts) == 2 and parts[1].isdigit():
# select cuda device's id
cuda_index = int(parts[1])
if cuda_index < torch.cuda.device_count():
device = f"cuda:{cuda_index}"
else:
_log.warning(
"CUDA device 'cuda:%d' is not available. Fall back to 'CPU'.",
cuda_index,
)
elif len(parts) == 1: # just "cuda"
device = "cuda:0"
else:
_log.warning(
"Invalid CUDA device format '%s'. Fall back to 'CPU'",
accelerator_device,
)
else:
_log.warning("CUDA is not available in the system. Fall back to 'CPU'")
elif accelerator_device == AcceleratorDevice.MPS.value:
if has_mps:
device = "mps"
else:
_log.warning("MPS is not available in the system. Fall back to 'CPU'")
elif accelerator_device == AcceleratorDevice.CPU.value:
device = "cpu"
else:
_log.warning(
"Unknown device option '%s'. Fall back to 'CPU'", accelerator_device
)
_log.info("Accelerator device: '%s'", device)
return device