mirror of
https://github.com/DS4SD/docling.git
synced 2025-07-26 20:14:47 +00:00
refactoring the download_model
Signed-off-by: Peter Staar <taa@zurich.ibm.com>
This commit is contained in:
Peter Staar
parent
3407955a47
commit
4c0bc61e54
@ -269,7 +269,9 @@ class InferenceFramework(str, Enum):
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OPENAI = "openai"
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TRANSFORMERS_AutoModelForVision2Seq = "transformers-AutoModelForVision2Seq"
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TRANSFORMERS_AutoModelForCausalLM = "transformers-AutoModelForCausalLM"
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TRANSFORMERS_LlavaForConditionalGeneration = "transformers-LlavaForConditionalGeneration"
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TRANSFORMERS_LlavaForConditionalGeneration = (
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"transformers-LlavaForConditionalGeneration"
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)
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class HuggingFaceVlmOptions(BaseVlmOptions):
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@ -17,81 +17,7 @@ from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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class HuggingFaceVlmModel(BasePageModel):
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"""
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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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import torch
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from transformers import ( # type: ignore
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AutoModelForVision2Seq,
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AutoProcessor,
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BitsAndBytesConfig,
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)
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device = decide_device(accelerator_options.device)
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self.device = device
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_log.debug(f"Available device for HuggingFace VLM: {device}")
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repo_cache_folder = vlm_options.repo_id.replace("/", "--")
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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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self.param_quantization_config = BitsAndBytesConfig(
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load_in_8bit=vlm_options.load_in_8bit, # True,
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llm_int8_threshold=vlm_options.llm_int8_threshold, # 6.0
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)
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self.param_quantized = vlm_options.quantized # False
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self.processor = AutoProcessor.from_pretrained(
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artifacts_path,
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# trust_remote_code=True,
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)
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if not self.param_quantized:
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self.vlm_model = AutoModelForVision2Seq.from_pretrained(
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artifacts_path,
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device_map=self.device,
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torch_dtype=torch.bfloat16,
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_attn_implementation=(
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"flash_attention_2"
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if self.device.startswith("cuda")
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and accelerator_options.cuda_use_flash_attention2
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else "eager"
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),
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# trust_remote_code=True,
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) # .to(self.device)
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else:
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self.vlm_model = AutoModelForVision2Seq.from_pretrained(
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artifacts_path,
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device_map=self.device,
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torch_dtype="auto",
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quantization_config=self.param_quantization_config,
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_attn_implementation=(
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"flash_attention_2"
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if self.device.startswith("cuda")
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and accelerator_options.cuda_use_flash_attention2
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else "eager"
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),
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# trust_remote_code=True,
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) # .to(self.device)
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"""
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class HuggingFaceVlmModel:
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@staticmethod
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def download_models(
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repo_id: str,
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@ -112,80 +38,3 @@ class HuggingFaceVlmModel(BasePageModel):
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)
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return Path(download_path)
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"""
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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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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "This is a page from a document.",
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},
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{"type": "image"},
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{"type": "text", "text": self.param_question},
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],
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}
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]
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prompt = self.processor.apply_chat_template(
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messages, add_generation_prompt=False
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)
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inputs = self.processor(
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text=prompt, images=[hi_res_image], return_tensors="pt"
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)
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inputs = {k: v.to(self.device) for k, v in inputs.items()}
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start_time = time.time()
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# Call model to generate:
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generated_ids = self.vlm_model.generate(
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**inputs, max_new_tokens=4096, use_cache=True
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)
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generation_time = time.time() - start_time
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generated_texts = self.processor.batch_decode(
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generated_ids[:, inputs["input_ids"].shape[1] :],
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skip_special_tokens=False,
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)[0]
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num_tokens = len(generated_ids[0])
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page_tags = generated_texts
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_log.debug(
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f"Generated {num_tokens} tokens in time {generation_time:.2f} seconds."
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)
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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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"""
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@ -11,6 +11,7 @@ from docling.datamodel.pipeline_options import (
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HuggingFaceVlmOptions,
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)
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from docling.models.base_model import BasePageModel
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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@ -44,7 +45,10 @@ class HuggingFaceMlxModel(BasePageModel):
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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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# artifacts_path = self.download_models(self.vlm_options.repo_id)
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artifacts_path = HuggingFaceVlmModel.download_models(
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self.vlm_options.repo_id
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)
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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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@ -54,6 +58,7 @@ class HuggingFaceMlxModel(BasePageModel):
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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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"""
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@staticmethod
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def download_models(
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repo_id: str,
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@ -74,6 +79,7 @@ class HuggingFaceMlxModel(BasePageModel):
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)
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return Path(download_path)
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"""
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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@ -11,6 +11,7 @@ from docling.datamodel.pipeline_options import (
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HuggingFaceVlmOptions,
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)
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from docling.models.base_model import BasePageModel
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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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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@ -30,7 +31,6 @@ class HuggingFaceVlmModel_AutoModelForCausalLM(BasePageModel):
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self.trust_remote_code = True
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self.vlm_options = vlm_options
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print(self.vlm_options)
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if self.enabled:
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import torch
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@ -49,7 +49,10 @@ class HuggingFaceVlmModel_AutoModelForCausalLM(BasePageModel):
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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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# artifacts_path = self.download_models(self.vlm_options.repo_id)
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artifacts_path = HuggingFaceVlmModel.download_models(
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self.vlm_options.repo_id
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)
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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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@ -99,6 +102,7 @@ class HuggingFaceVlmModel_AutoModelForCausalLM(BasePageModel):
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# Load generation config
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self.generation_config = GenerationConfig.from_pretrained(model_path)
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"""
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@staticmethod
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def download_models(
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repo_id: str,
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@ -119,6 +123,7 @@ class HuggingFaceVlmModel_AutoModelForCausalLM(BasePageModel):
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)
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return Path(download_path)
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"""
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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@ -11,6 +11,7 @@ from docling.datamodel.pipeline_options import (
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HuggingFaceVlmOptions,
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)
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from docling.models.base_model import BasePageModel
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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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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@ -46,7 +47,10 @@ class HuggingFaceVlmModel_AutoModelForVision2Seq(BasePageModel):
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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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# artifacts_path = self.download_models(self.vlm_options.repo_id)
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artifacts_path = HuggingFaceVlmModel.download_models(
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self.vlm_options.repo_id
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)
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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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@ -90,6 +94,7 @@ class HuggingFaceVlmModel_AutoModelForVision2Seq(BasePageModel):
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# trust_remote_code=True,
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) # .to(self.device)
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"""
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@staticmethod
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def download_models(
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repo_id: str,
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@ -110,6 +115,7 @@ class HuggingFaceVlmModel_AutoModelForVision2Seq(BasePageModel):
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)
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return Path(download_path)
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"""
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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@ -4,6 +4,8 @@ from collections.abc import Iterable
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from pathlib import Path
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from typing import Optional
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from transformers import AutoProcessor, LlavaForConditionalGeneration
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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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@ -11,11 +13,10 @@ from docling.datamodel.pipeline_options import (
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HuggingFaceVlmOptions,
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)
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from docling.models.base_model import BasePageModel
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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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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from transformers import AutoProcessor, LlavaForConditionalGeneration
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_log = logging.getLogger(__name__)
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@ -32,13 +33,12 @@ class HuggingFaceVlmModel_LlavaForConditionalGeneration(BasePageModel):
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self.trust_remote_code = True
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self.vlm_options = vlm_options
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print(self.vlm_options)
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if self.enabled:
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import torch
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from transformers import ( # type: ignore
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LlavaForConditionalGeneration,
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AutoProcessor,
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LlavaForConditionalGeneration,
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)
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self.device = decide_device(accelerator_options.device)
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@ -51,7 +51,10 @@ class HuggingFaceVlmModel_LlavaForConditionalGeneration(BasePageModel):
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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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# artifacts_path = self.download_models(self.vlm_options.repo_id)
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artifacts_path = HuggingFaceVlmModel.download_models(
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self.vlm_options.repo_id
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)
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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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@ -64,9 +67,11 @@ class HuggingFaceVlmModel_LlavaForConditionalGeneration(BasePageModel):
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artifacts_path,
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trust_remote_code=self.trust_remote_code,
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)
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self.vlm_model = LlavaForConditionalGeneration.from_pretrained(artifacts_path).to(self.device)
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self.vlm_model = LlavaForConditionalGeneration.from_pretrained(
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artifacts_path
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).to(self.device)
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"""
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@staticmethod
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def download_models(
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repo_id: str,
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@ -87,6 +92,7 @@ class HuggingFaceVlmModel_LlavaForConditionalGeneration(BasePageModel):
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)
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return Path(download_path)
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"""
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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@ -109,20 +115,22 @@ class HuggingFaceVlmModel_LlavaForConditionalGeneration(BasePageModel):
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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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images = [
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hi_res_image
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]
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images = [hi_res_image]
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prompt = "<s>[INST]Describe the images.\n[IMG][/INST]"
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inputs = self.processor(text=prompt, images=images, return_tensors="pt", use_fast=False).to(self.device) #.to("cuda")
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inputs = self.processor(
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text=prompt, images=images, return_tensors="pt", use_fast=False
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).to(self.device) # .to("cuda")
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generate_ids = self.vlm_model.generate(
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**inputs,
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max_new_tokens=self.max_new_tokens,
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use_cache=True # Enables KV caching which can improve performance
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use_cache=True, # Enables KV caching which can improve performance
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)
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response = self.processor.batch_decode(generate_ids,
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response = self.processor.batch_decode(
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generate_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)[0]
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clean_up_tokenization_spaces=False,
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)[0]
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print(f"response: {response}")
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"""
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_log.debug(
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@ -1,33 +0,0 @@
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import logging
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import time
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from collections.abc import Iterable
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from pathlib import Path
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from typing import 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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AcceleratorOptions,
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HuggingFaceVlmOptions,
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)
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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 HuggingFaceVlmModel_pixtral_12b_2409(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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import torch
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@ -24,18 +24,16 @@ from docling.datamodel.settings import settings
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from docling.models.api_vlm_model import ApiVlmModel
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# from docling.models.hf_vlm_model import HuggingFaceVlmModel
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from docling.models.hf_vlm_models.hf_vlm_mlx_model import (
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HuggingFaceMlxModel
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)
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from docling.models.hf_vlm_models.hf_vlm_model_LlavaForConditionalGeneration import (
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HuggingFaceVlmModel_LlavaForConditionalGeneration
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)
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from docling.models.hf_vlm_models.hf_vlm_mlx_model import HuggingFaceMlxModel
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from docling.models.hf_vlm_models.hf_vlm_model_AutoModelForCausalLM import (
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HuggingFaceVlmModel_AutoModelForCausalLM,
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)
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from docling.models.hf_vlm_models.hf_vlm_model_AutoModelForVision2Seq import (
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HuggingFaceVlmModel_AutoModelForVision2Seq,
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)
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from docling.models.hf_vlm_models.hf_vlm_model_LlavaForConditionalGeneration import (
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HuggingFaceVlmModel_LlavaForConditionalGeneration,
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)
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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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@ -126,7 +124,9 @@ class VlmPipeline(PaginatedPipeline):
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),
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]
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else:
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raise ValueError(f"Could not instantiate the right type of VLM pipeline: {vlm_options.inference_framework}")
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raise ValueError(
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f"Could not instantiate the right type of VLM pipeline: {vlm_options.inference_framework}"
|
||||
)
|
||||
|
||||
self.enrichment_pipe = [
|
||||
# Other models working on `NodeItem` elements in the DoclingDocument
|
||||
|
||||
Loading…
Reference in New Issue
Block a user