Replace torch.tensor() with torch.from_numpy() when processing numpy arrays

[shinyano]

System Info

• transformers version: 4.43.4
• Platform: Windows-10-10.0.22631-SP0
• Python version: 3.11.4
• Huggingface_hub version: 0.24.5
• Safetensors version: 0.4.4
• Accelerate version: not installed
• Accelerate config: not found
• PyTorch version (GPU?): 2.2.2+cpu (False)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?:

Who can help?

@ArthurZucker @gante @Rocketknight1

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

I'm using Pemja, a C extension using multiple python threads, to execute python scripts from java. I use Blip models in my python script and it hangs when I create a new python thread to execute this script.

This is my minimal script:

import numpy as np
import torch
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

class TensorTest:
    def __init__(self):
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

    def generate_caption(self, image_data):
        import io
        image = Image.open(io.BytesIO(image_data))

        # original way:
        inputs = self.processor(images=image, return_tensors="pt")   #  return_tensors="pt" is the pain point, my program would stuck here forever
        out = self.model.generate(**inputs)

        # workaround:
        # inputs = self.processor(images=image)               # This is a workaround to avoid hanging, it basically skips the torch.tensor() step and use torch.from_numpy() instead
        # pixel_values = np.array(inputs["pixel_values"][0])
        # pixel_values = torch.from_numpy(pixel_values).unsqueeze(0)
        # pixel_values = pixel_values.to(self.model.device)
        # out = self.model.generate(pixel_values)

        caption = self.processor.decode(out[0], skip_special_tokens=True)
        return caption

    def transform(self):
        with open("E:/pics/horse.jpg", "rb") as image_file:
            image_data = image_file.read()
            return self.generate_caption(image_data)

if __name__ == '__main__':
    t = TensorTest()
    t.transform()

The minimal Java code to call the script:

PythonInterpreterConfig config =
    PythonInterpreterConfig.newBuilder()
        .setPythonExec(pythonCMD)
        .addPythonPaths(PYTHON_PATH)
        .build();
try(PythonInterpreter interpreter = new PythonInterpreter(config)) {
  interpreter.exec(String.format("import %s", moduleName));
  interpreter.exec(String.format("t = %s.%s()", moduleName, className));
  interpreter.exec("t.transform()");                       // <- works fine
}
try(PythonInterpreter interpreter2 = new PythonInterpreter(config)) {
  interpreter2.exec(String.format("import %s", moduleName));
  interpreter2.exec(String.format("t = %s.%s()", moduleName, className));
  interpreter2.exec("t.transform()");                       // <- hangs here
}

As written in the comment, inputs = self.processor(images=image, return_tensors="pt") would stuck. By importing source code, I found that the processor was stuck here at line 149 and value is a numpy array:

transformers/src/transformers/feature_extraction_utils.py

Lines 139 to 150 in 5c1027b

	def as_tensor(value):
	if isinstance(value, (list, tuple)) and len(value) > 0:
	if isinstance(value[0], np.ndarray):
	value = np.array(value)
	elif (
	isinstance(value[0], (list, tuple))
	and len(value[0]) > 0
	and isinstance(value[0][0], np.ndarray)
	):
	value = np.array(value)
	return torch.tensor(value)

In research, I noticed similiar question had happened here: stackoverflow. And torch.from_numpy() works fine both in the stackoverflow link and in my case, as shown in the script above.

Although this bug won't appear if I call the script directly from python, still It seems that using torch.tensor() on numpy arrays is not a reliable practice when multiple processing or threading is involved.

So I would suggest that replacing torch.tensor() with torch.from_numpy() to process numpy arrays is a good idea. Please correct me if it's actually bad.

Expected behavior

The script should not stuck and finish its work.

[LysandreJik]

Thanks for your issue! cc @qubvel in case you have bandwidth :)

[qubvel]

Hi @shinyano, thanks for opening the issue!

I'm not sure about replacing torch.tensor() with torch.from_numpy() because I suspect there might be not only np.arrays but for example scalar values. However, we can resolve it by checking if the value is an array.

 def as_tensor(value): 
     if isinstance(value, (list, tuple)) and len(value) > 0: 
         if isinstance(value[0], np.ndarray): 
             value = np.array(value) 
         elif ( 
             isinstance(value[0], (list, tuple)) 
             and len(value[0]) > 0 
             and isinstance(value[0][0], np.ndarray) 
         ): 
             value = np.array(value)
             
     # Modified code 
     if isinstance(value, np.ndarray):
         tensor = torch.from_numpy(value)
     else:
         tensor = torch.tensor(value)
     return tensor

What do you think? Would you like to submit a PR to fix it?

[shinyano]

What do you think? Would you like to submit a PR to fix it?

The modification is exactly what I wanted :). And I would be glad to submit the PR.

[qubvel]

@shinyano great! ping me for review when it's ready 🤗

[shinyano]

@shinyano great! ping me for review when it's ready 🤗

@qubvel I have created a PR, Would you like to review it?