How to use this with multiple GPU and SentenceTransformer?

#32
by yansanity - opened

I have 4 40GB GPUs, but the demo sentence transformer code only used one. And I got this error:
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacity of 39.38 GiB of which 90.12 MiB is free. Including non-PyTorch memory, this process has 39.28 GiB memory in use. Of the allocated memory 38.69 GiB is allocated by PyTorch, and 104.60 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

I tried following code using device_map = auto.

import torch
from sentence_transformers import SentenceTransformer

# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}

query_prefix = "Instruct: "+task_name_to_instruct["example"]+"\nQuery: "
queries = [
    'are judo throws allowed in wrestling?', 
    'how to become a radiology technician in michigan?'
    ]

# No instruction needed for retrieval passages
passages = [
    "Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
    "Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]

# load model with tokenizer
model = SentenceTransformer('nvidia/NV-Embed-v2', trust_remote_code=True, device='cuda',model_kwargs={"device_map":"auto", 'device':'auto'})
model.max_seq_length = 32768
model.tokenizer.padding_side="right"

def add_eos(input_examples):
  input_examples = [input_example + model.tokenizer.eos_token for input_example in input_examples]
  return input_examples

# get the embeddings
batch_size = 2
query_embeddings = model.encode(add_eos(queries), batch_size=batch_size, prompt=query_prefix, normalize_embeddings=True)
passage_embeddings = model.encode(add_eos(passages), batch_size=batch_size, normalize_embeddings=True)

scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())

It starts to complain that:
FileNotFoundError: [Errno 2] No such file or directory: 'nvidia/NV-Embed-v2/sentence_xlnet_config.json'

Well, I think this might just be a bug of SentenceTransformer

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