CODEWITHSUNDEEP
python-3.xpytorchpython-embeddingbart
Tombawomba
Tombawomba

Reputation: 1

Does Pytorch automaticly use the GPU

I'm new to using torch and calculate on huge amount of data. I want to create embeddings for an large text corpus and write my embedding function, it works well but it seems pretty slow, so I'm not sure if it really uses the GPU or does still uses the CPU.

import torch
from transformers import BartTokenizer, BartForConditionalGeneration

model_name = 'facebook/bart-base'

def load_model():
    # load the pretrained bart model
    model = BartForConditionalGeneration.from_pretrained(model_name)
    # load the tokenizer
    tokenizer = BartTokenizer.from_pretrained(model_name)
    return model, tokenizer

def calculate_text_embeddings(text, model, tokenizer):
    # tokenize the text
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
    # generate the embedding
    with torch.no_grad():
        outputs = model.encoder(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)
    # get the last hidden state
    last_hidden_states = outputs.encoder_last_hidden_state
    
    return last_hidden_states

    return embeddings

torch.cuda.is_available() returns true

I want to calculate the embeddings on the GPU using CUDA.

Upvotes: 0

Views: 176

Answers (1)

Karl
Karl

Reputation: 5283

You need to explicitly move the model and the model inputs to the GPU.

You can run nvidia-smi to verify things are running on the GPU.

import torch
from transformers import BartTokenizer, BartForConditionalGeneration

model_name = 'facebook/bart-base'
device = 'cuda'

def load_model():
    # load the pretrained bart model
    model = BartForConditionalGeneration.from_pretrained(model_name)
    model.to(device)
    # load the tokenizer
    tokenizer = BartTokenizer.from_pretrained(model_name)
    return model, tokenizer

def calculate_text_embeddings(text, model, tokenizer):
    # tokenize the text
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
    # generate the embedding
    with torch.no_grad():
        outputs = model.encoder(input_ids=inputs.input_ids.to(device), attention_mask=inputs.attention_mask.to(device))
    # get the last hidden state
    last_hidden_states = outputs.encoder_last_hidden_state
    
    return last_hidden_states.cpu()

Upvotes: 0

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