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| import torch | |
| import numpy as np | |
| from magma import Magma | |
| from magma.language_model import get_language_model | |
| from magma.utils import get_tokenizer | |
| if __name__ == "__main__": | |
| # model = Magma.from_checkpoint( | |
| # "configs/MAGMA_v1.yml", | |
| # "/mnt/localdisk/mp_rank_00_model_states.pt", | |
| # model_dir="/mnt/localdisk/gptj", | |
| # lm_from_pretrained=True, | |
| # ) | |
| # gptj_model = model.lm | |
| # model.half().cuda().eval() | |
| tokenizer = get_tokenizer() | |
| input_text = tokenizer.encode("this is a test", return_tensors="pt").cuda() | |
| input_img = torch.ones(1, 3, 384, 384).half().cuda() | |
| # input = model.embed([input_img, input_text]) | |
| # logits = gptj_model(inputs_embeds=input).logits | |
| # logits = logits.detach().cpu().numpy() | |
| # np.save("/mnt/localdisk/logits_new.npy", logits) | |
| from transformers import GPTJForCausalLM | |
| import torch | |
| # load new model | |
| model = GPTJForCausalLM.from_pretrained( | |
| "EleutherAI/gpt-j-6B", | |
| revision="float16", | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| ) | |
| model.cuda() | |
| model.eval() | |
| logits = model(input_text).logits | |
| logits = logits.detach().cpu().numpy() | |
| np.save("/mnt/localdisk/gptj_logits_new.npy", logits) | |
| print("test") | |