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import os
import torch
import yaml
from models.multimodal_encoder.t5_encoder import T5Embedder
GPU = 0
MODEL_PATH = "google/t5-v1_1-xxl"
CONFIG_PATH = "configs/base.yaml"
SAVE_DIR = "lang_embed/"
# Modify this to your task name and instruction
TASK_NAME = "anubis_carrot_to_bag"
# INSTRUCTION = "take the towel off the kirby doll"
# INSTRUCTION = "insert the brush to the dustpan"
INSTRUCTION = "pick up the carrot and put into the bag"
# Note: if your GPU VRAM is less than 24GB,
# it is recommended to enable offloading by specifying an offload directory.
# OFFLOAD_DIR = '/home/jellyho/OFFLOAD' # Specify your offload directory here, ensuring the directory exists.
def main():
with open(CONFIG_PATH, "r") as fp:
config = yaml.safe_load(fp)
device = torch.device(f"cuda:{GPU}")
text_embedder = T5Embedder(
from_pretrained=MODEL_PATH,
model_max_length=config["dataset"]["tokenizer_max_length"],
device=device,
# use_offload_folder=OFFLOAD_DIR
)
tokenizer, text_encoder = text_embedder.tokenizer, text_embedder.model
tokens = tokenizer(
INSTRUCTION, return_tensors="pt",
padding="longest",
truncation=True
)["input_ids"].to(device)
tokens = tokens.view(1, -1)
with torch.no_grad():
pred = text_encoder(tokens).last_hidden_state.detach().cpu()
save_path = os.path.join(SAVE_DIR, f"{TASK_NAME}.pt")
# We save the embeddings in a dictionary format
torch.save({
"name": TASK_NAME,
"instruction": INSTRUCTION,
"embeddings": pred
}, save_path
)
print(f'\"{INSTRUCTION}\" from \"{TASK_NAME}\" is encoded by \"{MODEL_PATH}\" into shape {pred.shape} and saved to \"{save_path}\"')
if __name__ == "__main__":
main()
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