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prompt.py
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prompt.py
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import os
from llm_studio.src.utils.config_utils import load_config_yaml
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import argparse
import numpy as np
import torch
from llm_studio.src.datasets.text_utils import get_tokenizer
from llm_studio.src.utils.modeling_utils import load_checkpoint, set_generation_config
def parse_param(cfg, prompt):
prompt = prompt.replace("--", "")
parts = prompt.split(" ")
args = [" ".join(parts[i : i + 2]) for i in range(0, len(parts), 2)]
for arg in args:
splitted_arg = arg.split(" ")
setattr(
cfg.prediction,
splitted_arg[0],
type(getattr(cfg.prediction, splitted_arg[0]))(splitted_arg[1]),
)
print(
f"Permanently changed {splitted_arg[0]} to",
getattr(cfg.prediction, splitted_arg[0]),
)
return cfg
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Sample prompting.")
parser.add_argument(
"-e",
"--experiment",
type=str,
required=True,
help="Name of the experiment output folder",
)
parser.add_argument(
"-d", "--device", type=str, required=False, default="cuda:0", help="Device"
)
args, unknown = parser.parse_known_args()
DEVICE = args.device
cfg = load_config_yaml(os.path.join(args.experiment, "cfg.yaml"))
cfg.training.epochs = 0
cfg.environment._device = DEVICE
cfg.environment._local_rank = 0
cfg.tokenizer.padding_quantile = 0
cfg.environment.mixed_precision = True
cfg.architecture.gradient_checkpointing = False
cfg.architecture.pretrained = False
cfg.prediction.max_length_inference = 256
if cfg.dataset.text_prompt_start == "":
cfg.dataset.text_prompt_start = "\n"
# cfg.prediction.min_length_inference = 2
# cfg.prediction.max_length_inference = 256
# cfg.prediction.repetition_penalty = 1.5
# cfg.prediction.temperature = 0.3
# cfg.prediction.num_beams = 2
# cfg.prediction.do_sample = False
# cfg.prediction.top_p = 0.9
# cfg.prediction.top_k = 40
tokenizer = get_tokenizer(cfg)
print("Loading model weights...")
with torch.device(DEVICE):
model = cfg.architecture.model_class(cfg)
cfg.architecture.pretrained_weights = os.path.join(
args.experiment, "checkpoint.pth"
)
load_checkpoint(cfg, model, strict=True)
model = model.to(DEVICE).eval()
model.backbone.use_cache = True
model.backbone = set_generation_config(model.backbone, cfg.prediction)
print()
print("=============")
print(
"You can change inference parameters on the fly by typing --param value, "
"such as --num_beams 4. You can also chain them such as --num_beams 4 "
"--top_k 30."
)
print()
while True:
prompt = input("Please enter some prompt (type 'exit' to stop): ")
try:
if prompt.lower() == "exit":
break
if prompt.lower().startswith("--"):
cfg = parse_param(cfg, prompt)
model.backbone = set_generation_config(model.backbone, cfg.prediction)
continue
prompt = cfg.dataset.dataset_class.parse_prompt(cfg, prompt)
print(prompt)
inputs = cfg.dataset.dataset_class.encode(
tokenizer, prompt, cfg.tokenizer.max_length, "left"
)
inputs["prompt_input_ids"] = inputs.pop("input_ids").unsqueeze(0).to(DEVICE)
inputs["prompt_attention_mask"] = (
inputs.pop("attention_mask").unsqueeze(0).to(DEVICE)
)
output = {}
with torch.no_grad():
with torch.cuda.amp.autocast():
output["predicted_answer_ids"] = (
model.generate(inputs, cfg).detach().cpu()
)
predicted_text = [
tokenizer.decode(ids, skip_special_tokens=True)
for ids in output["predicted_answer_ids"]
]
output["predicted_text"] = np.array(predicted_text)
output = cfg.dataset.dataset_class.clean_output(output, cfg)
output = output["predicted_text"][0]
print(output)
print()
except Exception as e:
print("Error: {}".format(e))
print("Something went wrong, please try again.")