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import argparse
import os
from string import Template
import torch
from huggingface_hub import snapshot_download
from torch import nn
from transformers import AutoModelForCausalLM, AutoTokenizer
parser = argparse.ArgumentParser()
parser.add_argument("--base_model", type=str, required=True, help="Path to HF checkpoint with the base model")
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to either a local directory or a HF checkpoint with reward model's weights",
)
parser.add_argument("--revision", type=str, required=False, help="Optional branch/commit of the HF checkpoint")
parser.add_argument("--device", type=int, default=0)
args = parser.parse_args()
model_name = args.checkpoint.split("/")[-1]
device = torch.device(args.device)
class RewardModel(nn.Module):
def __init__(self, checkpoint_path, eos_token_id):
super().__init__()
model = AutoModelForCausalLM.from_pretrained(checkpoint_path)
self.transformer = model.transformer
self.v_head = nn.Linear(model.config.n_embd, 1, bias=False)
self.eos_token_id = eos_token_id
def forward(self, input_ids):
states = self.transformer(input_ids)[0]
rewards = self.v_head(states).squeeze(-1)
ends = torch.argmax((input_ids == self.eos_token_id).float(), dim=1).view(-1, 1)
returns = torch.gather(rewards, 1, ends).squeeze(-1)
return returns
if os.path.isdir(args.checkpoint):
directory = args.checkpoint
else:
directory = snapshot_download(args.checkpoint, revision=args.revision)
print(f"searching through {os.listdir(directory)} in {directory}")
for fpath in os.listdir(directory):
if fpath.endswith(".pt") or fpath.endswith(".bin"):
checkpoint = os.path.join(directory, fpath)
break
tokenizer = AutoTokenizer.from_pretrained(args.base_model)
model = RewardModel(args.base_model, tokenizer.eos_token_id)
model.load_state_dict(torch.load(checkpoint))
model.eval()
model.requires_grad_(False)
model = model.half().to(device)
input = tokenizer("reward model's hash", return_tensors="pt").to(device)
print(f"{model(input.input_ids)=}")
traced_script_module = torch.jit.trace(model, input.input_ids)
os.makedirs(f"model_store/{model_name}/1", exist_ok=True)
traced_script_module.save(f"model_store/{model_name}/1/traced-model.pt")
config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "triton_config.pbtxt")
with open(config_path) as f:
template = Template(f.read())
config = template.substitute({"model_name": model_name})
with open(f"model_store/{model_name}/config.pbtxt", "w") as f:
f.write(config)
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