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# Copyright 2025 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# Modified from LLaDA repos: https://github.com/ML-GSAI/LLaDA
import torch
import argparse
from generate import generate, generate_with_prefix_cache, generate_with_dual_cache
from transformers import AutoTokenizer, AutoModel
from model.modeling_llada import LLaDAModelLM
def chat(args):
model = LLaDAModelLM.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True, torch_dtype=torch.float16, device_map = 'auto').eval()
tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True)
device = next(iter(model.parameters())).device.type
gen_length = args.gen_length
steps = args.steps
print('*' * 66)
print(f'** Answer Length: {gen_length} | Sampling Steps: {steps} **')
print('*' * 66)
conversation_num = 0
#while True:
#user_input = input("Enter your question: ")
user_input = args.question
m = [{"role": "user", "content": user_input}]
user_input = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False)
input_ids = tokenizer(user_input)['input_ids']
input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)
if conversation_num == 0:
prompt = input_ids
else:
prompt = torch.cat([prompt, input_ids[:, 1:]], dim=1)
print(f'use cache: {args.use_cache} use cache position: {args.if_cache_position} threshold: {args.threshold} block size: {args.block_size}')
if args.use_cache:
if args.if_cache_position:
out, nfe = generate_with_dual_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
else:
out, nfe = generate_with_prefix_cache(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
else:
out, nfe = generate(model, prompt, steps=steps, gen_length=gen_length, block_length=args.block_size, temperature=0., remasking='low_confidence', threshold=args.threshold)
answer = tokenizer.batch_decode(out[:, prompt.shape[1]:], skip_special_tokens=True)[0]
print(f"Bot's reply: {answer}")
print(f"Number of forward passes: {nfe}")
# remove the <EOS>
prompt = out[out != 126081].unsqueeze(0)
conversation_num += 1
#print('-----------------------------------------------------------------------')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gen_length", type=int, default=128)
parser.add_argument("--steps", type=int, default=128)
parser.add_argument("--block_size", type=int, default=32)
parser.add_argument("--use_cache", action="store_true")
parser.add_argument("--if_cache_position", action="store_true")
parser.add_argument("--threshold", type=float, default=None)
parser.add_argument("--question", type=str, default='How are you ?')
args = parser.parse_args()
chat(args)
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