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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer, Qwen3MoeForCausalLM
import torch
import torch.nn as nn
import os
import signal
from typing import Optional, Tuple
import einops
import jaxtyping
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
MODEL_ID = "kalomaze/Qwen3-16B-A3B"
print(f"Load Model {MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = Qwen3MoeForCausalLM.from_pretrained(
MODEL_ID,
device_map="cpu",
trust_remote_code=True,
#quantization_config=quant_config_4,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
enable_thinking = True
skip_prompt=True
skip_special_tokens=True
def direction_ablation_hook(activation: jaxtyping.Float[torch.Tensor, "... d_act"],
direction: jaxtyping.Float[torch.Tensor, "d_act"]):
proj = einops.einsum(activation, direction.view(-1, 1), '... d_act, d_act single -> ... single') * direction
return activation - proj
class AblationDecoderLayer(nn.Module):
def __init__(self, original_layer, refusal_dir):
super(AblationDecoderLayer, self).__init__()
self.original_layer = original_layer
self.refusal_dir = refusal_dir
def forward(self, *args, **kwargs):
hidden_states = args[0]
ablated = direction_ablation_hook(hidden_states, self.refusal_dir.to(hidden_states.device)).to(hidden_states.device)
args = (ablated,) + args[1:]
return self.original_layer.forward(*args, **kwargs)
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
def on_finalized_text(self, text: str, stream_end: bool = False):
self.generated_text += text
print(text, end="", flush=True)
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = enable_thinking,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
use_cache=False,
max_new_tokens=max_new_tokens,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag
final_refusal_dirs= torch.load(MODEL_ID + "/hidden_states/final_refusal_dirs.pt", map_location='cpu', weights_only=True)
candidate_layer = 20
refusal_dir = final_refusal_dirs[candidate_layer]
layer = model.model.layers[20]
for name, param in layer.named_parameters():
print(f"layer0 {name} ")
original_params = {name: param.clone() for name, param in layer.named_parameters()}
for idx in range(len(model.model.layers)):
model.model.layers[idx] = AblationDecoderLayer(model.model.layers[idx], refusal_dir)
while True:
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/no_think":
if enable_thinking:
enable_thinking = False
print("Thinking = False.")
else:
enable_thinking = True
print("Thinking = True.")
continue
if user_input.lower() == "/skip_prompt":
if skip_prompt:
skip_prompt = False
print("skip_prompt = False.")
else:
skip_prompt = True
print("skip_prompt = True.")
continue
if user_input.lower() == "/skip_special_tokens":
if skip_special_tokens:
skip_special_tokens = False
print("skip_special_tokens = False.")
else:
skip_special_tokens = True
print("skip_special_tokens = True.")
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
response, stop_flag = generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, 2)
print("", flush=True)
messages.append({"role": "assistant", "content": response})
layer2 = model.model.layers[20]
for name, param in layer2.named_parameters():
print(f"layer1 {name} ")
layer2 = layer2.original_layer
for name, param in layer2.named_parameters():
print(f"layer2 {name} ")
for name, param in layer2.named_parameters():
if not torch.equal(original_params[name], param):
print(f"参数 {name} 被修改！")
if stop_flag:
continue
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