00-test-vector-results-Qwen3-16B-A3B.py

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