Add custom modeling code for abliteration

modeling_qwen3_moe.py

@@ -0,0 +1,174 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
+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 = "Qwen/Qwen3-30B-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 = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    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 + "/final_refusal_dirs.pt", map_location='cpu', weights_only=True)
+# candidate layer, 16, 21 ...
+candidate_layer = 16
+
+refusal_dir = final_refusal_dirs[candidate_layer]
+
+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, 8192)
+    print("", flush=True)
+    if stop_flag:
+        continue
+    messages.append({"role": "assistant", "content": response})