support Baichuan-13B

Former-commit-id: f3edfe7d42d5513fb4177be61ec4f88f1edffb1e

tests/quantize.py

@@ -0,0 +1,50 @@
+# coding=utf-8
+# Quantizes fine-tuned models with AutoGPTQ (https://github.com/PanQiWei/AutoGPTQ).
+# Usage: python auto_gptq.py --input_dir path_to_llama_model --output_dir path_to_quant_model --data_file alpaca.json
+#                            --max_length 1024 --max_samples 1024
+# dataset format: instruction (string), input (string), output (string), history (List[string])
+
+
+import fire
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+
+def quantize(input_dir: str, output_dir: str, data_file: str, max_length: int, max_samples: int):
+    tokenizer = AutoTokenizer.from_pretrained(input_dir, use_fast=False, padding_side="left")
+
+    def format_example(examples):
+        prefix=("A chat between a curious user and an artificial intelligence assistant. "
+                "The assistant gives helpful, detailed, and polite answers to the user's questions.")
+        texts = []
+        for i in range(len(examples["instruction"])):
+            prompt = prefix + "\n"
+            if "history" in examples:
+                for user_query, bot_resp in examples["history"][i]:
+                    prompt += "Human: {}\nAssistant: {}\n".format(user_query, bot_resp)
+            prompt += "Human: {}\nAssistant: {}".format(
+                examples["instruction"][i] + "\n" + examples["input"][i], examples["output"][i]
+            )
+            texts.append(prompt)
+        return tokenizer(texts, truncation=True, max_length=max_length)
+
+    dataset = load_dataset("json", data_files=data_file)["train"]
+    column_names = list(dataset.column_names)
+    dataset = dataset.select(range(min(len(dataset), max_samples)))
+    dataset = dataset.map(format_example, batched=True, remove_columns=column_names)
+    dataset = dataset.shuffle()
+
+    quantize_config = BaseQuantizeConfig(
+        bits=4,
+        group_size=128,
+        desc_act=False
+    )
+
+    model = AutoGPTQForCausalLM.from_pretrained(input_dir, quantize_config, trust_remote_code=True)
+    model.quantize(dataset)
+    model.save_quantized(output_dir)
+
+
+if __name__ == "__main__":
+    fire.Fire(quantize)