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quant_with_alpaca.py

+import json
+import random
+import time
+from argparse import ArgumentParser
+
+import torch
+from datasets import Dataset
+from transformers import AutoTokenizer, TextGenerationPipeline
+
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+
+def load_data(data_path, tokenizer, n_samples):
+    with open(data_path, "r", encoding="utf-8") as f:
+        raw_data = json.load(f)
+
+    raw_data = random.sample(raw_data, k=min(n_samples, len(raw_data)))
+
+    def dummy_gen():
+        return raw_data
+
+    def tokenize(examples):
+        instructions = examples["instruction"]
+        inputs = examples["input"]
+        outputs = examples["output"]
+
+        prompts = []
+        texts = []
+        input_ids = []
+        attention_mask = []
+        for istr, inp, opt in zip(instructions, inputs, outputs):
+            if inp:
+                prompt = f"Instruction:\n{istr}\nInput:\n{inp}\nOutput:\n"
+                text = prompt + opt
+            else:
+                prompt = f"Instruction:\n{istr}\nOutput:\n"
+                text = prompt + opt
+            if len(tokenizer(prompt)["input_ids"]) >= tokenizer.model_max_length:
+                continue
+
+            tokenized_data = tokenizer(text)
+
+            input_ids.append(tokenized_data["input_ids"][: tokenizer.model_max_length])
+            attention_mask.append(tokenized_data["attention_mask"][: tokenizer.model_max_length])
+            prompts.append(prompt)
+            texts.append(text)
+
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "prompt": prompts,
+        }
+
+    dataset = Dataset.from_generator(dummy_gen)
+
+    dataset = dataset.map(
+        tokenize,
+        batched=True,
+        batch_size=len(dataset),
+        num_proc=1,
+        keep_in_memory=True,
+        load_from_cache_file=False,
+        remove_columns=["instruction", "input"],
+    )
+
+    dataset = dataset.to_list()
+
+    for sample in dataset:
+        sample["input_ids"] = torch.LongTensor(sample["input_ids"])
+        sample["attention_mask"] = torch.LongTensor(sample["attention_mask"])
+
+    return dataset
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("--pretrained_model_dir", type=str)
+    parser.add_argument("--quantized_model_dir", type=str, default="./Qwen1.5-32B-Chat-gptq-4-test10.24")
+    parser.add_argument("--bits", type=int, default=4, choices=[2, 3, 4, 8])
+    parser.add_argument(
+        "--group_size",
+        type=int,
+        default=128,
+        help="group size, -1 means no grouping or full rank",
+    )
+    parser.add_argument("--desc_act", action="store_true", help="whether to quantize with desc_act", default=True)
+    parser.add_argument(
+        "--num_samples",
+        type=int,
+        default=128,
+        help="how many samples will be used to quantize model",
+    )
+    parser.add_argument(
+        "--save_and_reload",
+        action="store_true",
+        help="whether save quantized model to disk and reload back",
+        default=True
+    )
+    parser.add_argument("--fast_tokenizer", action="store_true", help="whether use fast tokenizer")
+    parser.add_argument(
+        "--use_triton",
+        action="store_true",
+        help="whether use triton to speedup at inference",
+    )
+    parser.add_argument(
+        "--per_gpu_max_memory",
+        type=int,
+        default=None,
+        help="max memory used to load model per gpu",
+    )
+    parser.add_argument(
+        "--cpu_max_memory",
+        type=int,
+        default=None,
+        help="max memory used to offload model to cpu",
+    )
+    parser.add_argument(
+        "--quant_batch_size",
+        type=int,
+        default=1,
+        help="examples batch size for quantization",
+    )
+    parser.add_argument(
+        "--trust_remote_code",
+        action="store_true",
+        help="whether to trust remote code when loading model",
+    )
+    args = parser.parse_args()
+
+    max_memory = {}
+    if args.per_gpu_max_memory is not None and args.per_gpu_max_memory > 0:
+        if torch.cuda.is_available():
+            max_memory.update({i: f"{args.per_gpu_max_memory}GIB" for i in range(torch.cuda.device_count())})
+    if args.cpu_max_memory is not None and args.cpu_max_memory > 0 and max_memory:
+        max_memory["cpu"] = f"{args.cpu_max_memory}GIB"
+    if not max_memory:
+        max_memory = None
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.pretrained_model_dir,
+        use_fast=args.fast_tokenizer,
+        trust_remote_code=args.trust_remote_code,
+    )
+    model = AutoGPTQForCausalLM.from_pretrained(
+        args.pretrained_model_dir,
+        quantize_config=BaseQuantizeConfig(bits=args.bits, group_size=args.group_size, desc_act=args.desc_act),
+        max_memory=max_memory,
+        trust_remote_code=args.trust_remote_code,
+    )
+
+    examples = load_data("dataset/alpaca_data_cleaned.json", tokenizer, args.num_samples)
+    examples_for_quant = [
+        {"input_ids": example["input_ids"], "attention_mask": example["attention_mask"]} for example in examples
+    ]
+
+    start = time.time()
+    model.quantize(
+        examples_for_quant,
+        batch_size=args.quant_batch_size,
+        use_triton=args.use_triton,
+        autotune_warmup_after_quantized=args.use_triton,
+    )
+    end = time.time()
+    print(f"quantization took: {end - start: .4f}s")
+
+    if not args.quantized_model_dir:
+        args.quantized_model_dir = args.pretrained_model_dir
+
+    if args.save_and_reload:
+        model.save_quantized(args.quantized_model_dir)
+        del model
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        model = AutoGPTQForCausalLM.from_quantized(
+            args.quantized_model_dir,
+            device="cuda:0",
+            use_triton=args.use_triton,
+            max_memory=max_memory,
+            inject_fused_mlp=True,
+            inject_fused_attention=True,
+            trust_remote_code=args.trust_remote_code,
+        )
+
+    pipeline_init_kwargs = {"model": model, "tokenizer": tokenizer}
+    if not max_memory:
+        pipeline_init_kwargs["device"] = "cuda:0"
+    pipeline = TextGenerationPipeline(**pipeline_init_kwargs)
+    for example in random.sample(examples, k=min(4, len(examples))):
+        print(f"prompt: {example['prompt']}")
+        print("-" * 42)
+        print(f"golden: {example['output']}")
+        print("-" * 42)
+        start = time.time()
+        generated_text = pipeline(
+            example["prompt"],
+            return_full_text=False,
+            num_beams=1,
+            max_length=len(example["input_ids"])
+            + 128,  # use this instead of max_new_token to disable UserWarning when integrate with logging
+        )[0]["generated_text"]
+        end = time.time()
+        print(f"quant: {generated_text}")
+        num_new_tokens = len(tokenizer(generated_text)["input_ids"])
+        print(f"generate {num_new_tokens} tokens using {end-start: .4f}s, {num_new_tokens / (end - start)} tokens/s.")
+        print("=" * 42)
+
+
+if __name__ == "__main__":
+    import logging
+
+    logging.basicConfig(
+        format="%(asctime)s %(levelname)s [%(name)s] %(message)s",
+        level=logging.INFO,
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+    main()