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| import sys | |
| import time | |
| from pathlib import Path | |
| from typing import Literal, Optional | |
| import lightning as L | |
| import torch | |
| from lightning.fabric.plugins import BitsandbytesPrecision | |
| from lightning.fabric.strategies import FSDPStrategy | |
| # support running without installing as a package | |
| wd = Path(__file__).parent.parent.resolve() | |
| sys.path.append(str(wd)) | |
| from generate.base import generate | |
| from lit_gpt import Tokenizer | |
| from lit_gpt.adapter_v2 import GPT, Block, Config | |
| from lit_gpt.utils import check_valid_checkpoint_dir, get_default_supported_precision, gptq_quantization, lazy_load | |
| from scripts.prepare_alpaca import generate_prompt | |
| def main( | |
| prompt: str = "What food do llamas eat?", | |
| input: str = "", | |
| adapter_path: Path = Path("out/adapter_v2/alpaca/lit_model_adapter_finetuned.pth"), | |
| checkpoint_dir: Path = Path("checkpoints/stabilityai/stablelm-base-alpha-3b"), | |
| quantize: Optional[Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8", "gptq.int4"]] = None, | |
| max_new_tokens: int = 100, | |
| top_k: Optional[int] = 200, | |
| temperature: float = 0.8, | |
| strategy: str = "auto", | |
| devices: int = 1, | |
| precision: Optional[str] = None, | |
| ) -> None: | |
| """Generates a response based on a given instruction and an optional input. | |
| This script will only work with checkpoints from the instruction-tuned GPT-AdapterV2 model. | |
| See `finetune/adapter_v2.py`. | |
| Args: | |
| prompt: The prompt/instruction (Alpaca style). | |
| input: Optional input (Alpaca style). | |
| adapter_path: Path to the checkpoint with trained adapter weights, which are the output of | |
| `finetune/adapter_v2.py`. | |
| checkpoint_dir: The path to the checkpoint folder with pretrained GPT weights. | |
| quantize: Whether to quantize the model and using which method: | |
| - bnb.nf4, bnb.nf4-dq, bnb.fp4, bnb.fp4-dq: 4-bit quantization from bitsandbytes | |
| - bnb.int8: 8-bit quantization from bitsandbytes | |
| - gptq.int4: 4-bit quantization from GPTQ | |
| for more details, see https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials/quantize.md | |
| max_new_tokens: The number of generation steps to take. | |
| top_k: The number of top most probable tokens to consider in the sampling process. | |
| temperature: A value controlling the randomness of the sampling process. Higher values result in more random | |
| samples. | |
| strategy: Indicates the Fabric strategy setting to use. | |
| devices: How many devices to use. | |
| precision: Indicates the Fabric precision setting to use. | |
| """ | |
| precision = precision or get_default_supported_precision(training=False) | |
| plugins = None | |
| if quantize is not None: | |
| if devices > 1: | |
| raise NotImplementedError( | |
| "Quantization is currently not supported for multi-GPU training. Please set devices=1 when using the" | |
| " --quantize flag." | |
| ) | |
| if quantize.startswith("bnb."): | |
| if "mixed" in precision: | |
| raise ValueError("Quantization and mixed precision is not supported.") | |
| dtype = {"16-true": torch.float16, "bf16-true": torch.bfloat16, "32-true": torch.float32}[precision] | |
| plugins = BitsandbytesPrecision(quantize[4:], dtype) | |
| precision = None | |
| if strategy == "fsdp": | |
| strategy = FSDPStrategy(auto_wrap_policy={Block}, cpu_offload=False) | |
| fabric = L.Fabric(devices=devices, precision=precision, strategy=strategy, plugins=plugins) | |
| fabric.launch() | |
| check_valid_checkpoint_dir(checkpoint_dir) | |
| config = Config.from_json(checkpoint_dir / "lit_config.json") | |
| if quantize is not None and devices > 1: | |
| raise NotImplementedError | |
| if quantize == "gptq.int4": | |
| model_file = "lit_model_gptq.4bit.pth" | |
| if not (checkpoint_dir / model_file).is_file(): | |
| raise ValueError("Please run `python quantize/gptq.py` first") | |
| else: | |
| model_file = "lit_model.pth" | |
| checkpoint_path = checkpoint_dir / model_file | |
| tokenizer = Tokenizer(checkpoint_dir) | |
| sample = {"instruction": prompt, "input": input} | |
| prompt = generate_prompt(sample) | |
| encoded = tokenizer.encode(prompt, device=fabric.device) | |
| prompt_length = encoded.size(0) | |
| max_returned_tokens = prompt_length + max_new_tokens | |
| fabric.print(f"Loading model {str(checkpoint_path)!r} with {config.__dict__}", file=sys.stderr) | |
| t0 = time.perf_counter() | |
| with fabric.init_module(empty_init=True), gptq_quantization(quantize == "gptq.int4"): | |
| model = GPT(config) | |
| fabric.print(f"Time to instantiate model: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr) | |
| with fabric.init_tensor(): | |
| # set the max_seq_length to limit the memory usage to what we need | |
| model.max_seq_length = max_returned_tokens | |
| # enable the kv cache | |
| model.set_kv_cache(batch_size=1) | |
| model.eval() | |
| t0 = time.perf_counter() | |
| checkpoint = lazy_load(checkpoint_path) | |
| adapter_checkpoint = lazy_load(adapter_path) | |
| checkpoint.update(adapter_checkpoint.get("model", adapter_checkpoint)) | |
| model.load_state_dict(checkpoint) | |
| fabric.print(f"Time to load the model weights: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr) | |
| model = fabric.setup(model) | |
| L.seed_everything(1234) | |
| t0 = time.perf_counter() | |
| y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k, eos_id=tokenizer.eos_id) | |
| t = time.perf_counter() - t0 | |
| output = tokenizer.decode(y) | |
| output = output.split("### Response:")[1].strip() | |
| fabric.print(output) | |
| tokens_generated = y.size(0) - prompt_length | |
| fabric.print(f"\n\nTime for inference: {t:.02f} sec total, {tokens_generated / t:.02f} tokens/sec", file=sys.stderr) | |
| if fabric.device.type == "cuda": | |
| fabric.print(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", file=sys.stderr) | |
| if __name__ == "__main__": | |
| from jsonargparse import CLI | |
| torch.set_float32_matmul_precision("high") | |
| CLI(main) | |