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| import sys | |
| import time | |
| from pathlib import Path | |
| from typing import Any, Literal, Optional | |
| import lightning as L | |
| import torch | |
| import torch._dynamo.config | |
| import torch._inductor.config | |
| 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 lit_gpt import GPT, Config, Tokenizer | |
| from lit_gpt.model import Block | |
| from lit_gpt.utils import ( | |
| check_valid_checkpoint_dir, | |
| get_default_supported_precision, | |
| gptq_quantization, | |
| load_checkpoint, | |
| ) | |
| def multinomial_num_samples_1(probs: torch.Tensor) -> torch.Tensor: | |
| if torch._dynamo.is_compiling(): | |
| # Faster alternative to `torch.multinomial(probs, num_samples=1)` that is also CUDAGraph friendly | |
| distribution = torch.empty_like(probs).exponential_(1) | |
| return torch.argmax(probs / distribution, dim=-1, keepdim=True) | |
| return torch.multinomial(probs, num_samples=1) | |
| def sample( | |
| logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None | |
| ) -> torch.Tensor: | |
| logits = logits[0, -1] | |
| # optionally crop the logits to only the top k options | |
| if top_k is not None: | |
| v, i = torch.topk(logits, min(top_k, logits.size(-1))) | |
| # do not use `torch.where` as in nanogpt because it will repeat top-k collisions | |
| logits = torch.full_like(logits, float("-inf")).scatter_(-1, i, v) | |
| # optionally scale the logits and sample from a probability distribution | |
| if temperature > 0.0: | |
| probs = torch.nn.functional.softmax(logits / temperature, dim=-1) | |
| return multinomial_num_samples_1(probs) | |
| return torch.argmax(logits, dim=-1, keepdim=True) | |
| def next_token( | |
| model: GPT, input_pos: torch.Tensor, x: torch.Tensor, **kwargs: Any | |
| ) -> torch.Tensor: | |
| logits = model(x, input_pos) | |
| next = sample(logits, **kwargs) | |
| return next.type_as(x) | |
| def generate( | |
| model: GPT, | |
| prompt: torch.Tensor, | |
| max_returned_tokens: int, | |
| *, | |
| temperature: float = 1.0, | |
| top_k: Optional[int] = None, | |
| eos_id: Optional[int] = None, | |
| ) -> torch.Tensor: | |
| """Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested. | |
| The implementation of this function is modified from A. Karpathy's nanoGPT. | |
| Args: | |
| model: The model to use. | |
| prompt: Tensor of shape (T) with indices of the prompt sequence. | |
| max_returned_tokens: The maximum number of tokens to return (given plus generated). | |
| temperature: Scales the predicted logits by 1 / temperature. | |
| top_k: If specified, only sample among the tokens with the k highest probabilities. | |
| eos_id: If specified, stop generating any more token once the <eos> token is triggered. | |
| """ | |
| T = prompt.size(0) | |
| assert max_returned_tokens > T | |
| if model.max_seq_length < max_returned_tokens - 1: | |
| # rolling the kv cache based on the `input_pos` value would be necessary. However, doing so would introduce a | |
| # data dependency on the `input_pos` tensor and impact model compilation. Since this setting is uncommon, we do | |
| # not support it to avoid negatively impacting the overall speed | |
| raise NotImplementedError( | |
| f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}" | |
| ) | |
| device = prompt.device | |
| tokens = [prompt] | |
| input_pos = torch.tensor([T], device=device) | |
| token = next_token( | |
| model, | |
| torch.arange(0, T, device=device), | |
| prompt.view(1, -1), | |
| temperature=temperature, | |
| top_k=top_k, | |
| ).clone() | |
| tokens.append(token) | |
| for _ in range(2, max_returned_tokens - T + 1): | |
| token = next_token( | |
| model, input_pos, token.view(1, -1), temperature=temperature, top_k=top_k | |
| ).clone() | |
| tokens.append(token) | |
| if token == eos_id: | |
| break | |
| input_pos = input_pos.add_(1) | |
| return torch.cat(tokens) | |
| def main( | |
| prompt: str = "What food do llamas eat?", | |
| *, | |
| num_samples: int = 1, | |
| max_new_tokens: int = 50, | |
| top_k: Optional[int] = 200, | |
| temperature: float = 0.8, | |
| 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, | |
| strategy: str = "auto", | |
| devices: int = 1, | |
| precision: Optional[str] = None, | |
| compile: bool = False, | |
| ) -> None: | |
| """Generates text samples based on a pre-trained model and tokenizer. | |
| Args: | |
| prompt: The prompt string to use for generating the samples. | |
| num_samples: The number of text samples to generate. | |
| 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. | |
| checkpoint_dir: The checkpoint directory to load. | |
| 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 | |
| strategy: Indicates the Fabric strategy setting to use. | |
| devices: How many devices to use. | |
| precision: Indicates the Fabric precision setting to use. | |
| compile: Whether to compile the model. | |
| """ | |
| 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 == "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) | |
| 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() | |
| if compile: | |
| torch._dynamo.config.automatic_dynamic_shapes = True | |
| torch._inductor.config.triton.unique_kernel_names = True | |
| torch._inductor.config.coordinate_descent_tuning = True | |
| global next_token | |
| next_token = torch.compile(next_token, mode="reduce-overhead") | |
| model = fabric.setup_module(model) | |
| t0 = time.perf_counter() | |
| load_checkpoint(fabric, model, checkpoint_path) | |
| fabric.print( | |
| f"Time to load the model weights: {time.perf_counter() - t0:.02f} seconds.", | |
| file=sys.stderr, | |
| ) | |
| L.seed_everything(1234) | |
| responses = [] | |
| for i in range(num_samples): | |
| t0 = time.perf_counter() | |
| y = generate( | |
| model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k | |
| ) | |
| t = time.perf_counter() - t0 | |
| for block in model.transformer.h: | |
| block.attn.kv_cache.reset_parameters() | |
| fabric.print(tokenizer.decode(y)) | |
| tokens_generated = y.size(0) - prompt_length | |
| fabric.print( | |
| f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_generated / t:.02f} tokens/sec", | |
| file=sys.stderr, | |
| ) | |
| responses.append( | |
| { | |
| "response": tokenizer.decode(y), | |
| "latency": f"{round(t, 2)} seconds", | |
| "generation_rate": f"{round(tokens_generated / t, 2)} tokens per sec", | |
| } | |
| ) | |
| if fabric.device.type == "cuda": | |
| fabric.print( | |
| f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", | |
| file=sys.stderr, | |
| ) | |
| return responses | |
| if __name__ == "__main__": | |
| from jsonargparse import CLI | |
| torch.set_float32_matmul_precision("high") | |
| CLI(main) | |