base.py

# Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.

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 litgpt import GPT, Config, PromptStyle, Tokenizer
from litgpt.prompts import has_prompt_style, load_prompt_style
from litgpt.utils import CLI, check_valid_checkpoint_dir, get_default_supported_precision, 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_top_p(logits: torch.Tensor, top_p: float) -> torch.Tensor:
    sorted_logits, sorted_indices = torch.sort(logits, descending=False)
    cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
    # Example:
    # sorted_probs=[0.1, 0.15, 0.2, 0.25, 0.3] -> sorted_cumprobs=[0.1, 0.25, 0.45, 0.7, 1.0]
    # sorted_indices_to_remove = [1, 1, 0, 0, 0] if top_p=0.7
    sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
    # Keep at least 1 token always to prevent the case where no token is selected
    # In this case the most probable one is always kept
    sorted_indices_to_remove[-1:] = 0
    indices_to_remove = sorted_indices_to_remove.scatter(0, sorted_indices, sorted_indices_to_remove)
    logits = logits.masked_fill(indices_to_remove, float("-inf"))
    return logits


def sample(
    logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None, top_p: float = 1.0
) -> torch.Tensor:
    if top_p < 0.0 or top_p > 1.0:
        raise ValueError(f"top_p must be in [0, 1], got {top_p}")
    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 or top_p > 0.0:
        if temperature > 0.0:
            logits = logits / temperature
        # optionally crop the logits to smallest set of logits with a cumulative probability above top_p
        if top_p < 1.0:
            logits = sample_top_p(logits, top_p)
        probs = torch.nn.functional.softmax(logits, 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.to(dtype=x.dtype)


@torch.inference_mode()
def generate(
    model: GPT,
    prompt: torch.Tensor,
    max_returned_tokens: int,
    *,
    temperature: float = 1.0,
    top_k: Optional[int] = None,
    top_p: float = 1.0,
    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.
        top_p: If specified, it represents the cumulative probability threshold to consider in the sampling process.
            In top-p sampling, the next token is sampled from the highest probability tokens
            whose cumulative probability exceeds the threshold `top_p`. When specified,
            it must be `0 <= top_p <= 1`. Here, `top_p=0` is equivalent
            to sampling the most probable token, while `top_p=1` samples from the whole distribution.
            It can be used in conjunction with `top_k` and `temperature` with the following order
            of application:

            1. `top_k` sampling
            2. `temperature` scaling
            3. `top_p` sampling

            For more details, see https://arxiv.org/abs/1904.09751
            or https://huyenchip.com/2024/01/16/sampling.html#top_p
        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, top_p=top_p
    ).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, top_p=top_p
        ).clone()
        tokens.append(token)
        if token == eos_id:
            break
        input_pos = input_pos.add_(1)
    return torch.cat(tokens)


@torch.inference_mode()
def main(
    prompt: str = "What food do llamas eat?",
    *,
    num_samples: int = 1,
    max_new_tokens: int = 50,
    top_k: Optional[int] = 50,
    top_p: float = 1.0,
    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"]] = None,
    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.
        top_p: If specified, it represents the cumulative probability threshold to consider in the sampling process.
            In top-p sampling, the next token is sampled from the highest probability tokens
            whose cumulative probability exceeds the threshold `top_p`. When specified,
            it must be `0 <= top_p <= 1`. Here, `top_p=0` is equivalent
            to sampling the most probable token, while `top_p=1` samples from the whole distribution.
            It can be used in conjunction with `top_k` and `temperature` with the following order
            of application:

            1. `top_k` sampling
            2. `temperature` scaling
            3. `top_p` sampling

            For more details, see https://arxiv.org/abs/1904.09751
            or https://huyenchip.com/2024/01/16/sampling.html#top_p
        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
            for more details, see https://github.com/Lightning-AI/litgpt/blob/main/tutorials/quantize.md
        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 and 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

    fabric = L.Fabric(devices=1, precision=precision, plugins=plugins)

    check_valid_checkpoint_dir(checkpoint_dir)
    config = Config.from_file(checkpoint_dir / "model_config.yaml")

    checkpoint_path = checkpoint_dir / "lit_model.pth"

    tokenizer = Tokenizer(checkpoint_dir)
    prompt_style = (
        load_prompt_style(checkpoint_dir) if has_prompt_style(checkpoint_dir) else PromptStyle.from_config(config)
    )

    prompt = prompt_style.apply(prompt)
    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):
        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)
    for i in range(num_samples):
        t0 = time.perf_counter()
        y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k, top_p=top_p, eos_id=tokenizer.eos_id)
        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
        )
    if fabric.device.type == "cuda":
        fabric.print(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", file=sys.stderr)


if __name__ == "__main__":
    torch.set_float32_matmul_precision("high")

    CLI(main)