DeepSeek-V3/inference/generate.py

import os
import json
from argparse import ArgumentParser
from typing import List

import torch
import torch.distributed as dist
from transformers import AutoTokenizer
from safetensors.torch import load_model

from model import Transformer, ModelArgs


def sample(logits, temperature: float = 1.0):
    """
    Samples a token from the logits using temperature scaling.

    Args:
        logits (torch.Tensor): The logits tensor for token predictions.
        temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.

    Returns:
        torch.Tensor: The sampled token.
    """
    logits = logits / max(temperature, 1e-5)
    probs = torch.softmax(logits, dim=-1)
    return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)


@torch.inference_mode()
def generate_single_sequence(args):
    """
    Generates tokens for a single sequence.
    
    Args:
        args: Tuple containing (model, tokens, max_new_tokens, eos_id, temperature)
        
    Returns:
        List of generated tokens.
    """
    model, tokens, max_new_tokens, eos_id, temperature = args
    total_len = min(model.max_seq_len, max_new_tokens + tokens.shape[1])
    tokens = torch.cat([tokens, torch.full((1, total_len - tokens.shape[1]), -1, dtype=torch.long, device="cuda")], dim=1)
    
    prev_pos = tokens.shape[1] - max_new_tokens
    finished = torch.tensor([False], device="cuda")

    for cur_pos in range(prev_pos, total_len):
        logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
        next_token = sample(logits, temperature) if temperature > 0 else logits.argmax(dim=-1)
        tokens[:, cur_pos] = next_token
        finished |= next_token == eos_id
        if finished.all():
            break

    generated_tokens = tokens.tolist()[0]
    return generated_tokens[tokens.shape[1] - max_new_tokens :]

@torch.inference_mode()
def generate_parallel(
    model: Transformer,
    prompt_tokens: List[List[int]],
    max_new_tokens: int,
    eos_id: int,
    temperature: float = 1.0,
    num_workers: int = 4
) -> List[List[int]]:
    """
    Parallelized token generation using multiprocessing.

    Args:
        model (Transformer): The transformer model used for token generation.
        prompt_tokens (List[List[int]]): A list of lists containing the prompt tokens for each sequence.
        max_new_tokens (int): The maximum number of new tokens to generate.
        eos_id (int): The end-of-sequence token ID.
        temperature (float, optional): Temperature for sampling. Defaults to 1.0.
        num_workers (int, optional): Number of worker processes for parallel generation.

    Returns:
        List[List[int]]: A list of lists containing the generated tokens for each sequence.
    """
    model.share_memory()  # Make the model shareable across processes
    tokens_list = [torch.tensor(t, dtype=torch.long, device="cuda").unsqueeze(0) for t in prompt_tokens]

    args_list = [(model, tokens, max_new_tokens, eos_id, temperature) for tokens in tokens_list]

    with mp.Pool(num_workers) as pool:
        results = pool.map(generate_single_sequence, args_list)

    return results


def main(
    ckpt_path: str,
    config: str,
    input_file: str = "",
    interactive: bool = True,
    max_new_tokens: int = 100,
    temperature: float = 1.0,
) -> None:
    """
    Main function to load the model and perform interactive or batch text generation.

    Args:
        ckpt_path (str): Path to the model checkpoint directory.
        config (str): Path to the model configuration file.
        input_file (str, optional): Path to a file containing input prompts. Defaults to "".
        interactive (bool, optional): Whether to run in interactive mode. Defaults to True.
        max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 100.
        temperature (float, optional): Temperature for sampling. Defaults to 1.0.
    """
    world_size = int(os.getenv("WORLD_SIZE", "1"))
    rank = int(os.getenv("RANK", "0"))
    local_rank = int(os.getenv("LOCAL_RANK", "0"))
    if world_size > 1:
        dist.init_process_group("nccl")
    global print
    if rank != 0:
        print = lambda *_, **__: None
    torch.cuda.set_device(local_rank)
    torch.set_default_dtype(torch.bfloat16)
    torch.set_num_threads(8)
    torch.manual_seed(965)
    with open(config) as f:
        args = ModelArgs(**json.load(f))
    print(args)
    with torch.device("cuda"):
        model = Transformer(args)
    tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
    tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
    load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))

    if interactive:
        messages = []
        while True:
            if world_size == 1:
                prompt = input(">>> ")
            elif rank == 0:
                prompt = input(">>> ")
                objects = [prompt]
                dist.broadcast_object_list(objects, 0)
            else:
                objects = [None]
                dist.broadcast_object_list(objects, 0)
                prompt = objects[0]
            if prompt == "/exit":
                break
            elif prompt == "/clear":
                messages.clear()
                continue
            messages.append({"role": "user", "content": prompt})
            prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
            completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
            completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
            print(completion)
            messages.append({"role": "assistant", "content": completion})
    else:
        with open(input_file) as f:
            prompts = [line.strip() for line in f.readlines()]
        assert len(prompts) <= args.max_batch_size
        prompt_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True) for prompt in prompts]
        completion_tokens = generate_parallel(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature, num_workers=4)
        completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
        for prompt, completion in zip(prompts, completions):
            print("Prompt:", prompt)
            print("Completion:", completion)
            print()

    if world_size > 1:
        dist.destroy_process_group()


if __name__ == "__main__":
    """
    Command-line interface for distributed text generation.

    Arguments:
        --ckpt-path (str): Path to the model checkpoint directory.
        --config (str): Path to the model configuration file.
        --input-file (str, optional): File containing prompts for batch processing.
        --interactive (bool, optional): Enable interactive mode for generating text.
        --max-new-tokens (int, optional): Maximum number of new tokens to generate. Defaults to 200.
        --temperature (float, optional): Temperature for sampling. Defaults to 0.2.

    Raises:
        AssertionError: If neither input-file nor interactive mode is specified.
    """
    parser = ArgumentParser()
    parser.add_argument("--ckpt-path", type=str, required=True)
    parser.add_argument("--config", type=str, required=True)
    parser.add_argument("--input-file", type=str, default="")
    parser.add_argument("--interactive", action="store_true")
    parser.add_argument("--max-new-tokens", type=int, default=200)
    parser.add_argument("--temperature", type=float, default=0.2)
    args = parser.parse_args()
    assert args.input_file or args.interactive
    main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)