mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-02-23 14:18:57 -05:00
89882a94f6
Changes: init_distributed function: Extracted the distributed setup logic into a separate function. sample function: Modified it to use torch.multinomial instead of an exponentiation-based approach for sampling. Argument Validation: Replaced the assert with a more user-friendly validation in main to ensure that at least one of the parameters (input-file or interactive) is provided. Interactive Code Refactoring: The user interaction logic was kept, but the init_distributed function is now called separately at the beginning of main.
208 lines
8.0 KiB
Python
208 lines
8.0 KiB
Python
import os
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import json
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from argparse import ArgumentParser
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from typing import List
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import torch
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import torch.distributed as dist
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from transformers import AutoTokenizer
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from safetensors.torch import load_model
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from model import Transformer, ModelArgs
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def sample(logits, temperature: float = 1.0):
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"""
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Samples a token from the logits using temperature scaling.
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Args:
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logits (torch.Tensor): The logits tensor for token predictions.
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temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.
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Returns:
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torch.Tensor: The sampled token.
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"""
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logits = logits / max(temperature, 1e-5)
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probs = torch.softmax(logits, dim=-1)
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return torch.multinomial(probs, 1) # Usando uma distribuição de probabilidade
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def init_distributed(rank: int, world_size: int, local_rank: int):
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"""
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Initialize the distributed process group and set device configurations.
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Args:
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rank (int): The rank of the current process.
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world_size (int): Total number of processes.
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local_rank (int): The local rank for multi-GPU configurations.
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"""
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if world_size > 1:
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dist.init_process_group("nccl")
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global print
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if rank != 0:
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print = lambda *_, **__: None
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torch.cuda.set_device(local_rank)
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torch.set_default_dtype(torch.bfloat16)
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torch.set_num_threads(8)
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torch.manual_seed(965)
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@torch.inference_mode()
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def generate(
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model: Transformer,
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prompt_tokens: List[List[int]],
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max_new_tokens: int,
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eos_id: int,
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temperature: float = 1.0
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) -> List[List[int]]:
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"""
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Generates new tokens based on the given prompt tokens using the specified model.
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Args:
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model (Transformer): The transformer model used for token generation.
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prompt_tokens (List[List[int]]): A list of lists containing the prompt tokens for each sequence.
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max_new_tokens (int): The maximum number of new tokens to generate.
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eos_id (int): The end-of-sequence token ID.
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temperature (float, optional): The temperature value for sampling. Defaults to 1.0.
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Returns:
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List[List[int]]: A list of lists containing the generated tokens for each sequence.
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"""
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prompt_lens = [len(t) for t in prompt_tokens]
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assert max(prompt_lens) <= model.max_seq_len
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total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens))
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tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long, device="cuda")
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for i, t in enumerate(prompt_tokens):
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tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
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prev_pos = 0
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finished = torch.tensor([False] * len(prompt_tokens), device="cuda")
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prompt_mask = tokens != -1
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for cur_pos in range(min(prompt_lens), total_len):
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logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
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if temperature > 0:
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next_token = sample(logits, temperature)
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else:
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next_token = logits.argmax(dim=-1)
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next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token)
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tokens[:, cur_pos] = next_token
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finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id)
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prev_pos = cur_pos
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if finished.all():
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break
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completion_tokens = []
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for i, toks in enumerate(tokens.tolist()):
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toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
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if eos_id in toks:
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toks = toks[:toks.index(eos_id)]
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completion_tokens.append(toks)
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return completion_tokens
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def main(
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ckpt_path: str,
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config: str,
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input_file: str = "",
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interactive: bool = True,
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max_new_tokens: int = 100,
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temperature: float = 1.0,
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) -> None:
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"""
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Main function to load the model and perform interactive or batch text generation.
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Args:
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ckpt_path (str): Path to the model checkpoint directory.
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config (str): Path to the model configuration file.
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input_file (str, optional): Path to a file containing input prompts. Defaults to "".
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interactive (bool, optional): Whether to run in interactive mode. Defaults to True.
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max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 100.
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temperature (float, optional): Temperature for sampling. Defaults to 1.0.
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"""
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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rank = int(os.getenv("RANK", "0"))
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local_rank = int(os.getenv("LOCAL_RANK", "0"))
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# Initialize distributed configuration
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init_distributed(rank, world_size, local_rank)
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with open(config) as f:
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args = ModelArgs(**json.load(f))
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print(args)
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with torch.device("cuda"):
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model = Transformer(args)
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
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tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
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load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
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if interactive:
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messages = []
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while True:
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if world_size == 1:
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prompt = input(">>> ")
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elif rank == 0:
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prompt = input(">>> ")
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objects = [prompt]
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dist.broadcast_object_list(objects, 0)
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else:
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objects = [None]
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dist.broadcast_object_list(objects, 0)
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prompt = objects[0]
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if prompt == "/exit":
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break
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elif prompt == "/clear":
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messages.clear()
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continue
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messages.append({"role": "user", "content": prompt})
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prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
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completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
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print(completion)
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messages.append({"role": "assistant", "content": completion})
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else:
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with open(input_file) as f:
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prompts = [line.strip() for line in f.readlines()]
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assert len(prompts) <= args.max_batch_size
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prompt_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True) for prompt in prompts]
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completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature)
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completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
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for prompt, completion in zip(prompts, completions):
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print("Prompt:", prompt)
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print("Completion:", completion)
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print()
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if world_size > 1:
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dist.destroy_process_group()
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if __name__ == "__main__":
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"""
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Command-line interface for distributed text generation.
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Arguments:
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--ckpt-path (str): Path to the model checkpoint directory.
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--config (str): Path to the model configuration file.
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--input-file (str, optional): File containing prompts for batch processing.
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--interactive (bool, optional): Enable interactive mode for generating text.
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--max-new-tokens (int, optional): Maximum number of new tokens to generate. Defaults to 200.
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--temperature (float, optional): Temperature for sampling. Defaults to 0.2.
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Raises:
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AssertionError: If neither input-file nor interactive mode is specified.
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"""
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parser = ArgumentParser()
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parser.add_argument("--ckpt-path", type=str, required=True)
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parser.add_argument("--config", type=str, required=True)
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parser.add_argument("--input-file", type=str, default="")
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parser.add_argument("--interactive", action="store_true")
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parser.add_argument("--max-new-tokens", type=int, default=200)
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parser.add_argument("--temperature", type=float, default=0.2)
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args = parser.parse_args()
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# Validate input
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if not (args.input_file or args.interactive):
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print("Erro: É necessário especificar --input-file ou ativar --interactive.")
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exit(1)
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main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)
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