| 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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|
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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 probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
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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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|
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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, f"Prompt length exceeds model maximum sequence length (max_seq_len={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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| Main function to load the model and perform interactive or batch text generation.
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|
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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.
|
| """
|
| 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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| 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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| 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, f"Number of prompts exceeds maximum batch size ({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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| Command-line interface for distributed text generation.
|
|
|
| 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.
|
|
|
| Raises:
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| AssertionError: If neither input-file nor interactive mode is specified.
|
| """
|
| 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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| assert args.input_file or args.interactive, "Either input-file or interactive mode must be specified"
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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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|
|
