mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-04-19 10:08:59 -04:00
af62bffeff
Error Handling: Improved error handling for file operations and JSON loading. Logging: Clearer logging messages for better debugging and monitoring. Code Comments: Added more descriptive comments to enhance code readability. Dynamic Sequence Management: Clarified the logic for managing prompt lengths and token generation. Performance: Minor optimizations in code structure and logic flow for better performance. Code Structure: Organized functions and constants for better readability and maintainability.
348 lines
12 KiB
Python
348 lines
12 KiB
Python
import os
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import json
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import logging
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from argparse import ArgumentParser
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from pathlib import Path
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from typing import List, Optional, Dict, Tuple
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from datetime import timedelta
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from contextlib import nullcontext
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import torch
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import torch.distributed as dist
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from transformers import AutoTokenizer, AutoConfig
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from safetensors.torch import load_model
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from tqdm import tqdm
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from model import Transformer, ModelArgs
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Constants
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DEFAULT_EOS_TOKEN = "</s>"
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MAX_SEQ_LEN_WARNING_THRESHOLD = 0.9
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TORCH_DTYPE = torch.bfloat16
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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def setup_distributed() -> Tuple[int, int, int]:
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"""Initialize distributed training environment."""
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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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if world_size > 1:
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dist.init_process_group(
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backend="nccl" if torch.cuda.is_available() else "gloo",
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timeout=timedelta(minutes=5)
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)
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logger.info(f"Initialized process group (rank {rank}/{world_size})")
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torch.cuda.set_device(local_rank)
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return world_size, rank, local_rank
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def validate_paths(ckpt_path: Path, config_path: Path) -> None:
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"""Validate model checkpoint and config paths."""
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if not ckpt_path.exists():
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raise FileNotFoundError(f"Checkpoint directory {ckpt_path} not found")
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if not config_path.exists():
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raise FileNotFoundError(f"Config file {config_path} not found")
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def load_model_config(config_path: Path) -> ModelArgs:
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"""Load and validate model configuration."""
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try:
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with open(config_path) as f:
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config_data = json.load(f)
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return ModelArgs(**config_data)
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except (json.JSONDecodeError, TypeError) as e:
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logger.error(f"Invalid model config: {str(e)}")
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raise
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def initialize_model(args: ModelArgs, device: str) -> Transformer:
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"""Initialize model with proper device placement and dtype."""
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model = Transformer(args).to(TORCH_DTYPE)
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model.eval()
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return model
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def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: int = 50) -> torch.Tensor:
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"""
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Sample token from logits with temperature and top-k filtering.
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Args:
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logits: Unnormalized log probabilities (batch_size, vocab_size)
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temperature: Sampling temperature (0.0 = greedy)
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top_k: Top-k tokens to consider (0 = no filtering)
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Returns:
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Sampled token indices (batch_size, 1)
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"""
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if temperature <= 0:
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return logits.argmax(dim=-1)
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if top_k > 0:
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v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
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logits[logits < v[:, [-1]]] = float('-inf')
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probs = torch.softmax(logits / max(temperature, 1e-5), dim=-1)
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return torch.multinomial(probs, num_samples=1).squeeze(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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top_k: int = 50,
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repetition_penalty: float = 1.1
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) -> List[List[int]]:
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"""
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Generate text with dynamic sequence length management.
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Args:
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model: Initialized transformer model
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prompt_tokens: List of tokenized prompts
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max_new_tokens: Maximum new tokens to generate
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eos_id: End-of-sequence token ID
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temperature: Sampling temperature
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top_k: Top-k sampling parameter
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repetition_penalty: Penalty for repeated tokens
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Returns:
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List of generated token sequences
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"""
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batch_size = len(prompt_tokens)
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device = next(model.parameters()).device
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max_seq_len = model.max_seq_len
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prompt_lens = [len(p) for p in prompt_tokens]
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# Adjust max_new_tokens based on input length
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if max(prompt_lens) + max_new_tokens > max_seq_len:
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logger.warning(f"Truncating sequence length to {max_seq_len}")
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max_new_tokens = max_seq_len - max(prompt_lens)
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# Initialize token tensor
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tokens = torch.full((batch_size, max_seq_len), -1, dtype=torch.long, device=device)
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for i, seq in enumerate(prompt_tokens):
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tokens[i, :len(seq)] = torch.tensor(seq, device=device)
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# Generation loop
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prev_pos = 0
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finished = torch.zeros(batch_size, dtype=torch.bool, device=device)
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prompt_mask = tokens != -1
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progress_bar = tqdm(total=max_new_tokens, desc="Generating", disable=not logger.isEnabledFor(logging.INFO))
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try:
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for cur_pos in range(max(prompt_lens), min(max_seq_len, max(prompt_lens) + max_new_tokens)):
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# Model forward pass
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logits = model(tokens[:, prev_pos:cur_pos], prev_pos)
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# Apply repetition penalty
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if repetition_penalty != 1.0:
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for idx in range(batch_size):
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unique_tokens, counts = torch.unique(tokens[idx], return_counts=True)
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logits[idx, unique_tokens] /= counts.float() ** (repetition_penalty - 1.0)
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# Sample next tokens
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next_tokens = sample(logits[:, -1], temperature, top_k)
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# Update tokens
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tokens[:, cur_pos] = torch.where(
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prompt_mask[:, cur_pos],
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tokens[:, cur_pos],
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next_tokens
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)
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# Update completion status
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finished |= (~prompt_mask[:, cur_pos] & (next_tokens == eos_id))
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prev_pos = cur_pos
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progress_bar.update(1)
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if finished.all():
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break
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finally:
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progress_bar.close()
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# Process outputs
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return [seq[pl:pl + max_new_tokens].tolist() for pl, seq in zip(prompt_lens, tokens)]
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def interactive_loop(
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model: Transformer,
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tokenizer: AutoTokenizer,
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world_size: int,
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rank: int,
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max_new_tokens: int,
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temperature: float
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) -> None:
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"""Interactive chat interface with history management."""
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messages = []
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eos_id = tokenizer.eos_token_id or tokenizer.convert_tokens_to_ids(DEFAULT_EOS_TOKEN)
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while True:
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try:
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# Distributed input handling
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prompt = None
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if world_size > 1:
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if rank == 0:
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prompt = input("\nUser: ")
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dist.broadcast_object_list([prompt], src=0)
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else:
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dist.broadcast_object_list([prompt], src=0)
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else:
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prompt = input("\nUser: ")
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# Command handling
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if prompt in ["/exit", "/clear"]:
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if prompt == "/exit":
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break
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messages.clear()
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logger.info("History cleared")
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continue
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# Tokenize and generate
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messages.append({"role": "user", "content": prompt})
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prompt_tokens = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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truncation=True,
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max_length=model.max_seq_len - max_new_tokens
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)
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completion_tokens = generate(
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model,
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[prompt_tokens],
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max_new_tokens,
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eos_id,
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temperature
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)[0]
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# Decode and update history
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completion = tokenizer.decode(completion_tokens, skip_special_tokens=True)
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messages.append({"role": "assistant", "content": completion})
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print(f"\nAssistant: {completion}")
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except KeyboardInterrupt:
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logger.info("\nExiting...")
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break
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except Exception as e:
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logger.error(f"Generation error: {str(e)}")
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messages.pop() # Remove failed prompt
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def batch_process(
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model: Transformer,
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tokenizer: AutoTokenizer,
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input_file: Path,
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max_new_tokens: int,
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temperature: float
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) -> None:
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"""Batch processing mode with progress tracking."""
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try:
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with open(input_file) as f:
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prompts = [line.strip() for line in f if line.strip()]
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if not prompts:
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raise ValueError("Input file is empty")
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# Tokenize with parallel processing
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tokenizer_fn = lambda p: tokenizer.apply_chat_template(
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[{"role": "user", "content": p}],
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add_generation_prompt=True,
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truncation=True,
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max_length=model.max_seq_len - max_new_tokens
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)
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prompt_tokens = [tokenizer_fn(p) for p in tqdm(prompts, desc="Tokenizing")]
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# Generate in batches
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completions = []
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for i in tqdm(range(0, len(prompt_tokens), model.args.max_batch_size)):
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batch = prompt_tokens[i:i + model.args.max_batch_size]
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completions += generate(model, batch, max_new_tokens, tokenizer.eos_token_id, temperature)
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# Decode and print
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for prompt, tokens in zip(prompts, completions):
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completion = tokenizer.decode(tokens, skip_special_tokens=True)
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print(f"\nPrompt: {prompt}\nCompletion: {completion}\n{'='*50}")
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except Exception as e:
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logger.error(f"Batch processing failed: {str(e)}")
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raise
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def main(
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ckpt_path: str,
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config_path: str,
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input_file: str = "",
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interactive: bool = True,
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max_new_tokens: int = 200,
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temperature: float = 0.2
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) -> None:
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"""Main execution flow with proper resource management."""
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# Distributed setup
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world_size, rank, local_rank = setup_distributed()
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try:
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# Path validation
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ckpt_dir = Path(ckpt_path)
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config_file = Path(config_path)
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validate_paths(ckpt_dir, config_file)
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# Model initialization
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model_args = load_model_config(config_file)
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model = initialize_model(model_args, DEVICE)
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load_model(model, ckpt_dir / f"model{rank}-mp{world_size}.safetensors")
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# Tokenizer setup
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tokenizer = AutoTokenizer.from_pretrained(
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ckpt_dir,
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use_fast=True,
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trust_remote_code=True
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)
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# Generation mode selection
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if interactive:
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interactive_loop(model, tokenizer, world_size, rank, max_new_tokens, temperature)
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else:
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batch_process(model, tokenizer, Path(input_file), max_new_tokens, temperature)
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finally:
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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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parser = ArgumentParser(description="Distributed Transformer Text Generation")
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parser.add_argument("--ckpt-path", type=str, required=True,
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help="Path to model checkpoint directory")
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parser.add_argument("--config", type=str, required=True,
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help="Path to model config JSON file")
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parser.add_argument("--input-file", type=str, default="",
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help="Path to input file for batch processing")
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parser.add_argument("--interactive", action="store_true",
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help="Enable interactive chat mode")
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parser.add_argument("--max-new-tokens", type=int, default=200,
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help="Maximum new tokens to generate")
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parser.add_argument("--temperature", type=float, default=0.2,
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help="Sampling temperature (0.0 = greedy)")
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parser.add_argument("--log-level", choices=["DEBUG", "INFO", "WARNING"], default="INFO",
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help="Set logging verbosity")
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args = parser.parse_args()
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# Validate arguments
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if not args.interactive and not args.input_file:
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parser.error("Must specify either --interactive or --input-file")
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# Configure logging
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logger.setLevel(args.log_level)
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try:
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main(
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args.ckpt_path,
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args.config,
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args.input_file,
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args.interactive,
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args.max_new_tokens,
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args.temperature
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)
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except Exception as e:
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logger.critical(f"Critical error: {str(e)}", exc_info=True)
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exit(1)
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