mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-04-19 10:08:59 -04:00
Critical Improvements for Model Correctness, Efficiency, and Robustness
This commit is contained in:
Abdur Rahman
GitHub
parent
f09f5fa321
commit
d257cbe733
@ -1,178 +1,122 @@
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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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from typing import List, Optional
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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 probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
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def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None) -> torch.Tensor:
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if temperature <= 1e-5:
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return logits.argmax(dim=-1)
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logits = logits / temperature
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if top_k is not None:
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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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if top_p is not None and top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True)
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cum_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
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remove_mask = cum_probs > top_p
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remove_mask[..., 1:] = remove_mask[..., :-1].clone()
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remove_mask[..., 0] = False
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remove_indices = remove_mask.scatter(-1, sorted_indices, remove_mask)
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logits[remove_indices] = -float('Inf')
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gumbel_noise = -torch.log(-torch.log(torch.rand_like(logits) + 1e-10))
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return (logits + gumbel_noise).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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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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def generate(model: Transformer, prompt_tokens: List[List[int]], max_new_tokens: int, eos_id: int, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None) -> List[List[int]]:
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model.reset_cache()
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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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tokens[i, :len(t)] = torch.tensor(t, 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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finished = torch.zeros(len(prompt_tokens), dtype=torch.bool, 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 = sample(logits, temperature, top_k, top_p)
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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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finished |= (~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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completions = []
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for i, seq in enumerate(tokens.tolist()):
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seq = seq[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
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completions.append(seq[:seq.index(eos_id)] if eos_id in seq else seq)
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return completions
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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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def main(ckpt_path: str, config: str, input_file: str = "", interactive: bool = True, max_new_tokens: int = 100, temperature: float = 0.2, top_k: Optional[int] = None, top_p: Optional[float] = None) -> None:
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if not os.path.isdir(ckpt_path):
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raise FileNotFoundError(f"Checkpoint directory missing: {ckpt_path}")
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if not os.path.isfile(config):
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raise FileNotFoundError(f"Config file missing: {config}")
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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("nccl")
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global print
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dist.init_process_group("nccl", init_method="env://")
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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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model_args = ModelArgs(**json.load(f))
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model = Transformer(model_args).to(torch.bfloat16).cuda()
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load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
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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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prompt = get_input(rank, world_size)
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if prompt == "/exit":
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break
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elif prompt == "/clear":
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if 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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try:
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prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
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except Exception as e:
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print(f"Tokenization error: {e}")
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continue
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completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature, top_k, top_p)
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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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prompts = [line.strip() for line in f if line.strip()]
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batch_size = model_args.max_batch_size
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completions = []
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for i in range(0, len(prompts), batch_size):
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batch_prompts = prompts[i:i+batch_size]
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batch_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": p}], add_generation_prompt=True) for p in batch_prompts]
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completion_tokens = generate(model, batch_tokens, max_new_tokens, tokenizer.eos_token_id, temperature, top_k, top_p)
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completions.extend(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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print(f"Prompt: {prompt}\nCompletion: {completion}\n{'-'*50}")
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if world_size > 1:
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dist.destroy_process_group()
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def get_input(rank: int, world_size: int) -> str:
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if world_size == 1 or rank == 0:
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prompt = input(">>> ")
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if world_size > 1:
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dist.broadcast_object_list([prompt], src=0)
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return prompt
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else:
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res = [None]
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dist.broadcast_object_list(res, src=0)
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return res[0]
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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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@ -180,6 +124,8 @@ if __name__ == "__main__":
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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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parser.add_argument("--top-k", type=int, default=None)
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parser.add_argument("--top-p", type=float, default=None)
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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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main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature, args.top_k, args.top_p)
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