diff --git a/inference/generate.py b/inference/generate.py index 7e9bffe..a9d5a6d 100644 --- a/inference/generate.py +++ b/inference/generate.py @@ -1,178 +1,122 @@ import os import json from argparse import ArgumentParser -from typing import List - +from typing import List, Optional 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) - +def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None) -> torch.Tensor: + if temperature <= 1e-5: + return logits.argmax(dim=-1) + logits = logits / temperature + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + logits[logits < v[:, [-1]]] = -float('Inf') + if top_p is not None and top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cum_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) + remove_mask = cum_probs > top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = False + remove_indices = remove_mask.scatter(-1, sorted_indices, remove_mask) + logits[remove_indices] = -float('Inf') + gumbel_noise = -torch.log(-torch.log(torch.rand_like(logits) + 1e-10)) + return (logits + gumbel_noise).argmax(dim=-1) @torch.inference_mode() -def generate( - model: Transformer, - prompt_tokens: List[List[int]], - max_new_tokens: int, - eos_id: int, - temperature: float = 1.0 -) -> List[List[int]]: - """ - Generates new tokens based on the given prompt tokens using the specified model. - - 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): The temperature value for sampling. Defaults to 1.0. - - Returns: - List[List[int]]: A list of lists containing the generated tokens for each sequence. - """ +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]]: + model.reset_cache() prompt_lens = [len(t) for t in prompt_tokens] - assert max(prompt_lens) <= model.max_seq_len, f"Prompt length exceeds model maximum sequence length (max_seq_len={model.max_seq_len})" total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens)) tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long, device="cuda") for i, t in enumerate(prompt_tokens): - tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda") + tokens[i, :len(t)] = torch.tensor(t, device="cuda") prev_pos = 0 - finished = torch.tensor([False] * len(prompt_tokens), device="cuda") + finished = torch.zeros(len(prompt_tokens), dtype=torch.bool, device="cuda") prompt_mask = tokens != -1 for cur_pos in range(min(prompt_lens), total_len): logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos) - if temperature > 0: - next_token = sample(logits, temperature) - else: - next_token = logits.argmax(dim=-1) + next_token = sample(logits, temperature, top_k, top_p) next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token) tokens[:, cur_pos] = next_token - finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id) + finished |= (~prompt_mask[:, cur_pos] & (next_token == eos_id)) prev_pos = cur_pos if finished.all(): break - completion_tokens = [] - for i, toks in enumerate(tokens.tolist()): - toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens] - if eos_id in toks: - toks = toks[:toks.index(eos_id)] - completion_tokens.append(toks) - return completion_tokens + completions = [] + for i, seq in enumerate(tokens.tolist()): + seq = seq[prompt_lens[i]:prompt_lens[i]+max_new_tokens] + completions.append(seq[:seq.index(eos_id)] if eos_id in seq else seq) + return completions - -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. - """ +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: + if not os.path.isdir(ckpt_path): + raise FileNotFoundError(f"Checkpoint directory missing: {ckpt_path}") + if not os.path.isfile(config): + raise FileNotFoundError(f"Config file missing: {config}") 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 + dist.init_process_group("nccl", init_method="env://") 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]) + model_args = ModelArgs(**json.load(f)) + model = Transformer(model_args).to(torch.bfloat16).cuda() load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors")) - + tokenizer = AutoTokenizer.from_pretrained(ckpt_path) 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] + prompt = get_input(rank, world_size) if prompt == "/exit": break - elif prompt == "/clear": + if 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) + try: + prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True) + except Exception as e: + print(f"Tokenization error: {e}") + continue + completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature, top_k, top_p) 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, f"Number of prompts exceeds maximum batch size ({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(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature) - completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True) + prompts = [line.strip() for line in f if line.strip()] + batch_size = model_args.max_batch_size + completions = [] + for i in range(0, len(prompts), batch_size): + batch_prompts = prompts[i:i+batch_size] + batch_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": p}], add_generation_prompt=True) for p in batch_prompts] + completion_tokens = generate(model, batch_tokens, max_new_tokens, tokenizer.eos_token_id, temperature, top_k, top_p) + completions.extend(tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)) for prompt, completion in zip(prompts, completions): - print("Prompt:", prompt) - print("Completion:", completion) - print() - + print(f"Prompt: {prompt}\nCompletion: {completion}\n{'-'*50}") if world_size > 1: dist.destroy_process_group() +def get_input(rank: int, world_size: int) -> str: + if world_size == 1 or rank == 0: + prompt = input(">>> ") + if world_size > 1: + dist.broadcast_object_list([prompt], src=0) + return prompt + else: + res = [None] + dist.broadcast_object_list(res, src=0) + return res[0] 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) @@ -180,6 +124,8 @@ if __name__ == "__main__": 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) + parser.add_argument("--top-k", type=int, default=None) + parser.add_argument("--top-p", type=float, default=None) args = parser.parse_args() assert args.input_file or args.interactive, "Either input-file or interactive mode must be specified" - main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature) + main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature, args.top_k, args.top_p)