# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. """Sample Generate GPT""" import json import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))) import torch from megatron import get_args from megatron import get_tokenizer from megatron import print_rank_0 from megatron.checkpointing import load_checkpoint from megatron.core import mpu from megatron.initialize import initialize_megatron from megatron.model import GPTModel from megatron.training import get_model from megatron.arguments import core_transformer_config_from_args from megatron.text_generation import generate_and_post_process def model_provider(pre_process=True, post_process=True): """Build the model.""" config = core_transformer_config_from_args(args) print_rank_0('building GPT model ...') model = GPTModel(config=config, num_tokentypes=0, parallel_output=False, pre_process=pre_process, post_process=post_process) return model def add_text_generate_args(parser): """Text generation arguments.""" group = parser.add_argument_group(title='text generation') group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.') group.add_argument("--greedy", action='store_true', default=False, help='Use greedy sampling.') group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.') group.add_argument("--top_k", type=int, default=0, help='Top k sampling.') group.add_argument("--out-seq-length", type=int, default=1024, help='Size of the output generated text.') group.add_argument("--sample-input-file", type=str, default=None, help='Get input from file instead of interactive mode, ' 'each line is an input.') group.add_argument("--sample-output-file", type=str, default=None, help='Output file got from --sample-input-file') group.add_argument("--num-samples", type=int, default=0, help='Number of samples to generate unconditionally, ' 'defaults to 0 and interactive conditional sampling') group.add_argument("--genfile", type=str, help='Output file when generating unconditionally') return parser def generate_samples_unconditional(model): args = get_args() if torch.distributed.get_rank() == 0: cnt = 0 num_samples = args.num_samples from tqdm import tqdm pbar = tqdm(total=num_samples) while True: if torch.distributed.get_rank() == 0: sentences = [''] * args.global_batch_size print("global batch size", args.global_batch_size) max_len = args.out_seq_length resp_sentences, resp_sentences_seg, output_logits, \ tokens = generate_and_post_process(model, prompts=sentences, tokens_to_generate=max_len, return_output_log_probs=False, top_k_sampling=args.top_k, top_p_sampling=args.top_p, add_BOS=True, temperature=1.0) for prompt, generation, token in zip(sentences, resp_sentences, tokens): datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} yield datum cnt += 1 pbar.update() if cnt >= num_samples: break if cnt >= num_samples: pbar.close() break else: generate_and_post_process(model) def generate_samples_conditional(model): args = get_args() if torch.distributed.get_rank() == 0: num_samples = args.num_samples cnt = 0 from tqdm import tqdm pbar = tqdm(total=num_samples) fname = open(args.sample_input_file, "r") lines = fname.readlines() all_raw_text = [json.loads(line)['prompt']['text'] for line in lines] input_count = len(all_raw_text) input_pos = 0 while True: torch.distributed.barrier() if torch.distributed.get_rank() == 0: sentences = [] print("global batch size", args.global_batch_size) for _ in range(args.global_batch_size): if input_pos >= input_count: print(f"input pos: {input_pos}, input count: {input_count}") raw_text = "EMPTY TEXT" else: raw_text = all_raw_text[input_pos] input_pos += 1 sentences.append(raw_text) max_len = args.out_seq_length resp_sentences, resp_sentences_seg, output_logits, \ tokens = generate_and_post_process(model, prompts=sentences, tokens_to_generate=max_len, return_output_log_probs=False, top_k_sampling=args.top_k, top_p_sampling=args.top_p, add_BOS=False, temperature=1.0) for prompt, generation, token in zip(sentences, resp_sentences, tokens): datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} yield datum cnt += 1 pbar.update() if cnt >= num_samples: break if cnt >= num_samples: pbar.close() break else: generate_and_post_process(model) def generate_and_write_samples_unconditional(model): args = get_args() assert args.genfile is not None with open(args.genfile, 'w') as f: for datum in generate_samples_unconditional(model): if torch.distributed.get_rank() == 0: f.write(json.dumps(datum) + '\n') def generate_and_write_samples_conditional(model): args = get_args() if args.sample_output_file is None: sample_output_file = args.sample_input_file + ".out" print('`sample-output-file` not specified, setting ' 'it to {}'.format(sample_output_file)) else: sample_output_file = args.sample_output_file with open(sample_output_file, 'w') as f: for datum in generate_samples_conditional(model): if torch.distributed.get_rank() == 0: f.write(json.dumps(datum) + '\n') def main(): """Main program.""" initialize_megatron(extra_args_provider=add_text_generate_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer', 'no_load_rng': True, 'no_load_optim': True, 'seq_length': 2048}) # Set up model and load checkpoint model = get_model(model_provider, wrap_with_ddp=False) args = get_args() if args.load is not None: _ = load_checkpoint(model, None, None) model = model[0] # Generate samples. if args.sample_input_file != None: print(f"{args.sample_input_file}") generate_and_write_samples_conditional(model) else: generate_and_write_samples_unconditional(model) if __name__ == "__main__": main()