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"""Sample Generate GPT""" |
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import os |
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import sys |
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), |
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os.path.pardir))) |
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import torch |
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import megatron.training |
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from megatron import get_args |
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from megatron import print_rank_0 |
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from megatron.core import mpu |
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from megatron.checkpointing import load_checkpoint |
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import megatron.initialize |
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from megatron.model import GPTModel |
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from megatron.text_generation_server import MegatronServer |
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from megatron.text_generation import generate_and_post_process |
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from megatron.text_generation import beam_search_and_post_process |
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from megatron.model import ModelType |
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def model_provider(pre_process=True, post_process=True): |
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"""Build the model.""" |
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print_rank_0('building GPT model ...') |
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model = GPTModel(num_tokentypes=0, |
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parallel_output=False, |
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pre_process=pre_process, |
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post_process=post_process) |
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return model |
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def add_text_generate_args(parser): |
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group = parser.add_argument_group(title='text generation') |
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group.add_argument("--temperature", type=float, default=1.0, |
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help='Sampling temperature.') |
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group.add_argument("--top_p", type=float, default=0.0, |
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help='Top p sampling.') |
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group.add_argument("--top_k", type=int, default=0, |
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help='Top k sampling.') |
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group.add_argument("--out_seq_length", type=int, default=1024, |
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help='Size of the output generated text.') |
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return parser |
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if __name__ == "__main__": |
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megatron.initialize.initialize_megatron(extra_args_provider=add_text_generate_args, |
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args_defaults={'tokenizer_type': 'GPT2BPETokenizer', |
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'no_load_rng': True, |
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'no_load_optim': True}) |
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args = get_args() |
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if args.num_layers_per_virtual_pipeline_stage is not None: |
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print("Interleaved pipeline schedule is not yet supported for text generation.") |
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exit() |
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model_type = ModelType.encoder_or_decoder |
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model = megatron.training.get_model(model_provider, model_type, wrap_with_ddp=False, args=args) |
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if args.load is not None: |
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_ = load_checkpoint(model, None, None) |
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assert len(model) == 1, "Above condition should have caught this" |
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model = model[0] |
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if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0: |
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server = MegatronServer(model) |
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server.run("0.0.0.0") |
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while True: |
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choice = torch.cuda.LongTensor(1) |
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torch.distributed.broadcast(choice, 0) |
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if choice[0].item() == 0: |
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try: |
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generate_and_post_process(model) |
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except ValueError as ve: |
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pass |
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elif choice[0].item() == 1: |
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try: |
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beam_search_and_post_process(model) |
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except ValueError as ve: |
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pass |
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