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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Sample Generate GPT"""
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir)))
import torch
import megatron.training
from megatron import get_args
from megatron import print_rank_0
from megatron.core import mpu
from megatron.checkpointing import load_checkpoint
import megatron.initialize
from megatron.model import GPTModel
from megatron.text_generation_server import MegatronServer
from megatron.text_generation import generate_and_post_process
from megatron.text_generation import beam_search_and_post_process
from megatron.model import ModelType
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
model = GPTModel(num_tokentypes=0,
parallel_output=False,
pre_process=pre_process,
post_process=post_process)
return model
def add_text_generate_args(parser):
group = parser.add_argument_group(title='text generation')
group.add_argument("--temperature", type=float, default=1.0,
help='Sampling temperature.')
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.')
return parser
if __name__ == "__main__":
megatron.initialize.initialize_megatron(extra_args_provider=add_text_generate_args,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
'no_load_rng': True,
'no_load_optim': True})
args = get_args()
if args.num_layers_per_virtual_pipeline_stage is not None:
print("Interleaved pipeline schedule is not yet supported for text generation.")
exit()
# Set up model and load checkpoint
model_type = ModelType.encoder_or_decoder
model = megatron.training.get_model(model_provider, model_type, wrap_with_ddp=False, args=args)
if args.load is not None:
_ = load_checkpoint(model, None, None)
assert len(model) == 1, "Above condition should have caught this"
model = model[0]
if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
server = MegatronServer(model)
server.run("0.0.0.0")
while True:
choice = torch.cuda.LongTensor(1)
torch.distributed.broadcast(choice, 0)
if choice[0].item() == 0:
try:
generate_and_post_process(model)
except ValueError as ve:
pass
elif choice[0].item() == 1:
try:
beam_search_and_post_process(model)
except ValueError as ve:
pass
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