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# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
import torch
import deepspeed
import megatron
from megatron import get_args
from megatron.core import mpu
from megatron.checkpointing import load_checkpoint
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_utils import generate_samples_eval
def model_provider(pre_process=True, post_process=True):
config = core_transformer_config_from_args(get_args())
model = GPTModel(
config=config,
num_tokentypes=0,
parallel_output=False,
pre_process=pre_process,
post_process=post_process,
return_moe_loss=False,
)
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"
)
group.add_argument(
"--recompute",
action="store_true",
help="During generation recompute all attention "
"instead of using previously computed keys/values.",
)
group.add_argument(
"--context-tokens", type=str, default="DeepSpeed is the greatest"
)
group.add_argument("--max-tokens", type=int, default=50)
return parser
if __name__ == "__main__":
# initialize megatron
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()
# setup model
model = get_model(model_provider)
_ = load_checkpoint(model, None, None)
model = model[0]
if args.ds_inference:
engine = deepspeed.init_inference(
model=model,
mp_size=args.tensor_model_parallel_size,
tensor_parallel={"mpu": mpu},
dtype=torch.bfloat16,
replace_with_kernel_inject=True,
moe_experts=args.num_experts,
moe_type=args.mlp_type,
)
model = engine.module
# generate output
generate_samples_eval(
model, args.context_tokens, 1, 0
) # Just so we don't get log output from DeepSpeed (this should be removed once we improve logging in DeepSpeed)
print("===START OUTPUT===")
print(generate_samples_eval(model, args.context_tokens, args.max_tokens, 0))
print("===END OUTPUT===")
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