Merge branch 'remove_generate_samples_gpt' into 'main'

# coding=utf-8

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

"""Sample Generate GPT"""

import os

import sys

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),

                                             os.path.pardir)))

from megatron import get_args

from megatron import print_rank_0

from megatron import get_tokenizer

from megatron 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.text_generation_utils import generate_and_write_samples_unconditional

from megatron.text_generation_utils import generate_samples_input_from_file

from megatron.text_generation_utils import generate_samples_interactive

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):

    """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.')

    return parser

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})

    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 = get_model(model_provider)

    if args.load is not None:

        _ = load_checkpoint(model, None, None)

    assert len(model) == 1, "Above condition should have caught this"

    model = model[0]

    # Generate samples.

    if args.num_samples == 0:

        args.micro_batch_size = 1

        if args.sample_input_file != None:

            generate_samples_input_from_file(model)

        else:

            generate_samples_interactive(model)

    else:

        generate_and_write_samples_unconditional(model)

if __name__ == "__main__":

    main()