Merge branch 'remove_generate_samples_gpt' into 'main'

This file doesn't work withthe new inference code, so it should be remove

See merge request ADLR/megatron-lm!322

tools/generate_samples_gpt.py

-# 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()