delete interactive file

tools/control_dialog_interactive.py

-
-"""Sample Generate Controllable Dialog Model"""
-
-import os
-import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                             os.path.pardir)))
-import argparse
-import torch
-from transformers import DPRQuestionEncoderTokenizer
-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 dialog_with_gpt_control_interactive, dialog_with_dpr_control_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_control_dialog_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("--recompute", action='store_true',
-                       help='During generation recompute all attention '
-                       'instead of using previously computed keys/values.')
-    group.add_argument("--ctrl-type", type=str, default="", 
-                        help="Either dpr or gpt")
-    group.add_argument("--ctrl-hidden-size", type=int, default=1024, 
-                        help="hidden-size of gpt control model")
-    group.add_argument("--ctrl-num-layers", type=int, default=24, 
-                        help="num-layers of gpt control model")
-    group.add_argument("--ctrl-num-attention-heads", type=int, default=16,
-                        help="num-attention-heads of gpt control model")
-    group.add_argument("--ctrl-gpt-load", type=str, default="",
-                        help="checkpoint path of the gpt control model")
-    group.add_argument("--ctrl-dpr-load", type=str, default="",
-                        help="checkpoint path of the dpr control model")
-    group.add_argument("--knowledge-corpus-path", type=str, default="",
-                        help="The path for the knowledge corpus")
-    group.add_argument("--knowledge-corpus-emb", type=str, default="",
-                        help="The path for the knowledge embedding")                 
-    group.add_argument('--spec-toks', type=str, default=None,
-                        help='additional special tokens')
-    group.add_argument('--add-separator', action="store_true",
-                        help='Add separator for the inputs')
-    
-    return parser
-
-
-def main():
-    """Main program."""
-
-    initialize_megatron(extra_args_provider=add_control_dialog_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 conversational model
-    conv_model = get_model(model_provider)
-    if args.load is not None:
-        _ = load_checkpoint(conv_model, None, None)
-
-    assert len(conv_model) == 1, "Above condition should have caught this"
-    conv_model = conv_model[0]
-
-    # Set up control model
-    assert args.ctrl_type in ["gpt", "dpr"], \
-                "please input a correct control model type"
-    
-    if args.ctrl_type == "gpt":
-        args.consumed_train_samples = 0
-        args.consumed_valid_samples = 0
-        args.hidden_size = args.ctrl_hidden_size
-        args.ffn_hidden_size = 4 * args.hidden_size
-        args.num_layers = args.ctrl_num_layers
-        args.num_attention_heads = args.ctrl_num_attention_heads
-        args.load = args.ctrl_gpt_load
-
-        ctrl_model = get_model(model_provider)
-        if args.load is not None:
-            _ = load_checkpoint(ctrl_model, None, None)
-        ctrl_model = ctrl_model[0]
-        
-        dialog_with_gpt_control_interactive(conv_model, ctrl_model, args.add_separator)
-
-    else:
-        print_rank_0("> Loading model from %s" % args.ctrl_dpr_load)
-        ctrl_model = torch.load(args.ctrl_dpr_load)
-        ctrl_model.cuda()
-        ctrl_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
-        
-        print_rank_0("> Loading knowledge corpus and embeddings")
-        with open(args.knowledge_corpus_path, "r") as f:
-            knowledge_corpus = f.readlines()
-        knowledge_corpus_emb = torch.load(args.knowledge_corpus_emb)
-        knowledge_corpus_emb = knowledge_corpus_emb.cuda()
-
-        assert knowledge_corpus_emb.size()[0] == len(knowledge_corpus), \
-            "The size of knowledge corpus and embeddings should be the same"
-
-        dialog_with_dpr_control_interactive(conv_model, ctrl_model,
-                                            ctrl_tokenizer, knowledge_corpus, 
-                                            knowledge_corpus_emb, args.add_separator)
-
-
-if __name__ == "__main__":
-
-    main()