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107 lines
4.0 KiB
Python
107 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright (c) 2024, HUAWEI CORPORATION. All rights reserved.
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# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Union
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import modellink
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from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, \
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get_gpt_layer_local_spec
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from megatron.core.transformer.spec_utils import import_module
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from megatron.training import get_args, print_rank_0
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from megatron.legacy.model import GPTModel
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from megatron.training.initialize import initialize_megatron
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from megatron.training.arguments import core_transformer_config_from_args
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from megatron.training.yaml_arguments import core_transformer_config_from_yaml
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from modellink.tasks.inference.text_generation.infer_base import task_factory, add_text_generate_args
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from modellink.tasks.inference.text_generation.module import GPTModelInfer, MegatronModuleForCausalLM
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def model_provider(pre_process=True, post_process=True) -> Union[GPTModelInfer, GPTModel]:
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"""Builds the model.
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If you set the use_mcore_models to True, it will return the mcore GPT model and if not the legacy GPT model.
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Args:
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pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
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post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.
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Returns:
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Union[GPTModelInfer, GPTModel]: The returned model
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"""
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args = get_args()
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use_te = args.transformer_impl == "transformer_engine"
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print_rank_0('building GPT model ...')
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# Experimental loading arguments from yaml
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if args.yaml_cfg is not None:
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config = core_transformer_config_from_yaml(args, "language_model")
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else:
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config = core_transformer_config_from_args(args)
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if args.use_mcore_models:
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if args.spec is not None:
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transformer_layer_spec = import_module(args.spec)
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else:
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if use_te:
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transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm)
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else:
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transformer_layer_spec = get_gpt_layer_local_spec(args.num_experts, args.moe_grouped_gemm)
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model = GPTModelInfer(
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config=config,
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transformer_layer_spec=transformer_layer_spec,
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vocab_size=args.padded_vocab_size,
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max_sequence_length=args.max_position_embeddings,
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pre_process=pre_process,
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post_process=post_process,
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fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
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parallel_output=False,
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share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
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position_embedding_type=args.position_embedding_type,
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rotary_percent=args.rotary_percent,
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)
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else:
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if not args.context_parallel_size == 1:
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raise ValueError("Context parallelism is only supported with Megatron Core!")
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model = GPTModel(
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config,
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parallel_output=False,
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pre_process=pre_process,
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post_process=post_process
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)
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return model
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def main():
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initialize_megatron(extra_args_provider=add_text_generate_args,
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args_defaults={'no_load_rng': True,
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'no_load_optim': True})
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args = get_args()
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model = MegatronModuleForCausalLM.from_pretrained(
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model_provider=model_provider,
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pretrained_model_name_or_path=args.load
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)
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task_factory(args, model)
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if __name__ == "__main__":
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main() |