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b386992 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # Copyright (c) 2021, 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.
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
#
# 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.
import os
from lightning.pytorch import Trainer
from omegaconf import OmegaConf, open_dict
from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel
from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
try:
from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel
except ModuleNotFoundError:
from abc import ABC
MegatronNMTModel = ABC
from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
from nemo.core import ModelPT
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.app_state import AppState
from nemo.utils.model_utils import inject_model_parallel_rank
def get_model_class(cfg):
if cfg.model_type == 'gpt':
return MegatronGPTModel
elif cfg.model_type == 'bert':
return MegatronBertModel
elif cfg.model_type == 't5':
return MegatronT5Model
elif cfg.model_type == 'bart':
return MegatronBARTModel
elif cfg.model_type == 'nmt':
return MegatronNMTModel
elif cfg.model_type == 'retro':
return MegatronRetrievalModel
else:
raise ValueError("Invalid Model Type")
@hydra_runner(config_path="conf", config_name="megatron_gpt_export")
def nemo_export(cfg):
"""Convert a nemo model into .onnx ONNX format."""
nemo_in = None
if cfg.gpt_model_file:
nemo_in = cfg.gpt_model_file
elif cfg.checkpoint_dir:
nemo_in = os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)
assert nemo_in is not None, "NeMo model not provided. Please provide the path to the .nemo or .ckpt file"
onnx_out = cfg.onnx_model_file
trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
assert (
cfg.trainer.devices * cfg.trainer.num_nodes
== cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size
), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size"
logging.info("Restoring NeMo model from '{}'".format(nemo_in))
try:
if cfg.gpt_model_file:
save_restore_connector = NLPSaveRestoreConnector()
if os.path.isdir(cfg.gpt_model_file):
save_restore_connector.model_extracted_dir = cfg.gpt_model_file
pretrained_cfg = ModelPT.restore_from(
restore_path=cfg.gpt_model_file,
trainer=trainer,
return_config=True,
save_restore_connector=save_restore_connector,
)
OmegaConf.set_struct(pretrained_cfg, True)
with open_dict(pretrained_cfg):
pretrained_cfg.sequence_parallel = False
pretrained_cfg.activations_checkpoint_granularity = None
pretrained_cfg.activations_checkpoint_method = None
pretrained_cfg.precision = trainer.precision
if trainer.precision == "16":
pretrained_cfg.megatron_amp_O2 = False
model = ModelPT.restore_from(
restore_path=cfg.gpt_model_file,
trainer=trainer,
override_config_path=pretrained_cfg,
save_restore_connector=save_restore_connector,
)
elif cfg.checkpoint_dir:
app_state = AppState()
if cfg.tensor_model_parallel_size > 1 or cfg.pipeline_model_parallel_size > 1:
app_state.model_parallel_size = cfg.tensor_model_parallel_size * cfg.pipeline_model_parallel_size
app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size
app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size
(
app_state.tensor_model_parallel_rank,
app_state.pipeline_model_parallel_rank,
app_state.model_parallel_size,
app_state.data_parallel_size,
app_state.pipeline_model_parallel_split_rank,
app_state.virtual_pipeline_model_parallel_rank,
) = fake_initialize_model_parallel(
world_size=app_state.model_parallel_size,
rank=trainer.global_rank,
tensor_model_parallel_size_=cfg.tensor_model_parallel_size,
pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size,
pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank,
)
checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name))
model_cls = get_model_class(cfg)
model = model_cls.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer)
else:
raise ValueError("need at least a nemo file or checkpoint dir")
except Exception as e:
logging.error(
"Failed to restore model from NeMo file : {}. Please make sure you have the latest NeMo package installed with [all] dependencies.".format(
nemo_in
)
)
raise e
logging.info("Model {} restored from '{}'".format(model.__class__.__name__, nemo_in))
# Export
check_trace = cfg.export_options.runtime_check
try:
model.to(device=cfg.export_options.device).freeze()
model.eval()
model.export(
onnx_out,
onnx_opset_version=cfg.export_options.onnx_opset,
do_constant_folding=cfg.export_options.do_constant_folding,
dynamic_axes={
'input_ids': {0: "sequence", 1: "batch"},
'position_ids': {0: "sequence", 1: "batch"},
'logits': {0: "sequence", 1: "batch"},
},
check_trace=check_trace,
check_tolerance=cfg.export_options.check_tolerance,
verbose=cfg.export_options.verbose,
)
except Exception as e:
logging.error(
"Export failed. Please make sure your NeMo model class ({}) has working export() and that you have the latest NeMo package installed with [all] dependencies.".format(
model.__class__
)
)
raise e
if __name__ == '__main__':
nemo_export()
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