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# Copyright (c) 2022, 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.
import os
from pytorch_lightning.trainer.trainer import Trainer
from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import (
MegatronGPTPromptLearningModel,
)
from nemo.collections.nlp.models.language_modeling.megatron_t5_prompt_learning_model import (
MegatronT5PromptLearningModel,
)
from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
from nemo.core.config import hydra_runner
from nemo.utils.app_state import AppState
from nemo.utils.model_utils import inject_model_parallel_rank
try:
from apex.transformer import parallel_state
HAVE_APEX = True
except (ImportError, ModuleNotFoundError):
HAVE_APEX = False
"""
This is the script to convert the p-tuning PTL checkpoint file to nemo file for evaluation.
Example usage:
Assume the model has TP=2, PP=2 in the following use cases.
```
python scripts/nlp_language_modeling/convert_prompt_learning_ckpt_to_nemo.py \
trainer.devices=4 \
trainer.num_nodes=1 \
trainer.precision=bf16 \
tensor_model_parallel_size=2 \
pipeline_model_parallel_size=2 \
checkpoint_dir=/results/ptune_squad/checkpoints \
checkpoint_name='megatron_gpt_prompt_tune--val_loss=3.401-step=500.ckpt' \
hparams_file=/results/ptune_squad/version_1/hparams.yaml
```
Note, the hparam file can be found under the pytorch lightning experiment result directory. The filename is `hparams.yaml`
"""
@hydra_runner(config_path="conf", config_name="prompt_learning_ckpt_to_nemo")
def main(cfg) -> None:
# trainer required for restoring model parallel models
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"
if 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_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,
)
app_state.tensor_model_parallel_size = cfg.tensor_model_parallel_size
app_state.pipeline_model_parallel_size = cfg.pipeline_model_parallel_size
checkpoint_path = inject_model_parallel_rank(os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name))
# check model type
if cfg.model_type.lower() == 't5':
model: MegatronT5PromptLearningModel = MegatronT5PromptLearningModel.load_from_checkpoint(
checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer
)
elif cfg.model_type.lower() == 'gpt':
model: MegatronGPTPromptLearningModel = MegatronGPTPromptLearningModel.load_from_checkpoint(
checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer
)
else:
raise ValueError("Model Type Not Supported!")
else:
raise ValueError("need at least a nemo file or checkpoint dir")
# check whether the DDP is initialized
if parallel_state.is_unitialized():
def dummy():
return
if trainer.strategy.launcher is not None:
trainer.strategy.launcher.launch(dummy, trainer=trainer)
trainer.strategy.setup_environment()
model = model.cuda()
model.on_train_end()
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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