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# 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.
import torch
from omegaconf.omegaconf import open_dict
from pytorch_lightning.trainer.trainer import Trainer
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
try:
from apex.transformer import parallel_state
HAVE_APEX = True
except (ImportError, ModuleNotFoundError):
HAVE_APEX = False
if not torch.cuda.is_available():
raise EnvironmentError("GPU is needed for the inference")
@hydra_runner(config_path="conf", config_name="megatron_t5_prompt_learning_inference")
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"
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,
)
# Load prompt tuned model, virtual_prompt_model_file and language_model_path must be provided in config
if cfg.get('virtual_prompt_model_file', None) is not None and cfg.get('language_model_path', None) is not None:
# Update frozen T5 model path in case it has changed
prompt_learning_cfg = MegatronT5PromptLearningModel.restore_from(
cfg.virtual_prompt_model_file, trainer=trainer, return_config=True
)
with open_dict(prompt_learning_cfg):
if cfg.get("language_model_path"):
# This is for backward compatibility with old checkpoints that used `pretrained_language_model_path` instead of `language_model_path`.
if hasattr(prompt_learning_cfg, 'pretrained_language_model_path'):
prompt_learning_cfg.pretrained_language_model_path = cfg.language_model_path
else:
prompt_learning_cfg.language_model_path = cfg.language_model_path
prompt_learning_cfg.micro_batch_size = cfg.data.get('micro_batch_size', 4)
prompt_learning_cfg.global_batch_size = cfg.data.get('global_batch_size', 4)
# Now load prompt learning model with frozen T5 model base
model = MegatronT5PromptLearningModel.restore_from(
restore_path=cfg.virtual_prompt_model_file, trainer=trainer, override_config_path=prompt_learning_cfg
)
else:
raise ValueError("virtual_prompt_model_file and pretrained_language_model_file must be provided in config")
# check whether the DDP is initialized
if parallel_state.is_unitialized():
def dummy():
return
if model.trainer.strategy.launcher is not None:
model.trainer.strategy.launcher.launch(dummy, trainer=model.trainer)
model.trainer.strategy.setup_environment()
model.freeze()
_, test_dl = model.build_virtual_prompt_dataset(
dataset_paths=cfg.data.test_ds,
batch_size=cfg.data.global_batch_size,
for_train=False,
drop_last=False,
shuffle=False,
num_workers=cfg.data.num_workers,
pin_memory=True,
)
outputs = trainer.predict(model, test_dl)
with open(cfg.pred_file_path, "w", encoding="utf-8") as pred_file:
for batch in outputs:
preds = batch["preds_text"]
for pred in preds:
pred = pred.strip().replace("\n", " ")
pred_file.write(pred + "\n")
print('test finish---------------------------------')
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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