File size: 4,485 Bytes
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 | # Copyright (c) 2023, 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 time
from typing import Optional
from lightning.pytorch import Callback, LightningModule, Trainer
from nemo.utils import logging
class BenchmarkCallback(Callback):
def __init__(
self,
start_benchmark_at_step: int = 0,
stop_benchmark_at_step: Optional[int] = None,
log_every_n_steps: int = 10,
):
super().__init__()
self.start_benchmark_at_step = start_benchmark_at_step
self.stop_benchmark_at_step = stop_benchmark_at_step
self.log_every_n_steps = log_every_n_steps
self.train_times = []
self.val_times = []
self.train_steps_times = []
self.val_steps_times = []
def should_benchmark(self, trainer: Trainer):
if self.stop_benchmark_at_step is None:
return trainer.global_step >= self.start_benchmark_at_step
return self.start_benchmark_at_step <= trainer.global_step <= self.stop_benchmark_at_step
def on_train_epoch_start(self, trainer: Trainer, pl_module: LightningModule):
self.epoch_start_time = time.time()
def on_train_epoch_end(self, trainer: Trainer, pl_module: LightningModule):
if self.should_benchmark(trainer):
epoch_time = time.time() - self.epoch_start_time
self.train_times.append(epoch_time)
logging.info(f'Training-Epoch-{trainer.current_epoch}-Time: {epoch_time} [sec]')
def on_train_batch_start(self, trainer: Trainer, pl_module: LightningModule, batch, batch_idx: int):
self.step_start_time = time.time()
def on_train_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs, batch, batch_idx: int):
if self.should_benchmark(trainer):
step_time = time.time() - self.step_start_time
self.train_steps_times.append(step_time)
if trainer.global_step % self.log_every_n_steps == 0:
logging.info(f'Training-Step-{trainer.global_step}-Time: {step_time} [sec]')
def on_validation_epoch_start(self, trainer: Trainer, pl_module: LightningModule):
self.val_start_time = time.time()
def on_validation_epoch_end(self, trainer: Trainer, pl_module: LightningModule):
if self.should_benchmark(trainer):
val_time = time.time() - self.val_start_time
self.val_times.append(val_time)
logging.info(f'Validation-Epoch-{trainer.current_epoch}-Time: {val_time} [sec]')
def on_validation_batch_start(
self, trainer: Trainer, pl_module: LightningModule, batch, batch_idx: int, dataloader_idx: int
):
self.val_step_start_time = time.time()
def on_validation_batch_end(
self, trainer: Trainer, pl_module: LightningModule, outputs, batch, batch_idx: int, dataloader_idx: int
):
if self.should_benchmark(trainer):
val_step_time = time.time() - self.val_step_start_time
self.val_steps_times.append(val_step_time)
if trainer.global_step % self.log_every_n_steps == 0:
logging.info(f'Validation-Step-{trainer.global_step}-Time: {val_step_time} [sec]')
def on_fit_end(self, trainer: Trainer, pl_module: LightningModule):
if self.should_benchmark(trainer):
avg_train_time = sum(self.train_times) / len(self.train_times)
avg_val_time = sum(self.val_times) / len(self.val_times)
avg_train_step_time = sum(self.train_steps_times) / len(self.train_steps_times)
avg_val_step_time = sum(self.val_steps_times) / len(self.val_steps_times)
logging.info(f'Average-Training-Epoch-Time: {avg_train_time} [sec]')
logging.info(f'Average-Validation-Epoch-Time: {avg_val_time} [sec]')
logging.info(f'Average-Training-Step-Time: {avg_train_step_time} [sec]')
logging.info(f'Average-Validation-Step-Time: {avg_val_step_time} [sec]')
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