Spaces:
Build error
Build error
File size: 3,407 Bytes
b6af722 |
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 |
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from abc import abstractmethod
from typing import Optional
import torch
from cosmos_predict1.utils import distributed, log
from cosmos_predict1.utils.callback import Callback
from cosmos_predict1.utils.model import Model
from cosmos_predict1.utils.trainer import Trainer
class EveryN(Callback):
def __init__(
self,
every_n: Optional[int] = None,
step_size: int = 1,
barrier_after_run: bool = True,
run_at_start: bool = False,
) -> None:
"""Constructor for `EveryN`.
Args:
every_n (int): Frequency with which callback is run during training.
step_size (int): Size of iteration step count. Default 1.
barrier_after_run (bool): Whether to have a distributed barrier after each execution. Default True, to avoid timeouts.
run_at_start (bool): Whether to run at the beginning of training. Default False.
"""
self.every_n = every_n
if self.every_n == 0:
log.warning(
f"every_n is set to 0. Callback {self.__class__.__name__} will be invoked only once in the beginning of the training. Calls happens on_training_step_end will be skipped."
)
self.step_size = step_size
self.barrier_after_run = barrier_after_run
self.run_at_start = run_at_start
def on_training_step_end(
self,
model: Model,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
iteration: int = 0,
) -> None:
# every_n = 0 is a special case which means every_n_impl will be called only once in the beginning of the training
if self.every_n != 0:
trainer = self.trainer
global_step = iteration // self.step_size
should_run = (iteration == 1 and self.run_at_start) or (
global_step % self.every_n == 0
) # (self.every_n - 1)
if should_run:
log.debug(f"Callback {self.__class__.__name__} fired on train_batch_end step {global_step}")
self.every_n_impl(trainer, model, data_batch, output_batch, loss, iteration)
log.debug(f"Callback {self.__class__.__name__} finished on train_batch_end step {global_step}")
# add necessary barrier to avoid timeout
if self.barrier_after_run:
distributed.barrier()
@abstractmethod
def every_n_impl(
self,
trainer: Trainer,
model: Model,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
iteration: int,
) -> None:
...
|