# Copyright 2022 Cerebras Systems.
#
# 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.
"""
Utilities for saving scalar summaries.
"""
from typing import Optional
import torch
from torch.utils.tensorboard import SummaryWriter
from modelzoo.common.pytorch.summaries.cb_summary import CBSummary
class _ScalarSummary(CBSummary):
"""A class for providing scalar summaries on CS/CPU/GPU devices."""
_REDUCTION_FN_MAP = {
"mean": torch.mean,
"max": torch.max,
"min": torch.min,
"sum": torch.sum,
}
def __init__(self, name: str, reduction: Optional[str] = None):
"""Constructs a `ScalarSummary` instance.
Args:
name: Name of the summary. This is the tag that appears in
TensorBoard.
reduction: The reduction function to apply to the input tensors.
Defaults to no reduction.
"""
super().__init__(name)
if reduction is None:
self._reduction_fn = lambda x: x # no-op
else:
self._reduction_fn = self._REDUCTION_FN_MAP.get(reduction)
if self._reduction_fn is None:
raise ValueError(
f"Unknown reduction `{reduction}`. Available reduction "
f"types are: {list(self._REDUCTION_FN_MAP)}."
)
# pylint: disable=arguments-differ
def run_on_host(self, tensor: torch.Tensor) -> float:
"""Runs the host portion of the summary computation.
Args:
tensor: The tensor to be summarized.
Returns:
The summarized float value.
"""
return float(self._reduction_fn(tensor))
def save_on_host(
self, host_outputs: float, writer: SummaryWriter, step: int,
) -> None:
"""Saves the scalar summary to events file.
Args:
host_outputs: The summarized float value to write to events file.
writer: A writer for writing summaries to events files.
step: The current global step.
"""
writer.add_scalar(self.name, host_outputs, global_step=step)
[docs]def scalar_summary(
name: str, tensor: torch.Tensor, reduction: Optional[str] = None
):
"""Convenience method for creating and running scalar summaries.
This method searches registered summaries for the given name. If one is
found, it uses it. Otherwise, it creates a new summary and runs the tensor
through that summary.
Args:
name: Name of the summary. This is the tag that appears in TensorBoard.
tensor: The tensor to be summarized.
reduction: The reduction function to apply to the tensor. Defaults to
no reduction.
"""
# See if a summary with the given name already exists
summary = _ScalarSummary(name, reduction=reduction)
# Run the summary op
summary(tensor)