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Diego Urgellfacebook-github-bot
Diego Urgell
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facebook-github-bot
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Add CheckpointPath abstraction in utils/checkpoint.py
Differential Revision: D56260188
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‎tests/utils/test_checkpoint.py

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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# pyre-strict
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import unittest
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from torchtnt.utils.checkpoint import CheckpointPath, MetricData
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class CheckpointPathTest(unittest.TestCase):
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def test_from_str(self) -> None:
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# invalid paths
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malformed_paths = [
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"foo/step_20",
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"foo/epoch_50",
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"epoch_30",
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"foo/epoch_20_step",
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"foo/epoch_20_step_30_val_loss=1a",
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"foo/epoch_2_step_15_mean=hello",
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"foo/epoch_2.6_step_23",
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]
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for path in malformed_paths:
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with self.assertRaisesRegex(
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ValueError, f"Attempted to parse malformed checkpoint path: {path}"
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):
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CheckpointPath.from_str(path)
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# valid paths
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valid_paths = [
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("foo/epoch_0_step_1", CheckpointPath("foo", epoch=0, step=1)),
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(
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"foo/epoch_14_step_3_mean=15.0",
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CheckpointPath(
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"foo", epoch=14, step=3, metric_data=MetricData("mean", 15.0)
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),
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),
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(
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"foo/epoch_14_step_3_loss=-27.35",
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CheckpointPath(
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"foo", epoch=14, step=3, metric_data=MetricData("loss", -27.35)
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),
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),
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(
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"/foo/epoch_14_step_3_loss=-27.35",
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CheckpointPath(
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"/foo", epoch=14, step=3, metric_data=MetricData("loss", -27.35)
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),
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),
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(
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"foo/bar/epoch_23_step_31_mean_loss_squared=0.0",
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CheckpointPath(
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"foo/bar/",
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epoch=23,
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step=31,
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metric_data=MetricData("mean_loss_squared", 0.0),
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),
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),
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(
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"oss://some/path/checkpoints/0b20e70f-9ad2-4904-b7d6-e8da48087d61/epoch_2_step_1_acc=0.98",
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CheckpointPath(
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"oss://some/path/checkpoints/0b20e70f-9ad2-4904-b7d6-e8da48087d61",
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epoch=2,
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step=1,
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metric_data=MetricData("acc", 0.98),
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),
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),
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]
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for path, expected_ckpt in valid_paths:
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parsed_ckpt = CheckpointPath.from_str(path)
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self.assertEqual(parsed_ckpt, expected_ckpt)
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self.assertEqual(parsed_ckpt.path, path)
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# with a trailing slash
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ckpt = CheckpointPath.from_str("foo/epoch_0_step_1/")
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self.assertEqual(ckpt, CheckpointPath("foo", epoch=0, step=1))
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self.assertEqual(ckpt.path, "foo/epoch_0_step_1")
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def test_compare_by_recency(self) -> None:
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old = CheckpointPath("foo", epoch=0, step=1)
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new = CheckpointPath("foo", epoch=1, step=1)
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self.assertTrue(new.newer_than(old))
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self.assertFalse(old.newer_than(new))
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self.assertFalse(new == old)
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old = CheckpointPath("foo", epoch=3, step=5)
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new = CheckpointPath("foo", epoch=3, step=9)
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self.assertTrue(new.newer_than(old))
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self.assertFalse(old.newer_than(new))
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self.assertFalse(new == old)
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twin1 = CheckpointPath(
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"foo", epoch=2, step=5, metric_data=MetricData("foo", 1.0)
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)
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almost_twin = CheckpointPath(
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"foo", epoch=2, step=5, metric_data=MetricData("bar", 2.0)
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)
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self.assertFalse(twin1.newer_than(almost_twin))
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self.assertFalse(almost_twin.newer_than(twin1))
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self.assertFalse(twin1 == almost_twin)
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twin2 = CheckpointPath(
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"foo", epoch=2, step=5, metric_data=MetricData("foo", 1.0)
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)
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self.assertTrue(twin1 == twin2)
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def test_compare_by_optimality(self) -> None:
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# not both metric aware
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ckpt1 = CheckpointPath("foo", epoch=0, step=1)
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ckpt2 = CheckpointPath("foo", epoch=1, step=1)
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ckpt3 = CheckpointPath(
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"foo", epoch=1, step=1, metric_data=MetricData("bar", 1.0)
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)
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for ckpt in [ckpt2, ckpt3]:
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with self.assertRaisesRegex(
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AssertionError,
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"Attempted to compare optimality of non metric-aware checkpoints",
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):
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ckpt1.more_optimal_than(ckpt, mode="min")
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# tracking different metrics
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ckpt4 = CheckpointPath(
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"foo", epoch=1, step=1, metric_data=MetricData("baz", 1.0)
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)
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with self.assertRaisesRegex(
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AssertionError,
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"Attempted to compare optimality of checkpoints tracking different metrics",
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):
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ckpt3.more_optimal_than(ckpt4, mode="min")
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smaller = CheckpointPath(
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"foo", epoch=0, step=1, metric_data=MetricData("foo", 1.0)
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)
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larger = CheckpointPath(
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"foo", epoch=0, step=1, metric_data=MetricData("foo", 2.0)
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)
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self.assertTrue(larger.more_optimal_than(smaller, mode="max"))
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self.assertFalse(smaller.more_optimal_than(larger, mode="max"))
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self.assertTrue(smaller.more_optimal_than(larger, mode="min"))
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self.assertFalse(larger.more_optimal_than(smaller, mode="min"))

‎torchtnt/utils/__init__.py

+3
Original file line numberDiff line numberDiff line change
@@ -6,6 +6,7 @@
66

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# pyre-strict
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from .checkpoint import CheckpointPath, MetricData
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from .device import (
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copy_data_to_device,
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CPUStats,
@@ -148,4 +149,6 @@
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"is_windows",
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"get_pet_launch_config",
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"spawn_multi_process",
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"CheckpointPath",
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"MetricData",
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]

‎torchtnt/utils/checkpoint.py

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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# pyre-strict
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import os
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import re
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from dataclasses import dataclass
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from functools import total_ordering
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from typing import Literal, Optional, Pattern
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from pyre_extensions import none_throws
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@dataclass
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class MetricData:
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"""
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Representation of a metric instance. Should provide both a metric name and it's value.
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"""
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name: str
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value: float
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@total_ordering
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class CheckpointPath:
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"""
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Representation of a checkpoint path. Handles parsing and serialization of the specific path format.
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Currently, the basic compliant path format is: <dirpath>/epoch_<epoch>_step_<step>
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If a metric is being tracked, it's added to the name: <dirpath>/epoch_<epoch>_step_<step>_<metric_name>=<metric_value>
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This class is well-ordered by checkpoint recency, so any comparisons will operate using the epoch + step. Sorting by
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metric can be done by extracting the metric value from the metric_data attribute.
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"""
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PATH_REGEX: Pattern = re.compile(
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r"^(.+)epoch_(\d+)_step_(\d+)(?:_(.+)=(-?\d+\.?\d*))?\/?$"
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)
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def __init__(
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self,
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dirpath: str,
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epoch: int,
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step: int,
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metric_data: Optional[MetricData] = None,
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) -> None:
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"""
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Args:
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dirpath: The base directory path that checkpoints are saved in.
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epoch: The epoch number of this checkpoint.
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step: The step number of this checkpoint.
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metric_data: Optional data about the metric being tracked. Should contain both metric name and value.
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"""
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self.dirpath: str = dirpath.rstrip("/")
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self.epoch = epoch
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self.step = step
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self.metric_data = metric_data
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@classmethod
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def from_str(cls, checkpoint_path: str) -> "CheckpointPath":
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"""
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Given a directory path, try to parse it and extract the checkpoint data.
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The expected format is: <dirpath>/epoch_<epoch>_step_<step>_<metric_name>=<metric_value>,
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where the metric name and value are optional.
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Args:
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checkpoint_path: The path to the checkpoint directory.
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Returns:
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A CheckpointPath instance if the path is valid, otherwise None.
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Raises:
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ValueError: If the path is malformed and can't be parsed.
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"""
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path_match = cls.PATH_REGEX.match(checkpoint_path)
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if not path_match:
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raise ValueError(
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f"Attempted to parse malformed checkpoint path: {checkpoint_path}."
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)
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dirpath, epoch, step, metric_name, metric_value = path_match.groups()
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try:
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metric_data: Optional[MetricData] = None
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if metric_name:
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metric_value_f = float(metric_value)
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metric_data = MetricData(name=metric_name, value=metric_value_f)
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return CheckpointPath(
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dirpath=dirpath,
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epoch=int(epoch),
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step=int(step),
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metric_data=metric_data,
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)
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except ValueError:
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# Should never happen since path matches regex
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raise ValueError(
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f"Invalid data types found in checkpoint path: {checkpoint_path}."
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)
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@property
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def path(self) -> str:
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"""
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Returns:
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The full path to the checkpoint directory.
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"""
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name = f"epoch_{self.epoch}_step_{self.step}"
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if self.metric_data:
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name += f"_{self.metric_data.name}={self.metric_data.value}"
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return os.path.join(self.dirpath, name)
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def newer_than(self, other: "CheckpointPath") -> bool:
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"""
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Given another CheckpointPath instance, determine if this checkpoint is strictly newer than the other.
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Returns:
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True if this checkpoint is newer than the other, otherwise False.
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"""
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if self.epoch != other.epoch:
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return self.epoch > other.epoch
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return self.step > other.step
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def more_optimal_than(
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self, other: "CheckpointPath", mode: Literal["min", "max"]
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) -> bool:
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"""
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Given another CheckpointPath instance, determine if this checkpoint is strictly more optimal than the other.
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Optimality is determined by comparing the metric value of the two checkpoints. The mode indicates if the
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metric value should be minimized or maximized. This only works for metric-aware checkpoints.
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Args:
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other: The other checkpoint path to compare against.
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mode: The mode to use for comparison.
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Returns:
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True if this checkpoint is more optimal than the other, otherwise False.
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Note: This expects that both checkpoints are metric-aware, and that they are tracking the same metric.
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"""
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assert (
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self.metric_data and other.metric_data
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), f"Attempted to compare optimality of non metric-aware checkpoints: {self} and {other}"
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assert (
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self.metric_data.name == other.metric_data.name
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), f"Attempted to compare optimality of checkpoints tracking different metrics: {self} and {other}"
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if mode == "min":
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return (
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none_throws(self.metric_data).value
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< none_throws(other.metric_data).value
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)
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return (
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none_throws(self.metric_data).value > none_throws(other.metric_data).value
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)
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def __str__(self) -> str:
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return self.path
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def __repr__(self) -> str:
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return f"CheckpointPath(dirpath={self.dirpath}, epoch={self.epoch}, step={self.step}, metric_data={self.metric_data})"
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def __eq__(self, other: "CheckpointPath") -> bool:
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return (
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self.dirpath == other.dirpath
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and self.epoch == other.epoch
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and self.step == other.step
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and self.metric_data == other.metric_data
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)
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def __gt__(self, other: "CheckpointPath") -> bool:
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return self.newer_than(other)

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