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Update precision on aggregation metrics to fp64. #123

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Update precision on aggregation metrics to fp64. #123

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@bobakfb bobakfb commented Jan 18, 2023

Summary: Updating metrics states with aggregation/ to have 64 bit floating point precision to support large numbers.

Differential Revision: D42482647

@bobakfb @facebook-github-bot
Update precision on aggregation metrics to fp64.
d966f90 
Summary: Updating metrics states with `aggregation/` to have 64 bit floating point precision to support large numbers.

Differential Revision: D42482647

fbshipit-source-id: d2dee288522db21103dc8a035fa65d8e8ec65b2b
@facebook-github-bot facebook-github-bot added CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. fb-exported labels Jan 18, 2023
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This pull request was exported from Phabricator. Differential Revision: D42482647

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codecov bot commented Jan 18, 2023 •
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Codecov Report

Merging #123 (d966f90) into main (6dcff7f) will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main     #123   +/-   ##
=======================================
  Coverage   95.63%   95.63%           
=======================================
  Files         156      156           
  Lines        8794     8794           
=======================================
  Hits         8410     8410           
  Misses        384      384
Impacted Files Coverage Δ
tests/metrics/aggregation/test_mean.py 100.00% <100.00%> (ø)
tests/metrics/aggregation/test_throughput.py 100.00% <100.00%> (ø)
torcheval/metrics/aggregation/mean.py 100.00% <100.00%> (ø)
torcheval/metrics/aggregation/throughput.py 100.00% <100.00%> (ø)

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bobakfb added a commit to bobakfb/torcheval that referenced this pull request Jan 25, 2023
@bobakfb @facebook-github-bot
Bumping to Version 0.0.6
8383a0e 
Summary:
# TorchEval Version 0.0.6

## Change Log

 - New metrics:
   - AUC
   - Binary, Multiclass, Multilabel AUPRC (also called Average Precision) pytorch#108 pytorch#109
   - Multilabel Precision Recall Curve pytorch#87
   - Recall at Fixed Precision pytorch#88 pytorch#91
   - Windowed Mean Square Error pytorch#72 pytorch#86
   - Blue Score pytorch#93 pytorch#95
   - Perplexity pytorch#90
   - Word Error Rate pytorch#97
   - Word Information Loss pytorch#111
   - Word Information Preserved pytorch#110
 - Features
   - Added Sync for Dictionaries of Metrics pytorch#98
   - Improved FLOPS counter pytorch#81
   - Improved Module Summary, added forward elapsed times pytorch#100 pytorch#103 pytorch#104 pytorch#105 pytorch#114
   - AUROC now supports weighted inputs pytorch#94
 - Other
   - Improved Documentation pytorch#80 pytorch#117 pytorch#121
   - Added Module Summary to Quickstart pytorch#113
   - Updates several unit tests pytorch#77 pytorch#96 pytorch#101 pytorch#73
   - Docs Automatically Add New Metrics pytorch#118
   - Several Aggregation Metrics now Support fp64 pytorch#116 pytorch#123

### [BETA] Sync Dictionaries of Metrics

We're looking forward to building tooling for metric collections. The first important feature towards this end is collective syncing of groups of metrics. In the example below, we show how easy it is to sync all your metrics at the same time with `sync_and_compute_collection`.

This method is not only for convenience, on the backend we only use one torch distributed sync collective for the entire group of metrics, meaning that the overhead from repeated network directives is maximally reduced.

```python
import torch
from torcheval.metrics import BinaryAUPRC, BinaryAUROC, BinaryAccuracy
from torcheval.metrics.toolkit import sync_and_compute_collection, reset_metrics

# Collections should be Dict[str, Metric]
train_metrics = {
    "train_auprc": BinaryAUPRC(),
    "train_auroc": BinaryAUROC(),
    "train_accuracy": BinaryAccuracy(),
}

# Hydrate metrics with some random data
preds = torch.rand(size=(100,))
targets = torch.randint(low=0, high=2, size=(100,))

for name, metric in train_metrics.items():
    metric.update(preds, targets)

# Sync the whole group with a single gather
print(sync_and_compute_collection(train_metrics))
>>> {'train_auprc': tensor(0.5913), 'train_auroc': tensor(0.5161, dtype=torch.float64), 'train_accuracy': tensor(0.5100)}

# reset all metrics in collection
reset_metrics(train_metrics.values())
```

Be on the lookout for more metric collection code coming in future releases.

## Contributors

We're grateful for our community, which helps us improving torcheval by highlighting issues and contributing code. The following persons have contributed patches for this release: Rohit Alekar lindawangg Julia Reinspach jingchi-wang Ekta Sardana williamhufb @\andreasfloros Erika Lal samiwilf

Reviewed By: ananthsub

Differential Revision: D42737308

fbshipit-source-id: 4c9d72ce73a35636d7cd6421926a23a80250e267
@bobakfb bobakfb mentioned this pull request Jan 25, 2023
Closed
facebook-github-bot pushed a commit that referenced this pull request Jan 25, 2023
@bobakfb @facebook-github-bot
Bumping to Version 0.0.6 (#124)
8d46858 
Summary:
Pull Request resolved: #124

# TorchEval Version 0.0.6

## Change Log

 - New metrics:
   - AUC
   - Binary, Multiclass, Multilabel AUPRC (also called Average Precision) #108 #109
   - Multilabel Precision Recall Curve #87
   - Recall at Fixed Precision #88 #91
   - Windowed Mean Square Error #72 #86
   - Blue Score #93 #95
   - Perplexity #90
   - Word Error Rate #97
   - Word Information Loss #111
   - Word Information Preserved #110
 - Features
   - Added Sync for Dictionaries of Metrics #98
   - Improved FLOPS counter #81
   - Improved Module Summary, added forward elapsed times #100 #103 #104 #105 #114
   - AUROC now supports weighted inputs #94
 - Other
   - Improved Documentation #80 #117 #121
   - Added Module Summary to Quickstart #113
   - Updates several unit tests #77 #96 #101 #73
   - Docs Automatically Add New Metrics #118
   - Several Aggregation Metrics now Support fp64 #116 #123

### [BETA] Sync Dictionaries of Metrics

We're looking forward to building tooling for metric collections. The first important feature towards this end is collective syncing of groups of metrics. In the example below, we show how easy it is to sync all your metrics at the same time with `sync_and_compute_collection`.

This method is not only for convenience, on the backend we only use one torch distributed sync collective for the entire group of metrics, meaning that the overhead from repeated network directives is maximally reduced.

```python
import torch
from torcheval.metrics import BinaryAUPRC, BinaryAUROC, BinaryAccuracy
from torcheval.metrics.toolkit import sync_and_compute_collection, reset_metrics

# Collections should be Dict[str, Metric]
train_metrics = {
    "train_auprc": BinaryAUPRC(),
    "train_auroc": BinaryAUROC(),
    "train_accuracy": BinaryAccuracy(),
}

# Hydrate metrics with some random data
preds = torch.rand(size=(100,))
targets = torch.randint(low=0, high=2, size=(100,))

for name, metric in train_metrics.items():
    metric.update(preds, targets)

# Sync the whole group with a single gather
print(sync_and_compute_collection(train_metrics))
>>> {'train_auprc': tensor(0.5913), 'train_auroc': tensor(0.5161, dtype=torch.float64), 'train_accuracy': tensor(0.5100)}

# reset all metrics in collection
reset_metrics(train_metrics.values())
```

Be on the lookout for more metric collection code coming in future releases.

## Contributors

We're grateful for our community, which helps us improving torcheval by highlighting issues and contributing code. The following persons have contributed patches for this release: Rohit Alekar lindawangg Julia Reinspach jingchi-wang Ekta Sardana williamhufb @\andreasfloros Erika Lal samiwilf

Reviewed By: ananthsub

Differential Revision: D42737308

fbshipit-source-id: dfd852345e1a9f3069ea33b056f5a60a3adde5aa
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