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0338cdc309
* fix imports referencing moved metrics.py file * Make representation computation branchless in TransformerEncoderBase (#4818) Summary: We want to make the computation branchless here because fairseq code may be exported and traced for deployment purposes, and tracing mechanisms can break the correctness for a captured program if it's dependent on input data. In this diff we try to rewrite the code to remove one branch so that tracer can proceed here and preserve the correct semantics of the model. Test Plan: CI Reviewers: Subscribers: Tasks: Tags: * Fix Torchscript typing in transformer_encoder.py (#4847) * Add Generative Spoken Dialogue Language Modeling (#4879) * Update deprecated torch.qr in glow.py example (#4685) torch.qr is deprecated for a long time and is being removed by https://github.com/pytorch/pytorch/pull/70989. This PR makes the example compatible with new and old PyTorch versions. * Emotion Conversion Paper Open Source (#4895) * data2vec v2.0 (#4903) data2v2c 2.0 Co-authored-by: Arun Babu <arbabu@fb.com> Co-authored-by: Wei-Ning Hsu <wnhsu@csail.mit.edu> * remove missing config entries when loading task from checkpoint (#4905) * make apex optional (#4906) * Add file to generate manifests for stop dataset. (#4891) * Update STOP dataset README to include proper link. (#4892) * Update README.md (#4893) * using foreach to reduce kernel (#4904) * using foreach to reduce kernel * set reproducibility to looser threshold * revert optimzer * update * update * update * update * update * update * update Co-authored-by: juntengjia <juntengjia@fb.com> * Update README.md to add data2vec blog post (#4913) * Update README.md * Update config to fix circleci failure (#4949) https://app.circleci.com/pipelines/github/fairinternal/fairseq-py/12635/workflows/3befbae2-79c4-458d-9fc4-aad4484183b4/jobs/26767 * Generative Spoken Dialogue Language Modeling Paper Open Source (#4957) * wav2vec2_laser (#4968) * ASR BLEU tool copied from ust branch into main (#4914) * Add transcript option for asr-bleu (#4981) --------- Co-authored-by: zhxchen17 <zhxchen17@outlook.com> Co-authored-by: zhxchen17 <zhxchen17@fb.com> Co-authored-by: Nguyen Tu Anh <nguyentuanh208@gmail.com> Co-authored-by: Sergii Dymchenko <kit1980@gmail.com> Co-authored-by: Felix Kreuk <felixkreuk@gmail.com> Co-authored-by: Alexei Baevski <alexei.b@gmail.com> Co-authored-by: padentomasello <pdtomasello@gmail.com> Co-authored-by: Junteng Jia <juntengjia@hotmail.com> Co-authored-by: juntengjia <juntengjia@fb.com> Co-authored-by: arbabu123 <arbabu@fb.com> Co-authored-by: dianaml0 <82468439+dianaml0@users.noreply.github.com> Co-authored-by: Pierre Andrews <mortimer@fb.com> Co-authored-by: Ilia Kulikov <kulikov@cs.nyu.edu> Co-authored-by: Xutai Ma <xutaima@gmail.com>
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import uuid
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from fairseq.logging import metrics
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class TestMetrics(unittest.TestCase):
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def test_nesting(self):
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with metrics.aggregate() as a:
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metrics.log_scalar("loss", 1)
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with metrics.aggregate() as b:
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metrics.log_scalar("loss", 2)
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self.assertEqual(a.get_smoothed_values()["loss"], 1.5)
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self.assertEqual(b.get_smoothed_values()["loss"], 2)
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def test_new_root(self):
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with metrics.aggregate() as a:
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metrics.log_scalar("loss", 1)
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with metrics.aggregate(new_root=True) as b:
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metrics.log_scalar("loss", 2)
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self.assertEqual(a.get_smoothed_values()["loss"], 1)
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self.assertEqual(b.get_smoothed_values()["loss"], 2)
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def test_nested_new_root(self):
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with metrics.aggregate() as layer1:
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metrics.log_scalar("loss", 1)
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with metrics.aggregate(new_root=True) as layer2:
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metrics.log_scalar("loss", 2)
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with metrics.aggregate() as layer3:
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metrics.log_scalar("loss", 3)
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with metrics.aggregate(new_root=True) as layer4:
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metrics.log_scalar("loss", 4)
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metrics.log_scalar("loss", 1.5)
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self.assertEqual(layer4.get_smoothed_values()["loss"], 4)
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self.assertEqual(layer3.get_smoothed_values()["loss"], 3)
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self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5)
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self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25)
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def test_named(self):
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name = str(uuid.uuid4())
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metrics.reset_meters(name)
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with metrics.aggregate(name):
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metrics.log_scalar("loss", 1)
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metrics.log_scalar("loss", 3)
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with metrics.aggregate(name):
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metrics.log_scalar("loss", 2)
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self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5)
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def test_nested_duplicate_names(self):
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name = str(uuid.uuid4())
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metrics.reset_meters(name)
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with metrics.aggregate(name):
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metrics.log_scalar("loss", 1)
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with metrics.aggregate() as other:
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with metrics.aggregate(name):
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metrics.log_scalar("loss", 2)
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metrics.log_scalar("loss", 6)
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self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3)
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self.assertEqual(other.get_smoothed_values()["loss"], 2)
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if __name__ == "__main__":
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unittest.main()
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