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Return scalar losses instead of per-sample means #18013

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merged 6 commits into from
Jul 4, 2022
Merged

Return scalar losses instead of per-sample means #18013

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2521c68
Return scalar losses instead of per-sample means
Rocketknight1 Jul 4, 2022
b339284
Make loss shape (1,) instead of scalar
Rocketknight1 Jul 4, 2022
d6f834e
Allow scalar losses in test_loss_computation
Rocketknight1 Jul 4, 2022
4918a76
Allow scalar losses in test_loss_computation
Rocketknight1 Jul 4, 2022
633664a
Allow scalar losses in test_loss_computation
Rocketknight1 Jul 4, 2022
f3515aa
Remove XLA loss function for RAG
Rocketknight1 Jul 4, 2022
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Allow scalar losses in test_loss_computation
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@Rocketknight1
Rocketknight1 committed Jul 4, 2022
commit 633664a00779db441b1b7f18d11d7ae3eafddbe8
2 changes: 1 addition & 1 deletion tests/test_modeling_tf_common.py
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Expand Up @@ -1307,7 +1307,7 @@ def test_loss_computation(self):
labels[0] = -100
prepared_for_class["labels"] = tf.convert_to_tensor(labels)
loss = model(model_input, **prepared_for_class)[0]
self.assertEqual(loss.shape.as_list(), expected_loss_size)
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
self.assertTrue(not np.any(np.isnan(loss.numpy())))

# Test that model correctly compute the loss with a dict
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