Fix GPT-NeoX-20B past handling, attention computation (#17811)
* Fix GPT-NeoX-20B past handling, swap attention computation to hopefully avoid NaN, update docs * 20B tests
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@@ -226,6 +226,10 @@ class GPTNeoXModelTest(ModelTesterMixin, unittest.TestCase):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
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@unittest.skip(reason="Feed forward chunking is not implemented")
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def test_feed_forward_chunking(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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@@ -247,7 +251,7 @@ class GPTNeoXModelIntegrationTest(unittest.TestCase):
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[[[33.8045, 2.3958, 34.2816], [63.7805, 4.8332, 63.5882], [66.9116, 5.2198, 63.1185]]]
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[[[33.5938, 2.3789, 34.0312], [63.4688, 4.8164, 63.3438], [66.8750, 5.2422, 63.0625]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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