use torch.testing.assertclose instead to get more details about error in cis (#35659)
* use torch.testing.assertclose instead to get more details about error in cis * fix * style * test_all * revert for I bert * fixes and updates * more image processing fixes * more image processors * fix mamba and co * style * less strick * ok I won't be strict * skip and be done * up
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@@ -352,7 +352,7 @@ class OlmoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
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# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
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# maximum sequence length, so the outputs for the short input should match.
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if scaling_type == "dynamic":
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self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
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else:
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self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
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@@ -369,10 +369,10 @@ class OlmoeIntegrationTest(unittest.TestCase):
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out = model(torch.tensor(input_ids)).logits.float()
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-1.3814, -3.4450, -2.2990, -1.9542, -2.4387, -2.7941, -2.9312, -2.8309]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
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# slicing logits[0, 0, 0:30]
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EXPECTED_SLICE = torch.tensor([-2.3874, -2.4076, -2.4995, 4.2278, 1.4004, -0.0252, 0.4189, -2.7560, 0.3531, 1.6678, -0.7941, -1.1818, -0.2920, 0.7131, -1.4173, 1.6723, 0.5406, 0.1345, -0.1800, 0.2304, 1.2791, 0.7489, 0.6341, -0.0151, -1.3693, -1.2532, -2.3921, 0.7376, 1.6876, 0.5483]) # fmt: skip
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-2, rtol=1e-2)
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
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@slow
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def test_model_7b_greedy_generation(self):
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