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Merged
ydshieh merged 5 commits into from
Mar 7, 2023
Merged

Update Jukebox tests #21984

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43f964f
update expected values for jukebox
ydshieh Mar 6, 2023
4ef84d7
update expected values for jukebox
ydshieh Mar 6, 2023
e9008e0
update expected values for jukebox
ydshieh Mar 6, 2023
437d611
update expected values for jukebox
ydshieh Mar 6, 2023
0a89027
update expected values for jukebox
ydshieh Mar 6, 2023
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63 changes: 56 additions & 7 deletions tests/models/jukebox/test_modeling_jukebox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,19 +57,38 @@ class Jukebox1bModelTester(unittest.TestCase):
1405, 1276, 1455, 1228
]

EXPECTED_OUTPUT_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653
]

EXPECTED_OUTPUT_1 = [
1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
1262, 869, 1728, 747
]
EXPECTED_OUTPUT_1_PT_2 = [
416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416
]

EXPECTED_OUTPUT_0 = [
1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
1186, 1015, 1777, 268
]
EXPECTED_OUTPUT_0_PT_2 = [
854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
185, 842, 591, 89
]

EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

Expand Down Expand Up @@ -135,15 +154,15 @@ def test_sampling(self):
set_seed(0)
zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2))
self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])
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@ydshieh ydshieh Mar 6, 2023

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These are integer tensors, we don't need assert_allclose - equal or in is super OK!


set_seed(0)
zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1))
self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

set_seed(0)
zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0))
self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

@slow
def test_conditioning(self):
Expand Down Expand Up @@ -248,6 +267,13 @@ class Jukebox5bModelTester(unittest.TestCase):
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
]
EXPECTED_OUTPUT_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
]

EXPECTED_OUTPUT_1 = [
1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
Expand All @@ -256,6 +282,13 @@ class Jukebox5bModelTester(unittest.TestCase):
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
]
EXPECTED_OUTPUT_1_PT_2 = [
416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416
]

EXPECTED_OUTPUT_0 = [
1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
Expand All @@ -264,6 +297,13 @@ class Jukebox5bModelTester(unittest.TestCase):
234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
]
EXPECTED_OUTPUT_0_PT_2 = [
854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
185, 89, 591, 842, 185, 842, 591, 632
]

EXPECTED_GPU_OUTPUTS_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
Expand All @@ -272,6 +312,15 @@ class Jukebox5bModelTester(unittest.TestCase):
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
]
EXPECTED_GPU_OUTPUTS_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
252, 1229, 1434, 2001
]

EXPECTED_GPU_OUTPUTS_1 = [
1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
Expand Down Expand Up @@ -301,15 +350,15 @@ def test_sampling(self):
set_seed(0)
zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_OUTPUT_2))
self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

set_seed(0)
zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[1][0], torch.tensor(self.EXPECTED_OUTPUT_1))
self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

set_seed(0)
zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[2][0], torch.tensor(self.EXPECTED_OUTPUT_0))
self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

@slow
@skip("Not enough GPU memory on CI runners")
Expand Down Expand Up @@ -344,4 +393,4 @@ def test_fp16_slow_sampling(self):
metadata = model.get_metadata(labels, 0, 7680, 0)
set_seed(0)
outputs = model.sample(1, metadata=metadata, sample_tokens=60)
torch.testing.assert_allclose(outputs[0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
self.assertIn(outputs[0].cpu().tolist(), [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])