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fix tests that failed locally on mac (#13872)

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keccak output was silently broken without contiguous
This commit is contained in:
chenyu
2025-12-29 11:23:38 -05:00
committed by GitHub
parent 0e409ff5ce
commit 0a98fd38b3
4 changed files with 6 additions and 3 deletions
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2
test/models/test_bert.py
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@@ -1,4 +1,6 @@
#!/usr/bin/env python #!/usr/bin/env python
import os
os.environ['USE_TF'] = '0' # prevent transformers from importing tensorflow
import unittest import unittest
from tinygrad import Tensor from tinygrad import Tensor
import numpy as np import numpy as np

2
test/models/test_efficientnet.py
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@@ -68,7 +68,7 @@ class TestEfficientNet(unittest.TestCase):
self.assertEqual(_LABELS[labels[0]], "sports car, sport car") self.assertEqual(_LABELS[labels[0]], "sports car, sport car")
def test_chicken_car(self): def test_chicken_car(self):
labels = _infer(self.model, np.concat([chicken_img, car_img], axis=0)) labels = _infer(self.model, np.concatenate([chicken_img, car_img], axis=0))
self.assertEqual(_LABELS[labels[0]], "hen") self.assertEqual(_LABELS[labels[0]], "hen")
self.assertEqual(_LABELS[labels[1]], "sports car, sport car") self.assertEqual(_LABELS[labels[1]], "sports car, sport car")

4
test/unit/test_hashing.py
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@@ -47,10 +47,10 @@ class TestKeccak(unittest.TestCase):
ha_ref, hb_ref = hasher(a), hasher(b) ha_ref, hb_ref = hasher(a), hasher(b)
tres = Tensor.stack(*(Tensor(d) for d in (a, b))).keccak(name) tres = Tensor.stack(*(Tensor(d) for d in (a, b))).keccak(name)
ha, hb = tres[0].data(), tres[1].data() ha, hb = bytes(tres[0].data()), bytes(tres[1].data())
self.assertEqual(ha_ref, ha) self.assertEqual(ha_ref, ha)
self.assertEqual(ha_ref, Tensor(a).keccak(name).data()) self.assertEqual(ha_ref, bytes(Tensor(a).keccak(name).data()))
self.assertEqual(hb_ref, hb) self.assertEqual(hb_ref, hb)
def test_referenced(self): def test_referenced(self):

1
tinygrad/tensor.py
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@@ -1845,6 +1845,7 @@ class Tensor(OpMixin):
p = state.reshape(bs, 5, 5).transpose(2, 1) p = state.reshape(bs, 5, 5).transpose(2, 1)
t1 = (p[:,:,0] ^ p[:,:,1] ^ p[:,:,2] ^ p[:,:,3] ^ p[:,:,4]).roll(-1, 1) # xor reduce t1 = (p[:,:,0] ^ p[:,:,1] ^ p[:,:,2] ^ p[:,:,3] ^ p[:,:,4]).roll(-1, 1) # xor reduce
state = state ^ (t1.roll(2, 1).bitwise_xor((t1 << 1) ^ (t1 >> 63)).unsqueeze(2).expand(bs, 5, 5).transpose(2, 1).flatten(1)) state = state ^ (t1.roll(2, 1).bitwise_xor((t1 << 1) ^ (t1 >> 63)).unsqueeze(2).expand(bs, 5, 5).transpose(2, 1).flatten(1))
state = state.contiguous() # required for correct indexing in π step # TODO: why is it needed?
# ρ and π steps # ρ and π steps
state = state[:, reorder_indexes] state = state[:, reorder_indexes]
state = (state * rot_offsets_v0).bitwise_or(state // rot_offsets_v1).reshape(bs, 5, 5) state = (state * rot_offsets_v0).bitwise_or(state // rot_offsets_v1).reshape(bs, 5, 5)