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upcast float exp to at least float32 (#11758)

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* upcast float exp to at least float32

* unlucky seed
This commit is contained in:
chenyu
2025-08-22 20:16:34 -04:00
committed by GitHub
parent b057a90d49
commit e39b25cd36
3 changed files with 4 additions and 2 deletions
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2
examples/hlb_cifar10.py
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@@ -118,7 +118,7 @@ class SpeedyResNet:
# hyper-parameters were exactly the same as the original repo
bias_scaler = 58
hyp = {
'seed' : 200,
'seed' : 201,
'opt': {
'bias_lr': 1.76 * bias_scaler/512,
'non_bias_lr': 1.76 / 512,

1
test/test_dtype_alu.py
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@@ -114,7 +114,6 @@ class TestDTypeALU(unittest.TestCase):
@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16, Device.DEFAULT), f"no bfloat16 on {Device.DEFAULT}")
@given(ht.bfloat16, strat.sampled_from(unary_operations))
@unittest.skipIf(Device.DEFAULT in ["AMD"], "broken on AMD?")
def test_bfloat16_unary(self, a, op): universal_test_unary(a, dtypes.bfloat16, op)
@given(ht.uint8, ht.uint8, strat.sampled_from(integer_binary_operations))

3
tinygrad/tensor.py
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@@ -2996,6 +2996,9 @@ class Tensor(MathTrait):
print(Tensor([0., 1., 2., 3.]).exp().numpy())
```
"""
# TODO: make it generic, and same thing to log and cos
if self.is_floating_point(): return self.cast(least_upper_dtype(self.dtype, dtypes.float32)).mul(1/math.log(2)).exp2().cast(self.dtype)
# TODO: behavior when DEFAULT_FLOAT is bfloat16 and input is int32?
return self.mul(1/math.log(2)).exp2()
def exp2(self) -> Tensor: