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tinygrad
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skip a few clang test that took > 30 seconds in CI (#4126) 

* skip slow CLANG test test_train_cifar

* skip those too

* and that

* only CI

* one more
This commit is contained in:
chenyu 2024-04-10 02:00:34 -04:00 committed by GitHub
parent 08ddeb5685
commit 6bbbeb93ac
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3 changed files with 9 additions and 9 deletions
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4
test/models/test_mnist.py
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@ -1,8 +1,9 @@
#!/usr/bin/env python #!/usr/bin/env python
import unittest import unittest
import numpy as np import numpy as np
from tinygrad import Tensor, Device
from tinygrad.helpers import CI
from tinygrad.nn.state import get_parameters from tinygrad.nn.state import get_parameters
from tinygrad.tensor import Tensor
from tinygrad.nn import optim, BatchNorm2d from tinygrad.nn import optim, BatchNorm2d
from extra.training import train, evaluate from extra.training import train, evaluate
from extra.datasets import fetch_mnist from extra.datasets import fetch_mnist
@ -48,6 +49,7 @@ class TinyConvNet:
x = x.reshape(shape=[x.shape[0], -1]) x = x.reshape(shape=[x.shape[0], -1])
return x.dot(self.l1) return x.dot(self.l1)
@unittest.skipIf(CI and Device.DEFAULT == "CLANG", "slow")
class TestMNIST(unittest.TestCase): class TestMNIST(unittest.TestCase):
def test_sgd_onestep(self): def test_sgd_onestep(self):
np.random.seed(1337) np.random.seed(1337)

6
test/models/test_real_world.py
View File

@ -64,7 +64,6 @@ class TestRealWorld(unittest.TestCase):
return t.realize() return t.realize()
helper_test("test_mini_sd", lambda: (Tensor.empty(4, 16, 8, 8), Tensor.empty(1, 24)), test, 0.01, 43) helper_test("test_mini_sd", lambda: (Tensor.empty(4, 16, 8, 8), Tensor.empty(1, 24)), test, 0.01, 43)
@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16") @unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
def test_llama(self): def test_llama(self):
dtypes.default_float = dtypes.float16 dtypes.default_float = dtypes.float16
@ -89,6 +88,7 @@ class TestRealWorld(unittest.TestCase):
def test(t, v): return model(t, v).realize() def test(t, v): return model(t, v).realize()
helper_test("test_gpt2", lambda: (Tensor([[1,]]),Variable("pos", 1, 100).bind(1)), test, 0.23 if CI else 0.9, 164 if CI else 468, all_jitted=True) helper_test("test_gpt2", lambda: (Tensor([[1,]]),Variable("pos", 1, 100).bind(1)), test, 0.23 if CI else 0.9, 164 if CI else 468, all_jitted=True)
@unittest.skipIf(CI and Device.DEFAULT == "CLANG", "slow")
def test_train_mnist(self): def test_train_mnist(self):
from examples.beautiful_mnist import Model from examples.beautiful_mnist import Model
with Tensor.train(): with Tensor.train():
@ -106,8 +106,7 @@ class TestRealWorld(unittest.TestCase):
helper_test("train_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 127) helper_test("train_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 127)
@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault") @unittest.skipIf(CI and Device.DEFAULT in {"CLANG", "GPU"}, "slow")
@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
def test_train_cifar(self): def test_train_cifar(self):
with Tensor.train(): with Tensor.train():
model = SpeedyResNet(Tensor.ones((12,3,2,2))) model = SpeedyResNet(Tensor.ones((12,3,2,2)))
@ -125,7 +124,6 @@ class TestRealWorld(unittest.TestCase):
helper_test("train_cifar", lambda: (Tensor.randn(BS, 3, 32, 32),), train, (1.0/48)*BS, 142 if CI else 154) # it's 154 on metal helper_test("train_cifar", lambda: (Tensor.randn(BS, 3, 32, 32),), train, (1.0/48)*BS, 142 if CI else 154) # it's 154 on metal
@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16") @unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
def test_train_cifar_hyp(self): def test_train_cifar_hyp(self):
dtypes.default_float = dtypes.float16 dtypes.default_float = dtypes.float16

8
test/test_fuzz_shape_ops.py
View File

@ -6,7 +6,7 @@ from hypothesis.extra import numpy as stn
import numpy as np import numpy as np
import torch import torch
import tinygrad from tinygrad import Tensor, Device
from tinygrad.helpers import CI from tinygrad.helpers import CI
@ -26,9 +26,9 @@ def st_shape(draw) -> tuple[int, ...]:
return s return s
def tensors_for_shape(s:tuple[int, ...]) -> tuple[torch.tensor, tinygrad.Tensor]: def tensors_for_shape(s:tuple[int, ...]) -> tuple[torch.tensor, Tensor]:
x = np.arange(prod(s)).reshape(s) x = np.arange(prod(s)).reshape(s)
return torch.from_numpy(x), tinygrad.Tensor(x) return torch.from_numpy(x), Tensor(x)
def apply(tor, ten, tor_fn, ten_fn=None): def apply(tor, ten, tor_fn, ten_fn=None):
ok = True ok = True
@ -38,7 +38,7 @@ def apply(tor, ten, tor_fn, ten_fn=None):
except: ten, ok = None, not ok # noqa: E722 except: ten, ok = None, not ok # noqa: E722
return tor, ten, ok return tor, ten, ok
@unittest.skipIf(CI and Device.DEFAULT == "CLANG", "slow")
class TestShapeOps(unittest.TestCase): class TestShapeOps(unittest.TestCase):
@settings.get_profile(__file__) @settings.get_profile(__file__)
@given(st_shape(), st_int32, st.one_of(st_int32, st.lists(st_int32))) @given(st_shape(), st_int32, st.one_of(st_int32, st.lists(st_int32)))