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a0965ee198
* models matrix * fix typo and install gpu deps * install llvm deps if needed * fix * testops with cuda * remove pip cache since not work * cuda env * install cuda deps * maybe it will work now * i can't read * all tests in matrix * trim down more * opencl stuff in matrix * opencl pip cache * test split * change cuda test exclusion * test * fix cuda maybe * add models * add more n=auto * third thing * fix bug * cache pip more * change name * update tests * try again cause why not * balance * try again... * try apt cache for cuda * try on gpu: * try cuda again * update packages step * replace libz-dev with zlib1g-dev * only cache cuda * why error * fix gpuocelot bug * apt cache err * apt cache to slow? * opt and image in single runner * add a couple n=autos * remove test matrix * try cuda apt cache again * libz-dev -> zlib1g-dev * remove -s since not supported by xdist * the cache takes too long and doesn't work * combine webgpu and metal tests * combine imagenet to c and cpu tests * torch tests with linters * torch back by itself * small windows clang test with torch tests * fix a goofy windows bug * im dumb * bro * clang with linters * fix pylint error * linter not work on windows * try with clang again * clang and imagenet? * install deps * fix * fix quote * clang by itself (windows too slow) * env vars for imagenet * cache pip for metal and webgpu tests * try torch with metal and webgpu * doesn't work, too long * remove -v * try -n=logical * don't use logical * revert accidental thing * remove some prints unless CI * fix print unless CI * ignore speed tests for slow tests * clang windows in matrix (ubuntu being tested in imagenet->c test) * try manual pip cache * fix windows pip cache path * all manual pip cache * fix pip cache dir for macos * print_ci function in helpers * CI as variable, no print_ci * missed one * cuda tests with docker image * remove setup-python action for cuda * python->python3? * remove -s -v * try fix pip cache * maybe fix * try to fix pip cache * is this the path? * maybe cache pip * try again * create wheels dir * ? * cuda pip deps in dockerfile * disable pip cache for clang * image from ghcr instead of docker hub * why is clang like this * fast deps * try use different caches * remove the fast thing * try with lighter image * remove setup python for cuda * small docker and cuda fast deps * ignore a few more tests * cool docker thing (maybe) * oops * quotes * fix docker command * fix bug * ignore train efficientnet test * remove dockerfile (docker stuff takes too long) * remove docker stuff and normal cuda * oops * ignore the tests for cuda * does this work * ignore test_train on slow backends * add space * llvm ignore same tests as cuda * nvm * ignore lr scheduler tests * get some stats * fix ignore bug * remove extra ' * remove and * ignore test for llvm * change ignored tests and durationon all backends * fix * and -> or * ignore some more cuda tests * finally? * does this fix it * remove durations=0 * add some more tests to llvm * make last pytest more readable * fix * don't train efficientnet on cpu * try w/out pip cache * pip cache seems to be generally better * pytest file markers * try apt fast for cuda * use quick install for apt-fast * apt-fast not worth * apt-get to apt * fix typo * suppress warnings * register markers * disable debug on fuzz tests * change marker names * apt update and apt install in one command * update marker names in test.yml * webgpu pytest marker
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import unittest
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import numpy as np
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from tinygrad.lazy import Device
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import getenv, CI
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def multidevice_test(fxn):
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exclude_devices = getenv("EXCLUDE_DEVICES", "").split(",")
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def ret(self):
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for device in Device._buffers:
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if device in ["DISK", "FAKE"]: continue
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if not CI: print(device)
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if device in exclude_devices:
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if not CI: print(f"WARNING: {device} test is excluded")
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continue
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with self.subTest(device=device):
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try:
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Device[device]
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except Exception:
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if not CI: print(f"WARNING: {device} test isn't running")
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continue
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fxn(self, device)
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return ret
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class TestExample(unittest.TestCase):
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@multidevice_test
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def test_convert_to_cpu(self, device):
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a = Tensor([[1,2],[3,4]], device=device)
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assert a.numpy().shape == (2,2)
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b = a.cpu()
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assert b.numpy().shape == (2,2)
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@multidevice_test
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def test_2_plus_3(self, device):
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a = Tensor([2], device=device)
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b = Tensor([3], device=device)
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result = a + b
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print(f"{a.numpy()} + {b.numpy()} = {result.numpy()}")
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assert result.numpy()[0] == 5.
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@multidevice_test
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def test_example_readme(self, device):
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x = Tensor.eye(3, device=device, requires_grad=True)
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y = Tensor([[2.0,0,-2.0]], device=device, requires_grad=True)
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z = y.matmul(x).sum()
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z.backward()
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x.grad.numpy() # dz/dx
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y.grad.numpy() # dz/dy
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assert x.grad.device == device
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assert y.grad.device == device
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@multidevice_test
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def test_example_matmul(self, device):
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try:
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Device[device]
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except Exception:
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print(f"WARNING: {device} test isn't running")
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return
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x = Tensor.eye(64, device=device, requires_grad=True)
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y = Tensor.eye(64, device=device, requires_grad=True)
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z = y.matmul(x).sum()
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z.backward()
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x.grad.numpy() # dz/dx
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y.grad.numpy() # dz/dy
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assert x.grad.device == device
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assert y.grad.device == device
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if __name__ == '__main__':
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unittest.main()
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