tinygrad/test/test_conv_shapetracker.py

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#!/usr/bin/env python
import unittest
from tinygrad.ops import UOps
from tinygrad.tensor import Tensor
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from tinygrad.nn import Conv2d
from tinygrad.engine.schedule import create_schedule
from tinygrad.shape.shapetracker import ShapeTracker, View
from tinygrad.helpers import prod
from test.unit.test_shapetracker import shapetracker_getitem
CI < 5 minutes (#1252) * 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
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class TestConvShapetracker(unittest.TestCase):
def test_conv_3x3_one_view(self):
conv = Conv2d(16, 32, (3, 3))
# first run to init the weights, they are scheduled.
create_schedule([conv(Tensor.empty(1, 16, 10, 10)).lazydata])
# run it again to get the kernels
sched = [si for si in create_schedule([conv(Tensor.empty(1, 16, 10, 10)).lazydata]) if si.ast.op is UOps.SINK]
assert len(sched) == 1, f"conv should only have one kernel, getting {len(sched)}"
for st in [x.st_arg for x in sched[0].ast.parents if x.op is UOps.LOAD]:
assert len(st.views) == 1
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def test_conv_2x2_backward_one_view(self):
X = Tensor.rand(1, 1, 3, 3, requires_grad=True)
conv = Conv2d(1, 1, (2, 2), bias=False)
conv(X).mean().backward()
si = X.grad.schedule()[-1]
print(si)
ldb = [x for x in si.ast.parents if x.op is UOps.LOAD][0]
st: ShapeTracker = ldb.st_arg.simplify()
# NOTE: st.real_size() is broken
print(si.inputs[0].size)
#self.assertEqual(si.inputs[0].size, st.real_size())
for v in st.views: print(v)
# same st
test_st = ShapeTracker((
View(shape=(1, 1, 2, 4, 2, 4), strides=(0, 0, 2, 8, 1, 4), offset=0, mask=((0, 1), (0, 1), (0, 2), (0, 2), (0, 2), (0, 2)), contiguous=False),
View(shape=(1, 1, 1, 1, 3, 3, 3, 3), strides=(0, 0, 0, 0, 24, 8, 3, 1), offset=0,
mask=((0, 1), (0, 1), (0, 1), (0, 1), (0, 2), (0, 3), (0, 2), (0, 3)), contiguous=False)))
#test_st = ShapeTracker((
# View(shape=(2,4), strides=(1,4), offset=0, mask=None, contiguous=False),
#)).simplify()
#View(shape=(1, 1, 2, 4, 2, 4), strides=(0, 0, 2, 8, 1, 4), offset=0, mask=((0, 1), (0, 1), (0, 2), (0, 2), (0, 2), (0, 2)), contiguous=False),
#View(shape=(1, 1, 1, 1, 3, 3, 3, 3), strides=(0, 0, 0, 0, 24, 8, 3, 1), offset=0,
# mask=((0, 1), (0, 1), (0, 1), (0, 1), (0, 2), (0, 3), (0, 2), (0, 3)), contiguous=False))).simplify()
print("*** new ***")
for v in test_st.views: print(v)
for i in range(prod(st.shape)):
i1, i2 = shapetracker_getitem(st, i), shapetracker_getitem(test_st, i)
print(i, i1, i2, si.inputs[0].size, i1==i2)
#self.assertEqual(i1, i2)
with self.assertRaises(AssertionError):
assert len(st.views) <= 2
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if __name__ == '__main__':
unittest.main()