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fix test_schedule conv2d bug (#7549)

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* tests tests tests

* slap a resolve on it

* fix comment
This commit is contained in:
geohotstan
2024-11-05 22:07:25 +08:00
committed by GitHub
parent 0db5f52b2a
commit 934fb73994
3 changed files with 15 additions and 5 deletions
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9
test/test_ops.py
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@@ -1629,6 +1629,15 @@ class TestOps(unittest.TestCase):
def test_conv2d_bs_1_cin_1(self): self._test_conv2d(bs=1, cin=1)
def test_conv2d_bs_4_cin_1(self): self._test_conv2d(bs=4, cin=1)
def test_conv2d_errors(self):
# kernel size cannot be larger than input size
self.helper_test_exception([(1,1,6,7), (6,1,3,3)],
lambda x,w:torch.nn.functional.conv2d(x,w,dilation=3),
lambda x,w: Tensor.conv2d(x,w,dilation=3), expected=(RuntimeError, AssertionError))
# regression test for https://github.com/tinygrad/tinygrad/pull/7549/
self.helper_test_exception([(2,16,2,2), (32,16,3,3)], lambda x,w:torch.nn.functional.conv2d(x,w), lambda x,w: Tensor.conv2d(x,w),
expected=(RuntimeError, AssertionError))
def test_large_input_conv2d(self):
bs = 4
cin = 16

10
test/test_schedule.py
View File

@@ -932,7 +932,7 @@ class TestSchedule(unittest.TestCase):
with Tensor.train():
img = Tensor.empty(2,3,4,4)
c1 = nn.Conv2d(3,16,3,bias=False)
c2 = nn.Conv2d(16,32,3,bias=False)
c2 = nn.Conv2d(16,32,2,bias=False)
_realize_weights([c1, c2])
opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
opt.zero_grad()
@@ -953,23 +953,23 @@ class TestSchedule(unittest.TestCase):
with Tensor.train():
img = Tensor.empty(2,3,4,4)
c1 = nn.Conv2d(3,16,3,bias=False)
c2 = nn.Conv2d(16,32,3,bias=False)
c2 = nn.Conv2d(16,32,2,bias=False)
_realize_weights([c1, c2])
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]))
opt.zero_grad()
c2(c1(img).relu()).relu().sum().backward()
check_schedule(opt.schedule_step(), 6)
check_schedule(opt.schedule_step(), 8)
def test_fold_2convs_sgd_nesterov_momentum_wd(self):
with Tensor.train():
img = Tensor.empty(2,3,4,4)
c1 = nn.Conv2d(3,16,3,bias=False)
c2 = nn.Conv2d(16,32,3,bias=False)
c2 = nn.Conv2d(16,32,2,bias=False)
_realize_weights([c1, c2])
opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]), nesterov=True, momentum=0.9, weight_decay=0.1)
opt.zero_grad()
c2(c1(img).relu()).relu().sum().backward()
check_schedule(opt.schedule_step(), 8)
check_schedule(opt.schedule_step(), 10)
def test_sgd_4convs_fuse(self):
with Tensor.train():