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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
import unittest, sys
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from tinygrad import Tensor, GlobalCounters, dtypes, Context
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from tinygrad.helpers import WINO
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from test.helpers import check_schedule
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@unittest.skipIf(sys.platform.startswith("win"), "flaky on Windows")
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class TestWinograd(unittest.TestCase):
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def setUp(self):
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self.old = WINO.value
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WINO.value = 1
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def tearDown(self):
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WINO.value = self.old
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def test_forward_kernels(self):
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x,w = Tensor.rand(1,4,9,9).realize(), Tensor.rand(4,4,3,3).realize()
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out = Tensor.conv2d(x,w)
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check_schedule(out, 4)
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def test_backward_counters(self):
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# contiguous_backward on the pooled input keeps the input-transform adjoint out of the overlap accumulation, so
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# winograd backward runs in a fraction of the direct-conv flops; NOOPT=1 keeps the raw flop ratio from drifting with the optimizer
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IC, OC, H = 64, 64, 28
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x,w = Tensor.empty(1,IC,H,H,device="NULL").realize(), Tensor.empty(OC,IC,3,3,device="NULL").realize()
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x.requires_grad = w.requires_grad = True
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def backward_ops(wino):
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x.grad = w.grad = None
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GlobalCounters.reset()
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with Context(NOOPT=1, WINO=wino):
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Tensor.conv2d(x,w,padding=1).mean().backward()
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Tensor.realize(x.grad, w.grad)
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return GlobalCounters.global_ops
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ops_wino, ops_normal = backward_ops(1), backward_ops(0)
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print(f"backward ops: normal {ops_normal} wino {ops_wino} ratio {ops_wino/ops_normal:.2f}")
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self.assertLess(ops_wino/ops_normal, 0.35)
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def test_counters(self):
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IC, OC, H = 64, 64, 28
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x,w = Tensor.empty(1,IC,H,H,device="NULL").realize(), Tensor.empty(OC,IC,3,3,device="NULL").realize()
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GlobalCounters.reset()
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with Context(NOOPT=0, WINO=1): Tensor.conv2d(x,w).realize()
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ops_wino = GlobalCounters.global_ops
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GlobalCounters.reset()
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with Context(NOOPT=0, WINO=0): Tensor.conv2d(x,w).realize()
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ops_normal = GlobalCounters.global_ops
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print(f"ops: normal {ops_normal} wino {ops_wino} ratio {ops_wino/ops_normal:.2f}")
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self.assertLess(ops_wino/ops_normal, 0.6)
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def test_dtype(self):
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IC, OC, X, Y = 4,4,9,9
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x,w = Tensor.empty(1,IC,Y,X), Tensor.empty(OC,IC,3,3)
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self.assertEqual(Tensor.conv2d(x,w).dtype, dtypes.default_float)
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x,w = Tensor.empty(1,IC,Y,X,dtype=dtypes.half), Tensor.empty(OC,IC,3,3,dtype=dtypes.half)
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self.assertEqual(Tensor.conv2d(x,w).dtype, dtypes.half)
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if __name__ == '__main__':
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unittest.main(verbosity=2)
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