forked from tinygrad/tinygrad
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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