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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.
99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
import unittest
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import functools
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from tinygrad import Tensor, Variable, UOp, function
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from tinygrad.uop.ops import KernelInfo
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from tinygrad.schedule import schedule_cache
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def custom_add_kernel(A:UOp, B:UOp, num:int=0) -> UOp:
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return A[0].set(B[0] + num).sink(arg=KernelInfo(f"custom_add_{num}"))
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def custom_add_backward(grad_output:UOp, _) -> tuple[None, UOp]:
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grad = Tensor.invalids(*grad_output.shape, dtype=grad_output.dtype, device=grad_output.device)
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grad = Tensor.custom_kernel(grad, Tensor(grad_output, device=grad_output.device), fxn=functools.partial(custom_add_kernel, num=0))[0]
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return None, grad.uop
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class TestScheduleCache(unittest.TestCase):
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def test_bound_variable_reuses_cache(self):
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schedule_cache.clear()
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v = Variable('v', 1, 100)
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x = Tensor.ones(10).contiguous().realize()
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# first run with v=5
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t1 = (x + Tensor(v.bind(5))).sum()
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self.assertEqual(t1.item(), 60.0)
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cache_size_after_first = len(schedule_cache)
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# second run with v=10 should reuse cache
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t2 = (x + Tensor(v.bind(10))).sum()
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self.assertEqual(t2.item(), 110.0)
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self.assertEqual(len(schedule_cache), cache_size_after_first)
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def test_custom_kernel(self):
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for i in range(4):
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a, b = Tensor.empty(1), Tensor.ones(1)
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a = Tensor.custom_kernel(a, b, fxn=functools.partial(custom_add_kernel, num=i))[0]
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a.realize()
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self.assertEqual(a.item(), i+1)
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def test_same_custom_function_reuses_cache(self):
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schedule_cache.clear()
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fxn = functools.partial(custom_add_kernel, num=10)
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# first run
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a, x = Tensor.empty(1), Tensor.ones(1)
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a = Tensor.custom_kernel(a, x, fxn=fxn)[0]
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a.realize()
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self.assertEqual(a.item(), 11)
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cache_size_after_first = len(schedule_cache)
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# second run with same function should reuse cache
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b, x = Tensor.empty(1), Tensor.ones(1)
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b = Tensor.custom_kernel(b, x, fxn=fxn)[0]
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b.realize()
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self.assertEqual(b.item(), 11)
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self.assertEqual(len(schedule_cache), cache_size_after_first)
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def test_simple(self):
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a = Tensor.ones(10).contiguous()
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b = Tensor.ones(10).contiguous()
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Tensor.realize(a, b)
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# warm up
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for _ in range(2):
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num = (a.sum().contiguous()+b.sum().contiguous()).item()
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print(num)
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# confirm schedule cache doesn't grow
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start_len_schedule_cache = len(schedule_cache)
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for _ in range(3):
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num = (a.sum().contiguous()+b.sum().contiguous()).item()
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print(num)
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self.assertEqual(len(schedule_cache), start_len_schedule_cache)
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def test_simple_precompile(self):
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@function(precompile=True, precompile_backward=True)
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def f(x:Tensor) -> Tensor:
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out = Tensor.invalids(*x.shape, dtype=x.dtype, device=x.device)
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out = Tensor.custom_kernel(out, x, fxn=functools.partial(custom_add_kernel, num=10), grad_fxn=custom_add_backward)[0]
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return out + x
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# warmup
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x = Tensor.ones(1).realize()
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out = f(x)
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out.backward(x)
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self.assertEqual(out.item(), 12)
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self.assertEqual(x.grad.item(), 2)
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# use the cache next time function is called
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start_len_schedule_cache = len(schedule_cache)
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for _ in range(3):
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x = Tensor.ones(1).realize()
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out = f(x)
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out.backward(x)
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self.assertEqual(out.item(), 12)
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self.assertEqual(x.grad.item(), 2)
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self.assertEqual(len(schedule_cache), start_len_schedule_cache)
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if __name__ == "__main__":
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unittest.main()
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