mirror of
https://github.com/sunnypilot/sunnypilot.git
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f398269b1a
version: sunnypilot v2026.001.000 (dev)
date: 2026-04-06T01:24:35
master commit: 8fec761f89
84 lines
3.5 KiB
Python
84 lines
3.5 KiB
Python
from typing import Callable
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import unittest, math
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import torch
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from tinygrad import Tensor
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from tinygrad.dtype import dtypes
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from tinygrad.uop.ops import UOp
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from tinygrad.gradient import compute_gradient
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class TestGradient(unittest.TestCase):
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def _cmp_nan_okay(self, x, y):
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if math.isnan(x) and math.isnan(y): return
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self.assertAlmostEqual(x, y, places=5)
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def _test_one_input_function(self, f:Callable, jf:Callable|None=None):
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if jf is None: jf = f
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x = UOp.variable('x', -math.inf, math.inf, dtype=dtypes.float)
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gx = compute_gradient(f(x), UOp.const(dtypes.float, 1.0), set([x]))[x]
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for val in [-5., -2.0, 0.0, 2.0, 5.]:
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tg_out = gx.substitute({x: x.const_like(val)}).ssimplify()
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tx = torch.tensor([val], dtype=torch.float, requires_grad=True)
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torch_out = torch.autograd.grad(jf(tx), tx)[0].item()
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self._cmp_nan_okay(tg_out, torch_out)
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def _test_two_input_function(self, f:Callable, jf:Callable|None=None):
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if jf is None: jf = f
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x = UOp.variable('x', -math.inf, math.inf, dtype=dtypes.float)
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y = UOp.variable('y', -math.inf, math.inf, dtype=dtypes.float)
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grads = compute_gradient(f(x, y), UOp.const(dtypes.float, 1.0), set([x, y]))
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gx, gy = grads[x], grads[y]
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for valx in [-5., -2.0, 0.0, 2.0, 5.]:
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for valy in [-5., -2.0, 0.0, 2.0, 5.]:
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# Substitute the values into the gradient expressions
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substitutions = {x: x.const_like(valx), y: y.const_like(valy)}
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tg_out_x = gx.substitute(substitutions).ssimplify()
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tg_out_y = gy.substitute(substitutions).ssimplify()
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tx = torch.tensor([valx], dtype=torch.float, requires_grad=True)
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ty = torch.tensor([valy], dtype=torch.float, requires_grad=True)
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torch_grad = torch.autograd.grad(jf(tx, ty), [tx, ty])
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torch_out_x, torch_out_y = [x.item() for x in torch_grad]
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self._cmp_nan_okay(tg_out_x, torch_out_x)
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self._cmp_nan_okay(tg_out_y, torch_out_y)
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# unary ops unit
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def test_recip(self): self._test_one_input_function(lambda x: 1.0/x)
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def test_sin(self): self._test_one_input_function(lambda x: x.sin())
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def test_sqrt(self): self._test_one_input_function(lambda x: x.sqrt())
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def test_log2(self): self._test_one_input_function(lambda x: x.log2())
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def test_exp2(self): self._test_one_input_function(lambda x: x.exp2())
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# binary ops unit
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def test_add(self): self._test_two_input_function(lambda x,y: x+y)
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def test_mul(self): self._test_two_input_function(lambda x,y: x*y)
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# chain rule
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def test_chain(self): self._test_one_input_function(lambda x: x.sin().sqrt())
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def test_chain_binop(self): self._test_two_input_function(lambda x,y: (x*y)+x*y)
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def test_big_add_sin(self): self._test_two_input_function(lambda x,y: x.sin()+3.0/y)
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def test_big_chain(self): self._test_two_input_function(lambda x,y: (1.0/x*y)+x*y)
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def test_where(self): self._test_two_input_function(lambda x,y: (x<y).where(x,y), lambda x,y: torch.where(x<y,x,y))
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class TestRealizeMeansRealize(unittest.TestCase):
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def test_randn_realizes(self):
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x = Tensor.randn(2, 3, 64, 64, requires_grad=True).realize()
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assert x.uop is not x.uop.base
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assert x.uop.is_realized
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def test_uniform_realizes(self):
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x = Tensor.uniform(16, 3, 3, 3, requires_grad=True).realize()
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print(x.uop)
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assert x.uop is not x.uop.base
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assert x.uop.is_realized
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def test_uniform_gradient(self):
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x = Tensor.uniform(16, 3, 3, 3, requires_grad=True).realize()
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y = x * 2
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y.sum().gradient(x)[0].realize()
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if __name__ == '__main__':
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unittest.main()
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