openpilot/tinygrad_repo/test/test_tiny.py

# basic self-contained tests of the external functionality of tinygrad
import unittest, random
from tinygrad import Tensor, Context, Variable, TinyJit, dtypes, Device, nn
from tinygrad.helpers import IMAGE, CI, getenv

class TestTiny(unittest.TestCase):

  # *** basic functionality ***

  def test_const(self):
    const = Tensor(2.0)
    self.assertEqual(const.item(), 2.0)

  def test_copy(self):
    out = Tensor([1.,2,3])
    self.assertListEqual(out.tolist(), [1.0, 2.0, 3.0])

  def test_plus(self):
    out = Tensor([1.,2,3]) + Tensor([4.,5,6])
    self.assertListEqual(out.tolist(), [5.0, 7.0, 9.0])

  def test_plus_int(self):
    out = Tensor([1,2,3], dtype=dtypes.int) + Tensor([4,5,6], dtype=dtypes.int)
    self.assertListEqual(out.tolist(), [5, 7, 9])

  def test_plus_big(self):
    out = Tensor.ones(16).contiguous() + Tensor.ones(16).contiguous()
    self.assertListEqual(out.tolist(), [2]*16)

  def test_cat(self):
    out = Tensor.cat(Tensor.ones(8).contiguous(), Tensor.ones(8).contiguous())
    self.assertListEqual(out.tolist(), [1]*16)

  def test_sum(self):
    out = Tensor.ones(256).contiguous().sum()
    self.assertEqual(out.item(), 256)

  def test_gemm(self, N=getenv("GEMM_N", 64), out_dtype=dtypes.float):
    a = Tensor.ones(N,N).contiguous()
    b = Tensor.eye(N).contiguous()
    lst = (out:=a@b).tolist()
    for y in range(N):
      for x in range(N):
        self.assertEqual(lst[y][x], 1.0, msg=f"mismatch at ({y},{x})")
    if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)

  def test_gemv(self, N=getenv("GEMV_N", 64), out_dtype=dtypes.float):
    a = Tensor.ones(1,N).contiguous()
    b = Tensor.eye(N).contiguous()
    lst = (out:=a@b).tolist()
    for x in range(N):
      self.assertEqual(lst[0][x], 1.0, msg=f"mismatch at {x}")
    if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)

  # *** randomness ***

  def test_random(self):
    out = Tensor.rand(10)
    for x in out.tolist():
      self.assertGreaterEqual(x, 0.0)
      self.assertLessEqual(x, 1.0)

  # *** JIT (for Python speed) ***

  def test_jit(self):
    cnt = 0
    random.seed(0)
    def new_rand_list(ln=10): return [random.randint(0, 100000) for _ in range(ln)]

    @TinyJit
    def fxn(a,b) -> Tensor:
      nonlocal cnt
      cnt += 1
      return a+b

    for _ in range(3):
      la,lb = new_rand_list(), new_rand_list()
      fa,fb = Tensor(la), Tensor(lb)
      ret = fxn(fa, fb)
      # math is correct
      self.assertListEqual(ret.tolist(), [a+b for a,b in zip(la, lb)])

    # function is only called twice
    self.assertEqual(cnt, 2)

  # *** BEAM (for Kernel speed) ***

  def test_beam(self):
    with Context(BEAM=1, IGNORE_BEAM_CACHE=1): self.test_plus()

  # *** symbolic (to allow less recompilation) ***

  def test_symbolic(self):
    i = Variable('i', 1, 10)
    ones = Tensor.ones(10).contiguous()
    for s in [2,5]:
      ret = ones[:i.bind(s)] + 1
      self.assertListEqual(ret.contiguous()[:s].tolist(), [2.0]*s)

  def test_symbolic_reduce(self):
    i = Variable('i', 1, 10)
    ones = Tensor.ones(10).contiguous()
    for s in [2,5]:
      ret = ones[:i.bind(s)].sum()
      self.assertEqual(ret.item(), s)

  # *** a model ***

  # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
  @unittest.skipIf(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
  def test_mnist(self):
    layers = [
      nn.Conv2d(1, 32, 5), Tensor.relu,
      nn.Conv2d(32, 32, 5), Tensor.relu,
      nn.BatchNorm(32), Tensor.max_pool2d,
      nn.Conv2d(32, 64, 3), Tensor.relu,
      nn.Conv2d(64, 64, 3), Tensor.relu,
      nn.BatchNorm(64), Tensor.max_pool2d,
      lambda x: x.flatten(1), nn.Linear(576, 10)]

    # replace random weights with ones
    Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])

    # run model inference
    probs = Tensor.rand(1, 1, 28, 28).sequential(layers).tolist()
    self.assertEqual(len(probs[0]), 10)

  # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
  @unittest.skipIf(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
  def test_mnist_backward(self):
    # NOTE: we don't have the whole model here for speed
    layers = [
      nn.Conv2d(1, 32, 5), Tensor.relu,
      nn.Conv2d(32, 32, 5), Tensor.relu]

    # replace random weights with ones
    # TODO: there's a bug here where it's tying two of the biases together. we need UNIQUE const
    #Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
    for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))

    # realize gradients
    for x in nn.state.get_parameters(layers): x.requires_grad_()
    Tensor.empty(4, 1, 28, 28).sequential(layers).sum().backward()
    Tensor.realize(*[x.grad for x in nn.state.get_parameters(layers) if x.grad is not None])

  # *** image ***

  @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
  def test_image(self):
    with Context(IMAGE=2): self.test_gemm(N=4, out_dtype=dtypes.imagef((4, 1, 4)))

  def test_beam_image(self):
    with Context(BEAM=1, IGNORE_BEAM_CACHE=1): self.test_image()

if __name__ == '__main__':
  unittest.main()