# basic self-contained tests of the external functionality of tinygrad import unittest from tinygrad import Tensor, Context, Variable, TinyJit, dtypes, Device from tinygrad.helpers import IMAGE class TestTiny(unittest.TestCase): # *** basic functionality *** 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_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=4, out_dtype=dtypes.float): a = Tensor.ones(N,N).contiguous() b = Tensor.eye(N).contiguous() self.assertListEqual((out:=a@b).flatten().tolist(), [1.0]*(N*N)) if IMAGE < 2: self.assertEqual(out.dtype, out_dtype) # *** JIT (for Python speed) *** def test_jit(self): cnt = 0 @TinyJit def fxn(a,b): nonlocal cnt cnt += 1 return a+b fa,fb = Tensor([1.,2,3]), Tensor([4.,5,6]) for _ in range(3): fxn(fa, fb) # function is only called twice self.assertEqual(cnt, 2) # *** BEAM (for Kernel speed) *** def test_beam(self): with Context(BEAM=1): self.test_plus() # *** symbolic (to allow less recompilation) *** def test_symbolic(self): i = Variable('i', 1, 10) for s in [2,5]: ret = Tensor.ones(s).contiguous().reshape(i.bind(s)) + 1 self.assertListEqual(ret.reshape(s).tolist(), [2.0]*s) def test_symbolic_reduce(self): i = Variable('i', 1, 10) for s in [2,5]: ret = Tensor.ones(s).contiguous().reshape(i.bind(s)).sum() self.assertEqual(ret.item(), s) # *** image *** @unittest.skipIf(Device.DEFAULT != "GPU", "image only supported on GPU") def test_image(self): with Context(IMAGE=2): self.test_gemm(out_dtype=dtypes.imagef((4, 1, 4))) def test_beam_image(self): with Context(BEAM=1): self.test_image() if __name__ == '__main__': unittest.main()