115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
#!/usr/bin/env python
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import numpy as np
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import unittest
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.engine.realize import run_schedule
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from tinygrad.ops import Ops, UOp
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from tinygrad.engine.schedule import create_schedule
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class TestLazyBuffer(unittest.TestCase):
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def test_fromcpu_shape_tracker(self):
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def helper(a: np.ndarray):
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print(a.shape, a.strides, a.flags.c_contiguous)
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b = Tensor(a).lazydata
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#assert b.st.contiguous == a.flags.c_contiguous
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assert b.st.shape == a.shape
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np.testing.assert_equal(a, Tensor(b).numpy())
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for ndims in range(1, 4):
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a = np.random.randn(*(4,)*ndims).astype(np.float32)
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for stride in [-2, 1, 2]:
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for start in [0, 1]:
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helper(a[(slice(start, None, stride),)*ndims])
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def test_shuffle_pad_ops_cmpeq(self):
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y = Tensor([1]).cat(Tensor([1]) == 0).numpy()
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z = Tensor([1, 0]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_div(self):
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y = Tensor([1]).cat(Tensor([1]).div(Tensor([2.0]))).numpy()
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z = Tensor([1, 0.5]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_log(self):
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y = Tensor([1]).cat(Tensor([1]).log()).numpy()
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z = Tensor([1, 0]).numpy()
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np.testing.assert_allclose(y, z)
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def test_shuffle_pad_ops_exp(self):
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y = Tensor([1]).cat(Tensor([1]).exp()).numpy()
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z = Tensor([1, np.e]).numpy()
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np.testing.assert_allclose(y, z)
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def test_device_0_is_the_same_device(self):
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a = Tensor([1, 2, 3], f"{Device.DEFAULT}")
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b = Tensor([1, 2, 3], f"{Device.DEFAULT}:0")
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assert a.device == b.device
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def test_shrink_const_into_zero(self):
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# regression test to make sure the shapetracker is preserved
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a = Tensor.zeros(4,4,4).shrink((None, (0,0), None))
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b = Tensor.zeros(4,1,4)
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c = a.cat(b, dim=1)
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np.testing.assert_allclose(c.numpy(), np.concatenate((a.numpy(), b.numpy()), axis=1))
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def test_shrink_const_then_cast(self):
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# regression test to make sure the shapetracker is preserved
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a = Tensor.zeros(4,4,4).shrink((None, (0,0), None)).cast(dtypes.int32)
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b = Tensor.zeros(4,1,4)
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c = a.cat(b, dim=1)
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np.testing.assert_allclose(c.numpy(), np.concatenate((a.numpy(), b.numpy()), axis=1))
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def test_const_dtype(self):
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lb: UOp = Tensor([1], dtype=dtypes.int).lazydata
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assert lb.const_like(1).const_arg == 1
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assert type(lb.const_like(1).const_arg) is int
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lb: UOp = Tensor([1], dtype=dtypes.float).lazydata
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assert lb.const_like(1).const_arg == 1.0
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assert type(lb.const_like(1).const_arg) is float
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def test_forced_realized_alu(self):
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a = Tensor.randn(2, 2).realize()
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b = Tensor.randn(2, 2).realize()
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add = (a+b).contiguous()
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out = add+2
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sched = create_schedule([out.lazydata])
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self.assertEqual(len(sched), 2)
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run_schedule(sched)
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np.testing.assert_allclose(out.numpy(), a.numpy()+b.numpy()+2)
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def test_forced_realized_metaop(self):
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empty = Tensor.empty(1).contiguous()
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sched = create_schedule([empty.lazydata])
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self.assertEqual(len(sched), 1)
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self.assertIs(sched[0].ast.op, Ops.EMPTY)
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run_schedule(sched)
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class TestReduceOp(unittest.TestCase):
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def test_no_split_reduce_kernel(self):
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a = Tensor.rand(4, 4).realize()
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a = a.sum()
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sched = create_schedule([a.lazydata])
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assert len(sched) == 1
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self.assertIs(sched[0].ast.src[0].src[2].op, Ops.REDUCE_AXIS)
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def test_split_reduce_kernel_dim0(self):
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a = Tensor.rand(256, 255).realize()
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a = a.sum()
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sched = create_schedule([a.lazydata])
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assert len(sched) == 2
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for s in sched:
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self.assertIs(s.ast.src[0].src[2].op, Ops.REDUCE_AXIS)
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def test_split_reduce_kernel_dim1(self):
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a = Tensor.rand(255, 256).realize()
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a = a.sum()
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sched = create_schedule([a.lazydata])
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assert len(sched) == 2
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for s in sched:
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self.assertIs(s.ast.src[0].src[2].op, Ops.REDUCE_AXIS)
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if __name__ == "__main__":
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unittest.main()
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