carrot/tinygrad_repo/test/test_tensor_uop.py

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#!/usr/bin/env python
import numpy as np
import unittest
from tinygrad import Tensor, Device, dtypes
from tinygrad.engine.realize import run_schedule
from tinygrad.uop.ops import Ops, UOp, UPat
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class TestTensorUOp(unittest.TestCase):
def test_fromcpu_shape_tracker(self):
def helper(a: np.ndarray):
print(a.shape, a.strides, a.flags.c_contiguous)
b = Tensor(a).lazydata
#assert b.st.contiguous == a.flags.c_contiguous
assert b.st.shape == a.shape
np.testing.assert_equal(a, Tensor(b).numpy())
for ndims in range(1, 4):
a = np.random.randn(*(4,)*ndims).astype(np.float32)
for stride in [-2, 1, 2]:
for start in [0, 1]:
helper(a[(slice(start, None, stride),)*ndims])
def test_shuffle_pad_ops_cmpeq(self):
y = Tensor([1]).cat(Tensor([1]) == 0).numpy()
z = Tensor([1, 0]).numpy()
np.testing.assert_allclose(y, z)
def test_shuffle_pad_ops_div(self):
y = Tensor([1]).cat(Tensor([1]).div(Tensor([2.0]))).numpy()
z = Tensor([1, 0.5]).numpy()
np.testing.assert_allclose(y, z)
def test_shuffle_pad_ops_log(self):
y = Tensor([1]).cat(Tensor([1]).log()).numpy()
z = Tensor([1, 0]).numpy()
np.testing.assert_allclose(y, z)
def test_shuffle_pad_ops_exp(self):
y = Tensor([1]).cat(Tensor([1]).exp()).numpy()
z = Tensor([1, np.e]).numpy()
np.testing.assert_allclose(y, z)
def test_device_0_is_the_same_device(self):
a = Tensor([1, 2, 3], f"{Device.DEFAULT}")
b = Tensor([1, 2, 3], f"{Device.DEFAULT}:0")
assert a.device == b.device
def test_shrink_const_into_zero(self):
# regression test to make sure the shapetracker is preserved
a = Tensor.zeros(4,4,4).shrink((None, (0,0), None))
b = Tensor.zeros(4,1,4)
c = a.cat(b, dim=1)
np.testing.assert_allclose(c.numpy(), np.concatenate((a.numpy(), b.numpy()), axis=1))
def test_shrink_const_then_cast(self):
# regression test to make sure the shapetracker is preserved
a = Tensor.zeros(4,4,4).shrink((None, (0,0), None)).cast(dtypes.int32)
b = Tensor.zeros(4,1,4)
c = a.cat(b, dim=1)
np.testing.assert_allclose(c.numpy(), np.concatenate((a.numpy(), b.numpy()), axis=1))
def test_const_dtype(self):
lb: UOp = Tensor([1], dtype=dtypes.int).lazydata
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assert lb.const_like(1).base.arg == 1
assert type(lb.const_like(1).base.arg) is int
lb: UOp = Tensor([1], dtype=dtypes.float).lazydata
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assert lb.const_like(1).base.arg == 1.0
assert type(lb.const_like(1).base.arg) is float
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def test_contiguous_alu(self):
a = Tensor.randn(2, 2).realize()
b = Tensor.randn(2, 2).realize()
add = (a+b).contiguous()
out = add+2
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sched = out.schedule()
self.assertEqual(len(sched), 2)
run_schedule(sched)
np.testing.assert_allclose(out.numpy(), a.numpy()+b.numpy()+2)
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# NOTE: contiguous on a buffer collapses
def test_contiguous_empty(self):
empty = Tensor.empty(1).contiguous()
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sched = empty.schedule()
self.assertEqual(len(sched), 0)
def test_contiguous_folded_alu(self):
a = Tensor.empty(8, 8)
# NOTE: the buffer for mul_0 late folds to just a CONST
mul_0 = a*0
out = mul_0.shrink(((4, 8), (0, 8))).contiguous()
out.realize()
self.assertEqual(out.tolist(), Tensor.zeros(4, 8).tolist())
reduce_kernel = UPat(Ops.SINK, src=(UPat(Ops.STORE, src=(UPat(), UPat(), UPat(Ops.REDUCE_AXIS)))))
class TestReduceOp(unittest.TestCase):
def test_no_split_reduce_kernel(self):
a = Tensor.rand(4, 4).realize()
a = a.sum()
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sched = a.schedule()
assert len(sched) == 1
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assert reduce_kernel.match(sched[0].ast, {})
def test_split_reduce_kernel_dim0(self):
a = Tensor.rand(256, 255).realize()
a = a.sum()
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sched = a.schedule()
assert len(sched) == 2
for s in sched:
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assert reduce_kernel.match(s.ast, {})
def test_split_reduce_kernel_dim1(self):
a = Tensor.rand(255, 256).realize()
a = a.sum()
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sched = a.schedule()
assert len(sched) == 2
for s in sched:
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assert reduce_kernel.match(s.ast, {})
if __name__ == "__main__":
unittest.main()