github/tinygrad
1
1
Fork 0
mirror of https://github.com/tinygrad/tinygrad.git synced 2026-01-10 07:28:15 -05:00
Code Issues Packages Projects Releases Wiki Activity

use empty for test instead of rand (#9332)

Browse Source
This commit is contained in:
George Hotz
2025-03-03 16:19:06 +08:00
committed by GitHub
parent 27e899aea5
commit ece0a0f305
1 changed files with 25 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
test/test_linearizer.py
View File

@@ -1435,8 +1435,8 @@ class TestFloat4(unittest.TestCase):
# TODO: express opts below as auto opts
def test_float4_basic(self):
a = Tensor.rand(2, 8).realize()
b = Tensor.rand(2, 8).realize()
a = Tensor.empty(2, 8).realize()
b = Tensor.empty(2, 8).realize()
c = a + b
s = c.schedule()[0]
@@ -1448,8 +1448,8 @@ class TestFloat4(unittest.TestCase):
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
def test_float4_multidim(self):
a = Tensor.rand(2, 8).realize()
b = Tensor.rand(2, 8).realize()
a = Tensor.empty(2, 8).realize()
b = Tensor.empty(2, 8).realize()
c = a + b
s = c.schedule()[0]
@@ -1466,8 +1466,8 @@ class TestFloat4(unittest.TestCase):
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
def test_float4_multidim_amx(self):
def kernel_for_shape(size, shift):
a = Tensor.rand(2, size).realize()
b = Tensor.rand(2, size).realize()
a = Tensor.empty(2, size).realize()
b = Tensor.empty(2, size).realize()
c = a + b
s = c.schedule()[0]
@@ -1490,8 +1490,8 @@ class TestFloat4(unittest.TestCase):
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
def test_float4_unaligned_load(self):
a = Tensor.rand(9).realize().shrink(((1, 9),))
b = Tensor.rand(9).realize().shrink(((1, 9),))
a = Tensor.empty(9).realize().shrink(((1, 9),))
b = Tensor.empty(9).realize().shrink(((1, 9),))
c = a + b
s = c.schedule()[0]
@@ -1503,8 +1503,8 @@ class TestFloat4(unittest.TestCase):
@unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
def test_float4_multidim_unaligned_load(self):
a = Tensor.rand(2, 9).realize().shrink(((0, 2), (1, 9),))
b = Tensor.rand(2, 9).realize().shrink(((0, 2), (1, 9),))
a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
c = a + b
s = c.schedule()[0]
@@ -1521,8 +1521,8 @@ class TestFloat4(unittest.TestCase):
@unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
def test_float4_multidim_unaligned_load_amx(self):
def kernel_for_shape(size, shift):
a = Tensor.rand(2, size).realize().shrink(((0, 2), (1, size),))
b = Tensor.rand(2, size).realize().shrink(((0, 2), (1, size),))
a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
c = a + b
s = c.schedule()[0]
@@ -1544,8 +1544,8 @@ class TestFloat4(unittest.TestCase):
assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), excepted_upcast_size[i]) == expected_output[i]
def test_float4_sometimes_unaligned(self):
a = Tensor.rand(1, 1, 8).realize()
b = Tensor.rand(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
a = Tensor.empty(1, 1, 8).realize()
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
c = a.conv2d(b)
# only the first and last conv dot products are aligned in a, and b is never aligned, so no
# float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
@@ -1558,8 +1558,8 @@ class TestFloat4(unittest.TestCase):
assert TestFloat4.count_float4(k) == (0, 0)
def test_float4_multidim_sometimes_unaligned(self):
a = Tensor.rand(1, 1, 7).realize()
b = Tensor.rand(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
a = Tensor.empty(1, 1, 7).realize()
b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
c = a.conv2d(b)
# the first conv dot product is aligned in a. If we upcast the output and reduce
# dimension, then we could do float4 for only that one set of loads, but we currently
@@ -1575,8 +1575,8 @@ class TestFloat4(unittest.TestCase):
assert TestFloat4.count_float4(k) in {(0,1), (1,1)}
def test_float4_noncontiguous(self):
a = Tensor.rand(4, 2).realize()
b = Tensor.rand(4, 2).realize()
a = Tensor.empty(4, 2).realize()
b = Tensor.empty(4, 2).realize()
c = a + b
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
@@ -1591,8 +1591,8 @@ class TestFloat4(unittest.TestCase):
assert TestFloat4.count_float4(k) == (0, 0)
def test_float4_expand(self):
a = Tensor.rand(9).realize().shrink(((1, 9),))
b = Tensor.rand(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
a = Tensor.empty(9).realize().shrink(((1, 9),))
b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
c = a + b
# we will upcast the top axis of sz 4. they should not be coalesced into float4,
@@ -1607,8 +1607,8 @@ class TestFloat4(unittest.TestCase):
assert TestFloat4.count_float4(k) == (0, 1)
def test_float4_heterogeneous(self):
a = Tensor.rand(8).realize()
b = Tensor.rand(9).realize().shrink(((1, 9),))
a = Tensor.empty(8).realize()
b = Tensor.empty(9).realize().shrink(((1, 9),))
c = a + b
# should float4 b but not a
@@ -1704,8 +1704,8 @@ class TestFloat4(unittest.TestCase):
class TestHandCodedOpts(unittest.TestCase):
def test_masked_upcast(self):
layer_1 = Tensor.cat(*[Tensor.rand(5) for _ in range(4)])
layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 20))
layer_1 = Tensor.cat(*[Tensor.empty(5) for _ in range(4)])
layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.empty(6, 20))
s = layer_2.schedule()[-1]
k = Kernel(s.ast)
@@ -1718,7 +1718,7 @@ class TestHandCodedOpts(unittest.TestCase):
@unittest.skipIf(Device.DEFAULT == "METAL", "METAL can only run kernels with up to 32 buffers")
def test_masked_upcast_wino(self):
monster = Tensor.stack(*[Tensor.stack(*[Tensor.rand(16) for _ in range(6)]) for _ in range(6)])
monster = Tensor.stack(*[Tensor.stack(*[Tensor.empty(16) for _ in range(6)]) for _ in range(6)])
s = monster.schedule()[-1]
k = Kernel(s.ast)