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Update251203 (#233)

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This commit is contained in:
carrot
2025-12-03 10:28:27 +09:00
committed by GitHub
parent d6899edd97
commit c5ebcbcb97
347 changed files with 8678 additions and 13489 deletions
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tinygrad_repo/test/test_tensor.py
+48 -15
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@@ -4,12 +4,12 @@ import torch
import unittest, copy, mmap, random, math, array
from tinygrad import Tensor, Device, dtypes
from tinygrad.tensor import _METADATA
from tinygrad.helpers import getenv, temp, mv_address
from tinygrad.helpers import getenv, temp, mv_address, RANGEIFY
from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
from hypothesis import given, settings, strategies as strat
from tinygrad.device import is_dtype_supported
from tinygrad.uop.ops import Ops, UOp
from tinygrad.runtime.support.compiler_cuda import PTX
from tinygrad.renderer.ptx import PTXRenderer
from tinygrad.codegen import full_rewrite
from tinygrad.dtype import DType
@@ -415,6 +415,21 @@ class TestTinygrad(unittest.TestCase):
data = _generate_data(depth)
np.testing.assert_allclose(Tensor(data).numpy(), np.array(data))
def test_tensor_list_implicit_cast(self):
data = [True, False]
np.testing.assert_equal(Tensor(data, dtype=dtypes.int).numpy(), torch.tensor(data, dtype=torch.int).numpy())
np.testing.assert_equal(Tensor(data, dtype=dtypes.uint8).numpy(), torch.tensor(data, dtype=torch.uint8).numpy())
np.testing.assert_equal(Tensor(data, dtype=dtypes.float).numpy(), torch.tensor(data, dtype=torch.float).numpy())
data = [-1, 0, 1, 2, 3]
np.testing.assert_equal(Tensor(data, dtype=dtypes.int).numpy(), torch.tensor(data, dtype=torch.int).numpy())
np.testing.assert_equal(Tensor(data, dtype=dtypes.uint8).numpy(), torch.tensor(data, dtype=torch.uint8).numpy())
np.testing.assert_equal(Tensor(data, dtype=dtypes.float).numpy(), torch.tensor(data, dtype=torch.float).numpy())
data = [-3.5, -2.5, -1.5, 0, 1.5, 2.5, 3.5]
np.testing.assert_equal(Tensor(data, dtype=dtypes.int).numpy(), torch.tensor(data, dtype=torch.int).numpy())
# NOTE: torch and jax raise OverflowError: Python integer -3 out of bounds for uint8
# np.testing.assert_equal(Tensor(data, dtype=dtypes.uint8).numpy(), torch.tensor(data, dtype=torch.uint8).numpy())
np.testing.assert_equal(Tensor(data, dtype=dtypes.float).numpy(), torch.tensor(data, dtype=torch.float).numpy())
def test_tensor_list_special_values(self):
if is_dtype_supported(dtypes.float16):
data = [math.nan, -math.inf, 65504, 65519, 65519.999, 65520, 65520.1]
@@ -501,10 +516,6 @@ class TestTinygrad(unittest.TestCase):
print(c)
def test_env_overwrite_default_device(self):
subprocess.run(['DISK=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"DISK\\""'],
shell=True, check=True)
subprocess.run(['NPY=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"NPY\\""'],
shell=True, check=True)
subprocess.run([f'{Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
shell=True, check=True)
subprocess.run([f'DISK=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
@@ -539,6 +550,11 @@ class TestTinygrad(unittest.TestCase):
def test_shrink(self):
t = Tensor.arange(32).contiguous().realize()
self.assertListEqual(t[16:20].tolist(), [16,17,18,19])
self.assertListEqual(t.shrink_to(16).tolist(), list(range(16)))
t = t.reshape(4, 8).contiguous().realize()
self.assertListEqual(t.shrink_to(2, 2).tolist(), [[0, 1], [8, 9]])
with self.assertRaises(ValueError): t.shrink_to(2)
with self.assertRaises(ValueError): t.shrink_to(2, 2, 2)
@unittest.skip("this test is just flaky, sync issue")
class TestMoveTensor(unittest.TestCase):
@@ -633,17 +649,22 @@ class TestZeroShapeTensor(unittest.TestCase):
def test_pad(self):
t = Tensor.rand(3, 2, 0).pad((None, None, (1, 1)), value=1)
assert t.shape == (3, 2, 2)
self.assertEqual(t.shape, (3, 2, 2))
np.testing.assert_equal(t.numpy(), np.ones((3, 2, 2)))
t = Tensor.rand(3, 2, 0).pad((None, (1, 1), None), value=1)
assert t.shape == (3, 4, 0)
self.assertEqual(t.shape, (3, 4, 0))
np.testing.assert_equal(t.numpy(), np.ones((3, 4, 0)))
t = Tensor.rand(3, 2, 0).pad(((1, 1), None, None), value=1)
assert t.shape == (5, 2, 0)
self.assertEqual(t.shape, (5, 2, 0))
np.testing.assert_equal(t.numpy(), np.ones((5, 2, 0)))
np.testing.assert_equal(Tensor([1, 2]).pad_to(4).numpy(), [1, 2, 0, 0])
np.testing.assert_equal(Tensor([[1, 2]]).pad_to(2, 3).numpy(), [[1, 2, 0], [0, 0, 0]])
with self.assertRaises(TypeError): Tensor([1, 2]).pad_to(2, 3)
with self.assertRaises(TypeError): Tensor([[1, 2]]).pad_to(3)
def test_shrink_into_zero(self):
t = Tensor.rand(3, 4).realize()
assert t.shrink((None, (2, 2))).realize().shape == (3, 0)
@@ -850,11 +871,18 @@ class TestTensorMetadata(unittest.TestCase):
self.assertEqual(y.grad.uop.metadata[0].name, "sigmoid")
self.assertTrue(y.grad.uop.metadata[0].backward)
si = Tensor.schedule(out, x.grad, y.grad)[-1]
self.assertEqual(len(si.metadata), 4, f"failed with {si.metadata}")
self.assertSetEqual(set(m.name for m in si.metadata), {"sigmoid", "__mul__", "relu"})
bw = [m for m in si.metadata if m.backward]
self.assertEqual(len(bw), 2)
self.assertEqual(bw[0].name, "sigmoid")
if not RANGEIFY:
self.assertEqual(len(si.metadata), 4, f"failed with {si.metadata}")
self.assertSetEqual(set(m.name for m in si.metadata), {"sigmoid", "__mul__", "relu"})
bw = [m for m in si.metadata if m.backward]
self.assertEqual(len(bw), 2)
self.assertEqual(bw[0].name, "sigmoid")
else:
self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
self.assertSetEqual(set(m.name for m in si.metadata), {"sigmoid", "relu"})
bw = [m for m in si.metadata if m.backward]
self.assertEqual(len(bw), 1)
self.assertEqual(bw[0].name, "sigmoid")
class TestIdxUpcast(unittest.TestCase):
def _find_op(self, ast: UOp, op: Ops):
@@ -900,19 +928,24 @@ class TestIdxUpcast(unittest.TestCase):
def test_regular_sym(self):
self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 1, 64).bind(32))
@unittest.skipIf(PTX, "PTX always convert Ops.INDEX to int64")
@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX always convert Ops.INDEX to int64")
def test_symfold(self):
# This would cause an overflow, but after sym fold it's within int32
a = Tensor.arange(65535)
uops = self._schedule_render(a)
assert all(uop.dtype is not dtypes.long for uop in uops)
def test_arange_raise_overflow(self):
with self.assertRaises(ValueError):
self._schedule_render(Tensor.arange(2**33, dtype=dtypes.int))
@unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
def test_int64_unsupported_overflow_sym(self):
with self.assertRaises(KeyError):
self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
@unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
@unittest.expectedFailure # bug in gpu dims limiting
def test_int64_unsupported_overflow(self):
with self.assertRaises(KeyError):
self.do_op_then_assert(dtypes.long, 2048, 2048, 2048)