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tinygrad
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fix out of bound python list into numpy array (#5043) 

numpy 2.0 does not allow oob python const and recommends writing as `np.array(value).astype(dtype)`
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chenyu 2024-06-18 18:05:21 -04:00 committed by GitHub
parent 4e5add4d01
commit cc2be9064f
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3 changed files with 11 additions and 16 deletions
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6
test/test_dtype.py
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@ -112,11 +112,7 @@ class TestDType(unittest.TestCase):
dtypes = list(map(np.dtype, ["bool", "uint8", "int8", "int16", "int32", "int64", "float32", "float64"])) dtypes = list(map(np.dtype, ["bool", "uint8", "int8", "int16", "int32", "int64", "float32", "float64"]))
data = [1., 2., 0., 0.5, -1.5, 5.25] data = [1., 2., 0., 0.5, -1.5, 5.25]
for dt in dtypes: for dt in dtypes:
try: arr = np.asarray(data).astype(dt)
arr = np.asarray(data, dtype=dt)
except OverflowError:
# TODO: this happens with numpy 2.0, update with proper behavior
continue
tin = Tensor(arr).numpy() tin = Tensor(arr).numpy()
tor = torch.as_tensor(arr).detach().numpy() tor = torch.as_tensor(arr).detach().numpy()
assert dt == tin.dtype == tor.dtype, f"dtype mismatch: expected={dt} | tinygrad={tin.dtype} | torch={tor.dtype}" assert dt == tin.dtype == tor.dtype, f"dtype mismatch: expected={dt} | tinygrad={tin.dtype} | torch={tor.dtype}"

19
test/test_tensor.py
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@ -307,18 +307,17 @@ class TestTinygrad(unittest.TestCase):
if is_dtype_supported(dtypes.float16): if is_dtype_supported(dtypes.float16):
data = [math.nan, -math.inf, 65504, 65519, 65519.999, 65520, 65520.1] data = [math.nan, -math.inf, 65504, 65519, 65519.999, 65520, 65520.1]
data = data + [-x for x in data] data = data + [-x for x in data]
np.testing.assert_allclose(Tensor(data, dtype=dtypes.float16).numpy(), np.array(data, dtype=np.float16)) np.testing.assert_allclose(Tensor(data, dtype=dtypes.float16).numpy(), np.array(data).astype(np.float16))
# TODO: numpy changed this behavior in 2.0 # uint32
# # uint32 data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
# data = [1 << 33, 1 << 32, 1 << 32 - 1, 1] data = data + [-x for x in data]
# data = data + [-x for x in data] np.testing.assert_allclose(Tensor(data, dtype=dtypes.uint32).numpy(), np.array(data).astype(np.uint32))
# np.testing.assert_allclose(Tensor(data, dtype=dtypes.uint32).numpy(), np.array(data, dtype=np.uint32))
# # int32 # int32
# data = [1 << 33, 1 << 32, 1 << 32 - 1, 1] data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
# data = data + [-x for x in data] data = data + [-x for x in data]
# np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data, dtype=np.int32)) np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data).astype(np.int32))
def test_tensor_list_ndarray(self): def test_tensor_list_ndarray(self):
data = [np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])] data = [np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])]

2
tinygrad/tensor.py
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@ -128,7 +128,7 @@ class Tensor:
if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool
else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float
if dtype == dtypes.bfloat16: data = Tensor(_fromnp(np.array(data, np.float32)), device=device).cast(dtypes.bfloat16).lazydata if dtype == dtypes.bfloat16: data = Tensor(_fromnp(np.array(data, np.float32)), device=device).cast(dtypes.bfloat16).lazydata
else: data = _fromnp(np.array(data, _to_np_dtype(dtype))) else: data = _fromnp(np.array(data).astype(_to_np_dtype(dtype)))
elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device) elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device)
elif isinstance(data, np.ndarray): elif isinstance(data, np.ndarray):
if data.shape == (): data = _loadop(LoadOps.CONST, tuple(), dtype or _from_np_dtype(data.dtype), device, data.item()) if data.shape == (): data = _loadop(LoadOps.CONST, tuple(), dtype or _from_np_dtype(data.dtype), device, data.item())