Bump numpy to <2.6.0

pyproject.toml

@@ -34,7 +34,7 @@ classifiers = [
 dependencies = [
     "networkx>=2.6, <=3.6.1",
     "ninja>=1.10.0.post2, <1.14",
-    "numpy>=1.24.0, <2.5.0",
+    "numpy>=1.24.0, <2.6.0",
     "openvino-telemetry>=2023.2.0",
     "packaging>=20.0",
     "psutil",

src/nncf/quantization/algorithms/weight_compression/constants.py

@@ -103,7 +103,7 @@
 )
 
 
-CENTER_OF_F4E2M1_QUANTILES = (F4E2M1_QUANTILES[1:] + F4E2M1_QUANTILES[:-1]) / 2
+CENTER_OF_F4E2M1_QUANTILES = (F4E2M1_QUANTILES[1:] + F4E2M1_QUANTILES[:-1]) / 2  # type: ignore[index]
 
 
 FP_MAX_VALUES = {

src/nncf/tensor/functions/numpy_io.py

@@ -13,7 +13,6 @@
 from typing import Any
 
 import numpy as np
-from numpy.typing import NDArray
 from safetensors.numpy import load_file as np_load_file
 from safetensors.numpy import save_file as np_save_file
 
@@ -22,7 +21,7 @@
 from nncf.tensor.functions import io as io
 from nncf.tensor.functions.numpy_numeric import validate_device
 
-T_NUMPY_ARRAY = NDArray[Any]
+T_NUMPY_ARRAY = np.ndarray[Any, np.dtype[Any]]
 T_NUMPY = T_NUMPY_ARRAY | np.generic  # type: ignore[type-arg]
 
 

src/nncf/tensor/functions/numpy_linalg.py

@@ -12,13 +12,12 @@
 from typing import Any, Literal
 
 import numpy as np
-from numpy.typing import NDArray
 from scipy.linalg import lstsq
 
 from nncf.tensor.definitions import T_AXIS
 from nncf.tensor.functions import linalg
 
-T_NUMPY_ARRAY = NDArray[Any]
+T_NUMPY_ARRAY = np.ndarray[Any, np.dtype[Any]]
 
 
 @linalg.norm.register

src/nncf/tensor/functions/numpy_numeric.py

@@ -12,7 +12,6 @@
 from typing import Any, Callable, Literal, Sequence
 
 import numpy as np
-from numpy.typing import NDArray
 
 from nncf.tensor.definitions import T_AXIS
 from nncf.tensor.definitions import T_NUMBER
@@ -25,7 +24,7 @@
 from nncf.tensor.functions import numeric as numeric
 from nncf.tensor.tensor import TTensor
 
-T_NUMPY_ARRAY = NDArray[Any]
+T_NUMPY_ARRAY = np.ndarray[Any, np.dtype[Any]]
 T_NUMPY = T_NUMPY_ARRAY | np.generic  # type: ignore[type-arg]
 
 DTYPE_MAP: dict[TensorDataType, np.dtype] = {  # type: ignore[type-arg]
@@ -80,12 +79,12 @@ def _(a: T_NUMPY) -> T_NUMPY_ARRAY:
 
 @numeric.max.register
 def _(a: T_NUMPY, axis: T_AXIS = None, keepdims: bool = False) -> T_NUMPY_ARRAY:
-    return np.array(np.max(a, axis=axis, keepdims=keepdims))
+    return np.array(np.max(a, axis=axis, keepdims=keepdims))  # type: ignore[call-overload]
 
 
 @numeric.min.register
 def _(a: T_NUMPY, axis: T_AXIS = None, keepdims: bool = False) -> T_NUMPY:
-    return np.array(np.min(a, axis=axis, keepdims=keepdims))
+    return np.array(np.min(a, axis=axis, keepdims=keepdims))  # type: ignore[call-overload]
 
 
 @numeric.abs.register
@@ -156,7 +155,7 @@ def _(
     *,
     range: tuple[float, float] | None = None,
 ) -> T_NUMPY:
-    return np.histogram(a=a, bins=bins, range=range)[0]
+    return np.histogram(a=a, bins=bins, range=range)[0]  # type: ignore[index]
 
 
 @numeric.isempty.register
@@ -172,7 +171,7 @@ def _(
     atol: float = 1e-08,
     equal_nan: bool = False,
 ) -> T_NUMPY_ARRAY:
-    return np.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)
+    return np.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)  # type: ignore[return-value]
 
 
 @numeric.maximum.register
@@ -244,7 +243,7 @@ def _(
     dtype: TensorDataType | None = None,
 ) -> T_NUMPY_ARRAY:
     np_dtype = convert_to_numpy_dtype(dtype)
-    return np.array(np.mean(a, axis=axis, keepdims=keepdims, dtype=np_dtype))  # type: ignore [arg-type]
+    return np.array(np.mean(a, axis=axis, keepdims=keepdims, dtype=np_dtype))  # type: ignore[call-overload]
 
 
 @numeric.median.register
@@ -253,7 +252,7 @@ def _(
     axis: T_SHAPE | None = None,
     keepdims: bool = False,
 ) -> T_NUMPY_ARRAY:
-    return np.array(np.median(a, axis=axis, keepdims=keepdims))  # type: ignore [arg-type]
+    return np.array(np.median(a, axis=axis, keepdims=keepdims))
 
 
 @numeric.floor.register
@@ -263,7 +262,7 @@ def _(a: T_NUMPY) -> T_NUMPY:
 
 @numeric.round.register
 def _(a: T_NUMPY, decimals: int = 0) -> T_NUMPY_ARRAY:
-    return np.round(a, decimals=decimals)
+    return np.round(a, decimals=decimals)  # type: ignore[return-value]
 
 
 @numeric.power.register
@@ -334,7 +333,7 @@ def _(a: T_NUMPY) -> T_NUMBER:
 
 @numeric.sum.register
 def _(a: T_NUMPY, axis: T_AXIS = None, keepdims: bool = False) -> T_NUMPY_ARRAY:
-    return np.array(np.sum(a, axis=axis, keepdims=keepdims))
+    return np.array(np.sum(a, axis=axis, keepdims=keepdims))  # type: ignore[call-overload]
 
 
 @numeric.cumsum.register
@@ -354,7 +353,7 @@ def _(
     keepdims: bool = False,
     ddof: int = 0,
 ) -> T_NUMPY_ARRAY:
-    return np.array(np.var(a, axis=axis, keepdims=keepdims, ddof=ddof))  # type: ignore[arg-type]
+    return np.array(np.var(a, axis=axis, keepdims=keepdims, ddof=ddof))  # type: ignore[call-overload]
 
 
 @numeric.size.register
@@ -409,7 +408,7 @@ def _(
     keepdims: bool = False,
 ) -> T_NUMPY_ARRAY:
     if mask is None:
-        return np.mean(x, axis=axis, keepdims=keepdims)
+        return np.mean(x, axis=axis, keepdims=keepdims)  # type: ignore[call-overload]
     masked_x = np.ma.array(x, mask=mask)
     result = np.ma.mean(masked_x, axis=axis, keepdims=keepdims)
     if isinstance(result, np.ma.MaskedArray):
@@ -422,7 +421,7 @@ def _(x: T_NUMPY_ARRAY, mask: T_NUMPY_ARRAY | None, axis: T_AXIS, keepdims: bool
     if mask is None:
         return np.median(x, axis=axis, keepdims=keepdims)
     masked_x = np.ma.array(x, mask=mask)
-    result = np.ma.median(masked_x, axis=axis, keepdims=keepdims)  # type: ignore[no-untyped-call]
+    result = np.ma.median(masked_x, axis=axis, keepdims=keepdims)
     if isinstance(result, np.ma.MaskedArray):
         return result.data
     return result
@@ -441,8 +440,8 @@ def _(a: T_NUMPY) -> T_NUMPY:
 @numeric.searchsorted.register
 def _(
     a: T_NUMPY_ARRAY, v: T_NUMPY_ARRAY, side: Literal["left", "right"] = "left", sorter: T_NUMPY_ARRAY | None = None
-) -> T_NUMPY_ARRAY:
-    return np.searchsorted(a, v, side, sorter)
+) -> T_NUMPY_ARRAY | float:
+    return np.searchsorted(a, v, side, sorter)  # type: ignore[return-value]
 
 
 @numeric.as_numpy_tensor.register

src/nncf/tensor/functions/openvino_numeric.py

@@ -10,8 +10,8 @@
 # limitations under the License.
 from typing import Any
 
+import numpy as np
 import openvino as ov  # type: ignore
-from numpy.typing import NDArray
 
 from nncf.tensor import Tensor
 from nncf.tensor import TensorDataType
@@ -59,7 +59,7 @@
 DTYPE_MAP_REV = {v: k for k, v in DTYPE_MAP.items()}
 
 
-def from_numpy(a: NDArray[Any]) -> ov.Tensor:
+def from_numpy(a: np.ndarray[Any, np.dtype[Any]]) -> ov.Tensor:
     """
     Convert a numpy array to an OpenVINO tensor.
 
@@ -104,7 +104,7 @@ def _(a: ov.Tensor, shape: int | tuple[int, ...]) -> ov.Tensor:
 
 
 @numeric.as_numpy_tensor.register
-def _(a: ov.Tensor) -> NDArray[Any]:
+def _(a: ov.Tensor) -> np.ndarray[Any, np.dtype[Any]]:
     # Cannot convert bfloat16, uint4, int4, nf4, f4e2m1, f8e8m0, f8e4m3, f8e5m2 to numpy directly
     a_dtype = DTYPE_MAP_REV[a.get_element_type()]
     if a_dtype in NATIVE_OV_CAST_DTYPES:

src/nncf/tensor/functions/torch_numeric.py

@@ -13,7 +13,6 @@
 
 import numpy as np
 import torch
-from numpy.typing import NDArray
 
 from nncf.tensor import TensorDataType
 from nncf.tensor import TensorDeviceType
@@ -433,7 +432,7 @@ def _(x: torch.Tensor, mask: torch.Tensor | None, axis: T_AXIS, keepdims: bool =
     if not isinstance(axis, int):
         device = x.device
         np_masked_x = np.ma.array(x.detach().cpu().numpy(), mask=mask.detach().cpu().numpy())
-        result = torch.tensor(np.ma.median(np_masked_x, axis=axis, keepdims=keepdims))  # type: ignore[no-untyped-call]
+        result = torch.tensor(np.ma.median(np_masked_x, axis=axis, keepdims=keepdims))
         return result.type(x.dtype).to(device)
     pt_masked_x = x.masked_fill(mask, torch.nan)
     ret = torch.nanquantile(pt_masked_x, q=0.5, dim=axis, keepdim=keepdims)
@@ -536,7 +535,7 @@ def arange(
     return torch.arange(start, end, step, dtype=pt_dtype, device=pt_device)
 
 
-def from_numpy(ndarray: NDArray[Any]) -> torch.Tensor:
+def from_numpy(ndarray: np.ndarray[Any, np.dtype[Any]]) -> torch.Tensor:
     return torch.from_numpy(ndarray)
 
 
@@ -562,7 +561,7 @@ def tensor(
 
 
 @numeric.as_numpy_tensor.register
-def _(a: torch.Tensor) -> NDArray[Any]:
+def _(a: torch.Tensor) -> np.ndarray[Any, np.dtype[Any]]:
     return a.cpu().detach().numpy()