add xlib.avecl

xlib/avecl/_internal/Tensor.py

@@ -0,0 +1,124 @@
+import weakref
+from typing import Iterable, Union
+
+import numpy as np
+
+from .AShape import *
+from .backend import *
+from .HType import *
+
+
+class Tensor:
+    ### CONSTRUCTORS
+
+    def __init__(self, shape : Union[AShape, Iterable],
+                       dtype : np.dtype,
+                       device : Union[None, int, Device, DeviceInfo] = None,
+                       initializer : 'Initializer' = None,
+                       ):
+        Tensor._object_count += 1
+
+        self._shape = shape = AShape(shape)
+        self._dtype = dtype = np.dtype(dtype)
+        self._device = device = get_device(device)
+        if device is None:
+            raise Exception('No device.')
+
+        self._seq_id = Tensor._seq_id = Tensor._seq_id + 1
+        self._buffer = Buffer(device, size=shape.size*dtype.itemsize,
+                              on_initialize= lambda initializer=initializer, wself=weakref.ref(self): initializer.initialize_tensor( wself() ) if initializer is not None else None )
+
+    @staticmethod
+    def from_value(value, dtype=None, device:Union[None,int,Device,DeviceInfo]=None) -> 'Tensor':
+        if isinstance(value, Tensor):
+            device = value.get_device()
+        elif not isinstance(value, np.ndarray):
+            if HType.is_scalar_type(value):
+                if dtype is None:
+                    raise ValueError('dtype must be specified for single value')
+                value = np.array([value], dtype=dtype)
+            elif isinstance(value, Iterable):
+                if dtype is None:
+                    raise ValueError('dtype must be specified for Iterable of values')
+                value = np.array(value, dtype=dtype)
+            else:
+                raise ValueError(f'Unsupported value type {value.__class__}')
+
+        return Tensor(shape=value.shape, dtype=value.dtype, device=device).set(value)
+
+    ### PROPERTIES
+
+    @property
+    def shape(self): return self._shape
+    @property
+    def ndim(self): return len(self._shape)
+    @property
+    def dtype(self) -> np.dtype: return self._dtype
+    @property
+    def name(self) -> str: return f'#{self.get_seq_id()}'
+
+    ### GETTERS/SETTERS
+
+    def get_buffer(self) -> Buffer: return self._buffer
+    def get_device(self) -> Device: return self._device
+    def get_seq_id(self) -> int: return self._seq_id
+
+    def set(self, value : Union['Tensor',np.ndarray,int,float]) -> 'Tensor':
+        if isinstance(value, Tensor):
+            if self.shape.size != value.shape.size:
+                raise ValueError('Unable to set the data from other tensor: shape.size is not the same.')
+            self.get_buffer().set(value.get_buffer())
+        else:
+            if isinstance(value, np.ndarray):
+                value = value.astype(self.dtype)
+            else:
+                if HType.is_scalar_type(value):
+                    value = np.array([value], dtype=self.dtype)
+                elif isinstance(value, Iterable):
+                    value = np.array(value, dtype=self.dtype)
+                else:
+                    raise ValueError(f'Unsupported value type {value.__class__}')
+
+            self.get_buffer().set(value)
+        return self
+
+    def np(self):
+        """Returns numpy value of a Tensor"""
+        return self.get_buffer().np(self.shape, self.dtype)
+
+
+    ### OPERATORS
+
+    def __add__(self, value) -> 'Tensor': ...
+    def __radd__(self, value) -> 'Tensor': ...
+    def __sub__(self, value) -> 'Tensor': ...
+    def __rsub__(self, value) -> 'Tensor': ...
+    def __mul__(self, value) -> 'Tensor': ...
+    def __rmul__(self, value) -> 'Tensor': ...
+    def __truediv__(self, value) -> 'Tensor': ...
+    def __rtruediv__(self, value) -> 'Tensor': ...
+    def __neg__(self) -> 'Tensor': ...
+    def __getitem__(self, slices) -> 'Tensor': ...
+    def __setitem__(self, slices, value): ...
+
+    def as_shape(self, shape) -> 'Tensor': ...
+    def copy(self) -> 'Tensor': ...
+    def max(self, axes=None, keepdims=False) -> 'Tensor': ...
+    def mean(self, axes=None, keepdims=False) -> 'Tensor': ...
+    def min(self, axes=None, keepdims=False) -> 'Tensor': ...
+    def reshape(self, new_shape) -> 'Tensor': ...
+    def sum(self, axes=None, keepdims=False) -> 'Tensor': ...
+    def transpose(self, axes_order, op_text=None, dtype=None) -> 'Tensor': ...
+
+    @property
+    def T(self): return self.transpose( tuple(range(self.shape.ndim-1,-1,-1)) )
+
+    ### INTERNAL METHODS
+
+    def __del__(self):
+        Tensor._object_count -= 1
+
+    _object_count = 0
+    _seq_id = 0
+
+__all__ = ['Tensor']