592 lines
16 KiB
Python
592 lines
16 KiB
Python
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# mypy: ignore-errors
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from __future__ import annotations
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import builtins
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import math
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import operator
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from typing import Sequence
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import torch
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from . import _dtypes, _dtypes_impl, _funcs, _ufuncs, _util
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from ._normalizations import (
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ArrayLike,
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normalize_array_like,
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normalizer,
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NotImplementedType,
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)
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newaxis = None
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FLAGS = [
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"C_CONTIGUOUS",
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"F_CONTIGUOUS",
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"OWNDATA",
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"WRITEABLE",
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"ALIGNED",
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"WRITEBACKIFCOPY",
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"FNC",
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"FORC",
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"BEHAVED",
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"CARRAY",
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"FARRAY",
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]
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SHORTHAND_TO_FLAGS = {
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"C": "C_CONTIGUOUS",
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"F": "F_CONTIGUOUS",
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"O": "OWNDATA",
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"W": "WRITEABLE",
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"A": "ALIGNED",
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"X": "WRITEBACKIFCOPY",
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"B": "BEHAVED",
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"CA": "CARRAY",
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"FA": "FARRAY",
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}
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class Flags:
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def __init__(self, flag_to_value: dict):
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assert all(k in FLAGS for k in flag_to_value.keys()) # sanity check
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self._flag_to_value = flag_to_value
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def __getattr__(self, attr: str):
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if attr.islower() and attr.upper() in FLAGS:
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return self[attr.upper()]
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else:
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raise AttributeError(f"No flag attribute '{attr}'")
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def __getitem__(self, key):
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if key in SHORTHAND_TO_FLAGS.keys():
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key = SHORTHAND_TO_FLAGS[key]
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if key in FLAGS:
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try:
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return self._flag_to_value[key]
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except KeyError as e:
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raise NotImplementedError(f"{key=}") from e
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else:
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raise KeyError(f"No flag key '{key}'")
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def __setattr__(self, attr, value):
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if attr.islower() and attr.upper() in FLAGS:
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self[attr.upper()] = value
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else:
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super().__setattr__(attr, value)
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def __setitem__(self, key, value):
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if key in FLAGS or key in SHORTHAND_TO_FLAGS.keys():
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raise NotImplementedError("Modifying flags is not implemented")
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else:
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raise KeyError(f"No flag key '{key}'")
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def create_method(fn, name=None):
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name = name or fn.__name__
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def f(*args, **kwargs):
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return fn(*args, **kwargs)
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f.__name__ = name
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f.__qualname__ = f"ndarray.{name}"
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return f
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# Map ndarray.name_method -> np.name_func
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# If name_func == None, it means that name_method == name_func
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methods = {
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"clip": None,
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"nonzero": None,
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"repeat": None,
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"round": None,
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"squeeze": None,
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"swapaxes": None,
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"ravel": None,
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# linalg
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"diagonal": None,
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"dot": None,
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"trace": None,
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# sorting
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"argsort": None,
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"searchsorted": None,
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# reductions
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"argmax": None,
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"argmin": None,
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"any": None,
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"all": None,
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"max": None,
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"min": None,
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"ptp": None,
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"sum": None,
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"prod": None,
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"mean": None,
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"var": None,
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"std": None,
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# scans
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"cumsum": None,
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"cumprod": None,
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# advanced indexing
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"take": None,
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"choose": None,
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}
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dunder = {
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"abs": "absolute",
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"invert": None,
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"pos": "positive",
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"neg": "negative",
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"gt": "greater",
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"lt": "less",
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"ge": "greater_equal",
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"le": "less_equal",
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}
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# dunder methods with right-looking and in-place variants
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ri_dunder = {
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"add": None,
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"sub": "subtract",
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"mul": "multiply",
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"truediv": "divide",
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"floordiv": "floor_divide",
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"pow": "power",
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"mod": "remainder",
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"and": "bitwise_and",
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"or": "bitwise_or",
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"xor": "bitwise_xor",
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"lshift": "left_shift",
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"rshift": "right_shift",
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"matmul": None,
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}
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def _upcast_int_indices(index):
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if isinstance(index, torch.Tensor):
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if index.dtype in (torch.int8, torch.int16, torch.int32, torch.uint8):
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return index.to(torch.int64)
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elif isinstance(index, tuple):
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return tuple(_upcast_int_indices(i) for i in index)
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return index
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# Used to indicate that a parameter is unspecified (as opposed to explicitly
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# `None`)
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class _Unspecified:
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pass
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_Unspecified.unspecified = _Unspecified()
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###############################################################
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# ndarray class #
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###############################################################
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class ndarray:
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def __init__(self, t=None):
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if t is None:
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self.tensor = torch.Tensor()
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elif isinstance(t, torch.Tensor):
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self.tensor = t
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else:
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raise ValueError(
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"ndarray constructor is not recommended; prefer"
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"either array(...) or zeros/empty(...)"
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)
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# Register NumPy functions as methods
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for method, name in methods.items():
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fn = getattr(_funcs, name or method)
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vars()[method] = create_method(fn, method)
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# Regular methods but coming from ufuncs
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conj = create_method(_ufuncs.conjugate, "conj")
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conjugate = create_method(_ufuncs.conjugate)
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for method, name in dunder.items():
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fn = getattr(_ufuncs, name or method)
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method = f"__{method}__"
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vars()[method] = create_method(fn, method)
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for method, name in ri_dunder.items():
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fn = getattr(_ufuncs, name or method)
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plain = f"__{method}__"
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vars()[plain] = create_method(fn, plain)
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rvar = f"__r{method}__"
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vars()[rvar] = create_method(lambda self, other, fn=fn: fn(other, self), rvar)
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ivar = f"__i{method}__"
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vars()[ivar] = create_method(
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lambda self, other, fn=fn: fn(self, other, out=self), ivar
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)
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# There's no __idivmod__
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__divmod__ = create_method(_ufuncs.divmod, "__divmod__")
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__rdivmod__ = create_method(
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lambda self, other: _ufuncs.divmod(other, self), "__rdivmod__"
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)
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# prevent loop variables leaking into the ndarray class namespace
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del ivar, rvar, name, plain, fn, method
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@property
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def shape(self):
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return tuple(self.tensor.shape)
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@property
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def size(self):
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return self.tensor.numel()
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@property
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def ndim(self):
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return self.tensor.ndim
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@property
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def dtype(self):
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return _dtypes.dtype(self.tensor.dtype)
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@property
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def strides(self):
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elsize = self.tensor.element_size()
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return tuple(stride * elsize for stride in self.tensor.stride())
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@property
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def itemsize(self):
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return self.tensor.element_size()
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@property
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def flags(self):
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# Note contiguous in torch is assumed C-style
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return Flags(
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{
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"C_CONTIGUOUS": self.tensor.is_contiguous(),
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"F_CONTIGUOUS": self.T.tensor.is_contiguous(),
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"OWNDATA": self.tensor._base is None,
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"WRITEABLE": True, # pytorch does not have readonly tensors
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}
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)
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@property
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def data(self):
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return self.tensor.data_ptr()
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@property
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def nbytes(self):
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return self.tensor.storage().nbytes()
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@property
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def T(self):
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return self.transpose()
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@property
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def real(self):
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return _funcs.real(self)
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@real.setter
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def real(self, value):
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self.tensor.real = asarray(value).tensor
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@property
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def imag(self):
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return _funcs.imag(self)
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@imag.setter
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def imag(self, value):
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self.tensor.imag = asarray(value).tensor
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# ctors
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def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True):
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if order != "K":
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raise NotImplementedError(f"astype(..., order={order} is not implemented.")
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if casting != "unsafe":
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raise NotImplementedError(
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f"astype(..., casting={casting} is not implemented."
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)
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if not subok:
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raise NotImplementedError(f"astype(..., subok={subok} is not implemented.")
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if not copy:
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raise NotImplementedError(f"astype(..., copy={copy} is not implemented.")
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torch_dtype = _dtypes.dtype(dtype).torch_dtype
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t = self.tensor.to(torch_dtype)
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return ndarray(t)
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@normalizer
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def copy(self: ArrayLike, order: NotImplementedType = "C"):
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return self.clone()
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@normalizer
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def flatten(self: ArrayLike, order: NotImplementedType = "C"):
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return torch.flatten(self)
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def resize(self, *new_shape, refcheck=False):
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# NB: differs from np.resize: fills with zeros instead of making repeated copies of input.
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if refcheck:
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raise NotImplementedError(
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f"resize(..., refcheck={refcheck} is not implemented."
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)
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if new_shape in [(), (None,)]:
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return
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# support both x.resize((2, 2)) and x.resize(2, 2)
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if len(new_shape) == 1:
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new_shape = new_shape[0]
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if isinstance(new_shape, int):
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new_shape = (new_shape,)
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if builtins.any(x < 0 for x in new_shape):
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raise ValueError("all elements of `new_shape` must be non-negative")
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new_numel, old_numel = math.prod(new_shape), self.tensor.numel()
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self.tensor.resize_(new_shape)
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if new_numel >= old_numel:
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# zero-fill new elements
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assert self.tensor.is_contiguous()
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b = self.tensor.flatten() # does not copy
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b[old_numel:].zero_()
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def view(self, dtype=_Unspecified.unspecified, type=_Unspecified.unspecified):
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if dtype is _Unspecified.unspecified:
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dtype = self.dtype
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if type is not _Unspecified.unspecified:
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raise NotImplementedError(f"view(..., type={type} is not implemented.")
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torch_dtype = _dtypes.dtype(dtype).torch_dtype
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tview = self.tensor.view(torch_dtype)
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return ndarray(tview)
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@normalizer
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def fill(self, value: ArrayLike):
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# Both Pytorch and NumPy accept 0D arrays/tensors and scalars, and
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# error out on D > 0 arrays
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self.tensor.fill_(value)
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def tolist(self):
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return self.tensor.tolist()
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def __iter__(self):
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return (ndarray(x) for x in self.tensor.__iter__())
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def __str__(self):
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return (
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str(self.tensor)
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.replace("tensor", "torch.ndarray")
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.replace("dtype=torch.", "dtype=")
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)
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__repr__ = create_method(__str__)
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def __eq__(self, other):
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try:
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return _ufuncs.equal(self, other)
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except (RuntimeError, TypeError):
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# Failed to convert other to array: definitely not equal.
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falsy = torch.full(self.shape, fill_value=False, dtype=bool)
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return asarray(falsy)
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def __ne__(self, other):
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return ~(self == other)
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def __index__(self):
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try:
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return operator.index(self.tensor.item())
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except Exception as exc:
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raise TypeError(
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"only integer scalar arrays can be converted to a scalar index"
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) from exc
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def __bool__(self):
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return bool(self.tensor)
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|
||
|
|
def __int__(self):
|
||
|
|
return int(self.tensor)
|
||
|
|
|
||
|
|
def __float__(self):
|
||
|
|
return float(self.tensor)
|
||
|
|
|
||
|
|
def __complex__(self):
|
||
|
|
return complex(self.tensor)
|
||
|
|
|
||
|
|
def is_integer(self):
|
||
|
|
try:
|
||
|
|
v = self.tensor.item()
|
||
|
|
result = int(v) == v
|
||
|
|
except Exception:
|
||
|
|
result = False
|
||
|
|
return result
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.tensor.shape[0]
|
||
|
|
|
||
|
|
def __contains__(self, x):
|
||
|
|
return self.tensor.__contains__(x)
|
||
|
|
|
||
|
|
def transpose(self, *axes):
|
||
|
|
# np.transpose(arr, axis=None) but arr.transpose(*axes)
|
||
|
|
return _funcs.transpose(self, axes)
|
||
|
|
|
||
|
|
def reshape(self, *shape, order="C"):
|
||
|
|
# arr.reshape(shape) and arr.reshape(*shape)
|
||
|
|
return _funcs.reshape(self, shape, order=order)
|
||
|
|
|
||
|
|
def sort(self, axis=-1, kind=None, order=None):
|
||
|
|
# ndarray.sort works in-place
|
||
|
|
_funcs.copyto(self, _funcs.sort(self, axis, kind, order))
|
||
|
|
|
||
|
|
def item(self, *args):
|
||
|
|
# Mimic NumPy's implementation with three special cases (no arguments,
|
||
|
|
# a flat index and a multi-index):
|
||
|
|
# https://github.com/numpy/numpy/blob/main/numpy/core/src/multiarray/methods.c#L702
|
||
|
|
if args == ():
|
||
|
|
return self.tensor.item()
|
||
|
|
elif len(args) == 1:
|
||
|
|
# int argument
|
||
|
|
return self.ravel()[args[0]]
|
||
|
|
else:
|
||
|
|
return self.__getitem__(args)
|
||
|
|
|
||
|
|
def __getitem__(self, index):
|
||
|
|
tensor = self.tensor
|
||
|
|
|
||
|
|
def neg_step(i, s):
|
||
|
|
if not (isinstance(s, slice) and s.step is not None and s.step < 0):
|
||
|
|
return s
|
||
|
|
|
||
|
|
nonlocal tensor
|
||
|
|
tensor = torch.flip(tensor, (i,))
|
||
|
|
|
||
|
|
# Account for the fact that a slice includes the start but not the end
|
||
|
|
assert isinstance(s.start, int) or s.start is None
|
||
|
|
assert isinstance(s.stop, int) or s.stop is None
|
||
|
|
start = s.stop + 1 if s.stop else None
|
||
|
|
stop = s.start + 1 if s.start else None
|
||
|
|
|
||
|
|
return slice(start, stop, -s.step)
|
||
|
|
|
||
|
|
if isinstance(index, Sequence):
|
||
|
|
index = type(index)(neg_step(i, s) for i, s in enumerate(index))
|
||
|
|
else:
|
||
|
|
index = neg_step(0, index)
|
||
|
|
index = _util.ndarrays_to_tensors(index)
|
||
|
|
index = _upcast_int_indices(index)
|
||
|
|
return ndarray(tensor.__getitem__(index))
|
||
|
|
|
||
|
|
def __setitem__(self, index, value):
|
||
|
|
index = _util.ndarrays_to_tensors(index)
|
||
|
|
index = _upcast_int_indices(index)
|
||
|
|
|
||
|
|
if not _dtypes_impl.is_scalar(value):
|
||
|
|
value = normalize_array_like(value)
|
||
|
|
value = _util.cast_if_needed(value, self.tensor.dtype)
|
||
|
|
|
||
|
|
return self.tensor.__setitem__(index, value)
|
||
|
|
|
||
|
|
take = _funcs.take
|
||
|
|
put = _funcs.put
|
||
|
|
|
||
|
|
def __dlpack__(self, *, stream=None):
|
||
|
|
return self.tensor.__dlpack__(stream=stream)
|
||
|
|
|
||
|
|
def __dlpack_device__(self):
|
||
|
|
return self.tensor.__dlpack_device__()
|
||
|
|
|
||
|
|
|
||
|
|
def _tolist(obj):
|
||
|
|
"""Recursively convert tensors into lists."""
|
||
|
|
a1 = []
|
||
|
|
for elem in obj:
|
||
|
|
if isinstance(elem, (list, tuple)):
|
||
|
|
elem = _tolist(elem)
|
||
|
|
if isinstance(elem, ndarray):
|
||
|
|
a1.append(elem.tensor.tolist())
|
||
|
|
else:
|
||
|
|
a1.append(elem)
|
||
|
|
return a1
|
||
|
|
|
||
|
|
|
||
|
|
# This is the ideally the only place which talks to ndarray directly.
|
||
|
|
# The rest goes through asarray (preferred) or array.
|
||
|
|
|
||
|
|
|
||
|
|
def array(obj, dtype=None, *, copy=True, order="K", subok=False, ndmin=0, like=None):
|
||
|
|
if subok is not False:
|
||
|
|
raise NotImplementedError("'subok' parameter is not supported.")
|
||
|
|
if like is not None:
|
||
|
|
raise NotImplementedError("'like' parameter is not supported.")
|
||
|
|
if order != "K":
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
# a happy path
|
||
|
|
if (
|
||
|
|
isinstance(obj, ndarray)
|
||
|
|
and copy is False
|
||
|
|
and dtype is None
|
||
|
|
and ndmin <= obj.ndim
|
||
|
|
):
|
||
|
|
return obj
|
||
|
|
|
||
|
|
if isinstance(obj, (list, tuple)):
|
||
|
|
# FIXME and they have the same dtype, device, etc
|
||
|
|
if obj and all(isinstance(x, torch.Tensor) for x in obj):
|
||
|
|
# list of arrays: *under torch.Dynamo* these are FakeTensors
|
||
|
|
obj = torch.stack(obj)
|
||
|
|
else:
|
||
|
|
# XXX: remove tolist
|
||
|
|
# lists of ndarrays: [1, [2, 3], ndarray(4)] convert to lists of lists
|
||
|
|
obj = _tolist(obj)
|
||
|
|
|
||
|
|
# is obj an ndarray already?
|
||
|
|
if isinstance(obj, ndarray):
|
||
|
|
obj = obj.tensor
|
||
|
|
|
||
|
|
# is a specific dtype requested?
|
||
|
|
torch_dtype = None
|
||
|
|
if dtype is not None:
|
||
|
|
torch_dtype = _dtypes.dtype(dtype).torch_dtype
|
||
|
|
|
||
|
|
tensor = _util._coerce_to_tensor(obj, torch_dtype, copy, ndmin)
|
||
|
|
return ndarray(tensor)
|
||
|
|
|
||
|
|
|
||
|
|
def asarray(a, dtype=None, order="K", *, like=None):
|
||
|
|
return array(a, dtype=dtype, order=order, like=like, copy=False, ndmin=0)
|
||
|
|
|
||
|
|
|
||
|
|
def ascontiguousarray(a, dtype=None, *, like=None):
|
||
|
|
arr = asarray(a, dtype=dtype, like=like)
|
||
|
|
if not arr.tensor.is_contiguous():
|
||
|
|
arr.tensor = arr.tensor.contiguous()
|
||
|
|
return arr
|
||
|
|
|
||
|
|
|
||
|
|
def from_dlpack(x, /):
|
||
|
|
t = torch.from_dlpack(x)
|
||
|
|
return ndarray(t)
|
||
|
|
|
||
|
|
|
||
|
|
def _extract_dtype(entry):
|
||
|
|
try:
|
||
|
|
dty = _dtypes.dtype(entry)
|
||
|
|
except Exception:
|
||
|
|
dty = asarray(entry).dtype
|
||
|
|
return dty
|
||
|
|
|
||
|
|
|
||
|
|
def can_cast(from_, to, casting="safe"):
|
||
|
|
from_ = _extract_dtype(from_)
|
||
|
|
to_ = _extract_dtype(to)
|
||
|
|
|
||
|
|
return _dtypes_impl.can_cast_impl(from_.torch_dtype, to_.torch_dtype, casting)
|
||
|
|
|
||
|
|
|
||
|
|
def result_type(*arrays_and_dtypes):
|
||
|
|
tensors = []
|
||
|
|
for entry in arrays_and_dtypes:
|
||
|
|
try:
|
||
|
|
t = asarray(entry).tensor
|
||
|
|
except (RuntimeError, ValueError, TypeError):
|
||
|
|
dty = _dtypes.dtype(entry)
|
||
|
|
t = torch.empty(1, dtype=dty.torch_dtype)
|
||
|
|
tensors.append(t)
|
||
|
|
|
||
|
|
torch_dtype = _dtypes_impl.result_type_impl(*tensors)
|
||
|
|
return _dtypes.dtype(torch_dtype)
|