Spaces:
Running
Running
File size: 17,226 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 |
# mypy: ignore-errors
from __future__ import annotations
import builtins
import math
import operator
from typing import Sequence
import torch
from . import _dtypes, _dtypes_impl, _funcs, _ufuncs, _util
from ._normalizations import (
ArrayLike,
normalize_array_like,
normalizer,
NotImplementedType,
)
newaxis = None
FLAGS = [
"C_CONTIGUOUS",
"F_CONTIGUOUS",
"OWNDATA",
"WRITEABLE",
"ALIGNED",
"WRITEBACKIFCOPY",
"FNC",
"FORC",
"BEHAVED",
"CARRAY",
"FARRAY",
]
SHORTHAND_TO_FLAGS = {
"C": "C_CONTIGUOUS",
"F": "F_CONTIGUOUS",
"O": "OWNDATA",
"W": "WRITEABLE",
"A": "ALIGNED",
"X": "WRITEBACKIFCOPY",
"B": "BEHAVED",
"CA": "CARRAY",
"FA": "FARRAY",
}
class Flags:
def __init__(self, flag_to_value: dict):
assert all(k in FLAGS for k in flag_to_value.keys()) # sanity check
self._flag_to_value = flag_to_value
def __getattr__(self, attr: str):
if attr.islower() and attr.upper() in FLAGS:
return self[attr.upper()]
else:
raise AttributeError(f"No flag attribute '{attr}'")
def __getitem__(self, key):
if key in SHORTHAND_TO_FLAGS.keys():
key = SHORTHAND_TO_FLAGS[key]
if key in FLAGS:
try:
return self._flag_to_value[key]
except KeyError as e:
raise NotImplementedError(f"{key=}") from e
else:
raise KeyError(f"No flag key '{key}'")
def __setattr__(self, attr, value):
if attr.islower() and attr.upper() in FLAGS:
self[attr.upper()] = value
else:
super().__setattr__(attr, value)
def __setitem__(self, key, value):
if key in FLAGS or key in SHORTHAND_TO_FLAGS.keys():
raise NotImplementedError("Modifying flags is not implemented")
else:
raise KeyError(f"No flag key '{key}'")
def create_method(fn, name=None):
name = name or fn.__name__
def f(*args, **kwargs):
return fn(*args, **kwargs)
f.__name__ = name
f.__qualname__ = f"ndarray.{name}"
return f
# Map ndarray.name_method -> np.name_func
# If name_func == None, it means that name_method == name_func
methods = {
"clip": None,
"nonzero": None,
"repeat": None,
"round": None,
"squeeze": None,
"swapaxes": None,
"ravel": None,
# linalg
"diagonal": None,
"dot": None,
"trace": None,
# sorting
"argsort": None,
"searchsorted": None,
# reductions
"argmax": None,
"argmin": None,
"any": None,
"all": None,
"max": None,
"min": None,
"ptp": None,
"sum": None,
"prod": None,
"mean": None,
"var": None,
"std": None,
# scans
"cumsum": None,
"cumprod": None,
# advanced indexing
"take": None,
"choose": None,
}
dunder = {
"abs": "absolute",
"invert": None,
"pos": "positive",
"neg": "negative",
"gt": "greater",
"lt": "less",
"ge": "greater_equal",
"le": "less_equal",
}
# dunder methods with right-looking and in-place variants
ri_dunder = {
"add": None,
"sub": "subtract",
"mul": "multiply",
"truediv": "divide",
"floordiv": "floor_divide",
"pow": "power",
"mod": "remainder",
"and": "bitwise_and",
"or": "bitwise_or",
"xor": "bitwise_xor",
"lshift": "left_shift",
"rshift": "right_shift",
"matmul": None,
}
def _upcast_int_indices(index):
if isinstance(index, torch.Tensor):
if index.dtype in (torch.int8, torch.int16, torch.int32, torch.uint8):
return index.to(torch.int64)
elif isinstance(index, tuple):
return tuple(_upcast_int_indices(i) for i in index)
return index
# Used to indicate that a parameter is unspecified (as opposed to explicitly
# `None`)
class _Unspecified:
pass
_Unspecified.unspecified = _Unspecified()
###############################################################
# ndarray class #
###############################################################
class ndarray:
def __init__(self, t=None):
if t is None:
self.tensor = torch.Tensor()
elif isinstance(t, torch.Tensor):
self.tensor = t
else:
raise ValueError(
"ndarray constructor is not recommended; prefer"
"either array(...) or zeros/empty(...)"
)
# Register NumPy functions as methods
for method, name in methods.items():
fn = getattr(_funcs, name or method)
vars()[method] = create_method(fn, method)
# Regular methods but coming from ufuncs
conj = create_method(_ufuncs.conjugate, "conj")
conjugate = create_method(_ufuncs.conjugate)
for method, name in dunder.items():
fn = getattr(_ufuncs, name or method)
method = f"__{method}__"
vars()[method] = create_method(fn, method)
for method, name in ri_dunder.items():
fn = getattr(_ufuncs, name or method)
plain = f"__{method}__"
vars()[plain] = create_method(fn, plain)
rvar = f"__r{method}__"
vars()[rvar] = create_method(lambda self, other, fn=fn: fn(other, self), rvar)
ivar = f"__i{method}__"
vars()[ivar] = create_method(
lambda self, other, fn=fn: fn(self, other, out=self), ivar
)
# There's no __idivmod__
__divmod__ = create_method(_ufuncs.divmod, "__divmod__")
__rdivmod__ = create_method(
lambda self, other: _ufuncs.divmod(other, self), "__rdivmod__"
)
# prevent loop variables leaking into the ndarray class namespace
del ivar, rvar, name, plain, fn, method
@property
def shape(self):
return tuple(self.tensor.shape)
@property
def size(self):
return self.tensor.numel()
@property
def ndim(self):
return self.tensor.ndim
@property
def dtype(self):
return _dtypes.dtype(self.tensor.dtype)
@property
def strides(self):
elsize = self.tensor.element_size()
return tuple(stride * elsize for stride in self.tensor.stride())
@property
def itemsize(self):
return self.tensor.element_size()
@property
def flags(self):
# Note contiguous in torch is assumed C-style
return Flags(
{
"C_CONTIGUOUS": self.tensor.is_contiguous(),
"F_CONTIGUOUS": self.T.tensor.is_contiguous(),
"OWNDATA": self.tensor._base is None,
"WRITEABLE": True, # pytorch does not have readonly tensors
}
)
@property
def data(self):
return self.tensor.data_ptr()
@property
def nbytes(self):
return self.tensor.storage().nbytes()
@property
def T(self):
return self.transpose()
@property
def real(self):
return _funcs.real(self)
@real.setter
def real(self, value):
self.tensor.real = asarray(value).tensor
@property
def imag(self):
return _funcs.imag(self)
@imag.setter
def imag(self, value):
self.tensor.imag = asarray(value).tensor
# ctors
def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True):
if order != "K":
raise NotImplementedError(f"astype(..., order={order} is not implemented.")
if casting != "unsafe":
raise NotImplementedError(
f"astype(..., casting={casting} is not implemented."
)
if not subok:
raise NotImplementedError(f"astype(..., subok={subok} is not implemented.")
if not copy:
raise NotImplementedError(f"astype(..., copy={copy} is not implemented.")
torch_dtype = _dtypes.dtype(dtype).torch_dtype
t = self.tensor.to(torch_dtype)
return ndarray(t)
@normalizer
def copy(self: ArrayLike, order: NotImplementedType = "C"):
return self.clone()
@normalizer
def flatten(self: ArrayLike, order: NotImplementedType = "C"):
return torch.flatten(self)
def resize(self, *new_shape, refcheck=False):
# NB: differs from np.resize: fills with zeros instead of making repeated copies of input.
if refcheck:
raise NotImplementedError(
f"resize(..., refcheck={refcheck} is not implemented."
)
if new_shape in [(), (None,)]:
return
# support both x.resize((2, 2)) and x.resize(2, 2)
if len(new_shape) == 1:
new_shape = new_shape[0]
if isinstance(new_shape, int):
new_shape = (new_shape,)
if builtins.any(x < 0 for x in new_shape):
raise ValueError("all elements of `new_shape` must be non-negative")
new_numel, old_numel = math.prod(new_shape), self.tensor.numel()
self.tensor.resize_(new_shape)
if new_numel >= old_numel:
# zero-fill new elements
assert self.tensor.is_contiguous()
b = self.tensor.flatten() # does not copy
b[old_numel:].zero_()
def view(self, dtype=_Unspecified.unspecified, type=_Unspecified.unspecified):
if dtype is _Unspecified.unspecified:
dtype = self.dtype
if type is not _Unspecified.unspecified:
raise NotImplementedError(f"view(..., type={type} is not implemented.")
torch_dtype = _dtypes.dtype(dtype).torch_dtype
tview = self.tensor.view(torch_dtype)
return ndarray(tview)
@normalizer
def fill(self, value: ArrayLike):
# Both Pytorch and NumPy accept 0D arrays/tensors and scalars, and
# error out on D > 0 arrays
self.tensor.fill_(value)
def tolist(self):
return self.tensor.tolist()
def __iter__(self):
return (ndarray(x) for x in self.tensor.__iter__())
def __str__(self):
return (
str(self.tensor)
.replace("tensor", "torch.ndarray")
.replace("dtype=torch.", "dtype=")
)
__repr__ = create_method(__str__)
def __eq__(self, other):
try:
return _ufuncs.equal(self, other)
except (RuntimeError, TypeError):
# Failed to convert other to array: definitely not equal.
falsy = torch.full(self.shape, fill_value=False, dtype=bool)
return asarray(falsy)
def __ne__(self, other):
return ~(self == other)
def __index__(self):
try:
return operator.index(self.tensor.item())
except Exception as exc:
raise TypeError(
"only integer scalar arrays can be converted to a scalar index"
) from exc
def __bool__(self):
return bool(self.tensor)
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)
|