File size: 26,814 Bytes
d1ceb73 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 |
# mypy: allow-untyped-defs
import contextlib
import dataclasses
import math
import textwrap
from typing import Any, Dict, Optional
import torch
from torch import inf
@dataclasses.dataclass
class __PrinterOptions:
precision: int = 4
threshold: float = 1000
edgeitems: int = 3
linewidth: int = 80
sci_mode: Optional[bool] = None
PRINT_OPTS = __PrinterOptions()
# We could use **kwargs, but this will give better docs
def set_printoptions(
precision=None,
threshold=None,
edgeitems=None,
linewidth=None,
profile=None,
sci_mode=None,
):
r"""Set options for printing. Items shamelessly taken from NumPy
Args:
precision: Number of digits of precision for floating point output
(default = 4).
threshold: Total number of array elements which trigger summarization
rather than full `repr` (default = 1000).
edgeitems: Number of array items in summary at beginning and end of
each dimension (default = 3).
linewidth: The number of characters per line for the purpose of
inserting line breaks (default = 80). Thresholded matrices will
ignore this parameter.
profile: Sane defaults for pretty printing. Can override with any of
the above options. (any one of `default`, `short`, `full`)
sci_mode: Enable (True) or disable (False) scientific notation. If
None (default) is specified, the value is defined by
`torch._tensor_str._Formatter`. This value is automatically chosen
by the framework.
Example::
>>> # Limit the precision of elements
>>> torch.set_printoptions(precision=2)
>>> torch.tensor([1.12345])
tensor([1.12])
>>> # Limit the number of elements shown
>>> torch.set_printoptions(threshold=5)
>>> torch.arange(10)
tensor([0, 1, 2, ..., 7, 8, 9])
>>> # Restore defaults
>>> torch.set_printoptions(profile='default')
>>> torch.tensor([1.12345])
tensor([1.1235])
>>> torch.arange(10)
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
if profile is not None:
if profile == "default":
PRINT_OPTS.precision = 4
PRINT_OPTS.threshold = 1000
PRINT_OPTS.edgeitems = 3
PRINT_OPTS.linewidth = 80
elif profile == "short":
PRINT_OPTS.precision = 2
PRINT_OPTS.threshold = 1000
PRINT_OPTS.edgeitems = 2
PRINT_OPTS.linewidth = 80
elif profile == "full":
PRINT_OPTS.precision = 4
PRINT_OPTS.threshold = inf
PRINT_OPTS.edgeitems = 3
PRINT_OPTS.linewidth = 80
if precision is not None:
PRINT_OPTS.precision = precision
if threshold is not None:
PRINT_OPTS.threshold = threshold
if edgeitems is not None:
PRINT_OPTS.edgeitems = edgeitems
if linewidth is not None:
PRINT_OPTS.linewidth = linewidth
PRINT_OPTS.sci_mode = sci_mode
def get_printoptions() -> Dict[str, Any]:
r"""Gets the current options for printing, as a dictionary that
can be passed as ``**kwargs`` to set_printoptions().
"""
return dataclasses.asdict(PRINT_OPTS)
@contextlib.contextmanager
def printoptions(**kwargs):
r"""Context manager that temporarily changes the print options. Accepted
arguments are same as :func:`set_printoptions`."""
old_kwargs = get_printoptions()
set_printoptions(**kwargs)
try:
yield
finally:
set_printoptions(**old_kwargs)
def tensor_totype(t):
dtype = torch.float if t.is_mps else torch.double
return t.to(dtype=dtype)
class _Formatter:
def __init__(self, tensor):
self.floating_dtype = tensor.dtype.is_floating_point
self.int_mode = True
self.sci_mode = False
self.max_width = 1
with torch.no_grad():
tensor_view = tensor.reshape(-1)
if not self.floating_dtype:
for value in tensor_view:
value_str = f"{value}"
self.max_width = max(self.max_width, len(value_str))
else:
nonzero_finite_vals = torch.masked_select(
tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0)
)
if nonzero_finite_vals.numel() == 0:
# no valid number, do nothing
return
# Convert to double for easy calculation. HalfTensor overflows with 1e8, and there's no div() on CPU.
nonzero_finite_abs = tensor_totype(nonzero_finite_vals.abs())
nonzero_finite_min = tensor_totype(nonzero_finite_abs.min())
nonzero_finite_max = tensor_totype(nonzero_finite_abs.max())
for value in nonzero_finite_vals:
if value != torch.ceil(value):
self.int_mode = False
break
if self.int_mode:
# in int_mode for floats, all numbers are integers, and we append a decimal to nonfinites
# to indicate that the tensor is of floating type. add 1 to the len to account for this.
if (
nonzero_finite_max / nonzero_finite_min > 1000.0
or nonzero_finite_max > 1.0e8
):
self.sci_mode = True
for value in nonzero_finite_vals:
value_str = f"{{:.{PRINT_OPTS.precision}e}}".format(value)
self.max_width = max(self.max_width, len(value_str))
else:
for value in nonzero_finite_vals:
value_str = f"{value:.0f}"
self.max_width = max(self.max_width, len(value_str) + 1)
else:
# Check if scientific representation should be used.
if (
nonzero_finite_max / nonzero_finite_min > 1000.0
or nonzero_finite_max > 1.0e8
or nonzero_finite_min < 1.0e-4
):
self.sci_mode = True
for value in nonzero_finite_vals:
value_str = f"{{:.{PRINT_OPTS.precision}e}}".format(value)
self.max_width = max(self.max_width, len(value_str))
else:
for value in nonzero_finite_vals:
value_str = f"{{:.{PRINT_OPTS.precision}f}}".format(value)
self.max_width = max(self.max_width, len(value_str))
if PRINT_OPTS.sci_mode is not None:
self.sci_mode = PRINT_OPTS.sci_mode
def width(self):
return self.max_width
def format(self, value):
if self.floating_dtype:
if self.sci_mode:
ret = f"{{:{self.max_width}.{PRINT_OPTS.precision}e}}".format(value)
elif self.int_mode:
ret = f"{value:.0f}"
if not (math.isinf(value) or math.isnan(value)):
ret += "."
else:
ret = f"{{:.{PRINT_OPTS.precision}f}}".format(value)
else:
ret = f"{value}"
return (self.max_width - len(ret)) * " " + ret
def _scalar_str(self, formatter1, formatter2=None):
if formatter2 is not None:
real_str = _scalar_str(self.real, formatter1)
imag_str = (_scalar_str(self.imag, formatter2) + "j").lstrip()
# handles negative numbers, +0.0, -0.0
if imag_str[0] == "+" or imag_str[0] == "-":
return real_str + imag_str
else:
return real_str + "+" + imag_str
else:
return formatter1.format(self.item())
def _vector_str(self, indent, summarize, formatter1, formatter2=None):
# length includes spaces and comma between elements
element_length = formatter1.width() + 2
if formatter2 is not None:
# width for imag_formatter + an extra j for complex
element_length += formatter2.width() + 1
elements_per_line = max(
1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length)))
)
def _val_formatter(val, formatter1=formatter1, formatter2=formatter2):
if formatter2 is not None:
real_str = formatter1.format(val.real)
imag_str = (formatter2.format(val.imag) + "j").lstrip()
# handles negative numbers, +0.0, -0.0
if imag_str[0] == "+" or imag_str[0] == "-":
return real_str + imag_str
else:
return real_str + "+" + imag_str
else:
return formatter1.format(val)
if summarize and not PRINT_OPTS.edgeitems:
# Deal with edge case that negative zero is zero
data = ["..."]
elif summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
data = (
[_val_formatter(val) for val in self[: PRINT_OPTS.edgeitems].tolist()]
+ [" ..."]
+ [_val_formatter(val) for val in self[-PRINT_OPTS.edgeitems :].tolist()]
)
else:
data = [_val_formatter(val) for val in self.tolist()]
data_lines = [
data[i : i + elements_per_line] for i in range(0, len(data), elements_per_line)
]
lines = [", ".join(line) for line in data_lines]
return "[" + ("," + "\n" + " " * (indent + 1)).join(lines) + "]"
# formatter2 is only used for printing complex tensors.
# For complex tensors, formatter1 and formatter2 are the formatters for tensor.real
# and tensor.imag respesectively
def _tensor_str_with_formatter(self, indent, summarize, formatter1, formatter2=None):
dim = self.dim()
if dim == 0:
return _scalar_str(self, formatter1, formatter2)
if dim == 1:
return _vector_str(self, indent, summarize, formatter1, formatter2)
if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
slices = (
[
_tensor_str_with_formatter(
self[i], indent + 1, summarize, formatter1, formatter2
)
for i in range(0, PRINT_OPTS.edgeitems)
]
+ ["..."]
+ [
_tensor_str_with_formatter(
self[i], indent + 1, summarize, formatter1, formatter2
)
for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))
]
)
else:
slices = [
_tensor_str_with_formatter(
self[i], indent + 1, summarize, formatter1, formatter2
)
for i in range(0, self.size(0))
]
tensor_str = ("," + "\n" * (dim - 1) + " " * (indent + 1)).join(slices)
return "[" + tensor_str + "]"
def _tensor_str(self, indent):
if self.numel() == 0:
return "[]"
if self.has_names():
# There are two main codepaths (possibly more) that tensor printing goes through:
# - tensor data can fit comfortably on screen
# - tensor data needs to be summarized
# Some of the codepaths don't fully support named tensors, so we send in
# an unnamed tensor to the formatting code as a workaround.
self = self.rename(None)
summarize = self.numel() > PRINT_OPTS.threshold
if self._is_zerotensor():
self = self.clone()
# handle the negative bit
if self.is_neg():
self = self.resolve_neg()
if self.dtype in [
torch.float16,
torch.bfloat16,
torch.float8_e5m2,
torch.float8_e5m2fnuz,
torch.float8_e4m3fn,
torch.float8_e4m3fnuz,
]:
self = self.float()
if self.dtype is torch.complex32:
self = self.cfloat()
if self.dtype.is_complex:
# handle the conjugate bit
self = self.resolve_conj()
real_formatter = _Formatter(
get_summarized_data(self.real) if summarize else self.real
)
imag_formatter = _Formatter(
get_summarized_data(self.imag) if summarize else self.imag
)
return _tensor_str_with_formatter(
self, indent, summarize, real_formatter, imag_formatter
)
else:
formatter = _Formatter(get_summarized_data(self) if summarize else self)
return _tensor_str_with_formatter(self, indent, summarize, formatter)
def _add_suffixes(tensor_str, suffixes, indent, force_newline):
tensor_strs = [tensor_str]
last_line_len = len(tensor_str) - tensor_str.rfind("\n") + 1
for suffix in suffixes:
suffix_len = len(suffix)
if force_newline or last_line_len + suffix_len + 2 > PRINT_OPTS.linewidth:
tensor_strs.append(",\n" + " " * indent + suffix)
last_line_len = indent + suffix_len
force_newline = False
else:
tensor_strs.append(", " + suffix)
last_line_len += suffix_len + 2
tensor_strs.append(")")
return "".join(tensor_strs)
def get_summarized_data(self):
dim = self.dim()
if dim == 0:
return self
if dim == 1:
if self.size(0) > 2 * PRINT_OPTS.edgeitems:
return torch.cat(
(self[: PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems :])
)
else:
return self
if not PRINT_OPTS.edgeitems:
return self.new_empty([0] * self.dim())
elif self.size(0) > 2 * PRINT_OPTS.edgeitems:
start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)]
end = [self[i] for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))]
return torch.stack([get_summarized_data(x) for x in (start + end)])
else:
return torch.stack([get_summarized_data(x) for x in self])
def _str_intern(inp, *, tensor_contents=None):
if torch._C._functorch.is_functorch_wrapped_tensor(inp):
return _functorch_wrapper_str_intern(inp, tensor_contents=tensor_contents)
is_plain_tensor = type(inp) is torch.Tensor or type(inp) is torch.nn.Parameter
if inp.is_nested:
prefix = "nested_tensor("
elif is_plain_tensor:
prefix = "tensor("
else:
prefix = f"{type(inp).__name__}("
indent = len(prefix)
suffixes = []
custom_contents_provided = tensor_contents is not None
if custom_contents_provided:
tensor_str = tensor_contents
# This is used to extract the primal value and thus disable the forward AD
# within this function.
# TODO(albanD) This needs to be updated when more than one level is supported
self, tangent = torch.autograd.forward_ad.unpack_dual(inp)
# Note [Print tensor device]:
# A general logic here is we only print device when it doesn't match
# the device specified in default tensor type.
# Currently torch.set_default_tensor_type() only supports CPU/CUDA, thus
# torch._C._get_default_device() only returns either cpu or cuda.
# In other cases, we don't have a way to set them as default yet,
# and we should always print out device for them.
if (
self.device.type != torch._C._get_default_device()
or (
self.device.type == "cuda"
and torch.cuda.current_device() != self.device.index
)
or (self.device.type == "mps")
):
suffixes.append("device='" + str(self.device) + "'")
# Tensor printing performs tensor operations like slice, indexing, etc to make it in a
# representable format. These operations on ipu/xla/lazy/mtia tensor results in compilations. Hence,
# to avoid compilations, copying the tensor to cpu before printing.
if self.device.type in ["xla", "lazy", "ipu", "mtia"]:
self = self.to("cpu")
# TODO: add an API to map real -> complex dtypes
_default_complex_dtype = (
torch.cdouble if torch.get_default_dtype() == torch.double else torch.cfloat
)
has_default_dtype = self.dtype in (
torch.get_default_dtype(),
_default_complex_dtype,
torch.int64,
torch.bool,
)
if self.is_sparse:
suffixes.append("size=" + str(tuple(self.shape)))
from torch._subclasses.fake_tensor import FakeTensor
is_meta = self.is_meta or isinstance(self, FakeTensor)
if not is_meta:
suffixes.append("nnz=" + str(self._nnz()))
if not has_default_dtype:
suffixes.append("dtype=" + str(self.dtype))
if not custom_contents_provided:
indices_prefix = "indices=tensor("
indices = self._indices().detach()
if is_meta:
indices_str = "..."
else:
indices_str = _tensor_str(indices, indent + len(indices_prefix))
if is_meta or indices.numel() == 0:
indices_str += ", size=" + str(tuple(indices.shape))
values_prefix = "values=tensor("
values = self._values().detach()
if is_meta:
values_str = "..."
else:
values_str = _tensor_str(values, indent + len(values_prefix))
if is_meta or values.numel() == 0:
values_str += ", size=" + str(tuple(values.shape))
tensor_str = (
indices_prefix
+ indices_str
+ "),\n"
+ " " * indent
+ values_prefix
+ values_str
+ ")"
)
elif self.layout in {
torch.sparse_csr,
torch.sparse_csc,
torch.sparse_bsr,
torch.sparse_bsc,
}:
from torch._subclasses.fake_tensor import FakeTensor
suffixes.append("size=" + str(tuple(self.shape)))
is_meta = self.is_meta or isinstance(self, FakeTensor)
if not is_meta:
suffixes.append("nnz=" + str(self._nnz()))
if not has_default_dtype:
suffixes.append("dtype=" + str(self.dtype))
if not custom_contents_provided:
compressed_indices_method, plain_indices_method = {
torch.sparse_csr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
torch.sparse_csc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
torch.sparse_bsr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
torch.sparse_bsc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
}[self.layout]
if self.layout in {torch.sparse_csr, torch.sparse_bsr}:
cdimname, pdimname = "row", "column"
else:
cdimname, pdimname = "column", "row"
compressed_indices_prefix = f"c{cdimname[:3]}_indices=tensor("
compressed_indices = compressed_indices_method(self).detach()
if is_meta:
compressed_indices_str = "..."
else:
compressed_indices_str = _tensor_str(
compressed_indices, indent + len(compressed_indices_prefix)
)
if compressed_indices.numel() == 0 or is_meta:
compressed_indices_str += ", size=" + str(
tuple(compressed_indices.shape)
)
plain_indices_prefix = f"{pdimname[:3]}_indices=tensor("
plain_indices = plain_indices_method(self).detach()
if is_meta:
plain_indices_str = "..."
else:
plain_indices_str = _tensor_str(
plain_indices, indent + len(plain_indices_prefix)
)
if plain_indices.numel() == 0 or is_meta:
plain_indices_str += ", size=" + str(tuple(plain_indices.shape))
values_prefix = "values=tensor("
values = self.values().detach()
if is_meta:
values_str = "..."
else:
values_str = _tensor_str(values, indent + len(values_prefix))
if values.numel() == 0 or is_meta:
values_str += ", size=" + str(tuple(values.shape))
tensor_str = (
compressed_indices_prefix
+ compressed_indices_str
+ "),\n"
+ " " * indent
+ plain_indices_prefix
+ plain_indices_str
+ "),\n"
+ " " * indent
+ values_prefix
+ values_str
+ ")"
)
elif self.is_quantized:
suffixes.append("size=" + str(tuple(self.shape)))
if not has_default_dtype:
suffixes.append("dtype=" + str(self.dtype))
suffixes.append("quantization_scheme=" + str(self.qscheme()))
if (
self.qscheme() == torch.per_tensor_affine
or self.qscheme() == torch.per_tensor_symmetric
):
suffixes.append("scale=" + str(self.q_scale()))
suffixes.append("zero_point=" + str(self.q_zero_point()))
elif (
self.qscheme() == torch.per_channel_affine
or self.qscheme() == torch.per_channel_symmetric
or self.qscheme() == torch.per_channel_affine_float_qparams
):
suffixes.append("scale=" + str(self.q_per_channel_scales()))
suffixes.append("zero_point=" + str(self.q_per_channel_zero_points()))
suffixes.append("axis=" + str(self.q_per_channel_axis()))
if not custom_contents_provided:
tensor_str = _tensor_str(self.dequantize(), indent)
elif self.is_nested:
if not custom_contents_provided:
def indented_str(s, indent):
return "\n".join(f" {line}" for line in s.split("\n"))
strs = ",\n".join(
indented_str(str(t), indent + 1)
for t in torch.ops.aten.unbind.int(self, 0)
)
tensor_str = f"[\n{strs}\n]"
elif torch._is_functional_tensor(self):
prefix = "_to_functional_tensor("
tensor_str = repr(torch._from_functional_tensor(self))
else:
# Circular import problem, so we import it here
from torch._subclasses.fake_tensor import FakeTensor
if self.is_meta or isinstance(self, FakeTensor):
suffixes.append("size=" + str(tuple(self.shape)))
if self.dtype != torch.get_default_dtype():
suffixes.append("dtype=" + str(self.dtype))
# TODO: This implies that ellipses is valid syntax for allocating
# a meta tensor or FakeTensor, which it could be, but it isn't right now
if not custom_contents_provided:
tensor_str = "..."
else:
if self.numel() == 0 and not self.is_sparse:
# Explicitly print the shape if it is not (0,), to match NumPy behavior
if self.dim() != 1:
suffixes.append("size=" + str(tuple(self.shape)))
# In an empty tensor, there are no elements to infer if the dtype
# should be int64, so it must be shown explicitly.
if self.dtype != torch.get_default_dtype():
suffixes.append("dtype=" + str(self.dtype))
if not custom_contents_provided:
tensor_str = "[]"
else:
if not PRINT_OPTS.edgeitems:
suffixes.append("size=" + str(tuple(self.shape)))
if not has_default_dtype:
suffixes.append("dtype=" + str(self.dtype))
if not custom_contents_provided:
if self.layout != torch.strided:
tensor_str = _tensor_str(self.to_dense(), indent)
else:
tensor_str = _tensor_str(self, indent)
if self.layout != torch.strided:
suffixes.append("layout=" + str(self.layout))
# Use inp here to get the original grad_fn and not the one generated by the forward grad
# unpacking.
grad_fn_name = None
try:
grad_fn = inp.grad_fn
except RuntimeError:
# Accessing the grad_fn calls rebasing logic which would cause an error
# if that tensor is a view created in no-grad mode modified in-place in
# no-grad mode. See: https://github.com/pytorch/pytorch/issues/99968
grad_fn_name = "Invalid"
if grad_fn_name is None and grad_fn is not None: # type: ignore[possibly-undefined]
grad_fn_name = type(grad_fn).__name__
if grad_fn_name == "CppFunction":
grad_fn_name = grad_fn.name().rsplit("::", 1)[-1]
if grad_fn_name is not None:
suffixes.append(f"grad_fn=<{grad_fn_name}>")
elif inp.requires_grad:
suffixes.append("requires_grad=True")
if self.has_names():
suffixes.append(f"names={self.names}")
if tangent is not None:
suffixes.append(f"tangent={tangent}")
string_repr = _add_suffixes(
prefix + tensor_str, suffixes, indent, force_newline=self.is_sparse # type: ignore[possibly-undefined]
)
# Check if this instance is flagged as a parameter and change the repr accordingly.
# Unfortunately, this function has to be aware of this detail.
# NB: This is currently skipped for plain tensor parameters to maintain BC. In the future,
# this should be done for those as well to produce a valid repr.
if isinstance(self, torch.nn.Parameter) and not is_plain_tensor:
string_repr = f"Parameter({string_repr})"
return string_repr
def _functorch_wrapper_str_intern(tensor, *, tensor_contents=None):
level = torch._C._functorch.maybe_get_level(tensor)
assert level != -1
if torch._C._functorch.is_functionaltensor(tensor):
# Since we're unwrapping the FunctionalTensorWrapper, we need to make sure
# that it's up to date first
torch._sync(tensor)
value = torch._C._functorch.get_unwrapped(tensor)
value_repr = repr(value)
indented_value_repr = textwrap.indent(value_repr, " " * 4)
if torch._C._functorch.is_batchedtensor(tensor):
bdim = torch._C._functorch.maybe_get_bdim(tensor)
assert bdim != -1
return (
f"BatchedTensor(lvl={level}, bdim={bdim}, value=\n"
f"{indented_value_repr}\n"
f")"
)
if torch._C._functorch.is_gradtrackingtensor(tensor):
return (
f"GradTrackingTensor(lvl={level}, value=\n" f"{indented_value_repr}\n" f")"
)
if torch._C._functorch.is_functionaltensor(tensor):
return f"FunctionalTensor(lvl={level}, value=\\\n{value_repr})"
raise ValueError("We don't know how to print this, please file us an issue")
def _str(self, *, tensor_contents=None):
with torch.no_grad(), torch.utils._python_dispatch._disable_current_modes():
guard = torch._C._DisableFuncTorch()
return _str_intern(self, tensor_contents=tensor_contents)
|