mirror of
https://github.com/pytorch/pytorch.git
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b0eeffdf6f
Fixes https://github.com/pytorch/functorch/issues/1026 We need to disable functorch's stack-based dispatching mechanism inside the tensor printing. Otherwise, all operations that clean up the data of the Tensor for printing dispatch through the entire functorch stack and causes problems. Disabling stack-based dispatching and printing a functorch wrapped tensor is not a problem; we're still able to get the attributes on the wrapped tensor that we want. Test Plan: - new test Pull Request resolved: https://github.com/pytorch/pytorch/pull/85556 Approved by: https://github.com/samdow
638 lines
24 KiB
Python
638 lines
24 KiB
Python
import math
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import textwrap
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from typing import Optional
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import torch
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from torch._six import inf
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class __PrinterOptions(object):
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precision: int = 4
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threshold: float = 1000
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edgeitems: int = 3
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linewidth: int = 80
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sci_mode: Optional[bool] = None
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PRINT_OPTS = __PrinterOptions()
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# We could use **kwargs, but this will give better docs
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def set_printoptions(
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precision=None,
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threshold=None,
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edgeitems=None,
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linewidth=None,
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profile=None,
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sci_mode=None,
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):
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r"""Set options for printing. Items shamelessly taken from NumPy
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Args:
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precision: Number of digits of precision for floating point output
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(default = 4).
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threshold: Total number of array elements which trigger summarization
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rather than full `repr` (default = 1000).
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edgeitems: Number of array items in summary at beginning and end of
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each dimension (default = 3).
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linewidth: The number of characters per line for the purpose of
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inserting line breaks (default = 80). Thresholded matrices will
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ignore this parameter.
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profile: Sane defaults for pretty printing. Can override with any of
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the above options. (any one of `default`, `short`, `full`)
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sci_mode: Enable (True) or disable (False) scientific notation. If
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None (default) is specified, the value is defined by
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`torch._tensor_str._Formatter`. This value is automatically chosen
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by the framework.
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Example::
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>>> # Limit the precision of elements
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>>> torch.set_printoptions(precision=2)
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>>> torch.tensor([1.12345])
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tensor([1.12])
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>>> # Limit the number of elements shown
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>>> torch.set_printoptions(threshold=5)
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>>> torch.arange(10)
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tensor([0, 1, 2, ..., 7, 8, 9])
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>>> # Restore defaults
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>>> torch.set_printoptions(profile='default')
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>>> torch.tensor([1.12345])
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tensor([1.1235])
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>>> torch.arange(10)
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tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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"""
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if profile is not None:
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if profile == "default":
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PRINT_OPTS.precision = 4
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PRINT_OPTS.threshold = 1000
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PRINT_OPTS.edgeitems = 3
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PRINT_OPTS.linewidth = 80
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elif profile == "short":
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PRINT_OPTS.precision = 2
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PRINT_OPTS.threshold = 1000
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PRINT_OPTS.edgeitems = 2
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PRINT_OPTS.linewidth = 80
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elif profile == "full":
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PRINT_OPTS.precision = 4
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PRINT_OPTS.threshold = inf
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PRINT_OPTS.edgeitems = 3
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PRINT_OPTS.linewidth = 80
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if precision is not None:
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PRINT_OPTS.precision = precision
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if threshold is not None:
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PRINT_OPTS.threshold = threshold
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if edgeitems is not None:
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PRINT_OPTS.edgeitems = edgeitems
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if linewidth is not None:
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PRINT_OPTS.linewidth = linewidth
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PRINT_OPTS.sci_mode = sci_mode
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def tensor_totype(t):
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dtype = torch.float if t.is_mps else torch.double
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return t.to(dtype=dtype)
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class _Formatter(object):
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def __init__(self, tensor):
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self.floating_dtype = tensor.dtype.is_floating_point
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self.int_mode = True
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self.sci_mode = False
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self.max_width = 1
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with torch.no_grad():
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tensor_view = tensor.reshape(-1)
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if not self.floating_dtype:
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for value in tensor_view:
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value_str = "{}".format(value)
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self.max_width = max(self.max_width, len(value_str))
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else:
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nonzero_finite_vals = torch.masked_select(
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tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0)
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)
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if nonzero_finite_vals.numel() == 0:
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# no valid number, do nothing
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return
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# Convert to double for easy calculation. HalfTensor overflows with 1e8, and there's no div() on CPU.
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nonzero_finite_abs = tensor_totype(nonzero_finite_vals.abs())
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nonzero_finite_min = tensor_totype(nonzero_finite_abs.min())
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nonzero_finite_max = tensor_totype(nonzero_finite_abs.max())
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for value in nonzero_finite_vals:
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if value != torch.ceil(value):
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self.int_mode = False
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break
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if self.int_mode:
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# in int_mode for floats, all numbers are integers, and we append a decimal to nonfinites
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# to indicate that the tensor is of floating type. add 1 to the len to account for this.
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if (
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nonzero_finite_max / nonzero_finite_min > 1000.0
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or nonzero_finite_max > 1.0e8
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):
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self.sci_mode = True
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for value in nonzero_finite_vals:
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value_str = (
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("{{:.{}e}}").format(PRINT_OPTS.precision).format(value)
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)
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self.max_width = max(self.max_width, len(value_str))
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else:
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for value in nonzero_finite_vals:
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value_str = ("{:.0f}").format(value)
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self.max_width = max(self.max_width, len(value_str) + 1)
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else:
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# Check if scientific representation should be used.
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if (
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nonzero_finite_max / nonzero_finite_min > 1000.0
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or nonzero_finite_max > 1.0e8
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or nonzero_finite_min < 1.0e-4
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):
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self.sci_mode = True
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for value in nonzero_finite_vals:
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value_str = (
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("{{:.{}e}}").format(PRINT_OPTS.precision).format(value)
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)
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self.max_width = max(self.max_width, len(value_str))
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else:
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for value in nonzero_finite_vals:
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value_str = (
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("{{:.{}f}}").format(PRINT_OPTS.precision).format(value)
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)
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self.max_width = max(self.max_width, len(value_str))
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if PRINT_OPTS.sci_mode is not None:
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self.sci_mode = PRINT_OPTS.sci_mode
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def width(self):
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return self.max_width
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def format(self, value):
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if self.floating_dtype:
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if self.sci_mode:
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ret = (
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("{{:{}.{}e}}")
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.format(self.max_width, PRINT_OPTS.precision)
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.format(value)
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)
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elif self.int_mode:
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ret = "{:.0f}".format(value)
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if not (math.isinf(value) or math.isnan(value)):
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ret += "."
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else:
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ret = ("{{:.{}f}}").format(PRINT_OPTS.precision).format(value)
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else:
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ret = "{}".format(value)
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return (self.max_width - len(ret)) * " " + ret
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def _scalar_str(self, formatter1, formatter2=None):
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if formatter2 is not None:
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real_str = _scalar_str(self.real, formatter1)
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imag_str = (_scalar_str(self.imag, formatter2) + "j").lstrip()
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# handles negative numbers, +0.0, -0.0
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if imag_str[0] == "+" or imag_str[0] == "-":
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return real_str + imag_str
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else:
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return real_str + "+" + imag_str
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else:
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return formatter1.format(self.item())
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def _vector_str(self, indent, summarize, formatter1, formatter2=None):
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# length includes spaces and comma between elements
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element_length = formatter1.width() + 2
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if formatter2 is not None:
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# width for imag_formatter + an extra j for complex
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element_length += formatter2.width() + 1
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elements_per_line = max(
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1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length)))
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)
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# char_per_line = element_length * elements_per_line # unused
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def _val_formatter(val, formatter1=formatter1, formatter2=formatter2):
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if formatter2 is not None:
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real_str = formatter1.format(val.real)
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imag_str = (formatter2.format(val.imag) + "j").lstrip()
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# handles negative numbers, +0.0, -0.0
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if imag_str[0] == "+" or imag_str[0] == "-":
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return real_str + imag_str
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else:
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return real_str + "+" + imag_str
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else:
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return formatter1.format(val)
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if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
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data = (
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[_val_formatter(val) for val in self[: PRINT_OPTS.edgeitems].tolist()]
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+ [" ..."]
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+ [_val_formatter(val) for val in self[-PRINT_OPTS.edgeitems :].tolist()]
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)
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else:
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data = [_val_formatter(val) for val in self.tolist()]
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data_lines = [
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data[i : i + elements_per_line] for i in range(0, len(data), elements_per_line)
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]
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lines = [", ".join(line) for line in data_lines]
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return "[" + ("," + "\n" + " " * (indent + 1)).join(lines) + "]"
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# formatter2 is only used for printing complex tensors.
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# For complex tensors, formatter1 and formatter2 are the formatters for tensor.real
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# and tensor.imag respesectively
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def _tensor_str_with_formatter(self, indent, summarize, formatter1, formatter2=None):
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dim = self.dim()
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if dim == 0:
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return _scalar_str(self, formatter1, formatter2)
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if dim == 1:
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return _vector_str(self, indent, summarize, formatter1, formatter2)
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if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
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slices = (
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[
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_tensor_str_with_formatter(
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self[i], indent + 1, summarize, formatter1, formatter2
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)
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for i in range(0, PRINT_OPTS.edgeitems)
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]
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+ ["..."]
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+ [
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_tensor_str_with_formatter(
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self[i], indent + 1, summarize, formatter1, formatter2
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)
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for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))
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]
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)
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else:
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slices = [
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_tensor_str_with_formatter(
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self[i], indent + 1, summarize, formatter1, formatter2
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)
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for i in range(0, self.size(0))
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]
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tensor_str = ("," + "\n" * (dim - 1) + " " * (indent + 1)).join(slices)
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return "[" + tensor_str + "]"
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def _tensor_str(self, indent):
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if self.numel() == 0:
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return "[]"
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if self.has_names():
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# There are two main codepaths (possibly more) that tensor printing goes through:
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# - tensor data can fit comfortably on screen
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# - tensor data needs to be summarized
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# Some of the codepaths don't fully support named tensors, so we send in
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# an unnamed tensor to the formatting code as a workaround.
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self = self.rename(None)
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summarize = self.numel() > PRINT_OPTS.threshold
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if self._is_zerotensor():
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self = self.clone()
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# handle the negative bit
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if self.is_neg():
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self = self.resolve_neg()
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if self.dtype is torch.float16 or self.dtype is torch.bfloat16:
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self = self.float()
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if self.dtype is torch.complex32:
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self = self.cfloat()
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if self.dtype.is_complex:
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# handle the conjugate bit
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self = self.resolve_conj()
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real_formatter = _Formatter(
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get_summarized_data(self.real) if summarize else self.real
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)
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imag_formatter = _Formatter(
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get_summarized_data(self.imag) if summarize else self.imag
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)
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return _tensor_str_with_formatter(
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self, indent, summarize, real_formatter, imag_formatter
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)
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else:
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formatter = _Formatter(get_summarized_data(self) if summarize else self)
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return _tensor_str_with_formatter(self, indent, summarize, formatter)
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def _add_suffixes(tensor_str, suffixes, indent, force_newline):
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tensor_strs = [tensor_str]
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last_line_len = len(tensor_str) - tensor_str.rfind("\n") + 1
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for suffix in suffixes:
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suffix_len = len(suffix)
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if force_newline or last_line_len + suffix_len + 2 > PRINT_OPTS.linewidth:
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tensor_strs.append(",\n" + " " * indent + suffix)
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last_line_len = indent + suffix_len
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force_newline = False
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else:
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tensor_strs.append(", " + suffix)
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last_line_len += suffix_len + 2
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tensor_strs.append(")")
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return "".join(tensor_strs)
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def get_summarized_data(self):
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dim = self.dim()
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if dim == 0:
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return self
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if dim == 1:
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if self.size(0) > 2 * PRINT_OPTS.edgeitems:
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return torch.cat(
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(self[: PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems :])
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)
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else:
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return self
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if self.size(0) > 2 * PRINT_OPTS.edgeitems:
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start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)]
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end = [self[i] for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))]
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return torch.stack([get_summarized_data(x) for x in (start + end)])
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else:
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return torch.stack([get_summarized_data(x) for x in self])
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def _str_intern(inp, *, tensor_contents=None):
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if torch._C._functorch.is_functorch_wrapped_tensor(inp):
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return _functorch_wrapper_str_intern(inp, tensor_contents=tensor_contents)
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is_plain_tensor = type(inp) is torch.Tensor or type(inp) is torch.nn.Parameter
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if inp.is_nested:
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prefix = "nested_tensor("
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elif is_plain_tensor:
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prefix = "tensor("
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else:
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prefix = f"{type(inp).__name__}("
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indent = len(prefix)
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suffixes = []
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custom_contents_provided = tensor_contents is not None
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if custom_contents_provided:
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tensor_str = tensor_contents
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# This is used to extract the primal value and thus disable the forward AD
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# within this function.
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# TODO(albanD) This needs to be updated when more than one level is supported
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self, tangent = torch.autograd.forward_ad.unpack_dual(inp)
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# Note [Print tensor device]:
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# A general logic here is we only print device when it doesn't match
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# the device specified in default tensor type.
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# Currently torch.set_default_tensor_type() only supports CPU/CUDA, thus
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# torch._C._get_default_device() only returns either cpu or cuda.
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# In other cases, we don't have a way to set them as default yet,
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# and we should always print out device for them.
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if (
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self.device.type != torch._C._get_default_device()
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or (
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self.device.type == "cuda"
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and torch.cuda.current_device() != self.device.index
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)
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or (self.device.type == "mps")
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):
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suffixes.append("device='" + str(self.device) + "'")
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# Tensor printing performs tensor operations like slice, indexing, etc to make it in a
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# representable format. These operations on ipu/xla/lazy tensor results in compilations. Hence,
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# to avoid compilations, copying the tensor to cpu before printing.
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if self.device.type in ["xla", "lazy", "ipu"]:
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self = self.to("cpu")
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# TODO: add an API to map real -> complex dtypes
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_default_complex_dtype = (
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torch.cdouble if torch.get_default_dtype() == torch.double else torch.cfloat
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)
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has_default_dtype = self.dtype in (
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torch.get_default_dtype(),
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_default_complex_dtype,
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torch.int64,
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torch.bool,
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)
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if self.is_sparse:
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suffixes.append("size=" + str(tuple(self.shape)))
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from torch._subclasses.fake_tensor import FakeTensor
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if not self.is_meta and not isinstance(self, FakeTensor):
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suffixes.append("nnz=" + str(self._nnz()))
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if not has_default_dtype:
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suffixes.append("dtype=" + str(self.dtype))
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if not custom_contents_provided:
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indices_prefix = "indices=tensor("
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indices = self._indices().detach()
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indices_str = _tensor_str(indices, indent + len(indices_prefix))
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if indices.numel() == 0:
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indices_str += ", size=" + str(tuple(indices.shape))
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values_prefix = "values=tensor("
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values = self._values().detach()
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values_str = _tensor_str(values, indent + len(values_prefix))
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if values.numel() == 0:
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values_str += ", size=" + str(tuple(values.shape))
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tensor_str = (
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indices_prefix
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+ indices_str
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+ "),\n"
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+ " " * indent
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+ values_prefix
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+ values_str
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+ ")"
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)
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elif self.layout in {
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torch.sparse_csr,
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torch.sparse_csc,
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torch.sparse_bsr,
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torch.sparse_bsc,
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}:
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suffixes.append("size=" + str(tuple(self.shape)))
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suffixes.append("nnz=" + str(self._nnz()))
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if not has_default_dtype:
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suffixes.append("dtype=" + str(self.dtype))
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if not custom_contents_provided:
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compressed_indices_method, plain_indices_method = {
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torch.sparse_csr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
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torch.sparse_csc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
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torch.sparse_bsr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
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torch.sparse_bsc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
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}[self.layout]
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if self.layout in {torch.sparse_csr, torch.sparse_bsr}:
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cdimname, pdimname = "row", "column"
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else:
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cdimname, pdimname = "column", "row"
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compressed_indices_prefix = f"c{cdimname[:3]}_indices=tensor("
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compressed_indices = compressed_indices_method(self).detach()
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compressed_indices_str = _tensor_str(
|
|
compressed_indices, indent + len(compressed_indices_prefix)
|
|
)
|
|
if compressed_indices.numel() == 0:
|
|
compressed_indices_str += ", size=" + str(
|
|
tuple(compressed_indices.shape)
|
|
)
|
|
plain_indices_prefix = f"{pdimname[:3]}_indices=tensor("
|
|
plain_indices = plain_indices_method(self).detach()
|
|
plain_indices_str = _tensor_str(
|
|
plain_indices, indent + len(plain_indices_prefix)
|
|
)
|
|
if plain_indices.numel() == 0:
|
|
plain_indices_str += ", size=" + str(tuple(plain_indices.shape))
|
|
values_prefix = "values=tensor("
|
|
values = self.values().detach()
|
|
values_str = _tensor_str(values, indent + len(values_prefix))
|
|
if values.numel() == 0:
|
|
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:
|
|
if self.is_meta:
|
|
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, 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 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.
|
|
if inp.grad_fn is not None:
|
|
name = type(inp.grad_fn).__name__
|
|
if name == "CppFunction":
|
|
name = inp.grad_fn.name().rsplit("::", 1)[-1]
|
|
suffixes.append("grad_fn=<{}>".format(name))
|
|
elif inp.requires_grad:
|
|
suffixes.append("requires_grad=True")
|
|
|
|
if self.has_names():
|
|
suffixes.append("names={}".format(self.names))
|
|
|
|
if tangent is not None:
|
|
suffixes.append("tangent={}".format(tangent))
|
|
|
|
string_repr = _add_suffixes(
|
|
prefix + tensor_str, suffixes, indent, force_newline=self.is_sparse
|
|
)
|
|
|
|
# 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():
|
|
guard = torch._C._DisableFuncTorch()
|
|
return _str_intern(self, tensor_contents=tensor_contents)
|