mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
027c0d7f8e
Summary:
Fixes multiple compilation on xla tensor print. Please check the conversation here: https://github.com/pytorch/xla/pull/3253
This is done to avoid compilations during tensor printing. Torch performs some tensor operations like slicing to make the tensor readable. These operations result in compilations. Hence to avoid the compilations, copying the tensor to cpu before printing.
example:
```
dev = xm.xla_device()
def test_linear(input_shape=(8, 1024)):
import pdb
pdb.set_trace()
linear = torch.nn.Linear(in_features=1024, out_features=4096, bias=True).to(dev)
inp = torch.randn(*input_shape).to(dev)
output = linear(inp)
xm.mark_step()
return output
```
Returning from this function would have resulted in 63 compiles, since PDB prints the value of the return output. In this case it is a xla tensor.
Now with the current change, there is no compilation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71147
Reviewed By: shunting314
Differential Revision: D33795177
Pulled By: wconstab
fbshipit-source-id: 74b53d9a1cb7ef67f9d8b0a32064f3896be449b5
(cherry picked from commit a9e0687fc5)
435 lines
18 KiB
Python
435 lines
18 KiB
Python
import math
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import torch
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from torch._six import inf
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from typing import Optional
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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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>>> torch.set_printoptions(precision=2)
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>>> torch.tensor([1.12345])
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tensor([1.12])
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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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"""
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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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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(tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0))
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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 = nonzero_finite_vals.abs().double()
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nonzero_finite_min = nonzero_finite_abs.min().double()
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nonzero_finite_max = nonzero_finite_abs.max().double()
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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 nonzero_finite_max / nonzero_finite_min > 1000. or nonzero_finite_max > 1.e8:
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self.sci_mode = True
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for value in nonzero_finite_vals:
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value_str = ('{{:.{}e}}').format(PRINT_OPTS.precision).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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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 nonzero_finite_max / nonzero_finite_min > 1000.\
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or nonzero_finite_max > 1.e8\
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or nonzero_finite_min < 1.e-4:
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self.sci_mode = True
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for value in nonzero_finite_vals:
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value_str = ('{{:.{}e}}').format(PRINT_OPTS.precision).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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for value in nonzero_finite_vals:
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value_str = ('{{:.{}f}}').format(PRINT_OPTS.precision).format(value)
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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 = ('{{:{}.{}e}}').format(self.max_width, PRINT_OPTS.precision).format(value)
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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(1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length))))
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char_per_line = element_length * elements_per_line
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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 = ([_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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else:
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data = [_val_formatter(val) for val in self.tolist()]
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data_lines = [data[i:i + elements_per_line] for i in range(0, len(data), elements_per_line)]
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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 = ([_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2)
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for i in range(0, PRINT_OPTS.edgeitems)] +
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['...'] +
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[_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2)
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for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))])
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else:
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slices = [_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2)
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for i in range(0, self.size(0))]
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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_complex:
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# handle the conjugate bit
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self = self.resolve_conj()
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real_formatter = _Formatter(get_summarized_data(self.real) if summarize else self.real)
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imag_formatter = _Formatter(get_summarized_data(self.imag) if summarize else self.imag)
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return _tensor_str_with_formatter(self, indent, summarize, real_formatter, imag_formatter)
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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((self[:PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems:]))
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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]
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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):
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prefix = 'tensor('
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indent = len(prefix)
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suffixes = []
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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 self.device.type != torch._C._get_default_device()\
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or (self.device.type == 'cuda' and torch.cuda.current_device() != self.device.index):
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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 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 == 'xla' or self.device.type == 'lazy':
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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 = torch.cdouble if torch.get_default_dtype() == torch.double else torch.cfloat
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has_default_dtype = self.dtype in (torch.get_default_dtype(), _default_complex_dtype, torch.int64, torch.bool)
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if self.is_sparse:
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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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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 = indices_prefix + indices_str + '),\n' + ' ' * indent + values_prefix + values_str + ')'
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elif self.is_sparse_csr:
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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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crow_indices_prefix = 'crow_indices=tensor('
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crow_indices = self.crow_indices().detach()
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crow_indices_str = _tensor_str(crow_indices, indent + len(crow_indices_prefix))
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if crow_indices.numel() == 0:
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crow_indices_str += ', size=' + str(tuple(crow_indices.shape))
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col_indices_prefix = 'col_indices=tensor('
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col_indices = self.col_indices().detach()
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col_indices_str = _tensor_str(col_indices, indent + len(col_indices_prefix))
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if col_indices.numel() == 0:
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col_indices_str += ', size=' + str(tuple(col_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 = crow_indices_prefix + crow_indices_str + '),\n' + ' ' * indent +\
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col_indices_prefix + col_indices_str + '),\n' + ' ' * indent +\
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values_prefix + values_str + ')'
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elif self.is_quantized:
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suffixes.append('size=' + str(tuple(self.shape)))
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if not has_default_dtype:
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suffixes.append('dtype=' + str(self.dtype))
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suffixes.append('quantization_scheme=' + str(self.qscheme()))
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if self.qscheme() == torch.per_tensor_affine or self.qscheme() == torch.per_tensor_symmetric:
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suffixes.append('scale=' + str(self.q_scale()))
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suffixes.append('zero_point=' + str(self.q_zero_point()))
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elif self.qscheme() == torch.per_channel_affine or self.qscheme() == torch.per_channel_symmetric \
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or self.qscheme() == torch.per_channel_affine_float_qparams:
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suffixes.append('scale=' + str(self.q_per_channel_scales()))
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suffixes.append('zero_point=' + str(self.q_per_channel_zero_points()))
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suffixes.append('axis=' + str(self.q_per_channel_axis()))
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tensor_str = _tensor_str(self.dequantize(), indent)
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else:
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if self.is_meta:
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suffixes.append('size=' + str(tuple(self.shape)))
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if self.dtype != torch.get_default_dtype():
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suffixes.append('dtype=' + str(self.dtype))
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# TODO: This implies that ellipses is valid syntax for allocating
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# a meta tensor, which it could be, but it isn't right now
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tensor_str = '...'
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else:
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if self.numel() == 0 and not self.is_sparse:
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# Explicitly print the shape if it is not (0,), to match NumPy behavior
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if self.dim() != 1:
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suffixes.append('size=' + str(tuple(self.shape)))
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# In an empty tensor, there are no elements to infer if the dtype
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# should be int64, so it must be shown explicitly.
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if self.dtype != torch.get_default_dtype():
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suffixes.append('dtype=' + str(self.dtype))
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tensor_str = '[]'
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else:
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if not has_default_dtype:
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suffixes.append('dtype=' + str(self.dtype))
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if self.layout != torch.strided:
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tensor_str = _tensor_str(self.to_dense(), indent)
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else:
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tensor_str = _tensor_str(self, indent)
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if self.layout != torch.strided:
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suffixes.append('layout=' + str(self.layout))
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# 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))
|
|
|
|
return _add_suffixes(prefix + tensor_str, suffixes, indent, force_newline=self.is_sparse)
|
|
|
|
def _str(self):
|
|
with torch.no_grad():
|
|
return _str_intern(self)
|