onnx_diagnostic.helpers¶

submodules

onnx_diagnostic.helpers.max_diff(expected: Any, got: Any, verbose: int = 0, level: int = 0, flatten: bool = False, debug_info: List[str] | None = None, begin: int = 0, end: int = -1, _index: int = 0, allow_unique_tensor_with_list_of_one_element: bool = True) → Dict[str, float][source]¶

Returns the maximum discrepancy.

Parameters:

expected – expected values
got – values
verbose – verbosity level
level – for embedded outputs, used for debug purpposes
flatten – flatten outputs
debug_info – debug information
begin – first output to considered
end – last output to considered (-1 for the last one)
_index – used with begin and end
allow_unique_tensor_with_list_of_one_element – allow a comparison between a single tensor and a list of one tensor

Returns:

dictionary with many values

abs: max absolute error
rel: max relative error
sum: sum of the errors
n: number of outputs values, if there is one
output, this number will be the number of elements of this output
dnan: difference in the number of nan

You may use string_diff() to display the discrepancies in one string.

onnx_diagnostic.helpers.string_diff(diff: Dict[str, Any]) → str[source]¶: Renders discrepancies return by max_diff() into one string.

onnx_diagnostic.helpers.string_sig(f: Callable, kwargs: Dict[str, Any] | None = None) → str[source]¶: Displays the signature of a function if the default if the given value is different from

onnx_diagnostic.helpers.string_type(obj: Any, with_shape: bool = False, with_min_max: bool = False, with_device: bool = False, ignore: bool = False, limit: int = 10) → str[source]¶

Displays the types of an object as a string.

Parameters:

obj – any
with_shape – displays shapes as well
with_min_max – displays information about the values
with_device – display the device
ignore – if True, just prints the type for unknown types

Returns:

str

<<<

from onnx_diagnostic.helpers import string_type

print(string_type((1, ["r", 6.6])))

>>>

    (int,#2[str,float])

With pytorch:

<<<

import torch
from onnx_diagnostic.helpers import string_type

inputs = (
    torch.rand((3, 4), dtype=torch.float16),
    [
        torch.rand((5, 6), dtype=torch.float16),
        torch.rand((5, 6, 7), dtype=torch.float16),
    ],
)

# with shapes
print(string_type(inputs, with_shape=True))

# with min max
print(string_type(inputs, with_shape=True, with_min_max=True))

>>>

    (T10s3x4,#2[T10s5x6,T10s5x6x7])
    (T10s3x4[0.00830078125,0.888671875:A0.4205729166666667],#2[T10s5x6[0.08984375,0.966796875:A0.4671875],T10s5x6x7[0.001953125,0.982421875:A0.5045340401785714]])