onnx_diagnostic.ext_test_case¶

The module contains the main class ExtTestCase which adds specific functionalities to this project.

class onnx_diagnostic.ext_test_case.ExtTestCase(methodName='runTest')[source]¶

Inherits from unittest.TestCase and adds specific comprison functions and other helper.

assertAlmostEqual(expected: ndarray, value: ndarray, atol: float = 0, rtol: float = 0)[source]¶: In the name

assertEmpty(value: Any)[source]¶: In the name

assertEqual(expected: Any, value: Any, msg: str = '')[source]¶: Overwrites the error message to get a more explicit message about what is what.

assertEqualArray(expected: Any, value: Any, atol: float = 0, rtol: float = 0, msg: str | None = None)[source]¶: In the name

assertEqualArrays(expected: Sequence[ndarray], value: Sequence[ndarray], atol: float = 0, rtol: float = 0, msg: str | None = None)[source]¶: In the name

assertExists(name)[source]¶: Checks the existing of a file.

assertGreaterOrEqual(a, b, msg=None)[source]¶: In the name

assertIn(tofind: str, text: str, msg: str = '')[source]¶: Just like self.assertTrue(a in b), but with a nicer default message.

assertNotEmpty(value: Any)[source]¶: In the name

assertRaise(fct: Callable, exc_type: type[Exception], msg: str | None = None)[source]¶: In the name

assertSetContained(set1, set2)[source]¶: Checks that set1 is contained in set2.

assertStartsWith(prefix: str, full: str)[source]¶: In the name

assert_onnx_disc(test_name: str, proto: onnx.ModelProto, model: torch.nn.Module, inputs: Tuple[Any] | Dict[str, Any], verbose: int = 0, atol: float = 1e-05, rtol: float = 0.001, copy_inputs: bool = True, expected: Any | None = None, use_ort: bool = False, **kwargs)[source]¶

Checks for discrepancies. Runs the onnx models, computes expected outputs, in that order. The inputs may be modified by this functions if the torch model modifies them inplace.

Parameters:

test_name – test name, dumps the model if not empty
proto – onnx model
model – torch model
inputs – inputs
verbose – verbosity
atol – absolute tolerance
rtol – relative tolerance
expected – expected values
copy_inputs – to copy the inputs
use_ort – use onnxruntime.InferenceSession
kwargs – arguments sent to onnx_diagnostic.helpers.ort_session.InferenceSessionForTorch

capture(fct: Callable)[source]¶

Runs a function and capture standard output and error.

Parameters:: fct – function to run
Returns:: result of fct, output, error

dump_onnx(name: str, proto: Any, folder: str | None = None) → str[source]¶: Dumps an onnx file.

get_dump_file(name: str, folder: str | None = None) → str[source]¶: Returns a filename to dump a model.

print_model(model: ModelProto)[source]¶: Prints a ModelProto

print_onnx(model: ModelProto)[source]¶: Prints a ModelProto

classmethod setUpClass()[source]¶: Hook method for setting up class fixture before running tests in the class.

subloop(*args, verbose: int = 0)[source]¶: Loops over elements and calls unittests.TestCase.subTest().

classmethod tearDownClass()[source]¶: Hook method for deconstructing the class fixture after running all tests in the class.

classmethod todo(f: Callable, msg: str)[source]¶: Adds a todo printed when all test are run.

tryCall(fct: Callable, msg: str | None = None, none_if: str | None = None) → Any | None[source]¶

Calls the function, catch any error.

Parameters:

fct – function to call
msg – error message to display if failing
none_if – returns None if this substring is found in the error message

Returns:

output of fct

onnx_diagnostic.ext_test_case.get_figure(ax)[source]¶: Returns the figure of a matplotlib figure.

onnx_diagnostic.ext_test_case.has_cuda() → bool[source]¶: Returns torch.cuda.device_count() > 0.

onnx_diagnostic.ext_test_case.has_onnxruntime_training(push_back_batch: bool = False)[source]¶: Tells if onnxruntime_training is installed.

onnx_diagnostic.ext_test_case.has_onnxscript(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxscript is not recent enough.

onnx_diagnostic.ext_test_case.has_torch(version: str) → bool[source]¶: Returns True if torch transformers is higher.

onnx_diagnostic.ext_test_case.has_transformers(version: str) → bool[source]¶: Returns True if transformers version is higher.

onnx_diagnostic.ext_test_case.hide_stdout(f: Callable | None = None) → Callable[source]¶

Catches warnings, hides standard output. The function may be disabled by setting UNHIDE=1 before running the unit test.

Parameters:: f – the function is called with the stdout as an argument

onnx_diagnostic.ext_test_case.ignore_errors(errors: Exception | Tuple[Exception]) → Callable[source]¶

Catches exception, skip the test if the error is expected sometimes.

Parameters:: errors – errors to ignore

onnx_diagnostic.ext_test_case.ignore_warnings(warns: List[Warning]) → Callable[source]¶

Catches warnings.

Parameters:: warns – warnings to ignore

onnx_diagnostic.ext_test_case.is_azure() → bool[source]¶: Tells if the job is running on Azure DevOps.

onnx_diagnostic.ext_test_case.long_test(msg: str = '') → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

onnx_diagnostic.ext_test_case.measure_time(stmt: str | Callable, context: Dict[str, Any] | None = None, repeat: int = 10, number: int = 50, warmup: int = 1, div_by_number: bool = True, max_time: float | None = None) → Dict[str, str | int | float][source]¶

Measures a statement and returns the results as a dictionary.

Parameters:

stmt – string or callable
context – variable to know in a dictionary
repeat – average over repeat experiment
number – number of executions in one row
warmup – number of iteration to do before starting the real measurement
div_by_number – divide by the number of executions
max_time – execute the statement until the total goes beyond this time (approximately), repeat is ignored, div_by_number must be set to True

Returns:

dictionary

<<<

from pprint import pprint
from math import cos
from onnx_diagnostic.ext_test_case import measure_time

res = measure_time(lambda: cos(0.5))
pprint(res)

>>>

    {'average': np.float64(7.743404421489687e-08),
     'context_size': 64,
     'deviation': np.float64(1.801079762106381e-08),
     'max_exec': np.float64(1.3126002158969642e-07),
     'min_exec': np.float64(6.950009264983237e-08),
     'number': 50,
     'repeat': 10,
     'ttime': np.float64(7.743404421489686e-07),
     'warmup_time': 0.0018865399979404174}

See Timer.repeat for a better understanding of parameter repeat and number. The function returns a duration corresponding to number times the execution of the main statement.

onnx_diagnostic.ext_test_case.never_test(msg: str = '') → Callable[source]¶: Skips a unit test.

onnx_diagnostic.ext_test_case.requires_cuda(msg: str = '', version: str = '', memory: int = 0)[source]¶

Skips a test if cuda is not available.

Parameters:

msg – to overwrite the message
version – minimum version
memory – minimum number of Gb to run the test

onnx_diagnostic.ext_test_case.requires_diffusers(version: str, msg: str = '', or_older_than: str | None = None) → Callable[source]¶: Skips a unit test if transformers is not recent enough.

onnx_diagnostic.ext_test_case.requires_numpy(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if numpy is not recent enough.

onnx_diagnostic.ext_test_case.requires_onnx(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnx is not recent enough.

onnx_diagnostic.ext_test_case.requires_onnx_array_api(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnx-array-api is not recent enough.

onnx_diagnostic.ext_test_case.requires_onnxruntime(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxruntime is not recent enough.

onnx_diagnostic.ext_test_case.requires_onnxruntime_training(push_back_batch: bool = False, ortmodule: bool = False, msg: str = '') → Callable[source]¶: Skips a unit test if onnxruntime is not onnxruntime_training.

onnx_diagnostic.ext_test_case.requires_onnxscript(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if onnxscript is not recent enough.

onnx_diagnostic.ext_test_case.requires_sklearn(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if scikit-learn is not recent enough.

onnx_diagnostic.ext_test_case.requires_torch(version: str, msg: str = '') → Callable[source]¶: Skips a unit test if pytorch is not recent enough.

onnx_diagnostic.ext_test_case.requires_transformers(version: str, msg: str = '', or_older_than: str | None = None) → Callable[source]¶: Skips a unit test if transformers is not recent enough.

onnx_diagnostic.ext_test_case.requires_zoo(msg: str = '') → Callable[source]¶: Skips a unit test if environment variable ZOO is not equal to 1.

onnx_diagnostic.ext_test_case.skipif_ci_apple(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

onnx_diagnostic.ext_test_case.skipif_ci_linux(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Linux.

onnx_diagnostic.ext_test_case.skipif_ci_windows(msg) → Callable[source]¶: Skips a unit test if it runs on azure pipeline on Windows.

onnx_diagnostic.ext_test_case.statistics_on_file(filename: str) → Dict[str, str | int | float][source]¶

Computes statistics on a file.

<<<

import pprint
from onnx_diagnostic.ext_test_case import statistics_on_file, __file__

pprint.pprint(statistics_on_file(__file__))

>>>

    {'chars': 26792, 'ext': '.py', 'lines': 921}

onnx_diagnostic.ext_test_case.statistics_on_folder(folder: str | List[str], pattern: str = '.*[.]((py|rst))$', aggregation: int = 0) → List[Dict[str, str | int | float]][source]¶

Computes statistics on files in a folder.

Parameters:

folder – folder or folders to investigate
pattern – file pattern
aggregation – show the first subfolders

Returns:

list of dictionaries

<<<

import os
import pprint
from onnx_diagnostic.ext_test_case import statistics_on_folder, __file__

pprint.pprint(statistics_on_folder(os.path.dirname(__file__)))

>>>

    [{'chars': 706, 'ext': '.py', 'lines': 27, 'name': 'doc.py'},
     {'chars': 26792, 'ext': '.py', 'lines': 921, 'name': 'ext_test_case.py'},
     {'chars': 136, 'ext': '.py', 'lines': 4, 'name': '__init__.py'},
     {'chars': 19723, 'ext': '.py', 'lines': 589, 'name': 'helpers/ort_session.py'},
     {'chars': 27703, 'ext': '.py', 'lines': 1019, 'name': 'helpers/helper.py'},
     {'chars': 2820, 'ext': '.py', 'lines': 75, 'name': 'helpers/cache_helper.py'},
     {'chars': 2878, 'ext': '.py', 'lines': 115, 'name': 'helpers/args_helper.py'},
     {'chars': 6983,
      'ext': '.py',
      'lines': 224,
      'name': 'helpers/torch_test_helper.py'},
     {'chars': 20798, 'ext': '.py', 'lines': 746, 'name': 'helpers/onnx_helper.py'},
     {'chars': 60, 'ext': '.py', 'lines': 1, 'name': 'helpers/__init__.py'},
     {'chars': 751,
      'ext': '.py',
      'lines': 34,
      'name': 'reference/quantized_tensor.py'},
     {'chars': 11323,
      'ext': '.py',
      'lines': 360,
      'name': 'reference/ort_evaluator.py'},
     {'chars': 6199, 'ext': '.py', 'lines': 219, 'name': 'reference/evaluator.py'},
     {'chars': 90, 'ext': '.py', 'lines': 2, 'name': 'reference/__init__.py'},
     {'chars': 439,
      'ext': '.py',
      'lines': 20,
      'name': 'reference/ops/op_complex.py'},
     {'chars': 647,
      'ext': '.py',
      'lines': 27,
      'name': 'reference/ops/op_fused_matmul.py'},
     {'chars': 380,
      'ext': '.py',
      'lines': 14,
      'name': 'reference/ops/op_tri_matrix.py'},
     {'chars': 458,
      'ext': '.py',
      'lines': 17,
      'name': 'reference/ops/op_quick_gelu.py'},
     {'chars': 531, 'ext': '.py', 'lines': 15, 'name': 'reference/ops/op_slice.py'},
     {'chars': 2582,
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      'lines': 88,
      'name': 'reference/ops/op_qlinear_conv.py'},
     {'chars': 1161,
      'ext': '.py',
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      'name': 'reference/ops/op_constant_of_shape.py'},
     {'chars': 667,
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      'name': 'reference/ops/op_scatternd_of_shape.py'},
     {'chars': 147,
      'ext': '.py',
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      'name': 'reference/ops/op_negxplus1.py'},
     {'chars': 220,
      'ext': '.py',
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      'name': 'reference/ops/op_simplified_layer_normalization.py'},
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      'name': 'reference/ops/op_transpose_cast.py'},
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      'name': 'reference/ops/op_average_pool_grad.py'},
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      'name': 'reference/ops/op_gather_grad.py'},
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      'name': 'reference/ops/op_qlinear_average_pool.py'},
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      'name': 'reference/ops/op_gather.py'},
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      'name': 'reference/ops/op_attention.py'},
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      'name': 'reference/ops/op_bias_softmax.py'},
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      'name': 'reference/ops/op_replace_zero.py'},
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      'ext': '.py',
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      'name': 'reference/ops/op_mul_sigmoid.py'},
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      'name': 'reference/ops/op_rotary.py'},
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      'name': 'reference/ops/op_concat.py'},
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      'ext': '.py',
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      'name': 'reference/ops/op_add_add_mul_mul.py'},
     {'chars': 2202,
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      'name': 'reference/ops/op_scatter_elements.py'},
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      'name': 'torch_export_patches/onnx_export_serialization.py'},
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      'name': 'torch_export_patches/patch_inputs.py'},
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      'name': 'torch_export_patches/__init__.py'},
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      'ext': '.py',
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      'name': 'torch_models/untrained/llm_phi2.py'},
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Aggregated:

<<<

import os
import pprint
from onnx_diagnostic.ext_test_case import statistics_on_folder, __file__

pprint.pprint(statistics_on_folder(os.path.dirname(__file__), aggregation=1))

>>>

    [{'chars': 706, 'dir': '', 'ext': '.py', 'lines': 27, 'name': 'doc.py'},
     {'chars': 26792,
      'dir': '',
      'ext': '.py',
      'lines': 921,
      'name': 'ext_test_case.py'},
     {'chars': 136, 'dir': '', 'ext': '.py', 'lines': 4, 'name': '__init__.py'},
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      'name': 'helpers/helper.py'},
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      'dir': 'helpers',
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      'name': 'helpers/cache_helper.py'},
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      'name': 'helpers/args_helper.py'},
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onnx_diagnostic.ext_test_case.unit_test_going()[source]¶: Enables a flag telling the script is running while testing it. Avois unit tests to be very long.