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https://github.com/pytorch/pytorch.git
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c26884f063
Summary: `ExportedProgram` is an artifact produced by torch.export, containing the graph that is exported, along with other attributes about the original program such as the graph signature, state dict, and constants. One slightly confusing thing that users run into is that they treat the `ExportedProgram` as a `torch.nn.Module`, since the object is callable. However, as we do not plan to support all features that `torch.nn.Module`s have, like hooks, we want to create a distinction between it and the `ExportedProgram` by removing the `__call__` method. Instead users can create a proper `torch.nn.Module` through `exported_program.module()` and use that as a callable. Test Plan: CI Differential Revision: D53075378 Pull Request resolved: https://github.com/pytorch/pytorch/pull/119466 Approved by: https://github.com/zhxchen17, https://github.com/thiagocrepaldi
795 lines
27 KiB
Python
795 lines
27 KiB
Python
# Owner(s): ["module: onnx"]
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from __future__ import annotations
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import contextlib
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import copy
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import dataclasses
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import io
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import logging
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import os
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import unittest
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import warnings
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from typing import (
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Any,
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Callable,
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Collection,
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Iterable,
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List,
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Mapping,
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Optional,
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Sequence,
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Tuple,
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Type,
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Union,
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)
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import numpy as np
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import onnxruntime
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import pytest
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import pytorch_test_common
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import torch
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from torch import export as torch_export
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from torch.onnx import _constants, verification
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from torch.onnx._internal import _beartype
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from torch.onnx._internal.fx import diagnostics
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from torch.testing._internal import common_utils
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from torch.testing._internal.opinfo import core as opinfo_core
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from torch.types import Number
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_NumericType = Union[Number, torch.Tensor, np.ndarray]
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_ModelType = Union[torch.nn.Module, Callable, torch_export.ExportedProgram]
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_InputArgsType = Optional[
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Union[torch.Tensor, int, float, bool, Sequence[Any], Mapping[str, Any]]
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]
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_OutputsType = Sequence[_NumericType]
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onnx_model_dir = os.path.join(
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os.path.dirname(os.path.realpath(__file__)),
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os.pardir,
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"repos",
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"onnx",
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"onnx",
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"backend",
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"test",
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"data",
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)
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pytorch_converted_dir = os.path.join(onnx_model_dir, "pytorch-converted")
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pytorch_operator_dir = os.path.join(onnx_model_dir, "pytorch-operator")
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def run_model_test(test_suite: _TestONNXRuntime, *args, **kwargs):
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options = verification.VerificationOptions()
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kwargs["opset_version"] = test_suite.opset_version
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kwargs["keep_initializers_as_inputs"] = test_suite.keep_initializers_as_inputs
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if hasattr(test_suite, "check_shape"):
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options.check_shape = test_suite.check_shape
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if hasattr(test_suite, "check_dtype"):
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options.check_dtype = test_suite.check_dtype
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names = {f.name for f in dataclasses.fields(options)}
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keywords_to_pop = []
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for k, v in kwargs.items():
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if k in names:
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setattr(options, k, v)
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keywords_to_pop.append(k)
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for k in keywords_to_pop:
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kwargs.pop(k)
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return verification.verify(*args, options=options, **kwargs)
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def assert_dynamic_shapes(onnx_program: torch.onnx.ONNXProgram, dynamic_shapes: bool):
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"""Assert whether the exported model has dynamic shapes or not.
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Args:
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onnx_program (torch.onnx.ONNXProgram): The output of torch.onnx.dynamo_export.
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dynamic_shapes (bool): Whether the exported model has dynamic shapes or not.
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When True, raises if graph inputs don't have at least one dynamic dimension
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When False, raises if graph inputs have at least one dynamic dimension.
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Raises:
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AssertionError: If the exported model has dynamic shapes and dynamic_shapes is False and vice-versa.
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"""
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if dynamic_shapes is None:
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return
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model_proto = onnx_program.model_proto
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# Process graph inputs
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dynamic_inputs = []
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for inp in model_proto.graph.input:
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dynamic_inputs += [
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dim
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for dim in inp.type.tensor_type.shape.dim
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if dim.dim_value == 0 and dim.dim_param != ""
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]
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assert dynamic_shapes == (
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len(dynamic_inputs) > 0
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), "Dynamic shape check failed for graph inputs"
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def parameterize_class_name(cls: Type, idx: int, input_dicts: Mapping[Any, Any]):
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"""Combine class name with the parameterized arguments.
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This function is passed to `parameterized.parameterized_class` as the
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`class_name_func` argument.
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"""
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suffix = "_".join(f"{k}_{v}" for k, v in input_dicts.items())
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return f"{cls.__name__}_{suffix}"
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class _TestONNXRuntime(pytorch_test_common.ExportTestCase):
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opset_version = _constants.ONNX_DEFAULT_OPSET
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keep_initializers_as_inputs = True # For IR version 3 type export.
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is_script = False
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check_shape = True
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check_dtype = True
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def setUp(self):
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super().setUp()
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onnxruntime.set_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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os.environ["ALLOW_RELEASED_ONNX_OPSET_ONLY"] = "0"
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self.is_script_test_enabled = True
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# The exported ONNX model may have less inputs than the pytorch model because of const folding.
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# This mostly happens in unit test, where we widely use torch.size or torch.shape.
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# So the output is only dependent on the input shape, not value.
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# remained_onnx_input_idx is used to indicate which pytorch model input idx is remained in ONNX model.
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def run_test(
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self,
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model,
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input_args,
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input_kwargs=None,
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rtol=1e-3,
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atol=1e-7,
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do_constant_folding=True,
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dynamic_axes=None,
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additional_test_inputs=None,
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input_names=None,
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output_names=None,
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fixed_batch_size=False,
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training=torch.onnx.TrainingMode.EVAL,
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remained_onnx_input_idx=None,
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verbose=False,
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):
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def _run_test(m, remained_onnx_input_idx, flatten=True, ignore_none=True):
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return run_model_test(
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self,
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m,
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input_args=input_args,
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input_kwargs=input_kwargs,
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rtol=rtol,
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atol=atol,
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do_constant_folding=do_constant_folding,
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dynamic_axes=dynamic_axes,
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additional_test_inputs=additional_test_inputs,
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input_names=input_names,
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output_names=output_names,
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fixed_batch_size=fixed_batch_size,
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training=training,
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remained_onnx_input_idx=remained_onnx_input_idx,
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flatten=flatten,
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ignore_none=ignore_none,
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verbose=verbose,
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)
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if isinstance(remained_onnx_input_idx, dict):
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scripting_remained_onnx_input_idx = remained_onnx_input_idx["scripting"]
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tracing_remained_onnx_input_idx = remained_onnx_input_idx["tracing"]
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else:
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scripting_remained_onnx_input_idx = remained_onnx_input_idx
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tracing_remained_onnx_input_idx = remained_onnx_input_idx
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is_model_script = isinstance(
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model, (torch.jit.ScriptModule, torch.jit.ScriptFunction)
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)
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if self.is_script_test_enabled and self.is_script:
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script_model = model if is_model_script else torch.jit.script(model)
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_run_test(
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script_model,
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scripting_remained_onnx_input_idx,
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flatten=False,
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ignore_none=False,
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)
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if not is_model_script and not self.is_script:
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_run_test(model, tracing_remained_onnx_input_idx)
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@_beartype.beartype
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def run_test_with_fx_to_onnx_exporter_and_onnx_runtime(
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self,
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model: _ModelType,
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input_args: Sequence[_InputArgsType],
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*,
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input_kwargs: Optional[Mapping[str, _InputArgsType]] = None,
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rtol: Optional[float] = 1e-3,
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atol: Optional[float] = 1e-7,
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has_mutation: bool = False,
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additional_test_inputs: Optional[
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List[
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Union[
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Tuple[Sequence[_InputArgsType], Mapping[str, _InputArgsType]],
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Tuple[Sequence[_InputArgsType]],
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]
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]
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] = None,
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skip_dynamic_shapes_check: bool = False,
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):
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"""Compare the results of PyTorch model with exported ONNX model
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Args:
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model (_ModelType): PyTorch model
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input_args (Sequence[_InputArgsType]): torch input arguments
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input_kwargs (Mapping[str, _InputArgsType]): torch input kwargs
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rtol (float, optional): relative tolerance. Defaults to 1e-3.
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atol (float, optional): absolute tolerance. Defaults to 1e-7.
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has_mutation (bool, optional): Whether the model mutates its input or state.
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`mutation` as `True` incurs extra overhead of cloning the inputs and model.
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Defaults to False.
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additional_test_inputs: Test the models with another dataset input, which
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is designed for dynamic axes testing. Defaults to None. It's a list of
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different input sets in tuples. Inside tuple, the first element is a tuple
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of args, and the second element is a dict of kwargs. Remember to put comma
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even if the following element is not provided.
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For example,
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additional_test_inputs = [((args1, args2), {"kwargs":1}), ((args1,),), ((), {"kwargs":1})]
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skip_dynamic_shapes_check: Whether to skip dynamic shape check. Defaults to False.
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Must be used when tests do not produce dynamic shapes even when dynamic shape feature is enabled.
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This is needed because Torch Dynamo uses the dynamic_shapes flag as a hint, only.
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"""
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# avoid mutable data structure
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if input_kwargs is None:
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input_kwargs = {}
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if (
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has_mutation
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and self.model_type
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!= pytorch_test_common.TorchModelType.TORCH_EXPORT_EXPORTEDPROGRAM
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):
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ref_model = _try_clone_model(model)
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ref_input_args, ref_input_kwargs = _try_clone_inputs(
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input_args, input_kwargs
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)
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else:
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ref_model = model
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ref_input_args = input_args
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ref_input_kwargs = input_kwargs
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assert isinstance(ref_model, torch.nn.Module) or callable(
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ref_model
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), "Model must be a torch.nn.Module or callable"
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if (
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self.model_type
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== pytorch_test_common.TorchModelType.TORCH_EXPORT_EXPORTEDPROGRAM
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):
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ref_model = torch.export.export(ref_model, args=ref_input_args)
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if (
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self.dynamic_shapes
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): # TODO: Support dynamic shapes for torch.export.ExportedProgram
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# https://github.com/pytorch/pytorch/issues/113705
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pytest.xfail(
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reason="torch.export.ExportedProgram does not support dynamic shapes"
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)
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# Feed args and kwargs into exporter.
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# Note that exporter should flatten kwargs into positional args the exported model;
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# since ONNX doesn't represent kwargs.
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export_error: Optional[torch.onnx.OnnxExporterError] = None
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try:
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onnx_program = torch.onnx.dynamo_export(
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ref_model,
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*ref_input_args,
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**ref_input_kwargs,
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export_options=torch.onnx.ExportOptions(
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op_level_debug=self.op_level_debug,
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dynamic_shapes=self.dynamic_shapes,
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diagnostic_options=torch.onnx.DiagnosticOptions(
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verbosity_level=logging.DEBUG
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),
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),
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)
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except torch.onnx.OnnxExporterError as e:
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export_error = e
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onnx_program = e.onnx_program
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if diagnostics.is_onnx_diagnostics_log_artifact_enabled():
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onnx_program.save_diagnostics(
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f"test_report_{self._testMethodName}"
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f"_op_level_debug_{self.op_level_debug}"
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f"_dynamic_axes_{self.dynamic_shapes}"
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f"_model_type_{self.model_type}"
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".sarif"
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)
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if export_error is not None:
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raise export_error
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if not skip_dynamic_shapes_check:
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assert_dynamic_shapes(onnx_program, self.dynamic_shapes)
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_compare_pytorch_onnx_with_ort(
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onnx_program,
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ref_model.module(),
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input_args,
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input_kwargs,
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atol,
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rtol,
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has_mutation=has_mutation,
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)
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# This confirms the exported mode accepts different input shapes
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# when dynamic shape is enabled.
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if additional_test_inputs and self.dynamic_shapes:
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for another_input in additional_test_inputs:
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if len(another_input) > 2:
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raise ValueError(
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f"test_inputs should only have tuple args and dictionary kwargs. But receives: {len(another_input)}"
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)
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additional_input_args = another_input[0]
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additional_input_kwargs = (
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another_input[1]
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if len(another_input) == 2 and another_input[1] is not None
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else {}
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)
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_compare_pytorch_onnx_with_ort(
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onnx_program,
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ref_model,
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additional_input_args,
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additional_input_kwargs,
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atol,
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rtol,
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has_mutation=has_mutation,
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)
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@_beartype.beartype
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def run_ort(
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onnx_model: Union[str, torch.onnx.ONNXProgram],
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pytorch_inputs: Sequence[_InputArgsType],
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) -> _OutputsType:
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"""Run ORT on the given ONNX model and inputs
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Used in test_fx_to_onnx_with_onnxruntime.py
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Args:
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onnx_model (Union[str, torch.onnx.ONNXProgram]): Converter ONNX model
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pytorch_inputs (Sequence[_InputArgsType]): The given torch inputs
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Raises:
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AssertionError: ONNX and PyTorch should have the same input sizes
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Returns:
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_OutputsType: ONNX model predictions
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"""
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if isinstance(onnx_model, torch.onnx.ONNXProgram):
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buffer = io.BytesIO()
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onnx_model.save(buffer)
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ort_model = buffer.getvalue()
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else:
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ort_model = onnx_model
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# Suppress floods of warnings from ONNX Runtime
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session_options = onnxruntime.SessionOptions()
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session_options.log_severity_level = 3 # Error
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session = onnxruntime.InferenceSession(
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ort_model, providers=["CPUExecutionProvider"], sess_options=session_options
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)
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input_names = [ort_input.name for ort_input in session.get_inputs()]
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if len(input_names) != len(pytorch_inputs):
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raise AssertionError(
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f"Expected {len(input_names)} inputs, got {len(pytorch_inputs)}"
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)
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ort_input = {
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k: torch.Tensor.numpy(v, force=True)
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for k, v in zip(input_names, pytorch_inputs)
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}
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return session.run(None, ort_input)
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@_beartype.beartype
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def _try_clone_model(model: _ModelType) -> _ModelType:
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"""Used for preserving original model in case forward mutates model states."""
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try:
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return copy.deepcopy(model)
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except Exception:
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warnings.warn(
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"Failed to clone model. Model state might be mutated during verification."
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)
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return model
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@_beartype.beartype
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def _try_clone_inputs(input_args, input_kwargs):
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ref_input_args = copy.deepcopy(input_args)
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ref_input_kwargs = copy.deepcopy(input_kwargs)
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return ref_input_args, ref_input_kwargs
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@_beartype.beartype
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def _compare_pytorch_onnx_with_ort(
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onnx_program: torch.onnx.ONNXProgram,
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model: _ModelType,
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input_args: Sequence[_InputArgsType],
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input_kwargs: Mapping[str, _InputArgsType],
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atol: Optional[float] = None,
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rtol: Optional[float] = None,
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has_mutation: bool = False,
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):
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if has_mutation:
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ref_model = _try_clone_model(model)
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ref_input_args, ref_input_kwargs = _try_clone_inputs(input_args, input_kwargs)
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else:
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ref_model = model
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ref_input_args = input_args
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ref_input_kwargs = input_kwargs
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# NOTE: ONNXProgram holds a reference (not copy) to the original ref_model, including its state_dict.
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# Thus, ONNXProgram() must run before ref_model() to prevent ref_model.forward() from changing the state_dict.
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# Otherwise, the ref_model can change buffers on state_dict which would be used by ONNXProgram.__call__()
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# NOTE: `model_with_state_dict=ref_model` is specified to cover runs with FakeTensor support
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ort_outputs = onnx_program(*input_args, **input_kwargs)
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ref_outputs = ref_model(*ref_input_args, **ref_input_kwargs)
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ref_outputs = onnx_program.adapt_torch_outputs_to_onnx(ref_outputs)
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if len(ref_outputs) != len(ort_outputs):
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raise AssertionError(
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f"Expected {len(ref_outputs)} outputs, got {len(ort_outputs)}"
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)
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for ref_output, ort_output in zip(ref_outputs, ort_outputs):
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torch.testing.assert_close(
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ref_output, torch.tensor(ort_output), rtol=rtol, atol=atol
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)
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# The min onnx opset version to test for
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MIN_ONNX_OPSET_VERSION = 9
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# The max onnx opset version to test for
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MAX_ONNX_OPSET_VERSION = _constants.ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET
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TESTED_OPSETS = range(MIN_ONNX_OPSET_VERSION, MAX_ONNX_OPSET_VERSION + 1)
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# The min onnx opset version to test for
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FX_MIN_ONNX_OPSET_VERSION = 18
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# The max onnx opset version to test for
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FX_MAX_ONNX_OPSET_VERSION = 18
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FX_TESTED_OPSETS = range(FX_MIN_ONNX_OPSET_VERSION, FX_MAX_ONNX_OPSET_VERSION + 1)
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BOOL_TYPES = (torch.bool,)
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INT_TYPES = (
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# torch.int8,
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# torch.int16,
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torch.int32,
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torch.int64,
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# torch.uint8,
|
|
)
|
|
|
|
QINT_TYPES = (
|
|
torch.qint8,
|
|
torch.quint8,
|
|
)
|
|
|
|
FLOAT_TYPES = (
|
|
torch.float16,
|
|
torch.float32,
|
|
# torch.float64, ORT doesn't support
|
|
)
|
|
|
|
COMPLEX_TYPES = (
|
|
# torch.complex32, NOTE: torch.complex32 is experimental in torch
|
|
torch.complex64,
|
|
# torch.complex128, ORT doesn't support
|
|
)
|
|
|
|
TESTED_DTYPES = (
|
|
# Boolean
|
|
torch.bool,
|
|
# Integers
|
|
*INT_TYPES,
|
|
# Floating types
|
|
*FLOAT_TYPES,
|
|
# Complex types
|
|
*COMPLEX_TYPES,
|
|
)
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class DecorateMeta:
|
|
"""Information about a test case to skip or xfail.
|
|
|
|
Adapted from functorch: functorch/test/common_utils.py
|
|
|
|
Attributes:
|
|
op_name: The name of the operator.
|
|
variant_name: The name of the OpInfo variant.
|
|
decorator: The decorator to apply to the test case.
|
|
opsets: The opsets to apply the decorator to.
|
|
dtypes: The dtypes to apply the decorator to.
|
|
reason: The reason for skipping.
|
|
test_behavior: The behavior of the test case. [skip or xfail]
|
|
matcher: The matcher to apply to the test case.
|
|
enabled_if: Whether to enable test behavior. Usually used on onnx/ort version control
|
|
model_type: The type of the torch model. Defaults to None.
|
|
"""
|
|
|
|
op_name: str
|
|
variant_name: str
|
|
decorator: Callable
|
|
opsets: Optional[Collection[Union[int, Callable[[int], bool]]]]
|
|
dtypes: Optional[Collection[torch.dtype]]
|
|
reason: str
|
|
test_behavior: str
|
|
matcher: Optional[Callable[[Any], bool]] = None
|
|
enabled_if: bool = True
|
|
model_type: Optional[pytorch_test_common.TorchModelType] = None
|
|
|
|
def contains_opset(self, opset: int) -> bool:
|
|
if self.opsets is None:
|
|
return True
|
|
return any(
|
|
opset == opset_spec if isinstance(opset_spec, int) else opset_spec(opset)
|
|
for opset_spec in self.opsets
|
|
)
|
|
|
|
|
|
def xfail(
|
|
op_name: str,
|
|
variant_name: str = "",
|
|
*,
|
|
reason: str,
|
|
opsets: Optional[Collection[Union[int, Callable[[int], bool]]]] = None,
|
|
dtypes: Optional[Collection[torch.dtype]] = None,
|
|
matcher: Optional[Callable[[Any], bool]] = None,
|
|
enabled_if: bool = True,
|
|
model_type: Optional[pytorch_test_common.TorchModelType] = None,
|
|
):
|
|
"""Expects a OpInfo test to fail.
|
|
|
|
Args:
|
|
op_name: The name of the operator.
|
|
variant_name: The name of the variant.
|
|
opsets: The opsets to expect the failure. e.g. [9, 10] or [opsets_before(11)]
|
|
dtypes: The dtypes to expect the failure.
|
|
reason: The reason for the failure.
|
|
matcher: A function that matches the test sample input. It is used only when
|
|
xfail is in the SKIP_XFAIL_SUBTESTS list.
|
|
enabled_if: Whether to enable xfail. Usually used on onnx/ort version control
|
|
model_type: The type of the torch model. Defaults to None.
|
|
"""
|
|
return DecorateMeta(
|
|
op_name=op_name,
|
|
variant_name=variant_name,
|
|
decorator=unittest.expectedFailure,
|
|
opsets=opsets,
|
|
dtypes=dtypes,
|
|
enabled_if=enabled_if,
|
|
matcher=matcher,
|
|
reason=reason,
|
|
test_behavior="xfail",
|
|
model_type=model_type,
|
|
)
|
|
|
|
|
|
def skip(
|
|
op_name: str,
|
|
variant_name: str = "",
|
|
*,
|
|
reason: str,
|
|
opsets: Optional[Collection[Union[int, Callable[[int], bool]]]] = None,
|
|
dtypes: Optional[Collection[torch.dtype]] = None,
|
|
matcher: Optional[Callable[[Any], Any]] = None,
|
|
enabled_if: bool = True,
|
|
model_type: Optional[pytorch_test_common.TorchModelType] = None,
|
|
):
|
|
"""Skips a test case in OpInfo that we don't care about.
|
|
|
|
Likely because ONNX does not support the use case or it is by design.
|
|
|
|
Args:
|
|
op_name: The name of the operator.
|
|
variant_name: The name of the variant.
|
|
opsets: The opsets to expect the failure. e.g. [9, 10] or [opsets_before(11)]
|
|
dtypes: The dtypes to expect the failure.
|
|
reason: The reason for the failure.
|
|
matcher: A function that matches the test sample input. It is used only when
|
|
skip is in the SKIP_XFAIL_SUBTESTS list.
|
|
enabled_if: Whether to enable skip. Usually used on onnx/ort version control
|
|
model_type: The type of the torch model. Defaults to None.
|
|
"""
|
|
return DecorateMeta(
|
|
op_name=op_name,
|
|
variant_name=variant_name,
|
|
decorator=unittest.skip(f"Skip: {reason}"),
|
|
opsets=opsets,
|
|
dtypes=dtypes,
|
|
reason=reason,
|
|
matcher=matcher,
|
|
enabled_if=enabled_if,
|
|
test_behavior="skip",
|
|
model_type=model_type,
|
|
)
|
|
|
|
|
|
def skip_slow(
|
|
op_name: str,
|
|
variant_name: str = "",
|
|
*,
|
|
reason: str,
|
|
opsets: Optional[Collection[Union[int, Callable[[int], bool]]]] = None,
|
|
dtypes: Optional[Collection[torch.dtype]] = None,
|
|
matcher: Optional[Callable[[Any], Any]] = None,
|
|
model_type: Optional[pytorch_test_common.TorchModelType] = None,
|
|
):
|
|
"""Skips a test case in OpInfo that is too slow.
|
|
|
|
It needs further investigation to understand why it is slow.
|
|
|
|
Args:
|
|
op_name: The name of the operator.
|
|
variant_name: The name of the variant.
|
|
opsets: The opsets to expect the failure. e.g. [9, 10] or [opsets_before(11)]
|
|
dtypes: The dtypes to expect the failure.
|
|
reason: The reason for the failure.
|
|
matcher: A function that matches the test sample input. It is used only when
|
|
skip is in the SKIP_XFAIL_SUBTESTS list.
|
|
model_type: The type of the torch model. Defaults to None.
|
|
"""
|
|
return DecorateMeta(
|
|
op_name=op_name,
|
|
variant_name=variant_name,
|
|
decorator=common_utils.slowTest,
|
|
opsets=opsets,
|
|
dtypes=dtypes,
|
|
reason=reason,
|
|
matcher=matcher,
|
|
enabled_if=not common_utils.TEST_WITH_SLOW,
|
|
test_behavior="skip",
|
|
model_type=model_type,
|
|
)
|
|
|
|
|
|
def add_decorate_info(
|
|
all_opinfos: Sequence[opinfo_core.OpInfo],
|
|
test_class_name: str,
|
|
base_test_name: str,
|
|
opset: int,
|
|
skip_or_xfails: Iterable[DecorateMeta],
|
|
):
|
|
"""Decorates OpInfo tests with decorators based on the skip_or_xfails list.
|
|
|
|
Args:
|
|
all_opinfos: All OpInfos.
|
|
test_class_name: The name of the test class.
|
|
base_test_name: The name of the test method.
|
|
opset: The opset to decorate for.
|
|
skip_or_xfails: DecorateMeta's.
|
|
"""
|
|
ops_mapping = {(info.name, info.variant_test_name): info for info in all_opinfos}
|
|
for decorate_meta in skip_or_xfails:
|
|
if not decorate_meta.contains_opset(opset):
|
|
# Skip does not apply to this opset
|
|
continue
|
|
opinfo = ops_mapping.get((decorate_meta.op_name, decorate_meta.variant_name))
|
|
assert (
|
|
opinfo is not None
|
|
), f"Couldn't find OpInfo for {decorate_meta}. Did you need to specify variant_name?"
|
|
decorators = list(opinfo.decorators)
|
|
new_decorator = opinfo_core.DecorateInfo(
|
|
decorate_meta.decorator,
|
|
test_class_name,
|
|
base_test_name,
|
|
dtypes=decorate_meta.dtypes,
|
|
active_if=decorate_meta.enabled_if,
|
|
)
|
|
decorators.append(new_decorator)
|
|
opinfo.decorators = tuple(decorators)
|
|
|
|
# This decorator doesn't modify fn in any way
|
|
def wrapped(fn):
|
|
return fn
|
|
|
|
return wrapped
|
|
|
|
|
|
def opsets_before(opset: int) -> Callable[[int], bool]:
|
|
"""Returns a comparison function that decides if the given opset is before the specified."""
|
|
|
|
def compare(other_opset: int):
|
|
return other_opset < opset
|
|
|
|
return compare
|
|
|
|
|
|
def opsets_after(opset: int) -> Callable[[int], bool]:
|
|
"""Returns a comparison function that decides if the given opset is after the specified."""
|
|
|
|
def compare(other_opset: int):
|
|
return other_opset > opset
|
|
|
|
return compare
|
|
|
|
|
|
def reason_onnx_script_does_not_support(
|
|
operator: str, dtypes: Optional[Sequence[str]] = None
|
|
) -> str:
|
|
"""Formats the reason: ONNX script doesn't support the given dtypes."""
|
|
return f"{operator} on {dtypes or 'dtypes'} not supported by ONNX script"
|
|
|
|
|
|
def reason_onnx_runtime_does_not_support(
|
|
operator: str, dtypes: Optional[Sequence[str]] = None
|
|
) -> str:
|
|
"""Formats the reason: ONNX Runtime doesn't support the given dtypes."""
|
|
return f"{operator} on {dtypes or 'dtypes'} not supported by ONNX Runtime"
|
|
|
|
|
|
def reason_onnx_does_not_support(
|
|
operator: str, dtypes: Optional[Sequence[str]] = None
|
|
) -> str:
|
|
"""Formats the reason: ONNX doesn't support the given dtypes."""
|
|
return f"{operator} on {dtypes or 'certain dtypes'} not supported by the ONNX Spec"
|
|
|
|
|
|
def reason_dynamo_does_not_support(
|
|
operator: str, dtypes: Optional[Sequence[str]] = None
|
|
) -> str:
|
|
"""Formats the reason: Dynamo doesn't support the given dtypes."""
|
|
return (
|
|
f"{operator} on {dtypes or 'certain dtypes'} not supported by the Dynamo Spec"
|
|
)
|
|
|
|
|
|
def reason_jit_tracer_error(info: str) -> str:
|
|
"""Formats the reason: JIT tracer errors."""
|
|
return f"JIT tracer error on {info}"
|
|
|
|
|
|
def reason_flaky() -> str:
|
|
"""Formats the reason: test is flaky."""
|
|
return "flaky test"
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def normal_xfail_skip_test_behaviors(
|
|
test_behavior: Optional[str] = None, reason: Optional[str] = None
|
|
):
|
|
"""This context manager is used to handle the different behaviors of xfail and skip.
|
|
|
|
Args:
|
|
test_behavior (optional[str]): From DecorateMeta name, can be 'skip', 'xfail', or None.
|
|
reason (optional[str]): The reason for the failure or skip.
|
|
|
|
Raises:
|
|
e: Any exception raised by the test case if it's not an expected failure.
|
|
"""
|
|
|
|
# We need to skip as soon as possible, as SegFault might also be a case.
|
|
if test_behavior == "skip":
|
|
pytest.skip(reason=reason)
|
|
|
|
try:
|
|
yield
|
|
# We could use `except (AssertionError, RuntimeError, ...) as e:`, but it needs
|
|
# to go over all test cases to find the right exception type.
|
|
except Exception as e: # pylint: disable=broad-exception-caught
|
|
if test_behavior is None:
|
|
raise e
|
|
if test_behavior == "xfail":
|
|
pytest.xfail(reason=reason)
|
|
else:
|
|
if test_behavior == "xfail":
|
|
pytest.fail("Test unexpectedly passed")
|