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https://github.com/zebrajr/pytorch.git
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7030403048
This PR is only relevant for the Fake tensor Mode ONNX export. For the conventional export, everything is unchanged. * An optional `rename_initializer=False` argument is added to an internal function `torch/onnx/_internal/fx/serialization.py::save_model_with_external_data` which is used by the public API `ExportOutput.save`. * The default behavior (`rename_initializer=False`) is meant to be used by public API `torch.onnx.dynamo_export` with the default Dynamo-based FX tracer (`DynamoExport`). In this scenario, both graph ONNX graph inputs and initializers have matching name with `.` in it (e.g. `linear.weight`) * `rename_initializer=True` is meant to be used by `torch.onnx.dynamo_export` with a non-publicly-supported FX tracer called `FXSymbolicTracer`. This tracer lifts the FX graph initializers as inputs before FX->ONNX start, and because of this, the initializer names must be valid python identifiers (meaning `.` are not supported argument name and must be replaced by `_` or similar). This causes the graph inputs to have names with `_` (e.g. `linear_weight`) while the initializers have `.` (e.g. `linear.weight`) in their name. This flag resolves this mismatch by replacing `.` by `_` when saving the ONNX proto (`save_model_with_external_data`). * This PR also adds unit tests for numerical validation against pytorch eager for onnx export using dynamo-based fx tracer and fake mode enabled. (There are already tests for export with fx symbolic tracer with fake mode) Pull Request resolved: https://github.com/pytorch/pytorch/pull/105002 Approved by: https://github.com/BowenBao
331 lines
9.3 KiB
Python
331 lines
9.3 KiB
Python
# Owner(s): ["module: onnx"]
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from __future__ import annotations
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import functools
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import os
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import random
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import sys
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import unittest
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from typing import Optional
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import numpy as np
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import packaging.version
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import torch
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from torch.autograd import function
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from torch.onnx._internal import diagnostics
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from torch.testing._internal import common_utils
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.insert(-1, pytorch_test_dir)
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torch.set_default_tensor_type("torch.FloatTensor")
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BATCH_SIZE = 2
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RNN_BATCH_SIZE = 7
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RNN_SEQUENCE_LENGTH = 11
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RNN_INPUT_SIZE = 5
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RNN_HIDDEN_SIZE = 3
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def _skipper(condition, reason):
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def decorator(f):
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@functools.wraps(f)
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def wrapper(*args, **kwargs):
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if condition():
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raise unittest.SkipTest(reason)
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return f(*args, **kwargs)
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return wrapper
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return decorator
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skipIfNoCuda = _skipper(lambda: not torch.cuda.is_available(), "CUDA is not available")
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skipIfTravis = _skipper(lambda: os.getenv("TRAVIS"), "Skip In Travis")
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skipIfNoBFloat16Cuda = _skipper(
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lambda: not torch.cuda.is_bf16_supported(), "BFloat16 CUDA is not available"
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)
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skipIfQuantizationBackendQNNPack = _skipper(
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lambda: torch.backends.quantized.engine == "qnnpack",
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"Not compatible with QNNPack quantization backend",
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)
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# skips tests for all versions below min_opset_version.
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# if exporting the op is only supported after a specific version,
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# add this wrapper to prevent running the test for opset_versions
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# smaller than the currently tested opset_version
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def skipIfUnsupportedMinOpsetVersion(min_opset_version):
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.opset_version < min_opset_version:
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raise unittest.SkipTest(
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f"Unsupported opset_version: {self.opset_version} < {min_opset_version}"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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# skips tests for all versions above max_opset_version.
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def skipIfUnsupportedMaxOpsetVersion(max_opset_version):
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.opset_version > max_opset_version:
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raise unittest.SkipTest(
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f"Unsupported opset_version: {self.opset_version} > {max_opset_version}"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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# skips tests for all opset versions.
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def skipForAllOpsetVersions():
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.opset_version:
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raise unittest.SkipTest(
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"Skip verify test for unsupported opset_version"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skipTraceTest(skip_before_opset_version: Optional[int] = None, reason: str = ""):
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"""Skip tracing test for opset version less than skip_before_opset_version.
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Args:
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skip_before_opset_version: The opset version before which to skip tracing test.
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If None, tracing test is always skipped.
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reason: The reason for skipping tracing test.
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Returns:
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A decorator for skipping tracing test.
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"""
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if skip_before_opset_version is not None:
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self.skip_this_opset = self.opset_version < skip_before_opset_version
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else:
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self.skip_this_opset = True
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if self.skip_this_opset and not self.is_script:
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raise unittest.SkipTest(f"Skip verify test for torch trace. {reason}")
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skipScriptTest(skip_before_opset_version: Optional[int] = None, reason: str = ""):
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"""Skip scripting test for opset version less than skip_before_opset_version.
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Args:
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skip_before_opset_version: The opset version before which to skip scripting test.
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If None, scripting test is always skipped.
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reason: The reason for skipping scripting test.
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Returns:
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A decorator for skipping scripting test.
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"""
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if skip_before_opset_version is not None:
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self.skip_this_opset = self.opset_version < skip_before_opset_version
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else:
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self.skip_this_opset = True
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if self.skip_this_opset and self.is_script:
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raise unittest.SkipTest(f"Skip verify test for TorchScript. {reason}")
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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# TODO(titaiwang): dynamic_only is specific to the situation that dynamic fx exporter
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# is not yet supported by ORT until 1.15.0. Remove dynamic_only once ORT 1.15.0 is released.
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def skip_min_ort_version(reason: str, version: str, dynamic_only: bool = False):
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if (
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packaging.version.parse(self.ort_version).release
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< packaging.version.parse(version).release
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):
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if dynamic_only and not self.dynamic_shapes:
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return func(self, *args, **kwargs)
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raise unittest.SkipTest(
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f"ONNX Runtime version: {version} is older than required version {version}. "
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f"Reason: {reason}."
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skip_dynamic_fx_test(reason: str):
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"""Skip dynamic exporting test.
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Args:
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reason: The reason for skipping dynamic exporting test.
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Returns:
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A decorator for skipping dynamic exporting test.
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"""
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.dynamic_shapes:
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raise unittest.SkipTest(
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f"Skip verify dynamic shapes test for FX. {reason}"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skip_op_level_debug_test(reason: str):
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"""Skip tests with op_level_debug enabled.
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Args:
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reason: The reason for skipping tests with op_level_debug enabled.
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Returns:
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A decorator for skipping tests with op_level_debug enabled.
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"""
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.op_level_debug:
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raise unittest.SkipTest(
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f"Skip test with op_level_debug enabled. {reason}"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skip_in_ci(reason: str):
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"""Skip test in CI.
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Args:
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reason: The reason for skipping test in CI.
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Returns:
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A decorator for skipping test in CI.
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"""
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if os.getenv("CI"):
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raise unittest.SkipTest(f"Skip test in CI. {reason}")
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def xfail(reason: str):
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"""Expect failure.
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Args:
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reason: The reason for expected failure.
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Returns:
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A decorator for expecting test failure.
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"""
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return unittest.expectedFailure
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# skips tests for opset_versions listed in unsupported_opset_versions.
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# if the caffe2 test cannot be run for a specific version, add this wrapper
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# (for example, an op was modified but the change is not supported in caffe2)
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def skipIfUnsupportedOpsetVersion(unsupported_opset_versions):
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def skip_dec(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.opset_version in unsupported_opset_versions:
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raise unittest.SkipTest(
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"Skip verify test for unsupported opset_version"
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)
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return func(self, *args, **kwargs)
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return wrapper
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return skip_dec
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def skipShapeChecking(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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self.check_shape = False
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return func(self, *args, **kwargs)
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return wrapper
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def skipDtypeChecking(func):
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@functools.wraps(func)
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def wrapper(self, *args, **kwargs):
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self.check_dtype = False
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return func(self, *args, **kwargs)
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return wrapper
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def flatten(x):
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return tuple(function._iter_filter(lambda o: isinstance(o, torch.Tensor))(x))
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def set_rng_seed(seed):
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torch.manual_seed(seed)
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random.seed(seed)
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np.random.seed(seed)
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class ExportTestCase(common_utils.TestCase):
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"""Test case for ONNX export.
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Any test case that tests functionalities under torch.onnx should inherit from this class.
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"""
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def setUp(self):
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super().setUp()
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# TODO(#88264): Flaky test failures after changing seed.
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set_rng_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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diagnostics.engine.clear()
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