[ONNX] Pretty print diagnostic logging #88261

BowenBao · 2022-11-01T22:43:52Z

Adds pretty print diagnostic logging. For example

import io
import torch
from torch.onnx._internal import diagnostics

class CustomAdd(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, y):
        return x + y

    @staticmethod
    def symbolic(g, x, y):
        return g.op("custom::CustomAdd", x, y)

class M(torch.nn.Module):
    def forward(self, x):
        return CustomAdd.apply(x, x)

# trigger warning for missing shape inference.
# rule = diagnostics.rules.node_missing_onnx_shape_inference
torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO())

By default, observe minimum summary of diagnostics

========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 =========
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ========================
3 WARNING were not printed due to the log level.

Adjusting the verbose and level argument.

diagnostics.engine.pretty_print(verbose=True, level=diagnostics.levels.WARNING)

Prints full log.

=============================== 1 Diagnostic Run ===============================
========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 =========
verbose: True, log level: Level.WARNING
======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ========================
WARNING: node-missing-onnx-shape-inference
==========================================
The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
--------------------------- Stack: Python call stack ---------------------------
frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151
frame: n, utils._params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/_patch_torch.py:82
frame: <@beartype(torch.onnx._patch_torch._graph_op) at 0x7f62184b6710>:78
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: return function(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_deprecation.py:30
frame: return g.op("custom::CustomAdd", x, y) test_pretty_print.py:14
frame: return symbolic_fn(g, *args) /home/bowbao/pytorch_dev/torch/onnx/utils.py:1716
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: graph = _C._jit_pass_onnx(graph, operator_export_type) /home/bowbao/pytorch_dev/torch/onnx/utils.py:663
frame: <@beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519
frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22
---------------------------- Stack: C++ call stack -----------------------------
frame: (<unknown frame>)
frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0xabd026 (0x7f625b599026 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown frame>)

WARNING: node-missing-onnx-shape-inference
==========================================
The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
--------------------------- Stack: Python call stack ---------------------------
frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151
frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:688
frame: <@beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519
frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22
---------------------------- Stack: C++ call stack -----------------------------
frame: (<unknown frame>)
frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown frame>)

WARNING: node-missing-onnx-shape-inference
==========================================
The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
--------------------------- Stack: Python call stack ---------------------------
frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151
frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:1179
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519
frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347
frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81
frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22
---------------------------- Stack: C++ call stack -----------------------------
frame: (<unknown frame>)
frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so))
frame: (<unknown frame>)

Stack from ghstack (oldest at bottom):

[ghstack-poisoned]

pytorch-bot · 2022-11-01T22:43:56Z

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ghstack-source-id: 315e0ced3ede9c1b929f491a5000d2285ccbf4b8 Pull Request resolved: #88261

[ghstack-poisoned]

ghstack-source-id: 89aed0703cdd60c71aba102648413893c7c193f8 Pull Request resolved: #88261

[ghstack-poisoned]

ghstack-source-id: 58a5c451fb49e80fd65a57df1448d0eaa097b196 Pull Request resolved: #88261

Adds pretty print diagnostic logging. For example ```python import io import torch from torch.onnx._internal import diagnostics class CustomAdd(torch.autograd.Function): staticmethod def forward(ctx, x, y): return x + y staticmethod def symbolic(g, x, y): return g.op("custom::CustomAdd", x, y) class M(torch.nn.Module): def forward(self, x): return CustomAdd.apply(x, x) # trigger warning for missing shape inference. rule = diagnostics.rules.node_missing_onnx_shape_inference torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) ``` By default, observe minimum summary of diagnostics ``` ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== 3 WARNING were not printed due to the log level. ``` Adjusting the `verbose` and `level` argument. ```python diagnostics.engine.pretty_print(verbose=True, level=diagnostics.levels.WARNING) ``` Prints full log. ``` =============================== 1 Diagnostic Run =============================== ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: True, log level: Level.WARNING ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: n, utils._params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/_patch_torch.py:82 frame: <beartype(torch.onnx._patch_torch._graph_op) at 0x7f62184b6710>:78 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: return function(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_deprecation.py:30 frame: return g.op("custom::CustomAdd", x, y) test_pretty_print.py:14 frame: return symbolic_fn(g, *args) /home/bowbao/pytorch_dev/torch/onnx/utils.py:1716 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: graph = _C._jit_pass_onnx(graph, operator_export_type) /home/bowbao/pytorch_dev/torch/onnx/utils.py:663 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabd026 (0x7f625b599026 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:688 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:1179 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) ``` [ghstack-poisoned]

ghstack-source-id: 48e91bf09ed64c9315959678fc24e3944de411ac Pull Request resolved: #88261

Adds pretty print diagnostic logging. For example ```python import io import torch from torch.onnx._internal import diagnostics class CustomAdd(torch.autograd.Function): staticmethod def forward(ctx, x, y): return x + y staticmethod def symbolic(g, x, y): return g.op("custom::CustomAdd", x, y) class M(torch.nn.Module): def forward(self, x): return CustomAdd.apply(x, x) # trigger warning for missing shape inference. rule = diagnostics.rules.node_missing_onnx_shape_inference torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) ``` By default, observe minimum summary of diagnostics ``` ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== 3 WARNING were not printed due to the log level. ``` Adjusting the `verbose` and `level` argument. ```python diagnostics.engine.pretty_print(verbose=True, level=diagnostics.levels.WARNING) ``` Prints full log. ``` =============================== 1 Diagnostic Run =============================== ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: True, log level: Level.WARNING ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: n, utils._params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/_patch_torch.py:82 frame: <beartype(torch.onnx._patch_torch._graph_op) at 0x7f62184b6710>:78 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: return function(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_deprecation.py:30 frame: return g.op("custom::CustomAdd", x, y) test_pretty_print.py:14 frame: return symbolic_fn(g, *args) /home/bowbao/pytorch_dev/torch/onnx/utils.py:1716 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: graph = _C._jit_pass_onnx(graph, operator_export_type) /home/bowbao/pytorch_dev/torch/onnx/utils.py:663 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabd026 (0x7f625b599026 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:688 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:1179 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) ``` [ghstack-poisoned]

ghstack-source-id: 1381bdcbe91583c5fde561f0815e074354347396 Pull Request resolved: #88261

abock

Very nice!

Adds pretty print diagnostic logging. For example ```python import io import torch from torch.onnx._internal import diagnostics class CustomAdd(torch.autograd.Function): staticmethod def forward(ctx, x, y): return x + y staticmethod def symbolic(g, x, y): return g.op("custom::CustomAdd", x, y) class M(torch.nn.Module): def forward(self, x): return CustomAdd.apply(x, x) # trigger warning for missing shape inference. # rule = diagnostics.rules.node_missing_onnx_shape_inference torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) ``` By default, observe minimum summary of diagnostics ``` ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== 3 WARNING were not printed due to the log level. ``` Adjusting the `verbose` and `level` argument. ```python diagnostics.engine.pretty_print(verbose=True, level=diagnostics.levels.WARNING) ``` Prints full log. ``` =============================== 1 Diagnostic Run =============================== ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: True, log level: Level.WARNING ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: n, utils._params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/_patch_torch.py:82 frame: <beartype(torch.onnx._patch_torch._graph_op) at 0x7f62184b6710>:78 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: return function(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_deprecation.py:30 frame: return g.op("custom::CustomAdd", x, y) test_pretty_print.py:14 frame: return symbolic_fn(g, *args) /home/bowbao/pytorch_dev/torch/onnx/utils.py:1716 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: graph = _C._jit_pass_onnx(graph, operator_export_type) /home/bowbao/pytorch_dev/torch/onnx/utils.py:663 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabd026 (0x7f625b599026 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:688 frame: <beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:1179 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) ``` [ghstack-poisoned]

ghstack-source-id: 397974bdef6b5e487123ffa85daa68a586543426 Pull Request resolved: #88261

BowenBao · 2022-11-11T19:16:00Z

@pytorchbot merge -g

pytorchmergebot · 2022-11-11T19:17:34Z

Merge started

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Learn more about merging in the wiki.

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@staticmethod

Adds pretty print diagnostic logging. For example ```python import io import torch from torch.onnx._internal import diagnostics class CustomAdd(torch.autograd.Function): @staticmethod def forward(ctx, x, y): return x + y @staticmethod def symbolic(g, x, y): return g.op("custom::CustomAdd", x, y) class M(torch.nn.Module): def forward(self, x): return CustomAdd.apply(x, x) # trigger warning for missing shape inference. # rule = diagnostics.rules.node_missing_onnx_shape_inference torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) ``` By default, observe minimum summary of diagnostics ``` ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: False, log level: Level.ERROR ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== 3 WARNING were not printed due to the log level. ``` Adjusting the `verbose` and `level` argument. ```python diagnostics.engine.pretty_print(verbose=True, level=diagnostics.levels.WARNING) ``` Prints full log. ``` =============================== 1 Diagnostic Run =============================== ========= Diagnostic Run torch.onnx.export version 1.14.0a0+git90a69c5 ========= verbose: True, log level: Level.WARNING ======================= 0 NONE 0 NOTE 3 WARNING 0 ERROR ======================== WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: n, utils._params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/_patch_torch.py:82 frame: <@beartype(torch.onnx._patch_torch._graph_op) at 0x7f62184b6710>:78 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: return function(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_deprecation.py:30 frame: return g.op("custom::CustomAdd", x, y) test_pretty_print.py:14 frame: return symbolic_fn(g, *args) /home/bowbao/pytorch_dev/torch/onnx/utils.py:1716 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: graph = _C._jit_pass_onnx(graph, operator_export_type) /home/bowbao/pytorch_dev/torch/onnx/utils.py:663 frame: <@beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabd026 (0x7f625b599026 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:688 frame: <@beartype(torch.onnx.utils._optimize_graph) at 0x7f62180e05f0>:85 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: module=module, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1123 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) WARNING: node-missing-onnx-shape-inference ========================================== The shape inference of custom::CustomAdd type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. --------------------------- Stack: Python call stack --------------------------- frame: diagnostic = ExportDiagnostic(rule, level, message, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/diagnostics/_diagnostic.py:151 frame: graph, params_dict, GLOBALS.export_onnx_opset_version /home/bowbao/pytorch_dev/torch/onnx/utils.py:1179 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: dynamic_axes=dynamic_axes, /home/bowbao/pytorch_dev/torch/onnx/utils.py:1539 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: export_modules_as_functions=export_modules_as_functions, /home/bowbao/pytorch_dev/torch/onnx/utils.py:519 frame: <@beartype(torch.onnx.utils.export) at 0x7f62180e0170>:347 frame: return beartyped(*args, **kwargs) /home/bowbao/pytorch_dev/torch/onnx/_internal/_beartype.py:81 frame: torch.onnx.export(M(), torch.randn(3, 4), io.BytesIO()) test_pretty_print.py:22 ---------------------------- Stack: C++ call stack ----------------------------- frame: (<unknown frame>) frame: (<unknown function> + 0x88411b (0x7f625b36011b in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Value*, std::pair<bool, bool> const&) + 0x7d3 (0x7f625b351743 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::UpdateReliable(torch::jit::Node*) + 0x4f (0x7f625b35198f in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(torch::jit::Node*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0xac9 (0x7f625b357179 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x87d6d1 (0x7f625b3596d1 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (torch::jit::ONNXShapeTypeInference(std::shared_ptr<torch::jit::Graph>&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, c10::IValue, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, c10::IValue> > > const&, int) + 0x33 (0x7f625b359cf3 in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0xabdbae (0x7f625b599bae in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown function> + 0x3c0fda (0x7f625ae9cfda in /home/bowbao/pytorch_dev/torch/lib/libtorch_python.so)) frame: (<unknown frame>) ``` Pull Request resolved: pytorch#88261 Approved by: https://github.com/abock, https://github.com/justinchuby

[ONNX] Pretty print diagnostic logging

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[ONNX] Generate diagnostic source files during build #86949

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[ONNX] Improve diagnostic message formatting #87830

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pytorch-bot bot added the release notes: onnx torch.onnx related changes that should show up in the release notes label Nov 1, 2022

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[ONNX] Create common ExportTestCase base class #88145

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[ONNX] Add stack info to diagnostics #87258

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BowenBao added a commit that referenced this pull request Nov 1, 2022

[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 315e0ced3ede9c1b929f491a5000d2285ccbf4b8 Pull Request resolved: #88261

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Update on "[ONNX] Pretty print diagnostic logging"

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[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 89aed0703cdd60c71aba102648413893c7c193f8 Pull Request resolved: #88261

Update on "[ONNX] Pretty print diagnostic logging"

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[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 58a5c451fb49e80fd65a57df1448d0eaa097b196 Pull Request resolved: #88261

BowenBao marked this pull request as ready for review November 2, 2022 02:44

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BowenBao added module: onnx Related to torch.onnx topic: improvements topic category labels Nov 2, 2022

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[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 48e91bf09ed64c9315959678fc24e3944de411ac Pull Request resolved: #88261

BowenBao added a commit that referenced this pull request Nov 3, 2022

[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 1381bdcbe91583c5fde561f0815e074354347396 Pull Request resolved: #88261

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[ONNX] Pretty print diagnostic logging

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ghstack-source-id: 397974bdef6b5e487123ffa85daa68a586543426 Pull Request resolved: #88261

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[ONNX] Pretty print diagnostic logging #88261

[ONNX] Pretty print diagnostic logging #88261

BowenBao commented Nov 1, 2022 •

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[ONNX] Pretty print diagnostic logging #88261

[ONNX] Pretty print diagnostic logging #88261

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BowenBao commented Nov 1, 2022 • edited

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