ROLLOUT 7 · caikit/caikit
caikit__caikit-233
Not solved
486 stotal time
7tool calls
7model calls
6,967tokens generated
61,834prompt tokens read
Test failures/errors recorded in JUnit XML (failures=1, errors=0)
Turn by turn
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System instructions 4,822 characters
You are a principal Python software engineer resolving a repository issue in `/workspace`. Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run. ## Critical Environment & Grader Rules 1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`. 2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files. 3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`. 4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks: `pytest path/to/test_file.py -k pattern -q --tb=short --no-header` If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off. 5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission. ## Execution Protocol ### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls) - The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories. - Run a single compound `run_command` with `grep -rn --include="*.py"` to locate: (a) the exact symbols, error strings, or functions from the problem description, (b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and (c) the closest existing unit test files under `tests/`. ### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls) - Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests. - Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types. ### Phase 3 — Reproduce Bug in One Call (1 tool call) - Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts. ### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls) - Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`. - Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests. - **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`. ### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls) - Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`. - Run `git status --short && git diff` via `run_command`. Confirm: - ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately), - ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched), - Minimal, clean diff with valid imports and indentation. - Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.
Task prompt 4,175 characters
You are evaluating a software engineering task for repository caikit/caikit.
Problem Statement:
Support Functional Inheritance
## Describe the bug
When creating a `class` that inherits from a `DataObjectBase` and overrides parent functions without adding or changing any of the schema, an exception is raised:
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/ghart/Projects/github/caikit/caikit2/caikit/core/data_model/dataobject.py", line 88, in __new__
raise TypeError(
TypeError: All DataObjectBase classes must follow dataclass syntax
```
## Sample Code
```py
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
## Expected behavior
```py
b_inst = Base(1)
assert b_inst.doit() == 2
d_inst = Derived(1)
assert d_inst.doit() == 3
```
## Observed behavior
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/ghart/Projects/github/caikit/caikit2/caikit/core/data_model/dataobject.py", line 88, in __new__
raise TypeError(
TypeError: All DataObjectBase classes must follow dataclass syntax
```
## Additional context
This will be very important for allowing content libraries to share common data structures with custom backend, construction, and functional interfaces
## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns
## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.
## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CODEOWNERS
./CONTRIBUTING.md
./LICENSE
./README.md
./SECURITY.md
./caikit
./caikit-overview.png
./caikit/__init__.py
./caikit/config
./caikit/config/__init__.py
./caikit/config/config.py
./caikit/config/config.yml
./caikit/core
./caikit/core/__init__.py
./caikit/core/augmentors
./caikit/core/data_model
./caikit/core/model_manager.py
./caikit/core/module_backends
./caikit/core/modules
./caikit/core/registries.py
./caikit/core/signature_parsing
./caikit/core/task.py
./caikit/core/toolkit
./caikit/interfaces
./caikit/interfaces/__init__.py
./caikit/interfaces/common
./caikit/interfaces/runtime
./caikit/runtime
./caikit/runtime/__init__.py
./caikit/runtime/dump_services.py
./caikit/runtime/grpc_server.py
./caikit/runtime/interceptors
./caikit/runtime/metrics
./caikit/runtime/model_management
./caikit/runtime/protobufs
./caikit/runtime/service_factory.py
./caikit/runtime/service_generation
./caikit/runtime/servicers
./caikit/runtime/types
./caikit/runtime/utils
./caikit/runtime/work_management
./code-of-conduct.md
./docs
./docs-requirements.txt
./examples
./pyproject.toml
./releases.md
./scripts
./scripts/check_deps.sh
./scripts/fmt.sh
./setup_requirements.txt
./tests
./tox.ini
```
Turn 1at 60 s426 tokens out3,251 prompt tokens
The user is reporting a bug where inheriting from a `DataObjectBase` class and overriding methods without adding new fields causes a `TypeError: All DataObjectBase classes must follow dataclass syntax`.
The sample code provided is:
```python
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
The error happens in `caikit/core/data_model/dataobject.py` at line 88 in `__new__`.
I need to investigate `caikit/core/data_model/dataobject.py` to understand why it raises this `TypeError` when no fields are added to the derived class.
Plan:
1. Reproduce the bug with a script.
2. Examine `caikit/core/data_model/dataobject.py`.
3. Fix the logic that checks if the class follows dataclass syntax.
4. Verify the fix.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\ntry:\n @dataobject\n class Base(DataObjectBase):\n foo: int\n def doit(self):\n return self.foo * 2\n\n @dataobject\n class Derived(Base):\n def doit(self):\n return self.foo * 3\n \n b_inst = Base(1)\n assert b_inst.doit() == 2\n\n d_inst = Derived(1)\n assert d_inst.doit() == 3\n print(\"Success!\")\nexcept Exception as e:\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 411 characters
{
"status": "ok",
"stdout": "",
"stderr": "<function register_backend_type at 0x7bb1829ae200> is still in the BETA phase and subject to change!\nTraceback (most recent call last):\n File \"<stdin>\", line 11, in <module>\n File \"/workspace/caikit/core/data_model/dataobject.py\", line 88, in __new__\n raise TypeError(\nTypeError: All DataObjectBase classes must follow dataclass syntax\n",
"exit_code": 0
}Generated tokens 426 tokens · stopped: STOP
<|channel>thought
The user is reporting a bug where inheriting from a `DataObjectBase` class and overriding methods without adding new fields causes a `TypeError: All DataObjectBase classes must follow dataclass syntax`.
The sample code provided is:
```python
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
The error happens in `caikit/core/data_model/dataobject.py` at line 88 in `__new__`.
I need to investigate `caikit/core/data_model/dataobject.py` to understand why it raises this `TypeError` when no fields are added to the derived class.
Plan:
1. Reproduce the bug with a script.
2. Examine `caikit/core/data_model/dataobject.py`.
3. Fix the logic that checks if the class follows dataclass syntax.
4. Verify the fix.<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
try:
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
b_inst = Base(1)
assert b_inst.doit() == 2
d_inst = Derived(1)
assert d_inst.doit() == 3
print("Success!")
except Exception as e:
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 2at 67 s74 tokens out3,825 prompt tokens
The bug is reproduced. Now let's examine `caikit/core/data_model/dataobject.py` around line 88.
TOOL CALL · read_file
{
"end_line": 150,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 1
}Result of read_file 5,897 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": "# Copyright The Caikit Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"This module defines the @schema decorator which can be used to declare data\nmodel objects inline without manually defining the protobufs representation\n\"\"\"\n\n\n# Standard\nfrom enum import Enum\nfrom typing import Any, Callable, List, Type, Union, get_args, get_origin\nimport dataclasses\n\n# Third Party\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import struct_pb2\nfrom google.protobuf.internal.enum_type_wrapper import EnumTypeWrapper\nimport numpy as np\n\n# First Party\nfrom py_to_proto.dataclass_to_proto import PY_TO_PROTO_TYPES, DataclassConverter\nimport alog\nimport py_to_proto\n\n# Local\nfrom ..toolkit.errors import error_handler\nfrom . import enums\nfrom .base import DataBase, _DataBaseMetaClass\nfrom .json_dict import JsonDict\n\n## Globals #####################################################################\n\nlog = alog.use_channel(\"SCHEMA\")\nerror = error_handler.get(log)\n\n# Type mapping for type hints in @dataobject classes\nDATAOBJECT_PY_TO_PROTO_TYPES = {\n JsonDict: struct_pb2.Struct,\n np.int32: _descriptor.FieldDescriptor.TYPE_INT32,\n np.int64: _descriptor.FieldDescriptor.TYPE_INT64,\n np.uint32: _descriptor.FieldDescriptor.TYPE_UINT32,\n np.uint64: _descriptor.FieldDescriptor.TYPE_UINT64,\n np.float32: _descriptor.FieldDescriptor.TYPE_FLOAT,\n np.float64: _descriptor.FieldDescriptor.TYPE_DOUBLE,\n **PY_TO_PROTO_TYPES,\n}\n\n# Common package prefix\nCAIKIT_DATA_MODEL = \"caikit_data_model\"\n\n# Registry of auto-generated protos so that they can be rendered to .proto\n_AUTO_GEN_PROTO_CLASSES = []\n\n# Special attribute used to indicate which defaults are user provided\n_USER_DEFINED_DEFAULTS = \"__user_defined_defaults__\"\n\n## Public ######################################################################\n\n\nclass _DataObjectBaseMetaClass(_DataBaseMetaClass):\n \"\"\"This metaclass is used for the DataObject base class so that all data\n objects can delay the creation of their proto class until after the\n metaclass has been instantiated.\n \"\"\"\n\n def __new__(mcs, name, bases, attrs):\n \"\"\"When instantiating a new DataObject class, the proto class will not\n yet have been generated, but the set of fields will be known since the\n class will be the raw input representation of a @dataclass\n \"\"\"\n\n # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is None:\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n\n # Delegate to the base metaclass\n return super().__new__(mcs, name, bases, attrs)\n\n\nclass DataObjectBase(DataBase, metaclass=_DataObjectBaseMetaClass):\n \"\"\"A DataObject is a data model class that is backed by a @dataclass.\n\n Data model classes that use the @dataobject decorator must derive from this\n base class.\n \"\"\"\n\n\ndef dataobject(*args, **kwargs) -> Callable[[Type], Type[DataBase]]:\n \"\"\"The @dataobject decorator can be used to define a Data Model object's\n schema inline with the definition of the python class rather than needing to\n bind to a pre-compiled protobufs class. For example:\n\n @dataobject(\"foo.bar\")\n @dataclass\n class MyDataObject(DataObjectBase):\n '''My Custom Data Object'''\n foo: str\n bar: int\n\n NOTE: The wrapped class must NOT inherit directly from DataBase. That\n inheritance will be added by this decorator, but if it is written\n directly, the metaclass that links protobufs to the class will be called\n before this decorator can auto-gen the protobufs class.\n\n Args:\n package: str\n The package name to use for the generated protobufs class\n\n Returns:\n decorator: Callable[[Type], Type[DataBase]]\n The decorator function that will wrap the given class\n \"\"\"\n\n def decorator(cls: Type) -> Type[DataBase]:\n # Make sure that the wrapped class does NOT inherit from DataBase\n error.value_check(\n \"<COR95184230E>\",\n issubclass(cls, (DataObjectBase, Enum)),\n \"{} must inherit from DataObjectBase/Enum when using @dataobject\",\n cls.__name__,\n )\n\n # Add the package to the kwargs\n kwargs.setdefault(\"package\", package)\n\n # If it's not an enum, fill in any missing field defaults as None\n # and make sure it's a dataclass\n if not issubclass(cls, Enum):\n log.debug2(\"Wrapping data class %s\", cls)\n user_defined_defaults = {}\n for annotation in getattr(cls, \"__annotations__\", {}):\n user_defined_default = getattr(cls, annotation, dataclasses.MISSING)\n if user_defined_default == dataclasses.MISSING:",
"start_line": 1,
"end_line": 150,
"total_lines": 386,
"is_truncated": false
}Generated tokens 74 tokens · stopped: STOP
The bug is reproduced. Now let's examine `caikit/core/data_model/dataobject.py` around line 88.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:1,end_line:150}<tool_call|><|tool_response>
Turn 3at 107 s659 tokens out5,511 prompt tokens
I see the issue in `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
"""When instantiating a new DataObject class, the proto class will not
yet have been generated, but the set of fields will be known since the
class will be the raw input representation of a @dataclass
"""
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
When a class inherits from `Base(DataObjectBase)`, and it doesn't define any new fields, `attrs.get("__annotations__")` will be `None` (because `__annotations__` are not in the `attrs` dictionary of the subclass if no annotations are defined in the class body).
Wait, `__annotations__` are defined at the class level. If `Derived` doesn't have any, `attrs.get("__annotations__")` is indeed `None`.
However, `Derived` should still be a `DataObjectBase` and should inherit the fields from `Base`.
The check `if field_names is None:` is too strict if we want to allow functional inheritance without adding new fields.
If `field_names` is `None`, we should check if any of the base classes have `__annotations__`. Or better yet, if we are inheriting from `DataObjectBase`, we should be able to just inherit the fields.
Let's see how `_DataBaseMetaClass._FWD_DECL_FIELDS` is used. `_DataBaseMetaClass` is likely where the field handling happens.
Let's look at `caikit/core/data_model/base.py` to see `_DataBaseMetaClass`.
Wait, if I change:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
to something that allows `field_names` to be `None` if it inherits from a `DataObjectBase` that already has annotations, that might work.
Actually, if `field_names` is `None`, and we just don't set `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS]`, will it use the fields from the base class?
Let's check `caikit/core/data_model/base.py`.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/base.py"
}Result of read_file 6,238 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/base.py",
"content": "# Copyright The Caikit Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\"\"\"Base classes and functionality for all data structures.\n\"\"\"\n\n# metaclass-generated field members cannot be detected by pylint\n# pylint: disable=no-member\n\n# Standard\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Any, Dict, Optional, Type, Union\nimport base64\nimport json\n\n# Third Party\nfrom google.protobuf import json_format\nfrom google.protobuf.descriptor import Descriptor\nfrom google.protobuf.internal import type_checkers as proto_type_checkers\nfrom google.protobuf.message import Message as ProtoMessageType\n\n# First Party\nimport alog\n\n# Local\nfrom ..toolkit.errors import error_handler\nfrom . import enums, json_dict\n\nlog = alog.use_channel(\"DATAM\")\nerror = error_handler.get(log)\n\n\nclass _DataBaseMetaClass(type):\n \"\"\"Meta class for all structures in the data model.\"\"\"\n\n # store a registry of all classes that use this metaclass, i.e.,\n # all classes that extend DataBase. This is used for constructing new\n # instances by name without having to introspect all modules in data_model.\n class_registry = {}\n\n # This sentinel value is used to determine whether a given attribute is\n # present on a class without doing `getattr` twice in the case where the\n # attribute does exist.\n _MISSING_ATTRIBUTE = \"missing attribute\"\n\n # Special attribute used to communicate that the proto fields are forward\n # declared and will be populated after the metaclass has completed\n # construction.\n _FWD_DECL_FIELDS = \"__fwd_decl_fields__\"\n\n # Special instance attributes that an instance of a class derived from\n # DataBase may have. These are added to __slots__.\n _BACKEND_ATTR = \"_backend\"\n _WHICH_ONEOF_ATTR = \"_which_oneof\"\n\n def __new__(mcs, name, bases, attrs):\n \"\"\"When constructing a new data model class, we set the 'fields' class variable from the\n protobufs descriptor and then set the '__slots__' magic class attribute to fields. This\n provides two benefits: (a) performance is improved since the classes only need to know\n about these attributes (b) it helps to enforce that all member variables in these classes\n are described in the protobufs.\n\n Note: If you want to add a variable for internal use that is not described in the\n protobufs, it can be named in the tuple class variable _private_slots and will\n automatically be added to __slots__.\n \"\"\"\n\n # Protobufs fields can be divided into these categories, which are used\n # to automatically determine appropriate behavior in a number of methods\n attrs[\"full_name\"] = name\n attrs[\"fields_enum_map\"] = {}\n attrs[\"fields_enum_rev\"] = {}\n attrs[\"_fields_oneofs_map\"] = {}\n attrs[\"_fields_to_oneof\"] = {}\n attrs[\"_fields_map\"] = ()\n attrs[\"_fields_message\"] = ()\n attrs[\"_fields_message_repeated\"] = ()\n attrs[\"_fields_enum\"] = ()\n attrs[\"_fields_enum_repeated\"] = ()\n attrs[\"_fields_primitive\"] = ()\n attrs[\"_fields_primitive_repeated\"] = ()\n\n # Look for the set of fields either from a predefined protobuf class or\n # from a forward declaration from @dataobject\n fields = ()\n proto_class = None\n if name not in [\"DataBase\", \"DataObjectBase\"]:\n # Look for a precompiled proto class and if found, parse its\n # descriptor\n proto_class = attrs.get(\"_proto_class\")\n if proto_class is not None:\n all_oneof_fields = [\n field.name\n for oneof in proto_class.DESCRIPTOR.oneofs\n for field in oneof.fields\n ]\n fields = tuple(\n (\n field\n for field in proto_class.DESCRIPTOR.fields_by_name\n if field not in all_oneof_fields\n )\n ) + tuple(proto_class.DESCRIPTOR.oneofs_by_name)\n\n # Otherwise, we need to get the fields from a \"special\" attribute\n else:\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\n log.debug4(\n \"Using dataclass forward declaration fields %s for %s\", fields, name\n )\n error.value_check(\n \"<COR49310991E>\",\n fields is not None,\n \"No proto class found for {}\",\n name,\n )\n attrs[\"fields\"] = fields\n attrs[\"_proto_class\"] = proto_class\n\n # Look if any private slots are declared as class variables\n private_slots = attrs.setdefault(\"_private_slots\", ())\n\n # Class slots are fields + private slots, this prevents other\n # member attributes from being set and also improves performance\n attrs[\"__slots__\"] = tuple(\n [f\"_{field}\" for field in fields]\n + list(private_slots)\n + [mcs._BACKEND_ATTR, mcs._WHICH_ONEOF_ATTR]\n )\n\n # Create the instance of the type\n instance = super().__new__(mcs, name, bases, attrs)\n\n # If there's a valid proto class, perform proto descriptor parsing\n if proto_class is not None:\n mcs.parse_proto_descriptor(instance)\n",
"start_line": 1,
"end_line": 150,
"total_lines": 990,
"is_truncated": true
}Generated tokens 659 tokens · stopped: STOP
I see the issue in `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
"""When instantiating a new DataObject class, the proto class will not
yet have been generated, but the set of fields will be known since the
class will be the raw input representation of a @dataclass
"""
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
When a class inherits from `Base(DataObjectBase)`, and it doesn't define any new fields, `attrs.get("__annotations__")` will be `None` (because `__annotations__` are not in the `attrs` dictionary of the subclass if no annotations are defined in the class body).
Wait, `__annotations__` are defined at the class level. If `Derived` doesn't have any, `attrs.get("__annotations__")` is indeed `None`.
However, `Derived` should still be a `DataObjectBase` and should inherit the fields from `Base`.
The check `if field_names is None:` is too strict if we want to allow functional inheritance without adding new fields.
If `field_names` is `None`, we should check if any of the base classes have `__annotations__`. Or better yet, if we are inheriting from `DataObjectBase`, we should be able to just inherit the fields.
Let's see how `_DataBaseMetaClass._FWD_DECL_FIELDS` is used. `_DataBaseMetaClass` is likely where the field handling happens.
Let's look at `caikit/core/data_model/base.py` to see `_DataBaseMetaClass`.
Wait, if I change:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
to something that allows `field_names` to be `None` if it inherits from a `DataObjectBase` that already has annotations, that might work.
Actually, if `field_names` is `None`, and we just don't set `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS]`, will it use the fields from the base class?
Let's check `caikit/core/data_model/base.py`.<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/base.py<|"|>}<tool_call|><|tool_response>
Turn 4at 175 s1,104 tokens out7,803 prompt tokens
In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
It also checks if `fields` is not None!
So both `_DataObjectBaseMetaClass` and `_DataBaseMetaClass` are enforcing that `_FWD_DECL_FIELDS` (which is populated from `__annotations__` in `_DataObjectBaseMetaClass`) must be present.
If we want to support functional inheritance without adding new fields, the derived class should inherit the `fields` from its base class if it doesn't define any new ones.
Wait, `DataObjectBase` classes are supposed to be used with `@dataobject` decorator, which then uses `dataclasses.dataclass` (implicitly or explicitly).
Let's re-read `_DataObjectBaseMetaClass.__new__` in `caikit/core/data_model/dataobject.py`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
return super().__new__(mcs, name, bases, attrs)
```
If `Derived` inherits from `Base`, `Derived` will have its own `__annotations__` only if it declares fields. If it doesn't, `attrs.get("__annotations__")` is `None`.
If we want `Derived` to inherit fields from `Base`, we should check if any of the bases are `DataObjectBase` or derivatives and if they have `__annotations__`.
But the `_DataBaseMetaClass` (the base metaclass) also has this check:
```python
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Wait, `_DataBaseMetaClass` is a base class for `_DataObjectBaseMetaClass`.
If `Derived` inherits from `Base`, and `Base` was already processed by `_DataObjectBaseMetaClass`, `Base` will have the `fields` attribute (set by `_DataBaseMetaClass.__new__`).
But `_DataBaseMetaClass.__new__` is called for `Derived` as well. And it looks for `_FWD_DECL_FIELDS` in `attrs` (the class dictionary of `Derived`), NOT in the base classes.
So if `Derived` doesn't provide its own `_FWD_DECL_FIELDS` (via `__annotations__` in `_DataObjectBaseMetaClass.__new__`), then `_DataBaseMetaClass.__new__` will fail with `error.value_check`.
To support inheriting fields, `_DataBaseMetaClass` should probably check if any base class has `fields` if it doesn't find them in `attrs`.
However, the goal is specifically "Support Functional Inheritance" for `DataObjectBase` classes.
If `Derived` inherits from `Base`, it should effectively have the same fields as `Base` unless it adds more.
In `_DataObjectBaseMetaClass.__new__`:
If `__annotations__` is missing, we could check if any base class has `__annotations__`. But `__annotations__` is only present on the class that defines the fields.
Actually, if `Derived` doesn't define any new fields, it should just inherit the `fields` from `Base`.
Let's see how `dataclasses` handles inheritance. When you have:
```python
@dataclass
class Base:
x: int
@dataclass
class Derived(Base):
pass
```
`Derived` has `x: int`.
In our case, `_DataObjectBaseMetaClass` is trying to capture the "dataclass syntax" before `dataclasses.dataclass` is even applied (the `@dataobject` decorator probably does that later).
Wait, the `@dataobject` decorator is applied to the class. Let's see the `@dataobject` implementation more closely.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 151
}Result of read_file 6,327 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": " log.debug3(\"Filling in None default for %s.%s\", cls, annotation)\n setattr(cls, annotation, None)\n else:\n user_defined_defaults[annotation] = user_defined_default\n # If the current __init__ is auto-generated by dataclass, remove\n # it so that a new one is created with the new defaults\n if _has_dataclass_init(cls):\n log.debug3(\"Resetting default dataclass init\")\n delattr(cls, \"__init__\")\n cls = dataclasses.dataclass(cls)\n setattr(cls, _USER_DEFINED_DEFAULTS, user_defined_defaults)\n\n descriptor = _dataobject_to_proto(dataclass_=cls, **kwargs)\n\n # Create the message class from the dataclass\n proto_class = py_to_proto.descriptor_to_message_class(descriptor)\n _AUTO_GEN_PROTO_CLASSES.append(proto_class)\n\n # Add enums to the global enums module\n for enum_class in _get_all_enums(proto_class):\n log.debug2(\"Importing enum [%s]\", enum_class.DESCRIPTOR.name)\n enums.import_enum(enum_class)\n\n # Declare the merged class that binds DataBase to the wrapped class with\n # this generated proto class\n if isinstance(proto_class, type):\n setattr(cls, \"_proto_class\", proto_class)\n cls = _make_data_model_class(proto_class, cls)\n\n # If this was a default-generated dataclass __init__ and there are\n # any oneofs, we need to augment the __init__ to support kwargs for\n # the individual fields\n if _has_dataclass_init(cls) and cls._fields_oneofs_map:\n setattr(cls, \"__init__\", _make_oneof_init(cls))\n\n else:\n enums.import_enum(proto_class, cls)\n setattr(cls, \"_proto_enum\", proto_class)\n\n # Return the decorated class\n return cls\n\n # If called without the function invocation, fill in the default argument\n if args and callable(args[0]):\n assert not kwargs, \"This shouldn't happen!\"\n package = CAIKIT_DATA_MODEL\n return decorator(args[0])\n\n # Pull the package as an arg or a keyword arg\n if args:\n package = args[0]\n if \"package\" in kwargs:\n raise TypeError(\"Got multiple values for argument 'package'\")\n else:\n package = kwargs.get(\"package\", CAIKIT_DATA_MODEL)\n return decorator\n\n\ndef render_dataobject_protos(interfaces_dir: str):\n \"\"\"Write out protobufs files for all proto classes generated from dataobjects\n to the target interfaces directory\n\n Args:\n interfaces_dir: str\n The target directory (must already exist)\n \"\"\"\n for proto_class in _AUTO_GEN_PROTO_CLASSES:\n proto_class.write_proto_file(interfaces_dir)\n\n\n## Implementation Details ######################################################\n\n\ndef _dataobject_to_proto(*args, **kwargs):\n kwargs.setdefault(\"type_mapping\", DATAOBJECT_PY_TO_PROTO_TYPES)\n return _DataobjectConverter(*args, **kwargs).descriptor\n\n\nclass _DataobjectConverter(DataclassConverter):\n \"\"\"Augment the dataclass converter to be able to pull descriptors from\n existing data objects\n \"\"\"\n\n def get_concrete_type(self, entry: Any) -> Any:\n \"\"\"Also include data model classes and enums as concrete types\"\"\"\n unwrapped = self._resolve_wrapped_type(entry)\n if (\n isinstance(unwrapped, type)\n and issubclass(unwrapped, DataBase)\n and unwrapped._proto_class is not None\n ) or hasattr(unwrapped, \"_proto_enum\"):\n return entry\n return super().get_concrete_type(entry)\n\n def get_descriptor(self, entry: Any) -> Any:\n \"\"\"Unpack data model classes and enums to their descriptors\"\"\"\n entry = self._resolve_wrapped_type(entry)\n if isinstance(entry, type) and issubclass(entry, DataBase):\n return entry._proto_class.DESCRIPTOR\n proto_enum = getattr(entry, \"_proto_enum\", None)\n if proto_enum is not None:\n return proto_enum.DESCRIPTOR\n return super().get_descriptor(entry)\n\n def get_optional_field_names(self, entry: Any) -> List[str]:\n \"\"\"Get the names of any fields which are optional. This will be any\n field that has a user-defined default or is marked as Optional[]\n \"\"\"\n optional_fields = list(getattr(entry, _USER_DEFINED_DEFAULTS, {}))\n for field_name, field in entry.__dataclass_fields__.items():\n if (\n field_name not in optional_fields\n and self._is_python_optional(field.type) is not None\n ):\n optional_fields.append(field_name)\n return optional_fields\n\n @staticmethod\n def _is_python_optional(entry: Any) -> Any:\n \"\"\"Detect if this type is a python optional\"\"\"\n if get_origin(entry) is Union:\n args = get_args(entry)\n return type(None) in args\n\n\ndef _get_all_enums(\n proto_class: Union[_message.Message, EnumTypeWrapper],\n) -> List[EnumTypeWrapper]:\n \"\"\"Given a generated proto class, recursively extract all enums\"\"\"\n all_enums = []\n if isinstance(proto_class, EnumTypeWrapper):\n all_enums.append(proto_class)\n else:\n for enum_descriptor in proto_class.DESCRIPTOR.enum_types:\n all_enums.append(getattr(proto_class, enum_descriptor.name))\n for nested_proto_descriptor in proto_class.DESCRIPTOR.nested_types:\n all_enums.extend(\n _get_all_enums(getattr(proto_class, nested_proto_descriptor.name))\n )\n\n return all_enums\n\n\ndef _make_data_model_class(proto_class, cls):\n if issubclass(cls, DataObjectBase):\n _DataBaseMetaClass.parse_proto_descriptor(cls)\n\n # Recursively make all nested message wrappers\n for nested_message_descriptor in proto_class.DESCRIPTOR.nested_types:\n nested_message_name = nested_message_descriptor.name",
"start_line": 151,
"end_line": 300,
"total_lines": 386,
"is_truncated": true
}Generated tokens 1,104 tokens · stopped: STOP
In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
It also checks if `fields` is not None!
So both `_DataObjectBaseMetaClass` and `_DataBaseMetaClass` are enforcing that `_FWD_DECL_FIELDS` (which is populated from `__annotations__` in `_DataObjectBaseMetaClass`) must be present.
If we want to support functional inheritance without adding new fields, the derived class should inherit the `fields` from its base class if it doesn't define any new ones.
Wait, `DataObjectBase` classes are supposed to be used with `@dataobject` decorator, which then uses `dataclasses.dataclass` (implicitly or explicitly).
Let's re-read `_DataObjectBaseMetaClass.__new__` in `caikit/core/data_model/dataobject.py`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
return super().__new__(mcs, name, bases, attrs)
```
If `Derived` inherits from `Base`, `Derived` will have its own `__annotations__` only if it declares fields. If it doesn't, `attrs.get("__annotations__")` is `None`.
If we want `Derived` to inherit fields from `Base`, we should check if any of the bases are `DataObjectBase` or derivatives and if they have `__annotations__`.
But the `_DataBaseMetaClass` (the base metaclass) also has this check:
```python
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Wait, `_DataBaseMetaClass` is a base class for `_DataObjectBaseMetaClass`.
If `Derived` inherits from `Base`, and `Base` was already processed by `_DataObjectBaseMetaClass`, `Base` will have the `fields` attribute (set by `_DataBaseMetaClass.__new__`).
But `_DataBaseMetaClass.__new__` is called for `Derived` as well. And it looks for `_FWD_DECL_FIELDS` in `attrs` (the class dictionary of `Derived`), NOT in the base classes.
So if `Derived` doesn't provide its own `_FWD_DECL_FIELDS` (via `__annotations__` in `_DataObjectBaseMetaClass.__new__`), then `_DataBaseMetaClass.__new__` will fail with `error.value_check`.
To support inheriting fields, `_DataBaseMetaClass` should probably check if any base class has `fields` if it doesn't find them in `attrs`.
However, the goal is specifically "Support Functional Inheritance" for `DataObjectBase` classes.
If `Derived` inherits from `Base`, it should effectively have the same fields as `Base` unless it adds more.
In `_DataObjectBaseMetaClass.__new__`:
If `__annotations__` is missing, we could check if any base class has `__annotations__`. But `__annotations__` is only present on the class that defines the fields.
Actually, if `Derived` doesn't define any new fields, it should just inherit the `fields` from `Base`.
Let's see how `dataclasses` handles inheritance. When you have:
```python
@dataclass
class Base:
x: int
@dataclass
class Derived(Base):
pass
```
`Derived` has `x: int`.
In our case, `_DataObjectBaseMetaClass` is trying to capture the "dataclass syntax" before `dataclasses.dataclass` is even applied (the `@dataobject` decorator probably does that later).
Wait, the `@dataobject` decorator is applied to the class. Let's see the `@dataobject` implementation more closely.
<channel|><|tool_call>call:read_file{start_line:151,filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>}<tool_call|><|tool_response>
Turn 5at 249 s1,319 tokens out10,667 prompt tokens
The `@dataobject` decorator calls `dataclasses.dataclass(cls)`.
In the problematic sample code:
```python
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
When `Base` is created, `_DataObjectBaseMetaClass.__new__` finds `foo` in `__annotations__` and sets `_FWD_DECL_FIELDS`. `_DataBaseMetaClass.__new__` then uses this to set `fields`.
When `Derived` is created, `_DataObjectBaseMetaClass.__new__` looks for `__annotations__` in `Derived`. It finds none, and raises `TypeError`.
If we allow `_DataObjectBaseMetaClass.__new__` to pass if `__annotations__` is missing, `_DataBaseMetaClass.__new__` will still raise `error.value_check("<COR49310991E>", fields is not None, "No proto class found for {}, name)`.
So we need to fix both.
Wait, if `Derived` inherits from `Base`, and we want it to have the same fields as `Base`, we should make sure `_FWD_DECL_FIELDS` is passed down or computed.
Actually, if `Derived` is a `DataObjectBase`, it *should* have fields. If it doesn't define any, it *must* inherit them from a base class that does.
If we change `_DataObjectBaseMetaClass.__new__` to:
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Check if any base class is a DataObjectBase
if not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
# If it inherits from DataObjectBase, it will inherit fields.
# We don't need to set _FWD_DECL_FIELDS because it's not adding new fields.
else:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
And then in `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Try to inherit fields from base classes
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Wait, if `Derived` adds *some* fields but not all, `__annotations__` will be present, and `_FWD_DECL_FIELDS` will be set to only those new fields. `_DataBaseMetaClass` will then set `attrs["fields"]` to only those new fields, potentially losing the fields from `Base`.
Let's see how `_DataBaseMetaClass` calculates `fields`.
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ... (proto_class check) ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ... (value_check) ...
attrs["fields"] = fields
```
If `Derived` adds a field, `_DataObjectBaseMetaClass` sets `_FWD_DECL_FIELDS` to `{"new_field": type}`. Then `_DataBaseMetaClass` sets `attrs["fields"]` to this. It doesn't seem to merge with `Base.fields`.
But `dataclasses` handles this merge automatically when `dataclass(cls)` is called. The `fields` attribute in `_DataBaseMetaClass` seems to be used for `__slots__`.
```python
# Class slots are fields + private slots, this prevents other
# member attributes from being set and also improves performance
attrs["__slots__"] = tuple(
[f"_{field}" for field in fields]
+ list(private_slots)
+ [mcs._BACKEND_ATTR, mcs._WHICH_ONEOF_ATTR]
)
```
If `fields` only contains new fields, `__slots__` will only contain those. But `Base` already has its own `__slots__`. In Python, if a subclass defines `__slots__`, it only adds to the slots of the parent classes. So that might be okay.
Wait, if `fields` is used for something else?
Let's look at `_DataItemMetaClass` (not present in the snippets but let's check `base.py` again).
In `_DataBaseMetaClass.__new__`:
`attrs["fields"] = fields`
Is `fields` used anywhere else?
I'll check the rest of `caikit/core/data_model/base.py`.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/base.py",
"start_line": 151
}Result of read_file 6,304 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/base.py",
"content": " # Return the constructed class instance\n return instance\n\n @classmethod\n def parse_proto_descriptor(mcs, cls):\n \"\"\"Encapsulate the logic for parsing the protobuf descriptor here. This\n allows the parsing to be done as a post-process after metaclass\n initialization\n \"\"\"\n\n # use the fully qualified protobuf name to avoid conflicts with\n # nested messages that have matching names\n cls.full_name = cls._proto_class.DESCRIPTOR.full_name\n\n # preserve old fields for _make_property_getter later\n old_fields = cls.fields\n\n # overwrite to only have proto-specific fields present\n cls.fields = tuple(cls._proto_class.DESCRIPTOR.fields_by_name)\n\n # map from all enum fields to their enum classes\n # note: enums are also primitives, these overlap\n cls.fields_enum_map = {\n field.name: getattr(enums, field.enum_type.name)\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.enum_type is not None\n }\n\n cls.fields_enum_rev = {\n field.name: getattr(enums, field.enum_type.name + \"Rev\")\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.enum_type is not None\n }\n\n # mapping of all oneofs and the fields that are part of them\n # NOTE: protobuf makes an interesting use of oneof to wrap types that\n # should be explicitly optional. We don't want to consider these\n # oneofs in the general oneof handling.\n cls._fields_oneofs_map = {\n oneof_name: [field.name for field in oneof.fields]\n for oneof_name, oneof in cls._proto_class.DESCRIPTOR.oneofs_by_name.items()\n if len(oneof.fields) != 1 or oneof.name != f\"_{oneof.fields[0].name}\"\n }\n cls._fields_to_oneof = {\n field_name: oneof_name\n for (oneof_name, oneof_fields) in cls._fields_oneofs_map.items()\n for field_name in oneof_fields\n }\n\n # all repeated fields\n fields_repeated = tuple(\n field.name\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.label == field.LABEL_REPEATED\n )\n\n # all messages, repeated or not\n _fields_message_all = tuple(\n field.name\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.type == field.TYPE_MESSAGE\n )\n\n # all enums, repeated or not\n _fields_enum_all = tuple(\n field.name\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.enum_type is not None\n )\n\n # all fields of type map\n cls._fields_map = tuple(\n field.name\n for field in cls._proto_class.DESCRIPTOR.fields\n if field.message_type and field.message_type.GetOptions().map_entry\n )\n\n # all primitives, repeated or not\n _fields_primitive_all = (\n frozenset(cls.fields)\n .difference(cls._fields_map)\n .difference(_fields_message_all)\n .difference(_fields_enum_all)\n )\n\n # messages that are not repeated\n cls._fields_message = frozenset(_fields_message_all).difference(fields_repeated)\n\n # messages that are repeated\n cls._fields_message_repeated = frozenset(fields_repeated).intersection(\n _fields_message_all\n )\n\n # enums that are not repeated\n cls._fields_enum = frozenset(_fields_enum_all).difference(fields_repeated)\n\n # enums that are repeated\n cls._fields_enum_repeated = frozenset(_fields_enum_all).intersection(\n fields_repeated\n )\n\n # primitives that are not repeated\n cls._fields_primitive = frozenset(_fields_primitive_all).difference(\n fields_repeated\n )\n\n # primitives that are repeated\n cls._fields_primitive_repeated = frozenset(fields_repeated).intersection(\n _fields_primitive_all\n )\n\n # Update the global class and proto registries\n # NOTE: Explicitly not respecting metaclass inheritance so single\n # registry shared for all\n _DataBaseMetaClass.class_registry[cls.full_name] = cls\n\n # Add properties that use the underlying backend. Also add fields that\n # existed in old_fields for supporting oneofs\n # see https://github.com/caikit/caikit/pull/107 for details\n for field in set(cls.fields + tuple(old_fields)):\n # If the field is the name of a field within a oneof and it was not\n # in the old fields, the data is held under the oneof's name if this\n # is the set value for the oneof\n if oneof_name := cls._fields_to_oneof.get(field):\n setattr(cls, field, mcs._make_property_getter(field, oneof_name))\n\n # If the field is a plain field or the name of a oneof, it will be\n # accessed directly\n else:\n setattr(cls, field, mcs._make_property_getter(field))\n\n # If there is not already an __init__ function defined, make one\n current_init = cls.__init__\n if current_init is None or current_init is DataBase.__init__:\n setattr(cls, \"__init__\", mcs._make_init(cls.fields))\n\n @classmethod\n def _make_property_getter(mcs, field, oneof_name=None):\n \"\"\"This helper creates an @property attribute getter for the given field\n\n NOTE: This needs to live as a standalone function in order for the given\n field name to be properly bound to the closure for the attrs\n \"\"\"\n private_name = f\"_{field}\" if oneof_name is None else oneof_name\n\n def _property_getter(self):\n # Check to see if the private name is defined and just return it if\n # it is\n current = getattr(self, private_name, mcs._MISSING_ATTRIBUTE)\n if current is not mcs._MISSING_ATTRIBUTE:",
"start_line": 151,
"end_line": 300,
"total_lines": 990,
"is_truncated": true
}Generated tokens 1,319 tokens · stopped: STOP
The `@dataobject` decorator calls `dataclasses.dataclass(cls)`.
In the problematic sample code:
```python
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
When `Base` is created, `_DataObjectBaseMetaClass.__new__` finds `foo` in `__annotations__` and sets `_FWD_DECL_FIELDS`. `_DataBaseMetaClass.__new__` then uses this to set `fields`.
When `Derived` is created, `_DataObjectBaseMetaClass.__new__` looks for `__annotations__` in `Derived`. It finds none, and raises `TypeError`.
If we allow `_DataObjectBaseMetaClass.__new__` to pass if `__annotations__` is missing, `_DataBaseMetaClass.__new__` will still raise `error.value_check("<COR49310991E>", fields is not None, "No proto class found for {}, name)`.
So we need to fix both.
Wait, if `Derived` inherits from `Base`, and we want it to have the same fields as `Base`, we should make sure `_FWD_DECL_FIELDS` is passed down or computed.
Actually, if `Derived` is a `DataObjectBase`, it *should* have fields. If it doesn't define any, it *must* inherit them from a base class that does.
If we change `_DataObjectBaseMetaClass.__new__` to:
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Check if any base class is a DataObjectBase
if not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
# If it inherits from DataObjectBase, it will inherit fields.
# We don't need to set _FWD_DECL_FIELDS because it's not adding new fields.
else:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
And then in `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Try to inherit fields from base classes
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Wait, if `Derived` adds *some* fields but not all, `__annotations__` will be present, and `_FWD_DECL_FIELDS` will be set to only those new fields. `_DataBaseMetaClass` will then set `attrs["fields"]` to only those new fields, potentially losing the fields from `Base`.
Let's see how `_DataBaseMetaClass` calculates `fields`.
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ... (proto_class check) ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ... (value_check) ...
attrs["fields"] = fields
```
If `Derived` adds a field, `_DataObjectBaseMetaClass` sets `_FWD_DECL_FIELDS` to `{"new_field": type}`. Then `_DataBaseMetaClass` sets `attrs["fields"]` to this. It doesn't seem to merge with `Base.fields`.
But `dataclasses` handles this merge automatically when `dataclass(cls)` is called. The `fields` attribute in `_DataBaseMetaClass` seems to be used for `__slots__`.
```python
# Class slots are fields + private slots, this prevents other
# member attributes from being set and also improves performance
attrs["__slots__"] = tuple(
[f"_{field}" for field in fields]
+ list(private_slots)
+ [mcs._BACKEND_ATTR, mcs._WHICH_ONEOF_ATTR]
)
```
If `fields` only contains new fields, `__slots__` will only contain those. But `Base` already has its own `__slots__`. In Python, if a subclass defines `__slots__`, it only adds to the slots of the parent classes. So that might be okay.
Wait, if `fields` is used for something else?
Let's look at `_DataItemMetaClass` (not present in the snippets but let's check `base.py` again).
In `_DataBaseMetaClass.__new__`:
`attrs["fields"] = fields`
Is `fields` used anywhere else?
I'll check the rest of `caikit/core/data_model/base.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/base.py<|"|>,start_line:151}<tool_call|><|tool_response>
Turn 6at 426 s3,037 tokens out13,721 prompt tokens
The `fields` attribute is used in `parse_proto_descriptor`, but it is immediately overwritten by the proto descriptor:
```python
# overwrite to only have proto-specific fields present
cls.fields = tuple(cls._proto_class.DESCRIPTOR.fields_by_name)
```
And the `_proto_class` is generated from the `dataclass` using `_dataobject_to_proto`.
So the `fields` attribute in `_DataBaseMetaClass` seems mostly used for `__slots__` and the initial `_make_init` (if no `__init__` is defined).
If `Derived` inherits from `Base`, and we want it to have the same `fields` as `Base` if no new fields are added:
1. `_DataObjectBaseMetaClass.__new__` should allow `__annotations__` to be `None` if a base class is a `DataObjectBase`.
2. `_DataBaseMetaClass.__new__` should allow `_FWD_DECL_FIELDS` to be `None` and try to inherit `fields` from base classes.
Wait, if I just remove the `TypeError` from `_DataObjectBaseMetaClass.__new__` and let `_DataBaseMetaClass.__new__` handle it:
In `_DataObjectBaseMetaClass.__new__`:
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Then in `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Inherit fields from base classes if possible
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Let's check if this is correct.
If `Base` is created:
1. `_DataObjectBaseMetaClass.__new__` finds `__annotations__`, sets `_FWD_DECL_FIELDS`.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS`, sets `fields`.
If `Derived` is created (inheriting from `Base`):
1. `_DataObjectBaseMetaClass.__new__` finds no `__annotations__`, does nothing.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS` (it's `None`).
3. `_DataBaseMetaClass.__new__` looks at `bases`. `Base` has `fields`. So `Derived.fields` becomes `Base.fields`.
4. `error.value_check` passes.
If `Derived` adds new fields:
1. `_DataObjectBaseMetaClass.__new__` finds `__annotations__`, sets `_FWD_DECL_FIELDS`.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS`. `fields` is now the new fields.
3. `error.value_check` passes.
4. `Derived.fields` is set to the new fields.
5. `__slots__` for `Derived` contains the new fields (plus private slots etc.).
6. `Base`'s `__slots__` are already set.
This seems correct.
One more thing: what if `Derived` inherits from `Base` but `Base` is not a `DataObjectBase`?
The check `if name not in ["DataBase", "DataObjectBase"]` in `_DataBaseMetaClass.__new__` handles the roots.
If `Derived` inherits from some other class that also uses `_DataBaseMetaClass`, it will also work.
Wait, what if `Derived` inherits from `Base(DataObjectBase)` but `Base` didn't have any fields?
`Base` would have had `_FWD_DECL_FIELDS` as `None` if we allow it.
But if `Base` had `__annotations__ = {}`, then `field_names` would be `{}` (not `None`).
If `Base` had no annotations, `_DataObjectBaseMetaClass.__new__` would have raised `TypeError`.
Wait, the user's `Base` class has annotations:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
So `Base` will have `fields`.
Let's refine the changes.
In `caikit/core/data_model/dataobject.py`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
Wait, if I just remove the `TypeError` and rely on `_DataBaseMetaClass` check, it's cleaner.
But `_DataBaseMetaClass` check says "No proto class found for {}". This is a bit different from "must follow dataclass syntax".
Actually, if I just change:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
To:
```python
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
```
This preserves the `TypeError` for classes that don't define fields AND don't inherit from a class that does.
And in `caikit/core/data_model/base.py`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Inherit fields from base classes if possible
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Let's try this.
Wait, one detail: if `fields` is inherited from `b.fields`, it's a tuple. If `_FWD_DECL_FIELDS` is set (from `__annotations__`), it's a dict (from `attrs.get("__annotations__")`).
Let's check what `__annotations__` is. It's a dict: `{'foo': int}`.
In `_DataBaseMetaClass.__new__`:
`fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)`
If `_FWD_DECL_FIELDS` was set from `__annotations__`, it's a dict.
Then `attrs["fields"] = fields` sets it to a dict.
Then `parse_proto_descriptor` does:
`old_fields = cls.fields`
`cls.fields = tuple(cls._proto_class.DESCRIPTOR.fields_by_name)`
So `cls.fields` becomes a tuple of field names.
If `Derived` inherits `fields` from `Base`:
`Derived.fields` = `Base.fields` (which is a tuple of field names).
Wait, if `Derived` is then decorated with `@dataobject`, `_dataobject_to_proto` is called.
```python
def _dataobject_to_proto(*args, **kwargs):
kwargs.setdefault("type_mapping", DATAOBJECT_PY_TO_PROTO_TYPES)
return _DataobjectConverter(*args, **kwargs).descriptor
```
And `_DataobjectConverter` inherits from `DataclassConverter`.
I suspect `DataclassConverter` uses the `dataclass` fields.
If I just change `_DataObjectBaseMetaClass` to allow missing annotations if inheriting from `DataObjectBase`, and `_DataBaseMetaClass` to allow missing `_FWD_DECL_FIELDS` if inheriting from a class with `fields`, it should work for the reproduction case.
Let's verify what happens when `Derived` has NO annotations.
1. `_DataObjectBaseMetaClass.__new__` does nothing.
2. `_DataBaseMetaClass.__new__` sets `Derived.fields = Base.fields`.
3. `@dataobject` is called on `Derived`.
4. `dataclasses.dataclass(Derived)` is called. Since `Derived` inherits from `Base` and `Base` was already a dataclass (because it was decorated with `@dataobject`), `Derived` also becomes a dataclass with the same fields as `Base`.
5. `_dataobject_to_proto` is called. It uses `DataclassConverter`. `DataclassConverter` should see the fields of `Derived` (which are the same as `Base`).
6. A proto class is generated for `Derived`.
7. `_make_data_model_class` is called. It calls `_DataBaseMetaClass.parse_proto_descriptor(Derived)`.
8. `parse_proto_descriptor` updates `Derived.fields` from the generated proto class.
This seems correct.
What if `Derived` adds new fields?
1. `_DataObjectBaseMetaClass.__new__` sets `_FWD_DECL_FIELDS` to `Derived`'s annotations.
2. `_DataBaseMetaClass.__new__` sets `Derived.fields` to these new fields.
3. `@dataobject` is called on `Derived`.
4. `dataclasses.dataclass(Derived)` merges `Base` fields and `Derived` fields.
5. `_dataobject_to_proto` generates a proto class with all fields.
6. `parse_proto_descriptor` updates `Derived.fields` to include ALL fields.
This also seems correct.
So the fix is indeed in these two places.
Wait, I should check if I need to handle the case where `__annotations__` is an empty dict `{}`.
`attrs.get("__annotations__")` returns `None` if not present. If present but empty, it returns `{}`.
In `_DataObjectBaseMetaClass.__new__`, `if field_names is None:` only catches the missing case. If it's `{}`, it proceeds and sets `_FWD_DECL_FIELDS = {}`.
If `_FWD_DECL_FIELDS` is `{}`, in `_DataBaseMetaClass.__new__`, `fields` will be `{}`.
Then `error.value_check` will see `fields` is not None (it's `{}`) and pass.
But is `{}` acceptable for `fields`?
Probably. If a class has no fields (even its parents), it might be allowed. But `DataObjectBase` classes are generally expected to have some fields.
Actually, the reproduction case is exactly what's needed.
Let's implement the fix.
First, `caikit/core/data_model/dataobject.py`.
TOOL CALL · edit_file
{
"filepath": "caikit/core/data_model/dataobject.py",
"new_string": " # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is not None:\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )",
"old_string": " # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is None:\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names"
}Result of edit_file 980 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/caikit/core/data_model/dataobject.py\n+++ b/caikit/core/data_model/dataobject.py\n@@ -84,11 +84,12 @@\n # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n- if field_names is None:\n+ if field_names is not None:\n+ attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n+ elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )\n- attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n \n # Delegate to the base metaclass\n return super().__new__(mcs, name, bases, attrs)\n",
"is_truncated": false
}Generated tokens 3,037 tokens · stopped: STOP
The `fields` attribute is used in `parse_proto_descriptor`, but it is immediately overwritten by the proto descriptor:
```python
# overwrite to only have proto-specific fields present
cls.fields = tuple(cls._proto_class.DESCRIPTOR.fields_by_name)
```
And the `_proto_class` is generated from the `dataclass` using `_dataobject_to_proto`.
So the `fields` attribute in `_DataBaseMetaClass` seems mostly used for `__slots__` and the initial `_make_init` (if no `__init__` is defined).
If `Derived` inherits from `Base`, and we want it to have the same `fields` as `Base` if no new fields are added:
1. `_DataObjectBaseMetaClass.__new__` should allow `__annotations__` to be `None` if a base class is a `DataObjectBase`.
2. `_DataBaseMetaClass.__new__` should allow `_FWD_DECL_FIELDS` to be `None` and try to inherit `fields` from base classes.
Wait, if I just remove the `TypeError` from `_DataObjectBaseMetaClass.__new__` and let `_DataBaseMetaClass.__new__` handle it:
In `_DataObjectBaseMetaClass.__new__`:
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Then in `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Inherit fields from base classes if possible
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Let's check if this is correct.
If `Base` is created:
1. `_DataObjectBaseMetaClass.__new__` finds `__annotations__`, sets `_FWD_DECL_FIELDS`.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS`, sets `fields`.
If `Derived` is created (inheriting from `Base`):
1. `_DataObjectBaseMetaClass.__new__` finds no `__annotations__`, does nothing.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS` (it's `None`).
3. `_DataBaseMetaClass.__new__` looks at `bases`. `Base` has `fields`. So `Derived.fields` becomes `Base.fields`.
4. `error.value_check` passes.
If `Derived` adds new fields:
1. `_DataObjectBaseMetaClass.__new__` finds `__annotations__`, sets `_FWD_DECL_FIELDS`.
2. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS`. `fields` is now the new fields.
3. `error.value_check` passes.
4. `Derived.fields` is set to the new fields.
5. `__slots__` for `Derived` contains the new fields (plus private slots etc.).
6. `Base`'s `__slots__` are already set.
This seems correct.
One more thing: what if `Derived` inherits from `Base` but `Base` is not a `DataObjectBase`?
The check `if name not in ["DataBase", "DataObjectBase"]` in `_DataBaseMetaClass.__new__` handles the roots.
If `Derived` inherits from some other class that also uses `_DataBaseMetaClass`, it will also work.
Wait, what if `Derived` inherits from `Base(DataObjectBase)` but `Base` didn't have any fields?
`Base` would have had `_FWD_DECL_FIELDS` as `None` if we allow it.
But if `Base` had `__annotations__ = {}`, then `field_names` would be `{}` (not `None`).
If `Base` had no annotations, `_DataObjectBaseMetaClass.__new__` would have raised `TypeError`.
Wait, the user's `Base` class has annotations:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
So `Base` will have `fields`.
Let's refine the changes.
In `caikit/core/data_model/dataobject.py`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
Wait, if I just remove the `TypeError` and rely on `_DataBaseMetaClass` check, it's cleaner.
But `_DataBaseMetaClass` check says "No proto class found for {}". This is a bit different from "must follow dataclass syntax".
Actually, if I just change:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
To:
```python
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
```
This preserves the `TypeError` for classes that don't define fields AND don't inherit from a class that does.
And in `caikit/core/data_model/base.py`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
# Inherit fields from base classes if possible
for b in bases:
if hasattr(b, "fields"):
fields = b.fields
break
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
Let's try this.
Wait, one detail: if `fields` is inherited from `b.fields`, it's a tuple. If `_FWD_DECL_FIELDS` is set (from `__annotations__`), it's a dict (from `attrs.get("__annotations__")`).
Let's check what `__annotations__` is. It's a dict: `{'foo': int}`.
In `_DataBaseMetaClass.__new__`:
`fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)`
If `_FWD_DECL_FIELDS` was set from `__annotations__`, it's a dict.
Then `attrs["fields"] = fields` sets it to a dict.
Then `parse_proto_descriptor` does:
`old_fields = cls.fields`
`cls.fields = tuple(cls._proto_class.DESCRIPTOR.fields_by_name)`
So `cls.fields` becomes a tuple of field names.
If `Derived` inherits `fields` from `Base`:
`Derived.fields` = `Base.fields` (which is a tuple of field names).
Wait, if `Derived` is then decorated with `@dataobject`, `_dataobject_to_proto` is called.
```python
def _dataobject_to_proto(*args, **kwargs):
kwargs.setdefault("type_mapping", DATAOBJECT_PY_TO_PROTO_TYPES)
return _DataobjectConverter(*args, **kwargs).descriptor
```
And `_DataobjectConverter` inherits from `DataclassConverter`.
I suspect `DataclassConverter` uses the `dataclass` fields.
If I just change `_DataObjectBaseMetaClass` to allow missing annotations if inheriting from `DataObjectBase`, and `_DataBaseMetaClass` to allow missing `_FWD_DECL_FIELDS` if inheriting from a class with `fields`, it should work for the reproduction case.
Let's verify what happens when `Derived` has NO annotations.
1. `_DataObjectBaseMetaClass.__new__` does nothing.
2. `_DataBaseMetaClass.__new__` sets `Derived.fields = Base.fields`.
3. `@dataobject` is called on `Derived`.
4. `dataclasses.dataclass(Derived)` is called. Since `Derived` inherits from `Base` and `Base` was already a dataclass (because it was decorated with `@dataobject`), `Derived` also becomes a dataclass with the same fields as `Base`.
5. `_dataobject_to_proto` is called. It uses `DataclassConverter`. `DataclassConverter` should see the fields of `Derived` (which are the same as `Base`).
6. A proto class is generated for `Derived`.
7. `_make_data_model_class` is called. It calls `_DataBaseMetaClass.parse_proto_descriptor(Derived)`.
8. `parse_proto_descriptor` updates `Derived.fields` from the generated proto class.
This seems correct.
What if `Derived` adds new fields?
1. `_DataObjectBaseMetaClass.__new__` sets `_FWD_DECL_FIELDS` to `Derived`'s annotations.
2. `_DataBaseMetaClass.__new__` sets `Derived.fields` to these new fields.
3. `@dataobject` is called on `Derived`.
4. `dataclasses.dataclass(Derived)` merges `Base` fields and `Derived` fields.
5. `_dataobject_to_proto` generates a proto class with all fields.
6. `parse_proto_descriptor` updates `Derived.fields` to include ALL fields.
This also seems correct.
So the fix is indeed in these two places.
Wait, I should check if I need to handle the case where `__annotations__` is an empty dict `{}`.
`attrs.get("__annotations__")` returns `None` if not present. If present but empty, it returns `{}`.
In `_DataObjectBaseMetaClass.__new__`, `if field_names is None:` only catches the missing case. If it's `{}`, it proceeds and sets `_FWD_DECL_FIELDS = {}`.
If `_FWD_DECL_FIELDS` is `{}`, in `_DataBaseMetaClass.__new__`, `fields` will be `{}`.
Then `error.value_check` will see `fields` is not None (it's `{}`) and pass.
But is `{}` acceptable for `fields`?
Probably. If a class has no fields (even its parents), it might be allowed. But `DataObjectBase` classes are generally expected to have some fields.
Actually, the reproduction case is exactly what's needed.
Let's implement the fix.
First, `caikit/core/data_model/dataobject.py`.
<channel|><|tool_call>call:edit_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,new_string:<|"|> # Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is not None:
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)<|"|>,old_string:<|"|> # Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names<|"|>}<tool_call|><|tool_response>
Turn 7at 440 s348 tokens out17,056 prompt tokens
Now `caikit/core/data_model/base.py`.
TOOL CALL · edit_file
{
"filepath": "caikit/core/data_model/base.py",
"new_string": " # Otherwise, we need to get the fields from a \\\"special\\\" attribute\\n else:\\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\\n if fields is None:\\n # Inherit fields from base classes if possible\\n for b in bases:\\n if hasattr(b, \\\"fields\\\"):\\n fields = b.fields\\n break\\n\\n log.debug4(\\n \\\"Using dataclass forward declaration fields %s for %s\\\", fields, name\\n )\\n error.value_check(\\n \\\"<COR49310991E>\\\",\\n fields is not None,\\n \\\"No proto class found for {}-\\\",\\n name,\\n )",
"old_string": " # Otherwise, we need to get the fields from a \\\"special\\\" attribute\\n else:\\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\\n log.debug4(\\n \\\"Using dataclass forward declaration fields %s for %s\\\", fields, name\\n )\\n error.value_check(\\n \\\"<COR49310991E>\\\",\\n fields is not None,\\n \\\"No proto class found for {}-\\\",\\n name,\\n )"
}Result of edit_file 206 characters
{
"status": "error",
"error_type": "FileEditError",
"error_message": "Failed to replace: old_string not found. Ensure you're not escaping content incorrectly and check whitespace, indentation, and context."
}Generated tokens 348 tokens · stopped: STOP
Now `caikit/core/data_model/base.py`.
<channel|><|tool_call>call:edit_file{filepath:<|"|>caikit/core/data_model/base.py<|"|>,new_string:<|"|> # Otherwise, we need to get the fields from a \"special\" attribute\n else:\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\n if fields is None:\n # Inherit fields from base classes if possible\n for b in bases:\n if hasattr(b, \"fields\"):\n fields = b.fields\n break\n\n log.debug4(\n \"Using dataclass forward declaration fields %s for %s\", fields, name\n )\n error.value_check(\n \"<COR49310991E>\",\n fields is not None,\n \"No proto class found for {}-\",\n name,\n )<|"|>,old_string:<|"|> # Otherwise, we need to get the fields from a \"special\" attribute\n else:\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\n log.debug4(\n \"Using dataclass forward declaration fields %s for %s\", fields, name\n )\n error.value_check(\n \"<COR49310991E>\",\n fields is not None,\n \"No proto class found for {}-\",\n name,\n )<|"|>}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
diff --git a/caikit/core/data_model/dataobject.py b/caikit/core/data_model/dataobject.py
index 00bc775..fc2ac1a 100644
--- a/caikit/core/data_model/dataobject.py
+++ b/caikit/core/data_model/dataobject.py
@@ -84,11 +84,12 @@ class _DataObjectBaseMetaClass(_DataBaseMetaClass):
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
- if field_names is None:
+ if field_names is not None:
+ attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
+ elif not any(isinstance(b, type) and issubclass(b, DataObjectBase) for b in bases):
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
- attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
Test output Included
============================= test session starts ==============================
collected 126 items
tests/core/data_model/test_dataobject.py ............................... [ 24%]
........................................................................ [ 81%]
......................F [100%]
=================================== FAILURES ===================================
E ValueError: value check failed: No proto class found for Derived
----------------------------- Captured stderr call -----------------------------
{"channel": "DATAM", "exception": null, "level": "error", "log_code": "<COR49310991E>", "message": "exception raised: ValueError('value check failed: No proto class found for Derived')", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.290321"}
------------------------------ Captured log call -------------------------------
ERROR DATAM:error_handler.py:80 {'log_code': '<COR49310991E>', 'message': "exception raised: ValueError('value check failed: No proto class found for Derived')", 'args': None}
/caikit/caikit/core/toolkit/errors/error_handler.py:130: ValueError: value check failed: No proto class found for Derived
=============================== warnings summary ===============================
tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_valid_dir
tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_no_dir
/usr/local/lib/python3.10/site-packages/py_to_proto/descriptor_to_file.py:86: RuntimeWarning: descriptor.syntax is deprecated. It will be removed soon. Most usages are checking field descriptors. Consider to use has_presence, is_packed on field descriptors.
proto_file_lines.append(f'syntax = "{descriptor.syntax}";')
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================== PASSES ====================================
_________________________ test_dataobject_native_types _________________________
---------------------------- Captured stderr setup -----------------------------
{"channel": "WIPDC", "exception": null, "level": "warning", "message": "<function register_backend_type at 0x79cf9ed9a950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.135821"}
{"channel": "WIPDC", "exception": null, "level": "warning", "message": "<function register_backend_type at 0x79cf9ed9a950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.135969"}
------------------------------ Captured log setup ------------------------------
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x79cf9ed9a950> is still in the BETA phase and subject to change!
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x79cf9ed9a950> is still in the BETA phase and subject to change!
__________________________ test_dataobject_with_oneof __________________________
----------------------------- Captured stderr call -----------------------------
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR09282193E>", "message": "exception raised: TypeError('Received conflicting oneof args/kwargs for data_stream/foo')", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.154609"}
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR59933157E>", "message": "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.154721"}
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR59933157E>", "message": "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.154776"}
------------------------------ Captured log call -------------------------------
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR09282193E>', 'message': "exception raised: TypeError('Received conflicting oneof args/kwargs for data_stream/foo')", 'args': None}
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR59933157E>', 'message': "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", 'args': None}
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR59933157E>', 'message': "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", 'args': None}
______________________ test_dataobject_invocation_flavors ______________________
----------------------------- Captured stderr call -----------------------------
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR95184230E>", "message": "exception raised: ValueError('value check failed: Foo4 must inherit from DataObjectBase/Enum when using @dataobject')", "num_indent": 0, "thread_id": 133932781075328, "timestamp": "2026-11-14T03:59:43.175375"}
------------------------------ Captured log call -------------------------------
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR95184230E>', 'message': "exception raised: ValueError('value check failed: Foo4 must inherit from DataObjectBase/Enum when using @dataobject')", 'args': None}
- generated xml file: /tmp/rebench-grade-ecf3f28889f143e29397e8135721ba15.xml --
=========================== short test summary info ============================
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_native_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_nested_objects
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_nested_enums
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_top_level_enums
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_arrays
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_obj_refs_no_opt_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_obj_refs_with_optional_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_additional_methods
PASSED tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_valid_dir
PASSED tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_no_dir
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_with_oneof
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_with_same_type_of_oneof
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_primitive_oneof_round_trips
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_oneof_from_backend
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_round_trip_json
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_round_trip_proto
PASSED tests/core/data_model/test_dataobject.py::test_dir_on_instance
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_invocation_flavors
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_pre_existing_dataclass
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_dataclass_non_default_init
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_dataclass_default_factory
PASSED tests/core/data_model/test_dataobject.py::test_enum_value_dereference
PASSED tests/core/data_model/test_dataobject.py::test_np_dtypes
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[0]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[1]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[2]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[3]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[4]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[5]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[6]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[7]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[8]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[9]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[10]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[11]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[12]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[13]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[14]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[15]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[16]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[17]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[18]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[19]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[20]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[21]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[22]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[23]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[24]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[25]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[26]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[27]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[28]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[29]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[30]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[31]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[32]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[33]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[34]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[35]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[36]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[37]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[38]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[39]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[40]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[41]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[42]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[43]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[44]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[45]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[46]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[47]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[48]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[49]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[50]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[51]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[52]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[53]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[54]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[55]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[56]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[57]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[58]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[59]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[60]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[61]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[62]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[63]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[64]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[65]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[66]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[67]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[68]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[69]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[70]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[71]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[72]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[73]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[74]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[75]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[76]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[77]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[78]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[79]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[80]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[81]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[82]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[83]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[84]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[85]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[86]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[87]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[88]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[89]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[90]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[91]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[92]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[93]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[94]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[95]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[96]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[97]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[98]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[99]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_to_kwargs
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_inheritance
FAILED tests/core/data_model/test_dataobject.py::test_dataobject_function_inheritance
================== 1 failed, 125 passed, 2 warnings in 0.21s ===================
Test errors Included
Test report (JUnit) Included
<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="1" skipped="0" tests="126" time="0.210" timestamp="2026-11-14T03:59:43.081532+00:00" hostname="8ef0ef209fa4"><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_native_types" time="0.006" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_nested_objects" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_nested_enums" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_top_level_enums" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_arrays" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_obj_refs_no_opt_types" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_obj_refs_with_optional_types" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_additional_methods" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_render_dataobject_protos_valid_dir" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_render_dataobject_protos_no_dir" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_with_oneof" time="0.002" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_with_same_type_of_oneof" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_primitive_oneof_round_trips" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_oneof_from_backend" time="0.013" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_round_trip_json" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_round_trip_proto" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dir_on_instance" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_invocation_flavors" time="0.002" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_pre_existing_dataclass" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_dataclass_non_default_init" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_dataclass_default_factory" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_enum_value_dereference" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_np_dtypes" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[0]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[1]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[2]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[3]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[4]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[5]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[6]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[7]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[8]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" 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name="test_dataobject_jsondict[81]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[82]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[83]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[84]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[85]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[86]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[87]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[88]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[89]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[90]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[91]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[92]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[93]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[94]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[95]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[96]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[97]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[98]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[99]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_to_kwargs" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_inheritance" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_function_inheritance" time="0.001"><failure message="ValueError: value check failed: No proto class found for Derived">E ValueError: value check failed: No proto class found for Derived</failure></testcase></testsuite></testsuites>
Agent log Included
[ 0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
[ 0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository caikit/caikit.
[ 28.1s] 💭 (902 chars) "The user is reporting a bug where inheriting from a `DataObjectBase` class and overriding
methods without adding new fie..."
[ 28.1s] 🔧 run_command (25 lines) python3 - <<'PY'
[ 28.8s] 🔧
[ 35.3s] 💭 (95 chars) "The bug is reproduced. Now let's examine `caikit/core/data_model/dataobject.py` around line
88."
[ 35.3s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 35.3s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '# Copyright The Caikit
Authors\n#\n# Li…
[ 74.6s] 💭 (2518 chars) "I see the issue in `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
"""W..."
[ 74.6s] 📖 read_file (caikit/core/data_model/base.py)
[ 74.7s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/base.py', 'content': '# Copyright The Caikit
Authors\n#\n# Licensed…
[ 143.2s] 💭 (4203 chars) "In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribut..."
[ 143.2s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 143.2s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '
log.debug3("Filling…
[ 217.3s] 💭 (4946 chars) "The `@dataobject` decorator calls `dataclasses.dataclass(cls)`.
In the problematic sample code:
```python
@dataobject
c..."
[ 217.3s] 📖 read_file (caikit/core/data_model/base.py)
[ 217.3s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/base.py', 'content': ' # Return the
constructed class instan…
[ 393.8s] 💭 (10247 chars) "The `fields` attribute is used in `parse_proto_descriptor`, but it is immediately
overwritten by the proto descriptor:
..."
[ 393.8s] ✏️ edit_file (caikit/core/data_model/dataobject.py)
[ 394.0s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'occurrences': 1, 'strategy':
'exact', 'diff': '---…
[ 407.7s] 💭 (37 chars) "Now `caikit/core/data_model/base.py`."
[ 407.7s] ✏️ edit_file (caikit/core/data_model/base.py)
[ 407.8s] ❌ {'status': 'error', 'error_type': 'FileEditError', 'error_message': "Failed to replace: old_string not
found. Ensure you…
Native result row
{
"agent_error": "Agent exceeded session timeout (7.5 min)",
"duration_seconds": 485.92,
"error": "Test failures/errors recorded in JUnit XML (failures=1, errors=0)",
"grade": {
"collected_tests": 126,
"competition_junit_ok": false,
"error": "Test failures/errors recorded in JUnit XML (failures=1, errors=0)",
"required_issues_count": 1,
"resolved": false,
"status_counts": {
"FAILED": 1,
"PASSED": 125
},
"strict_clean_patch": true,
"test_exit_code": 1
},
"instance_id": "caikit__caikit-233",
"pass_idx": 0,
"patch_chars": 986,
"phase1_seconds": 484.21,
"repo": "caikit/caikit",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/caikit__caikit-233__p0__r7.jsonl",
"resolved": false,
"rollout_idx": 7,
"run_key": "caikit__caikit-233__p0__r7",
"strict_clean_patch": true,
"tool_calls": 7,
"total_llm_calls": 7,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_caikit__caikit-233__p0__r7.json"
}