ROLLOUT 0 · caikit/caikit
caikit__caikit-233
Not solved
472 stotal time
11tool calls
11model calls
6,865tokens generated
91,443prompt tokens read
Empty agent patch
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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 37 s253 tokens out3,251 prompt tokens
The user is reporting a bug in `caikit.core.data_model.dataobject`.
When a class inherits from `DataObjectBase` and uses the `@dataobject` decorator, but doesn't add any new schema fields (only overrides methods), a `TypeError: All DataObjectBase classes must follow dataclass syntax` is raised.
Example provided:
```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 issue seems to be in the `__new__` method of `dataobject` decorator (or the class it returns) which is checking for dataclass-like syntax and failing if no new fields are defined in the subclass.
Let's start by locating the code and reproducing the bug.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"All DataObjectBase classes must follow dataclass syntax\" ."
}Result of run_command 183 characters
{
"status": "ok",
"stdout": "./caikit/core/data_model/dataobject.py:89: \"All DataObjectBase classes must follow dataclass syntax\"\n",
"stderr": "",
"exit_code": 0
}Generated tokens 253 tokens · stopped: STOP
<|channel>thought
The user is reporting a bug in `caikit.core.data_model.dataobject`.
When a class inherits from `DataObjectBase` and uses the `@dataobject` decorator, but doesn't add any new schema fields (only overrides methods), a `TypeError: All DataObjectBase classes must follow dataclass syntax` is raised.
Example provided:
```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 issue seems to be in the `__new__` method of `dataobject` decorator (or the class it returns) which is checking for dataclass-like syntax and failing if no new fields are defined in the subclass.
Let's start by locating the code and reproducing the bug.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "All DataObjectBase classes must follow dataclass syntax" .<|"|>}<tool_call|><|tool_response>
Turn 2at 50 s195 tokens out3,574 prompt tokens
Okay, the error is in `./caikit/core/data_model/dataobject.py` at line 89. Now, let's reproduce the bug using the sample code.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\n@dataobject\nclass Base(DataObjectBase):\n foo: int\n def doit(self):\n return self.foo * 2\n\ntry:\n @dataobject\n class Derived(Base):\n def doit(self):\n return self.foo * 3\n print(\"Success\")\nexcept TypeError as e:\n print(f\"Caught expected TypeError: {e}\")\nexcept Exception as e:\n print(f\"Caught unexpected exception: {type(e).__name__}: {e}\")\nPY\n"
}Result of run_command 246 characters
{
"status": "ok",
"stdout": "Caught expected TypeError: All DataObjectBase classes must follow dataclass syntax\n",
"stderr": "<function register_backend_type at 0x7159aef9e170> is still in the BETA phase and subject to change!\n",
"exit_code": 0
}Generated tokens 195 tokens · stopped: STOP
Okay, the error is in `./caikit/core/data_model/dataobject.py` at line 89.
Now, let's reproduce the bug using the sample code.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
try:
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
print("Success")
except TypeError as e:
print(f"Caught expected TypeError: {e}")
except Exception as e:
print(f"Caught unexpected exception: {type(e).__name__}: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Turn 3at 57 s71 tokens out3,858 prompt tokens
The reproduction confirms the bug. Now let's examine the code in `caikit/core/data_model/dataobject.py`.
TOOL CALL · read_file
{
"end_line": 120,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 1
}Result of read_file 4,712 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.",
"start_line": 1,
"end_line": 120,
"total_lines": 386,
"is_truncated": false
}Generated tokens 71 tokens · stopped: STOP
The reproduction confirms the bug. Now let's examine the code in `caikit/core/data_model/dataobject.py`.<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,end_line:120,start_line:1}<tool_call|><|tool_response>
Turn 4at 91 s439 tokens out5,230 prompt tokens
The issue is 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)
```
It checks if `__annotations__` exists in `attrs`. In Python, when a class inherits from another class and doesn't define any new annotated fields, `__annotations__` is NOT present in the `attrs` dictionary of the child class; it is only present in the parent class.
To support functional inheritance (overriding methods without adding new fields), we should check if any of the base classes have `__annotations__` if the current class doesn't.
Actually, `DataObjectBase` classes are supposed to behave like dataclasses. If a subclass doesn't have its own annotations, it should ideally inherit the annotations from its parent `DataObjectBase` subclass.
Wait, let's look at how `_DataBaseMetaClass._FWD_DECL_FIELDS` is used. I should probably read more of the file, especially `_DataBaseMetaClass` and how `_FWD_DECL_FIELDS` is used.
The `_DataBaseMetaClass` is defined in `.base`. Let's find where it is.
TOOL CALL · run_command
{
"command": "grep -rn \"class _DataBaseMetaClass\" ."
}Result of run_command 129 characters
{
"status": "ok",
"stdout": "./caikit/core/data_model/base.py:46:class _DataBaseMetaClass(type):\n",
"stderr": "",
"exit_code": 0
}Generated tokens 439 tokens · stopped: STOP
The issue is 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)
```
It checks if `__annotations__` exists in `attrs`. In Python, when a class inherits from another class and doesn't define any new annotated fields, `__annotations__` is NOT present in the `attrs` dictionary of the child class; it is only present in the parent class.
To support functional inheritance (overriding methods without adding new fields), we should check if any of the base classes have `__annotations__` if the current class doesn't.
Actually, `DataObjectBase` classes are supposed to behave like dataclasses. If a subclass doesn't have its own annotations, it should ideally inherit the annotations from its parent `DataObjectBase` subclass.
Wait, let's look at how `_DataBaseMetaClass._FWD_DECL_FIELDS` is used. I should probably read more of the file, especially `_DataBaseMetaClass` and how `_FWD_DECL_FIELDS` is used.
The `_DataBaseMetaClass` is defined in `.base`. Let's find where it is.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "class _DataBaseMetaClass" .<|"|>}<tool_call|><|tool_response>
Turn 5at 93 s46 tokens out5,732 prompt tokens
Let's read `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 46 tokens · stopped: STOP
Let's read `caikit/core/data_model/base.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/base.py<|"|>}<tool_call|><|tool_response>
Turn 6at 163 s1,108 tokens out7,410 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 expects `mcs._FWD_DECL_FIELDS` to be in `attrs`.
In `_DataObjectBaseMetaClass.__new__` (from `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)
```
The problem is that if a subclass doesn't define any new annotations, `attrs.get("__annotations__")` returns `None`, and it raises `TypeError`.
If the subclass doesn't define new annotations, it should probably inherit them from the parent `DataObjectBase` subclass.
However, if we just inherit the annotations, `_FWD_DECL_FIELDS` will be the same list of fields as the parent. Is that correct? Yes, if no new fields are added, the fields are the same.
But wait, if we have multiple levels of inheritance, we might want to merge annotations. Python's `__annotations__` are not automatically merged across inheritance levels in the `attrs` dict of `__new__`.
If I want to support inheritance of fields, I should check the base classes for `__annotations__` or `_FWD_DECL_FIELDS`.
Wait, if a class inherits from `Base` (which is a `DataObjectBase`), `Base` already went through `_DataObjectBaseMetaClass.__new__` and its `_FWD_DECL_FIELDS` was set. But `_FWD_DECL_FIELDS` is passed in `attrs` to `super().__new__`, and the resulting class has it? No, `_DataBaseMetaClass.__new__` does `attrs.pop(mcs._FWD_DECL_FIELDS, None)`. So `_FWD_DECL_FIELDS` is NOT kept on the class, it's used to set the `fields` attribute.
Wait, `_DataBaseMetaClass.__new__` does:
```python
attrs["fields"] = fields
attrs["_proto_class"] = proto_class
```
So the parent class will have a `fields` attribute.
But `_DataObjectBaseMetaClass` is trying to set `_FWD_DECL_FIELDS` before calling `super().__new__`.
If the subclass doesn't have `__annotations__`, it should try to find them in its bases.
Let's see what happens if we just remove the `TypeError` and try to collect annotations from bases.
Wait, if a class inherits from `Base`, it should have the same fields as `Base` unless it adds new ones.
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 collects annotations from bases.
Example:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
# Look in bases for annotations
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__.copy()
break
if field_names is None:
raise TypeError(...)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
But wait, `Base` might be a `DataObjectBase` but it might not have `__annotations__` anymore if it was processed by the metaclass? No, Python classes keep their `__annotations__`.
Let's check if `Base` has `__annotations__`.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\n@dataobject\nclass Base(DataObjectBase):\n foo: int\n\nprint(f\"Base __annotations__: {getattr(Base, '__annotations__', 'Not found')}\")\nPY\n"
}Result of run_command 208 characters
{
"status": "ok",
"stdout": "Base __annotations__: {'foo': <class 'int'>}\n",
"stderr": "<function register_backend_type at 0x7a46082f5240> is still in the BETA phase and subject to change!\n",
"exit_code": 0
}Generated tokens 1,108 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 expects `mcs._FWD_DECL_FIELDS` to be in `attrs`.
In `_DataObjectBaseMetaClass.__new__` (from `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)
```
The problem is that if a subclass doesn't define any new annotations, `attrs.get("__annotations__")` returns `None`, and it raises `TypeError`.
If the subclass doesn't define new annotations, it should probably inherit them from the parent `DataObjectBase` subclass.
However, if we just inherit the annotations, `_FWD_DECL_FIELDS` will be the same list of fields as the parent. Is that correct? Yes, if no new fields are added, the fields are the same.
But wait, if we have multiple levels of inheritance, we might want to merge annotations. Python's `__annotations__` are not automatically merged across inheritance levels in the `attrs` dict of `__new__`.
If I want to support inheritance of fields, I should check the base classes for `__annotations__` or `_FWD_DECL_FIELDS`.
Wait, if a class inherits from `Base` (which is a `DataObjectBase`), `Base` already went through `_DataObjectBaseMetaClass.__new__` and its `_FWD_DECL_FIELDS` was set. But `_FWD_DECL_FIELDS` is passed in `attrs` to `super().__new__`, and the resulting class has it? No, `_DataBaseMetaClass.__new__` does `attrs.pop(mcs._FWD_DECL_FIELDS, None)`. So `_FWD_DECL_FIELDS` is NOT kept on the class, it's used to set the `fields` attribute.
Wait, `_DataBaseMetaClass.__new__` does:
```python
attrs["fields"] = fields
attrs["_proto_class"] = proto_class
```
So the parent class will have a `fields` attribute.
But `_DataObjectBaseMetaClass` is trying to set `_FWD_DECL_FIELDS` before calling `super().__new__`.
If the subclass doesn't have `__annotations__`, it should try to find them in its bases.
Let's see what happens if we just remove the `TypeError` and try to collect annotations from bases.
Wait, if a class inherits from `Base`, it should have the same fields as `Base` unless it adds new ones.
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 collects annotations from bases.
Example:
```python
field_names = attrs.get("__annotations__")
if field_names is None:
# Look in bases for annotations
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__.copy()
break
if field_names is None:
raise TypeError(...)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
But wait, `Base` might be a `DataObjectBase` but it might not have `__annotations__` anymore if it was processed by the metaclass? No, Python classes keep their `__annotations__`.
Let's check if `Base` has `__annotations__`.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
print(f"Base __annotations__: {getattr(Base, '__annotations__', 'Not found')}")
PY
<|"|>}<tool_call|><|tool_response>
Turn 7at 186 s339 tokens out8,606 prompt tokens
Yes, `Base` has `__annotations__`.
What if `Derived` doesn't have any?
```python
@dataobject
class Derived(Base):
pass
```
In this case, `attrs` in `_DataObjectBaseMetaClass.__new__` for `Derived` will NOT have `__annotations__`.
If I want `Derived` to have the same fields as `Base`, I should get them from `Base`.
But what if `Derived` adds some fields?
```python
@dataobject
class Derived(Base):
bar: int
```
Then `attrs` for `Derived` WILL have `__annotations__ = {'bar': int}`. But it should also have `foo: int` from `Base`.
Wait, how does `@dataobject` handle this?
Usually `@dataobject` is used with `@dataclass`.
The docstring of `dataobject` says:
```python
@dataobject("foo.bar")
@dataclass
class MyDataObject(DataObjectBase):
```
Wait, the user's sample code is:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
The user is NOT using `@dataclass` explicitly. They are only using `@dataobject`.
Let's see the implementation of `dataobject` decorator.
TOOL CALL · read_file
{
"end_line": 386,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 121
}Result of read_file 6,374 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": "\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:\n 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\"\"\"",
"start_line": 121,
"end_line": 270,
"total_lines": 386,
"is_truncated": true
}Generated tokens 339 tokens · stopped: STOP
Yes, `Base` has `__annotations__`.
What if `Derived` doesn't have any?
```python
@dataobject
class Derived(Base):
pass
```
In this case, `attrs` in `_DataObjectBaseMetaClass.__new__` for `Derived` will NOT have `__annotations__`.
If I want `Derived` to have the same fields as `Base`, I should get them from `Base`.
But what if `Derived` adds some fields?
```python
@dataobject
class Derived(Base):
bar: int
```
Then `attrs` for `Derived` WILL have `__annotations__ = {'bar': int}`. But it should also have `foo: int` from `Base`.
Wait, how does `@dataobject` handle this?
Usually `@dataobject` is used with `@dataclass`.
The docstring of `dataobject` says:
```python
@dataobject("foo.bar")
@dataclass
class MyDataObject(DataObjectBase):
```
Wait, the user's sample code is:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
The user is NOT using `@dataclass` explicitly. They are only using `@dataobject`.
Let's see the implementation of `dataobject` decorator.
<channel|><|tool_call>call:read_file{end_line:386,filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:121}<tool_call|><|tool_response>
Turn 8at 243 s1,029 tokens out10,696 prompt tokens
The `dataobject` decorator does:
```python
if not issubclass(cls, Enum):
# ...
cls = dataclasses.dataclass(cls)
setattr(cls, _USER_DEFINED_DEFAULTS, user_defined_defaults)
```
It calls `dataclasses.dataclass(cls)`.
Now let's go back to `_DataObjectBaseMetaClass.__new__`.
```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)
```
The problem is that it insists that the current class being created must have `__annotations__` defined in its own `attrs`.
If we have:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
pass
```
When `Derived` is being created, `attrs` doesn't have `__annotations__`.
If we want `Derived` to be a valid `DataObjectBase` subclass, we should allow it to not have its own annotations, provided at least one of its base classes (other than `DataObjectBase` and `DataBase`) has them, or we just allow it and let `dataclasses.dataclass` handle it.
Wait, `_DataBaseMetaClass` uses `_FWD_DECL_FIELDS` to determine the `fields` of the class.
If `_FWD_DECL_FIELDS` is not provided, it throws an error:
```python
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
So we MUST provide `_FWD_DECL_FIELDS`.
If the subclass doesn't have `__annotations__`, it should inherit the annotations from its parent `DataObjectBase` subclass.
A `DataObjectBase` class in this context will have a `fields` attribute (set by `_DataBaseMetaClass`).
However, `_//DataObjectBaseMetaClass` runs *before* `_DataBaseMetaClass` because it inherits from it.
Let's see:
`class _DataObjectBaseMetaClass(_DataBaseMetaClass):`
So `_DataObjectBaseMetaClass.__new__` is called, then `_DataBaseMetaClass.__new__` is called via `super().__new__`.
If `Derived` doesn't have `__annotations__`, we can look at its bases to see if any of them are `DataObjectBase` subclasses and have annotations.
But wait, if the user just wants to override a method, the fields of `Derived` should be the same as `Base`.
Can we just look for `__annotations__` in the bases?
```python
def __new__(mcs, name, bases, attrs):
# ...
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Try to find annotations in base classes
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
```
Wait, if I just do `field_names = base.__annotations__`, I'm sharing the same dictionary. I should probably copy it.
And what if the subclass *does* have annotations? It should probably merge them with the parent's annotations if we want full dataclass-like inheritance.
But `dataclasses.dataclass` handles inheritance of fields automatically. The issue here is that `caikit` is doing its own proto generation based on these annotations *before* (or in parallel with) the dataclass conversion.
Let's see how `_dataobject_to_proto` is implemented.
TOOL CALL · read_file
{
"end_line": 386,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 271
}Result of read_file 4,549 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": " 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\n nested_proto_class = getattr(proto_class, nested_message_name)\n setattr(\n cls,\n nested_message_name,\n _make_data_model_class(\n nested_proto_class,\n _DataBaseMetaClass.__new__(\n _DataBaseMetaClass,\n name=nested_message_name,\n bases=(DataBase,),\n attrs={\"_proto_class\": getattr(proto_class, nested_message_name)},\n ),\n ),\n )\n for nested_enum_descriptor in proto_class.DESCRIPTOR.enum_types:\n setattr(\n cls,\n nested_enum_descriptor.name,\n getattr(enums, nested_enum_descriptor.name),\n )\n\n return cls\n\n\ndef _make_oneof_init(cls):\n \"\"\"Helper to augment a defaulted dataclass __init__ to support kwargs for\n oneof fields\n \"\"\"\n original_init = cls.__init__\n fields_to_oneofs = cls._fields_to_oneof\n oneofs_to_fields = cls._fields_oneofs_map\n\n def __init__(self, *args, **kwargs):\n new_kwargs = {}\n to_remove = []\n which_oneof = {}\n for field_name, val in kwargs.items():\n if oneof_name := fields_to_oneofs.get(field_name):\n oneof_pos_idx = list(cls.__dataclass_fields__.keys()).index(oneof_name)\n has_pos_val = len(args) > oneof_pos_idx\n if has_pos_val:\n error(\n \"<COR09282193E>\",\n TypeError(\n \"Received conflicting oneof args/kwargs for {}/{}\".format(\n oneof_name,\n field_name,\n )\n ),\n )\n\n other_oneof_fields = (\n field\n for field in [oneof_name] + oneofs_to_fields[oneof_name]\n if field != field_name\n )\n if any(field in kwargs for field in other_oneof_fields):\n error(\n \"<COR59933157E>\",\n TypeError(\n \"Received multiple keyword arguments for oneof {}\".format(\n oneof_name,\n )\n ),\n )\n new_kwargs[oneof_name] = val\n to_remove.append(field_name)\n which_oneof[oneof_name] = field_name\n\n for kwarg in to_remove:\n del kwargs[kwarg]\n kwargs.update(new_kwargs)\n original_init(self, *args, **kwargs)\n setattr(self, _DataBaseMetaClass._WHICH_ONEOF_ATTR, which_oneof)\n\n return __init__\n\n\ndef _has_dataclass_init(cls) -> bool:\n \"\"\"When the dataclass decorator adds an __init__ to a class, it adds\n __annotations__ to the init function itself. This function uses that fact to\n detect if the class's __init__ function was generated by @dataclass\n \"\"\"\n return bool(getattr(cls.__init__, \"__annotations__\", None)) and not any(\n cls.__init__ is base.__init__ for base in cls.__bases__\n )",
"start_line": 271,
"end_line": 386,
"total_lines": 386,
"is_truncated": false
}Generated tokens 1,029 tokens · stopped: STOP
The `dataobject` decorator does:
```python
if not issubclass(cls, Enum):
# ...
cls = dataclasses.dataclass(cls)
setattr(cls, _USER_DEFINED_DEFAULTS, user_defined_defaults)
```
It calls `dataclasses.dataclass(cls)`.
Now let's go back to `_DataObjectBaseMetaClass.__new__`.
```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)
```
The problem is that it insists that the current class being created must have `__annotations__` defined in its own `attrs`.
If we have:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
pass
```
When `Derived` is being created, `attrs` doesn't have `__annotations__`.
If we want `Derived` to be a valid `DataObjectBase` subclass, we should allow it to not have its own annotations, provided at least one of its base classes (other than `DataObjectBase` and `DataBase`) has them, or we just allow it and let `dataclasses.dataclass` handle it.
Wait, `_DataBaseMetaClass` uses `_FWD_DECL_FIELDS` to determine the `fields` of the class.
If `_FWD_DECL_FIELDS` is not provided, it throws an error:
```python
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
So we MUST provide `_FWD_DECL_FIELDS`.
If the subclass doesn't have `__annotations__`, it should inherit the annotations from its parent `DataObjectBase` subclass.
A `DataObjectBase` class in this context will have a `fields` attribute (set by `_DataBaseMetaClass`).
However, `_//DataObjectBaseMetaClass` runs *before* `_DataBaseMetaClass` because it inherits from it.
Let's see:
`class _DataObjectBaseMetaClass(_DataBaseMetaClass):`
So `_DataObjectBaseMetaClass.__new__` is called, then `_DataBaseMetaClass.__new__` is called via `super().__new__`.
If `Derived` doesn't have `__annotations__`, we can look at its bases to see if any of them are `DataObjectBase` subclasses and have annotations.
But wait, if the user just wants to override a method, the fields of `Derived` should be the same as `Base`.
Can we just look for `__annotations__` in the bases?
```python
def __new__(mcs, name, bases, attrs):
# ...
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Try to find annotations in base classes
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
```
Wait, if I just do `field_names = base.__annotations__`, I'm sharing the same dictionary. I should probably copy it.
And what if the subclass *does* have annotations? It should probably merge them with the parent's annotations if we want full dataclass-like inheritance.
But `dataclasses.dataclass` handles inheritance of fields automatically. The issue here is that `caikit` is doing its own proto generation based on these annotations *before* (or in parallel with) the dataclass conversion.
Let's see how `_dataobject_to_proto` is implemented.
<channel|><|tool_call>call:read_file{end_line:386,filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:271}<tool_call|><|tool_response>
Turn 9at 300 s1,059 tokens out12,966 prompt tokens
Actually, let's look at the reproduction code again.
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
When `Derived` is being processed by `_DataObjectBaseMetaClass.__new__`, it inherits from `Base`. `Base` already has `__annotations__`.
If we want `Derived` to have the same fields as `Base`, we should look up the annotations in the inheritance chain.
If I change `_DataObjectBaseMetaClass.__new__` to:
```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:
# Support functional inheritance: if no annotations are defined
# in the current class, look for them in the base classes.
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
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)
```
Wait, what if the subclass *does* have annotations? Should it merge them?
Usually, `@dataclass` merges annotations from bases.
The `_DataobjectConverter` seems to use the dataclass `__dataclass_fields__` later on:
```python
def get_optional_field_names(self, entry: Any) -> List[str]:
# ...
for field_name, field in entry.__dataclass_fields__.items():
# ...
```
The `descriptor` is created by `_dataobject_to_proto(dataclass_=cls, **kwargs)`.
`_DataobjectConverter` inherits from `DataclassConverter`.
If `DataclassConverter` also uses `__dataclass_fields__` or `__annotations__` of the dataclass, then since `dataclasses.dataclass` has already been called on `cls`, `cls` will already have the merged fields.
Wait, `_DataObjectBaseMetaClass.__new__` is called *whenever a class is defined*, regardless of whether `@dataobject` is used.
Wait, no, it's the metaclass of `DataObjectBase`. So any class inheriting from `DataObjectBase` will use it.
The `@dataobject` decorator is applied *after* the class is created by the metaclass.
So:
1. `Base` class is created. `_DataObjectBaseMetaClass.__new__` is called. `__annotations__` is present. `_FWD_DECL_FIELDS` is set. `_DataBaseMetaClass.__new__` is called. `fields` is set to `_FWD_DECL_FIELDS`.
2. `@dataobject` is called on `Base`. It calls `dataclasses.dataclass(Base)`. Then it generates a proto class and sets `Base._proto_class`. Then it calls `_make_data_model_class` which might call `_DataBaseMetaClass.parse_proto_descriptor(Base)`.
Now for `Derived`:
1. `Derived` class is created. `_DataObjectBaseMetaClass.__new__` is called. `__annotations__` is NOT present. It raises `TypeError`.
So we must fix `_DataObjectBaseMetaClass.__new__`.
If we just want to support the case where no new fields are added, looking at bases is enough.
But if we want to support adding new fields in `Derived`, we should merge annotations.
Let's test what happens if we add fields to `Derived`.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\n@dataobject\nclass Base(DataObjectBase):\n foo: int\n\ntry:\n @dataobject\n class Derived(Base):\n bar: int\n print(\"Success\")\nexcept TypeError as e:\n print(f\"Caught expected TypeError: {e}\")\nexcept Exception as e:\n print(f\"Caught unexpected exception: {type(e).__name__}: {e}\")\nPY\n"
}Result of run_command 171 characters
{
"status": "ok",
"stdout": "Success\n",
"stderr": "<function register_backend_type at 0x7b76acee9240> is still in the BETA phase and subject to change!\n",
"exit_code": 0
}Generated tokens 1,059 tokens · stopped: STOP
Actually, let's look at the reproduction code again.
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
When `Derived` is being processed by `_DataObjectBaseMetaClass.__new__`, it inherits from `Base`. `Base` already has `__annotations__`.
If we want `Derived` to have the same fields as `Base`, we should look up the annotations in the inheritance chain.
If I change `_DataObjectBaseMetaClass.__new__` to:
```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:
# Support functional inheritance: if no annotations are defined
# in the current class, look for them in the base classes.
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
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)
```
Wait, what if the subclass *does* have annotations? Should it merge them?
Usually, `@dataclass` merges annotations from bases.
The `_DataobjectConverter` seems to use the dataclass `__dataclass_fields__` later on:
```python
def get_optional_field_names(self, entry: Any) -> List[str]:
# ...
for field_name, field in entry.__dataclass_fields__.items():
# ...
```
The `descriptor` is created by `_dataobject_to_proto(dataclass_=cls, **kwargs)`.
`_DataobjectConverter` inherits from `DataclassConverter`.
If `DataclassConverter` also uses `__dataclass_fields__` or `__annotations__` of the dataclass, then since `dataclasses.dataclass` has already been called on `cls`, `cls` will already have the merged fields.
Wait, `_DataObjectBaseMetaClass.__new__` is called *whenever a class is defined*, regardless of whether `@dataobject` is used.
Wait, no, it's the metaclass of `DataObjectBase`. So any class inheriting from `DataObjectBase` will use it.
The `@dataobject` decorator is applied *after* the class is created by the metaclass.
So:
1. `Base` class is created. `_DataObjectBaseMetaClass.__new__` is called. `__annotations__` is present. `_FWD_DECL_FIELDS` is set. `_DataBaseMetaClass.__new__` is called. `fields` is set to `_FWD_DECL_FIELDS`.
2. `@dataobject` is called on `Base`. It calls `dataclasses.dataclass(Base)`. Then it generates a proto class and sets `Base._proto_class`. Then it calls `_make_data_model_class` which might call `_DataBaseMetaClass.parse_proto_descriptor(Base)`.
Now for `Derived`:
1. `Derived` class is created. `_DataObjectBaseMetaClass.__new__` is called. `__annotations__` is NOT present. It raises `TypeError`.
So we must fix `_DataObjectBaseMetaClass.__new__`.
If we just want to support the case where no new fields are added, looking at bases is enough.
But if we want to support adding new fields in `Derived`, we should merge annotations.
Let's test what happens if we add fields to `Derived`.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
try:
@dataobject
class Derived(Base):
bar: int
print("Success")
except TypeError as e:
print(f"Caught expected TypeError: {e}")
except Exception as e:
print(f"Caught unexpected exception: {type(e).__name__}: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Turn 10at 408 s1,827 tokens out14,100 prompt tokens
Interesting! `Derived(Base)` with `bar: int` succeeded. Why? Because `bar: int` means `__annotations__` is present in `attrs`.
So the only case that fails is when `__annotations__` is completely missing in the subclass.
If `__annotations__` is missing, it means no new fields are defined in the subclass.
In this case, the subclass should have the same fields as its parent.
If we just take `field_names` from the base class, will `_DataBaseMetaClass` (which sets `attrs["fields"] = fields`) work correctly? Yes, because it will set the same fields as the parent.
Is it possible that we need to merge annotations anyway?
If `Derived` adds a field, `attrs.get("__annotations__")` returns `{'bar': int}`.
Then `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = {'bar': int}`.
Then `_DataBaseMetaClass.__new__` sets `attrs["fields"] = {'bar': int}`.
Wait, if `Base` had `foo: int` and `Derived` has `bar: int`, but `Derived`'s `fields` attribute only contains `bar`, is that a problem?
Wait, `_DataBaseMetaClass.__new__` does:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
So `Derived.fields` would only be `{'bar': int}`. But `Base.fields` is `{'foo': int}`.
Does `DataclassConverter` (which is used by `@dataobject`) use these `fields` attributes or does it use the dataclass's `__dataclass_fields__`?
Let's check `_dataobject_to_proto` (actually `_DataobjectConverter` which inherits from `DataclassConverter`).
`DataclassConverter` is from `py_to_proto.dataclass_to_proto`. I don't have the source for that, but it's a third party library.
However, if `_DataBaseMetaClass` is used to set `__slots__`, it might be important.
```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 `Derived.fields` only has `bar`, then `Derived.__slots__` will only have `_bar`. But `Derived` also needs `_foo` because it inherits from `Base`.
But `Base` already has `__slots__` with `_foo`.
In Python, if a subclass defines `__slots__`, it only adds those slots. If it doesn't define `__slots__`, it has a `__dict__`.
If `Base` has `__slots__` and `Derived` also has `__slots__`, it's all good.
Wait, the problem is that if `Derived` defines `bar: int`, `_DataObjectBaseMetaClass` only passes `{'bar': int}` to `_DataBaseMetaClass`.
Does `_DataBaseMetaClass` handle inheritance of fields?
Looking at `_DataBaseMetaClass.__new__`:
```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"]:
# Look for a precompiled proto class and if found, parse its
# descriptor
proto_class = attrs.get("_proto_class")
if proto_class is not None:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
It does NOT seem to inherit `fields` from bases. It just takes what's in `attrs`.
Wait, if `Derived` is a subclass of `Base`, then `Derived` наследует all attributes of `Base` EXCEPT those that it overrides. `fields` is an attribute. If `_DataBaseMetaClass` sets `attrs["fields"] = fields`, it's overriding `Base.fields`.
So if `Derived` only has `bar: int`, `Derived.fields` will be `{'bar': int}`, while `Base.fields` was `{'foo': int}`.
Is this intended?
The `py_to_proto` converter probably handles the dataclass's combined fields.
But the `__slots__` are based on `fields`.
If `Derived.fields` is only `{'bar': int}`, then `Derived.__slots__` is `('_bar', ...)`.
Since `Base` already has `__slots__ = ('_foo', ...)`, `Derived` will have both.
Wait, if `Derived` doesn't define any annotations, `_DataObjectBaseMetaClass` currently raises `TypeError`.
If we want to support functional inheritance, we should allow it.
If we just remove the `TypeError` and let `field_names` be `None`, then `_DataBaseMetaClass` will raise its own error:
```python
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
So we MUST provide `field_names`.
If no annotations are provided in `Derived`, we should probably use the annotations of the first `DataObjectBase` parent.
```python
def __new__(mcs, name, bases, attrs):
# ...
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if the user did this:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
bar: int
```
Then `attrs` for `Derived` HAS `__annotations__ = {'bar': int}`.
So `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS]` will be `{'bar': int}`.
Then `Derived.fields` will be `{'bar': int}`.
But `Derived` is supposed to have both `foo` and `bar`.
If `@dataobject` (via `py_to_proto`) uses `dataclasses.dataclass`, it will see both.
Let's check if `Derived.fields` is supposed to be merged.
If I look at `_DataBaseMetaClass.__new__` again, it seems it's designed to just get the fields for the *current* class to set up `__slots__` and other metadata.
But wait, if `Derived` has `foo` and `bar`, then `_DataBaseMetaClass` probably wants `Derived.fields` to contain both.
Let's test this.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\n@dataobject\nclass Base(DataObjectBase):\n foo: int\n\n@dataobject\nclass Derived(Base):\n bar: int\n\nprint(f\"Base fields: {getattr(Base, 'fields', 'Not found')}\")\nprint(f\"Derived fields: {getattr(Derived, 'fields', 'Not found')}\")\nPY\n"
}Result of run_command 217 characters
{
"status": "ok",
"stdout": "Base fields: ('foo',)\nDerived fields: ('foo', 'bar')\n",
"stderr": "<function register_backend_type at 0x7c504f851240> is still in the BETA phase and subject to change!\n",
"exit_code": 0
}Generated tokens 1,827 tokens · stopped: STOP
Interesting! `Derived(Base)` with `bar: int` succeeded. Why? Because `bar: int` means `__annotations__` is present in `attrs`.
So the only case that fails is when `__annotations__` is completely missing in the subclass.
If `__annotations__` is missing, it means no new fields are defined in the subclass.
In this case, the subclass should have the same fields as its parent.
If we just take `field_names` from the base class, will `_DataBaseMetaClass` (which sets `attrs["fields"] = fields`) work correctly? Yes, because it will set the same fields as the parent.
Is it possible that we need to merge annotations anyway?
If `Derived` adds a field, `attrs.get("__annotations__")` returns `{'bar': int}`.
Then `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = {'bar': int}`.
Then `_DataBaseMetaClass.__new__` sets `attrs["fields"] = {'bar': int}`.
Wait, if `Base` had `foo: int` and `Derived` has `bar: int`, but `Derived`'s `fields` attribute only contains `bar`, is that a problem?
Wait, `_DataBaseMetaClass.__new__` does:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
So `Derived.fields` would only be `{'bar': int}`. But `Base.fields` is `{'foo': int}`.
Does `DataclassConverter` (which is used by `@dataobject`) use these `fields` attributes or does it use the dataclass's `__dataclass_fields__`?
Let's check `_dataobject_to_proto` (actually `_DataobjectConverter` which inherits from `DataclassConverter`).
`DataclassConverter` is from `py_to_proto.dataclass_to_proto`. I don't have the source for that, but it's a third party library.
However, if `_DataBaseMetaClass` is used to set `__slots__`, it might be important.
```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 `Derived.fields` only has `bar`, then `Derived.__slots__` will only have `_bar`. But `Derived` also needs `_foo` because it inherits from `Base`.
But `Base` already has `__slots__` with `_foo`.
In Python, if a subclass defines `__slots__`, it only adds those slots. If it doesn't define `__slots__`, it has a `__dict__`.
If `Base` has `__slots__` and `Derived` also has `__slots__`, it's all good.
Wait, the problem is that if `Derived` defines `bar: int`, `_DataObjectBaseMetaClass` only passes `{'bar': int}` to `_DataBaseMetaClass`.
Does `_DataBaseMetaClass` handle inheritance of fields?
Looking at `_DataBaseMetaClass.__new__`:
```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"]:
# Look for a precompiled proto class and if found, parse its
# descriptor
proto_class = attrs.get("_proto_class")
if proto_class is not None:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
It does NOT seem to inherit `fields` from bases. It just takes what's in `attrs`.
Wait, if `Derived` is a subclass of `Base`, then `Derived` наследует all attributes of `Base` EXCEPT those that it overrides. `fields` is an attribute. If `_DataBaseMetaClass` sets `attrs["fields"] = fields`, it's overriding `Base.fields`.
So if `Derived` only has `bar: int`, `Derived.fields` will be `{'bar': int}`, while `Base.fields` was `{'foo': int}`.
Is this intended?
The `py_to_proto` converter probably handles the dataclass's combined fields.
But the `__slots__` are based on `fields`.
If `Derived.fields` is only `{'bar': int}`, then `Derived.__slots__` is `('_bar', ...)`.
Since `Base` already has `__slots__ = ('_foo', ...)`, `Derived` will have both.
Wait, if `Derived` doesn't define any annotations, `_DataObjectBaseMetaClass` currently raises `TypeError`.
If we want to support functional inheritance, we should allow it.
If we just remove the `TypeError` and let `field_names` be `None`, then `_DataBaseMetaClass` will raise its own error:
```python
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
So we MUST provide `field_names`.
If no annotations are provided in `Derived`, we should probably use the annotations of the first `DataObjectBase` parent.
```python
def __new__(mcs, name, bases, attrs):
# ...
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
for base in bases:
if hasattr(base, "__annotations__"):
field_names = base.__annotations__
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if the user did this:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
bar: int
```
Then `attrs` for `Derived` HAS `__annotations__ = {'bar': int}`.
So `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS]` will be `{'bar': int}`.
Then `Derived.fields` will be `{'bar': int}`.
But `Derived` is supposed to have both `foo` and `bar`.
If `@dataobject` (via `py_to_proto`) uses `dataclasses.dataclass`, it will see both.
Let's check if `Derived.fields` is supposed to be merged.
If I look at `_DataBaseMetaClass.__new__` again, it seems it's designed to just get the fields for the *current* class to set up `__slots__` and other metadata.
But wait, if `Derived` has `foo` and `bar`, then `_DataBaseMetaClass` probably wants `Derived.fields` to contain both.
Let's test this.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
bar: int
print(f"Base fields: {getattr(Base, 'fields', 'Not found')}")
print(f"Derived fields: {getattr(Derived, 'fields', 'Not found')}")
PY
<|"|>}<tool_call|><|tool_response>
Turn 11at 430 s499 tokens out16,020 prompt tokens
Wait! `Derived fields` is `('foo', 'bar')`!
How did that happen?
Let's look at `_DataBaseMetaClass.__new__` again.
```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"]:
# Look for a precompiled proto class and if found, parse its
# descriptor
proto_class = attrs.get("_proto_class")
if proto_class is not None:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
In my test:
`Base` is created. `_FWD_DECL_FIELDS` is `{'foo': int}`. `_DataBaseMetaClass` sets `Base.fields = ('foo',)`. (Wait, why a tuple? Ah, maybe `_DataBaseMetaClass.parse_proto_descriptor` or something else converts it).
Wait, `_DataBaseMetaClass.__new__` sets `attrs["fields"] = fields`. If `fields` was `{'foo': int}`, then `Base.fields` should be `{'foo': int}`.
But the output says `Base fields: ('foo',)`.
Wait, I missed something.
Let's look at `_DataBaseMetaClass` again.
Ah! `_make_data_model_class` calls `_DataBaseMetaClass.parse_proto_descriptor(cls)`.
```python
def _make_data_model_class(proto_class, cls):
if issubclass(cls, DataObjectBase):
_DataBaseMetaClass.parse_proto_descriptor(cls)
# ...
```
Let's find `parse_proto_descriptor` in `caikit/core/data_model/base.py`.
TOOL CALL · run_command
{
"command": "grep -n \"def parse_proto_descriptor\" caikit/core/data_model/base.py"
}Result of run_command 107 characters
{
"status": "ok",
"stdout": "155: def parse_proto_descriptor(mcs, cls):\n",
"stderr": "",
"exit_code": 0
}Generated tokens 499 tokens · stopped: STOP
Wait! `Derived fields` is `('foo', 'bar')`!
How did that happen?
Let's look at `_DataBaseMetaClass.__new__` again.
```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"]:
# Look for a precompiled proto class and if found, parse its
# descriptor
proto_class = attrs.get("_proto_class")
if proto_class is not None:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
attrs["fields"] = fields
```
In my test:
`Base` is created. `_FWD_DECL_FIELDS` is `{'foo': int}`. `_DataBaseMetaClass` sets `Base.fields = ('foo',)`. (Wait, why a tuple? Ah, maybe `_DataBaseMetaClass.parse_proto_descriptor` or something else converts it).
Wait, `_DataBaseMetaClass.__new__` sets `attrs["fields"] = fields`. If `fields` was `{'foo': int}`, then `Base.fields` should be `{'foo': int}`.
But the output says `Base fields: ('foo',)`.
Wait, I missed something.
Let's look at `_DataBaseMetaClass` again.
Ah! `_make_data_model_class` calls `_DataBaseMetaClass.parse_proto_descriptor(cls)`.
```python
def _make_data_model_class(proto_class, cls):
if issubclass(cls, DataObjectBase):
_DataBaseMetaClass.parse_proto_descriptor(cls)
# ...
```
Let's find `parse_proto_descriptor` in `caikit/core/data_model/base.py`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -n "def parse_proto_descriptor" caikit/core/data_model/base.py<|"|>}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
Test output Included
============================= test session starts ==============================
collected 126 items
tests/core/data_model/test_dataobject.py ............................... [ 24%]
........................................................................ [ 81%]
......................F [100%]
=================================== FAILURES ===================================
E TypeError: All DataObjectBase classes must follow dataclass syntax
/caikit/caikit/core/data_model/dataobject.py:94: TypeError: All DataObjectBase classes must follow dataclass syntax
=============================== 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 0x7e256e7ca950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 138699383974784, "timestamp": "2026-11-14T02:49:23.263621"}
{"channel": "WIPDC", "exception": null, "level": "warning", "message": "<function register_backend_type at 0x7e256e7ca950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 138699383974784, "timestamp": "2026-11-14T02:49:23.263756"}
------------------------------ Captured log setup ------------------------------
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x7e256e7ca950> is still in the BETA phase and subject to change!
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x7e256e7ca950> 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": 138699383974784, "timestamp": "2026-11-14T02:49:23.282131"}
{"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": 138699383974784, "timestamp": "2026-11-14T02:49:23.282235"}
{"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": 138699383974784, "timestamp": "2026-11-14T02:49:23.282291"}
------------------------------ 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": 138699383974784, "timestamp": "2026-11-14T02:49:23.300506"}
------------------------------ 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-dceb388e75d94dab93fe152be28f998e.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="0.204" timestamp="2026-11-14T02:49:23.209709+00:00" hostname="0042cd093381"><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_native_types" time="0.005" /><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="0.011" /><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[72]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[73]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[74]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[75]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[76]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[77]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[78]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[79]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[80]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" 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="TypeError: All DataObjectBase classes must follow dataclass syntax">E TypeError: All DataObjectBase classes must follow dataclass syntax</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.
[ 17.4s] 💭 (823 chars) "The user is reporting a bug in `caikit.core.data_model.dataobject`.
When a class inherits from `DataObjectBase` and uses..."
[ 17.4s] 🔧 run_command grep -rn --include="*.py" "All DataObjectBase classes must follow dataclass synt
[ 17.5s] → ./caikit/core/data_model/dataobject.py:89: "All DataObjectBase classes must follow
dataclass syntax"
[ 31.0s] 💭 (127 chars) "Okay, the error is in `./caikit/core/data_model/dataobject.py` at line 89.
Now, let's reproduce the bug using the sampl..."
[ 31.0s] 🔧 run_command (20 lines) python3 - <<'PY'
[ 31.7s] → Caught expected TypeError: All DataObjectBase classes must follow dataclass syntax
[ 37.9s] 💭 (104 chars) "The reproduction confirms the bug. Now let's examine the code in
`caikit/core/data_model/dataobject.py`."
[ 37.9s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 38.0s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '# Copyright The Caikit
Authors\n#\n# Li…
[ 71.4s] 💭 (1742 chars) "The issue is in `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
"""When..."
[ 71.4s] 🔧 run_command grep -rn "class _DataBaseMetaClass" .
[ 71.5s] → ./caikit/core/data_model/base.py:46:class _DataBaseMetaClass(type):
[ 73.9s] 💭 (44 chars) "Let's read `caikit/core/data_model/base.py`."
[ 73.9s] 📖 read_file (caikit/core/data_model/base.py)
[ 73.9s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/base.py', 'content': '# Copyright The Caikit
Authors\n#\n# Licensed…
[ 143.6s] 💭 (4117 chars) "In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribut..."
[ 143.6s] 🔧 run_command (9 lines) python3 - <<'PY'
[ 143.9s] → Base __annotations__: {'foo': <class 'int'>}
[ 167.1s] 💭 (1007 chars) "Yes, `Base` has `__annotations__`.
What if `Derived` doesn't have any?
```python
@dataobject
class Derived(Base):
..."
[ 167.1s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 167.2s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '\n Args:\n
package: str\n …
[ 224.2s] 💭 (3952 chars) "The `dataobject` decorator does:
```python
if not issubclass(cls, Enum):
# ...
cls = dat..."
[ 224.2s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 224.3s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': ' if
get_origin(entry) is Union:\…
[ 281.2s] 💭 (3670 chars) "Actually, let's look at the reproduction code again.
```python
@dataobject
class Base(DataObjectBase):
foo: int
@da..."
[ 281.2s] 🔧 run_command (17 lines) python3 - <<'PY'
[ 281.5s] → Success
[ 388.9s] 💭 (6334 chars) "Interesting! `Derived(Base)` with `bar: int` succeeded. Why? Because `bar: int` means
`__annotations__` is present in `a..."
[ 388.9s] 🔧 run_command (14 lines) python3 - <<'PY'
[ 389.2s] → Base fields: ('foo',)
[ 411.0s] 💭 (1605 chars) "Wait! `Derived fields` is `('foo', 'bar')`!
How did that happen?
Let's look at `_DataBaseMetaClass.__new__` again.
```..."
[ 411.0s] 🔧 run_command grep -n "def parse_proto_descriptor" caikit/core/data_model/base.py
[ 411.1s] → 155: def parse_proto_descriptor(mcs, cls):
Native result row
{
"agent_error": "Agent exceeded session timeout (7.5 min)",
"duration_seconds": 471.52,
"error": "Empty agent patch",
"grade": {
"error": "Empty agent patch",
"resolved": false,
"strict_clean_patch": false,
"test_exit_code": -1
},
"instance_id": "caikit__caikit-233",
"pass_idx": 0,
"patch_chars": 0,
"phase1_seconds": 471.52,
"repo": "caikit/caikit",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/caikit__caikit-233__p0__r0.jsonl",
"resolved": false,
"rollout_idx": 0,
"run_key": "caikit__caikit-233__p0__r0",
"strict_clean_patch": false,
"tool_calls": 11,
"total_llm_calls": 11,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_caikit__caikit-233__p0__r0.json"
}