Fix Union type with dataclass ambiguous error and support superset comparison

[mao3267]

Summary

We only support json schema draft

1. same json schema
2. big -> small (superset)
3. inherit case (superset)

Tracking issue

Related to #5489

Why are the changes needed?

When a function accepts a Union of two dataclasses as input, Flyte cannot distinguish which dataclass matches the user's input. This is because Flyte only compares the simple types, and both dataclasses are identified as flyte.SimpleType_STRUCT in this scenario. As a result, there will be multiple matches, causing ambiguity and leading to an error.

union_test_dataclass.py

from typing import Union
from dataclasses import dataclass
from dataclasses_json import dataclass_json
from flytekit import task, workflow

@dataclass_json 
@dataclass
class A:
    a: int
    

@dataclass_json
@dataclass
class B:
    b: int


@task
def bar() -> A:
    return A(a=1)

@task
def foo(inp: Union[A, B]):
    print(inp)

@workflow
def wf():
    v = bar()
    foo(inp=v)

What changes were proposed in this pull request?

1. To distinguish between different types using protobuf struct (dataclass, Pydantic.BaseModel), we compare their JSON schemas generated by marshmallow_jsonschema.JSONSchema (draft-07) or mashumaro.jsonschema.build_json_schema (draft 2020-12) for dataclass and Pydantic.BaseModel for itself. To check equivalence, we compare the bytes from marshaling the json schemas if they are in the same draft version. For now, we only consider supporting the comparison of schemas with the same version.
2. We plan to support superset matching for dataclass/Pydantic.BaseModel with schemas in draft 2020-12, meaning that class A and class supersetA can be a match in the following example: (Pydantic.BaseModel example is in the screenshot section)

superset_A.py

# downstream 
@dataclass
class A:
    a: int
    b: Optional[int] = None
    c: str = "Flyte"

superset_dataclass.py

from dataclasses import dataclass
from typing import Optional
from superset_A import A as supersetA
# upstream 
@dataclass
class A:
    a: int

@dataclass
class B:
    b: str

@task
def foo() -> A:
    return A(a=1)

@task
def my_task(input: Union[supersetA, B]):
    print(input)

@workflow
def wf():
    a = foo()
    my_task(a)

3. Unit tests will be added for different versions of json schema, including one-level, two-level, and superset examples.

How was this patch tested? (my local testing repo here)

1. Run an example using union input with identical dataclass on remote (union_test_dataclass.py)
2. Run an example with superset on remote (superset_dataclass.py)
3. Run an example using union input with identical BaseModel class on remote (union_test_basemodel.py)

union_test_basemodel.py

from pydantic import BaseModel
from typing import Union
from flytekit import task, workflow
from flytekit.image_spec import ImageSpec

flytekit_hash = "3475ddc41f2ba31d23dd072362be704d7c2470a0"
flytekit = f"git+https://github.com/flyteorg/flytekit.git@{flytekit_hash}"

# Define custom image for the task
image = ImageSpec(
    packages=[
                flytekit,
                "pydantic>2",
                "pandas",
                "pyarrow"
],
    apt_packages=["git"],
    registry="localhost:30000",
    builder="default",
)

class A(BaseModel):
    a: int

class B(BaseModel):
    b: str

@task(container_image=image)
def bar() -> A:
    return A(a=1)

@task(container_image=image)
def foo(inp: Union[A, B]):
    print(inp)

@workflow
def wf():
    v = bar()
    foo(inp=v)

if __name__ == "__main__":
    wf()

4. Run an example with superset on remote (superset_basemodel.py)

superset_basemodel.py

from pydantic import BaseModel
from typing import Union
from flytekit import task, workflow
from flytekit.image_spec import ImageSpec
from superset_A import A as supersetA

flytekit_hash = "3475ddc41f2ba31d23dd072362be704d7c2470a0"
flytekit = f"git+https://github.com/flyteorg/flytekit.git@{flytekit_hash}"

# Define custom image for the task
image = ImageSpec(
    packages=[
                flytekit,
                "pydantic>2",
                "pandas",
                "pyarrow"
],
    apt_packages=["git"],
    registry="localhost:30000",
    builder="default",
)

# downstream (superset_A.py)
class A(BaseModel):
    a: int
    b: Optional[int] = None
    c: str = "Flyte"

# upstream
class A(BaseModel):
    a: int

class B(BaseModel):
    b: str

@task(container_image=image)
def bar() -> A:
    return A(a=1)

@task(container_image=image)
def foo(inp: Union[supersetA, B]):
    print(inp)

@workflow
def wf():
    v = bar()
    foo(inp=v)

if __name__ == "__main__":
    wf()

Setup process

git clone https://github.com/flyteorg/flyte.git
gh pr checkout 5858
make compile
POD_NAMESPACE=flyte ./flyte start --config flyte-single-binary-local.yaml

Screenshots

1. Example using union input with identical dataclass on remote (union_test_dataclass.py)

2. Example with superset on remote (superset_dataclass.py)

3. Example using union input with identical BaseModel class on remote (union_test_basemodel.py)

4. Example with superset on remote (superset_basemodel.py)

5. Input is dataclass and Superset is BaseModel inherited

Note for Optional values in JSON Schemas

While handling Optional values in Python, both NoneType and the target type are accepted. However, when defining such values, default values must still be provided. This is why Optional properties without default values are marked as required in JSON schemas.

Check all the applicable boxes

• I updated the documentation accordingly.
• All new and existing tests passed.
• All commits are signed-off.

Related PRs

None

Docs link

TODO

[codecov]

Codecov Report

Attention: Patch coverage is 72.09302% with 12 lines in your changes missing coverage. Please review.

Project coverage is 35.23%. Comparing base (b5f23a6) to head (7ed9be2).
Report is 22 commits behind head on master.

Files with missing lines	Patch %	Lines
flytepropeller/pkg/compiler/validators/typing.go	72.09%	8 Missing and 4 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #5858      +/-   ##
==========================================
- Coverage   36.90%   35.23%   -1.67%     
==========================================
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  Lines      131372   122879    -8493     
==========================================
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+ Misses      78682    75849    -2833     
+ Partials     4213     3739     -474     

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[fg91]

One note @mao3267, this problem does not only affect dataclasses but any other type that uses protobuf struct as transport. Even combinations of different types that all use protobuf struct.
We for instance have an internal type transformer for pydantic base models (historic reasons before an official one was introduced as a plugin). It has exactly the same problem because it also uses protobuf struct.
It would be nice if the solution found for this problem was general and not only working for dataclasses. That's what I'm sligthy worried about when reading

distinguish between different dataclasses, we compare their JSON schemas generated by either marshmallow_jsonschema.JSONSchema (draft-07) or mashumaro.jsonschema.build_json_schema

Maybe we can use the literal type's "type structure" field for this? Or we should document how transformers for other types can provide the schema in a way that they can "participate in the logic".

TL;DR: It would be nice if the compiler logic in flytepropeller didn't have "special treatment" for dataclasses but general treatment for json-like structures with schemas that the dataclass transformer makes use of - but other transformers can as well.

[Future-Outlier]

One note @mao3267, this problem does not only affect dataclasses but any other type that uses protobuf struct as transport. Even combinations of different types that all use protobuf struct. We for instance have an internal type transformer for pydantic base models (historic reasons before an official one was introduced as a plugin). It has exactly the same problem because it also uses protobuf struct. It would be nice if the solution found for this problem was general and not only working for dataclasses. That's what I'm sligthy worried about when reading

distinguish between different dataclasses, we compare their JSON schemas generated by either marshmallow_jsonschema.JSONSchema (draft-07) or mashumaro.jsonschema.build_json_schema

Maybe we can use the literal type's "type structure" field for this? Or we should document how transformers for other types can provide the schema in a way that they can "participate in the logic".

TL;DR: It would be nice if the compiler logic in flytepropeller didn't have "special treatment" for dataclasses but general treatment for json-like structures with schemas that the dataclass transformer makes use of - but other transformers can as well.

just dicussed with @mao3267 , he will explain how it works now, this will support both dataclass and pydantic basemodel in summary.

[Future-Outlier]

Let's get this done this week @mao3267

[mao3267]

just dicussed with @mao3267 , he will explain how it works now, this will support both dataclass and pydantic basemodel in summary.

Currently, we support both Pydantic BaseModel and dataclass, including their combinations and nested structures. For dataclass_json, we only support equivalence without superset matching due to significant differences between its JSON schema (draft-07) and the newer version used by both Pydantic and dataclass (draft 2020-12).

Although the schemas from Pydantic BaseModel and dataclass adhere to the same version, they differ in how they handle fields. For instance, only the schema generated by Pydantic BaseModel records the title in properties. Additionally, the required and additionalProperties fields are omitted if no required properties exist or if additional properties are disallowed. To address these discrepancies, we preprocess the schema before comparison, which involves removing the title field, and modify the logic while comparing for the required and additionalProperties field.

[wild-endeavor]

the exact match part is okay and we should fix that but I'm confused about the other part.

looking at this example

@task
def my_task(input: Union[supersetA, B]):
    print(input)

@workflow
def wf():
    a = foo()
    my_task(a)

can you explain why this should work? foo creates an A object with only one field. my_task is a task that takes in supersetA or B. B is not relevant here. supersetA takes in three fields.

Why doesn't mypy complain in this example? Or does it?

I almost feel like if we're going to go down this route, it should be the other way around. If foo returned supersetA and my_task took in union of A and B. The reason is because supersetA contains more fields than A

cc @fg91 if you want to take a look as well.

wasn't there another pr where we were discussing an LGPL library also?

[mao3267]

This is an example comparing dataclass and Pydantic BaseModel. As shown, the schema for dataclass includes a title field that records the name of the class. Additionally, the additionalProperties field is absent from the Pydantic BaseModel schema because its value is false. cc @eapolinario

dataclass	Pydantic.BaseModel

[mao3267]

To add the comment, writing the entire schema would make it too lengthy. Would it be acceptable to use something like this instead?

class A:
	a: int

Pydantic.BaseModel: 	{"properties": {"a": {"title": "A", "type": "integer"}}}
dataclass: 			{"properties": {"a": {"type": "integer"}}, "additionalProperties": false}

[fg91]

Are you proposing to preprocess the schemas so that one can mix and match dataclasses and base models given their schemas are aligned? I.e. task expects a dataclass with schema "A" and I pass a base model that has the same schema.

I personally feel this is not necessary and think it would be totally acceptable to consider a dataclass and a base model not a match by default. Especially if this makes things a lot more complicated in the backend otherwise because the schemas need to be aligned. What do you think about this?

If you are confident in the logic I'm of course not opposing the feature but if you feel this makes things complicated and brittle, I'd rather keep it simple and more robust.

[mao3267]

I think it actually make things more complicated, will remove related logic.

[mao3267]

can you explain why this should work? foo creates an A object with only one field. my_task is a task that takes in supersetA or B. B is not relevant here. supersetA takes in three fields.
Why doesn't mypy complain in this example? Or does it?

Class B is used as an example of a type that does not match Class A.
Mypy doesn't report any errors. I am not familiar with mypy, what kind of error do you expect mypy to raise?

I almost feel like if we're going to go down this route, it should be the other way around. If foo returned supersetA and my_task took in union of A and B. The reason is

In the discussion here, we assumed that upstream refers to the exact input type and downstream refers to the expected type for the task input. Did we misunderstand this? By the way, I’m also curious about the reason of supporting this superset matching. It will be helpful to decide our route.

wasn't there another pr where we were discussing an LGPL library also?

The LGPL discussion applies to this PR as well, it is not mentioned because we are no longer using it.

cc @Future-Outlier @wild-endeavor

[fg91]

Currently, we support both Pydantic BaseModel and dataclass, including their combinations and nested structures.

@Future-Outlier @mao3267
My question above wasn't specifically about base models but about how generalizable the solution is.
Let's consider the scenario that an org wants to build a custom internal type transformer for a json-like type similar to dataclasses or base models.
Is there a way they can provide the schema of their type in the to_literal_type method of their type transformer so that the backend can automatically perform schema checks for Union types? Or are there implementation details that are required in the backend too that limit this to dataclasses/base models and would be required for any additional json-like type?

The latter would be slightly concerning to me. Glancing over the code gives me the impression that there is quite a bit of dataclass/pydantic logic we need to apply. I wonder whether this could be done in the respective flytekit type transformer so that the backend is agnostic to the respective type and as long as the type transformer provides the schema in the right way, the backend can make use of it.

It would be really great if there was a tutorial in https://docs.flyte.org/en/latest/api/flytekit/types.extend.html in the end that documents how users need to provide the schema in their respective to_literal_type implementation so that the backend can automatically make use of it in the union type checker.

[wild-endeavor]

can you explain why this should work? foo creates an A object with only one field. my_task is a task that takes in supersetA or B. B is not relevant here. supersetA takes in three fields.
Why doesn't mypy complain in this example? Or does it?

Class B is used as an example of a type that does not match Class A. Mypy doesn't report any errors. I am not familiar with mypy, what kind of error do you expect mypy to raise?

I almost feel like if we're going to go down this route, it should be the other way around. If foo returned supersetA and my_task took in union of A and B. The reason is

In the discussion here, we assumed that upstream refers to the exact input type and downstream refers to the expected type for the task input. Did we misunderstand this?

Oh got it, but this only works because there are defaults right? The original case you linked to should work because
So if your supersetA was

@dataclass
class A:
    a: int
    b: Optional[int] = None
    c: str

then this should not work correct? (because c is missing).

By the way, I’m also curious about the reason of supporting this superset matching. It will be helpful to decide our route.

I don't think of this as superset matching. I think of this as schema compatibility, which is why I was thinking we'd find some off-the-shelf library that can just do it for us. 'Is this schema compatible with this other schema?'

wasn't there another pr where we were discussing an LGPL library also?
The LGPL discussion applies to this PR as well, it is not mentioned because we are no longer using it.

Is there a comment somewhere that explains why we no longer need, or can't use, that library or a library like it?

Re @fg91's comments

Is there a way they can provide the schema of their type in the to_literal_type method of their type transformer so that the backend can automatically perform schema checks for Union types? Or are there implementation details that are required in the backend too that limit this to dataclasses/base models and would be required for any additional json-like type?

I don't know the answer but it should be yes to the first question. It should be possible to easily provide a json schema and have everything on the backend just work. Isn't this the case @Future-Outlier? There should be no special logic for dataclasses or pydantic in the backend, at all. We should remove it if there is.

[mao3267]

Replying @wild-endeavor

then this should not work correct? (because c is missing).

Yes. This should not work.

Is there a comment somewhere that explains why we no longer need, or can't use, that library or a library like it?

No. I just decided to use another package that directly compares the JSON schema. This is not documented anywhere.

Replying @fg91

My question above wasn't specifically about base models but about how generalizable the solution is.
Let's consider the scenario that an org wants to build a custom internal type transformer for a json-like type similar to dataclasses or base models.
Is there a way they can provide the schema of their type in the to_literal_type method of their type transformer so that the backend can automatically perform schema checks for Union types? Or are there implementation details that are required in the backend too that limit this to dataclasses/base models and would be required for any additional json-like type?

The latter would be slightly concerning to me. Glancing over the code gives me the impression that there is quite a bit of dataclass/pydantic logic we need to apply. I wonder whether this could be done in the respective flytekit type transformer so that the backend is agnostic to the respective type and as long as the type transformer provides the schema in the right way, the backend can make use of it.

It would be really great if there was a tutorial in https://docs.flyte.org/en/latest/api/flytekit/types.extend.html in the end that documents how users need to provide the schema in their respective to_literal_type implementation so that the backend can automatically make use of it in the union type checker.

To support any other custom types, could we limit the provided schema from a certain package like Mashumaro for the compatibility check? Without limitations, it will be hard to cover all kinds of scenarios. Or does anyone know possible solutions to support JSON schema from different versions and packages?

[fg91]

There should be no special logic for dataclasses or pydantic in the backend, at all. We should remove it if there is.

This is exactly what I'm trying to say :)

To support any other custom types, could we limit the provided schema from a certain package like Mashumaro for the compatibility check? Without limitations, it will be hard to cover all kinds of scenarios. Or does anyone know possible solutions to support JSON schema from different versions and packages?

Yes, I think restricting what kind of schema needs to be supplied is absolutely reasonable! I think it would be good to add a tutorial here https://docs.flyte.org/en/latest/api/flytekit/types.extend.html that states something like "if you want propeller to understand the schema of your type e.g. to distinguish in union types, you need to provide a schema in the to_literal_type method in this specific way". And I personally feel that the dataclass and pydantic type transformers should provide the schema in this general way so that the backend doesn't have to have type specific implementations for dataclasses/base models.
What do you think about this?

(As a side note, as I described here, for cache checks we don't use the schema in metadata but the so-called type structure. Maybe it's difficult to fix this in hindsight but I kinda wished that there was a single unified way type transformers make the schema available to propeller that is used for everything, cache checks, union type checks, ...)

[wild-endeavor]

@fg91 and @EngHabu mind chiming in on dataclass compatibility? Just thinking about it from the basics, if I have json schemas representing two dataclasses, let's say,

@dataclass
class A2:
    a: int
    b: Optional[int] = None
    c: str = "hello"

and

@dataclass
class A1:
    a: int

which of the following two are valid?
Case 1

def wf():
  a1 = create_a1()  # -> A1
  use_a2(a2=a1)  # a2: A2

Case 2

def wf():
  a2 = create_a2()  # -> A2
  use_a1(a1=a2)  # a1: A1

Just thinking about compatibility in the loosest sense, both should be valid. The reason is that in the first case, when calling the downstream task use_a2, fields b and c have defaults.

In the second case, the a field can be taken from the a2 object and b and c discarded.

The implication here though is that if you have

@task
def make_a1() -> A1: ...

@task
def use_either(a: typing.Union[A1, A2]): ...

This this will fail

use_either(a=make_a1())

because A1 will match more than one variant. flytekit itself will not fail I think (right @Future-Outlier?) but we'll never get there because the compiler will fail.

Should we just do exact matches only? Plus of two examples earlier (case 1 & 2), both will fail mypy type checking.

[wild-endeavor]

Thank you @pingsutw and @eapolinario for reminding me... the original issue was an issue because of inheritance.

So the case to make work is

@dataclass
class Parent():
    a: int

@dataclass
class Child(Parent):
    b: int

with

def wf():
  c = create_child()  # -> Child
  use_parent(p=c)  # p: Parent

The reason we want to make this work is because this works in Python (type checks with mypy) and is common enough we feel that it should work.

So the upshot is, we should do strict subset matching. If the downstream schema is a subset of the upstream, and the fields that are there are an exact match, then we can. So in this example, you should be able to pass Child to something that expects Parent because Child has all the fields of Parent, and those fields are an exact type match.

Also, typing.Union[Parent, Child] is okay to fail because it's ambiguous.

This case should not compile but I think it's okay:
Child overwrites a field in Parent changing its type, which means it's no longer a strict subset match.

@dataclass
class Parent():
    a: int

@dataclass
class Child(Parent):
    a: Optional[int]

This case should compile fine.

@dataclass
class LeafBase():
    leaf: int

@dataclass
class Leaf(LeafBase):
    other: int

@dataclass
class Parent:
    l: LeafBase

A task that produces Parent(l=Leaf(leaf=5, other=3)) should be able to be passed to a downstream task that takes Parent.

[mao3267]

For your information, we originally tried to use the LGPL package json_schema_compare. to do the subset check. Although we are sure the license is compatible with Apache 2.0, we are not using it now because it only supports draft versions before draft-7.

Writing the comparison logic by ourselves may be our current direction.

[Future-Outlier]

Sorry I closed the wrong PR... XD

[wild-endeavor]

just use it if you need to. we can check with them.

[Future-Outlier]

just synced with @mao3267

• work

1. same
2. big -> small
3. child -> parent

• not work

1. different attribute

class A:
	a: int

class B:
	a: int

A -> B

2. parent -> child