Pydantic Transformer V2 #2792

Future-Outlier · 2024-10-08T10:08:11Z

Tracking issue

flyteorg/flyte#5033
flyteorg/flyte#5318

demo

Screen.Recording.2024-10-08.at.11.42.53.PM.mov

How to test it by others

git clone https://github.com/flyteorg/flytekit
gh pr checkout 2792
make setup-global-uv
cd plugins/flytekit-pydantic-v2 && pip install -e .
test a workflow example

not sure

the file tree structure.

Why are the changes needed?

why from_generic_idl?
flyteconsole/flytectl input to handle flytetypes
why _check_and_covert_int in the int transformer?
flyteconsole/flytectl input to handle float issue
why basemodel -> json str -> dict obj -> msgpack bytes?
for enum cases

What changes were proposed in this pull request?

attribute access (primitives and flyte types) (datetime not sure)
flyte types
nested cases
dataclasses.dataclass in pydantic.BaseModel
pydantic.dataclass in pydantic.BaseModel
pydantic.BaseModel in pydantic.BaseModel

note: we don't support pydantic BaseModel has a dataclass with FlyteTypes.
We support pydantic BaseModel has a dataclass with primitive types.

How was this patch tested?

Example code.
(nested cases, flyte types and attribute access.)

from pydantic import BaseModel, Field
import os
from typing import Dict, List
from flytekit.types.file import FlyteFile
from flytekit.types.directory import FlyteDirectory
from flytekit import task, workflow, ImageSpec
from enum import Enum


flytekit_hash = "afd43443acd541e8336db5335de364b5d23cfff6"
flytekit = f"git+https://github.com/flyteorg/flytekit.git@{flytekit_hash}"
pydantic_plugin = f"git+https://github.com/flyteorg/flytekit.git@{flytekit_hash}#subdirectory=plugins/flytekit-pydantic-v2"

# Define custom image for the task
image = ImageSpec(packages=[flytekit, pydantic_plugin],
                            apt_packages=["git"],
                            registry="localhost:30000",
                         )

class Status(Enum):
    PENDING = "pending"
    APPROVED = "approved"
    REJECTED = "rejected"

class InnerDC(BaseModel):
    a: int = -1
    b: float = 2.1
    c: str = "Hello, Flyte"
    d: bool = False
    e: List[int] = Field(default_factory=lambda: [0, 1, 2, -1, -2])
    f: List[FlyteFile] = Field(default_factory=lambda: [FlyteFile("s3://my-s3-bucket/example.txt")])
    g: List[List[int]] = Field(default_factory=lambda: [[0], [1], [-1]])
    h: List[Dict[int, bool]] = Field(default_factory=lambda: [{0: False}, {1: True}, {-1: True}])
    i: Dict[int, bool] = Field(default_factory=lambda: {0: False, 1: True, -1: False})
    j: Dict[int, FlyteFile] = Field(default_factory=lambda: {0: FlyteFile("s3://my-s3-bucket/example.txt"),
                                                             1: FlyteFile("s3://my-s3-bucket/example.txt"),
                                                             -1: FlyteFile("s3://my-s3-bucket/example.txt")})
    k: Dict[int, List[int]] = Field(default_factory=lambda: {0: [0, 1, -1]})
    l: Dict[int, Dict[int, int]] = Field(default_factory=lambda: {1: {-1: 0}})
    m: dict = Field(default_factory=lambda: {"key": "value"})
    n: FlyteFile = Field(default_factory=lambda: FlyteFile("s3://my-s3-bucket/example.txt"))
    o: FlyteDirectory = Field(default_factory=lambda: FlyteDirectory("s3://my-s3-bucket/s3_flyte_dir"))
    enum_status: Status = Status.PENDING


class DC(BaseModel):
    a: int = -1
    b: float = 2.1
    c: str = "Hello, Flyte"
    d: bool = False
    e: List[int] = Field(default_factory=lambda: [0, 1, 2, -1, -2])
    f: List[FlyteFile] = Field(default_factory=lambda: [FlyteFile("s3://my-s3-bucket/example.txt")])
    g: List[List[int]] = Field(default_factory=lambda: [[0], [1], [-1]])
    h: List[Dict[int, bool]] = Field(default_factory=lambda: [{0: False}, {1: True}, {-1: True}])
    i: Dict[int, bool] = Field(default_factory=lambda: {0: False, 1: True, -1: False})
    j: Dict[int, FlyteFile] = Field(default_factory=lambda: {0: FlyteFile("s3://my-s3-bucket/example.txt"),
                                                             1: FlyteFile("s3://my-s3-bucket/example.txt"),
                                                             -1: FlyteFile("s3://my-s3-bucket/example.txt")})
    k: Dict[int, List[int]] = Field(default_factory=lambda: {0: [0, 1, -1]})
    l: Dict[int, Dict[int, int]] = Field(default_factory=lambda: {1: {-1: 0}})
    m: dict = Field(default_factory=lambda: {"key": "value"})
    n: FlyteFile = Field(default_factory=lambda: FlyteFile("s3://my-s3-bucket/example.txt"))
    o: FlyteDirectory = Field(default_factory=lambda: FlyteDirectory("s3://my-s3-bucket/s3_flyte_dir"))
    inner_dc: InnerDC = Field(default_factory=lambda: InnerDC())
    enum_status: Status = Status.PENDING


@task(container_image=image)
def t_dc(dc: DC) -> DC:
    return dc
@task(container_image=image)
def t_inner(inner_dc: InnerDC):
    assert isinstance(inner_dc, InnerDC)

    expected_file_content = "Default content"

    # f: List[FlyteFile]
    for ff in inner_dc.f:
        assert isinstance(ff, FlyteFile)
        with open(ff, "r") as f:
            assert f.read() == expected_file_content
    # j: Dict[int, FlyteFile]
    for _, ff in inner_dc.j.items():
        assert isinstance(ff, FlyteFile)
        with open(ff, "r") as f:
            assert f.read() == expected_file_content
    # n: FlyteFile
    assert isinstance(inner_dc.n, FlyteFile)
    with open(inner_dc.n, "r") as f:
        assert f.read() == expected_file_content
    # o: FlyteDirectory
    assert isinstance(inner_dc.o, FlyteDirectory)
    assert not inner_dc.o.downloaded
    with open(os.path.join(inner_dc.o, "example.txt"), "r") as fh:
        assert fh.read() == expected_file_content
    assert inner_dc.o.downloaded
    print("Test InnerDC Successfully Passed")
    # enum: Status
    assert inner_dc.enum_status == Status.PENDING


@task(container_image=image)
def t_test_all_attributes(a: int, b: float, c: str, d: bool, e: List[int], f: List[FlyteFile], g: List[List[int]],
                          h: List[Dict[int, bool]], i: Dict[int, bool], j: Dict[int, FlyteFile],
                          k: Dict[int, List[int]], l: Dict[int, Dict[int, int]], m: dict,
                          n: FlyteFile, o: FlyteDirectory,
                          enum_status: Status
                          ):
    # Strict type checks for simple types
    assert isinstance(a, int), f"a is not int, it's {type(a)}"
    assert a == -1
    assert isinstance(b, float), f"b is not float, it's {type(b)}"
    assert isinstance(c, str), f"c is not str, it's {type(c)}"
    assert isinstance(d, bool), f"d is not bool, it's {type(d)}"

    # Strict type checks for List[int]
    assert isinstance(e, list) and all(isinstance(i, int) for i in e), "e is not List[int]"

    # Strict type checks for List[FlyteFile]
    assert isinstance(f, list) and all(isinstance(i, FlyteFile) for i in f), "f is not List[FlyteFile]"

    # Strict type checks for List[List[int]]
    assert isinstance(g, list) and all(
        isinstance(i, list) and all(isinstance(j, int) for j in i) for i in g), "g is not List[List[int]]"

    # Strict type checks for List[Dict[int, bool]]
    assert isinstance(h, list) and all(
        isinstance(i, dict) and all(isinstance(k, int) and isinstance(v, bool) for k, v in i.items()) for i in h
    ), "h is not List[Dict[int, bool]]"

    # Strict type checks for Dict[int, bool]
    assert isinstance(i, dict) and all(
        isinstance(k, int) and isinstance(v, bool) for k, v in i.items()), "i is not Dict[int, bool]"

    # Strict type checks for Dict[int, FlyteFile]
    assert isinstance(j, dict) and all(
        isinstance(k, int) and isinstance(v, FlyteFile) for k, v in j.items()), "j is not Dict[int, FlyteFile]"

    # Strict type checks for Dict[int, List[int]]
    assert isinstance(k, dict) and all(
        isinstance(k, int) and isinstance(v, list) and all(isinstance(i, int) for i in v) for k, v in
        k.items()), "k is not Dict[int, List[int]]"

    # Strict type checks for Dict[int, Dict[int, int]]
    assert isinstance(l, dict) and all(
        isinstance(k, int) and isinstance(v, dict) and all(
            isinstance(sub_k, int) and isinstance(sub_v, int) for sub_k, sub_v in v.items())
        for k, v in l.items()), "l is not Dict[int, Dict[int, int]]"

    # Strict type check for a generic dict
    assert isinstance(m, dict), "m is not dict"

    # Strict type check for FlyteFile
    assert isinstance(n, FlyteFile), "n is not FlyteFile"

    # Strict type check for FlyteDirectory
    assert isinstance(o, FlyteDirectory), "o is not FlyteDirectory"

    # Strict type check for Enum
    assert isinstance(enum_status, Status), "enum_status is not Status"

    print("All attributes passed strict type checks.")


@workflow
def wf(dc: DC):
    t_dc(dc=dc)
    t_inner(inner_dc=dc.inner_dc)
    t_test_all_attributes(a=dc.a, b=dc.b, c=dc.c,
                          d=dc.d, e=dc.e, f=dc.f,
                          g=dc.g, h=dc.h, i=dc.i,
                          j=dc.j, k=dc.k, l=dc.l,
                          m=dc.m, n=dc.n, o=dc.o,
                          enum_status=dc.enum_status
                          )

    t_test_all_attributes(a=dc.inner_dc.a, b=dc.inner_dc.b, c=dc.inner_dc.c,
                          d=dc.inner_dc.d, e=dc.inner_dc.e, f=dc.inner_dc.f,
                          g=dc.inner_dc.g, h=dc.inner_dc.h, i=dc.inner_dc.i,
                          j=dc.inner_dc.j, k=dc.inner_dc.k, l=dc.inner_dc.l,
                          m=dc.inner_dc.m, n=dc.inner_dc.n, o=dc.inner_dc.o,
                          enum_status=dc.inner_dc.enum_status
                          )

if __name__ == "__main__":
    from flytekit.clis.sdk_in_container import pyflyte
    from click.testing import CliRunner
    import os

    runner = CliRunner()
    path = os.path.realpath(__file__)
    input_val = '{"a": -1, "b": 3.14}'
    result = runner.invoke(pyflyte.main,
                           ["run", path, "wf", "--dc", input_val])
    print("Local Execution: ", result.output)
    #
    result = runner.invoke(pyflyte.main,
                           ["run", "--remote", path, "wf", "--dc", input_val])
    print("Remote Execution: ", result.output)

Setup process

Screenshots

Check all the applicable boxes

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

Related PRs

Docs link

Signed-off-by: Future-Outlier <[email protected]>

Future-Outlier · 2024-10-08T15:34:34Z

flytekit/core/type_engine.py

+    if lv.scalar.primitive.float_value is not None:
+        logger.info(f"Converting literal float {lv.scalar.primitive.float_value} to int, might have precision loss.")
+        return int(lv.scalar.primitive.float_value)


This is for cases when you input from the flyte console, and you use attribute access directly, you have to the float to int.
Since javascript has only number, it can't tell the difference between int and float, and when goland (propeller) doing attribute access, it doesn't have the expected python type

class TrainConfig(BaseModel): lr: float = 1e-3 batch_size: int = 32 @workflow def wf(cfg: TrainConfig) -> TrainConfig: return t_args(a=cfg.lr, batch_size=cfg.batch_size)

the javascript issue and the attribute access issue are orthogonal right?

this should only be a javascript problem. attribute access should work since msgpack preserves float/int even in attribute access correct?

YES, the attribute access works well, it's because javascript pass float to golang, and golang pass float to python.

this should only be a javascript problem. attribute access should work since msgpack preserves float/int even in attribute access correct?

Yes, but when you are accessing a simple type, you have to change the behavior of SimpleTransformer.

For Pydantic Transformer, we will use strict=False as argument to convert it to right type.

def from_binary_idl(self, binary_idl_object: Binary, expected_python_type: Type[BaseModel]) -> BaseModel: if binary_idl_object.tag == MESSAGEPACK: dict_obj = msgpack.loads(binary_idl_object.value) python_val = expected_python_type.model_validate(obj=dict_obj, strict=False) return python_val

Future-Outlier · 2024-10-08T16:03:08Z

@lukas503
Hi, I saw you add an emoji to this PR!
Do you want to help me test this out?
Search "How to test it by others?" will have a guide for you!

lukas503 · 2024-10-08T21:43:14Z

Hi @Future-Outlier,

Thanks for working on the Pydantic TypeTransformer! Which "How to test it by others?" guide are you referring to?

I've been testing the code locally and wondered about a behavior related to caching. Specifically, I’m curious if model_json_schema is considered in the hash used for caching.

Here’s an example:

from flytekit import task, workflow
from pydantic import BaseModel

class Config(BaseModel):
    x: int = 1
    # y: int = 4

@task(cache=True, cache_version="v1")
def task1(val: int) -> Config:
    return Config()

@task(cache=True, cache_version="v1")
def task2(cfg: Config) -> Config:
    print("CALLED!", cfg)
    return cfg

@workflow
def my_workflow():
    config = task1(val=5)
    task2(cfg=config)

if __name__ == "__main__":
    print(Config.model_json_schema())
    my_workflow()

When I run the workflow for the first time, nothing is cached. On the second run, the results are cached, as expected. However, if I uncomment y: int = 4, the tasks still remain cached. I would assume that this schema change would trigger a cache bust and re-execute the tasks. This causes failure if I update the attributes and the cache_version of task2.

Is this the expected behavior? Shouldn't schema changes like this invalidate the cache?

Future-Outlier · 2024-10-09T01:05:57Z

Hi @Future-Outlier,

Thanks for working on the Pydantic TypeTransformer! Which "How to test it by others?" guide are you referring to?

I've been testing the code locally and wondered about a behavior related to caching. Specifically, I’m curious if model_json_schema is considered in the hash used for caching.

Here’s an example:
from flytekit import task, workflow
from pydantic import BaseModel

class Config(BaseModel):
    x: int = 1
    # y: int = 4

@task(cache=True, cache_version="v1")
def task1(val: int) -> Config:
    return Config()

@task(cache=True, cache_version="v1")
def task2(cfg: Config) -> Config:
    print("CALLED!", cfg)
    return cfg

@workflow
def my_workflow():
    config = task1(val=5)
    task2(cfg=config)

if __name__ == "__main__":
    print(Config.model_json_schema())
    my_workflow()
When I run the workflow for the first time, nothing is cached. On the second run, the results are cached, as expected. However, if I uncomment y: int = 4, the tasks still remain cached. I would assume that this schema change would trigger a cache bust and re-execute the tasks. This causes failure if I update the attributes and the cache_version of task2.

Is this the expected behavior? Shouldn't schema changes like this invalidate the cache?

good question, will test this out and ask other maintainers if I don't know what happened, thank you <3

Future-Outlier · 2024-10-09T03:34:16Z

Hi @Future-Outlier,

Thanks for working on the Pydantic TypeTransformer! Which "How to test it by others?" guide are you referring to?

I've been testing the code locally and wondered about a behavior related to caching. Specifically, I’m curious if model_json_schema is considered in the hash used for caching.

Here’s an example:
from flytekit import task, workflow
from pydantic import BaseModel

class Config(BaseModel):
    x: int = 1
    # y: int = 4

@task(cache=True, cache_version="v1")
def task1(val: int) -> Config:
    return Config()

@task(cache=True, cache_version="v1")
def task2(cfg: Config) -> Config:
    print("CALLED!", cfg)
    return cfg

@workflow
def my_workflow():
    config = task1(val=5)
    task2(cfg=config)

if __name__ == "__main__":
    print(Config.model_json_schema())
    my_workflow()
When I run the workflow for the first time, nothing is cached. On the second run, the results are cached, as expected. However, if I uncomment y: int = 4, the tasks still remain cached. I would assume that this schema change would trigger a cache bust and re-execute the tasks. This causes failure if I update the attributes and the cache_version of task2.

Is this the expected behavior? Shouldn't schema changes like this invalidate the cache?

@lukas503
sorry can you try again?
I've updated the above description.

Signed-off-by: Future-Outlier <[email protected]>

codecov · 2024-10-09T04:15:00Z

Codecov Report

Attention: Patch coverage is 33.33333% with 50 lines in your changes missing coverage. Please review.

Project coverage is 74.83%. Comparing base (cd8216a) to head (773a3b6).
Report is 1 commits behind head on master.

Files with missing lines	Patch %	Lines
flytekit/types/schema/types.py	25.00%	9 Missing and 3 partials ⚠️
flytekit/types/structured/structured_dataset.py	25.00%	9 Missing and 3 partials ⚠️
flytekit/types/directory/types.py	40.00%	7 Missing and 2 partials ⚠️
flytekit/types/file/file.py	40.00%	7 Missing and 2 partials ⚠️
flytekit/core/type_engine.py	37.50%	4 Missing and 1 partial ⚠️
flytekit/interaction/click_types.py	0.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2792      +/-   ##
==========================================
- Coverage   76.82%   74.83%   -2.00%     
==========================================
  Files         196      196              
  Lines       20301    20331      +30     
  Branches     2610     2618       +8     
==========================================
- Hits        15596    15214     -382     
- Misses       4004     4364     +360     
- Partials      701      753      +52

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

Signed-off-by: Future-Outlier <[email protected]>

Future-Outlier · 2024-10-09T07:29:19Z

flytekit/types/file/file.py

+        if lv.scalar:
+            if lv.scalar.binary:
+                return self.from_binary_idl(lv.scalar.binary, expected_python_type)
+            if lv.scalar.generic:
+                return self.from_generic_idl(lv.scalar.generic, expected_python_type)


class DC(BaseModel): ff: FlyteFile = Field(default_factory=lambda: FlyteFile("s3://my-s3-bucket/example.txt")) @task(container_image=image) def t_args(dc: DC) -> DC: with open(dc.ff, "r") as f: print(f.read()) return dc @task(container_image=image) def t_ff(ff: FlyteFile) -> FlyteFile: with open(ff, "r") as f: print(f.read()) return ff @workflow def wf(dc: DC) -> DC: t_ff(dc.ff) return t_args(dc=dc)

this is for this case input from flyteconsole.

lukas503 · 2024-10-09T09:18:39Z

sorry can you try again?
I've updated the above description.

Thanks for updating the PR. I now understand the underlying issue better. It appears the caching mechanism is ignoring the output types/schema. What’s unclear to me is why the output types/schema aren’t factored into the hash used for caching. In my opinion, any interface change could invalidate the cache even the outputs. I don’t see how the old cached outputs can remain valid after an interface change.

That said, this concern isn’t directly related to the current PR, so feel free to proceed as is.

Signed-off-by: Future-Outlier <[email protected]>

Future-Outlier · 2024-10-11T02:43:19Z

flytekit/interaction/click_types.py

+            from pydantic import BaseModel as BaseModelV2
+            from pydantic.v1 import BaseModel as BaseModelV1
+
+            return issubclass(python_type, BaseModelV1) or issubclass(python_type, BaseModelV2)


for backward compatible

Future-Outlier · 2024-10-11T02:44:02Z

flytekit/types/directory/__init__.py

+   FlyteDirToMultipartBlobTransformer
   TensorboardLogs
   TFRecordsDirectory
 """

 import typing

-from .types import FlyteDirectory
+from .types import FlyteDirectory, FlyteDirToMultipartBlobTransformer


to import FlyteDirToMultipartBlobTransformer in the pydantic plugin, we have to import here.

Future-Outlier · 2024-10-11T02:45:24Z

flytekit/types/directory/types.py

+    def from_generic_idl(self, generic: Struct, expected_python_type: typing.Type[FlyteDirectory]) -> FlyteDirectory:
+        json_str = _json_format.MessageToJson(generic)
+        python_val = json.loads(json_str)
+        path = python_val.get("path", None)
+
+        if path is None:
+            raise ValueError("FlyteDirectory's path should not be None")
+
+        return FlyteDirToMultipartBlobTransformer().to_python_value(
+            FlyteContextManager.current_context(),
+            Literal(
+                scalar=Scalar(
+                    blob=Blob(
+                        metadata=BlobMetadata(
+                            type=_core_types.BlobType(
+                                format="", dimensionality=_core_types.BlobType.BlobDimensionality.MULTIPART
+                            )
+                        ),
+                        uri=path,
+                    )
+                )
+            ),
+            expected_python_type,
+        )


This is literally the same as _deserialize function for Structured Dataset

Signed-off-by: Future-Outlier <[email protected]>

Pydantic Transformer V2

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Future-Outlier requested review from wild-endeavor, kumare3, eapolinario, pingsutw, cosmicBboy, samhita-alla and thomasjpfan as code owners October 8, 2024 10:08

Future-Outlier changed the title ~~Pydantic Transformer V2~~ [wip] Pydantic Transformer V2 Oct 8, 2024

Future-Outlier added 3 commits October 8, 2024 18:09

add __init__.py

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convert float to int

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change gitsha in test script mode

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change gitsha

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use strict map=false

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Test flytefile console input + attr access

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Add flytedir generic -> flytedir

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Future-Outlier changed the title ~~[wip] Pydantic Transformer V2~~ Pydantic Transformer V2 Oct 10, 2024

Future-Outlier added 3 commits October 11, 2024 00:47

add tests for input from flyte console

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Merge branch 'master' into pydantic-plugin-v2-with-msgpack

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update thomas's advice

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Pydantic Transformer V2 #2792

Pydantic Transformer V2 #2792

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Pydantic Transformer V2 #2792

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Pydantic Transformer V2 #2792

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Future-Outlier commented Oct 8, 2024 • edited Loading

Tracking issue

demo

How to test it by others

not sure

Why are the changes needed?

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How was this patch tested?

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