[Validation] First implementation of `@strict` from `huggingface_hub` by gante · Pull Request #36534 · huggingface/transformers

gante · 2025-03-04T13:28:50Z

What does this PR do?

Testbed for huggingface/huggingface_hub#2895, released recently.

Core ideas:

All config arguments are individually validated at __init__ and assignment time, using strict type checks and custom value validators;
Class-level validation happens after __init__ through dataclass's __post_init__, or manually through .validate() (⚠️ when mutating the config after initializing, .validate() must be called to validate at a class-level). With this, we can check e.g. if a special token is within the vocabulary range;
Arbitrary validators can be added as validate_xxx methods: @strict adds them to its validation functions.

Minimal test script:

from transformers import AlbertConfig

# These should work
config = AlbertConfig()
config = AlbertConfig(eos_token_id=5)
config = AlbertConfig(foo="bar")  # Ensures backward compatibility

# Manual specification, traveling through an invalid config, should be allowed
config.hidden_size = 65 # the config is impossible here
config.num_attention_heads = 5
config.validate()

# These will raise an exception
config = AlbertConfig(vocab_size=10.0)  # vocab_size is an int
config = AlbertConfig(num_hidden_layers=None)  # num_hidden_layers is an int
config = AlbertConfig(position_embedding_type="foo")  # position_embedding_type is a Literal, foo is not one of the options
config = AlbertConfig(hidden_size=65)  # hidden_size % num_attention_heads must be 0

Wauplin

Thanks for giving it a try! I've left a few comments with some first thoughts

Wauplin · 2025-03-04T14:42:45Z

src/transformers/modeling_utils.py

+        if hasattr(config, "validate"):  # e.g. in @strict_dataclass
+            config.validate()


Not done yet but yes, could be a solution for "class-wide validation" in addition to "per-attribute validation"

src/transformers/models/albert/configuration_albert.py

Wauplin · 2025-03-04T14:46:34Z

src/transformers/models/albert/configuration_albert.py

+    def __post_init__(self):
+        self.validate()


Typically something we should move to @strict_dataclass definition.
The validate method itself would have to be implemented by each class though

Wauplin · 2025-03-10T10:05:22Z

src/transformers/validators.py

+def interval(min: Optional[int | float] = None, max: Optional[int | float] = None) -> Callable:
+    """
+    Parameterized validator that ensures that `value` is within the defined interval.
+    Expected usage: `validated_field(interval(min=0), default=8)`
+    """
+    error_message = "Value must be"
+    if min is not None:
+        error_message += f" at least {min}"
+    if min is not None and max is not None:
+        error_message += " and"
+    if max is not None:
+        error_message += f" at most {max}"
+    error_message += ", got {value}."
+
+    min = min or float("-inf")
+    max = max or float("inf")
+
+    def _inner(value: int | float):
+        if not min <= value <= max:
+            raise ValueError(error_message.format(value=value))
+
+    return _inner
+
+
+def probability(value: float):
+    """Ensures that `value` is a valid probability number, i.e. [0,1]."""
+    if not 0 <= value <= 1:
+        raise ValueError(f"Value must be a probability between 0.0 and 1.0, got {value}.")


pydantic defines conint and confloat which does similar things but in a more generic way. I'd be up to reuse the same naming/interface since that's what people are used to.

https://docs.pydantic.dev/1.10/usage/types/#arguments-to-conint

https://docs.pydantic.dev/1.10/usage/types/#arguments-to-confloat

For instance, interval(min=0) is unclear compared to conint(gt=0) or conint(ge=0). A probability would be confloat(ge=0.0, le=0.0), etc.

I'm open to suggestions, but let me share my thought process :D

Given that we can write arbitrary validators, I would like to avoid that particular interface for two reasons:

It heavily relies on non-obvious shortened names, which are a source of confusion -- and against our philosophy in transformers. My goal here was to write something a user without knowledge of strict_dataclass could read: for instance, interval with a min argument on an int should be immediately obvious, conint with gt requires some prior knowledge (con? gt?).

Partial redundancy can result in better UX: a probability is the same as a float between 0.0 and 1.0, but we can write a more precise error message for the user. Take the case of dropout-related variables -- while it is technically a probability, it is (was?) a bad practice to set it to a value larger than 0.5, and we could be interested in throwing a warning in that case.

Good points here! Ok to keep more explicit and redundant APIS then. I just want things to be precise when it comes to intervals (does "between 0.0 and 1.0" means "0.0" and "1.0" included or not?)

➕ for @gante 's comment

gante · 2025-03-10T17:32:02Z

@Wauplin notwithstanding the syntax for validation in discussion above, the current commit is a nearly working version. If we pass standard arguments, everything happens as expected 🙌

However, I've hit one limitation, and I have one further request:

limitation: our config classes accept arbitrary kwargs 😢 These are stored in the config as attributes. E.g. on main we can do this:

config = AlbertConfig(foo="bar")
print(config.foo)  # should print "bar"

I could keep the original __init__ method in AlbertConfig, which accepts **kwargs. However, that would add a lot of boilerplate to each config class, resulting in less readable code than without @strict_dataclass. Do you think it would be possible for strict_dataclass to overload the __init__ creating method in dataclass, so as to have an option to accept arbitrary kwargs? On each class, we could then do @strict_dataclass(accept_kwargs=True), storing the **kwargs on self.kwargs, and on __post_init__ we would handle them on our side.

wishlist: can we pass dataclass kwargs into strict_dataclass? I would like to keep the original __repr__ here for now, i.e. @strict_dataclass(repr=False) 🤗

Wauplin · 2025-03-12T09:16:48Z

Do you think it would be possible for strict_dataclass to overload the init creating method in dataclass, so as to have an option to accept arbitrary kwargs?

Definitely possible yes!

we could then do @strict_dataclass(accept_kwargs=True), storing the **kwargs on self.kwargs

So in the example above, you would have

config = AlbertConfig(foo="bar")
print(config.kwargs["foo"])  # should print "bar"

?
(just want to be sure we are aligned since it's not exactly the example you've mentioned. Both solutions are ok to me).

wishlist: can we pass dataclass kwargs into strict_dataclass? I would like to keep the original repr here for now, i.e. @strict_dataclass(repr=False) 🤗

Feature request accepted 🤗

gante · 2025-03-17T18:27:31Z

@Wauplin

So in the example above, you would have
config = AlbertConfig(foo="bar")
print(config.kwargs["foo"]) # should print "bar"
?
(just want to be sure we are aligned since it's not exactly the example you've mentioned. Both solutions are ok to me).

To be fully BC, config.foo (!= config.kwargs["foo"]) would have to be valid in this case 😢

What I had in mind, to avoid specifying an __init__ in each class to ensure BC:

decorate with @strict_dataclass(accept_kwargs=True)

and either (option A)

strict_dataclass took care of mapping kwargs into a self.kwargs attribute
__post_init__ in each class took care of setting attributes from self.kwargs

or (option B)

strict_dataclass would set the extra kwargs as instance attributes without validation, e.g. with a loop over kwargs with setattr

gante · 2025-04-29T13:51:59Z

@Wauplin: Reviving this project. The current issue is that transformers needs to accept (unvalidated) kwargs for BC. WDYT of having the hub class as-is, and we in transformers expanding the hub class to meet our unorthodox needs?

hub would then hold a technically sound class for everyone to use 🤗

gante · 2025-04-29T13:53:05Z

Potentially related: vllm-project/vllm#14764 -> add shape checkers

Wauplin · 2025-04-30T13:07:28Z

@gante I reviewed and updated my previous PR. The main change is that instead of the previous @strict_dataclass you must use both @strict and @dataclass. Without this I couldn't have perfect type hinting + autocompletion in IDEs which I think is super useful to have

The current issue is that transformers needs to accept (unvalidated) kwargs for BC. WDYT of having the hub class as-is, and we in transformers expanding the hub class to meet our unorthodox needs?

This is now doable yes!

@strict(accept_kwargs=True)
@dataclass
class ConfigWithKwargs:
    model_type: str
    vocab_size: int = 16

config = ConfigWithKwargs(model_type="bert", vocab_size=30000, extra_field="extra_value")
print(config)  # ConfigWithKwargs(model_type='bert', vocab_size=30000, *extra_field='extra_value')

Check out documentation on https://moon-ci-docs.huggingface.co/docs/huggingface_hub/pr_2895/en/package_reference/dataclasses

gante · 2025-05-02T11:33:52Z

src/transformers/models/albert/configuration_albert.py

+    def __post_init__(self):
+        """Called after `__init__`: validates the instance."""
+        self.validate()


@Wauplin as you mentioned in a comment in a prior commit: this could be moved to strict (but it's a minor thing, happy to keep in transformers)

let's have it in huggingface_hub directly!

gante · 2025-05-02T11:37:54Z

src/transformers/modeling_utils.py

+        # class-level validation of config (as opposed to the attribute-level validation provided by `@strict`)
+        if hasattr(config, "validate"):
+            config.validate()


validates the config at model init time

ArthurZucker

love it!
WOuld be nice to have:

    vocab_size: int = check(int, default=...)

something even more minimal

ArthurZucker · 2025-05-05T13:49:26Z

src/transformers/models/albert/configuration_albert.py

+        # Token validation
+        for token_name in ["pad_token_id", "bos_token_id", "eos_token_id"]:
+            token_id = getattr(self, token_name)
+            if token_id is not None and not 0 <= token_id < self.vocab_size:
+                raise ValueError(
+                    f"{token_name} must be in the vocabulary with size {self.vocab_size}, i.e. between 0 and "
+                    f"{self.vocab_size - 1}, got {token_id}."
+                )


these can be move the general PreTrainedConfig 's validate function for the common args

Agreed.

And they would have to be lifted to warnings, I'm sure there are config files out there with negative tokens (this was a common trick in the past to e.g. manipulate generation into not stopping on EOS)

ArthurZucker · 2025-05-08T10:33:56Z

src/transformers/models/albert/configuration_albert.py

    >>> configuration = model.config
    ```"""

+    vocab_size: int = validated_field(interval(min=1), default=30000)


is there a way to juste write validate or check instead of check_field?

(cc @Wauplin)

ArthurZucker · 2025-05-08T10:35:26Z

src/transformers/models/albert/configuration_albert.py

+    num_attention_heads: int = validated_field(interval(min=0), default=64)
+    intermediate_size: int = validated_field(interval(min=1), default=16384)
+    inner_group_num: int = validated_field(interval(min=0), default=1)
+    hidden_act: str = validated_field(activation_fn_key, default="gelu_new")


if activation_fn_key is a key, I would rather use ActivationFnKey as a class

Suggested change

hidden_act: str = validated_field(activation_fn_key, default="gelu_new")

hidden_act: str = check_activation_function(default="gelu_new")

In python 3.11, we can define something like ActivationFnKey = Literal[possible values], and then simply have

hidden_act: ActivationFnKey = "gelu_new"

Until then, I think it's best to keep the same pattern as in other validated fields, i.e. validated_field(validation_fn, default). WDYT?

ArthurZucker · 2025-05-08T10:35:57Z

src/transformers/models/albert/configuration_albert.py

+    embedding_size: int = validated_field(interval(min=1), default=128)
+    hidden_size: int = validated_field(interval(min=1), default=4096)
+    num_hidden_layers: int = validated_field(interval(min=1), default=12)
+    num_hidden_groups: int = validated_field(interval(min=1), default=1)
+    num_attention_heads: int = validated_field(interval(min=0), default=64)
+    intermediate_size: int = validated_field(interval(min=1), default=16384)
+    inner_group_num: int = validated_field(interval(min=0), default=1)


IMO the interval are too much, we only need the default here!

Would you prefer something like

embedding_size: int = validated_field(strictly_non_negative, default=128)

or simply

embedding_size: int = 128

?

IMO, since most people don't read the config classes, I think validation is a nice sanity check 🤗

@ArthurZucker @gante I've pushed an update that should greatly reduce the verbosity of validated fields. You can use the @as_validated_field decorator when defining your validators which avoid using validated_field on each field. Here is an example:

from dataclasses import dataclass from typing import Optional from huggingface_hub.dataclasses import as_validated_field, strict, validated_field # Case 1: decorated validator with no arguments @as_validated_field def probability(value: int): if not 0 <= value <= 1: raise ValueError(f"Value must be in interval [0, 1], got {value}") # Case 2: decorated validator with arguments def interval(min: Optional[int] = None, max: Optional[int] = None): @as_validated_field def _inner(value: int) -> None: if min is not None and value < min: raise ValueError(f"Value must be greater than {min}, got {value}") if max is not None and value > max: raise ValueError(f"Value must be less than {max}, got {value}") return _inner # Case 3: multiple validators def positive(value: int) -> None: if not value >= 0: raise ValueError(f"Value must be positive, got {value}") def multiple_of_2(value: int) -> None: if value % 2 != 0: raise ValueError(f"Value must be a multiple of 2, got {value}") @strict @dataclass class Config: # No custom validation (only type checking) model_type: str # Validator defined using the decorator hidden_dropout_prob: float = probability(default=0.0) # Validator with args defined using the decorator vocab_size: int = interval(min=10)(default=16) # Type checking + 2 validators (more verbose but more explicit) hidden_size: int = validated_field([positive, multiple_of_2], default=32)

With this syntax and we correctly defined validators, the AlbertConfig would become:

@strict(accept_kwargs=True) @dataclass class AlbertConfig(PretrainedConfig): vocab_size: int = interval(min=1)(default=30000) embedding_size: int = interval(min=1)(default=128) hidden_size: int = interval(min=1)(default=4096) num_hidden_layers: int = interval(min=1)(default=12) num_hidden_groups: int = interval(min=1)(default=1) num_attention_heads: int = interval(min=0)(default=64) intermediate_size: int = interval(min=1)(default=16384) inner_group_num: int = interval(min=0)(default=1) hidden_act: str = activation_fn_key(default="gelu_new") hidden_dropout_prob: float = probability(default=0.0) attention_probs_dropout_prob: float = probability(default=0.0) max_position_embeddings: int = interval(min=0)(default=512) type_vocab_size: int = interval(min=1)(default=2) initializer_range: float = interval(min=0.0)(default=0.02) layer_norm_eps: float = interval(min=0.0)(default=1e-12) classifier_dropout_prob: float = probability(default=0.1) position_embedding_type: Literal["absolute", "relative_key", "relative_key_query"] = "absolute" pad_token_id: Optional[int] = token(default=0) bos_token_id: Optional[int] = token(default=2) eos_token_id: Optional[int] = token(default=3)

which can hardly be less verbose IMO

gante · 2025-05-14T14:16:52Z

@Wauplin PR updated with the latest syntax 🙌 Moving __post_init__ to the hub-side would be a nice addition :)

@ArthurZucker syntax is much shorter now 🤗

Wauplin · 2025-05-14T14:20:25Z

PR updated with the latest syntax 🙌

🎉

Moving post_init to the hub-side would be a nice addition :)

huggingface/huggingface_hub@27ca2d7 🫶 with docs in https://moon-ci-docs.huggingface.co/docs/huggingface_hub/pr_2895/en/package_reference/dataclasses#class-validators

Wauplin · 2025-05-14T14:24:12Z

With class validators, you can now do that:

@strict(accept_kwargs=True)
@dataclass
class AlbertConfig(PretrainedConfig):
    vocab_size: int = interval(min=1)(default=30000)
    embedding_size: int = interval(min=1)(default=128)
    hidden_size: int = interval(min=1)(default=4096)
    num_hidden_layers: int = interval(min=1)(default=12)
    num_hidden_groups: int = interval(min=1)(default=1)
    num_attention_heads: int = interval(min=0)(default=64)
    intermediate_size: int = interval(min=1)(default=16384)
    inner_group_num: int = interval(min=0)(default=1)
    hidden_act: str = activation_fn_key(default="gelu_new")
    hidden_dropout_prob: float = probability(default=0.0)
    attention_probs_dropout_prob: float = probability(default=0.0)
    max_position_embeddings: int = interval(min=0)(default=512)
    type_vocab_size: int = interval(min=1)(default=2)
    initializer_range: float = interval(min=0.0)(default=0.02)
    layer_norm_eps: float = interval(min=0.0)(default=1e-12)
    classifier_dropout_prob: float = probability(default=0.1)
    position_embedding_type: Literal["absolute", "relative_key", "relative_key_query"] = "absolute"
    pad_token_id: Optional[int] = token(default=0)
    bos_token_id: Optional[int] = token(default=2)
    eos_token_id: Optional[int] = token(default=3)

    # Not part of __init__
    model_type = "albert"

    def validate_architecture(self):
        """Validates the architecture of the model."""
        # Check if the number of attention heads is a divisor of the hidden size
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) must be divisible by the number of attention "
                f"heads ({self.num_attention_heads})."
            )

=> validate_architecture is automatically considered as a class validator. Class validators are executed once after __post_init__ and can be re-executed anytime by calling config.validate() explicitly. Note that .validate() is not ran after each field attribution as it could block some use cases (e.g. if 2 related fields have to be updated at once)

Documentation: https://moon-ci-docs.huggingface.co/docs/huggingface_hub/pr_2895/en/package_reference/dataclasses#class-validators

Wauplin

(tiny review)

Wauplin · 2025-05-14T16:03:15Z

src/transformers/modeling_utils.py

+        # class-level validation of config (as opposed to the attribute-level validation provided by `@strict`)
+        if hasattr(config, "validate"):
+            config.validate()
+


Suggested change

# class-level validation of config (as opposed to the attribute-level validation provided by `@strict`)

if hasattr(config, "validate"):

config.validate()

not needed anymore

It's possible to load a config, modify it to an invalid state, and then use that config to instantiate a model :( as such, the model should ensure the config is valid before committing resources

src/transformers/validators.py

Co-authored-by: Lucain <lucainp@gmail.com>

HuggingFaceDocBuilderDev · 2025-05-29T16:34:24Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

ArthurZucker

In favor of removing interval when its obvious!

ArthurZucker · 2025-05-30T14:26:22Z

src/transformers/models/albert/configuration_albert.py

+    vocab_size: int = interval(min=1)(default=30000)
+    embedding_size: int = interval(min=1)(default=128)
+    hidden_size: int = interval(min=1)(default=4096)
+    num_hidden_layers: int = interval(min=1)(default=12)
+    num_hidden_groups: int = interval(min=1)(default=1)
+    num_attention_heads: int = interval(min=0)(default=64)
+    intermediate_size: int = interval(min=1)(default=16384)
+    inner_group_num: int = interval(min=0)(default=1)
+    hidden_act: str = activation_fn_key(default="gelu_new")


BTW I think we should only use default here. let's be less verbose when not needed! Not even sure we need to specify this being an interval!

gante · 2025-05-30T17:20:44Z

Note to myself before I go on holidays

Now that strict is released, we can easily run the full CI to detect issues. The PR is nearly in a compatible state (see CI status), but it wouldn't be wise to merge even if CI was green.

Let's ignore the validation logic itself, which is working well, and focus on the pressing issue: handling inheritance of PretrainedConfig. There are a few implementation details of the class that make inheritance challenging, which I'll try to isolate.

Issue: Base class inheritance

We are defining a model-level dataclass that inherits another class (non-dataclass). This makes attribute inheritance weird, especially given the base class init. I think it can be solved with:

converting the base class to a dataclass (with @strict, to accept kwargs)
adding a __post_init__ to add defaults
attributes that are not stored under the same name will need a getter and a setter (e.g. attn_implementation, see latest commit)

molbap · 2025-07-29T11:50:41Z

This is excellent work, it will be amazing to have 🚀 super looking forward!

gante · 2025-11-05T11:17:39Z

(If someone stumbles upon this in the future: @zucchini-nlp is working on a better @strict usage :) )

gante mentioned this pull request Mar 4, 2025

New @strict decorator for dataclass validation huggingface/huggingface_hub#2895

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Wauplin reviewed Mar 4, 2025

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hmellor mentioned this pull request Apr 29, 2025

[RFC]: Schema for checking input shapes for multi-modal models vllm-project/vllm#14764

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gante marked this pull request as ready for review May 2, 2025 11:30

gante commented May 2, 2025

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gante requested a review from ArthurZucker May 2, 2025 11:34

gante changed the title ~~[Validation] First implementation of @strict_dataclass from huggingface_hub~~ [Validation] First implementation of @strict from huggingface_hub May 2, 2025

gante commented May 2, 2025

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ArthurZucker reviewed May 8, 2025

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gante requested a review from ArthurZucker May 14, 2025 14:17

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gante added 9 commits May 29, 2025 16:08

mvp

4b3a954

validate in PreTrainedModel

62b3e12

validation

654845e

shorter syntax; NOT FULLY OPERATIONAL

6318d84

almost working

7cc2a9a

update with latest hub changes

0c7c4c5

update with latest validator changes (shorter syntax)

4b14696

validate functions

e73505e

update thrown exceptions in test

9016a80

gante and others added 2 commits May 29, 2025 16:08

Update src/transformers/validators.py

e1c6e89

Co-authored-by: Lucain <lucainp@gmail.com>

lower severity of token checks (exception -> warning)

bf3944b

gante force-pushed the validate_config branch from 493cacb to bf3944b Compare May 29, 2025 16:21

gante added 3 commits May 29, 2025 18:06

import guards; reduce token checks; validate on save

47478c8

type hints

29a4f1b

type hints

a382414

gante force-pushed the validate_config branch from cf0e3ea to 7152997 Compare May 30, 2025 13:09

ArthurZucker reviewed May 30, 2025

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maybe like this?

48e9c71

gante force-pushed the validate_config branch from 64cd349 to 48e9c71 Compare May 30, 2025 15:49

gante added 3 commits May 30, 2025 16:03

special handling for tokens

e3c46a7

hidden_size value in tests

0d942f9

attn_implementation getter/setter

49d8fd2

gante closed this Nov 5, 2025

zucchini-nlp mentioned this pull request Mar 4, 2026

🚨 Validate config attributes #41250

Merged

		if hasattr(config, "validate"): # e.g. in @strict_dataclass
		config.validate()

	hidden_act: str = validated_field(activation_fn_key, default="gelu_new")
	hidden_act: str = check_activation_function(default="gelu_new")

	# class-level validation of config (as opposed to the attribute-level validation provided by `@strict`)
	if hasattr(config, "validate"):
	config.validate()

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Check out documentation on https://moon-ci-docs.huggingface.co/docs/huggingface_hub/pr_2895/en/package_reference/dataclasses

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gante commented May 14, 2025

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Wauplin commented May 14, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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gante commented Mar 4, 2025 •

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gante Mar 10, 2025 •

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gante commented Mar 10, 2025 •

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gante commented Apr 29, 2025 •

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gante May 2, 2025 •

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gante May 8, 2025 •

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gante May 8, 2025 •

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Wauplin commented May 14, 2025 •

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Wauplin commented May 14, 2025 •

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