[Flax] Align FlaxBertForMaskedLM with BertForMaskedLM, implement from_pretrained, init (#9054)

* save intermediate

* save intermediate

* save intermediate

* correct flax bert model file

* new module / model naming

* make style

* almost finish BERT

* finish roberta

* make fix-copies

* delete keys file

* last refactor

* fixes in run_mlm_flax.py

* remove pooled from run_mlm_flax.py`

* fix gelu | gelu_new

* remove Module from inits

* splits

* dirty print

* preventing warmup_steps == 0

* smaller splits

* make fix-copies

* dirty print

* dirty print

* initial_evaluation argument

* declaration order fix

* proper model initialization/loading

* proper initialization

* run_mlm_flax improvements: improper model inputs bugfix + automatic dataset splitting + tokenizers parallelism warning + avoiding warmup_steps=0 bug

* removed tokenizers warning hack, fixed model re-initialization

* reverted training_args.py changes

* fix flax from pretrained

* improve test in flax

* apply sylvains tips

* update init

* make 0.3.0 compatible

* revert tevens changes

* revert tevens changes 2

* finalize revert

* fix bug

* add docs

* add pretrained to init

* Update src/transformers/modeling_flax_utils.py

* fix copies

* final improvements

Co-authored-by: TevenLeScao <teven.lescao@gmail.com>

docs/source/main_classes/model.rst

@@ -13,9 +13,10 @@
 Models
 -----------------------------------------------------------------------------------------------------------------------
 
-The base classes :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` implement the
+The base classes :class:`~transformers.PreTrainedModel`, :class:`~transformers.TFPreTrainedModel`, and
-common methods for loading/saving a model either from a local file or directory, or from a pretrained model
+:class:`~transformers.FlaxPreTrainedModel` implement the common methods for loading/saving a model either from a local
-configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).
+file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS
+S3 repository).
 
 :class:`~transformers.PreTrainedModel` and :class:`~transformers.TFPreTrainedModel` also implement a few methods which
 are common among all the models to:
@@ -57,6 +58,13 @@ TFModelUtilsMixin
     :members:
 
 
+FlaxPreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxPreTrainedModel
+    :members:
+
+
 Generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

examples/language-modeling/run_mlm_flax.py

@@ -385,7 +385,7 @@ def training_step(optimizer, batch, dropout_rng):
         # Hide away tokens which doesn't participate in the optimization
         token_mask = jnp.where(targets > 0, 1.0, 0.0)
 
-        pooled, logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)
+        logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
         loss, weight_sum = cross_entropy(logits, targets, token_mask)
         return loss / weight_sum
 
@@ -407,7 +407,7 @@ def eval_step(params, batch):
 
     # Hide away tokens which doesn't participate in the optimization
     token_mask = jnp.where(targets > 0, 1.0, 0.0)
-    _, logits = model(**batch, params=params, train=False)
+    logits = model(**batch, params=params, train=False)[0]
 
     return compute_metrics(logits, targets, token_mask)
 
@@ -572,8 +572,13 @@ if __name__ == "__main__":
     rng = jax.random.PRNGKey(training_args.seed)
     dropout_rngs = jax.random.split(rng, jax.local_device_count())
 
-    model = FlaxBertForMaskedLM.from_pretrained("bert-base-cased", dtype=jnp.float32, dropout_rate=0.1)
+    model = FlaxBertForMaskedLM.from_pretrained(
-    model.init(jax.random.PRNGKey(training_args.seed), (training_args.train_batch_size, model.config.max_length))
+        "bert-base-cased",
+        dtype=jnp.float32,
+        input_shape=(training_args.train_batch_size, config.max_position_embeddings),
+        seed=training_args.seed,
+        dropout_rate=0.1,
+    )
 
     # Setup optimizer
     optimizer = Adam(

setup.py

@@ -97,7 +97,7 @@ _deps = [
     "fastapi",
     "filelock",
     "flake8>=3.8.3",
-    "flax==0.2.2",
+    "flax>=0.2.2",
     "fugashi>=1.0",
     "ipadic>=1.0.0,<2.0",
     "isort>=5.5.4",
@@ -175,7 +175,7 @@ class DepsTableUpdateCommand(Command):
             "deps = {",
             entries,
             "}",
-            ""
+            "",
         ]
         target = "src/transformers/dependency_versions_table.py"
         print(f"updating {target}")
@@ -232,14 +232,14 @@ extras["dev"] = (
 # when modifying the following list, make sure to update src/transformers/dependency_versions_check.py
 install_requires = [
     deps["dataclasses"] + ";python_version<'3.7'",  # dataclasses for Python versions that don't have it
-    deps["filelock"],   # filesystem locks, e.g., to prevent parallel downloads
+    deps["filelock"],  # filesystem locks, e.g., to prevent parallel downloads
     deps["numpy"],
     deps["packaging"],  # utilities from PyPA to e.g., compare versions
-    deps["regex"],      # for OpenAI GPT
+    deps["regex"],  # for OpenAI GPT
-    deps["requests"],   # for downloading models over HTTPS
+    deps["requests"],  # for downloading models over HTTPS
-    deps["sacremoses"], # for XLM
+    deps["sacremoses"],  # for XLM
     deps["tokenizers"],
-    deps["tqdm"],       # progress bars in model download and training scripts
+    deps["tqdm"],  # progress bars in model download and training scripts
 ]
 
 setup(

src/transformers/__init__.py

@@ -945,6 +945,7 @@ else:
 
 
 if is_flax_available():
+    from .modeling_flax_utils import FlaxPreTrainedModel
     from .models.auto import FLAX_MODEL_MAPPING, FlaxAutoModel
     from .models.bert import FlaxBertForMaskedLM, FlaxBertModel
     from .models.roberta import FlaxRobertaModel

src/transformers/dependency_versions_table.py

@@ -10,7 +10,7 @@ deps = {
     "fastapi": "fastapi",
     "filelock": "filelock",
     "flake8": "flake8>=3.8.3",
-    "flax": "flax==0.2.2",
+    "flax": "flax>=0.2.2",
     "fugashi": "fugashi>=1.0",
     "ipadic": "ipadic>=1.0.0,<2.0",
     "isort": "isort>=5.5.4",
@@ -40,8 +40,8 @@ deps = {
     "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
     "sphinx": "sphinx==3.2.1",
     "starlette": "starlette",
-    "tensorflow-cpu": "tensorflow-cpu>=2.0",
+    "tensorflow-cpu": "tensorflow-cpu>=2.0,<2.4",
-    "tensorflow": "tensorflow>=2.0",
+    "tensorflow": "tensorflow>=2.0,<2.4",
     "timeout-decorator": "timeout-decorator",
     "tokenizers": "tokenizers==0.9.4",
     "torch": "torch>=1.0",

src/transformers/file_utils.py

@@ -270,6 +270,7 @@ TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
 WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = "tf_model.h5"
 TF_WEIGHTS_NAME = "model.ckpt"
+FLAX_WEIGHTS_NAME = "flax_model.msgpack"
 CONFIG_NAME = "config.json"
 MODEL_CARD_NAME = "modelcard.json"
 

src/transformers/modeling_flax_utils.py

@@ -15,64 +15,65 @@
 
 import os
 from abc import ABC, abstractmethod
+from functools import partial
 from pickle import UnpicklingError
-from typing import Dict
+from typing import Dict, Set, Tuple, Union
 
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
-from flax.serialization import to_bytes
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
-from flax.traverse_util import unflatten_dict
+from flax.serialization import from_bytes, to_bytes
+from flax.traverse_util import flatten_dict, unflatten_dict
 from jax.random import PRNGKey
 
 from .configuration_utils import PretrainedConfig
-from .file_utils import WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
+from .file_utils import FLAX_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
 from .utils import logging
 
 
 logger = logging.get_logger(__name__)
 
 
-@jax.jit
-def gelu(x):
-    r"""
-    Gaussian error linear unit activation function.
-
-    Computes the element-wise function:
-
-    .. math::
-      \mathrm{gelu}(x) = \frac{x}{2} \left(1 + \mathrm{tanh} \left(
-        \sqrt{\frac{2}{\pi}} \left(x + 0.044715 x^3 \right) \right) \right)
-
-    We explicitly use the approximation rather than the exact formulation for speed. For more information, see
-    `Gaussian Error Linear Units (GELUs) <https://arxiv.org/abs/1606.08415>`_, section 2.
-    """
-    return x * 0.5 * (1.0 + jax.lax.erf(x / jnp.sqrt(2.0)))
-
-
 ACT2FN = {
     "gelu": nn.gelu,
     "relu": nn.relu,
     "silu": nn.swish,
     "swish": nn.swish,
-    "gelu_new": gelu,
+    "gelu_new": partial(nn.gelu, approximate=True),
 }
 
 
 class FlaxPreTrainedModel(ABC):
+    r"""
+    Base class for all models.
+
+    :class:`~transformers.FlaxPreTrainedModel` takes care of storing the configuration of the models and handles
+    methods for loading, downloading and saving models.
+
+    Class attributes (overridden by derived classes):
+
+        - **config_class** (:class:`~transformers.PretrainedConfig`) -- A subclass of
+          :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
+        - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
+          derived classes of the same architecture adding modules on top of the base model.
+    """
     config_class = None
-    pretrained_model_archive_map = {}
     base_model_prefix = ""
-    model_class = None
 
     def __init__(
-        self, config: PretrainedConfig, module: nn.Module, params: Dict, seed: int = 0, dtype: jnp.dtype = jnp.float32
+        self,
+        config: PretrainedConfig,
+        module: nn.Module,
+        input_shape: Tuple = (1, 1),
+        seed: int = 0,
+        dtype: jnp.dtype = jnp.float32,
     ):
         if config is None:
             raise ValueError("config cannot be None")
 
-        if params is None:
+        if module is None:
-            raise ValueError("state cannot be None")
+            raise ValueError("module cannot be None")
 
         # Those are private to be exposed as typed property on derived classes.
         self._config = config
@@ -80,9 +81,18 @@ class FlaxPreTrainedModel(ABC):
 
         # Those are public as their type is generic to every derived classes.
         self.key = PRNGKey(seed)
-        self.params = params
         self.dtype = dtype
 
+        # randomely initialized parameters
+        random_params = self.init(self.key, input_shape)
+
+        # save required_params as set
+        self._required_params = set(flatten_dict(unfreeze(random_params)).keys())
+        self.params = random_params
+
+    def init(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> Dict:
+        raise NotImplementedError(f"init method has to be implemented for {self}")
+
     @property
     def config(self) -> PretrainedConfig:
         return self._config
@@ -91,24 +101,130 @@ class FlaxPreTrainedModel(ABC):
     def module(self) -> nn.Module:
         return self._module
 
+    @property
+    def params(self) -> Union[Dict, FrozenDict]:
+        return self._params
+
+    @property
+    def required_params(self) -> Set:
+        return self._required_params
+
+    @params.setter
+    def params(self, params: Union[Dict, FrozenDict]):
+        if isinstance(params, FrozenDict):
+            params = unfreeze(params)
+        param_keys = set(flatten_dict(params).keys())
+        if len(self.required_params - param_keys) > 0:
+            raise ValueError(
+                "Some parameters are missing. Make sure that `params` include the following "
+                f"parameters {self.required_params - param_keys}"
+            )
+        self._params = freeze(params)
+
     @staticmethod
     @abstractmethod
     def convert_from_pytorch(pt_state: Dict, config: PretrainedConfig) -> Dict:
         raise NotImplementedError()
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, dtype: jnp.dtype = jnp.float32, *model_args, **kwargs):
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        dtype: jnp.dtype = jnp.float32,
+        *model_args,
+        **kwargs
+    ):
+
         r"""
-        Instantiate a pretrained Flax model from a pre-trained model configuration.
+        Instantiate a pretrained flax model from a pre-trained model configuration.
+
+        The warning `Weights from XXX not initialized from pretrained model` means that the weights of XXX do not come
+        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
+        task.
+
+        The warning `Weights from XXX not used in YYY` means that the layer XXX is not used by YYY, therefore those
+        weights are discarded.
+
+        Parameters:
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+                Can be either:
+
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
+                    - A path to a `directory` containing model weights saved using
+                      :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
+                    - A path or url to a `pt index checkpoint file` (e.g, ``./tf_model/model.ckpt.index``). In this
+                      case, ``from_pt`` should be set to :obj:`True`.
+            model_args (sequence of positional arguments, `optional`):
+                All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
+            config (:obj:`Union[PretrainedConfig, str, os.PathLike]`, `optional`):
+                Can be either:
+
+                    - an instance of a class derived from :class:`~transformers.PretrainedConfig`,
+                    - a string or path valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.
+
+                Configuration for the model to use instead of an automatically loaded configuation. Configuration can
+                be automatically loaded when:
+
+                    - The model is a model provided by the library (loaded with the `model id` string of a pretrained
+                      model).
+                    - The model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
+                      by supplying the save directory.
+                    - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
+                      configuration JSON file named `config.json` is found in the directory.
+            cache_dir (:obj:`Union[str, os.PathLike]`, `optional`):
+                Path to a directory in which a downloaded pretrained model configuration should be cached if the
+                standard cache should not be used.
+            from_pt (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Load the model weights from a PyTorch checkpoint save file (see docstring of
+                ``pretrained_model_name_or_path`` argument).
+            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
+                file exists.
+            proxies (:obj:`Dict[str, str], `optional`):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to only look at local files (i.e., do not try to download the model).
+            revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
+                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+                git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
+                identifier allowed by git.
+            kwargs (remaining dictionary of keyword arguments, `optional`):
+                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
+                :obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
+                automatically loaded:
+
+                    - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
+                      underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
+                      already been done)
+                    - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
+                      initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
+                      ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
+                      with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
+                      attribute will be passed to the underlying model's ``__init__`` function.
+
+        Examples::
+
+            >>> from transformers import BertConfig, FlaxBertModel
+            >>> # Download model and configuration from huggingface.co and cache.
+            >>> model = FlaxBertModel.from_pretrained('bert-base-cased')
+            >>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
+            >>> model = FlaxBertModel.from_pretrained('./test/saved_model/')
+            >>> # Loading from a PyTorch checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
+            >>> config = BertConfig.from_json_file('./pt_model/config.json')
+            >>> model = FlaxBertModel.from_pretrained('./pt_model/pytorch_model.bin', from_pt=True, config=config)
         """
         config = kwargs.pop("config", None)
-        # state_dict = kwargs.pop("state_dict", None)
         cache_dir = kwargs.pop("cache_dir", None)
-        # from_tf = kwargs.pop("from_tf", False)
+        from_pt = kwargs.pop("from_pt", False)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
         proxies = kwargs.pop("proxies", None)
-        # output_loading_info = kwargs.pop("output_loading_info", False)
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
 
@@ -135,10 +251,28 @@ class FlaxPreTrainedModel(ABC):
 
         # Load model
         if pretrained_model_name_or_path is not None:
-            if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+            if os.path.isdir(pretrained_model_name_or_path):
+                if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
+                    # Load from a PyTorch checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)):
+                    # Load from a Flax checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)
+                else:
+                    raise EnvironmentError(
+                        "Error no file named {} found in directory {} or `from_pt` set to False".format(
+                            [FLAX_WEIGHTS_NAME, WEIGHTS_NAME],
+                            pretrained_model_name_or_path,
+                        )
+                    )
+            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
             else:
-                archive_file = hf_bucket_url(pretrained_model_name_or_path, filename=WEIGHTS_NAME, revision=revision)
+                archive_file = hf_bucket_url(
+                    pretrained_model_name_or_path,
+                    filename=WEIGHTS_NAME if from_pt else FLAX_WEIGHTS_NAME,
+                    revision=revision,
+                )
 
             # redirect to the cache, if necessary
             try:
@@ -169,31 +303,96 @@ class FlaxPreTrainedModel(ABC):
         # Instantiate model.
         with open(resolved_archive_file, "rb") as state_f:
             try:
-                from flax.serialization import from_bytes
+                if from_pt:
-
-                state = from_bytes(cls.model_class, state_f)
-            except TypeError:
-                try:
                     import torch
 
                     state = torch.load(state_f)
-                    state = {k: v.numpy() for k, v in state.items()}
-                    state = cls.convert_from_pytorch(state, config)
-                    state = unflatten_dict({tuple(k.split(".")[1:]): v for k, v in state.items()})
-                except UnpicklingError:
-                    raise EnvironmentError(
-                        f"Unable to convert model {archive_file} to Flax deserializable object. "
-                        "Supported format are PyTorch archive or Flax msgpack"
-                    )
 
-        return cls(config, state, *model_args, **model_kwargs)
+                    state = convert_state_dict_from_pt(cls, state, config)
+                else:
+                    state = from_bytes(cls, state_f.read())
+            except UnpicklingError:
+                raise EnvironmentError(
+                    f"Unable to convert pytorch model {archive_file} to Flax deserializable object. "
+                )
 
-    def save_pretrained(self, folder):
+        # init random models
-        folder_abs = os.path.abspath(folder)
+        model = cls(config, *model_args, **model_kwargs)
 
-        if not os.path.exists(folder_abs):
+        # if model is base model only use model_prefix key
-            os.mkdir(folder_abs)
+        if cls.base_model_prefix not in dict(model.params) and cls.base_model_prefix in state:
+            state = state[cls.base_model_prefix]
 
-        with open(os.path.join(folder_abs, f"{self._config.model_type}.flax", "wb")) as f:
+        # flatten dicts
+        state = flatten_dict(state)
+        random_state = flatten_dict(unfreeze(model.params))
+
+        missing_keys = model.required_params - set(state.keys())
+        unexpected_keys = set(state.keys()) - model.required_params
+
+        # add missing keys as random parameters
+        for missing_key in missing_keys:
+            state[missing_key] = random_state[missing_key]
+
+        if len(unexpected_keys) > 0:
+            logger.warning(
+                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
+                f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
+                f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
+                f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n"
+                f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
+                f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
+            )
+        else:
+            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+
+        if len(missing_keys) > 0:
+            logger.warning(
+                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
+                f"and are newly initialized: {missing_keys}\n"
+                f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
+            )
+        else:
+            logger.info(
+                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
+                f"If your task is similar to the task the model of the checkpoint was trained on, "
+                f"you can already use {model.__class__.__name__} for predictions without further training."
+            )
+
+        # set correct parameters
+        model.params = unflatten_dict(state)
+        return model
+
+    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
+        """
+        Save a model and its configuration file to a directory, so that it can be re-loaded using the
+        `:func:`~transformers.FlaxPreTrainedModel.from_pretrained`` class method
+
+        Arguments:
+            save_directory (:obj:`str` or :obj:`os.PathLike`):
+                Directory to which to save. Will be created if it doesn't exist.
+        """
+        if os.path.isfile(save_directory):
+            logger.error("Provided path ({}) should be a directory, not a file".format(save_directory))
+            return
+        os.makedirs(save_directory, exist_ok=True)
+
+        # get abs dir
+        save_directory = os.path.abspath(save_directory)
+        # save config as well
+        self.config.save_pretrained(save_directory)
+
+        # save model
+        with open(os.path.join(save_directory, FLAX_WEIGHTS_NAME), "wb") as f:
             model_bytes = to_bytes(self.params)
             f.write(model_bytes)
+
+
+def convert_state_dict_from_pt(model_class: ABC, state: Dict, config: PretrainedConfig):
+    """
+    Converts a PyTorch parameter state dict to an equivalent Flax parameter state dict
+    """
+    state = {k: v.numpy() for k, v in state.items()}
+    state = model_class.convert_from_pytorch(state, config)
+    state = unflatten_dict({tuple(k.split(".")): v for k, v in state.items()})
+    return state

src/transformers/models/auto/modeling_flax_auto.py

@@ -27,15 +27,6 @@ from .configuration_auto import AutoConfig, BertConfig, RobertaConfig
 logger = logging.get_logger(__name__)
 
 
-ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
-    (key, value)
-    for pretrained_map in [
-        FlaxBertModel.pretrained_model_archive_map,
-        FlaxRobertaModel.pretrained_model_archive_map,
-    ]
-    for key, value, in pretrained_map.items()
-)
-
 FLAX_MODEL_MAPPING = OrderedDict(
     [
         (RobertaConfig, FlaxRobertaModel),
@@ -114,10 +105,9 @@ class FlaxAutoModel(object):
                   organization name, like ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing model weights saved using
                   :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
-                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this
+                - a path or url to a `pytorch index checkpoint file` (e.g. `./pt_model/pytorch_model.bin`). In this
-                  case, ``from_tf`` should be set to True and a configuration object should be provided as ``config``
+                  case, ``from_pt`` should be set to True and a configuration object should be provided as ``config``
-                  argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model
+                  argument.
-                  using the provided conversion scripts and loading the PyTorch model afterwards.
 
             model_args: (`optional`) Sequence of positional arguments:
                 All remaining positional arguments will be passed to the underlying model's ``__init__`` method
@@ -133,13 +123,6 @@ class FlaxAutoModel(object):
                 - the model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
                   configuration JSON file named `config.json` is found in the directory.
 
-            state_dict: (`optional`) dict:
-                an optional state dictionary for the model to use instead of a state dictionary loaded from saved
-                weights file. This option can be used if you want to create a model from a pretrained configuration but
-                load your own weights. In this case though, you should check if using
-                :func:`~transformers.FlaxPreTrainedModel.save_pretrained` and
-                :func:`~transformers.FlaxPreTrainedModel.from_pretrained` is not a simpler option.
-
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model configuration should be cached if the
                 standard cache should not be used.

src/transformers/models/bert/modeling_flax_bert.py

@@ -20,10 +20,11 @@ import numpy as np
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict
 from jax.random import PRNGKey
 
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
-from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
+from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel
 from ...utils import logging
 from .configuration_bert import BertConfig
 
@@ -205,7 +206,7 @@ class FlaxBertAttention(nn.Module):
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
         # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
         # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
@@ -219,27 +220,28 @@ class FlaxBertAttention(nn.Module):
             bias_init=jax.nn.initializers.zeros,
             name="self",
             dtype=self.dtype,
-        )(hidden_state, attention_mask)
+        )(hidden_states, attention_mask)
 
-        layer_norm = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_state)
+        layer_norm = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_states)
         return layer_norm
 
 
 class FlaxBertIntermediate(nn.Module):
     output_size: int
+    hidden_act: str = "gelu"
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state):
+    def __call__(self, hidden_states):
-        # TODO: Add ACT2FN reference to change activation function
+        hidden_states = nn.Dense(
-        dense = nn.Dense(
             features=self.output_size,
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
-        )(hidden_state)
+        )(hidden_states)
-        return gelu(dense)
+        hidden_states = ACT2FN[self.hidden_act](hidden_states)
+        return hidden_states
 
 
 class FlaxBertOutput(nn.Module):
@@ -249,27 +251,28 @@ class FlaxBertOutput(nn.Module):
 
     @nn.compact
     def __call__(self, intermediate_output, attention_output, deterministic: bool = True):
-        hidden_state = nn.Dense(
+        hidden_states = nn.Dense(
             attention_output.shape[-1],
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
         )(intermediate_output)
-        hidden_state = nn.Dropout(rate=self.dropout_rate)(hidden_state, deterministic=deterministic)
+        hidden_states = nn.Dropout(rate=self.dropout_rate)(hidden_states, deterministic=deterministic)
-        hidden_state = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_state + attention_output)
+        hidden_states = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_states + attention_output)
-        return hidden_state
+        return hidden_states
 
 
 class FlaxBertLayer(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         attention = FlaxBertAttention(
             self.num_heads,
             self.head_size,
@@ -277,9 +280,13 @@ class FlaxBertLayer(nn.Module):
             dropout_rate=self.dropout_rate,
             name="attention",
             dtype=self.dtype,
-        )(hidden_state, attention_mask, deterministic=deterministic)
+        )(hidden_states, attention_mask, deterministic=deterministic)
         intermediate = FlaxBertIntermediate(
-            self.intermediate_size, kernel_init_scale=self.kernel_init_scale, name="intermediate", dtype=self.dtype
+            self.intermediate_size,
+            kernel_init_scale=self.kernel_init_scale,
+            hidden_act=self.hidden_act,
+            name="intermediate",
+            dtype=self.dtype,
         )(attention)
         output = FlaxBertOutput(
             kernel_init_scale=self.kernel_init_scale, dropout_rate=self.dropout_rate, name="output", dtype=self.dtype
@@ -297,6 +304,7 @@ class FlaxBertLayerCollection(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
@@ -316,6 +324,7 @@ class FlaxBertLayerCollection(nn.Module):
                 self.intermediate_size,
                 kernel_init_scale=self.kernel_init_scale,
                 dropout_rate=self.dropout_rate,
+                hidden_act=self.hidden_act,
                 name=f"{i}",
                 dtype=self.dtype,
             )
@@ -328,22 +337,24 @@ class FlaxBertEncoder(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         layer = FlaxBertLayerCollection(
             self.num_layers,
             self.num_heads,
             self.head_size,
             self.intermediate_size,
+            hidden_act=self.hidden_act,
             kernel_init_scale=self.kernel_init_scale,
             dropout_rate=self.dropout_rate,
             name="layer",
             dtype=self.dtype,
-        )(hidden_state, attention_mask, deterministic=deterministic)
+        )(hidden_states, attention_mask, deterministic=deterministic)
         return layer
 
 
@@ -352,10 +363,10 @@ class FlaxBertPooler(nn.Module):
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state):
+    def __call__(self, hidden_states):
-        cls_token = hidden_state[:, 0]
+        cls_token = hidden_states[:, 0]
         out = nn.Dense(
-            hidden_state.shape[-1],
+            hidden_states.shape[-1],
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
@@ -363,62 +374,20 @@ class FlaxBertPooler(nn.Module):
         return nn.tanh(out)
 
 
-class FlaxBertModule(nn.Module):
-    vocab_size: int
-    hidden_size: int
-    type_vocab_size: int
-    max_length: int
-    num_encoder_layers: int
-    num_heads: int
-    head_size: int
-    intermediate_size: int
-    dropout_rate: float = 0.0
-    kernel_init_scale: float = 0.2
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    @nn.compact
-    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
-
-        # Embedding
-        embeddings = FlaxBertEmbeddings(
-            self.vocab_size,
-            self.hidden_size,
-            self.type_vocab_size,
-            self.max_length,
-            kernel_init_scale=self.kernel_init_scale,
-            dropout_rate=self.dropout_rate,
-            name="embeddings",
-            dtype=self.dtype,
-        )(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
-
-        # N stacked encoding layers
-        encoder = FlaxBertEncoder(
-            self.num_encoder_layers,
-            self.num_heads,
-            self.head_size,
-            self.intermediate_size,
-            kernel_init_scale=self.kernel_init_scale,
-            dropout_rate=self.dropout_rate,
-            name="encoder",
-            dtype=self.dtype,
-        )(embeddings, attention_mask, deterministic=deterministic)
-
-        pooled = FlaxBertPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
-        return encoder, pooled
-
-
 class FlaxBertPredictionHeadTransform(nn.Module):
+    hidden_act: str = "gelu"
     dtype: jnp.dtype = jnp.float32
 
     @nn.compact
     def __call__(self, hidden_states):
         hidden_states = nn.Dense(hidden_states.shape[-1], name="dense", dtype=self.dtype)(hidden_states)
-        hidden_states = nn.elu(hidden_states)  # TODO: ACT2FN[config.hidden_act]
+        hidden_states = ACT2FN[self.hidden_act](hidden_states)
-        return FlaxBertLayerNorm(name="LayerNorm", dtype=self.dtype)(hidden_states)
+        return FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_states)
 
 
 class FlaxBertLMPredictionHead(nn.Module):
     vocab_size: int
+    hidden_act: str = "gelu"
     dtype: jnp.dtype = jnp.float32
 
     @nn.compact
@@ -428,64 +397,57 @@ class FlaxBertLMPredictionHead(nn.Module):
         #   Need a link between the two variables so that the bias is correctly
         #   resized with `resize_token_embeddings`
 
-        hidden_states = FlaxBertPredictionHeadTransform(name="transform", dtype=self.dtype)(hidden_states)
+        hidden_states = FlaxBertPredictionHeadTransform(
+            name="transform", hidden_act=self.hidden_act, dtype=self.dtype
+        )(hidden_states)
         hidden_states = nn.Dense(self.vocab_size, name="decoder", dtype=self.dtype)(hidden_states)
         return hidden_states
 
 
 class FlaxBertOnlyMLMHead(nn.Module):
     vocab_size: int
-    hidden_size: int
+    hidden_act: str = "gelu"
-    intermediate_size: int
-    head_size: int
-    num_heads: int
-    num_encoder_layers: int
-    type_vocab_size: int
-    max_length: int
-    dropout_rate: float = 0.0
     dtype: jnp.dtype = jnp.float32
 
     @nn.compact
-    def __call__(
+    def __call__(self, hidden_states):
-        self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
+        hidden_states = FlaxBertLMPredictionHead(
-    ):
+            vocab_size=self.vocab_size, hidden_act=self.hidden_act, name="predictions", dtype=self.dtype
-        # Model
+        )(hidden_states)
-        encoder, pooled = FlaxBertModule(
+        return hidden_states
-            vocab_size=self.vocab_size,
-            type_vocab_size=self.type_vocab_size,
-            hidden_size=self.hidden_size,
-            intermediate_size=self.intermediate_size,
-            head_size=self.hidden_size,
-            num_heads=self.num_heads,
-            num_encoder_layers=self.num_encoder_layers,
-            max_length=self.max_length,
-            dropout_rate=self.dropout_rate,
-            dtype=self.dtype,
-        )(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
-
-        # Compute the prediction scores
-        encoder = nn.Dropout(rate=self.dropout_rate)(encoder, deterministic=deterministic)
-        logits = FlaxBertLMPredictionHead(vocab_size=self.vocab_size, name="predictions", dtype=self.dtype)(encoder)
-
-        return logits, pooled
 
 
-@add_start_docstrings(
+class FlaxBertPreTrainedModel(FlaxPreTrainedModel):
-    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
-    BERT_START_DOCSTRING,
-)
-class FlaxBertModel(FlaxPreTrainedModel):
     """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    models.
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
     """
 
-    model_class = FlaxBertModule
     config_class = BertConfig
     base_model_prefix = "bert"
 
+    def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
+        if token_type_ids is None:
+            token_type_ids = jnp.ones_like(input_ids)
+
+        if position_ids is None:
+            position_ids = jnp.arange(jnp.atleast_2d(input_ids).shape[-1])
+
+        if attention_mask is None:
+            attention_mask = jnp.ones_like(input_ids)
+
+        return input_ids, attention_mask, token_type_ids, position_ids
+
+    def init(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
+        input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
+            jnp.zeros(input_shape, dtype="i4"), None, None, None
+        )
+
+        params_rng, dropout_rng = jax.random.split(rng)
+        rngs = {"params": params_rng, "dropout": dropout_rng}
+
+        return self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
+
     @staticmethod
     def convert_from_pytorch(pt_state: Dict, config: BertConfig) -> Dict:
         jax_state = dict(pt_state)
@@ -501,6 +463,11 @@ class FlaxBertModel(FlaxPreTrainedModel):
                 key = key.replace("weight", "kernel")
                 jax_state[key] = tensor
 
+            if "decoder.weight" in key:
+                del jax_state[key]
+                key = key.replace("weight", "kernel")
+                jax_state[key] = tensor.T
+
             # SelfAttention needs also to replace "weight" by "kernel"
             if {"query", "key", "value"} & key_parts:
 
@@ -526,7 +493,7 @@ class FlaxBertModel(FlaxPreTrainedModel):
                 jax_state[key] = tensor
 
             # There are some transposed parameters w.r.t their PyTorch counterpart
-            if "intermediate.dense.kernel" in key or "output.dense.kernel" in key:
+            if "intermediate.dense.kernel" in key or "output.dense.kernel" in key or "transform.dense.kernel" in key:
                 jax_state[key] = tensor.T
 
             # Self Attention output projection needs to be transposed
@@ -539,6 +506,11 @@ class FlaxBertModel(FlaxPreTrainedModel):
             if "pooler.dense.kernel" in key:
                 jax_state[key] = tensor.T
 
+            # Hack to correctly load some pytorch models
+            if "predictions.bias" in key:
+                del jax_state[key]
+                jax_state[".".join(key.split(".")[:2]) + ".decoder.bias"] = tensor
+
             # Handle LayerNorm conversion
             if "LayerNorm" in key:
                 del jax_state[key]
@@ -555,7 +527,22 @@ class FlaxBertModel(FlaxPreTrainedModel):
 
         return jax_state
 
-    def __init__(self, config: BertConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32):
+
+@add_start_docstrings(
+    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
+    BERT_START_DOCSTRING,
+)
+class FlaxBertModel(FlaxBertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    """
+
+    def __init__(
+        self, config: BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs
+    ):
         module = FlaxBertModule(
             vocab_size=config.vocab_size,
             hidden_size=config.hidden_size,
@@ -566,10 +553,12 @@ class FlaxBertModel(FlaxPreTrainedModel):
             head_size=config.hidden_size,
             intermediate_size=config.intermediate_size,
             dropout_rate=config.hidden_dropout_prob,
+            hidden_act=config.hidden_act,
             dtype=dtype,
+            **kwargs,
         )
 
-        super().__init__(config, module, state, seed)
+        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     def __call__(
@@ -601,34 +590,62 @@ class FlaxBertModel(FlaxPreTrainedModel):
             rngs=rngs,
         )
 
-    def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
-        if token_type_ids is None:
-            token_type_ids = jnp.ones_like(input_ids)
 
-        if position_ids is None:
+class FlaxBertModule(nn.Module):
-            position_ids = jnp.arange(jnp.atleast_2d(input_ids).shape[-1])
+    vocab_size: int
+    hidden_size: int
+    type_vocab_size: int
+    max_length: int
+    num_encoder_layers: int
+    num_heads: int
+    head_size: int
+    intermediate_size: int
+    hidden_act: str = "gelu"
+    dropout_rate: float = 0.0
+    kernel_init_scale: float = 0.2
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    add_pooling_layer: bool = True
 
-        if attention_mask is None:
+    @nn.compact
-            attention_mask = jnp.ones_like(input_ids)
+    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
 
-        return input_ids, attention_mask, token_type_ids, position_ids
+        # Embedding
+        embeddings = FlaxBertEmbeddings(
+            self.vocab_size,
+            self.hidden_size,
+            self.type_vocab_size,
+            self.max_length,
+            kernel_init_scale=self.kernel_init_scale,
+            dropout_rate=self.dropout_rate,
+            name="embeddings",
+            dtype=self.dtype,
+        )(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
 
-    def init(self, rng: jax.random.PRNGKey, input_shape: Tuple):
+        # N stacked encoding layers
-        input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
+        encoder = FlaxBertEncoder(
-            jnp.zeros(input_shape, dtype="i4"), None, None, None
+            self.num_encoder_layers,
-        )
+            self.num_heads,
+            self.head_size,
+            self.intermediate_size,
+            kernel_init_scale=self.kernel_init_scale,
+            dropout_rate=self.dropout_rate,
+            hidden_act=self.hidden_act,
+            name="encoder",
+            dtype=self.dtype,
+        )(embeddings, attention_mask, deterministic=deterministic)
 
-        params_rng, dropout_rng = jax.random.split(rng)
+        if not self.add_pooling_layer:
-        rngs = {"params": params_rng, "dropout": dropout_rng}
+            return encoder
 
-        self.params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
+        pooled = FlaxBertPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
+        return encoder, pooled
 
 
-class FlaxBertForMaskedLM(FlaxBertModel):
+class FlaxBertForMaskedLM(FlaxBertPreTrainedModel):
-    def __init__(self, config: BertConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs):
+    def __init__(
-        super().__init__(config, state, seed, dtype)
+        self, config: BertConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs
-
+    ):
-        self._module = FlaxBertOnlyMLMHead(
+        module = FlaxBertForMaskedLMModule(
             vocab_size=config.vocab_size,
             type_vocab_size=config.type_vocab_size,
             hidden_size=config.hidden_size,
@@ -636,10 +653,13 @@ class FlaxBertForMaskedLM(FlaxBertModel):
             head_size=config.hidden_size,
             num_heads=config.num_attention_heads,
             num_encoder_layers=config.num_hidden_layers,
-            max_length=config.max_length,
+            max_length=config.max_position_embeddings,
+            hidden_act=config.hidden_act,
             **kwargs,
         )
 
+        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
+
     def __call__(
         self,
         input_ids,
@@ -659,7 +679,7 @@ class FlaxBertForMaskedLM(FlaxBertModel):
         if dropout_rng is not None:
             rngs["dropout"] = dropout_rng
 
-        pooled, logits = self.module.apply(
+        return self.module.apply(
             {"params": params or self.params},
             jnp.array(input_ids, dtype="i4"),
             jnp.array(attention_mask, dtype="i4"),
@@ -669,4 +689,45 @@ class FlaxBertForMaskedLM(FlaxBertModel):
             rngs=rngs,
         )
 
-        return logits, pooled
+
+class FlaxBertForMaskedLMModule(nn.Module):
+    vocab_size: int
+    hidden_size: int
+    intermediate_size: int
+    head_size: int
+    num_heads: int
+    num_encoder_layers: int
+    type_vocab_size: int
+    max_length: int
+    hidden_act: str
+    dropout_rate: float = 0.0
+    dtype: jnp.dtype = jnp.float32
+
+    @nn.compact
+    def __call__(
+        self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
+    ):
+        # Model
+        encoder = FlaxBertModule(
+            vocab_size=self.vocab_size,
+            type_vocab_size=self.type_vocab_size,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            head_size=self.hidden_size,
+            num_heads=self.num_heads,
+            num_encoder_layers=self.num_encoder_layers,
+            max_length=self.max_length,
+            dropout_rate=self.dropout_rate,
+            hidden_act=self.hidden_act,
+            dtype=self.dtype,
+            add_pooling_layer=False,
+            name="bert",
+        )(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
+
+        # Compute the prediction scores
+        encoder = nn.Dropout(rate=self.dropout_rate)(encoder, deterministic=deterministic)
+        logits = FlaxBertOnlyMLMHead(
+            vocab_size=self.vocab_size, hidden_act=self.hidden_act, name="cls", dtype=self.dtype
+        )(encoder)
+
+        return (logits,)

src/transformers/models/roberta/modeling_flax_roberta.py

@@ -19,10 +19,11 @@ import numpy as np
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict
 from jax.random import PRNGKey
 
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
-from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
+from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel
 from ...utils import logging
 from .configuration_roberta import RobertaConfig
 
@@ -33,6 +34,23 @@ _CONFIG_FOR_DOC = "RobertaConfig"
 _TOKENIZER_FOR_DOC = "RobertaTokenizer"
 
 
+def create_position_ids_from_input_ids(input_ids, padding_idx):
+    """
+    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+    are ignored. This is modified from fairseq's `utils.make_positions`.
+
+    Args:
+        input_ids: jnp.ndarray
+        padding_idx: int
+
+    Returns: jnp.ndarray
+    """
+    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+    mask = (input_ids != padding_idx).astype("i4")
+    incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
+    return incremental_indices.astype("i4") + padding_idx
+
+
 ROBERTA_START_DOCSTRING = r"""
 
     This model inherits from :class:`~transformers.FlaxPreTrainedModel`. Check the superclass documentation for the
@@ -208,7 +226,7 @@ class FlaxRobertaAttention(nn.Module):
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
         # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
         # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
@@ -222,28 +240,29 @@ class FlaxRobertaAttention(nn.Module):
             bias_init=jax.nn.initializers.zeros,
             name="self",
             dtype=self.dtype,
-        )(hidden_state, attention_mask)
+        )(hidden_states, attention_mask)
 
-        layer_norm = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_state)
+        layer_norm = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_states)
         return layer_norm
 
 
 # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta
 class FlaxRobertaIntermediate(nn.Module):
     output_size: int
+    hidden_act: str = "gelu"
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state):
+    def __call__(self, hidden_states):
-        # TODO: Add ACT2FN reference to change activation function
+        hidden_states = nn.Dense(
-        dense = nn.Dense(
             features=self.output_size,
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
-        )(hidden_state)
+        )(hidden_states)
-        return gelu(dense)
+        hidden_states = ACT2FN[self.hidden_act](hidden_states)
+        return hidden_states
 
 
 # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Roberta
@@ -254,27 +273,28 @@ class FlaxRobertaOutput(nn.Module):
 
     @nn.compact
     def __call__(self, intermediate_output, attention_output, deterministic: bool = True):
-        hidden_state = nn.Dense(
+        hidden_states = nn.Dense(
             attention_output.shape[-1],
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
         )(intermediate_output)
-        hidden_state = nn.Dropout(rate=self.dropout_rate)(hidden_state, deterministic=deterministic)
+        hidden_states = nn.Dropout(rate=self.dropout_rate)(hidden_states, deterministic=deterministic)
-        hidden_state = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_state + attention_output)
+        hidden_states = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_states + attention_output)
-        return hidden_state
+        return hidden_states
 
 
 class FlaxRobertaLayer(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         attention = FlaxRobertaAttention(
             self.num_heads,
             self.head_size,
@@ -282,10 +302,11 @@ class FlaxRobertaLayer(nn.Module):
             dropout_rate=self.dropout_rate,
             name="attention",
             dtype=self.dtype,
-        )(hidden_state, attention_mask, deterministic=deterministic)
+        )(hidden_states, attention_mask, deterministic=deterministic)
         intermediate = FlaxRobertaIntermediate(
             self.intermediate_size,
             kernel_init_scale=self.kernel_init_scale,
+            hidden_act=self.hidden_act,
             name="intermediate",
             dtype=self.dtype,
         )(attention)
@@ -306,6 +327,7 @@ class FlaxRobertaLayerCollection(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
@@ -325,6 +347,7 @@ class FlaxRobertaLayerCollection(nn.Module):
                 self.intermediate_size,
                 kernel_init_scale=self.kernel_init_scale,
                 dropout_rate=self.dropout_rate,
+                hidden_act=self.hidden_act,
                 name=f"{i}",
                 dtype=self.dtype,
             )
@@ -338,22 +361,24 @@ class FlaxRobertaEncoder(nn.Module):
     num_heads: int
     head_size: int
     intermediate_size: int
+    hidden_act: str = "gelu"
     dropout_rate: float = 0.0
     kernel_init_scale: float = 0.2
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
+    def __call__(self, hidden_states, attention_mask, deterministic: bool = True):
         layer = FlaxRobertaLayerCollection(
             self.num_layers,
             self.num_heads,
             self.head_size,
             self.intermediate_size,
+            hidden_act=self.hidden_act,
             kernel_init_scale=self.kernel_init_scale,
             dropout_rate=self.dropout_rate,
             name="layer",
             dtype=self.dtype,
-        )(hidden_state, attention_mask, deterministic=deterministic)
+        )(hidden_states, attention_mask, deterministic=deterministic)
         return layer
 
 
@@ -363,10 +388,10 @@ class FlaxRobertaPooler(nn.Module):
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
 
     @nn.compact
-    def __call__(self, hidden_state):
+    def __call__(self, hidden_states):
-        cls_token = hidden_state[:, 0]
+        cls_token = hidden_states[:, 0]
         out = nn.Dense(
-            hidden_state.shape[-1],
+            hidden_states.shape[-1],
             kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
             name="dense",
             dtype=self.dtype,
@@ -374,64 +399,12 @@ class FlaxRobertaPooler(nn.Module):
         return nn.tanh(out)
 
 
-# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta
+class FlaxRobertaPreTrainedModel(FlaxPreTrainedModel):
-class FlaxRobertaModule(nn.Module):
-    vocab_size: int
-    hidden_size: int
-    type_vocab_size: int
-    max_length: int
-    num_encoder_layers: int
-    num_heads: int
-    head_size: int
-    intermediate_size: int
-    dropout_rate: float = 0.0
-    kernel_init_scale: float = 0.2
-    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
-
-    @nn.compact
-    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
-
-        # Embedding
-        embeddings = FlaxRobertaEmbeddings(
-            self.vocab_size,
-            self.hidden_size,
-            self.type_vocab_size,
-            self.max_length,
-            kernel_init_scale=self.kernel_init_scale,
-            dropout_rate=self.dropout_rate,
-            name="embeddings",
-            dtype=self.dtype,
-        )(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
-
-        # N stacked encoding layers
-        encoder = FlaxRobertaEncoder(
-            self.num_encoder_layers,
-            self.num_heads,
-            self.head_size,
-            self.intermediate_size,
-            kernel_init_scale=self.kernel_init_scale,
-            dropout_rate=self.dropout_rate,
-            name="encoder",
-            dtype=self.dtype,
-        )(embeddings, attention_mask, deterministic=deterministic)
-
-        pooled = FlaxRobertaPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
-        return encoder, pooled
-
-
-@add_start_docstrings(
-    "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
-    ROBERTA_START_DOCSTRING,
-)
-class FlaxRobertaModel(FlaxPreTrainedModel):
     """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    models.
-    all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
-    Kaiser and Illia Polosukhin.
     """
 
-    model_class = FlaxRobertaModule
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 
@@ -504,7 +477,49 @@ class FlaxRobertaModel(FlaxPreTrainedModel):
 
         return jax_state
 
-    def __init__(self, config: RobertaConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32):
+    def init(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
+        input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
+            jnp.zeros(input_shape, dtype="i4"), None, None, None
+        )
+
+        params_rng, dropout_rng = jax.random.split(rng)
+        rngs = {"params": params_rng, "dropout": dropout_rng}
+
+        return self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
+
+    def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
+        if token_type_ids is None:
+            token_type_ids = jnp.ones_like(input_ids)
+
+        if position_ids is None:
+            position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
+
+        if attention_mask is None:
+            attention_mask = jnp.ones_like(input_ids)
+
+        return input_ids, attention_mask, token_type_ids, position_ids
+
+
+@add_start_docstrings(
+    "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
+    ROBERTA_START_DOCSTRING,
+)
+class FlaxRobertaModel(FlaxRobertaPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
+    Kaiser and Illia Polosukhin.
+    """
+
+    def __init__(
+        self,
+        config: RobertaConfig,
+        input_shape: Tuple = (1, 1),
+        seed: int = 0,
+        dtype: jnp.dtype = jnp.float32,
+        **kwargs
+    ):
         module = FlaxRobertaModule(
             vocab_size=config.vocab_size,
             hidden_size=config.hidden_size,
@@ -513,12 +528,14 @@ class FlaxRobertaModel(FlaxPreTrainedModel):
             num_encoder_layers=config.num_hidden_layers,
             num_heads=config.num_attention_heads,
             head_size=config.hidden_size,
+            hidden_act=config.hidden_act,
             intermediate_size=config.intermediate_size,
             dropout_rate=config.hidden_dropout_prob,
             dtype=dtype,
+            **kwargs,
         )
 
-        super().__init__(config, module, state, seed)
+        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
 
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     def __call__(
@@ -550,42 +567,53 @@ class FlaxRobertaModel(FlaxPreTrainedModel):
             rngs=rngs,
         )
 
-    def init(self, rng: jax.random.PRNGKey, input_shape: Tuple):
-        input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
-            jnp.zeros(input_shape, dtype="i4"), None, None, None
-        )
 
-        params_rng, dropout_rng = jax.random.split(rng)
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta
-        rngs = {"params": params_rng, "dropout": dropout_rng}
+class FlaxRobertaModule(nn.Module):
+    vocab_size: int
+    hidden_size: int
+    type_vocab_size: int
+    max_length: int
+    num_encoder_layers: int
+    num_heads: int
+    head_size: int
+    intermediate_size: int
+    hidden_act: str = "gelu"
+    dropout_rate: float = 0.0
+    kernel_init_scale: float = 0.2
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    add_pooling_layer: bool = True
 
-        self.params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
+    @nn.compact
+    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
 
-    def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
+        # Embedding
+        embeddings = FlaxRobertaEmbeddings(
+            self.vocab_size,
+            self.hidden_size,
+            self.type_vocab_size,
+            self.max_length,
+            kernel_init_scale=self.kernel_init_scale,
+            dropout_rate=self.dropout_rate,
+            name="embeddings",
+            dtype=self.dtype,
+        )(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
 
-        if token_type_ids is None:
+        # N stacked encoding layers
-            token_type_ids = jnp.ones_like(input_ids)
+        encoder = FlaxRobertaEncoder(
+            self.num_encoder_layers,
+            self.num_heads,
+            self.head_size,
+            self.intermediate_size,
+            kernel_init_scale=self.kernel_init_scale,
+            dropout_rate=self.dropout_rate,
+            hidden_act=self.hidden_act,
+            name="encoder",
+            dtype=self.dtype,
+        )(embeddings, attention_mask, deterministic=deterministic)
 
-        if position_ids is None:
+        if not self.add_pooling_layer:
-            position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
+            return encoder
 
-        if attention_mask is None:
+        pooled = FlaxRobertaPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
-            attention_mask = jnp.ones_like(input_ids)
+        return encoder, pooled
-
-        return input_ids, attention_mask, token_type_ids, position_ids
-
-
-def create_position_ids_from_input_ids(input_ids, padding_idx):
-    """
-    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
-    are ignored. This is modified from fairseq's `utils.make_positions`.
-
-    Args:
-        input_ids: jnp.ndarray
-        padding_idx: int
-
-    Returns: jnp.ndarray
-    """
-    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
-    mask = (input_ids != padding_idx).astype("i4")
-    incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
-    return incremental_indices.astype("i4") + padding_idx

src/transformers/utils/dummy_flax_objects.py

@@ -2,6 +2,15 @@
 from ..file_utils import requires_flax
 
 
+class FlaxPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
 FLAX_MODEL_MAPPING = None
 
 

tests/test_modeling_flax_bert.py

@@ -14,14 +14,16 @@
 
 import unittest
 
+import numpy as np
+
 from transformers import BertConfig, is_flax_available
-from transformers.testing_utils import require_flax
+from transformers.testing_utils import require_flax, slow
 
 from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
 
 
 if is_flax_available():
-    from transformers.models.bert.modeling_flax_bert import FlaxBertModel
+    from transformers.models.bert.modeling_flax_bert import FlaxBertForMaskedLM, FlaxBertModel
 
 
 class FlaxBertModelTester(unittest.TestCase):
@@ -105,7 +107,14 @@ class FlaxBertModelTester(unittest.TestCase):
 @require_flax
 class FlaxBertModelTest(FlaxModelTesterMixin, unittest.TestCase):
 
-    all_model_classes = (FlaxBertModel,) if is_flax_available() else ()
+    all_model_classes = (FlaxBertModel, FlaxBertForMaskedLM) if is_flax_available() else ()
 
     def setUp(self):
         self.model_tester = FlaxBertModelTester(self)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("bert-base-cased")
+            outputs = model(np.ones((1, 1)))
+            self.assertIsNotNone(outputs)

tests/test_modeling_flax_common.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import random
+import tempfile
 
 import numpy as np
 
@@ -26,7 +27,7 @@ if is_flax_available():
 
     import jax
     import jax.numpy as jnp
-    from flax.traverse_util import unflatten_dict
+    from transformers.modeling_flax_utils import convert_state_dict_from_pt
 
     os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12"  # assumed parallelism: 8
 
@@ -59,21 +60,13 @@ def random_attention_mask(shape, rng=None):
     return attn_mask
 
 
-def convert_pt_model_to_flax(pt_model, config, flax_model_cls):
-    state = pt_model.state_dict()
-    state = {k: v.numpy() for k, v in state.items()}
-    state = flax_model_cls.convert_from_pytorch(state, config)
-    state = unflatten_dict({tuple(k.split(".")): v for k, v in state.items()})
-    return flax_model_cls(config, state, dtype=jnp.float32)
-
-
 @require_flax
 class FlaxModelTesterMixin:
     model_tester = None
     all_model_classes = ()
 
     def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
-        diff = np.abs((a - b)).sum()
+        diff = np.abs((a - b)).max()
         self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
 
     @require_torch
@@ -86,30 +79,54 @@ class FlaxModelTesterMixin:
                 pt_model_class = getattr(transformers, pt_model_class_name)
                 pt_model = pt_model_class(config).eval()
 
-                fx_model = convert_pt_model_to_flax(pt_model, config, model_class)
+                fx_state = convert_state_dict_from_pt(model_class, pt_model.state_dict(), config)
+                fx_model = model_class(config, dtype=jnp.float32)
+                fx_model.params = fx_state
 
                 pt_inputs = {k: torch.tensor(v.tolist()) for k, v in inputs_dict.items()}
 
                 with torch.no_grad():
                     pt_outputs = pt_model(**pt_inputs).to_tuple()
+
                 fx_outputs = fx_model(**inputs_dict)
                 self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
                 for fx_output, pt_output in zip(fx_outputs, pt_outputs):
-                    self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-3)
+                    self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-3)
+
+                with tempfile.TemporaryDirectory() as tmpdirname:
+                    pt_model.save_pretrained(tmpdirname)
+                    fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
+
+                fx_outputs_loaded = fx_model_loaded(**inputs_dict)
+                self.assertEqual(
+                    len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
+                )
+                for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
+                    self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 5e-3)
+
+    def test_from_pretrained_save_pretrained(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            with self.subTest(model_class.__name__):
+                model = model_class(config)
+
+                outputs = model(**inputs_dict)
+
+                with tempfile.TemporaryDirectory() as tmpdirname:
+                    model.save_pretrained(tmpdirname)
+                    model_loaded = model_class.from_pretrained(tmpdirname)
+
+                outputs_loaded = model_loaded(**inputs_dict)
+                for output_loaded, output in zip(outputs_loaded, outputs):
+                    self.assert_almost_equals(output_loaded, output, 5e-3)
 
-    @require_torch
     def test_jit_compilation(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__):
-
+                model = model_class(config)
-                # TODO later: have some way to initialize easily a Flax model from config, for now I go through PT
-                pt_model_class_name = model_class.__name__[4:]  # Skip the "Flax" at the beginning
-                pt_model_class = getattr(transformers, pt_model_class_name)
-                pt_model = pt_model_class(config).eval()
-
-                model = convert_pt_model_to_flax(pt_model, config, model_class)
 
                 @jax.jit
                 def model_jitted(input_ids, attention_mask=None, token_type_ids=None):
@@ -125,3 +142,14 @@ class FlaxModelTesterMixin:
                 self.assertEqual(len(outputs), len(jitted_outputs))
                 for jitted_output, output in zip(jitted_outputs, outputs):
                     self.assertEqual(jitted_output.shape, output.shape)
+
+    def test_naming_convention(self):
+        for model_class in self.all_model_classes:
+            model_class_name = model_class.__name__
+            module_class_name = (
+                model_class_name[:-5] + "Module" if model_class_name[-5:] == "Model" else model_class_name + "Module"
+            )
+            bert_modeling_flax_module = __import__(model_class.__module__, fromlist=[module_class_name])
+            module_cls = getattr(bert_modeling_flax_module, module_class_name)
+
+            self.assertIsNotNone(module_cls)

tests/test_modeling_flax_roberta.py

@@ -14,8 +14,10 @@
 
 import unittest
 
+import numpy as np
+
 from transformers import RobertaConfig, is_flax_available
-from transformers.testing_utils import require_flax
+from transformers.testing_utils import require_flax, slow
 
 from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
 
@@ -109,3 +111,10 @@ class FlaxRobertaModelTest(FlaxModelTesterMixin, unittest.TestCase):
 
     def setUp(self):
         self.model_tester = FlaxRobertaModelTester(self)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("roberta-base")
+            outputs = model(np.ones((1, 1)))
+            self.assertIsNotNone(outputs)