tests/test_modeling_flax_common.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import inspect
import json
import random
import tempfile
from typing import List, Tuple

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import CaptureLogger, is_pt_flax_cross_test, require_flax, torch_device
from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
from transformers.utils.generic import ModelOutput


if is_flax_available():
    import os

    import jax
    import jax.numpy as jnp
    from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
    from flax.serialization import from_bytes
    from flax.traverse_util import flatten_dict, unflatten_dict

    from transformers import (
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForSequenceClassification,
        FlaxBertModel,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.modeling_flax_utils import FLAX_WEIGHTS_INDEX_NAME, FLAX_WEIGHTS_NAME

    os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12"  # assumed parallelism: 8

if is_torch_available():
    import torch


def ids_tensor(shape, vocab_size, rng=None):
    """Creates a random int32 tensor of the shape within the vocab size."""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.randint(0, vocab_size - 1))

    output = np.array(values, dtype=jnp.int32).reshape(shape)

    return output


def floats_tensor(shape, scale=1.0, rng=None, name=None):
    """Creates a random float32 tensor"""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.random() * scale)

    return np.array(values, dtype=jnp.float32).reshape(shape)


def random_attention_mask(shape, rng=None):
    attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
    # make sure that at least one token is attended to for each batch
    attn_mask[:, -1] = 1
    return attn_mask


def get_params(params, from_head_prefix=None):
    """Function extracts relevant parameters into flatten dict from model params,
    appends batch normalization statistics if present"""

    # If Both parameters and batch normalization statistics are present
    if "batch_stats" in params:
        # Extract only parameters for the specified head prefix (if specified) and add batch statistics
        if from_head_prefix is not None:
            extracted_params = flatten_dict(unfreeze(params["params"][from_head_prefix]))
            extracted_params.update(flatten_dict(params["batch_stats"][from_head_prefix]))
        else:
            extracted_params = flatten_dict(unfreeze(params["params"]))
            extracted_params.update(flatten_dict(params["batch_stats"]))

    # Only parameters are present
    else:
        if from_head_prefix is not None:
            extracted_params = flatten_dict(unfreeze(params[from_head_prefix]))
        else:
            extracted_params = flatten_dict(unfreeze(params))

    return extracted_params


@require_flax
class FlaxModelTesterMixin:
    model_tester = None
    all_model_classes = ()
    test_mismatched_shapes = True
    is_encoder_decoder = False
    test_head_masking = False
    has_attentions = True

    def _prepare_for_class(self, inputs_dict, model_class):
        inputs_dict = copy.deepcopy(inputs_dict)

        # hack for now until we have AutoModel classes
        if "ForMultipleChoice" in model_class.__name__:
            inputs_dict = {
                k: jnp.broadcast_to(v[:, None], (v.shape[0], self.model_tester.num_choices, v.shape[-1]))
                if isinstance(v, (jnp.ndarray, np.ndarray)) and k != "indices_prng_key"
                else v
                for k, v in inputs_dict.items()
            }

        return inputs_dict

    def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
        diff = np.abs((a - b)).max()
        self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")

    def test_model_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
            dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

            def recursive_check(tuple_object, dict_object):
                if isinstance(tuple_object, (List, Tuple)):
                    for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                        recursive_check(tuple_iterable_value, dict_iterable_value)
                elif tuple_object is None:
                    return
                else:
                    self.assert_almost_equals(jnp.nan_to_num(tuple_object), jnp.nan_to_num(dict_object), 1e-5)

            recursive_check(tuple_output, dict_output)

        for model_class in self.all_model_classes:
            model = model_class(config)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

    # (Copied from tests.test_modeling_common.ModelTesterMixin.check_pt_flax_outputs)
    def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
        """
        Args:
            model_class: The class of the model that is currently testing. For example, ..., etc.
            Currently unused, but it could make debugging easier and faster.

            names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs.
                Currently unused, but in the future, we could use this information to make the error message clearer
                by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax.
        """

        self.assertEqual(type(name), str)
        if attributes is not None:
            self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")

        # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
        if isinstance(fx_outputs, ModelOutput):
            self.assertTrue(
                isinstance(pt_outputs, ModelOutput),
                f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is",
            )

            fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
            pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

            self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch")

            # convert to the case of `tuple`
            # appending each key to the current (string) `name`
            attributes = tuple([f"{name}.{k}" for k in fx_keys])
            self.check_pt_flax_outputs(
                fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
            )

        # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
        elif type(fx_outputs) in [tuple, list]:
            self.assertEqual(
                type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch"
            )
            self.assertEqual(
                len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch"
            )

            if attributes is not None:
                # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
                self.assertEqual(
                    len(attributes),
                    len(fx_outputs),
                    f"{name}: The tuple `attributes` should have the same length as `fx_outputs`",
                )
            else:
                # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
                attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])

            for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
                self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)

        elif isinstance(fx_outputs, jnp.ndarray):
            self.assertTrue(
                isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is"
            )

            # Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`.
            fx_outputs = np.array(fx_outputs)
            pt_outputs = pt_outputs.detach().to("cpu").numpy()

            self.assertEqual(
                fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch"
            )

            # deal with NumPy's scalars to make replacing nan values by 0 work.
            if np.isscalar(fx_outputs):
                fx_outputs = np.array([fx_outputs])
                pt_outputs = np.array([pt_outputs])

            fx_nans = np.isnan(fx_outputs)
            pt_nans = np.isnan(pt_outputs)

            pt_outputs[fx_nans] = 0
            fx_outputs[fx_nans] = 0
            pt_outputs[pt_nans] = 0
            fx_outputs[pt_nans] = 0

            max_diff = np.amax(np.abs(fx_outputs - pt_outputs))
            self.assertLessEqual(
                max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})."
            )
        else:
            raise ValueError(
                "`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got"
                f" {type(fx_outputs)} instead."
            )

    @is_pt_flax_cross_test
    def test_equivalence_pt_to_flax(self):
        # It might be better to put this inside the for loop below (because we modify the config there).
        # But logically, it is fine.
        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__):
                # Output all for aggressive testing
                config.output_hidden_states = True
                config.output_attentions = self.has_attentions

                # prepare inputs
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                pt_inputs = {k: torch.tensor(v.tolist(), device=torch_device) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                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()
                # Flax models don't use the `use_cache` option and cache is not returned as a default.
                # So we disable `use_cache` here for PyTorch model.
                pt_model.config.use_cache = False
                fx_model = model_class(config, dtype=jnp.float32)

                fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
                fx_model.params = fx_state

                # send pytorch model to the correct device
                pt_model.to(torch_device)

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs)
                fx_outputs = fx_model(**prepared_inputs_dict)

                fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
                pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

                self.assertEqual(fx_keys, pt_keys)
                self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)

                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(**prepared_inputs_dict)

                fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
                pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

                self.assertEqual(fx_keys, pt_keys)
                self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)

    @is_pt_flax_cross_test
    def test_equivalence_flax_to_pt(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__):
                # Output all for aggressive testing
                config.output_hidden_states = True
                config.output_attentions = self.has_attentions

                # prepare inputs
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                pt_inputs = {k: torch.tensor(v.tolist(), device=torch_device) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                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()
                # Flax models don't use the `use_cache` option and cache is not returned as a default.
                # So we disable `use_cache` here for PyTorch model.
                pt_model.config.use_cache = False
                fx_model = model_class(config, dtype=jnp.float32)

                pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                # send pytorch model to the correct device
                pt_model.to(torch_device)

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs)
                fx_outputs = fx_model(**prepared_inputs_dict)

                fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
                pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

                self.assertEqual(fx_keys, pt_keys)
                self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(tmpdirname)
                    pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)

                # send pytorch model to the correct device
                pt_model_loaded.to(torch_device)
                pt_model_loaded.eval()

                with torch.no_grad():
                    pt_outputs_loaded = pt_model_loaded(**pt_inputs)

                fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
                pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])

                self.assertEqual(fx_keys, pt_keys)
                self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)

    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)

                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                outputs = model(**prepared_inputs_dict).to_tuple()

                # verify that normal save_pretrained works as expected
                with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(tmpdirname)

                    # the config file (and the generation config file, if it can generate) should be saved
                    self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME)))
                    self.assertEqual(
                        model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME))
                    )

                    model_loaded = model_class.from_pretrained(tmpdirname)

                outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
                for output_loaded, output in zip(outputs_loaded, outputs):
                    self.assert_almost_equals(output_loaded, output, 1e-3)

                # verify that save_pretrained for distributed training
                # with `params=params` works as expected
                with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(tmpdirname, params=model.params)
                    model_loaded = model_class.from_pretrained(tmpdirname)

                outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
                for output_loaded, output in zip(outputs_loaded, outputs):
                    self.assert_almost_equals(output_loaded, output, 1e-3)

    def test_save_load_from_base(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = FLAX_MODEL_MAPPING[config.__class__]

        for model_class in self.all_model_classes:
            if model_class == base_class:
                continue

            model = base_class(config)
            base_params = get_params(model.params)

            # check that all base model weights are loaded correctly
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                head_model = model_class.from_pretrained(tmpdirname)

                base_param_from_head = get_params(head_model.params, from_head_prefix=head_model.base_model_prefix)

                for key in base_param_from_head.keys():
                    max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
                    self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    def test_save_load_to_base(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = FLAX_MODEL_MAPPING[config.__class__]

        for model_class in self.all_model_classes:
            if model_class == base_class:
                continue

            model = model_class(config)
            base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)

            # check that all base model weights are loaded correctly
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                base_model = base_class.from_pretrained(tmpdirname)

                base_params = get_params(base_model.params)

                for key in base_params_from_head.keys():
                    max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
                    self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    @is_pt_flax_cross_test
    def test_save_load_from_base_pt(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = FLAX_MODEL_MAPPING[config.__class__]

        for model_class in self.all_model_classes:
            if model_class == base_class:
                continue

            model = base_class(config)
            base_params = get_params(model.params)

            # convert Flax model to PyTorch model
            pt_model_class = getattr(transformers, base_class.__name__[4:])  # Skip the "Flax" at the beginning
            pt_model = pt_model_class(config).eval()
            pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)

            # check that all base model weights are loaded correctly
            with tempfile.TemporaryDirectory() as tmpdirname:
                # save pt model
                pt_model.save_pretrained(tmpdirname)
                head_model = model_class.from_pretrained(tmpdirname, from_pt=True)

                base_param_from_head = get_params(head_model.params, from_head_prefix=head_model.base_model_prefix)

                for key in base_param_from_head.keys():
                    max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
                    self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    @is_pt_flax_cross_test
    def test_save_load_to_base_pt(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = FLAX_MODEL_MAPPING[config.__class__]

        for model_class in self.all_model_classes:
            if model_class == base_class:
                continue

            model = model_class(config)
            base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)

            # convert Flax model to PyTorch model
            pt_model_class = getattr(transformers, model_class.__name__[4:])  # Skip the "Flax" at the beginning
            pt_model = pt_model_class(config).eval()
            pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)

            # check that all base model weights are loaded correctly
            with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(tmpdirname)
                base_model = base_class.from_pretrained(tmpdirname, from_pt=True)

                base_params = get_params(base_model.params)

                for key in base_params_from_head.keys():
                    max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
                    self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    @is_pt_flax_cross_test
    def test_save_load_bf16_to_base_pt(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = FLAX_MODEL_MAPPING[config.__class__]

        for model_class in self.all_model_classes:
            if model_class == base_class:
                continue

            model = model_class(config)
            model.params = model.to_bf16(model.params)
            base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)

            # convert Flax model to PyTorch model
            pt_model_class = getattr(transformers, model_class.__name__[4:])  # Skip the "Flax" at the beginning
            pt_model = pt_model_class(config).eval()
            pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)

            # check that all base model weights are loaded correctly
            with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(tmpdirname)
                base_model = base_class.from_pretrained(tmpdirname, from_pt=True)

                base_params = get_params(base_model.params)

                for key in base_params_from_head.keys():
                    max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
                    self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    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__):
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                model = model_class(config)

                @jax.jit
                def model_jitted(input_ids, attention_mask=None, **kwargs):
                    return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)

                with self.subTest("JIT Enabled"):
                    jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = model_jitted(**prepared_inputs_dict).to_tuple()

                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_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.__call__)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            if model.config.is_encoder_decoder:
                expected_arg_names = [
                    "input_ids",
                    "attention_mask",
                    "decoder_input_ids",
                    "decoder_attention_mask",
                ]
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
            else:
                expected_arg_names = ["input_ids", "attention_mask"]
                self.assertListEqual(arg_names[:2], expected_arg_names)

    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)

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            if hasattr(self.model_tester, "encoder_seq_length"):
                seq_length = self.model_tester.encoder_seq_length
            else:
                seq_length = self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

            if config.is_encoder_decoder:
                hidden_states = outputs.decoder_hidden_states

                self.assertIsInstance(hidden_states, (list, tuple))
                self.assertEqual(len(hidden_states), expected_num_layers)
                seq_len = getattr(self.model_tester, "seq_length", None)
                decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [decoder_seq_length, self.model_tester.hidden_size],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    def test_attention_outputs(self):
        if not self.has_attentions:
            self.skipTest(reason="Model does not output attentions")

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        seq_length = getattr(self.model_tester, "seq_length", None)
        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length)
        decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            model = model_class(config)
            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
            )
            out_len = len(outputs)

            if self.is_encoder_decoder:
                correct_outlen = 5

                # Question Answering model returns start_logits and end_logits
                if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                    correct_outlen += 1  # start_logits and end_logits instead of only 1 output

                self.assertEqual(out_len, correct_outlen)

                # decoder attentions
                decoder_attentions = outputs.decoder_attentions
                self.assertIsInstance(decoder_attentions, (list, tuple))
                self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(decoder_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
                )

                # cross attentions
                cross_attentions = outputs.cross_attentions
                self.assertIsInstance(cross_attentions, (list, tuple))
                self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(cross_attentions[0].shape[-3:]),
                    [
                        self.model_tester.num_attention_heads,
                        decoder_seq_length,
                        encoder_key_length,
                    ],
                )

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            if hasattr(self.model_tester, "num_hidden_states_types"):
                added_hidden_states = self.model_tester.num_hidden_states_types
            elif self.is_encoder_decoder:
                added_hidden_states = 2
            else:
                added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
            )

    def test_load_with_mismatched_shapes(self):
        if not self.test_mismatched_shapes:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            if model_class not in get_values(FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
                continue

            with self.subTest(msg=f"Testing {model_class}"):
                with tempfile.TemporaryDirectory() as tmp_dir:
                    model = model_class(config)
                    model.save_pretrained(tmp_dir)

                    # Fails when we don't set ignore_mismatched_sizes=True
                    with self.assertRaises(ValueError):
                        new_model = FlaxAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
                    with self.assertRaises(ValueError):
                        new_model_without_prefix = FlaxAutoModel.from_pretrained(tmp_dir, vocab_size=10)

                    logger = logging.get_logger("transformers.modeling_flax_utils")
                    with CaptureLogger(logger) as cl:
                        new_model = FlaxAutoModelForSequenceClassification.from_pretrained(
                            tmp_dir, num_labels=42, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)

                    logits = new_model(**inputs_dict)["logits"]
                    self.assertEqual(logits.shape[1], 42)

                    with CaptureLogger(logger) as cl:
                        new_model_without_prefix = FlaxAutoModel.from_pretrained(
                            tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)
                    input_ids = ids_tensor((2, 8), 10)
                    if self.is_encoder_decoder:
                        new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
                    else:
                        new_model_without_prefix(input_ids)

    def test_default_params_dtype(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            # check if all params are still in float32 when dtype of computation is half-precision
            model = model_class(config, dtype=jnp.float16)
            types = jax.tree_util.tree_map(lambda x: x.dtype, model.params)
            types = flatten_dict(types)

            for name, type_ in types.items():
                self.assertEqual(type_, jnp.float32, msg=f"param {name} is not initialized in fp32.")

    def test_to_bf16(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

            # cast all params to bf16
            params = model.to_bf16(model.params)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            # test if all params are in bf16
            for name, type_ in types.items():
                self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")

            # test masking
            flat_params = flatten_dict(params)
            key = random.choice(list(flat_params.keys()))  # choose a random param
            mask = {path: path != key for path in flat_params}  # don't cast the key
            mask = unflatten_dict(mask)

            params = model.to_bf16(model.params, mask)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            # test if all params are in bf16 except key
            for name, type_ in types.items():
                if name == key:
                    self.assertEqual(type_, jnp.float32, msg=f"param {name} should be in fp32.")
                else:
                    self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")

    def test_to_fp16(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

            # cast all params to fp16
            params = model.to_fp16(model.params)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            # test if all params are in fp16
            for name, type_ in types.items():
                self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")

            # test masking
            flat_params = flatten_dict(params)
            key = random.choice(list(flat_params.keys()))  # choose a random param
            mask = {path: path != key for path in flat_params}  # don't cast the key
            mask = unflatten_dict(mask)

            params = model.to_fp16(model.params, mask)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            # test if all params are in fp16 except key
            for name, type_ in types.items():
                if name == key:
                    self.assertEqual(type_, jnp.float32, msg=f"param {name} should be in fp32.")
                else:
                    self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")

    def test_to_fp32(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

            # cast all params to fp16 and back to fp32
            params = model.to_fp16(model.params)
            params = model.to_fp32(params)

            # test if all params are in fp32
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            for name, type_ in types.items():
                self.assertEqual(type_, jnp.float32, msg=f"param {name} is not in fp32.")

            # test masking
            flat_params = flatten_dict(params)
            key = random.choice(list(flat_params.keys()))  # choose a random param
            mask = {path: path != key for path in flat_params}  # don't cast the key
            mask = unflatten_dict(mask)

            # cast to fp16 and back to fp32 with mask
            params = model.to_fp16(model.params)
            params = model.to_fp32(params, mask)

            # test if all params are in fp32 except key
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
            for name, type_ in types.items():
                if name == key:
                    self.assertEqual(type_, jnp.float16, msg=f"param {name} should be in fp16.")
                else:
                    self.assertEqual(type_, jnp.float32, msg=f"param {name} is not in fp32.")

    def test_save_load_in_fp16(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

        # convert weights to fp16 and save
        params = model.to_fp16(model.params)
        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname, params=params)

            # load the weights again and check if they are still in fp16
            model = model_class.from_pretrained(tmpdirname)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, model.params))
            for name, type_ in types.items():
                self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")

    def test_save_load_in_bf16(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

        # convert weights to bf16 and save
        params = model.to_bf16(model.params)
        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname, params=params)

            # load the weights again and check if they are still in fp16
            model = model_class.from_pretrained(tmpdirname)
            types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, model.params))
            for name, type_ in types.items():
                self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")

    def test_model_main_input_name(self):
        for model_class in self.all_model_classes:
            model_signature = inspect.signature(getattr(model_class, "__call__"))
            # The main input is the name of the argument after `self`
            observed_main_input_name = list(model_signature.parameters.keys())[1]
            self.assertEqual(model_class.main_input_name, observed_main_input_name)

    def test_headmasking(self):
        if not self.test_head_masking:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        def _prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
            if i == 0:
                return np.concatenate([np.zeros(1, dtype=jnp.int32), np.ones(attention_heads - 1, dtype=jnp.int32)])
            if i == num_hidden_layers - 1:
                return np.concatenate([np.zeros(attention_heads - 1, dtype=jnp.int32), np.ones(1, dtype=jnp.int32)])
            return np.ones(attention_heads, dtype=jnp.int32)

        for model_class in self.all_model_classes:
            model = model_class(config)

            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
            # Prepare head mask
            inputs["head_mask"] = np.stack(
                [
                    _prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
                    for i in range(config.num_hidden_layers)
                ]
            )
            outputs = model(**inputs)

            def _check_attentions_validity(attentions):
                # Remove NaN
                for t in attentions:
                    # Check we don't have more than 25% nans (arbitrary)
                    self.assertLess(np.isnan(t).sum(), t.size / 4)
                attentions = [np.where(np.isnan(t), 0.0, t) for t in attentions]

                self.assertAlmostEqual(attentions[0][..., 0, :, :].sum(), 0.0)
                self.assertNotEqual(attentions[0][..., -1, :, :].sum(), 0.0)
                if len(attentions) > 2:  # encoder-decodere models have only 2 layers in each modules
                    self.assertNotEqual(attentions[1][..., 0, :, :].sum(), 0.0)
                self.assertAlmostEqual(attentions[-1][..., -2, :, :].sum(), 0.0)
                self.assertNotEqual(attentions[-1][..., -1, :, :].sum(), 0.0)

            if model.config.is_encoder_decoder:
                raise NotImplementedError("The test has not been implemented for encoder-decoder models yet.")
            else:
                _check_attentions_validity(outputs.attentions)

    def test_no_automatic_init(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            model = model_class(config, _do_init=False)

            # Check that accesing parmas raises an ValueError when _do_init is False
            with self.assertRaises(ValueError):
                params = model.params

            # Check if we params can be properly initialized when calling init_weights
            params = model.init_weights(model.key, model.input_shape)
            assert isinstance(params, (dict, FrozenDict)), f"params are not an instance of {FrozenDict}"
            # Check if all required parmas are initialized
            keys = set(flatten_dict(unfreeze(params)).keys())
            self.assertTrue(all(k in keys for k in model.required_params))
            # Check if the shapes match
            flat_params = flatten_dict(unfreeze(params))
            for k, v in flatten_dict(unfreeze(model.params_shape_tree)).items():
                self.assertEqual(
                    v.shape,
                    flat_params[k].shape,
                    "Shapes of {} do not match. Expecting {}, got {}.".format(k, v.shape, flat_params[k].shape),
                )

            # Check that setting params raises an ValueError when _do_init is False
            with self.assertRaises(ValueError):
                model.params = params

            # Check if we can do a forward pass
            inputs_dict["output_hidden_states"] = True
            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
            model(**inputs, params=params)

    def test_from_pretrained_with_no_automatic_init(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        def _assert_all_params_initialised(model, params):
            # Check if all required parmas are loaded
            keys = set(flatten_dict(unfreeze(params)).keys())
            self.assertTrue(all(k in keys for k in model.required_params))
            # Check if the shapes match
            flat_params = flatten_dict(unfreeze(params))
            for k, v in flatten_dict(unfreeze(model.params_shape_tree)).items():
                self.assertEqual(
                    v.shape,
                    flat_params[k].shape,
                    "Shapes of {} do not match. Expecting {}, got {}.".format(k, v.shape, flat_params[k].shape),
                )

        for model_class in self.all_model_classes:
            # init the model
            model = model_class(config)

            # save the model in the temporary directory
            # load the saved model with _do_init=False
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model, params = model_class.from_pretrained(tmpdirname, _do_init=False)

            # Check that accesing parmas raises an ValueError when _do_init is False
            with self.assertRaises(ValueError):
                params = model.params

            # Check if all required parmas are loaded
            _assert_all_params_initialised(model, params)

            # Check that setting params raises an ValueError when _do_init is False
            with self.assertRaises(ValueError):
                model.params = params

            # Check if init_weights initializes missing keys from from_pretrained
            flat_params = flatten_dict(unfreeze(params))
            random_key = random.choice(list(flat_params.keys()))
            flat_params.pop(random_key)
            params = freeze(unflatten_dict(flat_params))

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, params=params)
                model, params = model_class.from_pretrained(tmpdirname, _do_init=False)

                params = model.init_weights(model.key, model.input_shape, params=params)
                # Check if all required parmas are loaded
                _assert_all_params_initialised(model, params)

    def test_checkpoint_sharding_from_hub(self):
        model = FlaxBertModel.from_pretrained("ArthurZ/flax-tiny-random-bert-sharded")
        # the model above is the same as the model below, just a sharded version.
        ref_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
        for p1, p2 in zip(flatten_dict(model.params).values(), flatten_dict(ref_model.params).values()):
            assert np.allclose(np.array(p1), np.array(p2))

    def test_checkpoint_sharding_local(self):
        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

        with tempfile.TemporaryDirectory() as tmp_dir:
            # We use the same folder for various sizes to make sure a new save erases the old checkpoint.
            for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
                model.save_pretrained(tmp_dir, max_shard_size=max_size)

                # Get each shard file and its size
                shard_to_size = {}
                for shard in os.listdir(tmp_dir):
                    if shard.endswith(".msgpack"):
                        shard_file = os.path.join(tmp_dir, shard)
                        shard_to_size[shard_file] = os.path.getsize(shard_file)

                index_file = os.path.join(tmp_dir, FLAX_WEIGHTS_INDEX_NAME)
                # Check there is an index but no regular weight file
                self.assertTrue(os.path.isfile(index_file))
                self.assertFalse(os.path.isfile(os.path.join(tmp_dir, FLAX_WEIGHTS_NAME)))

                # Check a file is bigger than max_size only when it has a single weight
                for shard_file, size in shard_to_size.items():
                    if max_size.endswith("kiB"):
                        max_size_int = int(max_size[:-3]) * 2**10
                    else:
                        max_size_int = int(max_size[:-2]) * 10**3
                    # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
                    # the size asked for (since we count parameters)
                    if size >= max_size_int + 50000:
                        with open(shard_file, "rb") as state_f:
                            state_file = from_bytes(FlaxBertModel, state_f.read())
                            self.assertEqual(len(state_file), 1)

                # Check the index and the shard files found match
                with open(index_file, "r", encoding="utf-8") as f:
                    index = json.loads(f.read())

                all_shards = set(index["weight_map"].values())
                shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".msgpack")}
                self.assertSetEqual(all_shards, shards_found)

                # Finally, check the model can be reloaded
                new_model = FlaxBertModel.from_pretrained(tmp_dir)
                for p1, p2 in zip(flatten_dict(model.params).values(), flatten_dict(new_model.params).values()):
                    self.assertTrue(np.allclose(np.array(p1), np.array(p2)))

    @is_pt_flax_cross_test
    def test_from_sharded_pt(self):
        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
        ref_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-fx-only")
        for key, ref_val in flatten_dict(ref_model.params).items():
            val = flatten_dict(model.params)[key]
            assert np.allclose(np.array(val), np.array(ref_val))

    def test_gradient_checkpointing(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            # prepare inputs
            prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)
            remat_model = model_class(config)

            try:
                remat_model.enable_gradient_checkpointing()
            except NotImplementedError:
                continue

            outputs = model(**prepared_inputs_dict)
            remat_outputs = remat_model(**prepared_inputs_dict)

            # ensure that the dicts of outputs contain the same keys
            self.assertEqual(outputs.keys(), remat_outputs.keys())

            outputs = outputs.to_tuple()
            remat_outputs = remat_outputs.to_tuple()

            # ensure that the outputs remain precisely equal
            for output, remat_output in zip(outputs, remat_outputs):
                self.assertTrue((output == remat_output).all())