test_modeling_common.py 83 KB
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# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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.
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import copy
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import gc
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import inspect
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import json
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import os
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import os.path
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import random
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import tempfile
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import unittest
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import warnings
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from typing import Dict, List, Tuple
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from huggingface_hub import HfApi, Repository
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from requests.exceptions import HTTPError
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from transformers import AutoModel, AutoModelForSequenceClassification, is_torch_available, logging
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from transformers.file_utils import WEIGHTS_NAME, is_torch_fx_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import (
    ENDPOINT_STAGING,
    PASS,
    USER,
    CaptureLogger,
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    TestCasePlus,
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    is_staging_test,
    require_torch,
    require_torch_multi_gpu,
    slow,
    torch_device,
)
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if is_torch_available():
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    import numpy as np
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    import torch
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    from torch import nn
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    from transformers import (
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        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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        MODEL_FOR_CAUSAL_LM_MAPPING,
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        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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        MODEL_FOR_MASKED_LM_MAPPING,
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        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
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        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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        MODEL_MAPPING,
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        AdaptiveEmbedding,
        BertConfig,
        BertModel,
        PretrainedConfig,
        PreTrainedModel,
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        T5Config,
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        T5ForConditionalGeneration,
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    )
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if is_torch_fx_available():
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    from transformers.utils.fx import symbolic_trace
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def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__.keys():
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        if "_range" in key or "_std" in key or "initializer_factor" in key:
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            setattr(configs_no_init, key, 1e-10)
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    return configs_no_init

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TINY_T5 = "patrickvonplaten/t5-tiny-random"


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@require_torch
class ModelTesterMixin:

    model_tester = None
    all_model_classes = ()
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    all_generative_model_classes = ()
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    fx_ready_model_classes = ()
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    fx_dynamic_ready_model_classes = ()
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    test_torchscript = True
    test_pruning = True
    test_resize_embeddings = True
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    test_resize_position_embeddings = False
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    test_head_masking = True
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    test_missing_keys = True
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    test_model_parallel = False
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    is_encoder_decoder = False
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    test_sequence_classification_problem_types = False
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    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = copy.deepcopy(inputs_dict)
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        if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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            inputs_dict = {
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                k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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                if isinstance(v, torch.Tensor) and v.ndim > 1
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                else v
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                for k, v in inputs_dict.items()
            }
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        if return_labels:
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            if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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                inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
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            elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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                inputs_dict["start_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
                inputs_dict["end_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
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            elif model_class in [
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                *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
                *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
                *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
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            ]:
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                inputs_dict["labels"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
            elif model_class in [
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                *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
                *get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
                *get_values(MODEL_FOR_MASKED_LM_MAPPING),
                *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
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            ]:
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                )
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        return inputs_dict

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            out_2 = outputs[0].cpu().numpy()
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            out_2[np.isnan(out_2)] = 0
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            with tempfile.TemporaryDirectory() as tmpdirname:
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                model.save_pretrained(tmpdirname)
                model = model_class.from_pretrained(tmpdirname)
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                model.to(torch_device)
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                with torch.no_grad():
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                    after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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                # Make sure we don't have nans
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
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                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)
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    def test_save_load_keys_to_ignore_on_save(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
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            _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
            if _keys_to_ignore_on_save is None:
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                continue

            # check the keys are in the original state_dict
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            for k in _keys_to_ignore_on_save:
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                self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys()))
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            # check that certain keys didn't get saved with the model
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
                state_dict_saved = torch.load(output_model_file)
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                for k in _keys_to_ignore_on_save:
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                    self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys()))
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                # Test we can load the state dict in the model, necessary for the checkpointing API in Trainer.
                load_result = model.load_state_dict(state_dict_saved, strict=False)
                self.assertTrue(
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                    len(load_result.missing_keys) == 0
                    or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save)
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                )
                self.assertTrue(len(load_result.unexpected_keys) == 0)

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

        for model_class in self.all_model_classes:
            if not model_class.supports_gradient_checkpointing:
                continue

            # at init model should have gradient checkpointing disabled
            model = model_class(config)
            self.assertFalse(model.is_gradient_checkpointing)

            # check enable works
            model.gradient_checkpointing_enable()
            self.assertTrue(model.is_gradient_checkpointing)

            # check disable works
            model.gradient_checkpointing_disable()
            self.assertFalse(model.is_gradient_checkpointing)

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    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)

    def test_save_load_fast_init_from_base(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = MODEL_MAPPING[config.__class__]

        if isinstance(base_class, tuple):
            base_class = base_class[0]

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

            # make a copy of model class to not break future tests
            # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
            class CopyClass(model_class):
                pass

            model_class_copy = CopyClass

            # make sure that all keys are expected for test
            model_class_copy._keys_to_ignore_on_load_missing = []

            # make init deterministic, but make sure that
            # non-initialized weights throw errors nevertheless
            model_class_copy._init_weights = self._mock_init_weights

            model = base_class(config)
            state_dict = model.state_dict()

            # this will often delete a single weight of a multi-weight module
            # to test an edge case
            random_key_to_del = random.choice(list(state_dict.keys()))
            del state_dict[random_key_to_del]

            # check that certain keys didn't get saved with the model
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))

                model_fast_init = model_class_copy.from_pretrained(tmpdirname)
                model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False)

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

    def test_save_load_fast_init_to_base(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        base_class = MODEL_MAPPING[config.__class__]

        if isinstance(base_class, tuple):
            base_class = base_class[0]

        for model_class in self.all_model_classes:

            if model_class == base_class:
                continue

            # make a copy of model class to not break future tests
            # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
            class CopyClass(base_class):
                pass

            base_class_copy = CopyClass

            # make sure that all keys are expected for test
            base_class_copy._keys_to_ignore_on_load_missing = []

            # make init deterministic, but make sure that
            # non-initialized weights throw errors nevertheless
            base_class_copy._init_weights = self._mock_init_weights

            model = model_class(config)
            state_dict = model.state_dict()

            # this will often delete a single weight of a multi-weight module
            # to test an edge case
            random_key_to_del = random.choice(list(state_dict.keys()))
            del state_dict[random_key_to_del]

            # check that certain keys didn't get saved with the model
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.config.save_pretrained(tmpdirname)
                torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))

                model_fast_init = base_class_copy.from_pretrained(tmpdirname)
                model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False)

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

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

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    self.assertIn(
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                        ((param.data.mean() * 1e9).round() / 1e9).item(),
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                        [0.0, 1.0],
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                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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                    )
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    def test_determinism(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
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                first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
                second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
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            out_1 = first.cpu().numpy()
            out_2 = second.cpu().numpy()
            out_1 = out_1[~np.isnan(out_1)]
            out_2 = out_2[~np.isnan(out_2)]
            max_diff = np.amax(np.abs(out_1 - out_2))
            self.assertLessEqual(max_diff, 1e-5)

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    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.forward)
            # 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",
                ]
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                expected_arg_names.extend(
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                    ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
                    if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
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                    else ["encoder_outputs"]
                )
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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            else:
                expected_arg_names = ["input_ids"]
                self.assertListEqual(arg_names[:1], expected_arg_names)

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    def test_training(self):
        if not self.model_tester.is_training:
            return

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

        for model_class in self.all_model_classes:
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            if model_class in get_values(MODEL_MAPPING):
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                continue
            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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        if not self.model_tester.is_training:
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            return

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        config.use_cache = False
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        config.return_dict = True

        for model_class in self.all_model_classes:
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            if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
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                continue
            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

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

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        seq_len = getattr(self.model_tester, "seq_length", None)
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        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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        decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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        chunk_length = getattr(self.model_tester, "chunk_length", None)
        if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
            encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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        for model_class in self.all_model_classes:
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            inputs_dict["output_attentions"] = True
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            inputs_dict["output_hidden_states"] = False
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            config.return_dict = True
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            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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            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)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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            if chunk_length is not None:
                self.assertListEqual(
                    list(attentions[0].shape[-4:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                )
            else:
                self.assertListEqual(
                    list(attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )
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            out_len = len(outputs)
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            if self.is_encoder_decoder:
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                correct_outlen = 5
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                # loss is at first position
                if "labels" in inputs_dict:
                    correct_outlen += 1  # loss is added to beginning
                # Question Answering model returns start_logits and end_logits
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                if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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                    correct_outlen += 1  # start_logits and end_logits instead of only 1 output
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                if "past_key_values" in outputs:
                    correct_outlen += 1  # past_key_values have been returned
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                self.assertEqual(out_len, correct_outlen)

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

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

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            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

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            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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            if chunk_length is not None:
                self.assertListEqual(
                    list(self_attentions[0].shape[-4:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
                )
            else:
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )
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    @slow
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    def test_torchscript(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        self._create_and_check_torchscript(config, inputs_dict)
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    @slow
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    def test_torchscript_output_attentions(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_attentions = True
        self._create_and_check_torchscript(config, inputs_dict)
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    @slow
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    def test_torchscript_output_hidden_state(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        self._create_and_check_torchscript(config, inputs_dict)
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    def _create_and_check_torchscript(self, config, inputs_dict):
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        if not self.test_torchscript:
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            return
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        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
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            inputs = self._prepare_for_class(inputs_dict, model_class)
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            try:
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                if model.config.is_encoder_decoder:
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                    model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
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                    input_ids = inputs["input_ids"]
                    attention_mask = inputs["attention_mask"]
                    decoder_input_ids = inputs["decoder_input_ids"]
                    decoder_attention_mask = inputs["decoder_attention_mask"]
                    traced_model = torch.jit.trace(
                        model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
                    )
                else:
                    input_ids = inputs["input_ids"]
                    traced_model = torch.jit.trace(model, input_ids)
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            except RuntimeError:
                self.fail("Couldn't trace module.")
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            with tempfile.TemporaryDirectory() as tmp_dir_name:
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                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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                try:
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                    torch.jit.save(traced_model, pt_file_name)
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                except Exception:
                    self.fail("Couldn't save module.")
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                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")
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            model.to(torch_device)
            model.eval()
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            loaded_model.to(torch_device)
            loaded_model.eval()
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            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

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            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

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            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

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            models_equal = True
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            for layer_name, p1 in model_state_dict.items():
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                if layer_name in loaded_model_state_dict:
                    p2 = loaded_model_state_dict[layer_name]
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False
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            self.assertTrue(models_equal)
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    def test_torch_fx(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        self._create_and_check_torch_fx_tracing(config, inputs_dict)

    def test_torch_fx_output_loss(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True)

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    def test_torch_fx_dynamic_axes(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        self._create_and_check_torch_fx_tracing(config, inputs_dict, dynamic_axes=True)

    def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False, dynamic_axes=False):
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        if not is_torch_fx_available():
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.return_dict = False

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        model_classes = self.fx_ready_model_classes if not dynamic_axes else self.fx_dynamic_ready_model_classes
        for model_class in model_classes:
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            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)

            try:
                if model.config.is_encoder_decoder:
                    model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                    input_ids = inputs["input_ids"]
                    decoder_attention_mask = inputs["decoder_attention_mask"]
                    labels = inputs.get("labels", None)
                    input_names = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask"]
                    if labels is not None:
                        input_names.append("labels")
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                    model_output = model(**filtered_inputs)
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                    batch_size = input_ids.shape[0]
                    encoder_sequence_length = input_ids.shape[1]
                    decoder_sequence_length = decoder_attention_mask.shape[1]

                    traced_model = symbolic_trace(
                        model,
                        input_names,
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                        batch_size=batch_size if not dynamic_axes else -1,
                        sequence_length=[encoder_sequence_length, decoder_sequence_length] if not dynamic_axes else -1,
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                    )

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                    traced_output = traced_model(**filtered_inputs)
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                else:
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                    input_names = ["input_ids", "attention_mask", "token_type_ids"]
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                    input_ids = inputs["input_ids"]
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                    labels = inputs.get("labels", None)
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                    start_positions = inputs.get("start_positions", None)
                    end_positions = inputs.get("end_positions", None)
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                    if labels is not None:
                        input_names.append("labels")
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                    if start_positions is not None:
                        input_names.append("start_positions")
                    if end_positions is not None:
                        input_names.append("end_positions")
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                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = filtered_inputs.keys()
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                    model_output = model(**filtered_inputs)
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                    rank = len(input_ids.shape)
                    if rank == 2:
                        batch_size, sequence_length = input_ids.shape
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                        num_choices = -1
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                    elif rank == 3:
                        batch_size, num_choices, sequence_length = input_ids.shape
                    else:
                        raise NotImplementedError(
                            f"symbolic_trace automatic parameters inference not implemented for input of rank {rank}."
                        )
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                    traced_model = symbolic_trace(
                        model,
                        input_names,
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                        batch_size=batch_size if not dynamic_axes else -1,
                        sequence_length=sequence_length if not dynamic_axes else -1,
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                        num_choices=num_choices,
                    )
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                    traced_output = traced_model(**filtered_inputs)
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            except RuntimeError:
                self.fail("Couldn't trace module.")

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            def flatten_output(output):
                flatten = []
                for x in output:
                    if isinstance(x, (tuple, list)):
                        flatten += flatten_output(x)
                    elif not isinstance(x, torch.Tensor):
                        continue
                    else:
                        flatten.append(x)
                return flatten

            model_output = flatten_output(model_output)
            traced_output = flatten_output(traced_output)
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            num_outputs = len(model_output)
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            for i in range(num_outputs):
                self.assertTrue(
                    torch.allclose(model_output[i], traced_output[i]),
                    f"traced {i}th output doesn't match model {i}th output for {model_class}",
                )
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    def test_headmasking(self):
        if not self.test_head_masking:
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            return
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        global_rng.seed(42)
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        global_rng.seed()
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        inputs_dict["output_attentions"] = True
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        config.output_hidden_states = True
        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
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            # Prepare head_mask
            # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
            head_mask = torch.ones(
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                self.model_tester.num_hidden_layers,
                self.model_tester.num_attention_heads,
                device=torch_device,
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            )
            head_mask[0, 0] = 0
            head_mask[-1, :-1] = 0
            head_mask.requires_grad_(requires_grad=True)
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            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
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            inputs["head_mask"] = head_mask
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            if model.config.is_encoder_decoder:
                signature = inspect.signature(model.forward)
                arg_names = [*signature.parameters.keys()]
                if "decoder_head_mask" in arg_names:  # necessary diferentiation because of T5 model
                    inputs["decoder_head_mask"] = head_mask
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                if "cross_attn_head_mask" in arg_names:
                    inputs["cross_attn_head_mask"] = head_mask
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            outputs = model(**inputs, return_dict=True)
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            # Test that we can get a gradient back for importance score computation
            output = sum(t.sum() for t in outputs[0])
            output = output.sum()
            output.backward()
            multihead_outputs = head_mask.grad

            self.assertIsNotNone(multihead_outputs)
            self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
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            def check_attentions_validity(attentions):
                # Remove Nan
                for t in attentions:
                    self.assertLess(
                        torch.sum(torch.isnan(t)), t.numel() / 4
                    )  # Check we don't have more than 25% nans (arbitrary)
                attentions = [
                    t.masked_fill(torch.isnan(t), 0.0) for t in attentions
                ]  # remove them (the test is less complete)

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

            if model.config.is_encoder_decoder:
                check_attentions_validity(outputs.encoder_attentions)
                check_attentions_validity(outputs.decoder_attentions)
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                check_attentions_validity(outputs.cross_attentions)
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            else:
                check_attentions_validity(outputs.attentions)
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    def test_head_pruning(self):
        if not self.test_pruning:
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            return

        for model_class in self.all_model_classes:
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            (
                config,
                inputs_dict,
            ) = self.model_tester.prepare_config_and_inputs_for_common()
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            if "head_mask" in inputs_dict:
                del inputs_dict["head_mask"]
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            inputs_dict["output_attentions"] = True
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            config.output_hidden_states = False
            model = model_class(config=config)
            model.to(torch_device)
            model.eval()
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            heads_to_prune = {
                0: list(range(1, self.model_tester.num_attention_heads)),
                -1: [0],
            }
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            model.prune_heads(heads_to_prune)
            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
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            self.assertEqual(attentions[0].shape[-3], 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
            self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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    def test_head_pruning_save_load_from_pretrained(self):
        if not self.test_pruning:
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            return
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        for model_class in self.all_model_classes:
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            (
                config,
                inputs_dict,
            ) = self.model_tester.prepare_config_and_inputs_for_common()
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            if "head_mask" in inputs_dict:
                del inputs_dict["head_mask"]
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            inputs_dict["output_attentions"] = True
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            config.output_hidden_states = False
            model = model_class(config=config)
            model.to(torch_device)
            model.eval()
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            heads_to_prune = {
                0: list(range(1, self.model_tester.num_attention_heads)),
                -1: [0],
            }
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            model.prune_heads(heads_to_prune)
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            with tempfile.TemporaryDirectory() as temp_dir_name:
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                model.save_pretrained(temp_dir_name)
                model = model_class.from_pretrained(temp_dir_name)
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                model.to(torch_device)
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
            self.assertEqual(attentions[0].shape[-3], 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
            self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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    def test_head_pruning_save_load_from_config_init(self):
        if not self.test_pruning:
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            return
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        for model_class in self.all_model_classes:
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            (
                config,
                inputs_dict,
            ) = self.model_tester.prepare_config_and_inputs_for_common()
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            if "head_mask" in inputs_dict:
                del inputs_dict["head_mask"]
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            inputs_dict["output_attentions"] = True
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            config.output_hidden_states = False
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            heads_to_prune = {
                0: list(range(1, self.model_tester.num_attention_heads)),
                -1: [0],
            }
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            config.pruned_heads = heads_to_prune
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            model = model_class(config=config)
            model.to(torch_device)
            model.eval()
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
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            self.assertEqual(attentions[0].shape[-3], 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
            self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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    def test_head_pruning_integration(self):
        if not self.test_pruning:
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            return
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        for model_class in self.all_model_classes:
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            (
                config,
                inputs_dict,
            ) = self.model_tester.prepare_config_and_inputs_for_common()
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            if "head_mask" in inputs_dict:
                del inputs_dict["head_mask"]
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            inputs_dict["output_attentions"] = True
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            config.output_hidden_states = False
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            heads_to_prune = {0: [0], 1: [1, 2]}
            config.pruned_heads = heads_to_prune
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            model = model_class(config=config)
            model.to(torch_device)
            model.eval()
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
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            self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
            self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
            self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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            with tempfile.TemporaryDirectory() as temp_dir_name:
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                model.save_pretrained(temp_dir_name)
                model = model_class.from_pretrained(temp_dir_name)
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                model.to(torch_device)
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
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            self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
            self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
            self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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            heads_to_prune = {0: [0], 2: [1, 2]}
            model.prune_heads(heads_to_prune)
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            attentions = outputs[-1]
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            self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
            self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
            self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
            self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
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            self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
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    def test_hidden_states_output(self):
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        def check_hidden_states_output(inputs_dict, config, model_class):
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            model = model_class(config)
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            model.to(torch_device)
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            model.eval()
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            with torch.no_grad():
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                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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            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)
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            if hasattr(self.model_tester, "encoder_seq_length"):
                seq_length = self.model_tester.encoder_seq_length
                if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
                    seq_length = seq_length * self.model_tester.chunk_length
            else:
                seq_length = self.model_tester.seq_length

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

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        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)

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    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)
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        output = outputs[0]

        if config.is_encoder_decoder:
            # Seq2Seq models
            encoder_hidden_states = outputs.encoder_hidden_states[0]
            encoder_attentions = outputs.encoder_attentions[0]
            encoder_hidden_states.retain_grad()
            encoder_attentions.retain_grad()

            decoder_hidden_states = outputs.decoder_hidden_states[0]
            decoder_attentions = outputs.decoder_attentions[0]
            decoder_hidden_states.retain_grad()
            decoder_attentions.retain_grad()

            cross_attentions = outputs.cross_attentions[0]
            cross_attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(encoder_hidden_states.grad)
            self.assertIsNotNone(encoder_attentions.grad)
            self.assertIsNotNone(decoder_hidden_states.grad)
            self.assertIsNotNone(decoder_attentions.grad)
            self.assertIsNotNone(cross_attentions.grad)
        else:
            # Encoder-/Decoder-only models
            hidden_states = outputs.hidden_states[0]
            attentions = outputs.attentions[0]

            hidden_states.retain_grad()
            attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(hidden_states.grad)
            self.assertIsNotNone(attentions.grad)

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    def test_feed_forward_chunking(self):
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        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()
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        for model_class in self.all_model_classes:
            torch.manual_seed(0)
            config = copy.deepcopy(original_config)
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]

            torch.manual_seed(0)
            config.chunk_size_feed_forward = 1
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
            self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))

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    def test_resize_position_vector_embeddings(self):
        if not self.test_resize_position_embeddings:
            return

        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config)
            model.to(torch_device)

            if self.model_tester.is_training is False:
                model.eval()

            max_position_embeddings = config.max_position_embeddings

            # Retrieve the embeddings and clone theme
            if model.config.is_encoder_decoder:
                encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
                encoder_cloned_embeddings = encoder_model_embed.weight.clone()
                decoder_cloned_embeddings = decoder_model_embed.weight.clone()
            else:
                model_embed = model.get_position_embeddings()
                cloned_embeddings = model_embed.weight.clone()

            # Check that resizing the position embeddings with a larger max_position_embeddings increases
            # the model's postion embeddings size
            model.resize_position_embeddings(max_position_embeddings + 10)
            self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10)

            # Check that it actually resizes the embeddings matrix
            if model.config.is_encoder_decoder:
                encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
                self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10)
                self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10)
            else:
                model_embed = model.get_position_embeddings()
                self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)

            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that resizing the position embeddings with a smaller max_position_embeddings decreases
            # the model's max_position_embeddings
            model.resize_position_embeddings(max_position_embeddings - 5)
            self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5)

            # Check that it actually resizes the embeddings matrix
            if model.config.is_encoder_decoder:
                encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
                self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5)
                self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5)
            else:
                model_embed = model.get_position_embeddings()
                self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5)

            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that adding and removing tokens has not modified the first part of the embedding matrix.
            models_equal = True

            if model.config.is_encoder_decoder:
                for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight):
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False
                for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight):
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False
            else:
                for p1, p2 in zip(cloned_embeddings, model_embed.weight):
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False

            self.assertTrue(models_equal)

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    def test_resize_tokens_embeddings(self):
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        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()
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        if not self.test_resize_embeddings:
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            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config)
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            model.to(torch_device)
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            if self.model_tester.is_training is False:
                model.eval()

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            model_vocab_size = config.vocab_size
            # Retrieve the embeddings and clone theme
            model_embed = model.resize_token_embeddings(model_vocab_size)
            cloned_embeddings = model_embed.weight.clone()

            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
            model_embed = model.resize_token_embeddings(model_vocab_size + 10)
            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
            # Check that it actually resizes the embeddings matrix
            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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            # Check that the model can still do a forward pass successfully (every parameter should be resized)
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            model(**self._prepare_for_class(inputs_dict, model_class))
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            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
            model_embed = model.resize_token_embeddings(model_vocab_size - 15)
            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
            # Check that it actually resizes the embeddings matrix
            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)

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            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            # Input ids should be clamped to the maximum size of the vocabulary
            inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
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            # make sure that decoder_input_ids are resized as well
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
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            model(**self._prepare_for_class(inputs_dict, model_class))
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            # Check that adding and removing tokens has not modified the first part of the embedding matrix.
            models_equal = True
            for p1, p2 in zip(cloned_embeddings, model_embed.weight):
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

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    def test_resize_embeddings_untied(self):
        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.test_resize_embeddings:
            return

        original_config.tie_word_embeddings = False

        # if model cannot untied embeddings -> leave test
        if original_config.tie_word_embeddings:
            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config).to(torch_device)

            # if no output embeddings -> leave test
            if model.get_output_embeddings() is None:
                continue

            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
            model_vocab_size = config.vocab_size
            model.resize_token_embeddings(model_vocab_size + 10)
            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
            output_embeds = model.get_output_embeddings()
            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
            # Check bias if present
            if output_embeds.bias is not None:
                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
            model.resize_token_embeddings(model_vocab_size - 15)
            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
            # Check that it actually resizes the embeddings matrix
            output_embeds = model.get_output_embeddings()
            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
            # Check bias if present
            if output_embeds.bias is not None:
                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            # Input ids should be clamped to the maximum size of the vocabulary
            inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

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

        for model_class in self.all_model_classes:
            model = model_class(config)
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            self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding))
            model.set_input_embeddings(nn.Embedding(10, 10))
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            x = model.get_output_embeddings()
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            self.assertTrue(x is None or isinstance(x, nn.Linear))
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    def test_correct_missing_keys(self):
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        if not self.test_missing_keys:
            return
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        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

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

            if hasattr(model, base_model_prefix):
                with tempfile.TemporaryDirectory() as temp_dir_name:
                    model.base_model.save_pretrained(temp_dir_name)
                    model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
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                    with self.subTest(msg=f"Missing keys for {model.__class__.__name__}"):
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                        self.assertGreater(len(loading_info["missing_keys"]), 0)

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    def test_tie_model_weights(self):
        if not self.test_torchscript:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_same_values(layer_1, layer_2):
            equal = True
            for p1, p2 in zip(layer_1.weight, layer_2.weight):
                if p1.data.ne(p2.data).sum() > 0:
                    equal = False
            return equal

        for model_class in self.all_model_classes:
            config.torchscript = True
            model_not_tied = model_class(config)
            if model_not_tied.get_output_embeddings() is None:
                continue

            config_tied = copy.deepcopy(config)
            config_tied.torchscript = False
            model_tied = model_class(config_tied)
            params_tied = list(model_tied.parameters())
            # Check that the embedding layer and decoding layer are the same in size and in value
            # self.assertTrue(check_same_values(embeddings, decoding))

            # # Check that after modification, they remain the same.
            # embeddings.weight.data.div_(2)
            # # Check that the embedding layer and decoding layer are the same in size and in value
            # self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
            # self.assertTrue(check_same_values(embeddings, decoding))

            # # Check that after modification, they remain the same.
            # decoding.weight.data.div_(4)
            # # Check that the embedding layer and decoding layer are the same in size and in value
            # self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
            # self.assertTrue(check_same_values(embeddings, decoding))

            # Check that after resize they remain tied.
            model_tied.resize_token_embeddings(config.vocab_size + 10)
            params_tied_2 = list(model_tied.parameters())
            self.assertEqual(len(params_tied_2), len(params_tied))

            # decoding.weight.data.mul_(20)
            # # Check that the embedding layer and decoding layer are the same in size and in value
            # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
            # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))

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

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        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t

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        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                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)
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                    elif isinstance(tuple_object, Dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
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                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
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                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                            ),
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                            msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
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                        )

                recursive_check(tuple_output, dict_output)

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

            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, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            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})

            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_attentions": True})

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

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

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

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

        for model_class in self.all_model_classes:
            model = model_class(config)
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            model.to(torch_device)
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            model.eval()
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            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

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            wte = model.get_input_embeddings()
            if not self.is_encoder_decoder:
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                inputs["inputs_embeds"] = wte(input_ids)
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            else:
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                inputs["inputs_embeds"] = wte(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
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            with torch.no_grad():
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                model(**inputs)[0]
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    @require_torch_multi_gpu
    def test_multi_gpu_data_parallel_forward(self):
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # some params shouldn't be scattered by nn.DataParallel
        # so just remove them if they are present.
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        blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
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        for k in blacklist_non_batched_params:
            inputs_dict.pop(k, None)

        # move input tensors to cuda:O
        for k, v in inputs_dict.items():
            if torch.is_tensor(v):
                inputs_dict[k] = v.to(0)

        for model_class in self.all_model_classes:
            model = model_class(config=config)
            model.to(0)
            model.eval()

            # Wrap model in nn.DataParallel
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            model = nn.DataParallel(model)
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            with torch.no_grad():
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                _ = model(**self._prepare_for_class(inputs_dict, model_class))
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    @require_torch_multi_gpu
    def test_model_parallelization(self):
        if not self.test_model_parallel:
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            return
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        # a candidate for testing_utils
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        def get_current_gpu_memory_use():
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            """returns a list of cuda memory allocations per GPU in MBs"""
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            per_device_memory = []
            for id in range(torch.cuda.device_count()):
                with torch.cuda.device(id):
                    per_device_memory.append(torch.cuda.memory_allocated() >> 20)
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            return per_device_memory

        # Needs a large model to see the difference.
        config = self.model_tester.get_large_model_config()

        for model_class in self.all_parallelizable_model_classes:
            torch.cuda.empty_cache()

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            # 1. single gpu memory load + unload + memory measurements
            # Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
            memory_at_start = get_current_gpu_memory_use()
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            # Put model on device 0 and take a memory snapshot
            model = model_class(config)
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            model.to("cuda:0")
            memory_after_model_load = get_current_gpu_memory_use()

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            # The memory use on device 0 should be higher than it was initially.
            self.assertGreater(memory_after_model_load[0], memory_at_start[0])

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            del model
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            gc.collect()
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            torch.cuda.empty_cache()

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            # 2. MP test
            # it's essential to re-calibrate the usage before the next stage
            memory_at_start = get_current_gpu_memory_use()
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            # Spread model layers over multiple devices
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            model = model_class(config)
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            model.parallelize()
            memory_after_parallelization = get_current_gpu_memory_use()

            # Assert that the memory use on all devices is higher than it was when loaded only on CPU
            for n in range(torch.cuda.device_count()):
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                self.assertGreater(memory_after_parallelization[n], memory_at_start[n])
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            # Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
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            self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])

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            # Assert that the memory use of device 1 is higher than it was when the entire model was loaded
            # on device 0 and device 1 wasn't used at all
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            self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])

            del model
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            gc.collect()
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            torch.cuda.empty_cache()

    @require_torch_multi_gpu
    def test_model_parallel_equal_results(self):
        if not self.test_model_parallel:
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            return
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        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_parallelizable_model_classes:
            inputs_dict = self._prepare_for_class(inputs_dict, model_class)

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            def cast_to_device(dictionary, device):
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                output = {}
                for k, v in dictionary.items():
                    if isinstance(v, torch.Tensor):
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                        output[k] = v.to(device)
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                    else:
                        output[k] = v

                return output

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            model = model_class(config)
            output = model(**cast_to_device(inputs_dict, "cpu"))

            model.parallelize()

            parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))
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            for value, parallel_value in zip(output, parallel_output):
                if isinstance(value, torch.Tensor):
                    self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
                elif isinstance(value, (Tuple, List)):
                    for value_, parallel_value_ in zip(value, parallel_value):
                        self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))

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    @require_torch_multi_gpu
    def test_model_parallel_beam_search(self):
        if not self.test_model_parallel:
            return

        all_generative_and_parallelizable_model_classes = tuple(
            set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
        )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in all_generative_and_parallelizable_model_classes:
            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)

            def cast_to_device(dictionary, device):
                output = {}
                for k, v in dictionary.items():
                    if isinstance(v, torch.Tensor):
                        output[k] = v.to(device)
                    else:
                        output[k] = v

                return output

            model.parallelize()
            model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)

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    def test_problem_types(self):
        if not self.test_sequence_classification_problem_types:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        problem_types = [
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

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

            for problem_type in problem_types:
                with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):

                    config.problem_type = problem_type["title"]
                    config.num_labels = problem_type["num_labels"]

                    model = model_class(config)
                    model.to(torch_device)
                    model.train()

                    inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

                    if problem_type["num_labels"] > 1:
                        inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])

                    inputs["labels"] = inputs["labels"].to(problem_type["dtype"])

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                    # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                    # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                    # they have the same size." which is a symptom something in wrong for the regression problem.
                    # See https://github.com/huggingface/transformers/issues/11780
                    with warnings.catch_warnings(record=True) as warning_list:
                        loss = model(**inputs).loss
                    self.assertListEqual(warning_list, [])

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                    loss.backward()

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    def test_load_with_mismatched_shapes(self):
        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(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(RuntimeError) as e:
                        print(type(e))
                        new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)

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

                    new_model.to(torch_device)
                    inputs = self._prepare_for_class(inputs_dict, model_class)
                    logits = new_model(**inputs).logits
                    self.assertEqual(logits.shape[1], 42)

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global_rng = random.Random()
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def ids_tensor(shape, vocab_size, rng=None, name=None):
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    #  Creates a random int32 tensor of the shape within the vocab size
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    if rng is None:
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        rng = global_rng
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    total_dims = 1
    for dim in shape:
        total_dims *= dim
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    values = []
    for _ in range(total_dims):
        values.append(rng.randint(0, vocab_size - 1))
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    return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()
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def random_attention_mask(shape, rng=None, name=None):
    attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None)
    # make sure that at least one token is attended to for each batch
    attn_mask[:, -1] = 1
    return attn_mask


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

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

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

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    return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
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@require_torch
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class ModelUtilsTest(TestCasePlus):
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    @slow
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    def test_model_from_pretrained(self):
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        for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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            config = BertConfig.from_pretrained(model_name)
            self.assertIsNotNone(config)
            self.assertIsInstance(config, PretrainedConfig)

            model = BertModel.from_pretrained(model_name)
            model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
            self.assertIsNotNone(model)
            self.assertIsInstance(model, PreTrainedModel)
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            self.assertEqual(len(loading_info["missing_keys"]), 0)
            self.assertEqual(len(loading_info["unexpected_keys"]), 8)
            self.assertEqual(len(loading_info["mismatched_keys"]), 0)
            self.assertEqual(len(loading_info["error_msgs"]), 0)
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            config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
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            # Not sure this is the intended behavior. TODO fix Lysandre & Thom
            config.name_or_path = model_name

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            model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
            self.assertEqual(model.config.output_hidden_states, True)
            self.assertEqual(model.config, config)
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    def test_model_from_pretrained_with_different_pretrained_model_name(self):
        model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
        self.assertIsNotNone(model)

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        logger = logging.get_logger("transformers.configuration_utils")
        with CaptureLogger(logger) as cl:
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            BertModel.from_pretrained(TINY_T5)
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        self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
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    @require_torch
    def test_model_from_config_torch_dtype(self):
        # test that the model can be instantiated with dtype of user's choice - as long as it's a
        # float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
        # model from the config object.

        config = T5Config.from_pretrained(TINY_T5)
        model = AutoModel.from_config(config)
        # XXX: isn't supported
        # model = T5ForConditionalGeneration.from_config(config)
        self.assertEqual(model.dtype, torch.float32)

        model = AutoModel.from_config(config, torch_dtype=torch.float16)
        self.assertEqual(model.dtype, torch.float16)

        # torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
        with self.assertRaises(ValueError):
            model = AutoModel.from_config(config, torch_dtype=torch.int64)

    @require_torch
    def test_model_from_pretrained_torch_dtype(self):
        # test that the model can be instantiated with dtype of either
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        # 1. explicit from_pretrained's torch_dtype argument
        # 2. via autodiscovery by looking at model weights (torch_dtype="auto")
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        # so if a model.half() was saved, we want it to be instantiated as such.
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        #
        # test an explicit model class, but also AutoModel separately as the latter goes through a different code path
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        model_path = self.get_auto_remove_tmp_dir()

        # baseline - we know TINY_T5 is fp32 model
        model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
        self.assertEqual(model.dtype, torch.float32)

        # test the default fp32 save_pretrained => from_pretrained cycle
        model.save_pretrained(model_path)
        model = T5ForConditionalGeneration.from_pretrained(model_path)
        self.assertEqual(model.dtype, torch.float32)
        # test with auto-detection
        model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
        self.assertEqual(model.dtype, torch.float32)

        # test forced loading in fp16 (even though the weights are in fp32)
        model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
        self.assertEqual(model.dtype, torch.float16)

        # test fp16 save_pretrained, loaded with auto-detection
        model = model.half()
        model.save_pretrained(model_path)
        model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
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        self.assertEqual(model.config.torch_dtype, torch.float16)
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        self.assertEqual(model.dtype, torch.float16)

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        # tests `config.torch_dtype` saving
        with open(f"{model_path}/config.json") as f:
            config_dict = json.load(f)
        self.assertEqual(config_dict["torch_dtype"], "float16")

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        # test fp16 save_pretrained, loaded with the explicit fp16
        model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
        self.assertEqual(model.dtype, torch.float16)

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        # test AutoModel separately as it goes through a different path
        # test auto-detection
        model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
        self.assertEqual(model.dtype, torch.float32)
        # test forcing an explicit dtype
        model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
        self.assertEqual(model.dtype, torch.float16)

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if is_torch_available():

    class FakeModel(PreTrainedModel):
        config_class = BertConfig
        base_model_prefix = "fake"

        def __init__(self, config):
            super().__init__(config)
            self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)

        def forward(self, x):
            return self.linear(x)

        def _init_weights(self, module):
            pass


# Make sure this is synchronized with the model above.
FAKE_MODEL_CODE = """
import torch
from transformers import BertConfig, PreTrainedModel

class FakeModel(PreTrainedModel):
    config_class = BertConfig
    base_model_prefix = "fake"

    def __init__(self, config):
        super().__init__(config)
        self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, x):
        return self.linear(x)

    def _init_weights(self, module):
        pass
"""


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@require_torch
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._api = HfApi(endpoint=ENDPOINT_STAGING)
        cls._token = cls._api.login(username=USER, password=PASS)

    @classmethod
    def tearDownClass(cls):
        try:
            cls._api.delete_repo(token=cls._token, name="test-model")
        except HTTPError:
            pass

        try:
            cls._api.delete_repo(token=cls._token, name="test-model-org", organization="valid_org")
        except HTTPError:
            pass

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        try:
            cls._api.delete_repo(token=cls._token, name="test-dynamic-model")
        except HTTPError:
            pass

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    def test_push_to_hub(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        model = BertModel(config)
        with tempfile.TemporaryDirectory() as tmp_dir:
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            model.save_pretrained(os.path.join(tmp_dir, "test-model"), push_to_hub=True, use_auth_token=self._token)
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            new_model = BertModel.from_pretrained(f"{USER}/test-model")
            for p1, p2 in zip(model.parameters(), new_model.parameters()):
                self.assertTrue(torch.equal(p1, p2))

    def test_push_to_hub_in_organization(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        model = BertModel(config)
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(
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                os.path.join(tmp_dir, "test-model-org"),
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                push_to_hub=True,
                use_auth_token=self._token,
                organization="valid_org",
            )

            new_model = BertModel.from_pretrained("valid_org/test-model-org")
            for p1, p2 in zip(model.parameters(), new_model.parameters()):
                self.assertTrue(torch.equal(p1, p2))
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    def test_push_to_hub_dynamic_model(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        config.auto_map = {"AutoModel": "modeling.FakeModel"}
        model = FakeModel(config)

        with tempfile.TemporaryDirectory() as tmp_dir:
            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-model", use_auth_token=self._token)
            model.save_pretrained(tmp_dir)
            with open(os.path.join(tmp_dir, "modeling.py"), "w") as f:
                f.write(FAKE_MODEL_CODE)

            repo.push_to_hub()
            print(os.listdir(tmp_dir))

        new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
        for p1, p2 in zip(model.parameters(), new_model.parameters()):
            self.assertTrue(torch.equal(p1, p2))