transformers/tests/models/jamba/test_modeling_jamba.py

# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Jamba model. """
import math
import tempfile
import unittest

import pytest
from parameterized import parameterized

from transformers import AutoTokenizer, JambaConfig, is_torch_available
from transformers.testing_utils import (
    require_bitsandbytes,
    require_flash_attn,
    require_torch,
    require_torch_gpu,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        JambaForCausalLM,
        JambaForSequenceClassification,
        JambaModel,
    )
    from transformers.models.jamba.modeling_jamba import (
        HybridMambaAttentionDynamicCache,
    )


class JambaModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=5,
        attn_layer_offset=1,
        attn_layer_period=8,
        num_attention_heads=4,
        num_key_value_heads=2,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.attn_layer_offset = attn_layer_offset
        self.attn_layer_period = attn_layer_period
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        sequence_labels = None
        token_labels = None
        choice_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return JambaConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            attn_layer_offset=self.attn_layer_offset,
            attn_layer_period=self.attn_layer_period,
            num_attention_heads=self.num_attention_heads,
            num_key_value_heads=self.num_key_value_heads,
            intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            is_decoder=True,
            initializer_range=self.initializer_range,
            use_mamba_kernels=False,
            num_experts=2,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = self.prepare_config_and_inputs()

        config.is_decoder = True

        return (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
        model = JambaModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        model = JambaForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        result = model(input_ids, attention_mask=input_mask)
        result = model(input_ids, labels=token_labels)
        result = model(input_ids)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = JambaForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        # Attention: Jamba needs the cache to be initialized to return a cache!
        past_key_values = HybridMambaAttentionDynamicCache(
            config, input_ids.shape[0], model.dtype, device=model.device
        )
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            past_key_values=past_key_values,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            attention_mask=next_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
            cache_position=torch.arange(
                input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
            ),
        )["hidden_states"][0]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_for_sequence_classification(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = JambaForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            JambaModel,
            JambaForCausalLM,
            JambaForSequenceClassification,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (JambaForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": JambaModel,
            "text-classification": JambaForSequenceClassification,
            "text-generation": JambaForCausalLM,
            "zero-shot": JambaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False

    def setUp(self):
        self.model_tester = JambaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=JambaConfig, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_for_casual_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)

    def test_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_load_balancing_loss(self):
        r"""
        Let's make sure we can actually compute the loss and do a backward on it.
        """
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.num_labels = 3
        config.num_experts = 16
        config.output_router_logits = True
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(config.pad_token_id).to(torch_device)
        model = JambaForCausalLM(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask)
        bs, seqlen = input_ids.shape
        self.assertEqual(result.router_logits[0].shape, (bs * seqlen, config.num_experts))
        torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)

        # First, we make sure that adding padding tokens doesn't change the loss
        # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
        pad_length = 1000
        # Add padding tokens to input_ids
        padding_block = config.pad_token_id * torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(
            torch_device
        )
        padded_input_ids = torch.cat((padding_block, input_ids), dim=1)  # this is to simulate padding to the left
        padded_attention_mask = padded_input_ids.ne(config.pad_token_id).to(torch_device)

        padded_result = model(padded_input_ids, attention_mask=padded_attention_mask)
        torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4)

        # We make sure that the loss of including padding tokens != the loss without padding tokens
        # if attention_mask=None --> we don't exclude padding tokens
        include_padding_result = model(padded_input_ids, attention_mask=None)

        # This is to mimic torch.testing.assert_not_close
        self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item())

    def test_initialization(self):
        r"""
        Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently
        """
        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:
                    if "A_log" in name:
                        A = torch.arange(1, config.mamba_d_state + 1, dtype=torch.float32)[None, :]
                        self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5))
                    elif "D" in name:
                        # check if it's a ones like
                        self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5))
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def test_mismatched_shapes_have_properly_initialized_weights(self):
        r"""
        Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
        Mamba block are initialized differently and we tested that in test_initialization
        """
        self.skipTest("Cumbersome and redundant for Jamba")

    def test_attention_outputs(self):
        r"""
        Overriding the test_attention_outputs test as the Jamba model outputs attention only for its attention layers
        """
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

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

        expected_num_attentions = math.ceil(
            (self.model_tester.num_hidden_layers - self.model_tester.attn_layer_offset)
            / self.model_tester.attn_layer_period
        )

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            self.assertEqual(len(attentions), expected_num_attentions)

            # 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():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            self.assertEqual(len(attentions), expected_num_attentions)

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

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

            added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.attentions

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

    def test_left_padding_compatibility(self):
        r"""
        Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences
        effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value.
        """
        import inspect
        # NOTE: left-padding results in small numerical differences. This is expected.
        # See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535

        # First, filter out models that don't support left padding - generative and decoder-only.
        # Jamba is a decoder-only architecture
        decoder_only_classes = self.all_generative_model_classes

        # Then, test left-padding
        def _prepare_model_kwargs(input_ids, attention_mask, signature):
            model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
            if "position_ids" in signature:
                position_ids = torch.cumsum(attention_mask, dim=-1) - 1
                position_ids.masked_fill_(attention_mask == 0, 1)
                model_kwargs["position_ids"] = position_ids
            if "cache_position" in signature:
                cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
                model_kwargs["cache_position"] = cache_position
            return model_kwargs

        for model_class in decoder_only_classes:
            config, input_ids, attention_mask, _ = self._get_input_ids_and_config()
            model = model_class(config).to(torch_device).eval()
            signature = inspect.signature(model.forward).parameters.keys()

            # Without padding
            model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
            next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]

            # With left-padding (length 32)
            pad_size = (input_ids.shape[0], 32)
            padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
            padded_input_ids = torch.cat((padding, input_ids), dim=1)
            padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
            model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
            next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]

            # They should result in very similar logits
            self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=3e-3))

    @require_flash_attn
    @require_torch_gpu
    @require_bitsandbytes
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_fp32_ln(self):
        r"""
        Overriding the test_flash_attn_2_fp32_ln test as the Jamba model, like Mixtral, doesn't support
        right padding + use cache with FA2
        """
        for model_class in self.all_generative_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)

                dummy_input = inputs_dict[model.main_input_name]
                dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
                # NOTE: Jamba does not support right padding + use_cache with FA2.
                dummy_attention_mask[:, -1] = 1

                model = model_class.from_pretrained(
                    tmpdirname,
                    torch_dtype=torch.float16,
                    attn_implementation="flash_attention_2",
                    low_cpu_mem_usage=True,
                    load_in_4bit=True,
                )

                for _, param in model.named_parameters():
                    # upcast only layer norms
                    if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
                        param.data = param.data.to(torch.float32)

                _ = model(dummy_input)
                # with attention mask
                _ = model(dummy_input, attention_mask=dummy_attention_mask)

    @require_flash_attn
    @require_torch_gpu
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_generate_padding_right(self):
        r"""
        Overriding the test_flash_attn_2_generate_padding_right test as the Jamba model, like Mixtral, doesn't support
        right padding + use cache with FA2
        """
        import torch

        for model_class in self.all_generative_model_classes:
            config, _ = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
                    torch_device
                )

                dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
                dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)

                model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)

                model = model_class.from_pretrained(
                    tmpdirname,
                    torch_dtype=torch.float16,
                    attn_implementation="flash_attention_2",
                    low_cpu_mem_usage=True,
                ).to(torch_device)

                with self.assertRaises(ValueError):
                    _ = model.generate(
                        dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
                    )

    @require_flash_attn
    @require_torch_gpu
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_generate_use_cache(self):
        r"""
        Overriding the test_flash_attn_2_generate_use_cache test as the Jamba model, like Mixtral, doesn't support
        right padding + use cache with FA2
        """
        import torch

        max_new_tokens = 30

        for model_class in self.all_generative_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

            dummy_input = inputs_dict[model_class.main_input_name]
            if dummy_input.dtype in [torch.float32, torch.bfloat16]:
                dummy_input = dummy_input.to(torch.float16)

            # make sure that all models have enough positions for generation
            if hasattr(config, "max_position_embeddings"):
                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1

            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)

                dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
                # NOTE: Jamba does not support right padding + use_cache with FA2.
                dummy_attention_mask[:, -1] = 1

                model = model_class.from_pretrained(
                    tmpdirname,
                    torch_dtype=torch.float16,
                    attn_implementation="flash_attention_2",
                    low_cpu_mem_usage=True,
                ).to(torch_device)

                # Just test that a large cache works as expected
                _ = model.generate(
                    dummy_input,
                    attention_mask=dummy_attention_mask,
                    max_new_tokens=max_new_tokens,
                    do_sample=False,
                    use_cache=True,
                )

    @require_flash_attn
    @require_torch_gpu
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_inference_equivalence_right_padding(self):
        r"""
        Overriding the test_flash_attn_2_inference_padding_right test as the Jamba model, like Mixtral, doesn't support
        right padding + use cache with FA2
        """
        self.skipTest("Jamba flash attention does not support right padding")

    @unittest.skip("Jamba has its own special cache type")
    @parameterized.expand([(1, False), (1, True), (4, False)])
    def test_new_cache_format(self, num_beams, do_sample):
        pass


@require_torch
class JambaModelIntegrationTest(unittest.TestCase):
    model = None
    tokenizer = None

    @classmethod
    def setUpClass(cls):
        model_id = "ai21labs/Jamba-tiny-random"
        cls.model = JambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
        cls.tokenizer = AutoTokenizer.from_pretrained(model_id)

    @slow
    def test_simple_generate(self):
        self.model.to(torch_device)

        input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
            "input_ids"
        ].to(torch_device)
        out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
        output_sentence = self.tokenizer.decode(out[0, :])
        self.assertEqual(
            output_sentence,
            "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
        )

        with torch.no_grad():
            logits = self.model(input_ids=input_ids).logits

        EXPECTED_LOGITS_NO_GRAD = torch.tensor(
            [
                0.0140, -0.2246,  0.0408, -0.1016,  0.0471,  0.2715, -0.1465,  0.1631,
               -0.2949, -0.0297,  0.0250, -0.5586, -0.2139, -0.1426, -0.1602,  0.1309,
                0.0703,  0.2236,  0.1729, -0.2285, -0.1152, -0.1177, -0.1367,  0.0289,
                0.1245,  0.2363,  0.0442,  0.1094, -0.1348, -0.2295,  0.1494, -0.3945,
                0.1777, -0.4570, -0.0408,  0.2412,  0.1562, -0.1943,  0.2373, -0.0593
            ]
            , dtype=torch.float32)  # fmt: skip

        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)

    @slow
    def test_simple_batched_generate_with_padding(self):
        self.model.to(torch_device)

        inputs = self.tokenizer(
            ["Hey how are you doing on this lovely evening?", "Tell me a story"], padding=True, return_tensors="pt"
        ).to(torch_device)
        out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
        output_sentences = self.tokenizer.batch_decode(out)
        self.assertEqual(
            output_sentences[0],
            "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats",
        )
        self.assertEqual(
            output_sentences[1],
            "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",
        )

        with torch.no_grad():
            logits = self.model(input_ids=inputs["input_ids"]).logits

        EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
            [
                0.0140, -0.2246,  0.0408, -0.1016,  0.0471,  0.2715, -0.1465,  0.1631,
               -0.2949, -0.0297,  0.0250, -0.5586, -0.2139, -0.1426, -0.1602,  0.1309,
                0.0703,  0.2236,  0.1729, -0.2285, -0.1152, -0.1177, -0.1367,  0.0289,
                0.1245,  0.2363,  0.0442,  0.1094, -0.1348, -0.2295,  0.1494, -0.3945,
                0.1777, -0.4570, -0.0408,  0.2412,  0.1562, -0.1943,  0.2373, -0.0593
            ]
            , dtype=torch.float32)  # fmt: skip

        EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
            [
               -0.1289,  0.2363, -0.4180, -0.0302, -0.0476,  0.0327,  0.2578,  0.0874,
                0.1484,  0.2305, -0.1152, -0.1396, -0.1494, -0.1113, -0.0021, -0.2832,
                0.2002, -0.2676,  0.0598, -0.1982, -0.2539, -0.1133, -0.1973,  0.2148,
                0.0559,  0.1670,  0.1846,  0.1270,  0.1680, -0.1250, -0.2656, -0.2871,
                0.2344,  0.2637,  0.0510, -0.1855,  0.2158, -0.1289,  0.1758,  0.0074
            ]
            , dtype=torch.float32)  # fmt: skip

        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3)
        torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1e-3)