init

transformers/tests/models/zamba2/test_modeling_zamba2.py

@@ -0,0 +1,662 @@
+# 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 Zamba model."""
+
+import math
+import tempfile
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import AutoTokenizer, Zamba2Config, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    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 (
+        Zamba2ForCausalLM,
+        Zamba2ForSequenceClassification,
+        Zamba2Model,
+    )
+    from transformers.models.zamba2.modeling_zamba2 import (
+        Zamba2HybridDynamicCache,
+    )
+
+
+class Zamba2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=14,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=16,
+        mamba_d_state=2,
+        chunk_size=8,
+        mamba_dt_rank="auto",
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        n_mamba_heads=8,
+        mamba_ngroups=8,
+        intermediate_size=4,
+        hidden_act="gelu",
+        hidden_mamba_act="silu",
+        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,
+        layers_block_type=["mamba", "hybrid"],
+        num_mem_blocks=1,
+        use_mem_rope=True,
+    ):
+        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.mamba_dt_rank = mamba_dt_rank
+        self.mamba_d_state = mamba_d_state
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_mamba_heads = n_mamba_heads
+        self.mamba_ngroups = mamba_ngroups
+        self.chunk_size = chunk_size
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_mamba_act = hidden_mamba_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
+        self.layers_block_type = layers_block_type
+        self.num_mem_blocks = num_mem_blocks
+        self.use_mem_rope = use_mem_rope
+
+    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 Zamba2Config(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            mamba_dt_rank=self.mamba_dt_rank,
+            mamba_d_state=self.mamba_d_state,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            n_mamba_heads=self.n_mamba_heads,
+            intermediate_size=self.intermediate_size,
+            chunk_size=self.chunk_size,
+            hidden_act=self.hidden_act,
+            mamba_ngroups=self.mamba_ngroups,
+            hidden_mamba_act=self.hidden_mamba_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,
+            layers_block_type=self.layers_block_type,
+            num_mem_blocks=self.num_mem_blocks,
+            use_mem_rope=self.use_mem_rope,
+        )
+
+    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 = Zamba2Model(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 = Zamba2ForCausalLM(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 = False
+        model = Zamba2ForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        # Attention: Zamba2 needs the cache to be initialized to return a cache!
+        past_key_values = Zamba2HybridDynamicCache(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, 1), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 1), 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[:, -1:, 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 = Zamba2ForSequenceClassification(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 Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    test_torchscript = False
+    all_model_classes = (
+        (
+            Zamba2Model,
+            Zamba2ForCausalLM,
+            Zamba2ForSequenceClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Zamba2Model,
+            "text-classification": Zamba2ForSequenceClassification,
+            "text-generation": Zamba2ForCausalLM,
+            "zero-shot": Zamba2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+
+    def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config):
+        self.assertIsInstance(decoder_past_key_values, Zamba2HybridDynamicCache)
+
+        # (batch, head, seq_length, head_features)
+        expected_shape = (
+            batch_size,
+            config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads,
+            cache_length,
+            config.hidden_size // config.num_attention_heads,
+        )
+
+        self.assertListEqual(
+            [key_tensor.shape for key_tensor in decoder_past_key_values.key_cache],
+            [expected_shape] * len(decoder_past_key_values.key_cache),
+        )
+        self.assertListEqual(
+            [value_cache.shape for value_cache in decoder_past_key_values.value_cache],
+            [expected_shape] * len(decoder_past_key_values.value_cache),
+        )
+
+    def setUp(self):
+        self.model_tester = Zamba2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=37)
+
+    @unittest.skip("position_ids cannot be used to pad due to Mamba2 layers")
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    def test_past_key_values_format(self):
+        """
+        Overwriting to pass the expected cache shapes (Zamba2 has cache shape = [batch_size, 0] for mamba layers)
+        """
+        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        batch_size, seq_length = inputs["input_ids"].shape
+        per_head_embed_dim = config.attention_head_dim  # note: this one is not a common attribute name
+        self_attention_cache_shape = (batch_size, config.num_key_value_heads, seq_length, per_head_embed_dim)
+        # build the full cache shapes, including mamba layers
+        all_cache_shapes = []
+        for i in range(config.num_hidden_layers):
+            if config.layers_block_type[i] == "mamba":
+                all_cache_shapes.append([torch.Size([batch_size, 0]), torch.Size([batch_size, 0])])
+            else:
+                all_cache_shapes.append([self_attention_cache_shape, self_attention_cache_shape])
+        super().test_past_key_values_format(custom_all_cache_shapes=all_cache_shapes)
+
+    @unittest.skip(reason="Zamba2 has hybrid cache.")
+    def test_generate_continue_from_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="A large mamba2 would be necessary (and costly) for that")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    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_causal_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_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.n_mamba_heads + 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))
+                    elif "dt_bias" in name:
+                        dt = torch.exp(
+                            torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min))
+                            + math.log(config.time_step_min)
+                        ).clamp(min=config.time_step_floor)
+                        inv_dt = dt + torch.log(-torch.expm1(-dt))
+                        if param.requires_grad:
+                            self.assertTrue(param.data.max().item() <= inv_dt[1])
+                            self.assertTrue(param.data.min().item() >= inv_dt[0])
+                    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_attention_outputs(self):
+        r"""
+        Overriding the test_attention_outputs test as the Zamba2 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)
+
+        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._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.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.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.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+
+    def _get_input_ids_and_config(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        return config, input_ids, input_mask
+
+    @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 Zamba2 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: Zamba2 does not support right padding + use_cache with FA2.
+                dummy_attention_mask[:, -1] = 1
+
+                model = model_class.from_pretrained(
+                    tmpdirname,
+                    dtype=torch.float16,
+                    attn_implementation="flash_attention_2",
+                    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)
+
+    @unittest.skip(reason="Zamba2 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_gpu
+    def test_flex_attention_with_grads(self):
+        """
+        Overwriting as the base hidden size is big enough for compile.
+        Manipulation of dims causes issues due to other constraints not being satisfied anymore.
+        """
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config._attn_implementation = "flex_attention"
+
+            model = model_class(config).to(device=torch_device)
+            self.assertTrue(model.config._attn_implementation == "flex_attention")
+
+            # Elaborate workaround for encoder-decoder models as some do not specify their main input
+            dummy_inputs = {model.main_input_name: inputs_dict[model.main_input_name].to(torch_device)}
+            if config.is_encoder_decoder:
+                dummy_inputs["decoder_input_ids"] = inputs_dict["decoder_input_ids"].to(torch_device)
+                dummy_inputs["decoder_attention_mask"] = inputs_dict["decoder_attention_mask"].to(torch_device)
+
+            # If this does not raise an error, the test passes (see https://github.com/huggingface/transformers/pull/35605)
+            _ = model(**dummy_inputs)
+
+
+@require_torch
+class Zamba2ModelIntegrationTest(unittest.TestCase):
+    model = None
+    tokenizer = None
+
+    @classmethod
+    @slow
+    def setUpClass(cls):
+        model_id = "Zyphra/Zamba2-1.2B"
+        cls.model = Zamba2ForCausalLM.from_pretrained(model_id, dtype=torch.float32, revision="PR")
+        cls.tokenizer = AutoTokenizer.from_pretrained(model_id, revision="PR")
+
+    @parameterized.expand([(torch_device,), ("cpu",)])
+    @slow
+    def test_simple_generate(self, torch_device):
+        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,
+            "<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=input_ids).logits.to(dtype=torch.float32)
+
+        EXPECTED_LOGITS_NO_GRAD = torch.tensor(
+            [
+               -5.9587, 10.5152,  7.0382, -2.8728, -4.8143, -4.8142, -4.8142, -4.8144,
+               -4.8143, -4.8143, -4.8142, -4.8142,  6.0185, 18.0037, -4.8142, -4.8144,
+               -4.8143, -4.8142, -4.8143, -4.8143, -4.8143, -4.8143, -4.8142, -4.8143,
+               -4.8144, -4.8143, -4.8143, -4.8141, -4.8142, -4.8142, -4.8142, -4.8144,
+               -4.8143, -4.8143, -4.8143, -4.8142, -4.8144, -4.8144, -4.8142, -4.8142
+            ]
+            , dtype=torch.float32)  # fmt: skip
+        torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3)
+
+    @parameterized.expand([(torch_device,), ("cpu",)])
+    @slow
+    def test_simple_batched_generate_with_padding(self, torch_device):
+        self.model.to(torch_device)
+
+        inputs = self.tokenizer(
+            ["Hey how are you doing on this lovely evening?", "When did the Roman empire "],
+            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],
+            "<s> Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for",
+        )
+
+        self.assertEqual(
+            output_sentences[1],
+            "[PAD][PAD][PAD][PAD]<s> When did the Roman empire 1st fall?\nThe Roman Empire fell in",
+        )
+
+        with torch.no_grad():
+            logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits.to(
+                dtype=torch.float32
+            )
+
+        EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
+            [
+                -5.9611, 10.5208,  7.0411, -2.8743, -4.8167, -4.8167, -4.8167, -4.8168,
+                -4.8167, -4.8167, -4.8167, -4.8166,  6.0218, 18.0062, -4.8167, -4.8168,
+                -4.8167, -4.8167, -4.8167, -4.8168, -4.8168, -4.8168, -4.8167, -4.8167,
+                -4.8168, -4.8167, -4.8167, -4.8165, -4.8167, -4.8167, -4.8167, -4.8169,
+                -4.8168, -4.8168, -4.8168, -4.8166, -4.8169, -4.8168, -4.8167, -4.8167
+            ]
+            , dtype=torch.float32)  # fmt: skip
+
+        EXPECTED_LOGITS_NO_GRAD_1S = Expectations(
+            {
+                ("xpu", 3): torch.tensor([0.2027,  6.3481,  3.8392, -5.7279, -6.5090, -6.5088, -6.5087, -6.5088,
+                                          -6.5087, -6.5088, -6.5090, -6.5089,  7.8796, 13.5483, -6.5088, -6.5080,
+                                          -6.5090, -6.5086, -6.5090, -6.5090, -6.5089, -6.5090, -6.5088, -6.5090,
+                                          -6.5089, -6.5090, -6.5090, -6.5097, -6.5086, -6.5089, -6.5092, -6.5089,
+                                          -6.5088, -6.5090, -6.5090, -6.5088, -6.5090, -6.5091, -6.5087, -6.5089],
+                                         dtype=torch.float32),
+                ("cuda", None): torch.tensor([0.1966,  6.3449,  3.8350, -5.7291, -6.5106, -6.5104, -6.5103, -6.5104,
+                                              -6.5103, -6.5104, -6.5106, -6.5105,  7.8700, 13.5434, -6.5104, -6.5096,
+                                              -6.5106, -6.5102, -6.5106, -6.5106, -6.5105, -6.5106, -6.5104, -6.5106,
+                                              -6.5105, -6.5106, -6.5106, -6.5113, -6.5102, -6.5105, -6.5108, -6.5105,
+                                              -6.5104, -6.5106, -6.5106, -6.5104, -6.5106, -6.5107, -6.5103, -6.5105],
+                                             dtype=torch.float32),
+            }
+        )  # fmt: skip
+        EXPECTED_LOGITS_NO_GRAD_1 = EXPECTED_LOGITS_NO_GRAD_1S.get_expectation()
+
+        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=6e-3 if torch_device == "cpu" else 1e-3,
+        )