transformers/tests/models/deepseek_v3/test_modeling_deepseek_v3.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 DeepseekV3 model."""

import unittest

from packaging import version
from parameterized import parameterized

from transformers import AutoTokenizer, DeepseekV3Config, is_torch_available, set_seed
from transformers.testing_utils import (
    require_read_token,
    require_torch,
    require_torch_accelerator,
    require_torch_sdpa,
    slow,
    torch_device,
)

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


if is_torch_available():
    import torch

    from transformers import (
        DeepseekV3ForCausalLM,
        DeepseekV3Model,
    )
    from transformers.models.deepseek_v3.modeling_deepseek_v3 import (
        DeepseekV3RotaryEmbedding,
    )


class DeepseekV3ModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=False,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        intermediate_size=37,
        moe_intermediate_size=12,
        num_hidden_layers=5,
        num_attention_heads=4,
        num_key_value_heads=4,
        n_shared_experts=1,
        n_routed_experts=8,
        routed_scaling_factor=2.5,
        kv_lora_rank=16,
        q_lora_rank=32,
        qk_rope_head_dim=16,
        v_head_dim=32,
        qk_nope_head_dim=32,
        n_group=2,
        topk_group=1,
        num_experts_per_tok=8,
        first_k_dense_replace=2,
        norm_topk_prob=True,
        aux_loss_alpha=0.001,
        hidden_act="silu",
        max_position_embeddings=512,
        initializer_range=0.02,
        attention_probs_dropout_prob=0.1,
        type_vocab_size=16,
        type_sequence_label_size=2,
        num_labels=3,
        num_choices=4,
        pad_token_id=0,
        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_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.moe_intermediate_size = moe_intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.n_shared_experts = n_shared_experts
        self.n_routed_experts = n_routed_experts
        self.routed_scaling_factor = routed_scaling_factor
        self.kv_lora_rank = kv_lora_rank
        self.q_lora_rank = q_lora_rank
        self.qk_rope_head_dim = qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.qk_nope_head_dim = qk_nope_head_dim
        self.n_group = n_group
        self.topk_group = topk_group
        self.num_experts_per_tok = num_experts_per_tok
        self.first_k_dense_replace = first_k_dense_replace
        self.norm_topk_prob = norm_topk_prob
        self.aux_loss_alpha = aux_loss_alpha
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.pad_token_id = pad_token_id
        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 = torch.tril(torch.ones_like(input_ids).to(torch_device))

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return DeepseekV3Config(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            intermediate_size=self.intermediate_size,
            moe_intermediate_size=self.moe_intermediate_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            num_key_value_heads=self.num_key_value_heads,
            n_shared_experts=self.n_shared_experts,
            n_routed_experts=self.n_routed_experts,
            routed_scaling_factor=self.routed_scaling_factor,
            kv_lora_rank=self.kv_lora_rank,
            q_lora_rank=self.q_lora_rank,
            qk_rope_head_dim=self.qk_rope_head_dim,
            v_head_dim=self.v_head_dim,
            qk_nope_head_dim=self.qk_nope_head_dim,
            n_group=self.n_group,
            topk_group=self.topk_group,
            num_experts_per_tok=self.num_experts_per_tok,
            first_k_dense_replace=self.first_k_dense_replace,
            norm_topk_prob=self.norm_topk_prob,
            aux_loss_alpha=self.aux_loss_alpha,
            hidden_act=self.hidden_act,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            use_cache=True,
            pad_token_id=self.pad_token_id,
            attention_dropout=self.attention_probs_dropout_prob,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = DeepseekV3Model(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_model_as_decoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = DeepseekV3Model(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(input_ids, attention_mask=input_mask)
        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,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = DeepseekV3ForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        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,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = DeepseekV3ForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            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,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["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 prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            token_type_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 DeepseekV3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            DeepseekV3Model,
            DeepseekV3ForCausalLM,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (DeepseekV3ForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": DeepseekV3Model,
            "text-generation": DeepseekV3ForCausalLM,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    fx_compatible = False

    # Need to use `0.8` instead of `0.9` for `test_cpu_offload`
    # This is because we are hitting edge cases with the causal_mask buffer
    model_split_percents = [0.5, 0.7, 0.8]

    # used in `test_torch_compile_for_training`
    _torch_compile_train_cls = DeepseekV3ForCausalLM if is_torch_available() else None

    def setUp(self):
        self.model_tester = DeepseekV3ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=DeepseekV3Config, hidden_size=37)

    @unittest.skip("Failing because of unique cache (HybridCache)")
    def test_model_outputs_equivalence(self, **kwargs):
        pass

    @parameterized.expand([("random",), ("same",)])
    @unittest.skip("DeepseekV3 has HybridCache which is not compatible with assisted decoding")
    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
        pass

    @unittest.skip("DeepseekV3 has HybridCache which is not compatible with assisted decoding")
    def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
        pass

    @unittest.skip("DeepseekV3 has HybridCache which is not compatible with assisted decoding")
    def test_assisted_decoding_sample(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache which is not compatible with dola decoding")
    def test_dola_decoding_sample(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache and doesn't support continue from past kv")
    def test_generate_continue_from_past_key_values(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache and doesn't support low_memory generation")
    def test_beam_search_low_memory(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate_dict_outputs_use_cache(self):
        pass

    @unittest.skip("DeepseekV3 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate_low_memory(self):
        pass

    @unittest.skip(
        "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
    )
    def test_generate_with_static_cache(self):
        pass

    @unittest.skip(
        "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
    )
    def test_generate_from_inputs_embeds_with_static_cache(self):
        pass

    @unittest.skip(
        "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
    )
    def test_generate_continue_from_inputs_embeds(self):
        pass

    @unittest.skip("DeepseekV3's eager attn/sdpa attn outputs are expected to be different")
    def test_sdpa_equivalence(self):
        pass

    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
    def test_beam_search_generate_dict_outputs_use_cache(self):
        pass

    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
    def test_generate_compilation_all_outputs(self):
        pass

    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
    def test_generate_compile_model_forward(self):
        pass

    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
    def test_greedy_generate_dict_outputs_use_cache(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_model_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config_and_inputs[0].position_embedding_type = type
            self.model_tester.create_and_check_model(*config_and_inputs)

    @parameterized.expand([("yarn",)])
    def test_model_rope_scaling_from_config(self, scaling_type):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        short_input = ids_tensor([1, 10], config.vocab_size)
        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        original_model = DeepseekV3Model(config)
        original_model.to(torch_device)
        original_model.eval()
        original_short_output = original_model(short_input).last_hidden_state
        original_long_output = original_model(long_input).last_hidden_state

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
        scaled_model = DeepseekV3Model(config)
        scaled_model.to(torch_device)
        scaled_model.eval()
        scaled_short_output = scaled_model(short_input).last_hidden_state
        scaled_long_output = scaled_model(long_input).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
        else:
            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

    def test_model_rope_scaling(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        scaling_factor = 10
        short_input_length = 10
        long_input_length = int(config.max_position_embeddings * 1.5)

        # Inputs
        x = torch.randn(1, dtype=torch.float32, device=torch_device)  # used exlusively to get the dtype and the device
        position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
        position_ids_short = position_ids_short.unsqueeze(0)
        position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)
        position_ids_long = position_ids_long.unsqueeze(0)

        # Sanity check original RoPE
        original_rope = DeepseekV3RotaryEmbedding(config=config).to(torch_device)
        original_cos_short, original_sin_short = original_rope(x, position_ids_short)
        original_cos_long, original_sin_long = original_rope(x, position_ids_long)
        torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :])
        torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :])

        # Sanity check linear RoPE scaling
        # New position "x" should match original position with index "x/scaling_factor"
        config.rope_scaling = {"type": "linear", "factor": scaling_factor}
        linear_scaling_rope = DeepseekV3RotaryEmbedding(config=config).to(torch_device)
        linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short)
        linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long)
        torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :])
        torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :])
        for new_position in range(0, long_input_length, scaling_factor):
            original_position = int(new_position // scaling_factor)
            torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :])
            torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :])

        # Sanity check Dynamic NTK RoPE scaling
        # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
        # with scaling_factor (or that `inv_freq` decreases)
        config.rope_scaling = {"type": "dynamic", "factor": scaling_factor}
        ntk_scaling_rope = DeepseekV3RotaryEmbedding(config=config).to(torch_device)
        ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short)
        ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long)
        torch.testing.assert_close(ntk_cos_short, original_cos_short)
        torch.testing.assert_close(ntk_sin_short, original_sin_short)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(ntk_cos_long, original_cos_long)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(ntk_sin_long, original_sin_long)
        self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())

        # Sanity check Yarn RoPE scaling
        # Scaling should be over the entire input
        config.rope_scaling = {"type": "yarn", "factor": scaling_factor}
        yarn_scaling_rope = DeepseekV3RotaryEmbedding(config=config).to(torch_device)
        yarn_cos_short, yarn_sin_short = yarn_scaling_rope(x, position_ids_short)
        yarn_cos_long, yarn_sin_long = yarn_scaling_rope(x, position_ids_long)
        torch.testing.assert_close(yarn_cos_short, yarn_cos_long[:, :short_input_length, :])
        torch.testing.assert_close(yarn_sin_short, yarn_sin_long[:, :short_input_length, :])
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(yarn_cos_short, original_cos_short)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(yarn_sin_short, original_sin_short)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(yarn_cos_long, original_cos_long)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(yarn_sin_long, original_sin_long)

    @unittest.skip(reason="Deepseek-V3 uses MLA on all models so the KV cache is a non standard format")
    def test_past_key_values_format(self):
        pass

    @require_torch_sdpa
    @slow
    def test_eager_matches_sdpa_generate(self):
        """
        Overwritting the common test as the test is flaky on tiny models
        """
        max_new_tokens = 30

        tokenizer = AutoTokenizer.from_pretrained("bzantium/tiny-deepseek-v3")

        model_sdpa = DeepseekV3ForCausalLM.from_pretrained(
            "bzantium/tiny-deepseek-v3",
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True,
        ).to(torch_device)

        self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")

        model_eager = DeepseekV3ForCausalLM.from_pretrained(
            "bzantium/tiny-deepseek-v3",
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True,
            attn_implementation="eager",
        ).to(torch_device)

        self.assertTrue(model_eager.config._attn_implementation == "eager")

        texts = [
            "hi here's a longer context, getting longer and",
            "Hello this is a very long sentence my friend, very long for real",
            "Today I am in Paris and",
        ]

        for padding_side in ["left", "right"]:
            tokenizer.padding_side = padding_side
            tokenizer.pad_token = tokenizer.eos_token

            inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device)

            res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
            res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)

            with self.subTest(f"{padding_side}"):
                torch.testing.assert_close(
                    res_eager,
                    res_sdpa,
                    msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
                )


@require_torch_accelerator
class DeepseekV3IntegrationTest(unittest.TestCase):
    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
    # Depending on the hardware we get different logits / generations
    cuda_compute_capability_major_version = None

    @classmethod
    def setUpClass(cls):
        if is_torch_available() and torch.cuda.is_available():
            # 8 is for A100 / A10 and 7 for T4
            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]

    @slow
    @require_torch_accelerator
    @require_read_token
    def test_compile_static_cache(self):
        # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2
        # work as intended. See https://github.com/pytorch/pytorch/issues/121943
        if version.parse(torch.__version__) < version.parse("2.3.0"):
            self.skipTest(reason="This test requires torch >= 2.3 to run.")

        NUM_TOKENS_TO_GENERATE = 40
        # Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test
        # was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs.
        EXPECTED_TEXT_COMPLETION = [
            "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
            "reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
            "theory of relativ",
            "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
            "my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
        ]

        prompts = [
            "Simply put, the theory of relativity states that ",
            "My favorite all time favorite condiment is ketchup.",
        ]
        tokenizer = AutoTokenizer.from_pretrained("bzantium/tiny-deepseek-v3", pad_token="</s>", padding_side="right")
        model = DeepseekV3ForCausalLM.from_pretrained(
            "bzantium/tiny-deepseek-v3", device_map=torch_device, torch_dtype=torch.float16
        )
        inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)

        # Dynamic Cache
        generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
        dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text)

        # Static Cache
        generated_ids = model.generate(
            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
        )
        static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)

        # Static Cache + compile
        model._cache = None  # clear cache object, initialized when we pass `cache_implementation="static"`
        model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
        generated_ids = model.generate(
            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
        )
        static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)