test_modeling_mistral.py

# coding=utf-8
# Copyright 2023 Mistral AI and 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 Mistral model."""

import gc
import tempfile
import unittest

import pytest
from packaging import version

from transformers import AutoTokenizer, MistralConfig, is_torch_available, set_seed
from transformers.testing_utils import (
    backend_empty_cache,
    is_flaky,
    require_bitsandbytes,
    require_flash_attn,
    require_read_token,
    require_torch,
    require_torch_gpu,
    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 (
        MistralForCausalLM,
        MistralForSequenceClassification,
        MistralForTokenClassification,
        MistralModel,
    )


class MistralModelTester:
    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,
        num_hidden_layers=2,
        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,
        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.num_hidden_layers = num_hidden_layers
        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.pad_token_id = pad_token_id
        self.scope = scope

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs
    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(self.batch_size, self.seq_length)).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 MistralConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            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=False,
            initializer_range=self.initializer_range,
            pad_token_id=self.pad_token_id,
        )

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Mistral
    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = MistralModel(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Mistral
    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 = MistralModel(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Mistral
    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 = MistralForCausalLM(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Mistral
    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 = MistralForCausalLM(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common
    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 MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (MistralModel, MistralForCausalLM, MistralForSequenceClassification, MistralForTokenClassification)
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (MistralForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": MistralModel,
            "text-classification": MistralForSequenceClassification,
            "token-classification": MistralForTokenClassification,
            "text-generation": MistralForCausalLM,
            "zero-shot": MistralForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    fx_compatible = True

    # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        return True

    # TODO: @Fxmarty
    @is_flaky(max_attempts=3, description="flaky on some models.")
    @require_torch_sdpa
    @slow
    def test_eager_matches_sdpa_generate(self):
        super().test_eager_matches_sdpa_generate()

    def setUp(self):
        self.model_tester = MistralModelTester(self)
        self.config_tester = ConfigTester(self, config_class=MistralConfig, 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_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)

    def test_Mistral_sequence_classification_model(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        print(config)
        config.num_labels = 3
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
        model = MistralForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))

    def test_Mistral_sequence_classification_model_for_single_label(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.num_labels = 3
        config.problem_type = "single_label_classification"
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
        model = MistralForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))

    def test_Mistral_sequence_classification_model_for_multi_label(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.num_labels = 3
        config.problem_type = "multi_label_classification"
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        sequence_labels = ids_tensor(
            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
        ).to(torch.float)
        model = MistralForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_token_classification_model with Llama->Mistral,llama->Mistral
    def test_Mistral_token_classification_model(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.num_labels = 3
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels)
        model = MistralForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=token_labels)
        self.assertEqual(
            result.logits.shape,
            (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels),
        )

    @unittest.skip("Mistral buffers include complex numbers, which breaks this test")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
    def test_past_key_values_format(self):
        pass

    @require_flash_attn
    @require_torch_gpu
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_generate_padding_right(self):
        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):
        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: Mistral apparently 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):
        self.skipTest("Mistral flash attention does not support right padding")


@require_torch_gpu
class MistralIntegrationTest(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]

    def tearDown(self):
        torch.cuda.empty_cache()
        gc.collect()

    @slow
    def test_model_7b_logits(self):
        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
        )
        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
        with torch.no_grad():
            out = model(input_ids).logits.cpu()
        # Expected mean on dim = -1
        EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]])
        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)

        # Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
        #
        # Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
        # considering differences in hardware processing and potential deviations in output.
        EXPECTED_SLICE = {
            7: torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]),
            8: torch.tensor([-5.8711, -5.8555, -0.1050, -4.7148, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -1.0781, 1.7568, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711]),
            9: torch.tensor([-5.8750, -5.8594, -0.1047, -4.7188, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -1.0781,  1.7578, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750, -5.8750]),
        }  # fmt: skip

        torch.testing.assert_close(
            out[0, 0, :30], EXPECTED_SLICE[self.cuda_compute_capability_major_version], atol=1e-4, rtol=1e-4
        )

        del model
        backend_empty_cache(torch_device)
        gc.collect()

    @slow
    @require_bitsandbytes
    def test_model_7b_generation(self):
        EXPECTED_TEXT_COMPLETION = {
            7: "My favourite condiment is 100% ketchup. I love it on everything. I'm not a big",
            8: "My favourite condiment is 100% ketchup. I’m not a fan of mustard, mayo,",
        }

        prompt = "My favourite condiment is "
        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True
        )
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)

        # greedy generation outputs
        generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text)

        del model
        backend_empty_cache(torch_device)
        gc.collect()

    @require_bitsandbytes
    @slow
    @require_flash_attn
    def test_model_7b_long_prompt(self):
        EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
        # An input with 4097 tokens that is above the size of the sliding window
        input_ids = [1] + [306, 338] * 2048
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1",
            device_map={"": torch_device},
            load_in_4bit=True,
            attn_implementation="flash_attention_2",
        )
        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())

        # Assisted generation
        assistant_model = model
        assistant_model.generation_config.num_assistant_tokens = 2
        assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())

        del assistant_model
        del model
        backend_empty_cache(torch_device)
        gc.collect()

    @slow
    @require_torch_sdpa
    def test_model_7b_long_prompt_sdpa(self):
        EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
        # An input with 4097 tokens that is above the size of the sliding window
        input_ids = [1] + [306, 338] * 2048
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1", device_map="auto", attn_implementation="sdpa", torch_dtype=torch.float16
        )
        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())

        # Assisted generation
        assistant_model = model
        assistant_model.generation_config.num_assistant_tokens = 2
        assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())

        del assistant_model

        backend_empty_cache(torch_device)
        gc.collect()

        EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
        prompt = "My favourite condiment is "
        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)

        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)

        # greedy generation outputs
        generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @slow
    def test_speculative_generation(self):
        # Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
        #
        # Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
        # considering differences in hardware processing and potential deviations in generated text.
        EXPECTED_TEXT_COMPLETION = {
            7: "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs",
            8: "My favourite condiment is 100% ketchup. I love it on everything. I’m not a big",
            9: "My favourite condiment is 100% ketchup. I love it on everything. I’m not a big",
        }
        prompt = "My favourite condiment is "
        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
        )
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)

        # greedy generation outputs
        set_seed(0)
        generated_ids = model.generate(
            input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=model
        )
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text)

        del model
        backend_empty_cache(torch_device)
        gc.collect()

    @slow
    @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("This test requires torch >= 2.3 to run.")

        if self.cuda_compute_capability_major_version == 7:
            self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU.")

        NUM_TOKENS_TO_GENERATE = 40
        EXPECTED_TEXT_COMPLETION = {
            8: [
                "My favourite condiment is 100% ketchup. I love it on everything. "
                "I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
            ],
            7: [
                "My favourite condiment is 100% ketchup. I love it on everything. "
                "I’m not a big fan of mustard, mayo, or relish. I’m not a fan of pickles"
            ],
        }

        prompts = ["My favourite condiment is "]
        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
        tokenizer.pad_token = tokenizer.eos_token
        model = MistralForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-v0.1", device_map="sequential", 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[self.cuda_compute_capability_major_version], 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[self.cuda_compute_capability_major_version], static_text)

        # Sliding Window Cache
        generated_ids = model.generate(
            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
        )
        static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)

        # Static Cache + compile
        forward_function = model.forward
        model.forward = torch.compile(forward_function, 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[self.cuda_compute_capability_major_version], static_compiled_text)

        # Sliding Window Cache + compile
        torch._dynamo.reset()
        model.forward = torch.compile(forward_function, mode="reduce-overhead", fullgraph=True)
        generated_ids = model.generate(
            **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="sliding_window"
        )
        static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)

        del model
        backend_empty_cache(torch_device)
        gc.collect()


@slow
@require_torch_gpu
class Mask4DTestHard(unittest.TestCase):
    model_name = "mistralai/Mistral-7B-v0.1"
    _model = None

    def tearDown(self):
        gc.collect()
        torch.cuda.empty_cache()

    @property
    def model(self):
        if self.__class__._model is None:
            self.__class__._model = MistralForCausalLM.from_pretrained(
                self.model_name, torch_dtype=self.model_dtype
            ).to(torch_device)
        return self.__class__._model

    def setUp(self):
        self.model_dtype = torch.float16
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)

    def get_test_data(self):
        template = "my favorite {}"
        items = ("pet is a", "artist plays a", "name is L")  # same number of tokens in each item

        batch_separate = [template.format(x) for x in items]  # 3 separate lines
        batch_shared_prefix = template.format(" ".join(items))  # 1 line with options concatenated

        input_ids = self.tokenizer(batch_separate, return_tensors="pt").input_ids.to(torch_device)
        input_ids_shared_prefix = self.tokenizer(batch_shared_prefix, return_tensors="pt").input_ids.to(torch_device)

        mask_shared_prefix = torch.tensor(
            [
                [
                    [
                        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
                        [1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0],
                        [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
                        [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],
                        [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
                        [1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1],
                    ]
                ]
            ],
            device=torch_device,
        )

        position_ids = torch.arange(input_ids.shape[1]).tile(input_ids.shape[0], 1).to(torch_device)

        # building custom positions ids based on custom mask
        position_ids_shared_prefix = (mask_shared_prefix.sum(dim=-1) - 1).reshape(1, -1)
        # effectively: position_ids_shared_prefix = torch.tensor([[0, 1, 2, 3, 4, 5, 3, 4, 5, 3, 4, 5]]).to(device)

        # inverting the mask
        min_dtype = torch.finfo(self.model_dtype).min
        mask_shared_prefix = (mask_shared_prefix.eq(0.0)).to(dtype=self.model_dtype) * min_dtype

        return input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix

    def test_stacked_causal_mask(self):
        (
            input_ids,
            position_ids,
            input_ids_shared_prefix,
            mask_shared_prefix,
            position_ids_shared_prefix,
        ) = self.get_test_data()

        # regular batch
        logits = self.model.forward(input_ids, position_ids=position_ids).logits
        logits_last = logits[:, -1, :]  # last tokens in each batch line
        decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]

        # single forward run with 4D custom mask
        logits_shared_prefix = self.model.forward(
            input_ids_shared_prefix, attention_mask=mask_shared_prefix, position_ids=position_ids_shared_prefix
        ).logits
        logits_shared_prefix_last = logits_shared_prefix[
            0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1], :
        ]  # last three tokens
        decoded_shared_prefix = [self.tokenizer.decode(t) for t in logits_shared_prefix_last.argmax(dim=-1)]

        self.assertEqual(decoded, decoded_shared_prefix)

    def test_partial_stacked_causal_mask(self):
        # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks

        (
            input_ids,
            position_ids,
            input_ids_shared_prefix,
            mask_shared_prefix,
            position_ids_shared_prefix,
        ) = self.get_test_data()

        # regular batch
        logits = self.model.forward(input_ids, position_ids=position_ids).logits
        logits_last = logits[:, -1, :]  # last tokens in each batch line
        decoded = [self.tokenizer.decode(t) for t in logits_last.argmax(dim=-1)]

        # 2 forward runs with custom 4D masks
        part_a = 3  # split point

        input_1a = input_ids_shared_prefix[:, :part_a]
        position_ids_1a = position_ids_shared_prefix[:, :part_a]
        mask_1a = mask_shared_prefix[:, :, :part_a, :part_a]

        outs_1a = self.model.forward(input_1a, attention_mask=mask_1a, position_ids=position_ids_1a)
        past_key_values_a = outs_1a["past_key_values"]

        # Case 1: we pass a 4D attention mask regarding the current sequence length (i.e. [..., seq_len, full_len])
        input_1b = input_ids_shared_prefix[:, part_a:]
        position_ids_1b = position_ids_shared_prefix[:, part_a:]
        mask_1b = mask_shared_prefix[:, :, part_a:, :]
        outs_1b = self.model.forward(
            input_1b, attention_mask=mask_1b, position_ids=position_ids_1b, past_key_values=past_key_values_a
        )
        decoded_1b = [
            self.tokenizer.decode(t)
            for t in outs_1b.logits.argmax(-1)[
                0, torch.where(position_ids_shared_prefix == position_ids_shared_prefix.max())[1] - part_a
            ]
        ]
        self.assertEqual(decoded, decoded_1b)