transformers/tests/models/llama/test_modeling_llama.py

# coding=utf-8
# Copyright 2022 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 LLaMA model."""

import unittest

from packaging import version
from parameterized import parameterized

from transformers import AutoTokenizer, LlamaConfig, StaticCache, is_torch_available, set_seed
from transformers.generation.configuration_utils import GenerationConfig
from transformers.testing_utils import (
    cleanup,
    require_read_token,
    require_torch,
    require_torch_accelerator,
    require_torch_gpu,
    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 (
        LlamaForCausalLM,
        LlamaForQuestionAnswering,
        LlamaForSequenceClassification,
        LlamaForTokenClassification,
        LlamaModel,
        LlamaTokenizer,
    )
    from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding


class LlamaModelTester:
    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,
        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.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

    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 LlamaConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_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,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LlamaModel(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 = LlamaModel(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 = LlamaForCausalLM(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 = LlamaForCausalLM(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 LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            LlamaModel,
            LlamaForCausalLM,
            LlamaForSequenceClassification,
            LlamaForQuestionAnswering,
            LlamaForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (LlamaForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": LlamaModel,
            "text-classification": LlamaForSequenceClassification,
            "text-generation": LlamaForCausalLM,
            "zero-shot": LlamaForSequenceClassification,
            "question-answering": LlamaForQuestionAnswering,
            "token-classification": LlamaForTokenClassification,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    fx_compatible = False  # Broken by attention refactor cc @Cyrilvallez

    # 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 = LlamaForCausalLM if is_torch_available() else None

    def setUp(self):
        self.model_tester = LlamaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LlamaConfig, 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_llama_sequence_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)
        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
        model = LlamaForSequenceClassification(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_llama_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 = LlamaForSequenceClassification(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_llama_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 = LlamaForSequenceClassification(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_llama_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 = LlamaForTokenClassification(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(reason="Llama buffers include complex numbers, which breaks this test")
    def test_save_load_fast_init_from_base(self):
        pass

    @parameterized.expand([("linear",), ("dynamic",), ("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 = LlamaModel(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 = LlamaModel(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":
            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, 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 = LlamaRotaryEmbedding(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 = LlamaRotaryEmbedding(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 = LlamaRotaryEmbedding(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 = LlamaRotaryEmbedding(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)

    def test_model_loading_old_rope_configs(self):
        def _reinitialize_config(base_config, new_kwargs):
            # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation
            # steps.
            base_config_dict = base_config.to_dict()
            new_config = LlamaConfig.from_dict(config_dict={**base_config_dict, **new_kwargs})
            return new_config

        # from untouched config -> ✅
        base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        original_model = LlamaForCausalLM(base_config).to(torch_device)
        original_model(**model_inputs)

        # from a config with the expected rope configuration -> ✅
        config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}})
        original_model = LlamaForCausalLM(config).to(torch_device)
        original_model(**model_inputs)

        # from a config with the old rope configuration ('type' instead of 'rope_type')  -> ✅ we gracefully handle BC
        config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}})
        original_model = LlamaForCausalLM(config).to(torch_device)
        original_model(**model_inputs)

        # from a config with both 'type' and 'rope_type'  -> ✅ they can coexist (and both are present in the config)
        config = _reinitialize_config(
            base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}}
        )
        self.assertTrue(config.rope_scaling["type"] == "linear")
        self.assertTrue(config.rope_scaling["rope_type"] == "linear")
        original_model = LlamaForCausalLM(config).to(torch_device)
        original_model(**model_inputs)

        # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning
        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}})
            original_model = LlamaForCausalLM(config).to(torch_device)
            original_model(**model_inputs)
            self.assertEqual(len(logs.output), 1)
            self.assertIn("factor field", logs.output[0])

        # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning
        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
            config = _reinitialize_config(
                base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}}
            )
            original_model = LlamaForCausalLM(config).to(torch_device)
            original_model(**model_inputs)
            self.assertEqual(len(logs.output), 1)
            self.assertIn("Unrecognized keys", logs.output[0])

        # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception
        with self.assertRaises(KeyError):
            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}})  # missing "factor"


@require_torch_gpu
class LlamaIntegrationTest(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_read_token
    def test_llama_3_1_hard(self):
        """
        An integration test for llama 3.1. It tests against a long output to ensure the subtle numerical differences
        from llama 3.1.'s RoPE can be detected
        """
        # diff on `EXPECTED_TEXT`:
        # 2024-08-26: updating from torch 2.3.1 to 2.4.0 slightly changes the results.
        EXPECTED_TEXT = (
            "Tell me about the french revolution. The french revolution was a period of radical political and social "
            "upheaval in France that lasted from 1789 until 1799. It was a time of great change and upheaval, marked "
            "by the overthrow of the monarchy, the rise of the middle class, and the eventual establishment of the "
            "First French Republic.\nThe revolution began in 1789 with the Estates-General, a representative "
            "assembly that had not met since 1614. The Third Estate, which represented the common people, "
            "demanded greater representation and eventually broke away to form the National Assembly. This marked "
            "the beginning of the end of the absolute monarchy and the rise of the middle class.\n"
        )

        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
        model = LlamaForCausalLM.from_pretrained(
            "meta-llama/Meta-Llama-3.1-8B-Instruct", device_map="auto", torch_dtype=torch.bfloat16
        )
        input_text = ["Tell me about the french revolution."]
        model_inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

        generated_ids = model.generate(**model_inputs, max_new_tokens=128, do_sample=False)
        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(generated_text, EXPECTED_TEXT)

    @slow
    @require_read_token
    def test_model_7b_logits_bf16(self):
        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]

        model = LlamaForCausalLM.from_pretrained(
            "meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager"
        )

        with torch.no_grad():
            out = model(torch.tensor([input_ids]).to(torch_device))
        # Expected mean on dim = -1

        # fmt: off
        EXPECTED_MEAN = {
            7: torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
            8: torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536,  0.8127, -2.9811,  1.2918, -3.3848]])
        }

        self.assertTrue(
            torch.allclose(
                EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
                out.logits.float().mean(-1),
                atol=1e-2,
                rtol=1e-2
            )
        )

        # slicing logits[0, 0, 0:15]
        EXPECTED_SLICE = {
            7: torch.tensor([[-12.5000, -7.0625, -0.6289, -7.8750, -6.9688, -7.8125, -6.4688, -7.4375, -7.6875, -6.9375, -6.0312, -7.0000, -1.8594, 1.8438, -8.5000]]),
            8: torch.tensor([[-12.5625,  -7.1250,  -0.6289,  -7.8750,  -6.9688,  -7.8125,  -6.5000, -7.4375,  -7.6562,  -6.9688,  -6.0312,  -7.0312,  -1.8203,   1.8750, -8.5000]])
        }
        # fmt: on

        self.assertTrue(
            torch.allclose(
                EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
                out.logits[0, 0, :15].float(),
                atol=1e-2,
                rtol=1e-2,
            )
        )

    @slow
    @require_read_token
    def test_model_7b_logits(self):
        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]

        model = LlamaForCausalLM.from_pretrained(
            "meta-llama/Llama-2-7b-hf", device_map="auto", torch_dtype=torch.float16
        )

        with torch.no_grad():
            out = model(torch.tensor([input_ids]).to(torch_device))

        # fmt: off
        # Expected mean on dim = -1
        EXPECTED_MEAN = {
            7: torch.tensor([[-6.6420, -4.1227, -4.9809, -3.2041, 0.8261, -3.0052, 1.2957, -3.3648]]),
            8: torch.tensor([[-6.6544, -4.1259, -4.9840, -3.2456,  0.8261, -3.0124,  1.2971, -3.3641]])
        }

        self.assertTrue(
            torch.allclose(
                EXPECTED_MEAN[self.cuda_compute_capability_major_version].to(torch_device),
                out.logits.float().mean(-1),
                atol=1e-2,
                rtol=1e-2
            )
        )

        # slicing logits[0, 0, 0:15]
        EXPECTED_SLICE = {
            7: torch.tensor([-12.8125, -7.3359, -0.4846, -8.0234, -7.2383, -7.9922, -6.4805, -7.7344, -7.8125, -7.0078, -6.1797, -7.1094, -1.8633, 1.9736, -8.6016]),
            8: torch.tensor([-12.8281,  -7.4609,  -0.4668,  -8.0703,  -7.2539,  -8.0078,  -6.4961, -7.7734,  -7.8516,  -7.0352,  -6.2188,  -7.1367,  -1.8564,   1.9922, -8.6328])
        }
        # fmt: on

        self.assertTrue(
            torch.allclose(
                EXPECTED_SLICE[self.cuda_compute_capability_major_version].to(torch_device),
                out.logits[0, 0, :15].float(),
                atol=1e-2,
                rtol=1e-2,
            )
        )

    @slow
    def test_model_7b_dola_generation(self):
        # ground truth text generated with dola_layers="low", repetition_penalty=1.2
        EXPECTED_TEXT_COMPLETION = (
            "Simply put, the theory of relativity states that 1) time and space are relative, and 2) the laws of "
            "physics are the same for all observers in uniform motion relative to one another.\n\nThe theory of "
            "relativity was developed by Albert Einstein in the early 20th century, and it revolutionized our "
            "understanding of space and time."
        )
        prompt = "Simply put, the theory of relativity states that "
        tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
        model = LlamaForCausalLM.from_pretrained(
            "meta-llama/Llama-2-7b-chat-hf", device_map="sequential", torch_dtype=torch.float16
        )
        model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

        # greedy generation outputs
        generated_ids = model.generate(
            **model_inputs, max_new_tokens=64, top_p=None, temperature=1, do_sample=False, dola_layers="low"
        )
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @slow
    @require_torch_gpu
    @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 = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="</s>", padding_side="right")
        model = LlamaForCausalLM.from_pretrained(
            "meta-llama/Llama-2-7b-hf", 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)

    @slow
    @require_read_token
    def test_export_static_cache(self):
        if version.parse(torch.__version__) < version.parse("2.4.0"):
            self.skipTest(reason="This test requires torch >= 2.4 to run.")

        from transformers.integrations.executorch import (
            TorchExportableModuleWithStaticCache,
            convert_and_export_with_cache,
        )

        llama_models = {
            "meta-llama/Llama-3.2-1B": [
                "Simply put, the theory of relativity states that 1) the speed of light is the same for all "
                "observers, regardless of their location, and 2) the laws of physics are the same for all observers"
            ],
            "meta-llama/Llama-3.2-3B": [
                "Simply put, the theory of relativity states that 1. the speed of light is constant, and 2. "
                "the speed of light is the fastest speed possible"
            ],
            "meta-llama/Llama-2-7b-hf": [
                "Simply put, the theory of relativity states that 1) the speed of light is a constant, and 2) "
                "the laws of physics are the same for all",
            ],
        }

        for llama_model_ckp, EXPECTED_TEXT_COMPLETION in llama_models.items():
            # Load tokenizer
            tokenizer = AutoTokenizer.from_pretrained(llama_model_ckp, pad_token="</s>", padding_side="right")
            max_generation_length = tokenizer(EXPECTED_TEXT_COMPLETION, return_tensors="pt", padding=True)[
                "input_ids"
            ].shape[-1]

            # Load model
            device = "cpu"
            dtype = torch.bfloat16
            cache_implementation = "static"
            attn_implementation = "sdpa"
            batch_size = 1
            model = LlamaForCausalLM.from_pretrained(
                llama_model_ckp,
                device_map=device,
                torch_dtype=dtype,
                attn_implementation=attn_implementation,
                generation_config=GenerationConfig(
                    use_cache=True,
                    cache_implementation=cache_implementation,
                    max_length=max_generation_length,
                    cache_config={
                        "batch_size": batch_size,
                        "max_cache_len": max_generation_length,
                    },
                ),
            )

            prompts = ["Simply put, the theory of relativity states that "]
            prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
            prompt_token_ids = prompt_tokens["input_ids"]
            max_new_tokens = max_generation_length - prompt_token_ids.shape[-1]

            # Static Cache + export
            exported_program = convert_and_export_with_cache(model)
            ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
                exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
            )
            ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True)
            self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)


@slow
@require_torch_accelerator
class Mask4DTestHard(unittest.TestCase):
    def tearDown(self):
        cleanup(torch_device, gc_collect=True)

    def setUp(self):
        model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
        self.model_dtype = torch.float32
        self.tokenizer = LlamaTokenizer.from_pretrained(model_name)
        self.model = LlamaForCausalLM.from_pretrained(model_name, torch_dtype=self.model_dtype).to(torch_device)

    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)

    def test_stacked_causal_mask_static_cache(self):
        """same as above but with StaticCache"""
        (
            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)]

        # upgrade the model with StaticCache
        max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
        past_key_values = StaticCache(
            config=self.model.config,
            batch_size=1,
            max_cache_len=max_cache_len,
            device=torch_device,
            dtype=self.model.dtype,
        )

        padded_attention_mask = torch.nn.functional.pad(
            input=mask_shared_prefix,
            pad=(0, max_cache_len - mask_shared_prefix.shape[-1]),
            mode="constant",
            value=torch.finfo(self.model_dtype).min,
        )

        # single forward run with 4D custom mask
        logits_shared_prefix = self.model.forward(
            input_ids_shared_prefix,
            attention_mask=padded_attention_mask,
            position_ids=position_ids_shared_prefix,
            cache_position=torch.arange(input_ids_shared_prefix.shape[-1], device=torch_device),
            past_key_values=past_key_values,
        ).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_static_cache(self):
        # Same as the test above, but the input is passed in two groups. It tests that we can pass partial 4D attention masks
        # we pass a 4D attention mask shaped [..., seq_len, full_static_cache_len])
        (
            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)]

        # upgrade the model with StaticCache
        max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
        past_key_values = StaticCache(
            config=self.model.config,
            batch_size=1,
            max_cache_len=max_cache_len,
            device=torch_device,
            dtype=self.model.dtype,
        )

        # forward run for the first part of input
        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]

        padded_mask_1a = torch.nn.functional.pad(
            input=mask_1a,
            pad=(0, max_cache_len - mask_1a.shape[-1]),
            mode="constant",
            value=torch.finfo(self.model_dtype).min,
        )

        _ = self.model.forward(
            input_1a,
            attention_mask=padded_mask_1a,
            position_ids=position_ids_1a,
            cache_position=torch.arange(part_a, device=torch_device),
            past_key_values=past_key_values,
        )

        # forward run for the second part of input
        input_1b = input_ids_shared_prefix[:, part_a:]
        position_ids_1b = position_ids_shared_prefix[:, part_a:]
        mask_1b = mask_shared_prefix[:, :, part_a:, :]

        padded_mask_1b = torch.nn.functional.pad(
            input=mask_1b, pad=(0, max_cache_len - mask_1b.shape[-1]), mode="constant", value=0
        )

        outs_1b = self.model.forward(
            input_1b,
            attention_mask=padded_mask_1b,
            position_ids=position_ids_1b,
            cache_position=torch.arange(
                part_a,
                input_ids_shared_prefix.shape[-1],
                device=torch_device,
            ),
            past_key_values=past_key_values,
        )
        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)