Delete modeling_elm.py

modeling_elm.py

@@ -1,1288 +0,0 @@
-#
-# For licensing see accompanying LICENSE file.
-# Copyright (C) 2024 Apple Inc. All Rights Reserved.
-#
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import Tensor, nn
-from torch.nn import CrossEntropyLoss
-from torch.nn import functional as F
-from transformers import PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-# this import has to be relative, otherwise, when setting trust_remote_code=True
-# huggingface transformers won't be able to load the module correctly
-from numbers import Number
-from typing import List, Optional, Union
-
-import numpy as np
-from transformers import PretrainedConfig, AutoTokenizer
-
-
-def make_divisible(
-    v: Union[float, int],
-    divisor: Optional[int] = 8,
-    min_value: Optional[Union[float, int]] = None,
-) -> Union[float, int]:
-    """
-    This function is taken from the original tf repo.
-    It ensures that all layers have a channel number that is divisible by the divisor
-    It can be seen at:
-    https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
-    Args:
-        v: input value
-        divisor: default to 8
-        min_value: minimum divisor value
-    Returns:
-        new_v: new divisible value
-    """
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-def compute_heads(model_dim: int, head_dim: int) -> int:
-    """Compute the number of heads.
-    Args:
-        model_dim: Model dimension.
-        head_dim: Head dimension.
-    Returns:
-        An integer denoting number of heads in multi-head attention is returned.
-    Raises:
-        ValueError: if model dimension is not divisible by head dimension.
-    """
-    if model_dim % head_dim == 0:
-        return model_dim // head_dim
-    else:
-        raise ValueError(
-            f"Model dimension should be divisible by head dimension. Got: {model_dim} and {head_dim}."
-        )
-
-
-OpenELM_CONFIGS = {
-    "OpenELM-270M": dict(
-        num_transformer_layers=16,
-        model_dim=1280,
-        head_dim=64,
-        num_gqa_groups=4,
-        normalize_qk_projections=True,
-        share_input_output_layers=True,
-        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
-        ffn_multipliers=(0.5, 4.0),
-        qkv_multipliers=(0.5, 1.0),
-    ),
-    "OpenELM-450M": dict(
-        num_transformer_layers=20,
-        model_dim=1536,
-        head_dim=64,
-        num_gqa_groups=4,
-        normalize_qk_projections=True,
-        share_input_output_layers=True,
-        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
-        ffn_multipliers=(0.5, 4.0),
-        qkv_multipliers=(0.5, 1.0),
-    ),
-    "OpenELM-1_1B": dict(
-        num_transformer_layers=28,
-        model_dim=2048,
-        head_dim=64,
-        num_gqa_groups=4,
-        normalize_qk_projections=True,
-        share_input_output_layers=True,
-        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
-        ffn_multipliers=(0.5, 4.0),
-        qkv_multipliers=(0.5, 1.0),
-    ),
-    "OpenELM-3B": dict(
-        num_transformer_layers=36,
-        model_dim=3072,
-        head_dim=128,
-        num_gqa_groups=4,
-        normalize_qk_projections=True,
-        share_input_output_layers=True,
-        # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
-        ffn_multipliers=(0.5, 4.0),
-        qkv_multipliers=(0.5, 1.0),
-    ),
-}
-
-
-class OpenELMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`OpenELMModel`]. It is used to instantiate an OpenELM model according to the specified arguments, defining the model architecture.
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the OpenELM model.
-        max_context_length (`int`, *optional*, defaults to 2048):
-            Maximum number of input tokens.
-        num_transformer_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer decoder.
-        model_dim (`int`, *optional*, defaults to 2048):
-            Dimension of the hidden representations.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        qkv_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 1.0):
-            If the qkv_multipliers is a Number, then all attention layers have the same latent dimensions,
-            resulting in uniform allocation of parameters.
-            If the qkv_multipliers is a List of Number, then each attention layer have different latent dimensions
-            assuming qkv_multipliers[0] != qkv_multipliers[1]. This results in variable allocation of parameters in attention layer.
-            This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
-        num_query_heads (`Union[int, None]`, *optional*, defaults to None):
-            The number of query heads, computed from `compute_heads(model_dim=model_dim, head_dim=head_dim)`.
-        num_gqa_groups (`int`, *optional*, defaults to 1):
-            This variable allows to switch between multi-head attention, group query attention, and multi-query attention.
-            When num_gqa_groups == 1, then it is multi-head attention.
-            When 1 < num_gqa_groups < num_heads and num_heads is divisible by num_gqa_groups, then it is group query attention
-            When num_gqa_groups == num_heads, then it is multi-query attention
-        ffn_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 4.0):
-            Feed-forward network (FFN) multipliers.
-            If the ffn_multipliers is a Number, then all FFN layers have the same latent dimensions,
-            resulting in uniform allocation of parameters.
-            If the ffn_multipliers is a List of Number, then each FFN layer have different latent dimensions
-            assuming ffn_multipliers[0] != ffn_multipliers[1]. This results in variable allocation of parameters in FFN layer.
-            This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
-        ffn_with_glu (`bool`, *optional*, defaults to True):
-            Whether to use FFN with Gated Linear Unit (GLU)
-        ffn_dim_divisor (`int`, *optional*, defaults to 256):
-            The ffn layer dimension divisor.
-        activation_fn_name (`str` or `function`, *optional*, defaults to `"swish"`):
-            The non-linear activation function (function or string) in the decoder.
-        normalization_layer_name (`str` or `function`, *optional*, defaults to `"rms_norm"`):
-            Type of normalization layer.
-        normalize_qk_projections (`bool`, *optional*, defaults to False):
-            Whether to normalize queries and keys after projections
-        share_input_output_layers (`bool`, *optional*, defaults to False):
-            Whether to share the embedding between input and output linear layer
-        rope_freq_constant (`int`, *optional*, defaults to 10000):
-            The base period of the RoPE embeddings.
-        rope_max_length (`int`, *optional*, defaults to 4096):
-            That rope_max_length is set to twice of max_context_length.
-            This allows flexibility in token lengths during training or fine-tuning.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        bos_token_id (`int`, *optional*, defaults to 2):
-            Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 1):
-            End of stream token id.
-    """
-
-    model_type = "openelm"
-
-    def __init__(
-        self,
-        vocab_size: int = 32000,
-        max_context_length: int = 2048,
-        num_transformer_layers: int = 12,
-        model_dim: int = 2048,
-        head_dim: int = 128,
-        qkv_multipliers: Union[Number, List[Number]] = 1.0,
-        num_query_heads: Union[int, None] = None,
-        num_gqa_groups: int = 1,
-        ffn_multipliers: Union[Number, List[Number]] = 4.0,
-        ffn_with_glu: bool = True,
-        ffn_dim_divisor: int = 256,
-        activation_fn_name: str = "swish",
-        normalization_layer_name: str = "rms_norm",
-        normalize_qk_projections: bool = False,
-        share_input_output_layers: bool = False,
-        rope_freq_constant: int = 10000,
-        rope_max_length: int = 4096,
-        initializer_range: float = 0.02,
-        use_cache: bool = True,
-        bos_token_id: int = 1,
-        eos_token_id: int = 2,
-        **kwargs,
-    ) -> None:
-        self.vocab_size = vocab_size
-        self.max_context_length = max_context_length
-        self.num_transformer_layers = num_transformer_layers
-        self.model_dim = model_dim
-        self.head_dim = head_dim
-        self.qkv_multipliers = qkv_multipliers
-        self.num_query_heads = num_query_heads
-        self.num_gqa_groups = num_gqa_groups
-        self.ffn_multipliers = ffn_multipliers
-        self.ffn_with_glu = ffn_with_glu
-        self.ffn_dim_divisor = ffn_dim_divisor
-        self.activation_fn_name = activation_fn_name
-        self.normalization_layer_name = normalization_layer_name
-        self.normalize_qk_projections = normalize_qk_projections
-        self.share_input_output_layers = share_input_output_layers
-        self.rope_freq_constant = rope_freq_constant
-        self.rope_max_length = rope_max_length
-        self.num_query_heads = (
-            compute_heads(model_dim=model_dim, head_dim=head_dim)
-            if num_query_heads is None
-            else num_query_heads
-        )
-        self.initializer_range = initializer_range
-
-        self.__post_init__()
-        super().__init__(
-            use_cache=use_cache,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            **kwargs,
-        )
-
-    def __post_init__(self) -> None:
-        if self.num_gqa_groups is not None:
-            head_multiple_of = self.num_gqa_groups
-        else:
-            head_multiple_of = 2
-
-        if isinstance(self.qkv_multipliers, Number):
-            # All attention layers have the same latent dimensions, resulting in uniform allocation of parameters.
-            qkv_dim = make_divisible(
-                self.model_dim * self.qkv_multipliers,
-                divisor=self.head_dim * head_multiple_of,
-            )
-            query_dims = [int(qkv_dim)] * self.num_transformer_layers
-
-        elif (
-            isinstance(self.qkv_multipliers, (tuple, list))
-            and len(self.qkv_multipliers) == 2
-        ):
-            # Each attention layer have different latent dimensions assuming qkv_multipliers[0] != qkv_multipliers[1].
-            # This results in variable allocation of parameters in attention layer.
-            # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
-            qkv_multipliers = [
-                round(v, 2)
-                for v in np.linspace(
-                    self.qkv_multipliers[0],
-                    self.qkv_multipliers[1],
-                    num=self.num_transformer_layers,
-                    dtype=float,
-                )
-            ]
-            # Make sure that scaled model dimension is divisible by scaled head dimension.
-            query_dims = [
-                int(
-                    make_divisible(
-                        self.model_dim * m, divisor=self.head_dim * head_multiple_of
-                    )
-                )
-                for m in qkv_multipliers
-            ]
-        else:
-            raise NotImplementedError(
-                f"QKV multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
-            )
-
-        # compute the number of query, key, and value heads
-        # For multi-head and multi-query attention, the number of heads for query, key, and value are the same.
-        # For group query attention, the number of key and value heads are the same.
-        self.num_query_heads = [
-            int(compute_heads(q_dim, self.head_dim)) for q_dim in query_dims
-        ]
-        self.num_kv_heads = [
-            q_heads // self.num_gqa_groups for q_heads in self.num_query_heads
-        ]
-
-        # Feed-forward network (FFN) multipliers
-        if isinstance(self.ffn_multipliers, Number):
-            # All FFN layers have the same latent dimensions, resulting in uniform allocation of parameters.
-            self.ffn_multipliers = [self.ffn_multipliers] * self.num_transformer_layers
-        elif isinstance(self.ffn_multipliers, (tuple, list)):
-            # Each FFN layer have different latent dimensions assuming ffn_multipliers[0] != ffn_multipliers[1].
-            # This results in variable allocation of parameters in FFN layer.
-            # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
-            if len(self.ffn_multipliers) == 2:
-                self.ffn_multipliers = [
-                    round(v, 2)
-                    for v in np.linspace(
-                        self.ffn_multipliers[0],
-                        self.ffn_multipliers[1],
-                        num=self.num_transformer_layers,
-                        dtype=float,
-                    )
-                ]
-            else:
-                assert (
-                    len(self.ffn_multipliers) == self.num_transformer_layers
-                ), f"{len(self.ffn_multipliers)=}!={self.num_transformer_layers=}"
-        else:
-            raise NotImplementedError(
-                f"FFN multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
-            )
-
-        # check num_query_heads divisible by num_kv_heads for every layer
-        for layer_idx in range(len(query_dims)):
-            assert self.num_query_heads[layer_idx] % self.num_kv_heads[layer_idx] == 0
-
-class OpenELMRMSNorm(nn.Module):
-    def __init__(self, num_features: int, eps: float = 1e-6):
-        """
-        Initialize the OpenELMRMSNorm normalization layer.
-        Args:
-            dim (int): The dimension of the input tensor.
-            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-        Attributes:
-            eps (float): A small value added to the denominator for numerical stability.
-            weight (nn.Parameter): Learnable scaling parameter.
-        """
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(num_features))
-        self.num_features = num_features
-
-    def _norm(self, x: Tensor) -> Tensor:
-        """
-        Apply the OpenELMRMSNorm normalization to the input tensor.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The normalized tensor.
-        """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """
-        Forward pass through the OpenELMRMSNorm layer.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The output tensor after applying OpenELMRMSNorm.
-        """
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-    def extra_repr(self) -> str:
-        return (
-            super().extra_repr() + f"num_features={self.num_features}, eps={self.eps}"
-        )
-
-
-class OpenELMPreTrainedModel(PreTrainedModel):
-    config_class = OpenELMConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["OpenELMDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-
-    def __init__(self, *inputs, **kwargs) -> None:
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module: nn.Module) -> None:
-        """Initialize the weights."""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, OpenELMRMSNorm):
-            module.weight.data.fill_(1.0)
-
-
-def _rotate_half(x: Tensor) -> Tensor:
-    x1, x2 = x.chunk(2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor:
-    return (x * pos_cos) + (_rotate_half(x) * pos_sin)
-
-
-class OpenELMRotaryEmbedding(torch.nn.Module):
-    """
-    The rotary position embeddings (aka RoPE) from `RoFormer <https://arxiv.org/abs/2104.09864>`_.
-    RoPE encodes the position information of tokens using a rotation matrix, and is able to capture
-    explicit relative positional dependencies.
-    Args:
-        model_dim: The dimensionality of the model's hidden state.
-        max_seq_length: Maximum sequence length.
-        freq_constant: A constant used for computing frequencies.
-    """
-
-    def __init__(
-        self, model_dim: int, max_seq_length: int, freq_constant: int = 10000
-    ) -> None:
-        inv_freq = 1.0 / (
-            freq_constant
-            ** (torch.arange(0, model_dim, 2, dtype=torch.float32) / model_dim)
-        )
-        super().__init__()
-
-        self.model_dim = model_dim
-        self.freq_constant = freq_constant
-        self.max_seq_length = max_seq_length
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self._cached_cos = None
-        self._cached_sin = None
-        self._cached_seq_length = max_seq_length
-        self._compute_sin_cos_embeddings(max_seq_length)
-
-    def extra_repr(self) -> str:
-        return f"\tmodel_dim={self.model_dim}, max_seq_length={self.max_seq_length}, freq_constant={self.freq_constant}"
-
-    def _compute_sin_cos_embeddings(
-        self,
-        key_len: int,
-        key_device: torch.device = torch.device("cpu"),
-        key_dtype: torch.dtype = torch.float32,
-    ) -> None:
-        """
-        Compute sine and cos embeddings.
-        Args:
-            key_len: Number of tokens in the key embeddings in the transformer model.
-            device: Device where the key embeddings are stored.
-            key_dtype: Data type of the key embeddings.
-        Returns:
-            None
-        ...note:
-            We recalculate the sine and cosine embeddings if any of the following conditions are met:
-                1. The number of tokens in key embeddings are greater than the cached sequence length.
-                2. Sine and cosine caches are empty.
-                3. The device and data type of sine and cosine embeddings does not match with the key embeddings.
-        """
-        if (
-            key_len > self._cached_seq_length
-            or self._cached_cos is None
-            or (self._cached_cos is not None and self._cached_cos.device != key_device)
-            or (self._cached_cos is not None and self._cached_cos.dtype != key_dtype)
-            or self._cached_sin is None
-            or (self._cached_sin is not None and self._cached_sin.device != key_device)
-            or (self._cached_sin is not None and self._cached_sin.dtype != key_dtype)
-        ):
-            self._cached_seq_length = max(key_len, self._cached_seq_length)
-
-            # The shape of 'pos_index' is [number of key tokens]
-            pos_index = torch.arange(
-                self._cached_seq_length,
-                dtype=torch.float32,
-                device=self.inv_freq.device,
-            )
-            # The shape of 'pos_index_theta' is [number of key tokens, model dimension]
-            pos_index_theta = torch.einsum("i,j->ij", pos_index, self.inv_freq)
-            # The shape of 'emb' is [number of key tokens, model dimension]
-            emb = torch.cat((pos_index_theta, pos_index_theta), dim=-1)
-
-            # the shape of cos and sin embeddings is [number of key tokens, model_dim]
-            cos_emb = emb.cos().to(dtype=key_dtype, device=key_device)
-            sin_emb = emb.sin().to(dtype=key_dtype, device=key_device)
-
-            # the shape of cached cos and sin embeddings is [1, 1, number of key tokens, model_dim]
-            self._cached_cos = cos_emb[None, None, :, :]
-            self._cached_sin = sin_emb[None, None, :, :]
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        The forward function of RoPE embeddings.
-        Args:
-            query: Query embeddings in the transformer model. The shape of query embeddings is
-                [Batch, number of query heads, number of query tokens, model dimension].
-            key: Key embeddings in the transformer model. The shape of key embeddings is
-                [Batch, number of key heads, number of key tokens, model dimension].
-        Returns:
-            A tuple containing the query and key embeddings with positional information. The shape of the returned query
-            and key embeddings is the same as the input query and key embeddings respectively.
-        ...note:
-            The RoPE embedding computation is done in full-precision. After the computation, input query and key tensors
-            are casted to original input datatype.
-        """
-        dim = key.shape[-1]
-        key_len = key.shape[2]
-        query_len = query.shape[2]
-
-        assert dim == self.model_dim
-        assert key.device == query.device
-        assert key.dtype == query.dtype
-
-        # In the context of self-attention, the lengths of keys and queries are equal.
-        # However, in generation tasks, such as predicting the next token in a sequence, the lengths of keys and queries
-        # can differ. For instance, when employing key-value (KV) caching for sequence prediction, the keys
-        # represent embeddings of previous tokens and the current token, while the query corresponds
-        # to the embedding of the current token only.
-        assert (
-            key_len >= query_len
-        ), "Number of keys has to be greater than or equal to number of queries."
-
-        query_float = query.float()
-        key_float = key.float()
-
-        self._compute_sin_cos_embeddings(
-            key_len, key_device=key_float.device, key_dtype=key_float.dtype
-        )
-        query_float = _apply_rotary_pos_emb(
-            x=query_float,
-            pos_sin=self._cached_sin[..., key_len - query_len : key_len, :],
-            pos_cos=self._cached_cos[..., key_len - query_len : key_len, :],
-        )
-        key_float = _apply_rotary_pos_emb(
-            x=key_float,
-            pos_sin=self._cached_sin[..., :key_len, :],
-            pos_cos=self._cached_cos[..., :key_len, :],
-        )
-
-        return query_float.type_as(query), key_float.type_as(key)
-
-
-class OpenELMMultiHeadCausalAttention(nn.Module):
-    def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
-        super().__init__()
-        self.layer_idx = layer_idx
-        head_dim = config.head_dim
-        q_heads = config.num_query_heads[layer_idx]
-        k_heads = config.num_kv_heads[layer_idx]
-        v_heads = config.num_kv_heads[layer_idx]
-
-        self.qkv_proj = nn.Linear(
-            in_features=config.model_dim,
-            out_features=(q_heads + k_heads + v_heads) * head_dim,
-            bias=False,
-        )
-
-        self.pos_embedding = OpenELMRotaryEmbedding(
-            model_dim=config.head_dim,
-            max_seq_length=config.rope_max_length,
-            freq_constant=config.rope_freq_constant,
-        )
-
-        if config.normalize_qk_projections:
-            self.q_norm = OpenELMRMSNorm(
-                num_features=config.head_dim,
-            )
-            self.k_norm = OpenELMRMSNorm(
-                num_features=config.head_dim,
-            )
-        else:
-            self.q_norm = None
-            self.k_norm = None
-
-        self.out_proj = nn.Linear(
-            in_features=q_heads * head_dim,
-            out_features=config.model_dim,
-            bias=False,
-        )
-
-        self.head_dim = config.head_dim
-        self.num_q_heads = q_heads
-        self.num_k_heads = k_heads
-        self.num_v_heads = v_heads
-        self.transformer_dim = config.model_dim
-        self.num_groups = self.num_q_heads // self.num_k_heads
-
-    def extra_repr(self) -> str:
-        return (
-            super().extra_repr()
-            + f"query_heads={self.num_q_heads}, key_heads={self.num_k_heads}, value_heads={self.num_v_heads}"
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """
-        Forward pass of multi-head self-attention.
-        Args:
-            hidden_states: Input tensor of the shape [batch size, sequence length, model dimension].
-            past_key_value: Tensor storing the cached keys and values.
-            output_attentions: output attention weights.
-            use_cache: Specifies whether to use kv-cache for generation.
-            cache_position: used for updating the kv-cache.
-        Returns:
-            The output of the same shape as the input, optionally with a tensor containing cached keys and values.
-        """
-
-        # scaled_dot_product_attention does not return attention weights, set output_attentions to False
-        output_attentions = False
-        batch_size, seq_length, d_model = hidden_states.size()
-
-        # [B, S, d] --> [B, S, (q_h + k_h + v_h) * h]
-        qkv = self.qkv_proj(hidden_states)
-        # [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h]
-        qkv = qkv.reshape(
-            batch_size,
-            seq_length,
-            self.num_q_heads + self.num_k_heads + self.num_v_heads,
-            self.head_dim,
-        )
-        # [B, S, (q_h + k_h + v_h), h] --> [B, (q_h + k_h + v_h), S, h]
-        qkv = qkv.transpose(1, 2)
-        # [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
-        queries, keys, values = qkv.split(
-            [self.num_q_heads, self.num_k_heads, self.num_v_heads], dim=1
-        )
-
-        if self.q_norm is not None:
-            queries = self.q_norm(queries)
-
-        if self.k_norm is not None:
-            keys = self.k_norm(keys)
-
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; position_ids needed for the static cache
-            # cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            cache_kwargs = {"cache_position": cache_position}
-            keys, values = past_key_value.update(
-                keys, values, self.layer_idx, cache_kwargs
-            )
-
-        # Add positional embedding
-        queries, keys = self.pos_embedding(queries, keys)
-
-        if self.num_groups != 1:
-            # GQA
-            # [B, k_h, S, h] --> [B, q_h, S, h]
-            keys = keys.repeat_interleave(self.num_groups, dim=1)
-            # [B, v_h, S, h] --> [B, q_h, S, h]
-            values = values.repeat_interleave(self.num_groups, dim=1)
-
-        causal_mask = attention_mask
-        if attention_mask is not None and cache_position is not None:
-            causal_mask = causal_mask[:, :, cache_position, : keys.shape[-2]]
-
-        attn_output = F.scaled_dot_product_attention(
-            queries,
-            keys,
-            values,
-            attn_mask=causal_mask,
-            dropout_p=0,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(
-            batch_size, seq_length, self.num_q_heads * self.head_dim
-        )
-        attn_output = self.out_proj(attn_output)
-        if not output_attentions:
-            attn_weights = None
-        return attn_output, attn_weights, past_key_value
-
-
-class OpenELMFeedForwardNetwork(nn.Module):
-    def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
-        super().__init__()
-        ffn_multiplier = config.ffn_multipliers[layer_idx]
-        intermediate_dim = int(
-            make_divisible(
-                ffn_multiplier * config.model_dim,
-                divisor=config.ffn_dim_divisor,
-            )
-        )
-        if config.ffn_with_glu:
-            # FFN with Gated linear unit, as described in https://arxiv.org/abs/2002.05202v1.
-            self.proj_1 = nn.Linear(
-                in_features=config.model_dim,
-                out_features=2 * intermediate_dim,
-                bias=False,
-            )
-            self.proj_2 = nn.Linear(
-                in_features=intermediate_dim,
-                out_features=config.model_dim,
-                bias=False,
-            )
-            self.ffn_with_glu = True
-        else:
-            # Standard FFN, as described in https://arxiv.org/abs/1706.03762
-            self.proj_1 = nn.Linear(
-                in_features=config.model_dim,
-                out_features=intermediate_dim,
-                bias=False,
-            )
-            self.proj_2 = nn.Linear(
-                in_features=intermediate_dim,
-                out_features=config.model_dim,
-                bias=False,
-            )
-            self.ffn_with_glu = False
-
-        self.act = ACT2FN[config.activation_fn_name]
-
-    def extra_repr(self) -> str:
-        return super().extra_repr() + f"(ffn_with_glu) : {self.ffn_with_glu}"
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward function of FFN layer.
-        Args:
-            x: Input tensor of the shape [batch size, sequence length, model dimension].
-        Returns:
-            A tensor of the same shape as the input.
-        """
-        if self.ffn_with_glu:
-            y_12 = self.proj_1(x)
-            y_1, y_2 = y_12.chunk(2, dim=-1)
-            y = self.act(y_1) * y_2
-            return self.proj_2(y)
-        else:
-            return self.proj_2(self.act(self.proj_1(x)))
-
-
-class OpenELMDecoderLayer(nn.Module):
-    def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
-        super().__init__()
-        self.attn = OpenELMMultiHeadCausalAttention(config=config, layer_idx=layer_idx)
-        self.ffn = OpenELMFeedForwardNetwork(config=config, layer_idx=layer_idx)
-        self.ffn_norm = OpenELMRMSNorm(
-            num_features=config.model_dim,
-        )
-        self.attn_norm = OpenELMRMSNorm(
-            num_features=config.model_dim,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*):
-                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        """
-        residual = hidden_states
-        hidden_states = self.attn_norm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            **kwargs,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.ffn_norm(hidden_states)
-        hidden_states = self.ffn(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-class OpenELMModel(OpenELMPreTrainedModel):
-    config_class = OpenELMConfig
-
-    def __init__(self, config: OpenELMConfig):
-        super().__init__(config)
-        self.config = config
-
-        self.token_embeddings = nn.Embedding(
-            embedding_dim=config.model_dim,
-            num_embeddings=config.vocab_size,
-        )
-
-        self.layers = nn.ModuleList(
-            OpenELMDecoderLayer(config=config, layer_idx=layer_idx)
-            for layer_idx in range(config.num_transformer_layers)
-        )
-        self.norm = OpenELMRMSNorm(num_features=config.model_dim)
-        if config.share_input_output_layers:
-            self.classifier = None
-        else:
-            self.classifier = nn.Linear(
-                in_features=config.model_dim,
-                out_features=config.vocab_size,
-                bias=False,
-            )
-        self.num_transformer_layers = config.num_transformer_layers
-        self.gradient_checkpointing = False
-
-        # Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
-        # NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_context_length`.
-        causal_mask = torch.full(
-            (config.max_context_length, config.max_context_length),
-            fill_value=True,
-            dtype=torch.bool,
-        )
-        self.register_buffer(
-            "causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-        self.reset_parameters(config=config)
-
-    def get_input_embeddings(self):
-        return self.token_embeddings
-
-    def set_input_embeddings(self, new_embeddings: torch.Tensor):
-        self.token_embeddings = new_embeddings
-
-    def reset_parameters(self, config: OpenELMConfig) -> None:
-        """Initialize the layers in Language Model
-        The initialization scheme is followed, following `OPT <https://arxiv.org/pdf/2205.01068.pdf>`_.
-        Args:
-            use_megatron_std: Use standard deviation as described in Megatron-LM.
-        Returns:
-            None
-        """
-        for module in self.modules():
-            if isinstance(module, nn.Linear):
-                std = module.in_features**-0.5
-                torch.nn.init.normal_(module.weight, mean=0.0, std=std)
-                if module.bias is not None:
-                    torch.nn.init.zeros_(module.bias)
-            elif isinstance(module, nn.Embedding):
-                std = module.embedding_dim**-0.5
-                torch.nn.init.normal_(module.weight, mean=0.0, std=std)
-            elif isinstance(module, OpenELMRMSNorm):
-                if module.weight is not None:
-                    torch.nn.init.ones_(module.weight)
-                if hasattr(module, "bias") and module.bias is not None:
-                    torch.nn.init.zeros_(module.bias)
-
-        model_dim = config.model_dim
-        n_layers = config.num_transformer_layers
-        std = (model_dim**-0.5) * ((2 * n_layers) ** -0.5)
-        for param_name, param in self.named_parameters():
-            if param_name.endswith("out_proj.weight") or param_name.endswith(
-                "ffn.proj_2.weight"
-            ):
-                torch.nn.init.normal_(param, mean=0.0, std=std)
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.token_embeddings(input_ids)
-
-        past_seen_tokens = 0
-        if use_cache:  # kept for BC (cache positions)
-            if not isinstance(past_key_values, StaticCache):
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_seen_tokens = past_key_values.get_seq_length()
-
-        if cache_position is None:
-            cache_position = torch.arange(
-                past_seen_tokens,
-                past_seen_tokens + inputs_embeds.shape[1],
-                device=inputs_embeds.device,
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()
-                if isinstance(next_decoder_cache, Cache)
-                else next_decoder_cache
-            )
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
-                if v is not None
-            )
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-    def _update_causal_mask(self, attention_mask, input_tensor):
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        batch_size, seq_length = input_tensor.shape[:2]
-        dtype = input_tensor.dtype
-        device = input_tensor.device
-
-        # support going beyond cached `max_position_embedding`
-        if seq_length > self.causal_mask.shape[-1]:
-            causal_mask = torch.full(
-                (2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]),
-                fill_value=1,
-            )
-            self.register_buffer(
-                "causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
-            )
-
-        # We use the current dtype to avoid any overflows
-        min_dtype = torch.finfo(dtype).min
-        causal_mask = (
-            self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype)
-            * min_dtype
-        )
-
-        causal_mask = causal_mask.to(dtype=dtype, device=device)
-        if attention_mask is not None and attention_mask.dim() == 2:
-            mask_length = attention_mask.shape[-1]
-            padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
-                :, None, None, :
-            ].eq(0.0)
-            causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(
-                padding_mask, min_dtype
-            )
-
-        if self.config._attn_implementation == "sdpa" and attention_mask is not None:
-            # For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
-            is_tracing = (
-                torch.jit.is_tracing()
-                or isinstance(input_tensor, torch.fx.Proxy)
-                or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
-            )
-            if not is_tracing and torch.any(attention_mask != 1):
-                # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
-                # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-                # Details: https://github.com/pytorch/pytorch/issues/110213
-                causal_mask = causal_mask.mul(
-                    ~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)
-                ).to(dtype)
-
-        return causal_mask
-
-
-class OpenELMForCausalLM(OpenELMPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: OpenELMConfig):
-        super().__init__(config)
-        self.transformer = OpenELMModel(config)
-        self.vocab_size = config.vocab_size
-        if config.share_input_output_layers:
-            self.lm_head = None
-        else:
-            self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.transformer.token_embeddings
-
-    def set_input_embeddings(self, value):
-        self.transformer.token_embeddings = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.transformer = decoder
-
-    def get_decoder(self):
-        return self.transformer
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.transformer(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-        if self.lm_head is None:
-            # shared
-            logits = F.linear(
-                hidden_states, weight=self.transformer.token_embeddings.weight
-            )
-        else:
-            logits = self.lm_head(hidden_states)
-        logits = logits[:, : self.config.vocab_size]
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        past_length = 0
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if (
-                attention_mask is not None
-                and attention_mask.shape[1] > input_ids.shape[1]
-            ):
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
-
-        if self.generation_config.cache_implementation == "static":
-            # generation with static cache
-            cache_position = kwargs.get("cache_position", None)
-            if cache_position is None:
-                past_length = 0
-            else:
-                past_length = cache_position[-1] + 1
-            input_ids = input_ids[:, past_length:]
-            position_ids = position_ids[:, past_length:]
-
-        # we should only keep a `cache_position` in generate, and do +=1.
-        # same goes for position ids. Could also help with continued generation.
-        cache_position = torch.arange(
-            past_length,
-            past_length + position_ids.shape[-1],
-            device=position_ids.device,
-        )
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
-            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
-            # We could use `next_tokens` directly instead.
-            model_inputs = {"input_ids": input_ids.contiguous()}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids.contiguous(),
-                "cache_position": cache_position,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(
-                    past_state.index_select(0, beam_idx.to(past_state.device))
-                    for past_state in layer_past
-                ),
-            )
-        return reordered_past