Delete .ipynb_checkpoints/modeling_dbrx-checkpoint.py

.ipynb_checkpoints/modeling_dbrx-checkpoint.py

@@ -1,1455 +0,0 @@
-"""PyTorch Dbrx model."""
-
-import math
-import warnings
-from copy import deepcopy
-from functools import partial
-from typing import Any, Callable, Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_outputs import (MoeCausalLMOutputWithPast,
-                                           MoeModelOutputWithPast)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import is_flash_attn_2_available, logging
-
-from .configuration_dbrx import DbrxAttentionConfig, DbrxConfig, DbrxFFNConfig
-
-if is_flash_attn_2_available():
-    try:
-        from flash_attn import flash_attn_func, flash_attn_varlen_func
-        from flash_attn.bert_padding import pad_input  # noqa
-        from flash_attn.bert_padding import index_first_axis, unpad_input
-    except:
-        pass
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = 'DbrxConfig'
-
-#############################################################################
-# Copied from LLaMaRotaryEmbedding
-#############################################################################
-
-
-class DbrxRotaryEmbedding(nn.Module):
-
-    def __init__(self,
-                 dim: int,
-                 max_position_embeddings: int = 2048,
-                 base: float = 10000.0,
-                 scaling_factor: float = 1.0):
-        super().__init__()
-        self.scaling_factor = scaling_factor
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base**(
-            torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
-        self.register_buffer('inv_freq', inv_freq, persistent=False)
-        # For BC we register cos and sin cached
-        self.max_seq_len_cached = max_position_embeddings
-
-    @torch.no_grad()
-    def forward(
-            self, x: torch.Tensor, position_ids: torch.LongTensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(
-            position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(
-            device_type, str) and device_type != 'mps' else 'cpu'
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float()
-                     @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-def rotate_half(x: torch.Tensor) -> torch.Tensor:
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., :x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2:]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(
-        q: torch.Tensor,
-        k: torch.Tensor,
-        cos: torch.Tensor,
-        sin: torch.Tensor,
-        unsqueeze_dim: int = 1) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos and
-            sin so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos and sin have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos and sin broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """Equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
-
-    The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
-    (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :,
-                                  None, :, :].expand(batch, num_key_value_heads,
-                                                     n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
-                                 head_dim)
-
-
-#############################################################################
-
-#############################################################################
-# Modified from modeling_mixtral
-#############################################################################
-
-
-def load_balancing_loss_func(
-    gate_logits: torch.Tensor,
-    num_experts: int,
-    top_k: int,
-    attention_mask: Optional[torch.Tensor],
-) -> torch.Tensor:
-    r"""Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
-            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
-            shape [batch_size X sequence_length, num_experts].
-        num_experts (`int`):
-            Number of experts.
-        top_k (`int`):
-            The number of experts each token is routed to.
-        attention_mask (`torch.Tensor`, None):
-            The attention_mask used in forward function
-            shape [batch_size X sequence_length] if not None.
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
-        return torch.tensor(0.0)
-
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat(
-            [layer_gate.to(compute_device) for layer_gate in gate_logits],
-            dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits,
-                                                  dim=-1)
-
-    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    if attention_mask is None:
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.mean(routing_weights, dim=0)
-    else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (
-            batch_size * sequence_length)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
-        expert_attention_mask = (attention_mask[None, :, :, None, None].expand(
-            (num_hidden_layers, batch_size, sequence_length, top_k,
-             num_experts)).reshape(-1, top_k, num_experts).to(compute_device))
-
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(
-            expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-                expert_attention_mask, dim=0)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
-        router_per_expert_attention_mask = (
-            attention_mask[None, :, :, None].expand(
-                (num_hidden_layers, batch_size, sequence_length,
-                 num_experts)).reshape(-1, num_experts).to(compute_device))
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(
-            routing_weights * router_per_expert_attention_mask,
-            dim=0) / torch.sum(router_per_expert_attention_mask, dim=0)
-
-    overall_loss = torch.sum(tokens_per_expert *
-                             router_prob_per_expert.unsqueeze(0))
-    return overall_loss * num_experts
-
-
-#############################################################################
-
-
-def resolve_ffn_act_fn(
-        ffn_act_fn: dict) -> Callable[[torch.Tensor], torch.Tensor]:
-    """Resolve the activation function for the feed-forward network.
-
-    Args:
-        ffn_act_fn (dict): The configuration dictionary for the activation function.
-            The dict config must specify the 'name' of a torch.nn.functional activation
-            function. All of other key values pairs are bound to the function as a partial.
-
-    Returns:
-        Callable[[torch.Tensor], torch.Tensor]: The activation function.
-    """
-    config = deepcopy(ffn_act_fn)
-    name = config.pop('name')
-    if not hasattr(nn.functional, name):
-        raise ValueError(f'Unrecognised activation function name ({name}).')
-    act = getattr(nn.functional, name)
-    return partial(act, **config)
-
-
-#############################################################################
-# Copied from LLaMaAttention
-#############################################################################
-
-
-def _get_unpad_data(attention_mask: torch.Tensor):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32),
-                       (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-class DbrxAttention(nn.Module):
-    """Multi-head self attention."""
-
-    def __init__(self,
-                 hidden_size: int,
-                 num_heads: int,
-                 max_position_embeddings: int,
-                 attn_config: DbrxAttentionConfig,
-                 block_idx: Optional[int] = None):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = max_position_embeddings
-        self.block_idx = block_idx
-        self.config = attn_config
-        if block_idx is None:
-            logger.warning_once(
-                f'Instantiating {self.__class__.__name__} without passing a `block_idx` is not recommended and will '
-                +
-                'lead to errors during the forward call if caching is used. Please make sure to provide a `block_idx` '
-                + 'when creating this class.')
-
-        self.attn_pdrop = attn_config.attn_pdrop
-        self.clip_qkv = attn_config.clip_qkv
-        self.num_key_value_heads = attn_config.kv_n_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.rope_theta = attn_config.rope_theta
-
-        self.Wqkv = nn.Linear(self.hidden_size,
-                              self.hidden_size +
-                              2 * self.num_key_value_heads * self.head_dim,
-                              bias=False)
-        self.out_proj = nn.Linear(self.hidden_size,
-                                  self.hidden_size,
-                                  bias=False)
-        self.rotary_emb = DbrxRotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.LongTensor,
-        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,
-        **kwargs: Any,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv_states = self.Wqkv(hidden_states)
-        if self.clip_qkv is not None:
-            qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
-
-        query_states, key_states, value_states = qkv_states.split(
-            [
-                self.hidden_size,
-                self.num_key_value_heads * self.head_dim,
-                self.num_key_value_heads * self.head_dim,
-            ],
-            dim=2,
-        )
-
-        query_states = query_states.view(bsz, q_len, self.num_heads,
-                                         self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
-                                     self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
-                                         self.head_dim).transpose(1, 2)
-
-        past_key_value = getattr(self, 'past_key_value', past_key_value)
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states,
-                                                        key_states, cos, sin)
-
-        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
-            }
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.block_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(
-            2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, :key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights,
-                                             dim=-1,
-                                             dtype=torch.float32).to(
-                                                 query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights,
-                                             p=self.attn_pdrop,
-                                             training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
-                + f' {attn_output.size()}')
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        attn_output = self.out_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class DbrxFlashAttention2(DbrxAttention):
-    """Dbrx flash attention module.
-
-    This module inherits from `DbrxAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it
-    calls the public API of flash attention.
-    """
-
-    def __init__(self, *args: Any, **kwargs: Any):
-        if not is_flash_attn_2_available():
-            raise ImportError(
-                'Flash Attention 2 is not available. Please install it with `pip install flash-attn`.'
-            )
-
-        super().__init__(*args, **kwargs)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Any,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
-               Optional[Tuple[torch.Tensor]]]:
-        logger.info(
-            'Implicitly setting `output_attentions` to False as it is not supported in Flash Attention.'
-        )
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv_states = self.Wqkv(hidden_states)
-        if self.clip_qkv is not None:
-            qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
-
-        query_states, key_states, value_states = qkv_states.split(
-            [
-                self.hidden_size,
-                self.num_key_value_heads * self.head_dim,
-                self.num_key_value_heads * self.head_dim,
-            ],
-            dim=2,
-        )
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        query_states = query_states.view(bsz, q_len, self.num_heads,
-                                         self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
-                                     self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
-                                         self.head_dim).transpose(1, 2)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states,
-                                                        key_states, cos, sin)
-
-        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; cache_position needed for the static cache
-            cache_kwargs = {
-                'sin': sin,
-                'cos': cos,
-                'cache_position': cache_position
-            }
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.block_idx, cache_kwargs)
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout
-        # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attn_pdrop if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (LlamaRMSNorm handles it correctly)
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, '_pre_quantization_dtype'):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = query_states.dtype
-
-            logger.warning_once(
-                f'The input hidden states seems to be silently casted in float32, this might be '
-                +
-                f'related to the fact you have upcasted embedding or layer norm layers in '
-                + f'float32. We will cast back the input in {target_dtype}.')
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len,
-                                          self.hidden_size).contiguous()
-        attn_output = self.out_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value  # type: ignore
-
-    def _flash_attention_forward(
-        self,
-        query_states: torch.Tensor,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        attention_mask: Union[torch.LongTensor, None],
-        query_length: int,
-        dropout: float = 0.0,
-        softmax_scale: Optional[float] = None,
-    ):
-        """Use FlashAttention, stripping padding tokens if necessary.
-
-        Args:
-            query_states (torch.Tensor): Input query states to be passed to Flash Attention API
-            key_states (torch.Tensor): Input key states to be passed to Flash Attention API
-            value_states (torch.Tensor): Input value states to be passed to Flash Attention API
-            attention_mask (torch.LongTensor | None): The padding mask - corresponds to a tensor of size
-                (batch_size, seq_len) where 0 stands for the position of padding tokens and 1
-                for the position of non-padding tokens.
-            query_length (int): The length of the query sequence
-            dropout (float): Attention dropout
-            softmax_scale (float, optional): The scaling of QK^T before applying softmax.
-                Defaults to 1 / sqrt(head_dim)
-        """
-        causal = True
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask,
-                query_length)
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(
-                attn_output_unpad,
-                indices_q,
-                batch_size,
-                query_length,
-            )
-        else:
-            attn_output = flash_attn_func(
-                query_states,
-                key_states,
-                value_states,
-                dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-        return attn_output
-
-    def _upad_input(self, query_layer: torch.Tensor, key_layer: torch.Tensor,
-                    value_layer: torch.Tensor, attention_mask: torch.Tensor,
-                    query_length: int):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(
-            attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
-                              head_dim), indices_k)
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
-                                head_dim), indices_k)
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads,
-                                    head_dim), indices_k)
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
-                query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-DBRX_ATTENTION_CLASSES = {
-    'eager': DbrxAttention,
-    'flash_attention_2': DbrxFlashAttention2,
-}
-
-
-class DbrxNormAttentionNorm(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        max_position_embeddings: int,
-        resid_pdrop: float,
-        attn_implementation: str,
-        attn_config: DbrxAttentionConfig,
-        block_idx: Optional[int] = None,
-    ):
-        super().__init__()
-        self.block_idx = block_idx
-        self.resid_pdrop = resid_pdrop
-        self.norm_1 = nn.LayerNorm(hidden_size, bias=False)
-        self.attn = DBRX_ATTENTION_CLASSES[attn_implementation](
-            hidden_size=hidden_size,
-            num_heads=num_heads,
-            max_position_embeddings=max_position_embeddings,
-            attn_config=attn_config,
-            block_idx=block_idx,
-        )
-        self.norm_2 = nn.LayerNorm(hidden_size, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.LongTensor,
-        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,
-        **kwargs: Any,
-    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor],
-               Optional[Cache]]:
-
-        residual_states = hidden_states
-        hidden_states = self.norm_1(hidden_states).to(hidden_states.dtype)
-
-        hidden_states, attn_weights, past_key_value = self.attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = nn.functional.dropout(hidden_states,
-                                              p=self.resid_pdrop,
-                                              training=self.training)
-        hidden_states = hidden_states + residual_states
-
-        residual_states = hidden_states
-        hidden_states = self.norm_2(hidden_states).to(hidden_states.dtype)
-
-        return residual_states, hidden_states, attn_weights, past_key_value
-
-
-class DbrxRouter(nn.Module):
-
-    def __init__(self, hidden_size: int, moe_num_experts: int, moe_top_k: int,
-                 moe_jitter_eps: Optional[float],
-                 moe_normalize_expert_weights: Optional[float],
-                 uniform_expert_assignment: bool):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.moe_num_experts = moe_num_experts
-        self.moe_top_k = moe_top_k
-        self.moe_jitter_eps = moe_jitter_eps
-        self.moe_normalize_expert_weights = moe_normalize_expert_weights
-        self.uniform_expert_assignment = uniform_expert_assignment
-
-        self.layer = nn.Linear(self.hidden_size,
-                               self.moe_num_experts,
-                               bias=False)
-
-    def jitter(self, x: torch.Tensor) -> torch.Tensor:
-        if self.moe_jitter_eps is None:
-            raise RuntimeError('The router does not have moe_jitter_eps set.')
-        low = 1.0 - self.moe_jitter_eps
-        high = 1.0 + self.moe_jitter_eps
-        noise = torch.rand(x.size(), dtype=x.dtype, device=x.device)
-        return low + noise * (high - low)
-
-    def forward(
-            self, x: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.LongTensor]:
-        if self.training and self.moe_jitter_eps is not None:
-            x = x * self.jitter(x)
-
-        weights = self.layer(x.view(-1,
-                                    x.shape[-1])).softmax(dim=-1,
-                                                          dtype=torch.float32)
-        top_weights, top_experts = torch.topk(weights, self.moe_top_k, dim=-1)
-
-        if self.moe_normalize_expert_weights:
-            top_weights = top_weights / torch.norm(
-                top_weights,
-                p=self.moe_normalize_expert_weights,
-                dim=-1,
-                keepdim=True)
-
-        if self.uniform_expert_assignment:
-            with torch.no_grad():
-                uniform_tensor = torch.arange(
-                    0,
-                    top_experts.numel(),
-                    device=top_experts.device,
-                    dtype=top_experts.dtype) % self.moe_num_experts
-                top_experts = uniform_tensor.reshape(top_experts.shape)
-                # Note, weights and top_weights are not changed
-
-        weights = weights.to(x.dtype)
-        top_weights = top_weights.to(x.dtype)
-        return weights, top_weights, top_experts  # type: ignore
-
-
-class DbrxMLP(nn.Module):
-
-    def __init__(self, hidden_size: int, ffn_hidden_size: int, ffn_act_fn: dict):
-        super().__init__()
-
-        self.w1 = nn.Linear(hidden_size, ffn_hidden_size, bias=False)
-        self.v1 = nn.Linear(hidden_size, ffn_hidden_size, bias=False)
-        self.w2 = nn.Linear(ffn_hidden_size, hidden_size, bias=False)
-        self.activation_fn = resolve_ffn_act_fn(ffn_act_fn)
-    
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-
-        return self.w2(self.activation_fn(self.w1(x)) * self.v1(x))
-
-
-class DbrxExperts(nn.Module):
-
-    def __init__(self, hidden_size: int, ffn_hidden_size: int,
-                 moe_num_experts: int, ffn_act_fn: dict):
-        super().__init__()
-        self.moe_num_experts = moe_num_experts
-        self.mlp = nn.ModuleList([DbrxMLP(hidden_size, ffn_hidden_size, ffn_act_fn) for _ in range(moe_num_experts)])
-
-    def forward(self, x: torch.Tensor, weights: torch.Tensor,
-                top_weights: torch.Tensor,
-                top_experts: torch.LongTensor) -> torch.Tensor:
-        bsz, q_len, hidden_size = x.shape
-        x = x.view(-1, hidden_size)
-        out = torch.zeros_like(x)
-
-        expert_mask = nn.functional.one_hot(
-            top_experts, num_classes=self.moe_num_experts).permute(2, 1, 0)
-        for expert_idx in range(0, self.moe_num_experts):
-            topk_idx, token_idx = torch.where(expert_mask[expert_idx])
-            if token_idx.shape[0] == 0:
-                continue
-
-            token_list = token_idx.tolist()
-            topk_list = topk_idx.tolist()
-
-            expert_tokens = x[None, token_list].reshape(-1, hidden_size)
-            expert_out = self.mlp[expert_idx](expert_tokens) * top_weights[token_list, topk_list, None]
-
-            out.index_add_(0, token_idx, expert_out)
-
-        out = out.reshape(bsz, q_len, hidden_size)
-        return out
-
-
-class DbrxFFN(nn.Module):
-
-    def __init__(self, hidden_size: int, ffn_config: DbrxFFNConfig):
-        super().__init__()
-
-        self.router = DbrxRouter(
-            hidden_size,
-            moe_num_experts=ffn_config.moe_num_experts,
-            moe_top_k=ffn_config.moe_top_k,
-            moe_jitter_eps=ffn_config.moe_jitter_eps,
-            moe_normalize_expert_weights=ffn_config.
-            moe_normalize_expert_weights,
-            uniform_expert_assignment=ffn_config.uniform_expert_assignment,
-        )
-
-        self.experts = DbrxExperts(
-            hidden_size=hidden_size,
-            ffn_hidden_size=ffn_config.ffn_hidden_size,
-            moe_num_experts=ffn_config.moe_num_experts,
-            ffn_act_fn=ffn_config.ffn_act_fn,
-        )
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        weights, top_weights, top_experts = self.router(x)
-        out = self.experts(x, weights, top_weights, top_experts)
-        return out, weights
-
-
-class DbrxBlock(nn.Module):
-
-    def __init__(self, config: DbrxConfig, block_idx: int):
-        super().__init__()
-        self.hidden_size = config.d_model
-        self.resid_pdrop = config.resid_pdrop
-        self.block_idx = block_idx
-        self.norm_attn_norm = DbrxNormAttentionNorm(
-            hidden_size=config.d_model,
-            num_heads=config.n_heads,
-            max_position_embeddings=config.max_seq_len,
-            resid_pdrop=config.resid_pdrop,
-            attn_implementation=config._attn_implementation,
-            attn_config=config.attn_config,
-            block_idx=block_idx,
-        )
-        self.ffn = DbrxFFN(hidden_size=config.d_model,
-                           ffn_config=config.ffn_config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.LongTensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        output_router_logits: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Any,
-    ) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, Optional[torch.Tensor]],
-               Tuple[torch.Tensor, Optional[Cache]], Tuple[
-                   torch.Tensor, Optional[torch.Tensor], Optional[Cache]],
-               Tuple[torch.Tensor, Optional[torch.Tensor],
-                     Optional[torch.Tensor]], Tuple[
-                         torch.Tensor, Optional[Cache], Optional[torch.Tensor]],
-               Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache],
-                     Optional[torch.Tensor]],]:
-        """Forward function for DbrxBlock.
-
-        Args:
-            hidden_states (`torch.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            position_ids (`torch.LongTensor`): position ids of shape `(batch, seq_len)`
-            attention_mask (`torch.Tensor`, 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.
-            past_key_value (`Tuple(torch.Tensor)`, optional): cached past key and value projection states
-            output_attentions (`bool`, optional): Whether or not to return the attentions tensors of all
-                attention layers. See `attentions` under returned tensors for more detail.
-            output_router_logits (`bool`, optional): Whether or not to return the router logits.
-            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`).
-            cache_position (`torch.LongTensor`, optional): position ids of the cache
-        """
-        if 'padding_mask' in kwargs:
-            warnings.warn(
-                'Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`'
-            )
-
-        # Norm + Attention + Norm
-        resid_states, hidden_states, self_attn_weights, present_key_value = self.norm_attn_norm(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        # Fully Connected
-        hidden_states, router_logits = self.ffn(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states,
-                                              p=self.resid_pdrop,
-                                              training=self.training)
-        hidden_states = resid_states + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        if output_router_logits:
-            outputs += (router_logits,)
-
-        return outputs
-
-
-class DbrxPreTrainedModel(PreTrainedModel):
-    config_class = DbrxConfig
-    base_model_prefix = 'transformer'
-    supports_gradient_checkpointing = True
-    _no_split_modules = ['DbrxBlock']
-    _skip_keys_device_placement = ['past_key_values']
-    _supports_flash_attn_2 = True
-    _supports_sdpa = False
-    _supports_cache_class = True
-
-    def _init_weights(self, module: nn.Module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-
-    def _setup_cache(self, cache_cls: Any, max_batch_size: int,
-                     max_cache_len: int):  # TODO: how to set var type of class?
-        if self.config._attn_implementation == 'flash_attention_2' and cache_cls == StaticCache:
-            raise ValueError(
-                '`static` cache implementation is not compatible with ' +
-                '`attn_implementation==flash_attention_2`. Make sure to use ' +
-                '`spda` in the mean time and open an issue at https://github.com/huggingface/transformers.'
-            )
-
-        for block in self.transformer.blocks:
-            device = block.norm_attn_norm.norm_1.weight.device
-            if hasattr(self.config, '_pre_quantization_dtype'):
-                dtype = self.config._pre_quantization_dtype
-            else:
-                dtype = block.norm_attn_norm.attn.out_proj.weight.dtype
-            block.norm_attn_norm.attn.past_key_value = cache_cls(self.config,
-                                                                 max_batch_size,
-                                                                 max_cache_len,
-                                                                 device=device,
-                                                                 dtype=dtype)
-
-    def _reset_cache(self):
-        for block in self.transformer.blocks:
-            block.norm_attn_norm.attn.past_key_value = None
-
-
-class DbrxModel(DbrxPreTrainedModel):
-    """Transformer decoder consisting of *config.num_hidden_layers*
-
-    [`DbrxBlock`] layers.
-
-    Args:
-        config: DbrxConfig
-    """
-
-    def __init__(self, config: DbrxConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.emb_pdrop = config.emb_pdrop
-
-        self.wte = nn.Embedding(config.vocab_size, config.d_model,
-                                self.padding_idx)
-        self.blocks = nn.ModuleList([
-            DbrxBlock(config, block_idx) for block_idx in range(config.n_layers)
-        ])
-        self.norm_f = nn.LayerNorm(config.d_model, bias=False)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.wte
-
-    def set_input_embeddings(self, value: nn.Embedding):
-        self.wte = value
-
-    def _autocast_input_embeddings(self,
-                                   inputs_embeds: torch.Tensor) -> torch.Tensor:
-        if inputs_embeds.device.type == 'cuda' and torch.is_autocast_enabled():
-            return inputs_embeds.to(dtype=torch.get_autocast_gpu_dtype())
-        elif inputs_embeds.device.type == 'cpu' and torch.is_autocast_cpu_enabled(
-        ):
-            return inputs_embeds.to(dtype=torch.get_autocast_cpu_dtype())
-        else:
-            return inputs_embeds
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, MoeModelOutputWithPast]:
-        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)
-        output_router_logits = (output_router_logits
-                                if output_router_logits is not None else
-                                self.config.output_router_logits)
-        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.wte(input_ids)
-
-        inputs_embeds = self._autocast_input_embeddings(
-            inputs_embeds)  # type: ignore
-        inputs_embeds = nn.functional.dropout(inputs_embeds,
-                                              p=self.emb_pdrop,
-                                              training=self.training)
-
-        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(  # type: ignore
-                )
-
-        if cache_position is None:
-            if isinstance(past_key_values, StaticCache):
-                raise ValueError(
-                    'cache_position is a required argument when using StaticCache.'
-                )
-            cache_position = torch.arange(  # type: ignore
-                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)  # type: ignore
-
-        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds,
-                                               cache_position)  # type: ignore
-
-        # 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
-        all_router_logits = () if output_router_logits else None
-        next_decoder_cache = None
-
-        for block in self.blocks:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)  # type: ignore
-
-            if self.gradient_checkpointing and self.training:
-                block_outputs = self._gradient_checkpointing_func(
-                    block.__call__,
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_values=past_key_values,
-                    output_attentions=output_attentions,
-                    output_router_logits=output_router_logits,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-            else:
-                block_outputs = block(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    output_router_logits=output_router_logits,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            hidden_states = block_outputs[0]
-
-            if use_cache:
-                next_decoder_cache = block_outputs[
-                    2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (block_outputs[1],)  # type: ignore
-
-            if output_router_logits:
-                all_router_logits += (block_outputs[-1],)  # type: ignore
-
-        hidden_states = self.norm_f(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)  # type: ignore
-
-        next_cache = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache()  # type: ignore
-                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,
-                all_router_logits
-            ] if v is not None)
-        return MoeModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            router_logits=all_router_logits,
-        )
-
-    # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
-    # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
-    # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
-    # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
-    def _update_causal_mask(
-            self, attention_mask: Optional[torch.Tensor],
-            input_tensor: torch.Tensor,
-            cache_position: torch.Tensor) -> Optional[torch.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
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if hasattr(self.blocks[0].norm_attn_norm.attn,
-                   'past_key_value'):  # static cache
-            target_length = self.config.max_position_embeddings
-        else:  # dynamic cache
-            target_length = (attention_mask.shape[-1] if isinstance(
-                attention_mask, torch.Tensor) else cache_position[-1] + 1)
-        target_length = int(target_length)
-
-        causal_mask = torch.full((sequence_length, target_length),
-                                 fill_value=min_dtype,
-                                 dtype=dtype,
-                                 device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(
-            target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None,
-                                  None, :, :].expand(input_tensor.shape[0], 1,
-                                                     -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone(
-            )  # copy to contiguous memory for in-place edit
-            if 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)
-            elif attention_mask.dim() == 4:
-                # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
-                # cache. In that case, the 4D attention mask attends to the newest tokens only.
-                if attention_mask.shape[
-                        -2] < cache_position[0] + sequence_length:
-                    offset = cache_position[0]
-                else:
-                    offset = 0
-                mask_shape = attention_mask.shape
-                mask_slice = (attention_mask.eq(0.0)).to(
-                    dtype=dtype) * min_dtype
-                causal_mask[:mask_shape[0], :mask_shape[1],
-                            offset:mask_shape[2] +
-                            offset, :mask_shape[3]] = mask_slice
-
-        if (self.config._attn_implementation == 'sdpa' and
-                attention_mask is not None and
-                attention_mask.device.type == 'cuda'):
-            # TODO: 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  # type: ignore
-                (hasattr(torch, '_dynamo') and torch._dynamo.is_compiling()))
-            if not is_tracing and torch.any(attention_mask != 1):
-                # Attend to all tokens in fully masked rows in 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 = AttentionMaskConverter._unmask_unattended(
-                    causal_mask, min_dtype)
-
-        return causal_mask
-
-
-class DbrxForCausalLM(DbrxPreTrainedModel):
-
-    def __init__(self, config: DbrxConfig):
-        super().__init__(config)
-        self.transformer = DbrxModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size,
-                                 config.vocab_size,
-                                 bias=False)
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.ffn_config.moe_num_experts
-        self.num_experts_per_tok = config.ffn_config.moe_top_k
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.transformer.get_input_embeddings()
-
-    def set_input_embeddings(self, value: nn.Embedding):
-        self.transformer.set_input_embeddings(value)
-
-    def get_output_embeddings(self) -> nn.Linear:
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings: nn.Linear):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder: DbrxModel):
-        self.transformer = decoder
-
-    def get_decoder(self) -> DbrxModel:
-        return self.transformer
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
-        r"""Forward function for causal language modeling.
-
-        Example:
-        ```python
-        >>> from transformers import AutoTokenizer, DbrxForCausalLM
-
-        >>> model = DbrxForCausalLM.from_pretrained("databricks/dbrx")
-        >>> tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```
-        """
-        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)
-        output_router_logits = (output_router_logits
-                                if output_router_logits is not None else
-                                self.config.output_router_logits)
-        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,
-            output_router_logits=output_router_logits,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-
-        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 = nn.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)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits if return_dict else outputs[-1],
-                self.num_experts,
-                self.num_experts_per_tok,
-                attention_mask,
-            )
-            if labels is not None and loss is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(
-                    loss.device)  # make sure to reside in the same device
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return MoeCausalLMOutputWithPast(
-            loss=loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )
-
-    def prepare_inputs_for_generation(
-            self,
-            input_ids: torch.Tensor,
-            past_key_values: Optional[Cache] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            **kwargs: Any) -> Dict[str, Any]:
-        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:] if position_ids is not None else None
-
-        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
-        # same goes for position ids. Could also help with continued generation.
-        input_length = position_ids.shape[
-            -1] if position_ids is not None else input_ids.shape[-1]
-        cache_position = torch.arange(past_length,
-                                      past_length + input_length,
-                                      device=input_ids.device)
-        position_ids = position_ids.contiguous(
-        ) if position_ids is not None else None
-
-        # 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
-            # TODO: use `next_tokens` directly instead.
-            model_inputs = {'input_ids': input_ids.contiguous()}
-
-        model_inputs.update(
-            { # type: ignore
-                'position_ids': position_ids,
-                '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: Cache, beam_idx: torch.LongTensor):
-        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