Create modeling_dbrx.py

modeling_dbrx.py

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+# code adapted from https://huggingface.co/fahadh4ilyas
+"""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.q_proj = nn.Linear(self.hidden_size,
+                              self.hidden_size,
+                              bias=False)
+        self.k_proj = nn.Linear(self.hidden_size,
+                              self.num_key_value_heads * self.head_dim,
+                              bias=False)
+        self.v_proj = nn.Linear(self.hidden_size,
+                              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()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        if self.clip_qkv is not None:
+            query_states = query_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+            key_states = key_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+            value_states = value_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+
+        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.debug(
+            'Implicitly setting `output_attentions` to False as it is not supported in Flash Attention.'
+        )
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        if self.clip_qkv is not None:
+            query_states = query_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+            key_states = key_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+            value_states = value_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+
+        # 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
+
+            expert_tokens = x[None, token_idx].reshape(-1, hidden_size)
+            expert_out = self.mlp[expert_idx](expert_tokens) * top_weights[token_idx, topk_idx, 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