Create modernberg_model.py

modernberg_model.py

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+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from contextlib import nullcontext
+from typing import Dict, Literal, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    logging,
+)
+from transformers.utils.import_utils import is_triton_available, is_torchdynamo_compiling
+from transformers.models.gemma.modeling_gemma import GemmaRotaryEmbedding, apply_rotary_pos_emb
+from transformers.models.modernbert.modular_modernbert import (_pad_modernbert_output, _unpad_modernbert_input, ModernBertEmbeddings, ModernBertMLP, ModernBertUnpaddedRotaryEmbedding, ModernBertEmbeddings)
+
+
+if is_flash_attn_2_available():
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+    from flash_attn.layers.rotary import RotaryEmbedding
+    from flash_attn.ops.triton.rotary import apply_rotary
+else:
+    RotaryEmbedding = object
+
+_CHECKPOINT_FOR_DOC = "answerdotai/ModernBERT-base"
+_CONFIG_FOR_DOC = "ModernBertConfig"
+_MAX_SQRT_GRADIENT = 1000.0
+
+logger = logging.get_logger(__name__)
+
+
+class ModernBergConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ModernBergModel`]. It is used to instantiate an ModernBerg
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the ModernBERT-base.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50368):
+            Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ModernBertModel`]
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 1152):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 22):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        lru_width (`int`, *optional*, defaults to 128):
+            The dimension of the RG-LRU -- if None, this will be set to `hidden_size`.
+        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+            if not specified.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+            The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        norm_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the normalization layers.
+        pad_token_id (`int`, *optional*, defaults to 50283):
+            Padding token id.
+        eos_token_id (`int`, *optional*, defaults to 50282):
+            End of stream token id.
+        bos_token_id (`int`, *optional*, defaults to 50281):
+            Beginning of stream token id.
+        cls_token_id (`int`, *optional*, defaults to 50281):
+            Classification token id.
+        sep_token_id (`int`, *optional*, defaults to 50282):
+            Separation token id.
+        global_rope_theta (`float`, *optional*, defaults to 160000.0):
+            The base period of the global RoPE embeddings.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        global_temporal_every_n_layers (`int`, *optional*, defaults to 3):
+            The number of layers between global temporal mixing layers.
+        local_attention (`int`, *optional*, defaults to 128):
+            The window size for local attention.
+        local_rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the local RoPE embeddings.
+        embedding_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the MLP layers.
+        mlp_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the MLP layers.
+        decoder_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the decoder layers.
+        classifier_pooling (`str`, *optional*, defaults to `"cls"`):
+            The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
+            CLS token doesn't attend to all tokens on long sequences.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the classifier.
+        classifier_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the classifier.
+        classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+            The activation function for the classifier.
+        deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
+            Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
+        sparse_prediction (`bool`, *optional*, defaults to `False`):
+            Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
+        sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
+            The index to ignore for the sparse prediction.
+        reference_compile (`bool`, *optional*):
+            Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
+            the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
+            shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
+            be faster in some scenarios.
+        repad_logits_with_grad (`bool`, *optional*, defaults to `False`):
+            When True, ModernBertForMaskedLM keeps track of the logits' gradient when repadding for output. This only
+            applies when using Flash Attention 2 with passed labels. Otherwise output logits always have a gradient.
+
+    Examples:
+
+    ```python
+    >>> from transformers import ModernBertModel, ModernBertConfig
+
+    >>> # Initializing a ModernBert style configuration
+    >>> configuration = ModernBertConfig()
+
+    >>> # Initializing a model from the modernbert-base style configuration
+    >>> model = ModernBertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "modernbert"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50368,
+        hidden_size=768,
+        intermediate_size=1152,
+        num_hidden_layers=22,
+        num_attention_heads=12,
+        lru_width=1152,
+        conv1d_width=4,
+        hidden_activation="gelu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        initializer_cutoff_factor=2.0,
+        norm_eps=1e-5,
+        norm_bias=False,
+        pad_token_id=50283,
+        eos_token_id=50282,
+        bos_token_id=50281,
+        cls_token_id=50281,
+        sep_token_id=50282,
+        global_rope_theta=160000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        global_temporal_every_n_layers=3,
+        local_attention=128,
+        local_rope_theta=10000.0,
+        embedding_dropout=0.0,
+        mlp_bias=False,
+        mlp_dropout=0.0,
+        decoder_bias=True,
+        classifier_pooling: Literal["cls", "mean"] = "cls",
+        classifier_dropout=0.0,
+        classifier_bias=False,
+        classifier_activation="gelu",
+        deterministic_flash_attn=False,
+        sparse_prediction=False,
+        sparse_pred_ignore_index=-100,
+        reference_compile=None,
+        repad_logits_with_grad=False,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            cls_token_id=cls_token_id,
+            sep_token_id=sep_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.lru_width = lru_width
+        self.conv1d_width = conv1d_width
+        self.initializer_range = initializer_range
+        self.initializer_cutoff_factor = initializer_cutoff_factor
+        self.norm_eps = norm_eps
+        self.norm_bias = norm_bias
+        self.global_rope_theta = global_rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_activation = hidden_activation
+        self.global_temporal_every_n_layers = global_temporal_every_n_layers
+        self.local_attention = local_attention
+        self.local_rope_theta = local_rope_theta
+        self.embedding_dropout = embedding_dropout
+        self.mlp_bias = mlp_bias
+        self.mlp_dropout = mlp_dropout
+        self.decoder_bias = decoder_bias
+        self.classifier_pooling = classifier_pooling
+        self.classifier_dropout = classifier_dropout
+        self.classifier_bias = classifier_bias
+        self.classifier_activation = classifier_activation
+        self.deterministic_flash_attn = deterministic_flash_attn
+        self.sparse_prediction = sparse_prediction
+        self.sparse_pred_ignore_index = sparse_pred_ignore_index
+        self.reference_compile = reference_compile
+        self.repad_logits_with_grad = repad_logits_with_grad
+
+        if self.classifier_pooling not in ["cls", "mean"]:
+            raise ValueError(
+                f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
+            )
+
+class SqrtBoundDerivative(torch.autograd.Function):
+    """Computes a square root with a gradient clipped at `_MAX_SQRT_GRADIENT`."""
+
+    @staticmethod
+    def forward(ctx, x: torch.Tensor) -> torch.Tensor:
+        """The forward pass, which is a normal `sqrt`."""
+        ctx.save_for_backward(x)
+        return torch.sqrt(x)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
+        """The backward pass, which clips the `sqrt` gradient."""
+        (x,) = ctx.saved_tensors
+        clipped_x_times_4 = torch.clip(4.0 * x, min=1 / (_MAX_SQRT_GRADIENT**2))
+        return grad_output / torch.sqrt(clipped_x_times_4)
+
+class GriffinRglru(nn.Module):
+    """A Real-Gated Linear Recurrent Unit (RG-LRU) layer."""
+
+    def __init__(self, config: ModernBergConfig):
+        super().__init__()
+        self.num_attention_heads = config.num_attention_heads
+        self.block_width = config.lru_width // self.num_attention_heads
+
+        self.recurrent_param = nn.Parameter(torch.empty([config.lru_width]))
+        self.input_gate_weight = nn.Parameter(
+            torch.empty([self.num_attention_heads, self.block_width, self.block_width])
+        )
+        self.input_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))
+
+        self.recurrent_gate_weight = nn.Parameter(
+            torch.empty([self.num_attention_heads, self.block_width, self.block_width])
+        )
+        self.recurrent_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))
+        self.recurrent_states = None
+
+    def forward(
+        self,
+        activations: torch.Tensor,
+        position_ids: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        batch_size, seq_len, lru_width = activations.shape
+        reset = position_ids[:, :, None] == 0
+
+        reshape_act = activations.reshape(batch_size * seq_len, self.num_attention_heads, self.block_width)
+        reshape_act = reshape_act.permute(1, 0, 2)
+
+        res = torch.baddbmm(self.input_gate_bias[:, None, :], reshape_act, self.input_gate_weight)
+        input_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))
+
+        res = torch.baddbmm(self.recurrent_gate_bias[:, None, :], reshape_act, self.recurrent_gate_weight)
+        recurrent_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))
+
+        # Compute the parameter `A` of the recurrence.
+        log_recurrent_gate = -8.0 * recurrent_gate * nn.functional.softplus(self.recurrent_param)
+        recurrent_gate = torch.exp(log_recurrent_gate)
+        a_square = torch.exp(2 * log_recurrent_gate)
+
+        # Gate the input.
+        gated_inputs = activations * input_gate
+
+        # Apply gamma normalization to the input. We need to clip the derivatives of
+        # `sqrt` in order to prevent NaNs during training in bfloat16. TODO a bit annoying
+        multiplier = 1
+        tracing = isinstance(activations, torch.fx.Proxy) or is_torchdynamo_compiling()
+        if not torch.jit.is_tracing() and not tracing:
+            multiplier = SqrtBoundDerivative.apply(1 - a_square)
+        multiplier = reset + ~reset * multiplier
+        normalized_x = gated_inputs * multiplier.type(activations.dtype)
+
+        hidden_states, recurrent_states = self._rnn_scan(
+            hidden_states=normalized_x,
+            recurrent_gate=recurrent_gate,
+            reset=reset,
+            recurrent_states=self.recurrent_states,
+        )
+        self.recurrent_states = recurrent_states
+        return hidden_states
+
+    # TODO refactor
+    def _rnn_scan(
+        self,
+        hidden_states: torch.Tensor,
+        recurrent_gate: torch.Tensor,
+        reset: torch.Tensor,
+        recurrent_states: Union[torch.Tensor, None],
+        acc_dtype: torch.dtype = torch.float32,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Runs the recurrence of a linear RNN.
+
+        Args:
+        hidden_states: The input sequence.
+        recurrent_gate: The diagonal of the recurrence matrix `A`.
+        reset: Indicator of document boundaries, e.g. when to reset the hidden state
+            of the RNN.
+        recurrent_states: The initial hidden state.
+        acc_dtype: The data type for the accumulation.
+
+        Returns:
+        The output of the linear recurrence.
+        """
+        # Multiply `a` by the reset.
+        recurrent_gate = recurrent_gate * ~reset
+
+        if hidden_states.shape[1] == 1:
+            # Using scan in sampling mode.
+            if recurrent_states is None:  # same here, when decoding you always have cache
+                return hidden_states, hidden_states[:, 0].type(acc_dtype)
+
+            else:
+                contextualized_states = recurrent_gate.type(acc_dtype) * recurrent_states[:, None].to(
+                    recurrent_gate.device
+                )
+                contextualized_states += hidden_states.type(acc_dtype)
+                return contextualized_states.type(hidden_states.dtype), contextualized_states[:, -1]
+
+        else:
+            # Using scan in linear mode.
+            if recurrent_states is None:
+                recurrent_states = torch.zeros(hidden_states[:, 0].shape, dtype=acc_dtype, device=hidden_states.device)
+
+            contextualized_states = torch.zeros_like(hidden_states)
+            for t in range(hidden_states.shape[1]):
+                recurrent_states = recurrent_gate[:, t].type(acc_dtype) * recurrent_states.to(recurrent_gate.device)
+                recurrent_states = recurrent_states + hidden_states[:, t].type(acc_dtype)
+                contextualized_states[:, t] = recurrent_states.type(hidden_states.dtype)
+
+        return contextualized_states, recurrent_states
+
+class GriffinRecurrentblock(nn.Module):
+    """Griffin and Hawk's recurrent block."""
+
+    def __init__(self, config: ModernBergConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.lru_width = config.lru_width
+        self.hidden_size = config.hidden_size
+        self.linear_y = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
+        self.linear_x = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
+        self.linear_out = nn.Linear(in_features=config.lru_width, out_features=config.hidden_size)
+        self.conv1d_width = config.conv1d_width
+        self.conv_1d = nn.Conv1d(
+            config.lru_width,
+            config.lru_width,
+            kernel_size=config.conv1d_width,
+            groups=config.lru_width,
+            padding=config.conv1d_width - 1,
+        )
+        self.rg_lru = GriffinRglru(config)
+        self.act_fn = ACT2FN[config.hidden_activation]
+
+        self.conv1d_state = None
+
+    def forward(
+        self,
+        input_states: torch.Tensor,
+        position_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        cache_position: torch.Tensor,
+        use_cache: bool = True,
+    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
+        _, seq_len, _ = input_states.shape
+
+        y_branch = self.linear_y(input_states)
+        y_branch = self.act_fn(y_branch)
+
+        x_branch = self.linear_x(input_states)
+        x_branch = x_branch.transpose(1, 2)
+
+        if use_cache:
+            if cache_position.shape[0] != 1:  # prefill
+                self.conv1d_state = nn.functional.pad(x_branch, (self.conv1d_width - x_branch.shape[-1] - 1, 0))
+                x_branch = self.conv_1d(x_branch)[..., :seq_len]
+            else:  # decoding
+                conv_state = torch.cat((self.conv1d_state, x_branch), -1)
+                x_branch = torch.sum(conv_state * self.conv_1d.weight[:, 0, :], dim=-1) + self.conv_1d.bias
+                x_branch = x_branch.unsqueeze(-1)
+                self.conv1d_state = conv_state[:, :, 1:]
+        else:
+            x_branch = self.conv_1d(x_branch)[..., :seq_len]
+
+        x_branch = self.rg_lru(x_branch.transpose(1, 2), position_ids)
+
+        hidden_states = x_branch * y_branch
+        hidden_states = self.linear_out(hidden_states)
+        return hidden_states
+
+    def _setup_cache(self, batch, device, dtype):
+        # recurrent_states always computed in full precision
+        self.rg_lru.recurrent_states = torch.zeros((batch, self.lru_width), device=device, dtype=torch.float32)
+        self.conv1d_state = torch.zeros((batch, self.hidden_size, self.conv1d_width - 1), device=device, dtype=dtype)
+
+def eager_attention_forward(
+    module: "ModernBergAttention",
+    qkv: torch.Tensor,
+    attention_mask: torch.Tensor,
+    sliding_window_mask: torch.Tensor,
+    position_ids: Optional[torch.LongTensor],
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    output_attentions: Optional[bool] = False,
+    **_kwargs,
+) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+    # qkv: [batch_size, seqlen, 3, nheads, headdim]
+    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+    # query, key, value: [batch_size, heads, seq_len, head_dim]
+    query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+    scale = module.head_dim**-0.5
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale
+
+    if local_attention != (-1, -1):
+        attention_mask = sliding_window_mask
+
+    attn_weights = attn_weights + attention_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.view(bs, -1, dim)
+    if output_attentions:
+        return (attn_output, attn_weights)
+    return (attn_output,)
+
+def flash_attention_forward(
+    module: "ModernBergAttention",
+    qkv: torch.Tensor,
+    rotary_emb: ModernBertUnpaddedRotaryEmbedding,
+    cu_seqlens: torch.Tensor,
+    max_seqlen: int,
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    target_dtype: torch.dtype = torch.bfloat16,
+    **_kwargs,
+) -> Tuple[torch.Tensor]:
+    # (total_seqlen, 3, nheads, headdim)
+    qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+
+    convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
+    if convert_dtype:
+        # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
+        # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
+        orig_dtype = qkv.dtype
+        qkv = qkv.to(target_dtype)
+
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=module.attention_dropout if module.training else 0.0,
+            deterministic=module.deterministic_flash_attn,
+            window_size=local_attention,
+        )
+        attn = attn.to(orig_dtype)  # type: ignore
+    else:
+        attn = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            dropout_p=module.attention_dropout if module.training else 0.0,
+            deterministic=module.deterministic_flash_attn,
+            window_size=local_attention,
+        )
+    return (attn.view(bs, dim),)
+
+def sdpa_attention_forward(
+    module: "ModernBergAttention",
+    qkv: torch.Tensor,
+    attention_mask: torch.Tensor,
+    sliding_window_mask: torch.Tensor,
+    position_ids: Optional[torch.LongTensor],
+    local_attention: Tuple[int, int],
+    bs: int,
+    dim: int,
+    **_kwargs,
+) -> Tuple[torch.Tensor]:
+    # qkv: [batch_size, seqlen, 3, nheads, headdim]
+    cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+    query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+    # query, key, value: [batch_size, heads, seq_len, head_dim]
+    query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+    if local_attention != (-1, -1):
+        attention_mask = sliding_window_mask
+
+    attn_output = (
+        F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=module.attention_dropout if module.training else 0.0,
+            attn_mask=attention_mask,
+        )
+        .transpose(1, 2)
+        .contiguous()
+    )
+    attn_output = attn_output.view(bs, -1, dim)
+    return (attn_output,)
+
+MODERNBERT_ATTENTION_FUNCTION = {
+    "flash_attention_2": flash_attention_forward,
+    "eager": eager_attention_forward,
+    "sdpa": sdpa_attention_forward,
+}
+
+class ModernBergRotaryEmbedding(GemmaRotaryEmbedding):
+    def __init__(self, config: ModernBergConfig, dim: int, base: float, device: Optional[torch.device] = None):
+        # JANK!!! JANK!!! JANK!!!
+        config.rope_theta = base
+        super().__init__(config=config, device=device)
+        inv_freq, self.attention_scaling = self.rope_init_fn(None, device, dim=dim, base=base)
+
+class ModernBergAttention(nn.Module):
+    """Performs multi-headed self attention on a batch of unpadded sequences.
+
+    If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
+    If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
+    which requires padding and unpadding inputs, adding some overhead.
+
+    See `forward` method for additional details.
+    """
+
+    def __init__(self, config: ModernBergConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_id = layer_id
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.deterministic_flash_attn = config.deterministic_flash_attn
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.all_head_size = self.head_dim * self.num_heads
+        self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)
+
+        assert layer_id % config.global_temporal_every_n_layers != 0, "ModernBerg does not support global self-attention"
+        self.local_attention = (config.local_attention // 2, config.local_attention // 2)
+
+        rope_theta = config.global_rope_theta
+        max_position_embeddings = config.max_position_embeddings
+        if self.local_attention != (-1, -1):
+            if config.local_rope_theta is not None:
+                rope_theta = config.local_rope_theta
+            max_position_embeddings = config.local_attention
+
+        if config._attn_implementation == "flash_attention_2":
+            self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
+                dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
+            )
+        else:
+            self.rotary_emb = ModernBergRotaryEmbedding(config=config, dim=self.head_dim, base=rope_theta)
+
+        self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+        self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
+        self.pruned_heads = set()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv = self.Wqkv(hidden_states)
+
+        bs = hidden_states.shape[0]
+        if self.config._attn_implementation == "flash_attention_2":
+            qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
+        else:
+            qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)
+
+        attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
+            self,
+            qkv=qkv,
+            rotary_emb=self.rotary_emb,
+            local_attention=self.local_attention,
+            bs=bs,
+            dim=self.all_head_size,
+            output_attentions=output_attentions,
+            **kwargs,
+        )
+        hidden_states = attn_outputs[0]
+        hidden_states = self.out_drop(self.Wo(hidden_states))
+
+        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted
+
+class ModernBergTemporalLayer(nn.Module):
+    def __init__(self, config: ModernBergConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        if layer_id % config.global_temporal_every_n_layers == 0:
+            self.temporal = GriffinRecurrentblock(config=config, layer_id=layer_id)
+        else:
+            self.temporal = ModernBergAttention(config=config, layer_id=layer_id)
+    
+    def forward(self, hidden_states: torch.Tensor, **kwargs):
+        return self.temporal(hidden_states, **kwargs)
+
+class ModernBergEncoderLayer(nn.Module):
+    def __init__(self, config: ModernBergConfig, layer_id: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        if layer_id == 0:
+            self.temporal_norm = nn.Identity()
+        else:
+            self.temporal_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.temporal = ModernBergTemporalLayer(config=config, layer_id=layer_id)
+        self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.mlp = ModernBertMLP(config)
+
+    @torch.compile(dynamic=True)
+    def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.mlp(self.mlp_norm(hidden_states))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> torch.Tensor:
+        attn_outputs = self.temporal(
+            self.temporal_norm(hidden_states),
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            output_attentions=output_attentions,
+        )
+        hidden_states = hidden_states + attn_outputs[0]
+        mlp_output = (
+            self.compiled_mlp(hidden_states)
+            if self.config.reference_compile
+            else self.mlp(self.mlp_norm(hidden_states))
+        )
+        hidden_states = hidden_states + mlp_output
+
+        return (hidden_states,) + attn_outputs[1:]  # add attentions if outputted
+
+MODERNBERG_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`ModernBergConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare ModernBerg Model outputting raw hidden-states without any specific head on top.",
+    MODERNBERG_START_DOCSTRING,
+)
+class ModernBergPreTrainedModel(PreTrainedModel):
+    config_class = ModernBergConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ModernBergEmbeddings", "ModernBergEncoderLayer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = False
+
+    def _init_weights(self, module: nn.Module):
+        cutoff_factor = self.config.initializer_cutoff_factor
+        if cutoff_factor is None:
+            cutoff_factor = 3
+
+        def init_weight(module: nn.Module, std: float):
+            nn.init.trunc_normal_(
+                module.weight,
+                mean=0.0,
+                std=std,
+                a=-cutoff_factor * std,
+                b=cutoff_factor * std,
+            )
+
+            if isinstance(module, nn.Linear):
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+        stds = {
+            "in": self.config.initializer_range,
+            "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+            "embedding": self.config.initializer_range,
+            "final_out": self.config.hidden_size**-0.5,
+        }
+
+        std = math.sqrt(self.config.initializer_range / self.config.conv1d_width)
+        if isinstance(module, ModernBertEmbeddings):
+            init_weight(module.tok_embeddings, stds["embedding"])
+        elif isinstance(module, ModernBertMLP):
+            init_weight(module.Wi, stds["in"])
+            init_weight(module.Wo, stds["out"])
+        elif isinstance(module, ModernBergAttention):
+            init_weight(module.Wqkv, stds["in"])
+            init_weight(module.Wo, stds["out"])
+        elif isinstance(module, GriffinRecurrentblock):
+            torch.nn.init.zeros_(module.linear_x.bias)
+            torch.nn.init.normal_(module.linear_x.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
+
+            torch.nn.init.zeros_(module.linear_y.bias)
+            torch.nn.init.normal_(module.linear_y.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
+
+            std = math.sqrt(self.config.initializer_range / self.config.lru_width)
+            torch.nn.init.normal_(module.linear_out.weight, mean=0.0, std=std)
+            torch.nn.init.zeros_(module.linear_out.bias)
+        elif isinstance(module, GriffinRglru):
+            std = math.sqrt(
+                self.config.initializer_range / (self.config.lru_width // self.config.num_attention_heads)
+            )
+            torch.nn.init.normal_(module.input_gate_weight, mean=0.0, std=std)
+            torch.nn.init.normal_(module.recurrent_gate_weight, mean=0.0, std=std)
+            torch.nn.init.zeros_(module.input_gate_bias)
+            torch.nn.init.zeros_(module.recurrent_gate_bias)
+
+            module.recurrent_param.data.uniform_(0.9**2 + 1e-8, 0.999**2 + 1e-8)
+            module.recurrent_param.data.log_().mul_(0.5)
+            module.recurrent_param.data.neg_().exp_().sub_(1.0).log_()
+        elif isinstance(module, ModernBergPredictionHead):
+            init_weight(module.dense, stds["out"])
+        elif isinstance(module, ModernBergForMaskedLM):
+            init_weight(module.decoder, stds["out"])
+        elif isinstance(module, (ModernBergForSequenceClassification, ModernBergForTokenClassification)):
+            init_weight(module.classifier, stds["final_out"])
+        elif isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            if getattr(module, "bias", None) is not None:
+                torch.nn.init.zeros_(module.bias)
+
+    @classmethod
+    def _autoset_attn_implementation(
+        cls,
+        config,
+        use_flash_attention_2: bool = False,
+        torch_dtype: Optional[torch.dtype] = None,
+        device_map: Optional[Union[str, Dict[str, int]]] = None,
+        check_device_map: bool = True,
+    ):
+        # If the user didn't specify anything, try to use flash_attention_2 if available.
+        # Otherwise we fall back to the default SDPA -> Eager from the super() method.
+        # ModernBert's FA2 implementation correctly handles non-fp16/bf16 dtypes, we don't
+        # need the FA2 warning for non-fp16/bf16 dtypes so we set fp16 for the FA2 check.
+        if config._attn_implementation_internal is None:
+            config._attn_implementation_internal = "flash_attention_2"
+            try:
+                return cls._check_and_enable_flash_attn_2(
+                    config,
+                    torch_dtype=torch.float16,
+                    device_map=device_map,
+                    hard_check_only=False,
+                    check_device_map=check_device_map,
+                )
+            except (ValueError, ImportError):
+                config._attn_implementation_internal = None
+        return super()._autoset_attn_implementation(
+            config,
+            use_flash_attention_2=use_flash_attention_2,
+            torch_dtype=torch.float16,
+            device_map=device_map,
+            check_device_map=check_device_map,
+        )
+
+    def _maybe_set_compile(self):
+        if self.config.reference_compile is False:
+            return
+
+        if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "If `accelerate` split the model across devices, `torch.compile` will not work. "
+                    "Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        if self.device.type == "mps":
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
+                    "Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        if self.device.type == "cpu":
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. "
+                    "Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        if self.config.reference_compile is None:
+            self.config.reference_compile = is_triton_available()
+
+    def resize_token_embeddings(self, *args, **kwargs):
+        model_embeds = super().resize_token_embeddings(*args, **kwargs)
+
+        if self.config.reference_compile in {True, None}:
+            if self.config.reference_compile:
+                logger.warning_once(
+                    "Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
+                )
+            self.config.reference_compile = False
+
+        return model_embeds
+
+
+MODERNBERG_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. With Flash Attention 2.0, padding will be ignored
+            by default should you provide it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        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_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare ModernBerg Model outputting raw hidden-states without any specific head on top.",
+    MODERNBERG_START_DOCSTRING,
+)
+class ModernBergModel(ModernBergPreTrainedModel):
+    def __init__(self, config: ModernBergConfig):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = ModernBertEmbeddings(config)
+        self.layers = nn.ModuleList(
+            [ModernBergEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
+        )
+        self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(MODERNBERG_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        self._maybe_set_compile()
+
+        if input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+        if batch_size is None and seq_len is None:
+            if inputs_embeds is not None:
+                batch_size, seq_len = inputs_embeds.shape[:2]
+            else:
+                batch_size, seq_len = input_ids.shape[:2]
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+        repad = False
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                repad = True
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask
+                    )
+        else:
+            if position_ids is None:
+                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+
+            attention_mask, sliding_window_mask = self._update_attention_mask(
+                attention_mask, output_attentions=output_attentions
+            )
+
+        hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    sliding_window_mask,
+                    position_ids,
+                    cu_seqlens,
+                    max_seqlen,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    sliding_window_mask=sliding_window_mask,
+                    position_ids=position_ids,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    output_attentions=output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions and len(layer_outputs) > 1:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        hidden_states = self.final_norm(hidden_states)
+
+        if repad:
+            hidden_states = _pad_modernbert_output(
+                inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+            )
+            if all_hidden_states is not None:
+                all_hidden_states = tuple(
+                    _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
+                    for hs in all_hidden_states
+                )
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+    def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
+        if output_attentions:
+            if self.config._attn_implementation == "sdpa":
+                logger.warning_once(
+                    "Outputting attentions is only supported with the 'eager' attention implementation, "
+                    'not with "sdpa". Falling back to `attn_implementation="eager"`.'
+                )
+                self.config._attn_implementation = "eager"
+            elif self.config._attn_implementation != "eager":
+                logger.warning_once(
+                    "Outputting attentions is only supported with the eager attention implementation, "
+                    f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
+                    " Setting `output_attentions=False`."
+                )
+
+        global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+        # Create position indices
+        rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
+        # Calculate distance between positions
+        distance = torch.abs(rows - rows.T)
+
+        # Create sliding window mask (1 for positions within window, 0 outside)
+        window_mask = (
+            (distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
+        )
+        # Combine with existing mask
+        sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)
+
+        return global_attention_mask, sliding_window_mask
+
+
+class ModernBergPredictionHead(nn.Module):
+    def __init__(self, config: ModernBergConfig):
+        super().__init__()
+        self.config = config
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
+        self.act = ACT2FN[config.classifier_activation]
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(self.act(self.dense(hidden_states)))
+
+
+@add_start_docstrings(
+    "The ModernBerg Model with a decoder head on top that is used for masked language modeling.",
+    MODERNBERG_START_DOCSTRING,
+)
+class ModernBergForMaskedLM(ModernBergPreTrainedModel):
+    _tied_weights_keys = ["decoder.weight"]
+
+    def __init__(self, config: ModernBergConfig):
+        super().__init__(config)
+        self.config = config
+        self.model = ModernBergModel(config)
+        self.head = ModernBergPredictionHead(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+        self.sparse_prediction = self.config.sparse_prediction
+        self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear):
+        self.decoder = new_embeddings
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    @add_start_docstrings_to_model_forward(MODERNBERG_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                    )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        if self.sparse_prediction and labels is not None:
+            # flatten labels and output first
+            labels = labels.view(-1)
+            last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+            # then filter out the non-masked tokens
+            mask_tokens = labels != self.sparse_pred_ignore_index
+            last_hidden_state = last_hidden_state[mask_tokens]
+            labels = labels[mask_tokens]
+
+        logits = (
+            self.compiled_head(last_hidden_state)
+            if self.config.reference_compile
+            else self.decoder(self.head(last_hidden_state))
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size)
+
+        if self.config._attn_implementation == "flash_attention_2":
+            with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
+                logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The ModernBerg Model with a sequence classification head on top that performs pooling.",
+    MODERNBERG_START_DOCSTRING,
+)
+class ModernBergForSequenceClassification(ModernBergPreTrainedModel):
+    def __init__(self, config: ModernBergConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.model = ModernBergModel(config)
+        self.head = ModernBergPredictionHead(config)
+        self.drop = torch.nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(MODERNBERG_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        if self.config.classifier_pooling == "cls":
+            last_hidden_state = last_hidden_state[:, 0]
+        elif self.config.classifier_pooling == "mean":
+            last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+                dim=1, keepdim=True
+            )
+
+        pooled_output = self.head(last_hidden_state)
+        pooled_output = self.drop(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The ModernBerg Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.",
+    MODERNBERG_START_DOCSTRING,
+)
+class ModernBergForTokenClassification(ModernBergPreTrainedModel):
+    def __init__(self, config: ModernBergConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.model = ModernBergModel(config)
+        self.head = ModernBergPredictionHead(config)
+        self.drop = torch.nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(MODERNBERG_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        self._maybe_set_compile()
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = outputs[0]
+
+        last_hidden_state = self.head(last_hidden_state)
+        last_hidden_state = self.drop(last_hidden_state)
+        logits = self.classifier(last_hidden_state)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "ModernBergConfig",
+    "ModernBergModel",
+    "ModernBergPreTrainedModel",
+    "ModernBergForMaskedLM",
+    "ModernBergForSequenceClassification",
+    "ModernBergForTokenClassification",
+]