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config.json

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+{
+  "A_initializer_range": [
+    1,
+    16
+  ],
+  "_name_or_path": "OuteAI/Lite-Oute-2-Mamba2Attn-Base",
+  "architectures": [
+    "Mamba2ForCausalLM"
+  ],
+  "attention_conv_kernel": 4,
+  "attention_head_dim": 128,
+  "attention_layers_idx": [
+    6,
+    12,
+    18,
+    24
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_mamba2attn.Mamba2Config",
+    "AutoModelForCausalLM": "modeling_mamba2attn.Mamba2ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "chunk_size": 256,
+  "classifier_dropout": 0.1,
+  "conv_initializer_range": null,
+  "emb_initializer_range": 0.02,
+  "eos_token_id": 2,
+  "expand": 2,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "intermediate_size": 2048,
+  "layer_norm_epsilon": 1e-05,
+  "mamba2_conv_kernel": 4,
+  "mamba2_head_dim": 64,
+  "mamba2_num_heads": 32,
+  "max_position_embeddings": 8192,
+  "mlp_intermediate_size": 0,
+  "mlp_padding_size": 128,
+  "model_type": "mamba2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 16,
+  "pad_token_id": 0,
+  "rescale_prenorm_residual": false,
+  "residual_in_fp32": true,
+  "rope_emb_dim": 64,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "state_size": 128,
+  "tie_embedding_weights": true,
+  "time_step_floor": 0.0001,
+  "time_step_limit": [
+    0.0,
+    Infinity
+  ],
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "use_attention_out_bias": false,
+  "use_attention_qkv_bias": false,
+  "use_cache": true,
+  "use_conv_bias": true,
+  "use_mamba2_bias": false,
+  "use_mlp_bias": false,
+  "vocab_size": 32768
+}

configuration_mamba2attn.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# 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.
+
+# Implementation from: https://github.com/huggingface/transformers/pull/32027
+
+"""MAMBA2 configuration"""
+
+from typing import List
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mamba2Config(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
+    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 MAMBA2
+    [state-spaces/mamba2-130m](https://huggingface.co/state-spaces/mamba2-130m) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50280):
+            Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Mamba2Model`].
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            The id of the beginning of sentence token in the vocabulary.
+        eos_token_id (`int`, *optional*, defaults to 0):
+            The id of the end of sentence token in the vocabulary.
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the embeddings and hidden states.
+        state_size (`int`, *optional*, defaults to 128):
+            Shape of the state space latents.
+        expand (`int`, *optional*, defaults to 2):
+            Expanding factor used to determine the intermediate size.
+        chunk_size (`int`, *optional*, defaults to 256):
+            Block / Chunk size for the HW-efficient algorithm which parallelizes on intra- and inter-chunk calculations.
+        mamba2_conv_kernel (`int`, *optional*, defaults to 4):
+            Size of the convolution kernel in the mamba2 mixer.
+        attention_conv_kernel (`int`, *optional*, defaults to 4):
+            Size of the convolution kernel in the attention block.
+        mlp_intermediate_size (`int`, *optional*, defaults to 0):
+            Dimensionality of up-projections within the MLP blocks. If set to <=0, then MLP blocks are disabled.
+        mlp_padding_size (`int`, *optional*, defaults to 128):
+            Padding `mlp_intermediate_size` to a multiple of this.
+        mamba2_head_dim (`int`, *optional*, defaults to 64):
+            Multi-input SSM head dimension.
+        attention_head_dim (`int`, *optional*, defaults to 128):
+            Multi-head attention's head dimension.
+        num_attention_heads (`int`, *optional*, defaults to 30):
+            The number of heads in multi-head attention.
+        num_key_value_heads (`int`, *optional*, defaults to 30):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `attention_num_key_value_heads=attention_num_heads`, the model will use Multi Head Attention (MHA), if
+            `attention_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `attention_num_heads`.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the model.
+        attention_layers_idx (`List[int]`, *optional*, defaults to `[]`):
+            The specific layers that exchange the mamba2 mixer block with the attention equivalent.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon to use in the layer normalization layers.
+        use_conv_bias (`bool`, *optional*, defaults to `True`):
+            Whether or not to use bias in the convolution layer of the mixer block.
+        use_mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether or not to use a bias in the up- and downprojections of the MLP block.
+        use_mamba2_bias (`bool`, *optional*, defaults to `False`):
+            Whether or not to use bias in ["in_proj", "out_proj"] of the mamba2 mixer block.
+        use_attention_qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether or not to use bias in the qkv projection of the attention block.
+        use_attention_out_bias (`bool`, *optional*, defaults to `False`):
+            Whether or not to use bias in the out projection of the attention block.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        emb_initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing the embedding weight matrix.
+        conv_initializer_range (`float`, *optional*):
+            The range for uniformly initializing the convolution weights.
+        A_initializer_range (`List[int]`, *optional*, defaults to `[1, 16]`):
+            The range for uniformly initializing the 1-SS(a) scalar.
+        time_step_min (`float`, *optional*, defaults to 0.001):
+            Minimum `time_step` used to bound `dt_proj.bias`.
+        time_step_max (`float`, *optional*, defaults to 0.1):
+            Maximum `time_step` used to bound `dt_proj.bias`.
+        time_step_floor (`float`, *optional*, defaults to 0.0001):
+            Minimum clamping value of the `dt_proj.bias` layer initialization.
+        time_step_limit (`List[float]`, *optional*, defaults to `[0.0, inf]`):
+            Clapping values for the dt weights.
+        residual_in_fp32 (`bool`, *optional*, defaults to `True`):
+            Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
+        rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
+            Whether or not to rescale `out_proj` weights when initializing.
+        rope_emb_dim (`int`, *optional*, defaults to 64):
+            Embedding dimension of the RoPE embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. This is based on the context length the
+            Mamba2 models have been trained on. Also necessary when using any sort of RoPE embeddings.
+        tie_embedding_weights (`bool`, *optional*, defaults to `True`):
+            Whether or not to tie the lm head to the input embeddings.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the cache should be used.
+        classifier_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the classification head in [`Mamba2ForSequenceClassification`] model.
+
+    Example:
+
+    ```python
+    >>> from transformers import Mamba2Config, Mamba2Model
+
+    >>> # Initializing a Mamba2 configuration
+    >>> configuration = Mamba2Config()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = Mamba2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "mamba2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50280,
+        pad_token_id=0,
+        bos_token_id=0,
+        eos_token_id=0,
+        hidden_size=768,
+        state_size=128,
+        expand=2,
+        chunk_size=256,
+        mamba2_conv_kernel=4,
+        attention_conv_kernel=4,
+        mlp_intermediate_size=0,
+        mlp_padding_size=128,
+        mamba2_head_dim=64,
+        attention_head_dim=128,
+        num_attention_heads=30,
+        num_key_value_heads=30,
+        num_hidden_layers=24,
+        attention_layers_idx=None,
+        layer_norm_epsilon=1e-5,
+        use_conv_bias=True,
+        use_mlp_bias=False,
+        use_mamba2_bias=False,
+        use_attention_qkv_bias=False,
+        use_attention_out_bias=False,
+        hidden_act="silu",
+        emb_initializer_range=0.02,
+        conv_initializer_range=None,
+        A_initializer_range=None,
+        time_step_min=0.001,
+        time_step_max=0.1,
+        time_step_floor=1e-4,
+        time_step_limit=None,
+        residual_in_fp32=True,
+        rescale_prenorm_residual=False,
+        rope_emb_dim=64,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        max_position_embeddings=2048,
+        tie_embedding_weights=True,
+        use_cache=True,
+        classifier_dropout=0.1,
+        **kwargs,
+    ):
+        # Avoid mutable default args
+        attention_layers_idx = [] if attention_layers_idx is None else attention_layers_idx
+        A_initializer_range = [1, 16] if A_initializer_range is None else A_initializer_range
+        time_step_limit = [0.0, float("inf")] if time_step_limit is None else time_step_limit
+
+        self.vocab_size = vocab_size
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.hidden_size = hidden_size
+        self.state_size = state_size
+        self.expand = expand
+        self.intermediate_size = int(expand * self.hidden_size)
+        self.chunk_size = chunk_size
+        self.mamba2_conv_kernel = mamba2_conv_kernel
+        self.attention_conv_kernel = attention_conv_kernel
+        self.mlp_padding_size = mlp_padding_size
+        self.mlp_intermediate_size = mlp_intermediate_size
+        self.mamba2_head_dim = mamba2_head_dim
+        self.mamba2_num_heads = self.intermediate_size // self.mamba2_head_dim
+        self.attention_head_dim = attention_head_dim
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.attention_layers_idx = attention_layers_idx
+        self._attention_layers_idx_validation()
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.use_conv_bias = use_conv_bias
+        self.use_mlp_bias = use_mlp_bias
+        self.use_mamba2_bias = use_mamba2_bias
+        self.use_attention_qkv_bias = use_attention_qkv_bias
+        self.use_attention_out_bias = use_attention_out_bias
+        self.hidden_act = hidden_act
+        self.emb_initializer_range = emb_initializer_range
+        self.conv_initializer_range = conv_initializer_range
+        self.A_initializer_range = A_initializer_range
+        self.time_step_min = time_step_min
+        self.time_step_max = time_step_max
+        self.time_step_floor = time_step_floor
+        self.time_step_limit = time_step_limit
+        self.residual_in_fp32 = residual_in_fp32
+        self.rescale_prenorm_residual = rescale_prenorm_residual
+        self.rope_emb_dim = rope_emb_dim
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        if self.rope_emb_dim > 0:
+            self._rope_scaling_validation()
+        self.max_position_embeddings = max_position_embeddings
+        self.tie_embedding_weights = tie_embedding_weights
+        self.use_cache = use_cache
+        self.classifier_dropout = classifier_dropout
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_embedding_weights=tie_embedding_weights,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+
+    def _attention_layers_idx_validation(self):
+        """
+        Validate the `attention_layers_idx` configuration.
+        """
+        if isinstance(self.attention_layers_idx, list) and len(self.attention_layers_idx) == 0:
+            return
+
+        if not isinstance(self.attention_layers_idx, List) and all(
+            isinstance(x, int) for x in self.attention_layers_idx
+        ):
+            raise ValueError(
+                "`attention_layers_idx` must be a list of integers indicating the attention layers, "
+                f"got {self.attention_layers_idx}"
+            )
+
+        if min(self.attention_layers_idx) < 0 or max(self.attention_layers_idx) >= self.num_hidden_layers:
+            raise ValueError(
+                "`attention_layers_idx` has out-of-range indices, "
+                f"got {self.attention_layers_idx}, but expected indices in {list(range(self.num_hidden_layers))}"
+            )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.44.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f180a1f218b19ad4a3986698893de8b773c11a7d3065d7a4b7adaf26e4babb3
+size 1008294800

modeling_mamba2attn.py

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+# coding=utf-8
+# Copyright 2024 state-spaces/mamba org and HuggingFace Inc. team.
+#
+# 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.
+
+# Implementation from: https://github.com/huggingface/transformers/pull/32027
+
+"""PyTorch MAMBA2 model."""
+
+import math
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import (
+    get_torch_version,
+    is_causal_conv1d_available,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+)
+from .configuration_mamba2attn import Mamba2Config
+
+
+logger = logging.get_logger(__name__)
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+try:
+    from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+    from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
+except:
+    selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined = None, None, None
+
+try:
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+except:
+    causal_conv1d_update, causal_conv1d_fn = None, None
+
+is_fast_path_available = all(
+    (
+        selective_state_update,
+        mamba_chunk_scan_combined,
+        mamba_split_conv1d_scan_combined,
+        causal_conv1d_fn,
+        causal_conv1d_update,
+    )
+)
+
+
+_CONFIG_FOR_DOC = "MambaConfig"
+
+
+# Adapted from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache with Mamba->Mamba2
+class HybridMamba2AttentionDynamicCache(DynamicCache):
+    """
+    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba2 cache
+    (which has a constant shape regardless of seq_len).
+
+    This cache has two sets of lists of tensors: `key_cache`, `value_cache`, and 'conv_states' for attention cache and
+    `conv_states` and `ssm_states` for mamba2 cache. Each of these lists has `num_layers` tensors.
+
+    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_key_value_heads, seq_len, attention_head_dim)`,
+    while `conv_states` has a shape of `(batch_size, attention_head_dim * (num_attention_heads + 2 * num_key_value_heads), attention_conv_kernel)`
+    or `(batch_size, 0)` (empty tensors) and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
+
+    For mamba2 layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
+    while `conv_states` represents the convolution state and has a shape of `(batch_size, intermediate_size + 2 * state_size, mamba2_conv_kernel)`,
+    and `ssm_states` represents the ssm state and has a shape of `(batch_size, mamba2_num_heads, mamba2_head_dim, state_size)`.
+    """
+
+    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
+        self.dtype = dtype
+        self.has_previous_state = False
+
+        in_channels = config.intermediate_size + 2 * config.state_size
+        ssm_state_size = config.state_size
+        mamba2_conv_kernel_size = config.mamba2_conv_kernel
+        attention_conv_kernel_size = config.attention_conv_kernel
+        mamba2_num_heads = config.mamba2_num_heads
+        mamba2_head_dim = config.mamba2_head_dim
+        attention_head_dim = config.attention_head_dim
+        attention_num_heads = config.num_attention_heads
+        attention_num_heads_kv = config.num_key_value_heads
+        attention_qkv_dim = attention_head_dim * (attention_num_heads + 2 * attention_num_heads_kv)
+
+        self.conv_states = []
+        self.ssm_states = []
+        self.transformer_layers = []
+        for i in range(config.num_hidden_layers):
+            if i not in config.attention_layers_idx:
+                self.conv_states += [
+                    torch.zeros(batch_size, in_channels, mamba2_conv_kernel_size, device=device, dtype=dtype)
+                ]
+                self.ssm_states += [
+                    torch.zeros(
+                        batch_size, mamba2_num_heads, mamba2_head_dim, ssm_state_size, device=device, dtype=dtype
+                    )
+                ]
+            else:
+                # Conv1d is optional for the attention layer
+                if attention_conv_kernel_size > 0:
+                    self.conv_states += [
+                        torch.zeros(
+                            batch_size, attention_qkv_dim, attention_conv_kernel_size, device=device, dtype=dtype
+                        )
+                    ]
+                else:
+                    self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
+                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
+                self.transformer_layers.append(i)
+
+        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+
+    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.update
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Update the cache
+        if self.key_cache[layer_idx].shape[-1] == 0:
+            self.key_cache[layer_idx] = key_states
+            self.value_cache[layer_idx] = value_states
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.reorder_cache
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        for layer_idx in range(len(self.key_cache)):
+            device = self.key_cache[layer_idx].device
+            self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+            device = self.value_cache[layer_idx].device
+            self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+            device = self.conv_states[layer_idx].device
+            self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
+            device = self.ssm_states[layer_idx].device
+            self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
+
+    # Adapted from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.get_seq_length
+    # Fixes issues when accessing on empty cache and allow mamba2 pure architectures
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # Mamba2 layers don't need the seq_len either way
+        if len(self.transformer_layers) == 0:
+            return 0
+
+        # Take any layer that contains cache and not empty tensor
+        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
+        if len(self.key_cache) <= layer_idx:
+            return 0
+
+        # We also allow seq_len checks on empty tensors
+        size_idx = -2 if len(self.key_cache[layer_idx].shape) > 2 else -1
+
+        return self.key_cache[layer_idx].shape[size_idx]
+
+    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.to_legacy_cache with Mamba->Mamba2
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        raise NotImplementedError("HybridMamba2AttentionDynamicCache does not have a legacy cache equivalent.")
+
+    @classmethod
+    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.from_legacy_cache with Mamba->Mamba2
+    def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
+        raise NotImplementedError("HybridMamba2AttentionDynamicCache does not have a legacy cache equivalent.")
+
+
+class Mamba2MLP(nn.Module):
+    def __init__(self, config: Mamba2Config, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+
+        self.hidden_size = config.hidden_size
+        self.original_intermediate_size = config.mlp_intermediate_size
+        self.mlp_padding_size = config.mlp_padding_size
+
+        self.intermediate_size = (
+            (self.original_intermediate_size + self.mlp_padding_size - 1)
+            // self.mlp_padding_size
+            * self.mlp_padding_size
+        )
+
+        self.fc1 = nn.Linear(self.hidden_size, 2 * self.intermediate_size, bias=config.use_mlp_bias)
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+        self.fc2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_mlp_bias)
+
+    def forward(self, x):
+        y = self.fc1(x)
+        y, z = y.chunk(2, dim=-1)
+        y = y * self.act(z)
+        y = self.fc2(y)
+        return y
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mamba2
+class Mamba2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=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().to(device) / 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, position_ids):
+        # 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)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Mamba2
+class Mamba2LinearScalingRotaryEmbedding(Mamba2RotaryEmbedding):
+    """Mamba2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: a scaling factor is aplied to the position ids
+        position_ids = position_ids.float() / self.scaling_factor
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Mamba2
+class Mamba2DynamicNTKScalingRotaryEmbedding(Mamba2RotaryEmbedding):
+    """Mamba2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def forward(self, x, position_ids):
+        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
+
+        cos, sin = super().forward(x, position_ids)
+        return cos, sin
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """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)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """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.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] 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[position_ids] and sin[position_ids] 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
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the 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)
+
+
+# Adapted from transformers.models.llama.modeling_llama.LlamaAttention with Llama->Mamba2
+class Mamba2Attention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Possible switch to MQA when num_heads_kv < num_heads_q.
+    """
+
+    def __init__(self, config: Mamba2Config, layer_idx: int):
+        super().__init__()
+        self.config = config
+
+        self.hidden_size = config.hidden_size
+        self.conv_kernel_size = config.attention_conv_kernel
+        self.head_dim = config.attention_head_dim
+        self.num_heads = config.num_attention_heads
+        self.num_heads_kv = config.num_key_value_heads
+        self.num_groups_kv = self.num_heads // self.num_heads_kv
+        # See https://github.com/state-spaces/mamba/issues/457#issuecomment-2221116217
+        # hidden_size % num_heads == 0 is not necessary due to this custom head projection dim
+        self.qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
+        self.out_dim = self.head_dim * self.num_heads
+
+        # Optional RoPE
+        self.rotary_emb_dim = config.rope_emb_dim
+        self.rope_theta = config.rope_theta
+        self._init_rope()
+
+        self.in_proj = nn.Linear(self.hidden_size, self.qkv_dim, bias=config.use_attention_qkv_bias)
+        # Optional conv1d
+        self._init_conv1d()
+        self.out_proj = nn.Linear(self.out_dim, self.hidden_size, bias=config.use_attention_out_bias)
+
+        self.is_causal = True
+        self.layer_idx = layer_idx
+
+        # We throw a similar fast path warning, in case no mamba2 block is used
+        if config.num_hidden_layers == len(config.attention_layers_idx):
+            if not is_causal_conv1d_available():
+                logger.warning_once(
+                    "Convolution implementation in Mamba2 attention is falling back to naive implementation because `(causal_conv1d_fn, causal_conv1d_update)`"
+                    "is None. To install follow https://github.com/Dao-AILab/causal-conv1d."
+                )
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaAttention._init_rope
+    # Rope is optional and can be ignored if rope_emb_dim <= 0
+    def _init_rope(self):
+        # RoPE is optional
+        if self.rotary_emb_dim < 1:
+            return
+
+        if self.config.rope_scaling is None:
+            self.rotary_emb = Mamba2RotaryEmbedding(
+                self.rotary_emb_dim,
+                max_position_embeddings=self.config.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = Mamba2LinearScalingRotaryEmbedding(
+                    self.rotary_emb_dim,
+                    max_position_embeddings=self.config.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = Mamba2DynamicNTKScalingRotaryEmbedding(
+                    self.rotary_emb_dim,
+                    max_position_embeddings=self.config.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _init_conv1d(self):
+        # Conv1d is optional
+        if self.conv_kernel_size < 1:
+            return
+
+        self.conv1d = nn.Conv1d(
+            self.qkv_dim,
+            self.qkv_dim,
+            kernel_size=self.conv_kernel_size,
+            padding=self.conv_kernel_size - 1,
+            groups=self.qkv_dim,
+        )
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaAttention.forward
+    # Custom projections involving optional causal-conv-1d and optional (partial) RoPE
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        cache: Optional[HybridMamba2AttentionDynamicCache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+        bsz, q_len, _ = hidden_states.shape
+
+        # Apply attention-conv1d-specific projections and rope
+        query, key, value = self._attn_conv1d_projections_and_rope(
+            hidden_states=hidden_states, position_ids=position_ids, cache=cache, use_cache=use_cache
+        )
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key = repeat_kv(key, self.num_groups_kv)
+        value = repeat_kv(value, self.num_groups_kv)
+
+        attn_weights = torch.matmul(query, key.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.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.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=0.0, training=self.training)
+        attn_output = torch.matmul(attn_weights, value)
+
+        # Reshape output
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        # Final projection
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights
+
+    def _conv1d(self, qkv, seq_len, cache, cached_start, cached_forward):
+        # Init cache with first "real" values
+        if cached_start:
+            qkv_t = qkv.transpose(1, 2)
+            cache.conv_states[self.layer_idx].copy_(
+                nn.functional.pad(qkv_t, (self.conv_kernel_size - qkv_t.shape[-1], 0))
+            )
+
+        if is_causal_conv1d_available():
+            if cached_forward:
+                qkv = causal_conv1d_update(
+                    x=qkv.squeeze(1),
+                    conv_state=cache.conv_states[self.layer_idx],
+                    weight=self.conv1d.weight.squeeze(1),
+                    bias=self.conv1d.bias,
+                ).unsqueeze(1)
+            else:
+                qkv = causal_conv1d_fn(
+                    x=qkv.transpose(1, 2),
+                    weight=self.conv1d.weight.squeeze(1),
+                    bias=self.conv1d.bias,
+                ).transpose(1, 2)
+        else:
+            if cached_forward:
+                cache.conv_states[self.layer_idx].copy_(
+                    torch.roll(cache.conv_states[self.layer_idx], shifts=-1, dims=-1)
+                )
+                cache.conv_states[self.layer_idx][:, :, -1] = qkv.squeeze(1)
+                qkv = torch.sum(cache.conv_states[self.layer_idx] * self.conv1d.weight.squeeze(1), dim=-1)
+                if self.conv1d.bias is not None:
+                    qkv = qkv + self.conv1d.bias
+                qkv = qkv.unsqueeze(1)
+            else:
+                qkv = self.conv1d(qkv.transpose(1, 2))[..., :seq_len].transpose(1, 2).contiguous()
+
+        return qkv
+
+    # Moved to a separate function since it's optional
+    # Mixture of transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXAttention._attn_projections_and_rope and
+    # transformers.models.llama.modeling_llama.LlamaAttention.forward RoPE parts
+    # GPTNeoX for the partial (on dim) RoPE application, Llama for the general RoPE embeddings
+    def _apply_rope(
+        self,
+        query: torch.FloatTensor,
+        key: torch.FloatTensor,
+        value: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+    ):
+        # Compute rotary embeddings on rotary_emb_dim
+        query_rot = query[..., : self.rotary_emb_dim]
+        query_pass = query[..., self.rotary_emb_dim :]
+        key_rot = key[..., : self.rotary_emb_dim]
+        key_pass = key[..., self.rotary_emb_dim :]
+
+        # Compute RoPE and stitch it back together
+        cos, sin = self.rotary_emb(value, position_ids)
+        query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
+        query = torch.cat((query, query_pass), dim=-1)
+        key = torch.cat((key, key_pass), dim=-1)
+
+        return query, key
+
+    def _attn_conv1d_projections_and_rope(
+        self,
+        hidden_states: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        cache: Optional[HybridMamba2AttentionDynamicCache] = None,
+        use_cache: Optional[bool] = False,
+    ):
+        bsz, q_len, _ = hidden_states.shape
+
+        # Managing cache state
+        has_layer_past = cache is not None
+        if has_layer_past:
+            cached_start = not cache.has_previous_state
+            cached_forward = not cached_start
+        else:
+            cached_start = False
+            cached_forward = False
+
+        # Compute QKV
+        # Attention heads [batch, seq_len, hidden_size]
+        #   --> [batch, seq_len, (head_dim * (num_heads(_q) + 2 * num_heads_kv)]
+        qkv = self.in_proj(hidden_states)
+
+        # (Optional) Apply Conv1d, caching is applied in-place
+        if self.conv_kernel_size > 0:
+            qkv = self._conv1d(
+                qkv, seq_len=qkv.shape[1], cache=cache, cached_start=cached_start, cached_forward=cached_forward
+            )
+
+        # Get the respective matrices from the parallel projection back
+        q, k, v = qkv.split(
+            [self.num_heads * self.head_dim, self.num_heads_kv * self.head_dim, self.num_heads_kv * self.head_dim],
+            dim=-1,
+        )
+
+        # Split combined hidden dims back into respective attention heads
+        # [batch, seq_len, hidden_size] --> [batch, seq_len, num_heads, head_dim]
+        query = q.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key = k.reshape(bsz, q_len, self.num_heads_kv, self.head_dim).transpose(1, 2)
+        value = v.reshape(bsz, q_len, self.num_heads_kv, self.head_dim).transpose(1, 2)
+
+        # (Optional) RoPE
+        if self.rotary_emb_dim > 0:
+            # TODO do we need to cache sin and cos for RoPE, llama doesn't seem to cache it (except when using sink cache)?
+            query, key = self._apply_rope(query, key, value, position_ids)
+
+        # Cache KV values
+        if has_layer_past:
+            key, value = cache.update(key, value, self.layer_idx)
+
+        return query, key, value
+
+
+# Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Mamba2
+class Mamba2FlashAttention2(Mamba2Attention):
+    """
+    Mamba2 flash attention module. This module inherits from `Mamba2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward
+    # Custom projections involving optional causal-conv-1d and optional (partial) RoPE
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        cache: Optional[HybridMamba2AttentionDynamicCache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+        bsz, q_len, _ = hidden_states.shape
+
+        # Apply attention-conv1d-specific projections and rope
+        query, key, value = self._attn_conv1d_projections_and_rope(
+            hidden_states=hidden_states, position_ids=position_ids, cache=cache, use_cache=use_cache
+        )
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key = repeat_kv(key, self.num_groups_kv)
+        value = repeat_kv(value, self.num_groups_kv)
+
+        # Permute to get the expected shape for Flash Attention
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        # 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 float16 / bfloat16 just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        input_dtype = query.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 = self.in_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query = query.to(target_dtype)
+            key = key.to(target_dtype)
+            value = value.to(target_dtype)
+
+        # Compute attention
+        attn_weights = _flash_attention_forward(
+            query,
+            key,
+            value,
+            attention_mask,
+            q_len,
+            dropout=0.0,
+            softmax_scale=None,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        # Reshape outputs
+        attn_output = attn_weights.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights
+
+
+# Adapted from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mamba2
+class Mamba2SdpaAttention(Mamba2Attention):
+    """
+    Mamba2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Mamba2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
+    to adapt to the SDPA API.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
+        # attn_mask, so we need to call `.contiguous()`. This was fixed in torch==2.2.0.
+        # Reference: https://github.com/pytorch/pytorch/issues/112577
+        self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0")
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaSdpaAttention.forward
+    # Custom projections involving optional causal-conv-1d and optional (partial) RoPE
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        cache: Optional[HybridMamba2AttentionDynamicCache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+        if output_attentions:
+            logger.warning_once(
+                "`Mamba2SdpaAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not support "
+                "`output_attentions=True`. Falling back to the manual attention implementation, but specifying the manual "
+                "implementation will be required from Transformers version v5.0.0 onwards. "
+                'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                output_attentions=output_attentions,
+                cache=cache,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        # Apply attention-conv1d-specific projections and rope
+        query, key, value = self._attn_conv1d_projections_and_rope(
+            hidden_states=hidden_states, position_ids=position_ids, cache=cache, use_cache=use_cache
+        )
+
+        # Repeat k/v heads if n_kv_heads < n_heads
+        key = repeat_kv(key, self.num_groups_kv)
+        value = repeat_kv(value, self.num_groups_kv)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+        # Avoid torch==2.1.2 specific bug for the memory-efficient backend in SDPA
+        if self.require_contiguous_qkv and query.device.type == "cuda" and attention_mask is not None:
+            query = query.contiguous()
+            key = key.contiguous()
+            value = value.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = True if attention_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query=query,
+            key=key,
+            value=value,
+            attn_mask=causal_mask,
+            dropout_p=0.0,
+            is_causal=is_causal,
+        )
+
+        # Reshape outputs
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, None
+
+
+MAMBA2_ATTENTION_CLASSES = {
+    "eager": Mamba2Attention,
+    "flash_attention_2": Mamba2FlashAttention2,
+    "sdpa": Mamba2SdpaAttention,
+}
+
+
+class Mamba2Mixer(nn.Module):
+    """
+    Using the found relation to the attention mechanism under certain conditions (State-Space-Duality SSD),
+    we use the Multi-input SSM which can be seen as a counterpart to the Multi-value Attention with analogues:
+    - X ~= V
+    - B ~= Q
+    - C ~= K
+    - A (1-SS(a)) ~= Attention Mask
+
+    For an overview, see the mamba2 paper, section 6, figure 4.
+    """
+
+    def __init__(self, config: Mamba2Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.ssm_state_size = config.state_size
+        self.conv_kernel_size = config.mamba2_conv_kernel
+        self.intermediate_size = config.intermediate_size
+        self.head_dim = config.mamba2_head_dim
+        self.num_heads = config.mamba2_num_heads
+        self.chunk_size = config.chunk_size
+        self.dt_min = config.time_step_limit[0]
+        self.dt_max = config.time_step_limit[1]
+        self.layer_idx = layer_idx
+        self.use_bias = config.use_mamba2_bias
+        self.use_conv_bias = config.use_conv_bias
+
+        # Parallel projection of the input hidden states
+        self.in_proj = nn.Linear(
+            in_features=self.hidden_size,
+            out_features=2 * (self.intermediate_size + self.ssm_state_size) + self.num_heads,
+            bias=self.use_bias,
+        )
+
+        conv1d_dim = self.intermediate_size + 2 * self.ssm_state_size
+        self.conv1d = nn.Conv1d(
+            in_channels=conv1d_dim,
+            out_channels=conv1d_dim,
+            bias=config.use_conv_bias,
+            kernel_size=config.mamba2_conv_kernel,
+            groups=conv1d_dim,
+            padding=config.mamba2_conv_kernel - 1,
+        )
+
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+
+        # We only use a bias as parameter
+        self.dt_bias = nn.Parameter(torch.rand(size=(self.num_heads,)))
+
+        # Scalar initialization of A, i.e. 1-Semi-Separable Matrix of A (== 1-SS(a))
+        A = torch.empty(self.num_heads, dtype=torch.float32).uniform_(*config.A_initializer_range)
+        self.A_log = nn.Parameter(torch.log(A))
+
+        # As D is a skip connection with A, it is also a scalar of the same shape as A
+        self.D = nn.Parameter(torch.ones(self.num_heads))
+
+        # Residual normalization introduced for instability, see section 7 of the paper
+        self.norm = Mamba2RMSNorm(self.intermediate_size, eps=1e-5)
+
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
+
+        if not is_fast_path_available:
+            logger.warning_once(
+                "The fast path is not available because on of "
+                "`(selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, causal_conv1d_fn, causal_conv1d_update)`"
+                " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
+                " https://github.com/Dao-AILab/causal-conv1d"
+            )
+
+    def triton_kernels_forward(self, hidden_states, cache):
+        # Managing cache state
+        if cache is not None:
+            cached_start = not cache.has_previous_state
+            cached_forward = not cached_start
+        else:
+            cached_start = False
+            cached_forward = False
+
+        # 1. Parallel projection for the input
+        zxbcdt = self.in_proj(hidden_states)
+
+        # 2-5. Training combined into one triton kernel
+        if self.training and cache is None:
+            y = mamba_split_conv1d_scan_combined(
+                zxbcdt=zxbcdt,
+                conv1d_weight=self.conv1d.weight.squeeze(1),
+                conv1d_bias=self.conv1d.bias,
+                dt_bias=self.dt_bias,
+                A=-torch.exp(self.A_log),
+                D=self.D,
+                chunk_size=self.chunk_size,
+                seq_idx=None,
+                activation=self.activation,
+                rmsnorm_weight=self.norm.weight,
+                rmsnorm_eps=self.norm.eps,
+                outproj_weight=self.out_proj.weight,
+                outproj_bias=self.out_proj.bias,
+                headdim=self.head_dim,
+                ngroups=1,
+                norm_before_gate=False,
+                dt_limit=(self.dt_min, self.dt_max),
+                initial_states=None,
+                return_final_states=False,
+            )
+            return y
+
+        # Reconstructing the necessary vars
+        d_mlp = (zxbcdt.shape[-1] - 2 * self.intermediate_size - 2 * self.ssm_state_size - self.num_heads) // 2
+        z0, x0, z, xBC, dt = torch.split(
+            zxbcdt,
+            [d_mlp, d_mlp, self.intermediate_size, self.intermediate_size + 2 * self.ssm_state_size, self.num_heads],
+            dim=-1,
+        )
+
+        # 2. Causal convolution for partial set of variables ("input" (x), B, C)
+        # Init cache with first "real" values
+        if cached_start:
+            xBC_t = xBC.transpose(1, 2)
+            cache.conv_states[self.layer_idx].copy_(F.pad(xBC_t, (self.conv_kernel_size - xBC_t.shape[-1], 0)))
+
+        if cached_forward:
+            xBC = causal_conv1d_update(
+                x=xBC.squeeze(1),
+                conv_state=cache.conv_states[self.layer_idx],
+                weight=self.conv1d.weight.squeeze(1),
+                bias=self.conv1d.bias,
+                activation=self.activation,
+            )
+        else:
+            xBC = causal_conv1d_fn(
+                x=xBC.transpose(1, 2),
+                weight=self.conv1d.weight.squeeze(1),
+                bias=self.conv1d.bias,
+                activation=self.activation,
+            ).transpose(1, 2)
+
+        # Reconstruct causal convolution vars
+        x, B, C = torch.split(xBC, [self.intermediate_size, self.ssm_state_size, self.ssm_state_size], dim=-1)
+
+        # 3. State Space Duality (SSD)
+        # Discretize 1-SS(a)
+        A = -torch.exp(self.A_log.float())  # .float() to avoid infs/nans
+
+        if not cached_forward:
+            y = mamba_chunk_scan_combined(
+                x=x.reshape(x.shape[0], x.shape[1], -1, self.head_dim),
+                dt=dt,
+                A=A,
+                B=B.unsqueeze(-2),
+                C=C.unsqueeze(-2),
+                chunk_size=self.chunk_size,
+                D=self.D,
+                z=None,
+                initial_states=None,
+                dt_bias=self.dt_bias,
+                dt_softplus=True,
+                seq_idx=None,
+                dt_limit=(self.dt_min, self.dt_max),
+                return_final_states=cached_start,
+            )
+
+            if cached_start:
+                y, last_state = y
+                if cached_start:
+                    cache.ssm_states[self.layer_idx].copy_(last_state)
+
+            # [bsz, seq_len, num_heads, head_dim] -> [bsz, seq_len, intermediate size]
+            y = y.reshape(y.shape[0], y.shape[1], -1)
+        else:
+            # Preparing values for single step
+            # [num_heads] -> [num_heads, head_dim, state_size]
+            A = (
+                A.unsqueeze(-1)
+                .unsqueeze(-1)
+                .expand(A.shape[0], self.head_dim, self.ssm_state_size)
+                .to(dtype=torch.float32)
+            )
+            # [bsz, 1, num_heads] -> [bsz, num_heads, head_dim]
+            dt = dt.transpose(1, 2).expand(dt.shape[0], dt.shape[-1], self.head_dim)
+            # [num_heads] -> [num_heads, head_dim]
+            dt_bias = self.dt_bias.unsqueeze(-1).expand(self.dt_bias.shape[0], self.head_dim)
+            # [num_heads] -> [num_heads, head_dim]
+            D = self.D.unsqueeze(-1).expand(self.D.shape[0], self.head_dim)
+            # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
+            x_reshaped = x.reshape(x.shape[0], -1, self.head_dim)
+
+            # Triton kernel for updating states in-place and returning the hidden state
+            y = selective_state_update(
+                state=cache.ssm_states[self.layer_idx],
+                x=x_reshaped,
+                dt=dt,
+                A=A,
+                B=B,
+                C=C,
+                D=D,
+                z=None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+            )
+
+            # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
+            y = y.reshape(y.shape[0], -1).unsqueeze(1)
+
+        # 4. Gate normalization introduced for instability, see section 7 of the paper
+        y = self.norm(y, residual=z)
+        if d_mlp > 0:
+            y = torch.cat([self.act(z0) * x0, y], dim=-1)
+
+        # 5. Out projecting
+        y = self.out_proj(y)
+
+        return y
+
+    @classmethod
+    def _ssd_naive(
+        cls, x, dt, A, B, C, D, chunk_size, dt_bias, dt_min, dt_max, initial_states=None, return_final_states=False
+    ):
+        """
+        Arguments:
+            x:       (batch_size, seq_len, num_heads, head_dim)
+            dt:      (batch_size, seq_len, num_heads)
+            A:       (num_heads)
+            B:       (batch_size, seq_len, num_heads, ssm_state_size)
+            C:       (batch_size, seq_len, num_heads, ssm_state_size)
+            D:       (num_heads)
+            dt_bias: (num_heads)
+        Return:
+            y:       (batch_size, seq_len, num_heads, head_dim)
+        """
+
+        def pad_by_size(x, pad_size):
+            """
+            Padding x tensor with `pad_size` on the seq_len dim (dim=1)
+
+            Assumes that we only have tensors of either size 4 or 3
+            """
+            assert 2 < len(x.shape) < 5
+
+            pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(x.shape) == 4 else (0, 0, 0, pad_size, 0, 0)
+
+            return F.pad(x, pad_shape, mode="constant", value=0)
+
+        def reshape_into_chunks(x, pad_size, chunk_size):
+            """
+            Padding x tensor with `pad_size` on the seq_len dim (dim=1) and
+            simultaneously splitting it into chunk sequences.
+
+            Assumes that we only have tensors of either size 4 or 3
+            """
+            # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
+            x = pad_by_size(x, pad_size)
+
+            if len(x.shape) == 3:
+                # b (l c) h -> b l c h with c=chunk_size
+                # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
+                return x.reshape(x.shape[0], -1, chunk_size, x.shape[2])
+            else:
+                # b (l c) h p -> b l c h p with c=chunk_size
+                # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size]
+                return x.reshape(x.shape[0], -1, chunk_size, x.shape[2], x.shape[3])
+
+        def segsum(x):
+            """
+            More stable segment sum calculation
+            """
+            T = x.size(-1)
+            # [..., chunk_size] -> [..., chunk_size, chunk_size]
+            x = x.unsqueeze(-1).expand(*x.size(), T)
+            mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=-1)
+            x = x.masked_fill(~mask, 0)
+            x_segsum = torch.cumsum(x, dim=-2)
+            mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=0)
+            x_segsum = x_segsum.masked_fill(~mask, -torch.inf)
+            return x_segsum
+
+        # Since it is parallelized by chunks they have to be of the same size which we ensure by padding
+        seq_len = x.shape[1]
+        pad_size = chunk_size - (seq_len % chunk_size)
+
+        # dt softplus and clamping
+        dt = F.softplus(dt + dt_bias)
+        dt = torch.clamp(dt, dt_min, dt_max)
+
+        D_residual = D.unsqueeze(-1) * pad_by_size(x, pad_size)
+
+        # Discretize x and A
+        x = x * dt.unsqueeze(-1)
+        A = A.to(x.dtype) * dt
+
+        # Rearrange into blocks/chunks
+        x, A, B, C = [reshape_into_chunks(t, pad_size, chunk_size) for t in (x, A, B, C)]
+
+        # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
+        A = A.permute(0, 3, 1, 2)
+        A_cumsum = torch.cumsum(A, dim=-1)
+
+        # 1. Compute the output for each intra-chunk (diagonal blocks)
+        L = torch.exp(segsum(A))
+        Y_diag = torch.einsum("bclhn,bcshn,bhcls,bcshp->bclhp", C, B, L, x)
+
+        # 2. Compute the state for each intra-chunk
+        # (right term of low-rank factorization of off-diagonal blocks; B terms)
+        decay_states = torch.exp((A_cumsum[:, :, :, -1:] - A_cumsum))
+        states = torch.einsum("bclhn,bhcl,bclhp->bchpn", B, decay_states, x)
+
+        # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
+        # (middle term of factorization of off-diag blocks; A terms)
+        if initial_states is None:
+            initial_states = torch.zeros_like(states[:, :1])
+        states = torch.cat([initial_states, states], dim=1)
+        decay_chunk = torch.exp(segsum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
+        new_states = torch.einsum("bhzc,bchpn->bzhpn", decay_chunk, states)
+        states, final_state = new_states[:, :-1], new_states[:, -1]
+
+        # 4. Compute state -> output conversion per chunk
+        # (left term of low-rank factorization of off-diagonal blocks; C terms)
+        state_decay_out = torch.exp(A_cumsum)
+        Y_off = torch.einsum("bclhn,bchpn,bhcl->bclhp", C, states, state_decay_out)
+
+        # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
+        y = Y_diag + Y_off
+        # [bsz, -1, chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
+        y = y.reshape(y.shape[0], -1, y.shape[-2], y.shape[-1])
+
+        # Add D residual to final output
+        y = y + D_residual
+
+        # Cutting off padded chunks
+        if pad_size > 0:
+            y = y[:, :seq_len, :, :]
+
+        if not return_final_states:
+            return y
+        else:
+            return y, final_state
+
+    def slow_forward(self, hidden_states, cache):
+        seq_len = hidden_states.shape[1]
+
+        # Managing cache state
+        if cache is not None:
+            cached_start = not cache.has_previous_state
+            cached_forward = not cached_start
+        else:
+            cached_start = False
+            cached_forward = False
+
+        # 1. Parallel projection for the input
+        zxbcdt = self.in_proj(hidden_states)
+
+        # Reconstructing the necessary vars
+        d_mlp = (zxbcdt.shape[-1] - 2 * self.intermediate_size - 2 * self.ssm_state_size - self.num_heads) // 2
+        z0, x0, z, xBC, dt = torch.split(
+            zxbcdt,
+            [d_mlp, d_mlp, self.intermediate_size, self.intermediate_size + 2 * self.ssm_state_size, self.num_heads],
+            dim=-1,
+        )
+
+        # 2. Causal convolution for partial set of variables ("input" (x), B, C)
+        # Init cache with first "real" values
+        if cached_start:
+            xBC_t = xBC.transpose(1, 2)
+            cache.conv_states[self.layer_idx].copy_(F.pad(xBC_t, (self.conv_kernel_size - xBC_t.shape[-1], 0)))
+
+        if cached_forward:
+            cache.conv_states[self.layer_idx].copy_(torch.roll(cache.conv_states[self.layer_idx], shifts=-1, dims=-1))
+            cache.conv_states[self.layer_idx][:, :, -1] = xBC.squeeze(1)
+            xBC = torch.sum(cache.conv_states[self.layer_idx] * self.conv1d.weight.squeeze(1), dim=-1)
+            if self.conv1d.bias is not None:
+                xBC = xBC + self.conv1d.bias
+            xBC = self.act(xBC)
+        else:
+            xBC = self.act(self.conv1d(xBC.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+
+        # Reconstruct causal convolution vars
+        x, B, C = torch.split(xBC, [self.intermediate_size, self.ssm_state_size, self.ssm_state_size], dim=-1)
+
+        # 3. State Space Duality (SSD)
+        # Discretize 1-SS(a)
+        A = -torch.exp(self.A_log.float())  # .float() to avoid infs/nans
+
+        if not cached_forward:
+            y = self._ssd_naive(
+                # [bsz, seq_len, intermediate_size] -> [bsz, seq_len, num_heads, head_dim]
+                x=x.reshape(x.shape[0], x.shape[1], -1, self.head_dim),
+                dt=dt,
+                A=A,
+                # [bsz, seq_len, state_size] -> [bsz, seq_len, num_groups=1, state_size]
+                B=B.unsqueeze(-2),
+                # [bsz, seq_len, state_size] -> [bsz, seq_len, num_groups=1, state_size]
+                C=C.unsqueeze(-2),
+                chunk_size=self.chunk_size,
+                D=self.D,
+                initial_states=None,
+                dt_bias=self.dt_bias,
+                dt_min=self.dt_min,
+                dt_max=self.dt_max,
+                return_final_states=cached_start,
+            )
+
+            if cached_start:
+                y, last_state = y
+                if cached_start:
+                    cache.ssm_states[self.layer_idx].copy_(last_state)
+
+            # [bsz, seq_len, num_heads, head_dim] -> [bsz, seq_len, intermediate_size]
+            y = y.reshape(y.shape[0], y.shape[1], -1)
+        else:
+            # Get time step with softplus and bias
+            dt = F.softplus(dt + self.dt_bias.to(dtype=dt.dtype))
+            dt = dt.squeeze(1)
+
+            # Discretize A
+            dA = torch.exp(dt * A)
+
+            # Discretize B and x
+            # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
+            x = x.reshape(x.shape[0], -1, self.head_dim)
+            dBx = torch.einsum("bh,bn,bhp->bhpn", dt, B, x)
+
+            # State calculation
+            cache.ssm_states[self.layer_idx].copy_(
+                cache.ssm_states[self.layer_idx] * dA.unsqueeze(-1).unsqueeze(-1) + dBx
+            )
+
+            # Subsequent output
+            y = torch.einsum("bhpn,bn->bhp", cache.ssm_states[self.layer_idx].to(C.dtype), C)
+
+            # D skip connection
+            y = y + self.D.unsqueeze(-1) * x
+
+            # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
+            y = y.reshape(y.shape[0], -1).unsqueeze(1)
+
+        # 4. Gate normalization introduced for instability, see section 7 of the paper
+        y = self.norm(y, residual=z)
+        if d_mlp > 0:
+            y = torch.cat([self.act(z0) * x0, y], dim=-1)
+
+        # 5. Out projecting
+        y = self.out_proj(y)
+
+        return y
+
+    def forward(self, hidden_states, cache: Optional[HybridMamba2AttentionDynamicCache] = None):
+        # TODO: check version for AMD support?
+        if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
+            return self.triton_kernels_forward(hidden_states, cache)
+        return self.slow_forward(hidden_states, cache)
+
+
+# Adapted from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mamba2
+# An optional residual normalization has been integrated
+class Mamba2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Mamba2RMSNorm is equivalent to LlamaRMSNorm but with optional residual normalizing
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.eps = eps
+
+    def forward(self, hidden_states, residual=None):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+
+        # Residual normalization introduced for instability, see section 7 of the paper
+        if residual is not None:
+            hidden_states = hidden_states * F.silu(residual.to(torch.float32))
+
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Adapted from transformers.models.mamba.modeling_mamba.MambaBlock
+# Allows attention instead of mamba2 and an optional MLP layer afterward
+class Mamba2Block(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.attention_layer = layer_idx in config.attention_layers_idx
+        self.mlp_layer = config.mlp_intermediate_size > 0
+        self.residual_in_fp32 = config.residual_in_fp32
+        self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        # Mixer is either attention layer or mamba2 layer
+        if self.attention_layer:
+            self.mixer = MAMBA2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
+        else:
+            self.mixer = Mamba2Mixer(config, layer_idx=layer_idx)
+
+        # Following mlp layer is optional
+        if self.mlp_layer:
+            self.norm2 = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+            self.mlp = Mamba2MLP(config, layer_idx=layer_idx)
+        else:
+            self.norm2 = None
+            self.mlp = None
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        cache: Optional[HybridMamba2AttentionDynamicCache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ):
+        dtype = hidden_states.dtype
+
+        residual = hidden_states
+        hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
+        if self.residual_in_fp32:
+            residual = residual.to(torch.float32)
+
+        # Mamba2 path
+        if not self.attention_layer:
+            hidden_states = self.mixer(hidden_states, cache=cache)
+            attn_weights = None
+        # Attention path
+        else:
+            hidden_states, attn_weights = self.mixer(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                cache=cache,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+        hidden_states = (residual + hidden_states).to(dtype)
+
+        if self.mlp_layer:
+            residual = hidden_states
+            hidden_states = self.norm2(hidden_states.to(dtype=self.norm2.weight.dtype))
+            if self.residual_in_fp32:
+                residual = residual.to(torch.float32)
+
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = (hidden_states + residual).to(dtype)
+
+        return hidden_states, attn_weights
+
+
+class Mamba2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = Mamba2Config
+    base_model_prefix = "backbone"
+    _no_split_modules = ["Mamba2Block"]
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True  # Note: only supports HybridMamba2AttentionDynamicCache
+    _is_stateful = True
+
+    # Adapted from transformers.models.mamba.modeling_mamba.MambaPreTrainedModel._init_weights
+    # Only using dt bias and rescale_prenorm_residual is expanded when using the additional MLP layer
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, Mamba2Mixer):
+            module.A_log._no_weight_decay = True
+            module.D._no_weight_decay = True
+
+            dt = torch.exp(
+                torch.rand(self.config.mamba2_num_heads)
+                * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+                + math.log(self.config.time_step_min)
+            ).clamp(min=self.config.time_step_floor)
+            # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+            inv_dt = dt + torch.log(-torch.expm1(-dt))
+            with torch.no_grad():
+                module.dt_bias.copy_(inv_dt)
+            module.dt_bias._no_reinit = True
+            module.dt_bias._no_weight_decay = True
+
+        if isinstance(module, nn.Linear):
+            if module.bias is not None:
+                if not getattr(module.bias, "_no_reinit", False):
+                    nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, std=self.config.emb_initializer_range)
+        elif isinstance(module, nn.Conv1d):
+            if self.config.conv_initializer_range is not None:
+                nn.init.uniform_(
+                    module.weight, -self.config.conv_initializer_range, self.config.conv_initializer_range
+                )
+
+        if self.config.rescale_prenorm_residual:
+            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+            #
+            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+            for name, p in module.named_parameters():
+                if name in ["out_proj.weight", "fc2.weight"]:
+                    # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+                    # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
+                    # We need to reinit p since this code could be called multiple times
+                    # Having just p *= scale would repeatedly scale it down
+                    nn.init.kaiming_uniform_(p, a=math.sqrt(5))
+
+                    # mlp layer is considered as an additional overhead
+                    n_residuals = 2 if self.config.mlp_intermediate_size > 0 else 1
+                    with torch.no_grad():
+                        p /= math.sqrt(n_residuals * self.config.num_hidden_layers)
+
+
+MAMBA2_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 ([`Mamba2Config`]): 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.
+"""
+
+MAMBA2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. 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 `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            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**.
+        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.
+        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)
+        past_key_values (`HybridMamba2AttentionDynamicCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            A HybridMamba2AttentionDynamicCache object containing pre-computed hidden-states (keys, values, and, if used, the convolution in the
+            self-attention blocks and convolution and ssm states in the mamba2 blocks) that can be used (see `past_key_values` input)
+            to speed up sequential decoding.
+            Key and value cache tensors have shape `(batch_size, num_key_value_heads, seq_len, attention_head_dim)`.
+            Convolution and ssm states tensors have shape `(batch_size, intermediate_size + 2 * state_size, mamba2_conv_kernel)` if used in the mamba2 block
+            else it has shape `(batch_size, attention_head_dim * (num_attention_heads + 2 * num_key_value_heads), attention_conv_kernel)`
+            and `(batch_size, mamba2_num_heads, mamba2_head_dim, state_size)` respectively.
+            See the `HybridMamba2AttentionDynamicCache` class for more details.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `input_ids` of shape `(batch_size, sequence_length)`.
+        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`).
+        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.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare MAMBA2 Model outputting raw hidden-states without any specific head on top.",
+    MAMBA2_START_DOCSTRING,
+)
+class Mamba2Model(Mamba2PreTrainedModel):
+    # Adapted from transformers.models.mamba.modeling_mamba.MambaModel.__init__ with Mamba->Mamba2
+    # Additional information about possible attention layers
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
+
+        self._attn_implementation = config._attn_implementation
+        self._uses_attention_layers = len(config.attention_layers_idx) > 0
+
+        self.gradient_checkpointing = False
+        self.norm_f = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        # Initialize weights and apply final processing
+        self._register_load_state_dict_pre_hook(self.load_hook)
+        self.post_init()
+
+    # Copied from transformers.models.mamba.modeling_mamba.MambaModel.load_hook
+    def load_hook(self, state_dict, prefix, *args):
+        for k in state_dict:
+            if "embedding." in k:
+                state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k)
+                break
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def set_input_embeddings(self, new_embeddings):
+        self.embeddings = new_embeddings
+
+    @add_start_docstrings_to_model_forward(MAMBA2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        output_type=BaseModelOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    # Adapted from transformers.models.jamba.modeling_jamba.JambaModel.forward
+    # No MoE logic, inits cache itself like Mamba does, and handles position_ids like Llama
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[HybridMamba2AttentionDynamicCache] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
+
+        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):  # ^ is python for xor
+            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.embeddings(input_ids)
+        hidden_states = inputs_embeds
+
+        # We allow empty caches on initial forward
+        if past_key_values is None and use_cache:
+            past_key_values = HybridMamba2AttentionDynamicCache(
+                config=self.config,
+                batch_size=inputs_embeds.shape[0],
+                device=inputs_embeds.device,
+                dtype=inputs_embeds.dtype,
+            )
+
+        # LLama based positions
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for mixer_block in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                out = self._gradient_checkpointing_func(
+                    mixer_block.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                out = mixer_block(
+                    hidden_states=hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    cache=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = out[0]
+
+            if output_attentions:
+                if out[1] is not None:
+                    # Append attentions only of attention layers. Mamba2 layers return `None` as the attention weights
+                    all_self_attns += (out[1],)
+
+        hidden_states = self.norm_f(hidden_states)
+
+        # Add hidden states from the last block
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if past_key_values and not past_key_values.has_previous_state:
+            past_key_values.has_previous_state = True
+
+        next_cache = None if not use_cache else past_key_values
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
+    # Custom hybrid cache instead
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: HybridMamba2AttentionDynamicCache,
+        output_attentions: bool,
+    ):
+        if not self._uses_attention_layers:
+            return None
+
+        if self._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+
+        # TODO: check if this is compatible with this custom cache format
+        if self._attn_implementation == "sdpa" and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        target_length = (
+            attention_mask.shape[-1]
+            if isinstance(attention_mask, torch.Tensor)
+            else past_seen_tokens + sequence_length
+        )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            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
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+        if (
+            self._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # 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
+
+
+@add_start_docstrings(
+    """
+    The MAMBA2 Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
+    """,
+    MAMBA2_START_DOCSTRING,
+)
+class Mamba2ForCausalLM(Mamba2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.backbone = Mamba2Model(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def get_input_embeddings(self):
+        return self.backbone.get_input_embeddings()
+
+    def set_input_embeddings(self, new_embeddings):
+        return self.backbone.set_input_embeddings(new_embeddings)
+
+    # Adapted from transformers.models.jamba.modeling_jamba.JambaForCausalLM.prepare_inputs_for_generation
+    # We omit some args Mamba2 doesn't use such as output_router_logits and num_logits_to_keep; additional optional reinit of the cache
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        empty_past_kv = past_key_values is None
+
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if not empty_past_kv:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        # Initialize cache, if necessary
+        if empty_past_kv:
+            past_key_values = HybridMamba2AttentionDynamicCache(
+                config=self.config,
+                batch_size=input_ids.shape[0],
+                device=self.device,
+                dtype=self.dtype,
+            )
+
+        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 not empty_past_kv:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and empty_past_kv:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+            }
+        )
+        return model_inputs
+
+    @add_start_docstrings_to_model_forward(MAMBA2_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        output_type=CausalLMOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[HybridMamba2AttentionDynamicCache] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.backbone(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
+
+        loss = None
+        if labels is not None:
+            # Move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->Mamba2, torch.tanh->F.silu
+class Mamba2ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        inner_dim: int,
+        num_classes: int,
+        pooler_dropout: float,
+    ):
+        super().__init__()
+        self.dense = nn.Linear(input_dim, inner_dim)
+        self.dropout = nn.Dropout(p=pooler_dropout)
+        self.out_proj = nn.Linear(inner_dim, num_classes)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = F.silu(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+@add_start_docstrings(
+    """
+    Mamba2 Model backbone with a sequence classification/regression head on top
+    (a linear layer on top of the pooled output) e.g. for GLUE tasks.
+
+    [`Mamba2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token.
+    If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row.
+    If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MAMBA2_START_DOCSTRING,
+)
+class Mamba2ForSequenceClassification(Mamba2PreTrainedModel):
+    # Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.__init__ with Bart->Mamba2,d_model->hidden_size,model->backbone
+    def __init__(self, config: Mamba2Config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.backbone = Mamba2Model(config)
+        self.classification_head = Mamba2ClassificationHead(
+            config.hidden_size,
+            config.hidden_size,
+            config.num_labels,
+            config.classifier_dropout,
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.backbone.embeddings
+
+    def set_input_embeddings(self, value):
+        self.backbone.embeddings = value
+
+    @add_start_docstrings_to_model_forward(MAMBA2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    # Copied from transformers.models.mixtral.modeling_mixtral.MixtralForSequenceClassification.forward with self.num_labels->self.config.num_labels,self.score->self.classification_head,self.model->self.backbone
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        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
+
+        transformer_outputs = self.backbone(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.classification_head(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.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.config.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,42 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}