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README.md

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+---
+license: other
+---
+
+# How to use
+```python
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "Taykhoom/Helix-mRNA-Wrapper",
+    trust_remote_code=True,
+)
+
+model = AutoModel.from_pretrained(
+    "Taykhoom/Helix-mRNA-Wrapper",
+    trust_remote_code=True,
+).eval()
+
+dna = "ACGUAGCAUCGGAUCUAUCUAUCGACACUUGGUUAUCGAUCUACGAGCAUCUCGUUAGC"
+inputs = tokenizer(
+    dna,
+    return_tensors="pt",
+    truncation=True,
+    padding="longest",
+    max_length=tokenizer.model_max_length,
+    return_special_tokens_mask=True,
+)
+
+special_tokens_mask = inputs["special_tokens_mask"]
+attention_mask = 1 - special_tokens_mask
+
+embedding = model(
+    input_ids=inputs["input_ids"],
+    attention_mask=attention_mask,
+).last_hidden_state  # [1, sequence_length, 256]
+```
+
+# Model Variants
+The following `base_model` options are available for embedding generation. The short name (keys) or the full model name (values) can be specified using the `base_model` argument.
+```python
+VARIANTS = {
+    "aido_rna_1m_mars": "genbio-ai/AIDO.RNA-1M-MARS",
+    "aido_rna_25m_mars": "genbio-ai/AIDO.RNA-25M-MARS",
+    "aido_rna_300m_mars": "genbio-ai/AIDO.RNA-300M-MARS",
+    "aido_rna_650m": "genbio-ai/AIDO.RNA-650M",
+    "aido_rna_650m_cds": "genbio-ai/AIDO.RNA-650M-CDS",
+    "aido_rna_1b600m": "genbio-ai/AIDO.RNA-1.6B",
+    "aido_rna_1b600m_cds": "genbio-ai/AIDO.RNA-1.6B-CDS",
+}
+```
+
+# Performance Vs Original Helix-mRNA Models
+
+Verify that the modified code produces the same embeddings as the original Helix-mRNA models.
+
+Original Helix-mRNA code snippet:
+```python
+from helical.models.helix_mrna import HelixmRNA, HelixmRNAConfig
+import torch
+
+input_sequences = ["ACGUAGCAUCGGAUCUAUCUAUCGACACUUGGUUAUCGAUCUACGAGCAUCUCGUUAGC"]
+
+helix_mrna_config = HelixmRNAConfig(batch_size=1)
+helix_mrna = HelixmRNA(configurer=helix_mrna_config)
+
+# prepare data for input to the model
+processed_input_data = helix_mrna.process_data(input_sequences)
+
+# generate the embeddings for the processed data
+embedding = torch.Tensor(helix_mrna.get_embeddings(processed_input_data))
+
+embedding_mean = torch.mean(embedding, dim=1) # [1, 256]
+print(torch.mean(embedding_mean)) # Outputs tensor(-0.0033)
+
+embedding_max = torch.max(embedding, dim=1)[0]
+print(torch.mean(embedding_max)) # Outputs tensor(0.0989)
+
+```
+
+Modified code snippet using the wrapper:
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "Taykhoom/Helix-mRNA-Wrapper",
+    trust_remote_code=True,
+)
+
+model = AutoModel.from_pretrained(
+    "Taykhoom/Helix-mRNA-Wrapper",
+    trust_remote_code=True,
+).eval()
+
+dna = "ACGUAGCAUCGGAUCUAUCUAUCGACACUUGGUUAUCGAUCUACGAGCAUCUCGUUAGC"
+inputs = tokenizer(
+    dna,
+    return_tensors="pt",
+    truncation=True,
+    padding="longest",
+    max_length=tokenizer.model_max_length,
+    return_special_tokens_mask=True,
+)
+
+special_tokens_mask = inputs["special_tokens_mask"]
+attention_mask = 1 - special_tokens_mask
+
+embedding = model(
+    input_ids=inputs["input_ids"],
+    attention_mask=attention_mask,
+).last_hidden_state  # [1, sequence_length, 256]
+
+embedding_mean = torch.mean(embedding, dim=1)
+print(torch.mean(embedding_mean)) # Outputs tensor(-0.0033, grad_fn=<MeanBackward0>)
+
+embedding_max = torch.max(embedding, dim=1)[0]
+print(torch.mean(embedding_max)) # Outputs tensor(0.0989, grad_fn=<MeanBackward0>)
+```
+
+# License Notice
+This repository contains modified versions of Helical code.
+Modifications include:
+- Removal of reliance on helical package
+- Removal of some ease-of-use embedding generation code (to standardize usage) and other checks (see original repository for more details)
+
+Not all of the original functionality may be preserved. These changes were made to better integrate with the mRNABench framework which focuses on embedding generation for mRNA sequences. Most of the required code was directly copied from the original Helical repository with minimal changes, so please refer to the original repository for full details on the implementation.
+
+When using this repository, please adhere to the original license terms of the Helical code. This license can be found in this directory as `LICENSE`.
+
+# Original Repository
+The original Helical repository can be found at: https://github.com/helicalAI/helical

config.json

@@ -0,0 +1,8 @@
+{
+  "model_name": "helical-ai/Helix-mRNA",
+  "max_length": 12288,
+  "auto_map": {
+    "AutoConfig": "configuration_helix_mrna.HelixmRNAConfig",
+    "AutoModel": "modeling_helix_mrna.HelixmRNAModel"
+  }
+}

configuration_helix_mrna.py

@@ -0,0 +1,48 @@
+from typing import Literal
+from transformers import PretrainedConfig
+
+class HelixmRNAConfig(PretrainedConfig):
+    """HelixmRNAConfig class to store the configuration of the Helix-mRNA model.
+
+    Parameters
+    ----------
+    batch_size : int, optional, default=10
+        The batch size
+    device : Literal["cpu", "cuda"], optional, default="cpu"
+        The device to use. Either use "cuda" or "cpu".
+    max_length : int, optional, default=12288
+        The maximum length of the input sequence.
+    nproc: int, optional, default=1
+        Number of processes to use for data processing.
+    """
+
+    model_name: Literal["helical-ai/Helix-mRNA"] = "helical-ai/Helix-mRNA"
+
+    def __init__(
+        self,
+        max_length: int = 12288,
+        **kwargs,
+    ):
+
+        self.config = {
+            "model_name": self.model_name,
+            "max_length": max_length,
+        }
+
+        super().__init__(**kwargs)
+
+    @property
+    def layers_block_type(self):
+        layers = []
+        if self.num_hidden_layers != len(self.layers_block_type_string):
+            raise ValueError(
+                f"num_hidden_layers should be equal to the number of layers in layers_block_type_string, but got {self.num_hidden_layers} and {len(self.layers_block_type_string)}"
+            )
+        for layer in self.layers_block_type_string:
+            if layer == "M":
+                layers.append("mamba")
+            elif layer == "*":
+                layers.append("attention")
+            elif layer == "+":
+                layers.append("mlp")
+        return layers

modeling_helix_mrna.py

@@ -0,0 +1,1701 @@
+# coding=utf-8
+# Copyright 2024 state-spaces/mamba2 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.
+"""PyTorch Helix-mRNA model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union, Dict, Any, List
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.cache_utils import DynamicCache
+from transformers.activations import ACT2FN
+
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput
+
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+)
+from .configuration_helix_mrna import HelixmRNAConfig
+
+from transformers.utils.import_utils import (
+    is_causal_conv1d_available,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    is_mamba_2_ssm_available,
+)
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+if is_mamba_2_ssm_available():
+    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,
+    )
+else:
+    selective_state_update = None
+
+if is_causal_conv1d_available():
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+    causal_conv1d_update, causal_conv1d_fn = None, None
+
+is_fast_path_available = all(
+    (selective_state_update, causal_conv1d_fn, causal_conv1d_update)
+)
+
+# copied from transformers.models.mistral.modeling_mistral.pad_tensor_by_size
+
+
+def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int):
+    """
+    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
+    """
+    pad_shape = (
+        (0, 0, 0, 0, 0, pad_size, 0, 0)
+        if len(input_tensor.shape) == 4
+        else (0, 0, 0, pad_size, 0, 0)
+    )
+
+    return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0)
+
+
+def reshape_into_chunks(input_tensor, pad_size, chunk_size):
+    """
+    Padding input_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, ...]
+    input_tensor = pad_tensor_by_size(input_tensor, pad_size)
+
+    if len(input_tensor.shape) == 3:
+        # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
+        return input_tensor.reshape(
+            input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2]
+        )
+    else:
+        # [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 input_tensor.reshape(
+            input_tensor.shape[0],
+            -1,
+            chunk_size,
+            input_tensor.shape[2],
+            input_tensor.shape[3],
+        )
+
+
+def segment_sum(input_tensor):
+    """
+    More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
+    """
+    chunk_size = input_tensor.size(-1)
+    # 1. expand input tensor to have an additional dimension and repeat along that dimension
+    # [..., chunk_size] -> [..., chunk_size, chunk_size]
+    input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
+    # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
+    mask = torch.tril(
+        torch.ones(
+            chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool
+        ),
+        diagonal=-1,
+    )
+    input_tensor = input_tensor.masked_fill(~mask, 0)
+    # 3. compute actual cumsum
+    tensor_segsum = torch.cumsum(input_tensor, dim=-2)
+
+    # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
+    mask = torch.tril(
+        torch.ones(
+            chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool
+        ),
+        diagonal=0,
+    )
+    tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf)
+    return tensor_segsum
+
+
+# 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)
+
+
+class HybridMambaAttentionDynamicCache(DynamicCache):
+    """
+    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
+    (which has a constant shape regardless of seq_len).
+
+    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
+    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
+    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
+    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
+    For mamba 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, d_inner, d_conv)`,
+    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
+    """
+
+    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
+        super().__init__()
+        self.dtype = dtype
+        self.layers_block_type = config.layers_block_type
+        self.has_previous_state = False  # only used by mamba
+        intermediate_size = config.expand * config.hidden_size
+        ssm_state_size = config.state_size
+        conv_kernel_size = config.conv_kernel
+        self.seqlen_offset = 0
+        self.conv_states = []
+        self.ssm_states = []
+        self.transformer_layers = []
+        for i in range(config.num_hidden_layers):
+            if self.layers_block_type[i] == "mamba":
+                self.conv_states += [
+                    torch.zeros(
+                        batch_size,
+                        intermediate_size,
+                        conv_kernel_size,
+                        device=device,
+                        dtype=dtype,
+                    )
+                ]
+                self.ssm_states += [
+                    torch.zeros(
+                        batch_size,
+                        intermediate_size,
+                        ssm_state_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)
+        ]
+
+    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]
+
+    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)
+            )
+
+    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."""
+        # 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
+        return self.key_cache[layer_idx].shape[-2]
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        raise NotImplementedError(
+            "HybridMambaAttentionDynamicCache does not have a legacy cache equivalent."
+        )
+
+    @classmethod
+    def from_legacy_cache(
+        cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    ) -> "DynamicCache":
+        raise NotImplementedError(
+            "HybridMambaAttentionDynamicCache does not have a legacy cache equivalent."
+        )
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Jamba
+class HelixmRNAAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(
+        self, config: HelixmRNAConfig, layer_idx: Optional[int] = None
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            print(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(
+            self.hidden_size, self.num_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba
+class HelixmRNAFlashAttention2(HelixmRNAAttention):
+    """
+    Jamba flash attention module. This module inherits from `JambaAttention` 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()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # 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 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            print(
+                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_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            sliding_window=getattr(self.config, "sliding_window", None),
+            is_causal=self.is_causal,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba
+class HelixmRNASdpaAttention(HelixmRNAAttention):
+    """
+    Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from JambaAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            print(
+                "JambaModel is using JambaSdpaAttention, 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,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.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.
+        # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+        is_causal = (
+            True if self.is_causal and causal_mask is None and q_len > 1 else False
+        )
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+HelixmRNA_ATTENTION_CLASSES = {
+    "eager": HelixmRNAAttention,
+    "flash_attention_2": HelixmRNAFlashAttention2,
+    "sdpa": HelixmRNASdpaAttention,
+}
+
+
+class Mamba2Cache:
+    """
+    Arguments:
+        config: Mamba2Config
+        batch_size: int
+        dtype: torch.dtype
+        device: torch.device
+
+    Attributes:
+        seqlen_offset: int
+        dtype: torch.dtype
+        conv_states: Dict[int, torch.Tensor] # layer_idx -> [batch_size, intermediate_size, conv_kernel_size]
+        ssm_states: Dict[int, torch.Tensor] # layer_idx -> [batch_size, intermediate_size, ssm_state_size]
+    """
+
+    def __init__(
+        self,
+        config: HelixmRNAConfig,
+        batch_size: int,
+        dtype: torch.dtype = torch.float16,
+        device: Optional[str] = None,
+    ):
+        self.seqlen_offset = 0
+        self.dtype = dtype
+        self.conv_kernel_size = config.conv_kernel
+        self.intermediate_size = int(config.expand * config.hidden_size)
+
+        self.conv_states = {
+            i: torch.zeros(
+                batch_size,
+                self.intermediate_size + 2 * config.n_groups * config.state_size,
+                self.conv_kernel_size,
+                device=device,
+                dtype=dtype,
+            )
+            for i in range(config.num_hidden_layers)
+        }
+        self.ssm_states = {
+            i: torch.zeros(
+                batch_size,
+                config.num_heads,
+                config.head_dim,
+                config.state_size,
+                device=device,
+                dtype=dtype,
+            )
+            for i in range(config.num_hidden_layers)
+        }
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+
+    def update_conv_state(
+        self,
+        layer_idx: int,
+        new_conv_state: torch.Tensor,
+        cache_position: torch.LongTensor,
+    ) -> torch.Tensor:
+        conv_state = self.conv_states[layer_idx]
+        cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
+
+        conv_state = conv_state.roll(shifts=-1, dims=-1)
+        conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device)
+        self.conv_states[layer_idx].zero_()
+        self.conv_states[layer_idx] += conv_state
+        return self.conv_states[layer_idx]
+
+    def reset(self):
+        self.conv_states.zero_()
+        self.ssm_states.zero_()
+
+
+class MambaRMSNormGated(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states, gate=None):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+
+        if gate is not None:
+            hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class Mamba2Mixer(nn.Module):
+    """
+    Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
+    A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
+    ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
+    and is why Mamba is called **selective** state spaces)
+    """
+
+    def __init__(self, config: HelixmRNAConfig, layer_idx: int):
+        super().__init__()
+        self.num_heads = config.num_heads
+        self.hidden_size = config.hidden_size
+        self.ssm_state_size = config.state_size
+        self.conv_kernel_size = config.conv_kernel
+        self.intermediate_size = int(config.expand * self.hidden_size)
+        self.time_step_rank = int(config.time_step_rank)
+        self.layer_idx = layer_idx
+        self.use_conv_bias = config.use_conv_bias
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+
+        self.layer_norm_epsilon = config.layer_norm_epsilon
+        self.rms_norm = config.rms_norm
+
+        self.n_groups = config.n_groups
+        self.head_dim = config.head_dim
+        self.chunk_size = config.chunk_size
+
+        self.time_step_limit = config.time_step_limit
+        self.time_step_min = config.time_step_min
+        self.time_step_max = config.time_step_max
+
+        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=config.use_conv_bias,
+            kernel_size=config.conv_kernel,
+            groups=self.conv_dim,
+            padding=config.conv_kernel - 1,
+        )
+
+        # projection of the input hidden states
+        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=config.use_bias,
+        )
+        # selective projection used to make dt, B and C input dependant
+
+        # time step projection (discretization)
+        # instantiate once and copy inv_dt in init_weights of PretrainedModel
+        self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
+
+        # S4D real initialization. These are not discretized!
+        # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+        A = torch.arange(1, self.num_heads + 1)
+        self.A_log = nn.Parameter(torch.log(A))
+        self.A_log._no_weight_decay = True
+        self.norm = MambaRMSNormGated(
+            self.intermediate_size, eps=self.layer_norm_epsilon
+        )
+        self.D = nn.Parameter(torch.ones(self.num_heads))
+        self.D._no_weight_decay = True
+
+        self.out_proj = nn.Linear(
+            self.intermediate_size, self.hidden_size, bias=config.use_bias
+        )
+        self.use_bias = config.use_bias
+
+        if not is_fast_path_available:
+            print(
+                "The fast path is not available because on of `(selective_state_update, 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 cuda_kernels_forward(
+        self,
+        hidden_states: torch.Tensor,
+        cache_params: Optional[HybridMambaAttentionDynamicCache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        # set up dimensions for reshapes later
+
+        batch_size, seq_len, _ = hidden_states.shape
+        groups_time_state_size = self.n_groups * self.ssm_state_size
+        d_to_remove = (
+            2 * self.intermediate_size
+            + 2 * self.n_groups * self.ssm_state_size
+            + self.num_heads
+        )
+
+        # getting projected states from cache if it exists
+        if cache_params is not None and cache_params.seqlen_offset > 0:
+            in_projected_states = self.in_proj(hidden_states.squeeze(1))  # (B 2D)
+            d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
+            split_projection_dim = [
+                d_mlp,
+                d_mlp,
+                self.intermediate_size,
+                self.conv_dim,
+                self.num_heads,
+            ]
+            _, _, gate, hidden_states_B_C, dt = torch.split(
+                in_projected_states, split_projection_dim, dim=-1
+            )
+
+            hidden_states_B_C = causal_conv1d_update(
+                hidden_states_B_C,
+                cache_params.conv_states[self.layer_idx],
+                self.conv1d.weight.squeeze(1),
+                self.conv1d.bias,
+                self.activation,
+            )
+
+            hidden_states, B, C = torch.split(
+                hidden_states_B_C,
+                [
+                    self.intermediate_size,
+                    groups_time_state_size,
+                    groups_time_state_size,
+                ],
+                dim=-1,
+            )
+            A = -torch.exp(self.A_log.float())  # (nheads,)
+
+            A = (
+                A[:, None, ...][:, :, None]
+                .expand(-1, self.head_dim, self.ssm_state_size)
+                .to(dtype=torch.float32)
+            )
+            dt = dt[:, :, None].expand(-1, -1, self.head_dim)
+            dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
+            D = self.D[:, None, ...].expand(-1, self.head_dim)
+            B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
+            C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
+            hidden_states_reshaped = hidden_states.view(
+                batch_size, self.num_heads, self.head_dim
+            )
+            hidden_states = selective_state_update(
+                cache_params.ssm_states[self.layer_idx],
+                hidden_states_reshaped,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+            )
+            hidden_states = hidden_states.view(
+                batch_size, self.num_heads * self.head_dim
+            )
+            hidden_states = self.norm(hidden_states, gate)
+            out = self.out_proj(hidden_states)[:, None, ...]
+        # if no cache is found, calling the kernel
+        else:
+            if (
+                attention_mask is not None
+                and attention_mask.shape[1] > 1
+                and attention_mask.shape[0] > 1
+            ):
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                dtype = hidden_states.dtype
+                hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+            # 1. Gated MLP's linear projection
+            projected_states = self.in_proj(hidden_states)
+            A = -torch.exp(
+                self.A_log.float()
+            )  # (num_heads) or (intermediate_size, state_size)
+            dt_limit_kwargs = (
+                {}
+                if self.time_step_limit == (0.0, float("inf"))
+                else {"dt_limit": self.time_step_limit}
+            )
+
+            if self.training and cache_params is None:
+                out, ssm_state = mamba_split_conv1d_scan_combined(
+                    projected_states,
+                    self.conv1d.weight.squeeze(1),
+                    self.conv1d.bias,
+                    self.dt_bias,
+                    A,
+                    D=self.D,
+                    chunk_size=self.chunk_size,
+                    seq_idx=None,  # was seq_idx
+                    activation=self.activation,
+                    rmsnorm_weight=self.norm.weight,
+                    rmsnorm_eps=self.norm.variance_epsilon,
+                    outproj_weight=self.out_proj.weight,
+                    outproj_bias=self.out_proj.bias,
+                    headdim=self.head_dim,
+                    ngroups=self.n_groups,
+                    norm_before_gate=False,
+                    return_final_states=True,
+                    **dt_limit_kwargs,
+                )
+
+            else:
+                gate, hidden_states_B_C, time_step = torch.split(
+                    projected_states,
+                    [self.intermediate_size, self.conv_dim, self.num_heads],
+                    dim=-1,
+                )
+
+                # 1D Convolution
+                if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
+                    hidden_states_B_C = self.act(
+                        self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[
+                            :, :seq_len
+                        ]
+                    )  # (B, L, self.d_inner + 2 * ngroups * d_state)
+                else:
+                    hidden_states_B_C = causal_conv1d_fn(
+                        x=hidden_states_B_C.transpose(1, 2),
+                        weight=self.conv1d.weight.squeeze(1),
+                        bias=self.conv1d.bias,
+                        activation=self.activation,
+                    ).transpose(1, 2)[:, :seq_len]
+                hidden_states, B, C = torch.split(
+                    hidden_states_B_C,
+                    [
+                        self.intermediate_size,
+                        groups_time_state_size,
+                        groups_time_state_size,
+                    ],
+                    dim=-1,
+                )
+                if (
+                    attention_mask is not None
+                    and attention_mask.shape[1] > 1
+                    and attention_mask.shape[0] > 1
+                ):
+                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                    dtype = hidden_states.dtype
+                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(
+                        dtype
+                    )
+                scan_output, ssm_state = mamba_chunk_scan_combined(
+                    hidden_states.view(batch_size, seq_len, -1, self.head_dim),
+                    time_step,
+                    A,
+                    B.view(batch_size, seq_len, self.n_groups, -1),
+                    C.view(batch_size, seq_len, self.n_groups, -1),
+                    chunk_size=self.chunk_size,
+                    D=self.D,
+                    z=None,
+                    seq_idx=None,
+                    return_final_states=True,
+                    dt_bias=self.dt_bias,
+                    dt_softplus=True,
+                    **dt_limit_kwargs,
+                )
+                if ssm_state is not None and cache_params is not None:
+                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                scan_output = scan_output.view(batch_size, seq_len, -1)
+                # Multiply "gate" branch and apply extra normalization layer
+                scan_output = self.norm(scan_output, gate)
+                out = self.out_proj(scan_output)
+        return out
+
+    # fmt: off
+    def torch_forward(self, input_states, cache_params: Optional[HybridMambaAttentionDynamicCache]=None, cache_position:Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
+        batch_size, seq_len, _ = input_states.shape
+        dtype = input_states.dtype
+        # Gated MLP's linear projection
+        projected_states =  self.in_proj(input_states.squeeze(1))
+        d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size -  2 * self.n_groups * self.ssm_state_size- self.num_heads) // 2
+        _, _, gate, hidden_states, dt = projected_states.split(
+                [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
+        )
+
+        # Convolution sequence transformation
+        if cache_params is not None:
+            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = ssm_state.to(hidden_states.device)
+            if cache_params.seqlen_offset > 0:
+                conv_state = cache_params.conv_states[self.layer_idx]                   # [batch, intermediate_size, conv_kernel_size]
+                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
+                # handle batched generation - states are copied through
+                conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
+                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+                if self.use_conv_bias:
+                    hidden_states += self.conv1d.bias
+                hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
+            else:
+                hidden_states = hidden_states.transpose(1,2)
+                conv_state = nn.functional.pad(
+                    hidden_states,
+                    (self.conv_kernel_size - hidden_states.shape[-1], 0)
+                )
+                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
+                if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
+                    dtype = hidden_states.dtype
+                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
+            hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+        hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
+        A = -torch.exp(self.A_log.float())                            # [num_heads]
+        if cache_params is not None and cache_params.seqlen_offset > 0:
+            # Note: there is no need to pad parameter matrices here, as there is just one new token
+            # for batched generation
+            dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
+            dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
+            # [num_heads] -> [num_heads, head_dim]
+            dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
+
+            dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
+            dt = torch.clamp(dt, self.time_step_min) #, self.time_step_max)
+            A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
+            # [bsz, num_heads, head_dim, state_size]
+            dA = torch.exp(dt[..., None] * A)
+
+            # Discretize B
+            # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
+            # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
+            B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
+            B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
+            B = B.reshape(batch_size, -1, B.shape[-1])
+            # [bsz, num_heads, head_dim, state_size]
+            dB = dt[..., None] * B[..., None, :]
+
+            # Discretize x into dB
+            # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
+            hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
+            dBx = dB * hidden_states[..., None]
+
+            # State calculation
+            cache_params.ssm_states[self.layer_idx].copy_(
+                cache_params.ssm_states[self.layer_idx] * dA + dBx
+            )
+
+            # Subsequent output
+            # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
+            C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
+            C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
+            C = C.reshape(batch_size, -1, C.shape[-1])
+            # [bsz, num_heads, head_dim]
+
+            ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype)  # Shape: [b, h, d, n]
+            # Reshape ssm_states to merge the first two dimensions
+            ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
+            C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
+            y = torch.bmm(ssm_states_reshaped, C_reshaped)
+            y = y.view(batch_size, self.num_heads, self.head_dim)
+
+            # D skip connection
+            # [num_heads] -> [num_heads, head_dim]
+            D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
+            y = (y + hidden_states * D).to(y.dtype)
+
+            # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
+            y = y.reshape(batch_size, -1)[:, None, ...]
+        else:
+            # begin ssd naive implementation without einsums
+            dt = nn.functional.softplus(dt + self.dt_bias)
+            dt = torch.clamp(dt, self.time_step_min)
+            hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
+            B = B.reshape(batch_size, seq_len,  -1, self.ssm_state_size).float()
+            C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
+            B = B.repeat(1, 1, self.num_heads // self.n_groups, 1)
+            C = C.repeat(1, 1, self.num_heads // self.n_groups, 1)
+            pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
+
+            D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
+
+            # Discretize x and A
+            hidden_states = hidden_states * dt[..., None]
+            A = A.to(hidden_states.dtype) * dt
+
+            # Rearrange into blocks/chunks
+            hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, 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)
+            # This is the analog of a causal mask
+            L = torch.exp(segment_sum(A))
+
+            # First, contraction of C and B to get G (attention-weights like)
+            G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, : ,:]  # shape: (b, c, l, s, h, n)
+            G = G_intermediate.sum(dim=-1)  # shape: (b, c, l, s, h)
+
+
+            # Step 2: Compute M, equivalent to applying attention mask to weights
+            M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
+            M = M_intermediate.sum(dim=-1)
+
+            # Step 3: Compute Y_diag (apply to values)
+            Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3)
+
+            # (right term of low-rank factorization of off-diagonal blocks; B terms)
+
+            decay_states = torch.exp((A_cumsum[:, :, :, -1:] - A_cumsum))
+            B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
+            # permute back B * decay states
+            states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None]  * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
+            if cache_params is not None and cache_params.seqlen_offset > 0:
+                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
+            else:
+                previous_states = torch.zeros_like(states[:, :1])
+            states = torch.cat([previous_states, states], dim=1)
+            decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
+
+            states_permuted = states.permute(0, 2, 1, 3, 4)
+            result = (decay_chunk[..., None, None] * states_permuted[:, :, None, ...]).sum(dim=2)
+            new_states = result.permute(0, 2, 1, 3, 4)
+            states, ssm_state = new_states[:, :-1], new_states[:, -1]
+
+            # 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)
+            # compute Yoff
+            C_times_states = (C[..., None, :] * states[:, :, None, ...])
+            state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
+            Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
+            # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
+
+            y = Y_diag + Y_off
+            # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
+            y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
+
+            y = y + D_residual
+            # Cutting off padded chunks
+            if pad_size > 0:
+                y = y[:, :seq_len, :, :]
+            y = y.reshape(batch_size, seq_len, -1)
+            if ssm_state is not None and cache_params is not None:
+                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+
+        scan_output = self.norm(y, gate)
+
+        # end ssd naive
+
+        # 4. Final linear projection
+        contextualized_states = self.out_proj(scan_output.to(dtype))  # [batch, seq_len, hidden_size]
+        return contextualized_states
+    # fmt: on
+
+    def forward(
+        self,
+        hidden_states,
+        cache_params: Optional[Mamba2Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
+            return self.cuda_kernels_forward(
+                hidden_states, cache_params, cache_position, attention_mask
+            )
+        dtype = hidden_states.dtype
+        if (
+            attention_mask is not None
+            and attention_mask.shape[1] > 1
+            and attention_mask.shape[0] > 1
+        ):
+            # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+            hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
+        return self.torch_forward(
+            hidden_states, cache_params, cache_position, attention_mask
+        )
+
+
+class Mamba2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class HelixmRNAMLP(nn.Module):
+    def __init__(self, config, layer_idx=None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = self.hidden_size * 4  # config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state, **kwargs):
+        hidden_states = self.down_proj(
+            self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)
+        )
+        return (hidden_states,)
+
+
+class HelixmRNAMLPLayer(nn.Module):
+    def __init__(self, config, layer_idx=None):
+        super().__init__()
+        ffn_layer_class = HelixmRNAMLP
+        self.feed_forward = ffn_layer_class(config)
+        self.input_layernorm = Mamba2RMSNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        use_cache=True,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+        **kwargs,
+    ):
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+        ff_outputs = self.feed_forward(hidden_states)
+
+        hidden_states = ff_outputs[0]
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if use_cache:
+            outputs += (past_key_value,)
+
+        return outputs
+
+
+class Mamba2Block(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.residual_in_fp32 = config.residual_in_fp32
+        self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        self.mixer = Mamba2Mixer(config, layer_idx=layer_idx)
+
+    def forward(
+        self,
+        hidden_states,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+    ):
+        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)
+
+        hidden_states = self.mixer(
+            hidden_states,
+            cache_params=past_key_value,
+            cache_position=cache_position,
+            attention_mask=attention_mask,
+        )
+        hidden_states = residual + hidden_states
+
+        hidden_states = (hidden_states,)
+        if output_attentions:
+            hidden_states += (None,)
+
+        if use_cache:
+            hidden_states += (past_key_value,)
+
+        return hidden_states
+
+
+class HelixmRNAAttentionDecoderLayer(nn.Module):
+    def __init__(self, config: HelixmRNAConfig, layer_idx: int):
+        super().__init__()
+        self.self_attn = HelixmRNA_ATTENTION_CLASSES[config._attn_implementation](
+            config, layer_idx
+        )
+
+        ffn_layer_class = HelixmRNAMLP
+        self.feed_forward = ffn_layer_class(config)
+        self.input_layernorm = Mamba2RMSNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+        self.pre_ff_layernorm = Mamba2RMSNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                Attention mask of size `(batch, sequence_length)` where padding elements are indicated by 0.
+            past_key_value (`HybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+                should not be returned during inference.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+
+        # residual connection after attention
+        hidden_states = residual + hidden_states
+
+        # feed-forward (experts/MLP)
+        residual = hidden_states
+        hidden_states = self.pre_ff_layernorm(hidden_states)
+        ff_outputs = self.feed_forward(hidden_states)
+
+        hidden_states = ff_outputs[0]
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class HelixmRNAPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = HelixmRNAConfig
+    base_model_prefix = "backbone"
+    supports_gradient_checkpointing = True
+    _is_stateful = True
+    _no_split_modules = ["HelixmRNAAttentionDecoderLayer", "Mamba2Block"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True  # Note: only supports HybridMambaAttentionDynamicCache
+
+    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.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
+
+        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.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"]:
+                    # 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))
+                    with torch.no_grad():
+                        p /= math.sqrt(self.config.num_hidden_layers)
+
+
+@dataclass
+# Copied from transformers.models.mamba.modeling_mamba.MambaOutput with MAMBA->MAMBA2,Mamba->Mamba2
+class HelixmRNAOutput(ModelOutput):
+    """
+    Class for the MAMBA2 model outputs.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        cache_params (`Mamba2Cache`):
+            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+            avoid providing the old `input_ids`.
+
+            Includes both the State space model state matrices after the selective scan, and the Convolutional states
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    cache_params: Optional[Mamba2Cache] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+ALL_DECODER_LAYER_TYPES = {
+    "attention": HelixmRNAAttentionDecoderLayer,
+    "mamba": Mamba2Block,
+    "mlp": HelixmRNAMLPLayer,
+}
+
+
+class HelixmRNAModel(HelixmRNAPreTrainedModel):
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        wrapper_config = kwargs.pop("config", None)
+        if wrapper_config is None:
+            raise ValueError("Config must be provided")
+
+        model_name = wrapper_config.model_name
+        cfg = HelixmRNAConfig.from_pretrained(model_name, **kwargs)
+        cfg.model_name = model_name
+
+        return super().from_pretrained(
+            model_name,
+            *model_args,
+            config=cfg,
+            **kwargs,
+        )
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        decoder_layers = []
+        for i in range(config.num_hidden_layers):
+            layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
+            decoder_layers.append(layer_class(config, layer_idx=i))
+        self.layers = nn.ModuleList(decoder_layers)
+        self.gradient_checkpointing = False
+        self.norm_f = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        self._attn_implementation = config._attn_implementation
+        # Initialize weights and apply final processing
+        self._register_load_state_dict_pre_hook(self.load_hook)
+        self.post_init()
+
+    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
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, HelixmRNAOutput]:
+        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 must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            use_cache = False
+        cache_params = past_key_values
+        if use_cache:
+            if cache_params is None:
+                cache_params = HybridMambaAttentionDynamicCache(
+                    self.config,
+                    inputs_embeds.size(0),
+                    device=inputs_embeds.device,
+                    dtype=inputs_embeds.dtype,
+                )
+                cache_position = torch.arange(
+                    0, self.config.conv_kernel, device=inputs_embeds.device
+                )
+            elif cache_position is None:
+                # cases when we do manual forward instead of using `model.generate` which will initiate
+                # `cache_position` and makes sure it is not None, throw error here instead of doing some
+                # hack to conjecture the current cache position
+                raise ValueError(
+                    "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, "
+                    "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will "
+                    "be initialized for you automatically"
+                )
+            if use_cache and past_key_values is None:
+                print(
+                    "HelixmRNA requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
+                    "provided, so no cache will be returned."
+                )
+        else:
+            cache_params = None
+
+        hidden_states = inputs_embeds
+        if cache_position is None:
+            cache_position = torch.arange(
+                hidden_states.shape[1], device=hidden_states.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position
+        )
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        for helix_block in self.layers:
+
+            layer_mask = (
+                attention_mask if isinstance(helix_block, Mamba2Block) else causal_mask
+            )
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    helix_block.__call__,
+                    hidden_states,
+                    layer_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = helix_block(
+                    hidden_states,
+                    attention_mask=layer_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            if output_hidden_states:
+                all_hidden_states += (layer_outputs[0],)
+
+        hidden_states = self.norm_f(layer_outputs[0])
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if output_attentions:
+                if layer_outputs[1] is not None:
+                    # append attentions only of attention layers. Mamba layers return `None` as the attention weights
+                    all_self_attns += (layer_outputs[1],)
+
+        if use_cache:
+            cache_params.seqlen_offset += inputs_embeds.shape[1]
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, cache_params, all_hidden_states]
+                if v is not None
+            )
+
+        return HelixmRNAOutput(
+            last_hidden_state=hidden_states,
+            cache_params=cache_params if use_cache else None,
+            hidden_states=all_hidden_states,
+        )
+
+    def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        target_length = cache_position[-1] + 1
+
+        causal_mask = torch.full(
+            (sequence_length, target_length),
+            fill_value=min_dtype,
+            dtype=dtype,
+            device=device,
+        )
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(
+            target_length, device=device
+        ) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(
+            input_tensor.shape[0], 1, -1, -1
+        )
+        if attention_mask is not None:
+            causal_mask = (
+                causal_mask.clone()
+            )  # copy to contiguous memory for in-place edit
+            if attention_mask.dim() == 2:
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
+                    :, None, None, :
+                ].eq(0.0)
+                causal_mask[..., :mask_length] = causal_mask[
+                    ..., :mask_length
+                ].masked_fill(padding_mask, min_dtype)
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+        ):
+            # 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
+
+    def _update_mamba_mask(self, attention_mask, cache_position):
+        """
+        No need for zeroing states when
+            1. Cached forward
+            2. Attending to all inputs
+        """
+        mamba_mask = attention_mask
+        if cache_position[0] > 0 or (
+            attention_mask is not None and torch.all(attention_mask == 1)
+        ):
+            mamba_mask = None
+        return mamba_mask

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+    "bos_token": "[BOS]",
+    "cls_token": "[CLS]",
+    "eos_token": "[SEP]",
+    "mask_token": "[MASK]",
+    "pad_token": "[PAD]",
+    "sep_token": "[SEP]",
+    "unk_token": "[UNK]"
+  }

tokenization_helix_mrna.py

@@ -0,0 +1,127 @@
+"""Character tokenizer for Hugging Face."""
+
+from typing import List, Optional, Dict, Sequence, Tuple
+
+from transformers import PreTrainedTokenizer
+
+
+class HelixmRNATokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids"]
+
+    def __init__(
+        self,
+        model_max_length: int,
+        bos_token="[BOS]",
+        eos_token="[SEP]",
+        sep_token="[SEP]",
+        cls_token="[CLS]",
+        pad_token="[PAD]",
+        mask_token="[MASK]",
+        unk_token="[UNK]",
+        **kwargs,
+    ):
+        """Character tokenizer for Hugging Face transformers.
+        Adapted from https://huggingface.co/LongSafari/hyenadna-tiny-1k-seqlen-hf/blob/main/tokenization_hyena.py
+        Args:
+            model_max_length (int): Model maximum sequence length.
+            characters (Sequence[str]): List of desired characters. Any character which
+                is not included in this list will be replaced by a special token called
+                [UNK] with id=6. Following is a list of the special tokens with
+                their corresponding ids:
+                    "[CLS]": 0
+                    "[SEP]": 1
+                    "[BOS]": 2
+                    "[MASK]": 3
+                    "[PAD]": 4
+                    "[RESERVED]": 5
+                    "[UNK]": 6
+                an id (starting at 7) will be assigned to each character.
+        """
+        self.characters = ("A", "C", "G", "U", "N", "E", "T")
+        self.model_max_length = model_max_length
+
+        self._vocab_str_to_int = {
+            "[CLS]": 2,
+            "[SEP]": 1,
+            "[BOS]": 0,
+            "[MASK]": 3,
+            "[PAD]": 1,
+            "[RESERVED]": 5,
+            "[UNK]": 6,
+            **{ch: i + 7 for i, ch in enumerate(self.characters)},
+        }
+
+        self._vocab_int_to_str = {v: k for k, v in self._vocab_str_to_int.items()}
+        add_prefix_space = kwargs.pop("add_prefix_space", False)
+        padding_side = kwargs.pop("padding_side", "left")
+
+        self._vocab_str_to_int["T"] = self._vocab_str_to_int["U"]
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            sep_token=sep_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            unk_token=unk_token,
+            add_prefix_space=add_prefix_space,
+            model_max_length=model_max_length,
+            padding_side=padding_side,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab_str_to_int)
+
+    def _tokenize(self, text: str, **kwargs) -> List[str]:
+        return list(text.upper())  # Convert all base pairs to uppercase
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab_str_to_int.get(token, self._vocab_str_to_int["[UNK]"])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._vocab_int_to_str[index]
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(
+            tokens
+        )  # Note: this operation has lost info about which base pairs were originally lowercase
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+
+        result = ([0] * len(token_ids_0)) + [1]
+        if token_ids_1 is not None:
+            result += ([0] * len(token_ids_1)) + [1]
+        return result
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        sep = [self.sep_token_id]
+        # cls = [self.cls_token_id]
+        result = token_ids_0 + sep
+        if token_ids_1 is not None:
+            result += token_ids_1 + sep
+        return result
+
+    def get_vocab(self) -> Dict[str, int]:
+        return self._vocab_str_to_int
+
+    # Fixed vocabulary with no vocab file
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple:
+        return ()

tokenizer_config.json

@@ -0,0 +1,71 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[BOS]",
+  "characters": [
+    "A",
+    "C",
+    "G",
+    "U",
+    "N",
+    "E",
+    "T"
+  ],
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 12288,
+  "pad_token": "[PAD]",
+  "padding_side": "left",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]",
+  "tokenizer_class": "HelixmRNATokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_helix_mrna.HelixmRNATokenizer",
+      null
+    ]
+  }
+}