Delete chatNT.py

chatNT.py

@@ -1,1850 +0,0 @@
-# This file stores ChatNT and all associated layers and configs
-
-from dataclasses import asdict, dataclass, field
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F  # noqa: N812
-from transformers import PretrainedConfig, PreTrainedModel
-
-
-@dataclass
-class RotaryEmbeddingConfig:
-    """
-    Rotary Positional Embedding configuration
-        max_seq_len: The number of positions to encode and cache.
-        dim: Dimension of RoPE.
-        theta: Rotation angle.
-    """
-
-    max_seq_len: int
-    dim: int
-    theta: float
-
-
-@dataclass
-class PerceiverResamplerConfig:
-    """
-    Parameters to initialize an PerceiverResampler model. Based on the ESM architecture.
-
-    Args:
-        emb_layer_norm_before: Whether to use layer norm before the first attention
-            layer.
-        attention_heads: Number of attention heads.
-        key_size: The dimension of the query, key, and values within each attention
-            head, if not specified, it is set to attention_heads//embed_dim.
-            It can be useful to set a custom key size if we want to impose the size of
-            the query, key and value tensor ( for example, tensors shaped with
-            power of 2 are more efficiently handled on TPUs ).
-            Note: Parametrizing the model with a custom key size has been done in :
-            Brown, Tom, et al. "Language models are few-shot learners."
-            Advances in neural information processing systems 33 (2020): 1877-1901.
-        embed_dim: Embedding dimension.
-        ffn_embed_dim: Feed forward embedding dimension.
-        num_layers: Number of attention blocks.
-        ffn_activation_name: Activation function to be used in FFN block. Supported
-            names are "gelu", "relu", "swish".
-        use_glu_in_ffn: Whether to use Gated Linear Unit (GLU) in Feed
-            Forward Network (FFN) block. To do a swiGLU (gated-swish) put this arg
-            to True and use swish as ffn_activation_name.
-            Same principle for a gated-relu. To keep the same number of parameters in
-            the FFN block, one should multiply by 2/3 the ffn_embed_dim when using GLU.
-            See https://arxiv.org/pdf/2002.05202.pdf for more details.
-        resampled_length: length of the resampled output of the module
-        use_gradient_checkpointing: Whether to use gradient checkpointing (checkpoint
-            gradients in the forward pass to reduce the computation in the backward).
-    """
-
-    # architecture
-    emb_layer_norm_before: bool = False
-    attention_heads: int = 20
-    key_size: Optional[int] = None
-    embed_dim: int = 1280
-    ffn_embed_dim: int = 5120
-    num_layers: int = 24
-    add_bias_kv: bool = False
-    add_bias_ffn: bool = True
-    ffn_activation_name: str = "gelu-no-approx"
-    use_glu_in_ffn: bool = False
-    resampled_length: int = 64
-
-    # performance
-    use_gradient_checkpointing: bool = False
-
-    def __post_init__(self) -> None:
-        """
-        Checks that the given values are compatible.
-        """
-
-        if self.key_size is None:
-            if not self.embed_dim % self.attention_heads == 0:
-                raise ValueError(
-                    f"When no key size is provided, the embedding dimension should be "
-                    f"divisible by the number of heads, however provided embedding "
-                    f"dimension is {self.embed_dim} and the number of heads is "
-                    f"{self.attention_heads}."
-                )
-            self.key_size = self.embed_dim // self.attention_heads
-
-
-@dataclass
-class GptConfig:
-    """
-    Parameters to initialize a Gpt model.
-
-    NOTE: the pad token is not defined
-
-    Args:
-        vocab_size: Token vocabulary.
-        eos_token_id: used to stop sentence generation
-        embed_dim: Embedding dimension.
-        ffn_embed_dim: Feed forward embedding dimension.
-        num_heads: Number of attention heads.
-        num_kv_heads: Number of key and value heads to support Grouped-Query and
-            Multi-Query Attention. If None, the number of key and value heads is
-            equal to the number of attention heads.
-        num_layers: Number of Decoder layer_stack
-        rope_config: The configuration for the rotary positional embeddings
-        add_bias_ffn: Add bias in feed forward network block.
-        ffn_activation_name: Activation function to be used in FFN block. Supported
-            names are "gelu", "gelu-no-approx", "relu", "swish".
-        use_glu_in_ffn: whether to use Gated Linear Unit (GLU) in Feed
-            Forward Network (FFN) block.
-            example: To do a swiGLU (gated-swish) put this arg
-            to True and use swish as ffn_activation_name.
-            Same principle for a gated-relu.
-        add_bias_lm_head: whether to use bias in the final LM layer
-        norm_type: The type of norm used ( pre normalization scheme ) used. can be
-            one of ["layer_norm", "RMS_norm"]
-        parallel_attention_ff: Whether to do the attention and the MLP in parallel,
-            and then sum up the results as it is done in Gpt-NeoX :
-            Black, Sid, et al. "Gpt-neox-20b: An open-source autoregressive
-            language model." arXiv preprint arXiv:2204.06745 (2022).
-            It is said to improve the training time of 15% when compiling with JAX
-        use_gradient_checkpointing: Whether to use gradient checkpointing (checkpoint
-            gradients in the forward pass to reduce the computation in the backward).
-        add_bias_attn: Add bias to the attention mechanism (key, query, value, and
-            output projections).
-    """
-
-    # vocabulary
-    vocab_size: int
-    eos_token_id: int
-
-    # architecture
-    embed_dim: int = 16
-    ffn_embed_dim: int = 64
-    num_heads: int = 2
-    num_kv_heads: Optional[int] = None
-    num_layers: int = 2
-    rope_config: RotaryEmbeddingConfig = field(
-        default_factory=lambda: RotaryEmbeddingConfig(
-            max_seq_len=512, dim=8, theta=10000.0
-        )
-    )
-    add_bias_ffn: bool = False
-    ffn_activation_name: str = "swish"
-    use_glu_in_ffn: bool = True
-    add_bias_lm_head: bool = False
-    norm_type: str = "RMS_norm"
-    rms_norm_eps: float = 1e-6
-    parallel_attention_ff: bool = True
-
-    # inference / backward behavior
-    use_gradient_checkpointing: bool = False
-
-    # architecture params with default values
-    add_bias_attn: bool = False
-
-    def __post_init__(self) -> None:
-        """
-        Checks that the given values are compatible.
-        """
-        if not self.embed_dim % self.num_heads == 0:
-            raise ValueError(
-                f"The embedding dimension should be "
-                f"divisible by the number of heads, however provided embedding "
-                f"dimension is {self.embed_dim} and the number of heads is "
-                f"{self.num_heads}."
-            )
-
-        if not self.embed_dim // self.num_heads > 1:
-            raise ValueError(
-                "embed_dim / num_heads must be higher than 2 to apply rotary embeddings"
-            )
-
-        if not self.embed_dim // self.num_heads >= self.rope_config.dim:
-            raise ValueError(
-                "embed_dim // num_heads must be higher than rope_config.dim "
-                "to apply rotary embeddings"
-            )
-
-    def to_dict(self):  # type: ignore
-        output = asdict(self)
-        output["rope_config"] = asdict(self.rope_config)
-        return output
-
-
-@dataclass
-class ESMTransformerConfig:
-    """
-    Parameters to initialize an ESM model. While the ESM architecture is an encoder-only
-    model, different choices have been made for each version and this configuration aims
-    to cover most of them.
-
-    Args:
-        alphabet_size: Token vocabulary.
-        pad_token_id: ID of pad token.
-        mask_token_id: ID of mask token.
-        max_positions: Maximum sequence length.
-        embed_scale: Correction ratio applied to the embeddings to make up for the
-            norm difference between the input during training and inference.
-        emb_layer_norm_before: Whether to use layer norm before the first attention
-            layer.
-        attention_heads: Number of attention heads.
-        key_size: The dimension of the query, key, and values within each attention
-            head, if not specified, it is set to attention_heads//embed_dim.
-            It can be useful to set a custom key size if we want to impose the size of
-            the query, key and value tensor ( for example, tensors shaped with
-            power of 2 are more efficiently handled on TPUs ).
-            Note: Parametrizing the model with a custom key size has been done in :
-            Brown, Tom, et al. "Language models are few-shot learners."
-            Advances in neural information processing systems 33 (2020): 1877-1901.
-        embed_dim: Embedding dimension.
-        ffn_embed_dim: Feed forward embedding dimension.
-        num_layers: Number of attention blocks.
-        positional_embedding: Type of positional embedding to use before the first
-            attention layer. Options: "learned", "learned_standard" "sinusoidal" or
-            None.
-            NOTE: "learned" is the positional embedding of ESM, and "learned_standard"
-            is a more standard one, used for example in DNAbert.
-        lm_head: type of language model head. Options: "simple", "roberta" or None.
-        add_bias_kv: Add bias in attention layer.
-        add_bias_ffn: Add bias in feed forward network block.
-        use_rotary_embedding: Whether to use rotary embeddings (for ESM2). Requires:
-            positional_embeddings = None.
-        rescaling_factor: Scaling factor to use for rotary embeddings.
-        ffn_activation_name: Activation function to be used in FFN block. Supported
-            names are "gelu", "relu", "swish".
-        use_glu_in_ffn: Whether to use Gated Linear Unit (GLU) in Feed
-            Forward Network (FFN) block. To do a swiGLU (gated-swish) put this arg
-            to True and use swish as ffn_activation_name.
-            Same principle for a gated-relu. To keep the same number of parameters in
-            the FFN block, one should multiply by 2/3 the ffn_embed_dim when using GLU.
-            See https://arxiv.org/pdf/2002.05202.pdf for more details.
-        mask_before_attention: Use mask before attention layers (for EMS1b and ESM2).
-        layer_norm_eps: the eps factor in the different layer norms of the model (refer
-            to layer norm implementation)
-        token_dropout: Token dropout.
-        masking_ratio: Masking ratio (used if token dropout is enabled).
-        masking_prob: Masking probability (used if token dropout is enabled).
-        use_gradient_checkpointing: Whether to use gradient checkpointing (checkpoint
-            gradients in the forward pass to reduce the computation in the backward).
-    """
-
-    alphabet_size: int
-    pad_token_id: int
-    mask_token_id: int
-
-    max_positions: int = 1024
-    embed_scale: float = 1.0
-
-    # architecture
-    emb_layer_norm_before: bool = False
-    attention_heads: int = 20
-    key_size: Optional[int] = None
-    embed_dim: int = 1280
-    ffn_embed_dim: int = 5120
-    num_layers: int = 24
-    positional_embedding: Optional[str] = "learned"
-    lm_head: Optional[str] = "simple"
-    add_bias_kv: bool = False
-    add_bias_ffn: bool = True
-    use_rotary_embedding: bool = False
-    rescaling_factor: Optional[float] = None
-    ffn_activation_name: str = "gelu-no-approx"
-    use_glu_in_ffn: bool = False
-    mask_before_attention: bool = False
-    layer_norm_eps: float = 1e-5
-    pre_layer_norm: bool = True
-    bias_word_embedding: bool = False
-
-    # dropout
-    token_dropout: bool = False
-    masking_ratio: float = 0.1
-    masking_prob: float = 0.8
-
-    # logging
-    use_gradient_checkpointing: bool = False
-
-    # return
-    embeddings_layers_to_save: List[int] = field(default_factory=list)
-    attention_maps_to_save: List[Tuple[int, int]] = field(default_factory=list)
-
-    def __post_init__(self) -> None:
-        """
-        Checks that the given values are compatible.
-        """
-
-        if self.key_size is None:
-            if not self.embed_dim % self.attention_heads == 0:
-                raise ValueError(
-                    f"When no key size is provided, the embedding dimension should be "
-                    f"divisible by the number of heads, however provided embedding "
-                    f"dimension is {self.embed_dim} and the number of heads is "
-                    f"{self.attention_heads}."
-                )
-            self.key_size = self.embed_dim // self.attention_heads
-        if self.positional_embedding is not None:
-            if type(self.positional_embedding) != str:
-                raise TypeError
-
-            if self.positional_embedding not in [
-                "learned",
-                "sinusoidal",
-                "learned_standard",
-                "alibi_dnabert_2",
-            ]:
-                raise ValueError(
-                    "The positional_embedding argument should either be None,"
-                    "`learned`, `sinusoidal`, 'learned_standard' or 'alibi_dnabert_2'."
-                )
-        if self.lm_head is not None:
-            if type(self.lm_head) != str:
-                raise TypeError
-
-            if self.lm_head not in ["simple", "roberta"]:
-                raise ValueError(
-                    "The lm_head argument should either be None,"
-                    "`simple` or `roberta`."
-                )
-
-        if self.use_rotary_embedding and self.positional_embedding is not None:
-            raise ValueError(
-                "When using rotary embedding, positional_embedding must be set to none"
-            )
-
-        if self.add_bias_kv and self.use_rotary_embedding:
-            raise ValueError(
-                "Biases on key and values are not compatible with Rotary embeddings."
-            )
-
-        if self.positional_embedding == "alibi_dnabert_2":
-            assert not self.add_bias_kv
-
-
-@dataclass
-class ChatNTConfig(PretrainedConfig):
-    model_type = "ChatNT"
-
-    def __init__(self, **kwargs):  # type: ignore
-        self.gpt_config: GptConfig = kwargs.get("gpt_config", GptConfig(32000, 3))
-        self.esm_config: ESMTransformerConfig = kwargs.get(
-            "esm_config", ESMTransformerConfig(4000, 1, 4)
-        )
-        self.perceiver_resampler_config: PerceiverResamplerConfig = kwargs.get(
-            "perceiver_resampler_config", PerceiverResamplerConfig()
-        )
-        self.seq_token_id: int = kwargs.get("seq_token_id", 32000)
-        self.bio_pad_token_id: int = kwargs.get("bio_pad_token_id", 1)
-        self.english_pad_token_id: int = kwargs.get("english_pad_token_id", 2)
-        super().__init__(**kwargs)
-
-    def to_dict(self):  # type: ignore
-        output = super().to_dict()
-
-        def serialize(obj):  # type: ignore
-            return obj.to_dict() if hasattr(obj, "to_dict") else vars(obj)
-
-        output["gpt_config"] = serialize(self.gpt_config)  # type: ignore
-        output["esm_config"] = serialize(self.esm_config)  # type: ignore
-        output["perceiver_resampler_config"] = serialize(  # type: ignore
-            self.perceiver_resampler_config
-        )
-        return output
-
-
-class TorchBioBrainDecoder(nn.Module):
-    def __init__(
-        self,
-        gpt_config: GptConfig,
-        seq_token_id: int,
-    ):
-        """
-        Initializes the BioBrain decoder, using a GPT model for text generation with
-        bio embeddings.
-
-        Args:
-            gpt_config: Configuration for the GPT model
-            seq_token_id: Index of the SEQ token
-        """
-        super(TorchBioBrainDecoder, self).__init__()
-        self.gpt_config = gpt_config
-        self.seq_token_id = seq_token_id
-
-        # Initialize the GPT model (assumed you have it already in PyTorch)
-        self.gpt_model = TorchGptDecoder(self.gpt_config)
-
-    def forward(
-        self, english_token_ids: torch.Tensor, projected_bio_embeddings: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Forward pass through the model.
-
-        Args:
-            english_token_ids: Tensor of English token IDs with shape
-                (batch_size, num_english_tokens).
-            projected_bio_embeddings: Optional tensor of bio embeddings with shape
-                                      (batch_size, num_bio_sequences, ?, embed_dim).
-
-        Returns:
-            torch.Tensor: The logits from the GPT model,
-                shaped (batch_size, num_english_tokens, vocab_size).
-        """
-
-        # Compute English token embeddings
-        tokens_embeddings = self.gpt_model.token_embed(english_token_ids)
-
-        if projected_bio_embeddings is not None:
-            (
-                batch_size,
-                num_bio_sequences,
-                _,
-                bio_embed_dim,
-            ) = projected_bio_embeddings.shape
-
-            # Insert the bio embeddings at the SEQ token positions
-            processed_tokens_ids = english_token_ids.clone()
-            for bio_seq_num in range(num_bio_sequences):
-                tokens_embeddings, processed_tokens_ids = self.insert_embeddings(
-                    processed_tokens_ids,
-                    tokens_embeddings,
-                    projected_bio_embeddings[:, bio_seq_num, :, :],
-                    bio_seq_num=bio_seq_num,
-                )
-
-        # Regular GPT pass through
-        embeddings = self.gpt_model.apply_transformer_layers(tokens_embeddings)
-        embeddings = self.gpt_model.final_norm(embeddings)
-
-        # Compute logits
-        logits = self.gpt_model.lm_head(embeddings)
-
-        if projected_bio_embeddings is not None:
-            # Clean logits sequentially
-            processed_tokens_ids = english_token_ids.clone()
-            resampled_length = projected_bio_embeddings.shape[-2]
-            for _ in range(num_bio_sequences):
-                logits, processed_tokens_ids = self.cleanup_logits(
-                    tokens=processed_tokens_ids,
-                    logits=logits,
-                    resampled_length=resampled_length,
-                )
-
-        return logits
-
-    def insert_embeddings(
-        self,
-        tokens: torch.Tensor,
-        input_embeddings: torch.Tensor,
-        resampled_embeddings: torch.Tensor,
-        bio_seq_num: int,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Inserts resampled embeddings in input_embeddings, starting at the SEQ token
-
-        Args:
-            tokens (torch.Tensor): Shape (batch_size, num_tokens)
-            input_embeddings (torch.Tensor): Shape (batch_size, num_tokens, embed_dim)
-            resampled_embeddings (torch.Tensor):
-                Shape (batch_size, num_bio_sequences, bio_sequence_length, embed_dim)
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]:
-                - input_embeddings with resampled_embeddings inserted at the SEQ token
-                - tokens with the SEQ token set to -1
-        """
-
-        def _insert(
-            tokens_1d: torch.Tensor,
-            input_embeddings_1d: torch.Tensor,
-            resampled_embeddings_1d: torch.Tensor,
-        ) -> Tuple[torch.Tensor, torch.Tensor]:
-            """
-            Args:
-                tokens (torch.Tensor): Shape (num_tokens,)
-                input_embeddings (torch.Tensor): Shape (num_tokens, embed_dim,)
-                resampled_embeddings (torch.Tensor):
-                    Shape (bio_sequence_length, embed_dim,)
-            """
-            indices = torch.where(tokens_1d == self.seq_token_id)[0]
-            if indices.numel() > 0:
-                idx = indices[0].item()
-                insertion_pos = idx + resampled_embeddings_1d.shape[-2] * bio_seq_num
-                x = torch.cat(
-                    [
-                        input_embeddings_1d[:insertion_pos, :],
-                        resampled_embeddings_1d,
-                        input_embeddings_1d[insertion_pos:, :],
-                    ],
-                    dim=0,
-                )[: tokens_1d.shape[0] + 1, :]
-                x = torch.roll(torch.roll(x, shifts=-idx, dims=0), shifts=idx, dims=0)[
-                    :-1, :
-                ]
-                tokens_1d[idx] = -1
-                return x, tokens_1d
-            else:
-                return (
-                    input_embeddings,
-                    tokens_1d,
-                )  # Return unchanged if seq_token_id is not found
-
-        tokens_acc = []
-        embeddings_acc = []
-
-        for i in range(tokens.shape[0]):
-            embeddings_out, tokens_out = _insert(
-                tokens[i].clone(),
-                input_embeddings[i].clone(),
-                resampled_embeddings[i].clone(),
-            )
-            tokens_acc.append(tokens_out)
-            embeddings_acc.append(embeddings_out)
-        tokens_acc = torch.stack(tokens_acc)
-        embeddings_acc = torch.stack(embeddings_acc)
-
-        return embeddings_acc, tokens_acc
-
-    def cleanup_logits(
-        self, tokens: torch.Tensor, logits: torch.Tensor, resampled_length: int
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Removes the logits corresponding to the unused embeddings.
-
-        Args:
-            tokens: Input english tokens.
-            logits: Input logits.
-
-        Returns:
-            Cleaned logits, last values will be equal to 0.
-        """
-
-        def _clean(
-            token: torch.Tensor, logit: torch.Tensor
-        ) -> Tuple[torch.Tensor, torch.Tensor]:
-            indices = torch.where(token == self.seq_token_id)[0]
-            if indices.numel() > 0:
-                idx = indices[0].item()
-
-                mask_idx = (
-                    torch.arange(logit.shape[0] - resampled_length, device=logit.device)
-                    > idx
-                )
-                mask_idx = mask_idx.unsqueeze(1)
-
-                # Remove values corresponding to bio tokens
-                logit = (
-                    logit[:-resampled_length] * (~mask_idx)
-                    + logit[resampled_length:] * mask_idx
-                )
-
-                # Append zeros at the end
-                logit = torch.cat(
-                    (
-                        logit,
-                        torch.zeros(
-                            (resampled_length, logit.shape[1]),
-                            dtype=logit.dtype,
-                            device=logit.device,
-                        ),
-                    )
-                )
-
-                # Update token
-                token[idx] = -1
-
-                return logit, token
-
-            else:
-                return logit, token
-
-        tokens_acc = []
-        logits_acc = []
-
-        for i in range(tokens.shape[0]):
-            logits_out, tokens_out = _clean(tokens[i].clone(), logits[i].clone())
-            tokens_acc.append(tokens_out)
-            logits_acc.append(logits_out)
-        tokens_acc = torch.stack(tokens_acc)
-        logits_acc = torch.stack(logits_acc)
-
-        return logits_acc, tokens_acc
-
-
-class TorchMultiOmicsModel(PreTrainedModel):
-    config_class = ChatNTConfig
-
-    def __init__(self, config: ChatNTConfig) -> None:
-        super().__init__(config=config)
-        self.gpt_config = config.gpt_config
-        self.esm_config = config.esm_config
-        self.perceiver_resampler_config = config.perceiver_resampler_config
-        self.seq_token_id = config.seq_token_id
-        self.bio_pad_token_id = config.bio_pad_token_id
-        self.english_pad_token_id = config.english_pad_token_id
-
-        # Correct seq_token_id
-        self.seq_token_id -= 1
-
-        self.biobrain_encoder = TorchBioBrainEncoder(esm_config=self.esm_config)
-        self.biobrain_decoder = TorchBioBrainDecoder(
-            gpt_config=self.gpt_config, seq_token_id=self.seq_token_id
-        )
-        self.projection_model = TorchMultiModalPerceiverResamplerProjection(
-            perceiver_resampler_config=self.perceiver_resampler_config,
-            input_embed_dim=self.esm_config.embed_dim,
-            embed_dim=self.gpt_config.embed_dim,
-            english_vocab_size=self.gpt_config.vocab_size,
-            bio_pad_token_id=self.bio_pad_token_id,
-            english_pad_token_id=self.english_pad_token_id,
-        )
-
-    def forward(
-        self,
-        multi_omics_tokens_ids: tuple[torch.Tensor, torch.Tensor],
-        projection_english_tokens_ids: torch.Tensor,
-        projected_bio_embeddings: torch.Tensor = None,
-    ) -> dict[str, torch.Tensor]:
-        """
-
-        Args:
-            multi_omics_tokens_ids (Tuple[torch.Tensor, torch.Tensor]):
-                english_tokens_ids: Represents the prompt tokens (english tokens)
-                    Shape (batch_size, num_english_tokens)
-
-                bio_tokens_ids: Represents the bio sequences tokens
-                    Shape (batch_size, num_bio_sequences, num_bio_tokens)
-
-            projection_english_tokens_ids (torch.Tensor):
-                Shape (batch_size, num_english_tokens)
-
-            projected_bio_embeddings (projected_bio_embeddings, optional):
-                Shape (batch_size, num_bio_sequencse, ?, embed_dim).
-                Defaults to None.
-
-        Returns:
-            dict[str, torch.Tensor] containing:
-                - logits:
-                    Shape (batch_size, num_tokens, vocab_size)
-
-                - projected_bio_embeddings:
-                    Shape (batch_size, num_bio_sequences, ?, embed_dim)
-        """
-        english_token_ids, bio_token_ids = multi_omics_tokens_ids
-
-        # Replace config.vocab_size value in english tokens
-        # We do this because the default vocab size (32000) doesn't match with the
-        # number of tokens because of seq_token_id(=32000) that was added
-        # Therefore, we will put seq_token_id to 31999
-        # (I will also put token n°31999 to 0, which is for unknown token)
-        # This is a workaround to avoid having to change the vocab size in the config
-        vocab_size = self.gpt_config.vocab_size
-        # Replace vocab
-        english_token_ids[english_token_ids == vocab_size - 1] = 0
-        projection_english_tokens_ids[
-            projection_english_tokens_ids == vocab_size - 1
-        ] = 0
-        english_token_ids[english_token_ids == vocab_size] = vocab_size - 1
-        projection_english_tokens_ids[projection_english_tokens_ids == vocab_size] = (
-            vocab_size - 1
-        )
-
-        if bio_token_ids is None:
-            projected_bio_embeddings = None
-        else:
-            num_bio_sequences = bio_token_ids.shape[1]
-
-            if projected_bio_embeddings is None:
-                # Compute bio sequences embeddings
-                bio_embeddings_list = [
-                    self.biobrain_encoder(bio_token_ids=bio_token_ids[:, bio_seq_num])
-                    for bio_seq_num in range(num_bio_sequences)
-                ]
-
-                # Project these embeddings
-                projected_bio_embeddings = [
-                    self.projection_model(
-                        bio_token_ids=bio_token_ids[:, bio_seq_num],
-                        bio_embeddings=bio_embeddings,
-                        english_token_ids=projection_english_tokens_ids,
-                    )
-                    for bio_seq_num, bio_embeddings in enumerate(bio_embeddings_list)
-                ]
-                projected_bio_embeddings = torch.stack(projected_bio_embeddings, dim=1)
-
-        # decode
-        logits = self.biobrain_decoder(
-            english_token_ids=english_token_ids,
-            projected_bio_embeddings=projected_bio_embeddings,
-        )
-
-        outs = {"logits": logits, "projected_bio_embeddings": projected_bio_embeddings}
-
-        return outs
-
-
-class TorchRotaryEmbedding(torch.nn.Module):
-    def __init__(self, config: RotaryEmbeddingConfig):
-        super().__init__()
-
-        self.max_seq_len = config.max_seq_len
-        self.dim = config.dim
-        self.theta = config.theta
-        self.sincos_cache = self._create_sinusoidal_positions()
-
-    def _create_sinusoidal_positions(self) -> torch.Tensor:
-        """
-        Create the sines and cosines for the RoPE.
-
-        Returns:
-            Sinusoidal positions of shape (self.max_seq_len, self.dim).
-        """
-        # Create the inverse frequency based on theta and dim
-        inv_freq = 1.0 / (
-            self.theta ** (torch.arange(0, self.dim, 2).float() / self.dim)
-        )
-
-        # Compute sinusoidal input using the broadcasting
-        sinusoid_inp = torch.einsum(
-            "i,j->ij", torch.arange(self.max_seq_len).float(), inv_freq
-        )
-
-        # Apply sin and cos to the sinusoidal input
-        sin, cos = sinusoid_inp.sin(), sinusoid_inp.cos()
-
-        # Allocate a tensor for the final sin-cos values
-        sincos = torch.zeros((self.max_seq_len, self.dim), dtype=torch.float32)
-
-        # Fill the sincos tensor with sin and cos values
-        sentinel = self.dim // 2 + self.dim % 2
-        sincos[:, :sentinel] = sin
-        sincos[:, sentinel:] = cos
-
-        return sincos
-
-    def _rotate_every_two(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Prepare a tensor to apply the RoPE mechanism.
-
-        Args:
-            x: Tensor of shape (batch_size, seq_len, num_heads, head_dim),
-            typically this is the key or query tensor.
-
-        Returns:
-            The even indices in the last dimension have their sign flipped.
-            Tensor of shape (batch_size, seq_len, num_heads, head_dim).
-        """
-        # Split the tensor into two halves (odd and even indexed dimensions)
-        rotate_half = torch.stack((-x[..., 1::2], x[..., ::2]), dim=-1)
-
-        # Reshape the tensor to the original shape
-        rotate_half = rotate_half.view(rotate_half.shape[:-2] + (-1,))
-        return rotate_half
-
-    def _apply_rotary_pos_emb(
-        self, x: torch.Tensor, sincos: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Applies rotary embeddings to x.
-
-        Args:
-            x: Tensor of shape (batch_size, seq_len, num_heads, head_dim),
-            typically this is the key or query tensor.
-            sincos: Tuple of sine and cosine tensors for position encoding.
-
-        Returns:
-            RoPE embeddings tensor.
-        """
-        sin_pos, cos_pos = sincos
-
-        # Reshape the sin and cos tensors for broadcasting
-        sin_pos = torch.repeat_interleave(sin_pos.unsqueeze(2), repeats=2, dim=-1)
-        cos_pos = torch.repeat_interleave(cos_pos.unsqueeze(2), repeats=2, dim=-1)
-
-        # Apply the rotary embedding mechanism
-        return (x * cos_pos) + (self._rotate_every_two(x) * sin_pos)
-
-    def __call__(
-        self, k: torch.Tensor, q: torch.Tensor, positions: Optional[torch.Tensor] = None
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Applies rotary embeddings to k and q.
-
-        Args:
-            k: key tensor of shape (batch_size, seq_len, num_heads, head_dim),
-            q: value tensor of shape (batch_size, seq_len, num_heads, head_dim),
-            positions: optional positions offset useful when caching,
-
-        Returns:
-            RoPE embeddings for the keys and values.
-        """
-        batch_size, seq_len, num_heads, head_dim = k.shape
-
-        # Generate position ids
-        position_ids = (
-            torch.arange(seq_len, device=k.device).unsqueeze(0).expand(batch_size, -1)
-        )
-
-        if positions is not None:
-            position_ids += positions
-
-        # Retrieve sincos values using the position_ids
-        sincos = self.sincos_cache[position_ids]
-
-        # Split sincos into sin_pos and cos_pos
-        sincos = torch.chunk(sincos, 2, dim=-1)
-
-        # Apply rotary position embedding to key (k) and query (q)
-        k_rot = self._apply_rotary_pos_emb(k[..., : self.dim], sincos)
-        k_pass = k[..., self.dim :]
-
-        q_rot = self._apply_rotary_pos_emb(q[..., : self.dim], sincos)
-        q_pass = q[..., self.dim :]
-
-        # Concatenate the rotated and non-rotated parts
-        keys = torch.cat([k_rot, k_pass], dim=-1)
-        values = torch.cat([q_rot, q_pass], dim=-1)
-
-        return keys, values
-
-
-class TorchGptGroupedQueryAttention(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        rope_config: RotaryEmbeddingConfig,
-        num_kv_heads: int = None,  # type: ignore
-        head_dim: int = None,  # type: ignore
-        add_bias_attn: bool = False,  # type: ignore
-    ) -> None:
-        super().__init__()
-        self.num_heads = num_heads
-        self.num_kv_heads = num_kv_heads or num_heads
-        self.embed_dim = embed_dim
-        self.head_dim = head_dim or (embed_dim // num_heads)
-        self.add_bias_attn = add_bias_attn
-        self.rope = TorchRotaryEmbedding(rope_config)
-
-        self.query_linear = nn.Linear(
-            embed_dim, self.num_heads * self.head_dim, bias=add_bias_attn
-        )
-        self.key_linear = nn.Linear(
-            embed_dim, self.num_kv_heads * self.head_dim, bias=add_bias_attn
-        )
-        self.value_linear = nn.Linear(
-            embed_dim, self.num_kv_heads * self.head_dim, bias=add_bias_attn
-        )
-        self.out_linear = nn.Linear(
-            self.num_heads * self.head_dim, embed_dim, bias=add_bias_attn
-        )
-
-    def forward(
-        self,
-        query_inputs: torch.Tensor,
-        key_inputs: torch.Tensor,
-        value_inputs: torch.Tensor,
-        attention_mask: torch.Tensor = None,
-    ) -> torch.Tensor:
-        batch_size, seq_len, _ = query_inputs.shape
-
-        queries = self.query_linear(query_inputs).view(  # noqa
-            batch_size, seq_len, self.num_heads, self.head_dim
-        )
-        keys = self.key_linear(key_inputs).view(  # noqa
-            batch_size, seq_len, self.num_kv_heads, self.head_dim
-        )
-        values = self.value_linear(value_inputs).view(  # noqa
-            batch_size, seq_len, self.num_kv_heads, self.head_dim
-        )
-
-        keys, queries = self.rope(keys, queries)
-
-        n_rep = self.num_heads // self.num_kv_heads
-        keys = keys.repeat_interleave(n_rep, dim=2)
-        values = values.repeat_interleave(n_rep, dim=2)
-
-        attention_logits = torch.einsum("bthd,bThd->bhtT", queries, keys) / (
-            self.head_dim**0.5
-        )
-
-        if attention_mask is not None:
-            attention_logits = attention_logits.masked_fill(
-                attention_mask == 0, float("-inf")
-            )
-
-        attention_weights = nn.functional.softmax(attention_logits, dim=-1)
-
-        values = torch.einsum("bhtT,bThd->bthd", attention_weights, values)
-        values = values.contiguous().view(batch_size, seq_len, -1)
-
-        return self.out_linear(values)
-
-
-class TorchGptDecoder(nn.Module):
-    def __init__(self, config: GptConfig, name: Optional[str] = None):
-        super().__init__()
-        self.config = config
-
-        self.token_embed = nn.Embedding(config.vocab_size, config.embed_dim)
-
-        if config.norm_type == "layer_norm":
-            self.final_norm = nn.LayerNorm(config.embed_dim)
-        elif config.norm_type == "RMS_norm":
-            self.final_norm = TorchRMSNorm(config.embed_dim, eps=config.rms_norm_eps)
-        else:
-            raise ValueError(f"unrecognized norm_type in config {config.norm_type}")
-
-        self.layers = nn.ModuleList(
-            [
-                TorchGptDecoderLayer(
-                    embed_dim=config.embed_dim,
-                    ffn_embed_dim=config.ffn_embed_dim,
-                    num_heads=config.num_heads,
-                    rope_config=config.rope_config,
-                    norm_type=config.norm_type,
-                    parallel_attention_ff=config.parallel_attention_ff,
-                    add_bias_ffn=config.add_bias_ffn,
-                    ffn_activation_name=config.ffn_activation_name,
-                    use_glu_in_ffn=config.use_glu_in_ffn,
-                    num_kv_heads=config.num_kv_heads,  # type: ignore
-                    add_bias_attn=config.add_bias_attn,
-                    rms_norm_eps=config.rms_norm_eps,
-                )
-                for _ in range(config.num_layers)
-            ]
-        )
-
-        self.lm_head = TorchSimpleLMHead(
-            embed_dim=config.embed_dim,
-            alphabet_size=config.vocab_size,
-            add_bias_lm_head=config.add_bias_lm_head,
-        )
-
-    def apply_transformer_layers(
-        self, embeddings: torch.Tensor, attention_mask: torch.Tensor = None
-    ) -> torch.Tensor:
-        if attention_mask is None:
-            attention_mask = build_causal_attention_mask(1, embeddings.shape[1])
-        for layer in self.layers:
-            embeddings = layer(embeddings, attention_mask)
-
-        return embeddings
-
-    def forward(
-        self, token_ids: torch.Tensor, attention_mask: torch.Tensor = None
-    ) -> dict[str, torch.Tensor]:
-        if attention_mask is None:
-            attention_mask = build_causal_attention_mask(1, token_ids.shape[1])
-
-        tokens_embeddings = self.token_embed(token_ids)
-
-        after_transformer_embeddings = self.apply_transformer_layers(
-            tokens_embeddings, attention_mask=attention_mask
-        )
-
-        embeddings = self.final_norm(after_transformer_embeddings)
-        logits = self.lm_head(embeddings)
-        return {"embeddings": embeddings, "logits": logits}
-
-
-class TorchSimpleLMHead(nn.Module):
-    def __init__(
-        self, embed_dim: int, alphabet_size: int, add_bias_lm_head: bool = True
-    ) -> None:
-        super().__init__()
-        self.fc = nn.Linear(embed_dim, alphabet_size, bias=add_bias_lm_head)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.fc(x)
-
-
-class TorchGptDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        ffn_embed_dim: int,
-        num_heads: int,
-        rope_config: RotaryEmbeddingConfig,
-        norm_type: str,
-        parallel_attention_ff: bool,
-        add_bias_ffn: bool,
-        ffn_activation_name: str,
-        use_glu_in_ffn: bool,
-        num_kv_heads: int,
-        add_bias_attn: bool,
-        rms_norm_eps: float = 1e-6,
-    ) -> None:
-        super().__init__()
-        self.num_heads = num_heads
-        self.parallel_attention_ff = parallel_attention_ff
-        self.use_glu_in_ffn = use_glu_in_ffn
-
-        # Self-Attention layer
-        self.self_attn = TorchGptGroupedQueryAttention(
-            embed_dim=embed_dim,
-            num_heads=num_heads,
-            num_kv_heads=num_kv_heads,
-            rope_config=rope_config,
-            add_bias_attn=add_bias_attn,
-        )
-
-        # Normalization layers
-        if norm_type == "layer_norm":
-            self.attn_norm = nn.LayerNorm(embed_dim)
-            if not self.parallel_attention_ff:
-                self.ffn_norm = nn.LayerNorm(embed_dim)
-        elif norm_type == "RMS_norm":
-            self.attn_norm = TorchRMSNorm(embed_dim, eps=rms_norm_eps)
-            if not self.parallel_attention_ff:
-                self.ffn_norm = TorchRMSNorm(embed_dim, eps=rms_norm_eps)
-        else:
-            raise ValueError(f"unrecognized norm_type: {norm_type}")
-
-        # Feedforward network
-        self.activation = get_activation_fn(ffn_activation_name)
-        ffn_hidden_dim = ffn_embed_dim * (2 if use_glu_in_ffn else 1)
-        self.fc1 = nn.Linear(embed_dim, ffn_hidden_dim, bias=add_bias_ffn)
-        self.fc2 = nn.Linear(ffn_embed_dim, embed_dim, bias=add_bias_ffn)
-
-    def forward(
-        self, embeddings: torch.Tensor, attention_mask: torch.Tensor
-    ) -> torch.Tensor:
-        residuals = embeddings
-
-        if self.parallel_attention_ff:
-            # Parallel Attention + MLP
-            embeddings_normed = self.attn_norm(embeddings)
-
-            attn_output, _ = self.self_attn(
-                embeddings_normed,
-                embeddings_normed,
-                embeddings_normed,
-                attn_mask=attention_mask,
-            )
-            ffn_output = self.mlp(embeddings_normed)  # type: ignore
-
-            return residuals + attn_output + ffn_output
-        else:
-            # Sequential Attention + MLP
-            normed_embeddings = self.attn_norm(embeddings)
-
-            attn_output = embeddings + self.self_attn(
-                normed_embeddings,
-                normed_embeddings,
-                normed_embeddings,
-                attention_mask=attention_mask,
-            )
-
-            normed_embeddings2 = self.ffn_norm(attn_output)
-            ffn_output = self.mlp(normed_embeddings2)  # type: ignore
-            return attn_output + ffn_output  # Residual connection
-
-    def mlp(self, x: torch.Tensor) -> torch.Tensor:
-        """Applies the feedforward network (MLP) with optional GLU."""
-        ffn_output = self.fc1(x)
-
-        if self.use_glu_in_ffn:
-            ffn_output1, ffn_output2 = ffn_output.chunk(2, dim=-1)
-            ffn_output = self.activation(ffn_output1) * ffn_output2
-        else:
-            ffn_output = self.activation(ffn_output)
-
-        return self.fc2(ffn_output)
-
-
-class TorchRMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.eps = eps
-        self.scale = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return (
-            x
-            * self.scale
-            / torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
-        )
-
-
-def get_activation_fn(activation_name: str):  # type: ignore
-    activations = {
-        "gelu": nn.functional.gelu,
-        "relu": nn.functional.relu,
-        "swish": nn.functional.silu,
-        "silu": nn.functional.silu,
-    }
-    return activations.get(activation_name, nn.functional.relu)
-
-
-def build_causal_attention_mask(batch_size: int, seq_len: int) -> torch.Tensor:
-    """
-    Builds a batch of causal masks of shape (batch_size, 1, seq_len, seq_len) to feed
-    to an attention layer.
-
-    Args:
-        batch_size: Batch size.
-        seq_len: Length of the sequences.
-
-    Returns:
-        Batch of causal masks.
-    """
-    mask = torch.ones((batch_size, 1, seq_len, seq_len))
-    causal_mask = torch.tril(mask)
-    return causal_mask
-
-
-@dataclass
-class RotaryEmbeddingConfigBis:
-    """
-    Parameters to initialize the RotaryEmbedding layer. The rescaling factor allows
-    to adapt the rotary embeddings to larger lengths than what was used for training.
-    One of this strategy is presented in the Yarn paper: https://arxiv.org/pdf/2309.00071.pdf. # noqa
-    Args:
-    """
-
-    rescaling_factor: Optional[float]
-
-
-class RotaryEmbeddingBis(torch.nn.Module):
-    """
-    Rotary position embeddings based on those in
-    [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer).
-    Query and keys are transformed by rotation
-    matrices which depend on their relative positions.
-    """
-
-    def __init__(self, dim: int, rotary_embedding_config: RotaryEmbeddingConfigBis):
-        super().__init__()
-
-        # Extract argument from the config
-        self.rescaling_factor = rotary_embedding_config.rescaling_factor
-        self.upper_freq = 10000
-        self.dim = dim
-
-        self._seq_len_cached = None
-        self._cos_cached = None
-        self._sin_cached = None
-
-    def _apply_rotary_pos_emb(
-        self,
-        heads: torch.Tensor,
-        cos: torch.Tensor,
-        sin: torch.Tensor,
-    ) -> torch.Tensor:
-        """ """
-        x_first, x_second = (
-            heads[..., : heads.shape[-1] // 2],
-            heads[..., heads.shape[-1] // 2 :],
-        )
-
-        first_part = x_first * cos - x_second * sin
-        second_part = x_second * cos + x_first * sin
-
-        return torch.cat((first_part, second_part), dim=-1)
-
-    def _compute_cos_sin_tables(
-        self, x: torch.Tensor, inv_freq: torch.Tensor, seq_dimension: int = 2
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        seq_len = x.shape[seq_dimension]
-        # Reset the tables if the sequence length has changed,
-        # or if we're on a new device (possibly due to tracing for instance)
-        self._seq_len_cached = seq_len
-        t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(inv_freq)
-        # freqs = torch.outer(t, inv_freq)
-        freqs = torch.einsum("i, j -> ij", t, inv_freq)
-
-        self._cos_cached = torch.cos(freqs)[None, :, None, :]
-        self._sin_cached = torch.sin(freqs)[None, :, None, :]
-        # emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-
-        # self._cos_cached = emb.cos()[None, None, :, :]
-        # self._sin_cached = emb.sin()[None, None, :, :]
-
-        return self._cos_cached, self._sin_cached
-
-    def forward(
-        self, q: torch.Tensor, k: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        if self.rescaling_factor is None:
-            inv_freq = 1.0 / (
-                self.upper_freq ** (torch.arange(0, self.dim, 2).float() / self.dim)
-            )
-        else:
-            updated_base = self.upper_freq * (
-                self.rescaling_factor ** (self.dim / (self.dim - 2))
-            )
-            inv_freq = 1.0 / (
-                updated_base ** (torch.arange(0, self.dim, 2).float() / self.dim)
-            )
-
-        self._cos_cached, self._sin_cached = self._compute_cos_sin_tables(
-            q,
-            inv_freq,
-            seq_dimension=-3,
-        )
-
-        return (
-            self._apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
-            self._apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
-        )
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        num_heads: int,
-        key_size: int,
-        rotary_embedding_config: Optional[RotaryEmbeddingConfigBis] = None,
-        add_bias_kv: bool = False,
-        value_size: Optional[int] = None,
-        model_size: Optional[int] = None,
-        name: Optional[str] = None,
-    ):
-        super().__init__()
-        if not model_size:
-            model_size = key_size * num_heads
-        if not value_size:
-            value_size = key_size
-        self.model_size = model_size
-        self.key_size = key_size
-        self.value_size = value_size
-        self.add_bias_kv = add_bias_kv
-        self.name = name
-        self.num_heads = num_heads
-        self._rotary_embedding_config = rotary_embedding_config
-
-        self.w_k = nn.Linear(self.model_size, self.num_heads * self.key_size)
-        self.w_q = nn.Linear(self.model_size, self.num_heads * self.key_size)
-        self.w_v = nn.Linear(self.model_size, self.num_heads * self.value_size)
-        self.output = nn.Linear(self.num_heads * self.value_size, self.model_size)
-        if self._rotary_embedding_config:
-            self._rotary_embedding = RotaryEmbeddingBis(
-                self.key_size, self._rotary_embedding_config
-            )
-
-    def apply_rotary_embeddings(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """ """
-        query, key = self._rotary_embedding(query, key)
-        return query, key
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        attention_weight_bias: Optional[torch.Tensor] = None,
-    ) -> dict[str, torch.Tensor]:
-        """
-        Returns:
-            dictionary containing attention weights
-            and outputs.
-        """
-        key_heads = self.w_k(key).reshape(
-            (*key.shape[:-1], self.num_heads, self.key_size)
-        )
-        query_heads = self.w_q(query).reshape(
-            (*query.shape[:-1], self.num_heads, self.key_size)
-        )
-        value_heads = self.w_v(value).reshape(
-            (*value.shape[:-1], self.num_heads, self.value_size)
-        )
-        if self._rotary_embedding_config:
-            query_heads, key_heads = self.apply_rotary_embeddings(
-                query_heads, key_heads
-            )
-        attention_weights = torch.einsum(
-            "...thd, ...Thd -> ...htT", query_heads, key_heads
-        )
-        sqrt_key_size = np.sqrt(self.key_size)
-        attention_weights = attention_weights / sqrt_key_size
-        if attention_mask is not None:
-            attention_weights = torch.where(attention_mask, attention_weights, -1e30)
-        if attention_weight_bias is not None:
-            attention_weights = F.softmax(
-                attention_weights + attention_weight_bias, dim=-1
-            )
-        else:
-            attention_weights = F.softmax(attention_weights, dim=-1)
-        value_out = torch.einsum(
-            "...htT, ...Thd->...thd", attention_weights, value_heads
-        )
-        value_out = value_out.reshape((*value_out.shape[:-2], -1))
-        embeddings = self.output(value_out)
-
-        return {"attention_weights": attention_weights, "embeddings": embeddings}
-
-
-class SelfAttentionBlock(nn.Module):
-    def __init__(
-        self,
-        num_heads: int,
-        embed_dim: int,
-        ffn_embed_dim: int,
-        key_size: Optional[int] = None,
-        add_bias_kv: bool = False,
-        add_bias_fnn: bool = True,
-        ffn_activation_name: str = "gelu-no-approx",
-        use_glu_in_ffn: bool = False,
-        layer_norm_eps: float = 1e-5,  # this is the default haiku value
-        pre_layer_norm: bool = True,
-        name: Optional[str] = None,
-        rotary_embedding_config: Optional[RotaryEmbeddingConfigBis] = None,
-    ):
-        super().__init__()
-        if key_size is None:
-            if embed_dim % num_heads != 0:
-                raise ValueError(
-                    f"The embedding dimension should be divisible by the number of "
-                    f"heads, however provided embedding dimension is {embed_dim} and "
-                    f"the number of heads is {num_heads}."
-                )
-            else:
-                key_size = embed_dim // num_heads
-
-        # Get ffn activation function
-        self._pre_layer_norm = pre_layer_norm
-        self._use_glu_in_fnn = use_glu_in_ffn
-        # Define layers
-        if use_glu_in_ffn:
-            # user should multiply ffn_embed_dim by 2/3 when using GLU
-            # to keep total number of parameters equal
-            # see https://arxiv.org/pdf/2002.05202.pdf. for more details
-            # we multiply by 2 here as the output will be split in 2 for GLU
-            self.fc1 = nn.Linear(embed_dim, int(2 * ffn_embed_dim), bias=add_bias_fnn)
-        else:
-            self.fc1 = nn.Linear(embed_dim, ffn_embed_dim, bias=add_bias_fnn)
-
-        self.fc2 = nn.Linear(ffn_embed_dim, embed_dim, bias=add_bias_fnn)
-
-        self.layer_norm_self_attention = nn.LayerNorm(
-            embed_dim,
-        )
-        self.layer_norm_mlp = nn.LayerNorm(embed_dim)
-        if ffn_activation_name == "swish":
-            self._ffn_activation_fn = nn.SiLU()
-        elif ffn_activation_name == "gelu-no-approx":
-            self._ffn_activation_fn = nn.GELU(approximate="tanh")
-        else:
-            self._ffn_activation_fn = getattr(torch.nn, ffn_activation_name)
-
-        self.mha = MultiHeadAttention(
-            num_heads=num_heads,
-            key_size=key_size,
-            add_bias_kv=add_bias_kv,
-            model_size=embed_dim,
-            name="self_attention",
-            rotary_embedding_config=rotary_embedding_config,
-        )
-
-    def mlp(self, embed: torch.Tensor) -> torch.Tensor:
-
-        if self._pre_layer_norm:
-            x = self.layer_norm_mlp(embed)
-        else:
-            x = embed
-
-        if self._use_glu_in_fnn:
-            x = self.fc1(x)
-            x1, x2 = torch.split(x, split_size_or_sections=x.shape[-1] // 2, dim=-1)
-            x = self._ffn_activation_fn(x1) * x2
-        else:
-            x = self._ffn_activation_fn(self.fc1(x))
-        x = self.fc2(x)
-
-        if not self._pre_layer_norm:
-            x = self.layer_norm_mlp(x + embed)
-        return x
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        attention_weight_bias: Optional[torch.Tensor] = None,
-    ) -> dict[str, torch.Tensor]:
-
-        res = x
-        if self._pre_layer_norm:
-            x = self.layer_norm_self_attention(x)
-
-        output: dict[str, torch.Tensor] = self.mha(
-            x,
-            x,
-            x,
-            attention_mask=attention_mask,
-            attention_weight_bias=attention_weight_bias,
-        )
-
-        if not self._pre_layer_norm:
-            output["embeddings"] = self.layer_norm_self_attention(
-                output["embeddings"] + res
-            )
-
-            x = output["embeddings"]
-        else:
-            x = output["embeddings"]
-            x = res + x
-
-        # MLP
-        if not self._pre_layer_norm:
-            x = self.mlp(x)
-        else:
-            x = x + self.mlp(x)
-
-        output["embeddings"] = x
-        return output
-
-
-class RobertaLMHead(nn.Module):
-    """
-    Roberta Language Model head. Transforms final attention layer output into a
-    distribution over tokens at each position.
-    """
-
-    def __init__(self, embed_dim: int, alphabet_size: int):
-        """
-        Args:
-            embed_dim: Embedding dimension.
-            alphabet_size: Number of tokens in the alphabet.
-        """
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.alphabet_size = alphabet_size
-
-        # Define layers
-        self._first_layer_norm = nn.LayerNorm(embed_dim, elementwise_affine=True)
-        self._fc1 = nn.Linear(embed_dim, embed_dim)
-        self._second_layer_norm = nn.LayerNorm(embed_dim, elementwise_affine=True)
-        self._final_fc = nn.Linear(embed_dim, alphabet_size)
-
-    def forward(self, x: torch.Tensor) -> dict:
-        x = self._first_layer_norm(x)
-        embeddings = x
-        x = self._fc1(x)
-        x = nn.functional.gelu(x)
-        x = self._second_layer_norm(x)
-        logits = self._final_fc(x)
-        return {"embeddings": embeddings, "logits": logits}
-
-
-class TorchESMTransformer(nn.Module):
-    def __init__(
-        self,
-        esm_config: ESMTransformerConfig,
-    ):
-        super(TorchESMTransformer, self).__init__()
-        self.esm_config = esm_config
-
-        # Other cases are not implemented
-        assert esm_config.positional_embedding is None
-        assert esm_config.lm_head == "roberta"
-        assert esm_config.use_rotary_embedding is True
-        assert esm_config.token_dropout is False
-        assert esm_config.emb_layer_norm_before is False
-        assert esm_config.mask_before_attention is False
-        assert esm_config.bias_word_embedding is False
-        assert esm_config.use_gradient_checkpointing is False
-
-        self.embed_layer = nn.Embedding(esm_config.alphabet_size, esm_config.embed_dim)
-
-        self.lm_head = RobertaLMHead(
-            embed_dim=esm_config.embed_dim,
-            alphabet_size=esm_config.alphabet_size,
-        )
-
-        self.rotary_embedding_config = RotaryEmbeddingConfigBis(
-            rescaling_factor=esm_config.rescaling_factor
-        )
-
-        self.attention_blocks = nn.ModuleList(
-            [
-                SelfAttentionBlock(  # type: ignore
-                    num_heads=esm_config.attention_heads,
-                    embed_dim=esm_config.embed_dim,
-                    key_size=esm_config.key_size,
-                    ffn_embed_dim=esm_config.ffn_embed_dim,
-                    add_bias_kv=esm_config.add_bias_kv,
-                    add_bias_fnn=esm_config.add_bias_ffn,
-                    ffn_activation_name=esm_config.ffn_activation_name,
-                    use_glu_in_ffn=esm_config.use_glu_in_ffn,
-                    rotary_embedding_config=self.rotary_embedding_config,
-                    layer_norm_eps=esm_config.layer_norm_eps,
-                    pre_layer_norm=esm_config.pre_layer_norm,
-                )
-                for _ in range(esm_config.num_layers)
-            ]
-        )
-
-    def forward(
-        self, tokens: torch.Tensor, attention_mask: torch.Tensor = None
-    ) -> torch.Tensor:
-        """
-        Computes the embeddings based on the input tokens.
-
-        Args:
-            tokens: Input tokens out of the tokenizer of shape (batch_size, seq_len).
-            attention_mask: Attention mask of shape (batch_size, 1, seq_len, seq_len).
-                If no mask is provided, a mask by default which equals 1 over all non
-                pad tokens and 0 over pad tokens is computed.
-
-        Returns:
-            Dictionary containing the final embeddings and logits.
-        """
-        x = self.embed_layer(tokens)
-
-        # RoBERTa's mask scaling factor
-        x = self.esm_config.embed_scale * x
-
-        if attention_mask is None:
-            attention_mask = build_padding_attention_mask(
-                tokens=tokens, pad_token_id=self.esm_config.pad_token_id
-            )
-
-        for layer in self.attention_blocks:
-            x = layer(x, attention_mask)["embeddings"]
-
-        assert self.esm_config.lm_head == "roberta"
-        x = self.lm_head(x)["embeddings"]
-
-        return x
-
-
-def build_padding_attention_mask(
-    tokens: torch.Tensor, pad_token_id: int
-) -> torch.Tensor:
-    """
-    Builds a padding mask from a sequence of tokens by masking <pad> in the attention.
-
-    Args:
-        tokens: Batch of sequences of shape (batch_size, seq_len).
-        pad_token_id: Int corresponding to the <pad> token to mask.
-
-    Returns:
-        Batch of attention masks, masking out <pad> tokens.
-    """
-    padding_mask = tokens != pad_token_id
-    padding_mask = padding_mask.unsqueeze(1)
-    padding_mask = torch.einsum("bhT, bht -> bhtT", padding_mask, padding_mask)
-    return padding_mask
-
-
-class TorchBioBrainEncoder(nn.Module):
-    def __init__(
-        self,
-        esm_config: ESMTransformerConfig,
-    ):
-        super(TorchBioBrainEncoder, self).__init__()
-        self.esm_config = esm_config
-        self.esm_model = TorchESMTransformer(self.esm_config)
-
-    def forward(
-        self,
-        bio_token_ids: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Args:
-            bio_token_ids (torch.Tensor):
-                Shape (batch_size, num_bio_tokens)
-
-        Returns:
-            torch.Tensor:
-                Shape (batch_size, num_bio_tokens, embed_dim)
-        """
-        bio_embeddings = self.esm_model(tokens=bio_token_ids)
-
-        return bio_embeddings
-
-
-class TorchMultiModalPerceiverResamplerBlock(nn.Module):
-    def __init__(
-        self,
-        num_heads: int,
-        embed_dim: int,
-        ffn_embed_dim: int,
-        key_size: Optional[int] = None,
-        add_bias_kv: bool = False,
-        add_bias_ffn: bool = True,
-        ffn_activation_name: str = "gelu",
-        use_glu_in_ffn: bool = False,
-    ):
-        super().__init__()
-
-        if key_size is None:
-            if embed_dim % num_heads != 0:
-                raise ValueError(
-                    f"Embedding dimension {embed_dim} should be divisible by "
-                    f"num_heads {num_heads}."
-                )
-            key_size = embed_dim // num_heads
-
-        self.num_heads = num_heads
-        self.embed_dim = embed_dim
-        self.ffn_embed_dim = ffn_embed_dim * 2 if use_glu_in_ffn else ffn_embed_dim
-        self.use_glu_in_ffn = use_glu_in_ffn
-
-        self.cross_attention_1 = MultiHeadAttention(
-            num_heads=num_heads, key_size=key_size, add_bias_kv=add_bias_kv
-        )
-        self.cross_attention_2 = MultiHeadAttention(
-            num_heads=num_heads, key_size=key_size, add_bias_kv=add_bias_kv
-        )
-
-        self.norm_cross_attention_1 = nn.LayerNorm(embed_dim)
-        self.norm_cross_attention_2 = nn.LayerNorm(embed_dim)
-        self.norm_mlp = nn.LayerNorm(embed_dim)
-
-        self.fc1 = nn.Linear(embed_dim, self.ffn_embed_dim, bias=add_bias_ffn)
-        self.fc2 = nn.Linear(self.ffn_embed_dim, embed_dim, bias=add_bias_ffn)
-
-        self.activation_fn = getattr(
-            nn.functional, ffn_activation_name, nn.functional.gelu
-        )
-
-    def mlp(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm_mlp(x)
-        if self.use_glu_in_ffn:
-            x1, x2 = torch.chunk(self.fc1(x), 2, dim=-1)
-            x = self.activation_fn(x1) * x2
-        else:
-            x = self.activation_fn(self.fc1(x))
-        return self.fc2(x)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        cross_attention_embeddings_1: torch.Tensor,
-        cross_attention_embeddings_2: torch.Tensor,
-        attention_mask_1: Optional[torch.Tensor] = None,
-        attention_mask_2: Optional[torch.Tensor] = None,
-    ) -> Dict[str, torch.Tensor]:
-        res = x
-        x = self.norm_cross_attention_1(x)
-
-        attn_output = self.cross_attention_1(
-            query=x,
-            key=cross_attention_embeddings_1,
-            value=cross_attention_embeddings_1,
-            attention_mask=attention_mask_1,
-        )["embeddings"]
-        x = res + attn_output
-
-        res = x
-        x = self.norm_cross_attention_2(x)
-        attn_output = self.cross_attention_2(
-            query=x,
-            key=cross_attention_embeddings_2,
-            value=cross_attention_embeddings_2,
-            attention_mask=attention_mask_2,
-        )["embeddings"]
-        x = res + attn_output
-
-        x = x + self.mlp(x)
-
-        return {"embeddings": x}
-
-
-class TorchMultiModalPerceiverResampler(nn.Module):
-    """
-    Perceiver Resampler model, made of successive PerceiverResamplerBlocks.
-    """
-
-    def __init__(
-        self,
-        config: PerceiverResamplerConfig,
-        name: Optional[str] = None,
-    ):
-        """
-        Initialize a Perceiver Resampler model.
-
-        Args:
-            config: Dataclass containing model hyperparameters.
-            name: Name for module (custom will break weight loading).
-        """
-        super().__init__()
-        self.config = config
-        self.name = name
-        self.layers = nn.ModuleList(
-            [
-                TorchMultiModalPerceiverResamplerBlock(
-                    num_heads=self.config.attention_heads,
-                    embed_dim=self.config.embed_dim,
-                    key_size=self.config.key_size,
-                    ffn_embed_dim=self.config.ffn_embed_dim,
-                    add_bias_kv=self.config.add_bias_kv,
-                    add_bias_ffn=self.config.add_bias_ffn,
-                    ffn_activation_name=self.config.ffn_activation_name,
-                    use_glu_in_ffn=self.config.use_glu_in_ffn,
-                )
-                for _ in range(self.config.num_layers)
-            ]
-        )
-
-        self.latent_queries = torch.nn.Parameter(
-            torch.randn(self.config.resampled_length, self.config.embed_dim)
-            * (
-                1.0
-                / torch.sqrt(torch.tensor(self.config.embed_dim, dtype=torch.float32))
-            )
-        )
-
-    def apply_attention_blocks(
-        self,
-        x: torch.Tensor,
-        xf_1: torch.Tensor,
-        xf_2: torch.Tensor,
-        outs: Dict[str, torch.Tensor],
-        attention_mask_1: Optional[torch.Tensor] = None,
-        attention_mask_2: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Create the blocks of attention layers and applies them.
-        """
-        for layer in self.layers:
-            concat_input_1 = torch.cat([xf_1, x], dim=1)
-            concat_input_2 = torch.cat([xf_2, x], dim=1)
-
-            output = layer(
-                x=x,
-                cross_attention_embeddings_1=concat_input_1,
-                cross_attention_embeddings_2=concat_input_2,
-                attention_mask_1=attention_mask_1,
-                attention_mask_2=attention_mask_2,
-            )
-            x = output["embeddings"]
-
-        return x, outs
-
-    def forward(
-        self,
-        input_embeddings_1: torch.Tensor,
-        input_embeddings_2: torch.Tensor,
-        attention_mask_1: Optional[torch.Tensor] = None,
-        attention_mask_2: Optional[torch.Tensor] = None,
-    ) -> Dict[str, torch.Tensor]:
-        """
-        Computes the embeddings based on the input tokens.
-        """
-        assert (
-            input_embeddings_1.shape[-1] == self.config.embed_dim
-        ), "The input embedding dim should match the model embed dim"
-        assert (
-            input_embeddings_2.shape[-1] == self.config.embed_dim
-        ), "The input embedding dim should match the model embed dim"
-
-        batch_size = input_embeddings_1.shape[0]
-
-        latent_queries = self.latent_queries.unsqueeze(0).repeat(batch_size, 1, 1)
-
-        outs: Dict[str, torch.Tensor] = {}
-        x = latent_queries
-
-        x, outs = self.apply_attention_blocks(
-            x=x,
-            xf_1=input_embeddings_1,
-            xf_2=input_embeddings_2,
-            outs=outs,
-            attention_mask_1=attention_mask_1,
-            attention_mask_2=attention_mask_2,
-        )
-
-        outs["embeddings"] = x
-
-        return outs
-
-
-class TorchMultiModalPerceiverResamplerProjection(nn.Module):
-    def __init__(
-        self,
-        perceiver_resampler_config: PerceiverResamplerConfig,
-        input_embed_dim: int,
-        embed_dim: int,
-        bio_pad_token_id: int,
-        english_pad_token_id: int,
-        english_vocab_size: int,
-    ):
-        super().__init__()
-        self.config = perceiver_resampler_config
-        self.input_embed_dim = input_embed_dim
-        self.embed_dim = embed_dim
-        self.bio_pad_token_id = bio_pad_token_id
-        self.english_pad_token_id = english_pad_token_id
-        self.english_vocab_size = english_vocab_size
-
-        self.bio_projection = nn.Linear(input_embed_dim, embed_dim)
-        self.token_embedding = nn.Embedding(english_vocab_size, embed_dim)
-        self.perceiver_resampler = TorchMultiModalPerceiverResampler(config=self.config)
-
-    def forward(
-        self,
-        bio_token_ids: torch.Tensor,
-        bio_embeddings: torch.Tensor,
-        english_token_ids: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Args:
-            bio_token_ids (torch.Tensor):
-                Shape (batch_size, num_bio_tokens)
-
-            bio_embeddings (torch.Tensor):
-                Shape (batch_size, num_bio_tokens, embed_dim)
-
-            english_token_ids (torch.Tensor):
-                Shape (batch_size, num_english_tokens)
-        """
-        projected_bio_embeddings = self.bio_projection(bio_embeddings)
-        english_embeddings = self.token_embedding(english_token_ids)
-
-        bio_attention_mask = build_perceiver_padding_attention_mask(
-            bio_token_ids, self.config.resampled_length, self.bio_pad_token_id
-        )
-        english_attention_mask = build_perceiver_padding_attention_mask(
-            english_token_ids, self.config.resampled_length, self.english_pad_token_id
-        )
-
-        projected_embeddings = self.perceiver_resampler(
-            input_embeddings_1=projected_bio_embeddings,
-            attention_mask_1=bio_attention_mask,
-            input_embeddings_2=english_embeddings,
-            attention_mask_2=english_attention_mask,
-        )["embeddings"]
-
-        return projected_embeddings
-
-
-def build_perceiver_padding_attention_mask(
-    tokens: torch.Tensor, resampled_length: int, pad_token_id: int
-) -> torch.Tensor:
-    batch_size, seq_len = tokens.shape
-    padding_mask = tokens != pad_token_id  # (batch_size, seq_len)
-
-    padding_mask = torch.cat(
-        [
-            padding_mask,
-            torch.ones(
-                (batch_size, resampled_length), dtype=torch.bool, device=tokens.device
-            ),
-        ],
-        dim=1,
-    )  # (batch_size, seq_len + resampled_length)
-
-    padding_mask = padding_mask[:, None, None, :]
-    padding_mask = padding_mask.repeat(1, 1, resampled_length, 1)  # noqa
-    return padding_mask