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chatNT.py

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+# 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:
+        if isinstance(config, dict):
+            # If config is a dictionary instead of ChatNTConfig (which can happen
+            # depending how the config was saved), we convert it to the config
+            config["gpt_config"]["rope_config"] = RotaryEmbeddingConfig(
+                **config["gpt_config"]["rope_config"]
+            )
+            config["gpt_config"] = GptConfig(**config["gpt_config"])
+            config["esm_config"] = ESMTransformerConfig(**config["esm_config"])
+            config["perceiver_resampler_config"] = PerceiverResamplerConfig(
+                **config["perceiver_resampler_config"]
+            )
+            config = ChatNTConfig(**config)  # type: ignore
+
+        else:
+            if isinstance(config.gpt_config, dict):
+                config.gpt_config["rope_config"] = RotaryEmbeddingConfig(
+                    **config.gpt_config["rope_config"]
+                )
+                config.gpt_config = GptConfig(**config.gpt_config)
+
+            if isinstance(config.esm_config, dict):
+                config.esm_config = ESMTransformerConfig(**config.esm_config)
+
+            if isinstance(config.perceiver_resampler_config, dict):
+                config.perceiver_resampler_config = PerceiverResamplerConfig(
+                    **config.perceiver_resampler_config
+                )
+
+        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
+        english_token_ids = english_token_ids.clone()
+        bio_token_ids = bio_token_ids.clone()
+        projection_english_tokens_ids = projection_english_tokens_ids.clone()
+        if projected_bio_embeddings is not None:
+            projected_bio_embeddings = projected_bio_embeddings.clone()
+
+        # 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

config.json

@@ -0,0 +1,85 @@
+{
+  "architectures": [
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