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dplm2.py

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+"""
+FastPLMs-compatible DPLM2 implementation.
+
+This module is based on:
+https://github.com/bytedance/dplm
+"""
+
+import entrypoint_setup
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, Union
+
+from transformers import EsmTokenizer
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    ModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.models.esm.configuration_esm import EsmConfig
+from transformers.models.esm.modeling_esm import (
+    EsmAttention,
+    EsmClassificationHead,
+    EsmEmbeddings,
+    EsmEncoder,
+    EsmIntermediate,
+    EsmLayer,
+    EsmLMHead,
+    EsmOutput,
+    EsmPooler,
+    EsmPreTrainedModel,
+    EsmSelfAttention,
+    EsmSelfOutput,
+    RotaryEmbedding,
+    apply_rotary_pos_emb,
+)
+
+try:
+    from torch.nn.attention.flex_attention import create_block_mask
+    from torch.nn.attention.flex_attention import flex_attention
+except ImportError:
+    create_block_mask = None
+    flex_attention = None
+
+try:
+    from .base_tokenizer import BaseSequenceTokenizer
+except ImportError:
+    from base_tokenizer import BaseSequenceTokenizer
+
+try:
+    from .embedding_mixin import EmbeddingMixin
+except ImportError:
+    try:
+        from ..embedding_mixin import EmbeddingMixin
+    except ImportError:
+        from embedding_mixin import EmbeddingMixin
+
+
+def _create_pad_block_mask(attention_mask_2d: torch.Tensor):
+    assert create_block_mask is not None, "Flex attention block mask requires create_block_mask."
+    token_valid = attention_mask_2d.bool()
+    batch_size, seq_len = token_valid.shape
+
+    def mask_mod(batch_idx, head_idx, q_idx, kv_idx):
+        return token_valid[batch_idx, q_idx] & token_valid[batch_idx, kv_idx]
+
+    return create_block_mask(
+        mask_mod,
+        batch_size,
+        1,
+        seq_len,
+        seq_len,
+        device=attention_mask_2d.device,
+    )
+
+
+def _infer_modality_type(input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+    input_mask = attention_mask.bool()
+    modality_type = ((input_ids < 33) & input_mask).int()
+    modality_type[~input_mask] = 2
+    return modality_type
+
+
+MODEL_REGISTRY = {}
+
+
+def register_model(name):
+    def decorator(cls):
+        MODEL_REGISTRY[name] = cls
+        return cls
+
+    return decorator
+
+
+@dataclass
+class DPLM2MaskedLMOutput(ModelOutput):
+    loss: Optional[torch.Tensor] = None
+    logits: Optional[torch.Tensor] = None
+    last_hidden_state: Optional[torch.Tensor] = None
+    hidden_states: Optional[Tuple[torch.Tensor, ...]] = None
+    attentions: Optional[Tuple[torch.Tensor, ...]] = None
+
+
+class DPLM2Config(EsmConfig):
+    model_type = "dplm2"
+
+    def __init__(
+        self,
+        attn_backend: str = "sdpa",
+        aa_type: int = 1,
+        struct_type: int = 0,
+        pad_type: int = 2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.attn_backend = attn_backend
+        self.aa_type = aa_type
+        self.struct_type = struct_type
+        self.pad_type = pad_type
+        self.tie_word_embeddings = False
+
+
+class DPLM2PreTrainedModel(EsmPreTrainedModel):
+    config_class = DPLM2Config
+    base_model_prefix = "dplm2"
+    supports_gradient_checkpointing = True
+    tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
+    all_tied_weights_keys = {}
+
+    def get_input_embeddings(self) -> nn.Module:
+        try:
+            return self.embeddings.word_embeddings
+        except AttributeError:
+            return self.esm.embeddings.word_embeddings
+
+
+class ModifiedRotaryEmbedding(RotaryEmbedding):
+    def __init__(self, dim: int, aa_type: int, struct_type: int):
+        super().__init__(dim)
+        self.aa_type = aa_type
+        self.struct_type = struct_type
+
+    def _has_multimodal_tokens(self, type_ids: Optional[torch.Tensor]) -> bool:
+        if type_ids is None:
+            return False
+        aa_present = (type_ids == self.aa_type).any()
+        struct_present = (type_ids == self.struct_type).any()
+        return bool(aa_present and struct_present)
+
+    def _update_cos_sin_tables(
+        self,
+        x: torch.Tensor,
+        type_ids: Optional[torch.Tensor],
+        seq_dimension: int = 2,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        seq_len = x.shape[seq_dimension]
+        if self._has_multimodal_tokens(type_ids):
+            seq_len = seq_len // 2
+
+        cache_is_stale = (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or seq_len != self._seq_len_cached
+            or self._cos_cached.device != x.device
+        )
+        if cache_is_stale:
+            self._seq_len_cached = seq_len
+            t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq)
+            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,
+        type_ids: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
+            k,
+            type_ids=type_ids,
+            seq_dimension=-2,
+        )
+
+        if self._has_multimodal_tokens(type_ids):
+            q_1, q_2 = q.chunk(2, dim=-2)
+            k_1, k_2 = k.chunk(2, dim=-2)
+            q_1 = apply_rotary_pos_emb(q_1, self._cos_cached, self._sin_cached)
+            q_2 = apply_rotary_pos_emb(q_2, self._cos_cached, self._sin_cached)
+            k_1 = apply_rotary_pos_emb(k_1, self._cos_cached, self._sin_cached)
+            k_2 = apply_rotary_pos_emb(k_2, self._cos_cached, self._sin_cached)
+            return torch.cat((q_1, q_2), dim=-2), torch.cat((k_1, k_2), dim=-2)
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+class ModifiedEsmSelfAttention(EsmSelfAttention):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__(config, position_embedding_type)
+        self.attn_backend = config.attn_backend
+        self.rotary_embeddings = ModifiedRotaryEmbedding(
+            dim=self.attention_head_size,
+            aa_type=config.aa_type,
+            struct_type=config.struct_type,
+        )
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+        type_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        flex_block_mask: Optional[object] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor]:
+        if past_key_values is not None:
+            past_key_value = past_key_values
+
+        mixed_query_layer = self.query(hidden_states)
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            key_layer = past_key_value[0]
+            value_layer = past_key_value[1]
+            attention_mask = encoder_attention_mask
+        elif is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer) * self.attention_head_size**-0.5
+
+        if self.is_decoder:
+            past_key_value = (key_layer, value_layer)
+
+        if self.position_embedding_type == "rotary":
+            query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer, type_ids)
+
+        if self.position_embedding_type in ["relative_key", "relative_key_query"]:
+            raise NotImplementedError
+
+        query_layer = query_layer.contiguous()
+        key_layer = key_layer.contiguous()
+        value_layer = value_layer.contiguous()
+
+        if output_attentions:
+            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+            if attention_mask is not None:
+                attention_scores = attention_scores + attention_mask
+            attention_probs = F.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype)
+            context_layer = torch.matmul(attention_probs, value_layer)
+        else:
+            attention_probs = None
+            if self.attn_backend == "flex":
+                assert flex_attention is not None, "Flex attention backend requested but torch.flex_attention is unavailable."
+                assert query_layer.dtype in (torch.float16, torch.bfloat16), (
+                    f"Flex attention backend requires float16 or bfloat16, got {query_layer.dtype}."
+                )
+                assert is_cross_attention is False, "Flex attention backend currently does not support cross-attention."
+                assert past_key_value is None, "Flex attention backend currently does not support KV caching."
+                if attention_mask is not None:
+                    assert flex_block_mask is not None, (
+                        "Flex attention backend requires a block mask when attention_mask is provided."
+                    )
+                context_layer = flex_attention(
+                    query_layer,
+                    key_layer,
+                    value_layer,
+                    block_mask=flex_block_mask,
+                    scale=1.0,
+                )
+            else:
+                context_layer = F.scaled_dot_product_attention(
+                    query_layer,
+                    key_layer,
+                    value_layer,
+                    attn_mask=attention_mask,
+                    scale=1.0,
+                )
+
+        if head_mask is not None and torch.is_tensor(head_mask):
+            context_layer = context_layer * head_mask
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs)
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class ModifiedEsmAttention(EsmAttention):
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        self.self = ModifiedEsmSelfAttention(config)
+        self.output = EsmSelfOutput(config)
+        self.pruned_heads = set()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        type_ids=None,
+        flex_block_mask=None,
+    ):
+        hidden_states_ln = self.LayerNorm(hidden_states)
+        self_outputs = self.self(
+            hidden_states_ln,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+            type_ids,
+            flex_block_mask=flex_block_mask,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]
+        return outputs
+
+
+class ModifiedEsmLayer(EsmLayer):
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = ModifiedEsmAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            if self.is_decoder is False:
+                raise RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
+            self.crossattention = ModifiedEsmAttention(config)
+        self.intermediate = EsmIntermediate(config)
+        self.output = EsmOutput(config)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        type_ids=None,
+        flex_block_mask=None,
+    ):
+        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+            type_ids=type_ids,
+            flex_block_mask=flex_block_mask,
+        )
+        attention_output = self_attention_outputs[0]
+
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[1:]
+
+        if self.is_decoder and encoder_hidden_states is not None:
+            if self.add_cross_attention is False:
+                raise AttributeError(
+                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention "
+                    "layers by setting `config.add_cross_attention=True`"
+                )
+
+            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                cross_attn_past_key_value,
+                output_attentions,
+                type_ids=None,
+                flex_block_mask=None,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:-1]
+            present_key_value = present_key_value + cross_attention_outputs[-1]
+
+        layer_output = self.feed_forward_chunk(attention_output)
+        outputs = (layer_output,) + outputs
+
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+        return outputs
+
+
+class ModifiedEsmEncoder(EsmEncoder):
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        self.config = config
+        self.layer = nn.ModuleList([ModifiedEsmLayer(config) for _ in range(config.num_hidden_layers)])
+        self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        type_ids=None,
+        flex_block_mask=None,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+        next_decoder_cache = () if use_cache else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    type_ids,
+                    flex_block_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    type_ids,
+                    flex_block_mask,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache = next_decoder_cache + (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        if self.emb_layer_norm_after:
+            hidden_states = self.emb_layer_norm_after(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if return_dict is False:
+            return tuple(
+                value
+                for value in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if value is not None
+            )
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class DPLM2Model(DPLM2PreTrainedModel, EmbeddingMixin):
+    config_class = DPLM2Config
+
+    def __init__(self, config, add_pooling_layer=True):
+        DPLM2PreTrainedModel.__init__(self, config)
+        self.config = config
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = ModifiedEsmEncoder(config)
+        self.pooler = EsmPooler(config) if add_pooling_layer else None
+        self.post_init()
+
+    def _convert_head_mask_to_5d(self, head_mask: torch.Tensor, num_hidden_layers: int) -> torch.Tensor:
+        if head_mask.dim() == 1:
+            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+            head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
+        elif head_mask.dim() == 2:
+            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+        assert head_mask.dim() == 5, f"head_mask.dim != 5, got {head_mask.dim()}"
+        head_mask = head_mask.to(dtype=self.dtype)
+        return head_mask
+
+    def get_head_mask(
+        self,
+        head_mask: Optional[torch.Tensor],
+        num_hidden_layers: int,
+        is_attention_chunked: bool = False,
+    ) -> Union[torch.Tensor, List[None]]:
+        if head_mask is None:
+            return [None] * num_hidden_layers
+        head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
+        if is_attention_chunked:
+            head_mask = head_mask.unsqueeze(-1)
+        return head_mask
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if attention_mask is None:
+            attention_mask = input_ids.ne(self.config.pad_token_id)
+        type_ids = _infer_modality_type(input_ids, attention_mask)
+        outputs = self(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            type_ids=type_ids,
+            output_hidden_states=False,
+            output_attentions=False,
+            return_dict=True,
+        )
+        return outputs.last_hidden_state
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        type_ids: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device)
+
+        token_attention_mask = None
+        if attention_mask.dim() == 2:
+            token_attention_mask = attention_mask.bool()
+            if self.config.attn_backend == "flex" and output_attentions is False:
+                extended_attention_mask = None
+            else:
+                extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
+        elif attention_mask.dim() == 4:
+            if self.config.attn_backend == "flex" and output_attentions is False:
+                extended_attention_mask = None
+            else:
+                extended_attention_mask = attention_mask
+            if input_ids is not None:
+                token_attention_mask = input_ids.ne(self.config.pad_token_id)
+        else:
+            raise ValueError(f"Unsupported attention_mask shape: {attention_mask.shape}")
+
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = encoder_attention_mask
+
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_attention_mask = token_attention_mask
+        if embedding_attention_mask is None and input_ids is not None:
+            embedding_attention_mask = input_ids.ne(self.config.pad_token_id)
+
+        flex_block_mask = None
+        if (
+            self.config.attn_backend == "flex"
+            and token_attention_mask is not None
+            and output_attentions is False
+        ):
+            assert create_block_mask is not None, (
+                "Flex attention backend requested but torch.create_block_mask is unavailable."
+            )
+            flex_block_mask = _create_pad_block_mask(token_attention_mask)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=embedding_attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            type_ids=type_ids,
+            flex_block_mask=flex_block_mask,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if return_dict is False:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=None,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class DPLM2ForMaskedLM(DPLM2PreTrainedModel, EmbeddingMixin):
+    config_class = DPLM2Config
+
+    def __init__(self, config, dropout: float = 0.1, vocab_size: Optional[int] = None):
+        config.hidden_dropout_prob = dropout
+        config.tie_word_embeddings = False
+        if vocab_size is not None:
+            config.vocab_size = vocab_size
+        DPLM2PreTrainedModel.__init__(self, config)
+        self.esm = DPLM2Model(config, add_pooling_layer=False)
+        self.lm_head = EsmLMHead(config)
+        self.loss_fct = nn.CrossEntropyLoss()
+        self.post_init()
+        self.pad_id = config.pad_token_id
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def _get_modality_type(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        return _infer_modality_type(input_ids, attention_mask)
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if attention_mask is None:
+            attention_mask = input_ids.ne(self.pad_id)
+        type_ids = self._get_modality_type(input_ids, attention_mask)
+        outputs = self.esm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            type_ids=type_ids,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=True,
+        )
+        return outputs.last_hidden_state
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        type_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        decoder_input_ids: Optional[torch.Tensor] = None,
+        decoder_attention_mask: Optional[torch.Tensor] = None,
+        decoder_inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple[torch.Tensor], DPLM2MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if attention_mask is None:
+            assert input_ids is not None
+            attention_mask = input_ids.ne(self.pad_id)
+
+        if type_ids is None:
+            assert input_ids is not None
+            type_ids = self._get_modality_type(input_ids, attention_mask)
+
+        outputs = self.esm(
+            input_ids=input_ids,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            type_ids=type_ids,
+        )
+
+        sequence_output = outputs.last_hidden_state
+        logits = self.lm_head(sequence_output)
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            loss = self.loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if return_dict is False:
+            output = (logits, sequence_output, outputs.hidden_states, outputs.attentions)
+            if loss is not None:
+                return (loss,) + output
+            return output
+
+        return DPLM2MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            last_hidden_state=sequence_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class DPLM2ForSequenceClassification(DPLM2PreTrainedModel, EmbeddingMixin):
+    config_class = DPLM2Config
+
+    def __init__(self, config):
+        DPLM2PreTrainedModel.__init__(self, config)
+        self.num_labels = config.num_labels
+        self.esm = DPLM2Model(config, add_pooling_layer=False)
+        self.classifier = EsmClassificationHead(config)
+        self.mse = nn.MSELoss()
+        self.ce = nn.CrossEntropyLoss()
+        self.bce = nn.BCEWithLogitsLoss()
+        self.post_init()
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        type_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        if type_ids is None and input_ids is not None:
+            if attention_mask is None:
+                attention_mask = input_ids.ne(self.config.pad_token_id)
+            type_ids = _infer_modality_type(input_ids, attention_mask)
+
+        outputs = self.esm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            type_ids=type_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        sequence_output = outputs.last_hidden_state
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                if self.num_labels == 1:
+                    loss = self.mse(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = self.mse(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss = self.ce(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss = self.bce(logits, labels)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class DPLM2ForTokenClassification(DPLM2PreTrainedModel, EmbeddingMixin):
+    config_class = DPLM2Config
+
+    def __init__(self, config):
+        DPLM2PreTrainedModel.__init__(self, config)
+        self.num_labels = config.num_labels
+        self.esm = DPLM2Model(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        self.loss_fct = nn.CrossEntropyLoss()
+        self.post_init()
+
+    def _embed(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.esm._embed(input_ids, attention_mask)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        type_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        if type_ids is None and input_ids is not None:
+            if attention_mask is None:
+                attention_mask = input_ids.ne(self.config.pad_token_id)
+            type_ids = _infer_modality_type(input_ids, attention_mask)
+
+        outputs = self.esm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            type_ids=type_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        sequence_output = self.dropout(outputs.last_hidden_state)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            loss = self.loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )