Upload ProPrimeForPretraining

config.json

@@ -6,6 +6,7 @@
   "attention_probs_dropout_prob": 0.0,
   "auto_map": {
     "AutoConfig": "configuration_proprime.ProPrimeConfig",
+    "AutoModel": "modeling_proprime.ProPrimeForPretraining",
     "AutoModelForMaskedLM": "AI4Protein/ProPrime_650M--modeling_proprime.ProPrimeForMaskedLM"
   },
   "emb_layer_norm_before": false,

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33c732ac89d871c8ec6ed53f710e03241c251865c52670f50ce7ae57d6293071
+size 2676905476

modeling_proprime.py

@@ -0,0 +1,1345 @@
+import math
+from typing import List, Optional, Tuple, Union
+import torch.nn.functional as F
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from dataclasses import dataclass
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    ModelOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from .configuration_proprime import ProPrimeConfig
+from torch.nn.functional import scaled_dot_product_attention
+
+logger = logging.get_logger(__name__)
+
+
+def consine_based_loss(x1, x2):
+    cos = nn.CosineSimilarity(dim=0, eps=1e-6)
+    x1 = x1 - x1.mean()
+    x2 = x2 - x2.mean()
+    return 1 - cos(x1, x2).mean()
+
+PROPRIME_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "AI4protein/ProPrime_650M",
+]
+
+
+def rotate_half(x):
+    return torch.cat((-x[..., x.shape[-1] // 2 :], x[..., : x.shape[-1] // 2]), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+def gelu(x):
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (
+            10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)
+        )
+        inv_freq = inv_freq
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_tables(self, x, seq_dimension=2):
+        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)
+        if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+            self._seq_len_cached = seq_len
+            t = torch.arange(x.shape[seq_dimension], 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
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
+            k, seq_dimension=-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 ProPrimeEmbeddings(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+
+        if config.emb_layer_norm_before:
+            self.layer_norm = nn.LayerNorm(
+                config.hidden_size, eps=config.layer_norm_eps
+            )
+        else:
+            self.layer_norm = None
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.register_buffer(
+            "position_ids",
+            torch.arange(config.max_position_embeddings).expand((1, -1)),
+            persistent=False,
+        )
+
+        self.padding_idx = config.pad_token_id
+        if self.position_embedding_type == "absolute":
+            self.position_embeddings = nn.Embedding(
+                config.max_position_embeddings,
+                config.hidden_size,
+                padding_idx=self.padding_idx,
+            )
+        self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        inputs_embeds=None,
+        past_key_values_length=0,
+    ):
+        if position_ids is None:
+            if input_ids is not None:
+                position_ids = create_position_ids_from_input_ids(
+                    input_ids, self.padding_idx, past_key_values_length
+                )
+            else:
+                position_ids = self.create_position_ids_from_inputs_embeds(
+                    inputs_embeds
+                )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+
+        if self.token_dropout:
+            embeddings = embeddings.masked_fill(
+                (input_ids == self.mask_token_id).unsqueeze(-1), 0.0
+            )
+            mask_ratio_train = 0.15 * 0.8
+            src_lengths = attention_mask.sum(-1)
+            mask_ratio_observed = (input_ids == self.mask_token_id).sum(
+                -1
+            ).float() / src_lengths
+            embeddings = (
+                embeddings
+                * (1 - mask_ratio_train)
+                / (1 - mask_ratio_observed)[:, None, None]
+            ).to(embeddings.dtype)
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = embeddings + position_embeddings
+
+        if self.layer_norm is not None:
+            embeddings = self.layer_norm(embeddings)
+        if attention_mask is not None:
+            embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(
+                embeddings.dtype
+            )
+        # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
+        # embeddings = self.dropout(embeddings)
+        return embeddings
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        input_shape = inputs_embeds.size()[:-1]
+        sequence_length = input_shape[1]
+
+        position_ids = torch.arange(
+            self.padding_idx + 1,
+            sequence_length + self.padding_idx + 1,
+            dtype=torch.long,
+            device=inputs_embeds.device,
+        )
+        return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class ProPrimeSelfAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+            config, "embedding_size"
+        ):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.rotary_embeddings = None
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(
+                2 * config.max_position_embeddings - 1, self.attention_head_size
+            )
+        elif self.position_embedding_type == "rotary":
+            self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+        self.flash_attention = config.flash_attention
+        self.is_decoder = config.is_decoder
+        self.config = config
+
+    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,
+    ) -> Tuple[torch.Tensor]:
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            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)
+
+        query_layer = 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)
+
+        if not self.flash_attention:
+            # Take the dot product between "query" and "key" to get the raw attention scores.
+            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+            if (
+                self.position_embedding_type == "relative_key"
+                or self.position_embedding_type == "relative_key_query"
+            ):
+                seq_length = hidden_states.size()[1]
+                position_ids_l = torch.arange(
+                    seq_length, dtype=torch.long, device=hidden_states.device
+                ).view(-1, 1)
+                position_ids_r = torch.arange(
+                    seq_length, dtype=torch.long, device=hidden_states.device
+                ).view(1, -1)
+                distance = position_ids_l - position_ids_r
+                positional_embedding = self.distance_embedding(
+                    distance + self.max_position_embeddings - 1
+                )
+                positional_embedding = positional_embedding.to(
+                    dtype=query_layer.dtype
+                )  # fp16 compatibility
+
+                if self.position_embedding_type == "relative_key":
+                    relative_position_scores = torch.einsum(
+                        "bhld,lrd->bhlr", query_layer, positional_embedding
+                    )
+                    attention_scores = attention_scores + relative_position_scores
+                elif self.position_embedding_type == "relative_key_query":
+                    relative_position_scores_query = torch.einsum(
+                        "bhld,lrd->bhlr", query_layer, positional_embedding
+                    )
+                    relative_position_scores_key = torch.einsum(
+                        "bhrd,lrd->bhlr", key_layer, positional_embedding
+                    )
+                    attention_scores = (
+                        attention_scores
+                        + relative_position_scores_query
+                        + relative_position_scores_key
+                    )
+
+            if attention_mask is not None:
+                attention_scores = attention_scores + attention_mask
+
+            # Normalize the attention scores to probabilities.
+            attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+            # This is actually dropping out entire tokens to attend to, which might
+            # seem a bit unusual, but is taken from the original Transformer paper.
+            attention_probs = self.dropout(attention_probs)
+
+            # Mask heads if we want to
+            if head_mask is not None:
+                attention_probs = attention_probs * head_mask
+
+            context_layer = torch.matmul(attention_probs, value_layer)
+        else:
+            if self.training:
+                context_layer = scaled_dot_product_attention(
+                    query_layer,
+                    key_layer,
+                    value_layer,
+                    attn_mask=attention_mask,
+                    dropout_p=self.config.attention_probs_dropout_prob,
+                    scale=1,  # we have query_layer = query_layer * self.attention_head_size**-0.5
+                )
+            else:
+                context_layer = scaled_dot_product_attention(
+                    query_layer,
+                    key_layer,
+                    value_layer,
+                    attn_mask=attention_mask,
+                    scale=1,  # we have query_layer = query_layer * self.attention_head_size**-0.5
+                )
+
+        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 output_attentions else (context_layer,)
+        )
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class ProPrimeSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = hidden_states + input_tensor
+        return hidden_states
+
+
+class ProPrimeAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = ProPrimeSelfAttention(config)
+        self.output = ProPrimeSelfOutput(config)
+        self.pruned_heads = set()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads,
+            self.self.num_attention_heads,
+            self.self.attention_head_size,
+            self.pruned_heads,
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = (
+            self.self.attention_head_size * self.self.num_attention_heads
+        )
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    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,
+    ):
+        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,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+class ProPrimeIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = gelu(hidden_states)
+        return hidden_states
+
+
+class ProPrimeOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = hidden_states + input_tensor
+        return hidden_states
+
+
+class ProPrimeLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = ProPrimeAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            if not self.is_decoder:
+                raise RuntimeError(
+                    f"{self} should be used as a decoder model if cross attention is added"
+                )
+            self.crossattention = ProPrimeAttention(config)
+        self.intermediate = ProPrimeIntermediate(config)
+        self.output = ProPrimeOutput(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,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        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,
+        )
+        attention_output = self_attention_outputs[0]
+
+        # if decoder, the last output is tuple of self-attn cache
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[
+                1:
+            ]  # add self attentions if we output attention weights
+
+        cross_attn_present_key_value = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            if not hasattr(self, "crossattention"):
+                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 cached key/values tuple is at positions 3,4 of past_key_value tuple
+            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,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = (
+                outputs + cross_attention_outputs[1:-1]
+            )  # add cross attentions if we output attention weights
+
+            # add cross-attn cache to positions 3,4 of present_key_value tuple
+            cross_attn_present_key_value = cross_attention_outputs[-1]
+            present_key_value = present_key_value + cross_attn_present_key_value
+
+        layer_output = self.feed_forward_chunk(attention_output)
+
+        outputs = (layer_output,) + outputs
+
+        # if decoder, return the attn key/values as the last output
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        attention_output_ln = self.LayerNorm(attention_output)
+        intermediate_output = self.intermediate(attention_output_ln)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class ProPrimeEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList(
+            [ProPrimeLayer(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,
+    ):
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                    "`use_cache=False`..."
+                )
+                use_cache = False
+        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,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                )
+
+            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 not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v 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 ProPrimePreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = ProPrimeConfig
+    base_model_prefix = "proprime"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "ProPrimeLayer",
+        "ProPrimeEmbeddings",
+    ]
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class ProPrimeModel(ProPrimePreTrainedModel):
+    base_model_prefix = "proprime"
+    
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = ProPrimeEmbeddings(config)
+        self.encoder = ProPrimeEncoder(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    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,
+    ) -> 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"
+            )
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            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_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
+            )
+
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_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 = None
+
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+        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,
+        )
+        sequence_output = encoder_outputs[0]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class ProPrimeForMaskedLM(ProPrimePreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `ProPrimeForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.pro_prime = ProPrimeModel(config, add_pooling_layer=False)
+        self.lm_head = ProPrimeLMHead(config)
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.pro_prime.embeddings.word_embeddings
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.pro_prime(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class ProPrimeLMHead(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x) + self.bias
+        return x
+
+
+class ProPrimeStructureHead(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.decoder = nn.Linear(config.hidden_size, config.structure_vocab_size, bias=False)
+        self.bias = nn.Parameter(torch.zeros(config.structure_vocab_size))
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x) + self.bias
+        return x
+
+
+def create_position_ids_from_input_ids(
+    input_ids, padding_idx, past_key_values_length=0
+):
+    """
+    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+    are ignored. This is modified from fairseq's `utils.make_positions`.
+
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
+    """
+    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+    mask = input_ids.ne(padding_idx).int()
+    incremental_indices = (
+        torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
+    ) * mask
+    return incremental_indices.long() + padding_idx
+
+
+# POOLING_HEAD
+class MaskedConv1d(nn.Conv1d):
+    """A masked 1-dimensional convolution layer.
+
+    Takes the same arguments as torch.nn.Conv1D, except that the padding is set automatically.
+
+         Shape:
+            Input: (N, L, in_channels)
+            input_mask: (N, L, 1), optional
+            Output: (N, L, out_channels)
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+    ):
+        """
+        :param in_channels: input channels
+        :param out_channels: output channels
+        :param kernel_size: the kernel width
+        :param stride: filter shift
+        :param dilation: dilation factor
+        :param groups: perform depth-wise convolutions
+        :param bias: adds learnable bias to output
+        """
+        padding = dilation * (kernel_size - 1) // 2
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding=padding,
+        )
+
+    def forward(self, x, input_mask=None):
+        if input_mask is not None:
+            x = x * input_mask
+        return super().forward(x.transpose(1, 2)).transpose(1, 2)
+
+
+class Attention1d(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.layer = MaskedConv1d(config.hidden_size, 1, 1)
+        self.out = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, x, input_mask=None):
+        batch_szie = x.shape[0]
+        attn = self.layer(x)
+        attn = attn.view(batch_szie, -1)
+        if input_mask is not None:
+            attn = attn.masked_fill_(
+                ~input_mask.view(batch_szie, -1).bool(), float("-inf")
+            )
+        attn = F.softmax(attn, dim=-1).view(batch_szie, -1, 1)
+        out = (attn * x).sum(dim=1)
+        out = self.out(out)
+        return out
+
+
+class FFN1d(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.act = nn.GELU()
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class Attention1dPooling(nn.Module):
+    """Outputs of the model with the attention1d"""
+
+    def __init__(
+        self, config
+    ):  # [batch x sequence(751) x embedding (1280)] --> [batch x embedding] --> [batch x 1]
+        super(Attention1dPooling, self).__init__()
+        self.attention1d = Attention1d(config)
+        self.ffn = FFN1d(config)
+        # self.norm1 = nn.BatchNorm1d(config.hidden_size)
+        # self.norm2 = nn.BatchNorm1d(config.hidden_size)
+        self.dropout1 = nn.Dropout(config.hidden_dropout_prob)
+        self.dropout2 = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, x, input_mask):
+        attn_out = self.attention1d(x, input_mask=input_mask.unsqueeze(-1))
+        x = self.dropout1(attn_out)
+        # x = self.norm1(x)
+        ffn_out = self.ffn(x)
+        x = x + self.dropout2(ffn_out)
+        # x = self.norm2(x)
+        return x
+
+
+@dataclass
+class MaskedLMOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    mlm_loss: Optional[torch.FloatTensor] = None
+    value_loss: Optional[torch.FloatTensor] = None
+    predicted_values: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    sequence_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+
+
+class ProPrimeMV(ProPrimePreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.pro_prime = ProPrimeModel(config, add_pooling_layer=False)
+        self.lm_head = ProPrimeLMHead(config)
+        self.sequence_pooling = Attention1dPooling(config)
+        self.value_projection = nn.Sequential(
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.Tanh(),
+            nn.Linear(config.hidden_size, 1),
+        )
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.pro_prime.embeddings.word_embeddings
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.pro_prime(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        if values is not None:
+            sequence_states = self.sequence_pooling(sequence_output, attention_mask)
+            predicted_values = self.value_projection(sequence_states)
+            values = values.to(predicted_values.dtype)
+            values = values.reshape(-1, 1)
+            value_loss = nn.MSELoss()(predicted_values, values)
+            loss = masked_lm_loss + 0.01 * value_loss
+        else:
+            sequence_states = self.sequence_pooling(sequence_output, attention_mask)
+            predicted_values = self.value_projection(sequence_states)
+            value_loss = None
+            loss = masked_lm_loss
+
+        return MaskedLMOutput(
+            loss=loss,
+            mlm_loss=masked_lm_loss,
+            value_loss=value_loss,
+            logits=prediction_scores,
+            predicted_values=predicted_values.reshape(-1),
+            hidden_states=outputs.hidden_states,
+            sequence_hidden_states=sequence_states,
+            attentions=outputs.attentions,
+        )
+
+
+@dataclass
+class PretrainedOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    mlm_loss: Optional[torch.FloatTensor] = None
+    structure_loss: Optional[torch.FloatTensor] = None
+    corr_loss: Optional[torch.FloatTensor] = None
+    value_loss: Optional[torch.FloatTensor] = None
+    predicted_values: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    structure_logits: torch.FloatTensor = None
+    sequence_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+
+
+class ProPrimeForPretraining(ProPrimePreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+    base_model_prefix = "proprime"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.pro_prime = ProPrimeModel(config, add_pooling_layer=False)
+        self.lm_head = ProPrimeLMHead(config)
+        self.structure_head = ProPrimeStructureHead(config)
+        self.sequence_pooling = Attention1dPooling(config)
+        self.value_projection = nn.Sequential(
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.Tanh(),
+            nn.Linear(config.hidden_size, 1),
+        )
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.pro_prime.embeddings.word_embeddings
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        structure_labels: Optional[torch.LongTensor] = None,
+        values: Optional[torch.FloatTensor] = None,
+        mutant_input_ids: Optional[torch.LongTensor] = None, # Corr
+        mutant_index: Optional[torch.LongTensor] = None, # Corr
+        mutant_type: Optional[torch.LongTensor] = None, # Corr
+        wild_type: Optional[torch.LongTensor] = None, # Corr
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        outputs = self.pro_prime(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        
+        mlm_scores = self.lm_head(sequence_output)
+        structure_scores = self.structure_head(sequence_output)
+        sequence_states = self.sequence_pooling(sequence_output, attention_mask)
+        predicted_values = self.value_projection(sequence_states)
+        
+        loss = 0
+        if mutant_input_ids is not None:
+            with torch.no_grad():
+                mutant_outputs = self.pro_prime(
+                    mutant_input_ids,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    head_mask=head_mask,
+                    inputs_embeds=inputs_embeds,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    output_attentions=output_attentions,
+                    output_hidden_states=output_hidden_states,
+                    return_dict=return_dict,
+                )
+                mutant_sequence_output = mutant_outputs[0]
+                mutant_sequence_states = self.sequence_pooling(mutant_sequence_output, attention_mask)
+                mutant_predicted_values = self.value_projection(mutant_sequence_states)            
+                values_diff = mutant_predicted_values - predicted_values
+            logits = mlm_scores.log_softmax(dim=-1)
+            mt_probs = logits[torch.arange(logits.size(0)), mutant_index, mutant_type]
+            wt_probs = logits[torch.arange(logits.size(0)), mutant_index, wild_type]
+            mutant_effects = mt_probs - wt_probs
+            corr_loss = consine_based_loss(values_diff.squeeze(), mutant_effects.squeeze())
+            loss += corr_loss
+        else:
+            corr_loss = None
+            
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            labels = labels.to(mlm_scores.device)
+            mlm_loss = loss_fct(
+                mlm_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+            loss += mlm_loss
+        else:
+            mlm_loss = None
+        
+        if structure_labels is not None:
+            loss_fct = CrossEntropyLoss()
+            structure_labels = structure_labels.to(structure_scores.device)
+            structure_loss = loss_fct(
+                structure_scores.view(-1, self.config.structure_vocab_size), structure_labels.view(-1)
+            )
+            loss += structure_loss
+        else:
+            structure_loss = None
+        
+        if values is not None:
+            loss_fct = nn.MSELoss()
+            values = values.to(predicted_values.dtype)
+            values = values.reshape(-1, 1)
+            value_loss = nn.MSELoss()(predicted_values, values)
+            loss += 0.01 * value_loss
+        else:
+            value_loss = None            
+    
+        return PretrainedOutput(
+            loss=loss,
+            mlm_loss=mlm_loss,
+            structure_loss=structure_loss,
+            value_loss=value_loss,
+            corr_loss=corr_loss,
+            logits=mlm_scores,
+            structure_logits=structure_scores,
+            predicted_values=predicted_values,
+            hidden_states=outputs.hidden_states,
+            sequence_hidden_states=sequence_states,
+            attentions=outputs.attentions,
+        )
+            
+ProPrimeForMaskedLM.register_for_auto_class("AutoModelForMaskedLM")
+ProPrimeForPretraining.register_for_auto_class("AutoModel")