upload tokenizer and model config

config.json

@@ -1,32 +1,91 @@
 {
-  "add_linear_bias": true,
+  "return_dict": true,
+  "output_hidden_states": false,
+  "output_attentions": false,
+  "torchscript": false,
+  "torch_dtype": "float32",
+  "use_bfloat16": false,
+  "tf_legacy_loss": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": false,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
   "architectures": [
     "FM4BioForMaskedLM"
   ],
-  "attention_probs_dropout_prob": 0.0,
-  "experts_per_token": 2,
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "tokenizer_class": null,
+  "prefix": null,
+  "bos_token_id": null,
+  "pad_token_id": 0,
+  "eos_token_id": null,
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "task_specific_params": null,
+  "problem_type": null,
+  "_name_or_path": "genbio-ai/AIDO.Protein-16B",
+  "transformers_version": "4.38.0",
+  "model_type": "fm4bio",
+  "vocab_size": 128,
+  "hidden_size": 2304,
+  "num_hidden_layers": 36,
+  "num_attention_heads": 36,
   "hidden_act": "swiglu",
+  "intermediate_size": 7680,
   "hidden_dropout_prob": 0.0,
-  "hidden_size": 2304,
+  "attention_probs_dropout_prob": 0.0,
+  "max_position_embeddings": 2048,
+  "type_vocab_size": 2,
   "initializer_range": 0.02,
-  "intermediate_size": 7680,
   "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 2048,
-  "model_type": "fm4bio",
-  "moe": true,
-  "normalization_type": "RMSNorm",
-  "num_attention_heads": 36,
-  "num_experts": 8,
-  "num_hidden_layers": 36,
-  "pad_token_id": 0,
   "position_embedding_type": "rope",
+  "use_cache": true,
+  "add_linear_bias": true,
+  "normalization_type": "RMSNorm",
   "rotary_percent": 1.0,
   "seq_len_interpolation_factor": null,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.38.0.dev0",
-  "type_vocab_size": 2,
-  "use_cache": true,
+  "moe": true,
+  "num_experts": 8,
+  "experts_per_token": 2,
   "use_lm_head": false,
-  "vocab_size": 128
-}
+  "output_vocab_size": null,
+  "gradient_checkpointing": false,
+  "str_embedding_in": null
+}

configuration_fm4bio.py

@@ -0,0 +1,65 @@
+from transformers.configuration_utils import PretrainedConfig
+class FM4BioConfig(PretrainedConfig):
+    model_type = "fm4bio"
+
+    def __init__(
+        self,
+        vocab_size=128,
+        hidden_size=1024,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        intermediate_size=4096,
+        hidden_act="swiglu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        max_position_embeddings=2048,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-05,
+        pad_token_id=0,
+        add_linear_bias=True,
+        position_embedding_type="rope",
+        normalization_type="RMSNorm",
+        use_cache=True,
+        rotary_percent=1.0,
+        seq_len_interpolation_factor=None,
+        moe=False,
+        num_experts=0,
+        experts_per_token=0,
+        use_lm_head=True,
+        tie_word_embeddings=True,
+        output_vocab_size: int = None,  # when set, the output vocab size is different from the input vocab size
+        gradient_checkpointing=False, # << Pan: Gradient checkpoint for memory saving
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.add_linear_bias = add_linear_bias
+        assert normalization_type in [
+            "RMSNorm",
+            "LayerNorm",
+        ], "normalization_type must be 'RMSNorm' or 'LayerNorm'"
+        self.normalization_type = normalization_type
+        self.rotary_percent = rotary_percent
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
+        self.moe = moe
+        self.num_experts = num_experts
+        self.experts_per_token = experts_per_token
+        self.use_lm_head = use_lm_head
+        self.tie_word_embeddings = tie_word_embeddings
+        self.output_vocab_size = output_vocab_size
+        self.gradient_checkpointing = gradient_checkpointing # << Pan: Gradient checkpoint for memory saving

modeling_fm4bio.py

@@ -0,0 +1,2148 @@
+# coding=utf-8
+# Copyright 2024 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018-2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch MegatronBERT model."""
+
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+import sys
+from functools import partial
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, HuberLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_fm4bio import FM4BioConfig
+from collections import namedtuple
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "FM4BioConfig"
+_CHECKPOINT_FOR_DOC = ""
+
+FM4BIO_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "",
+    # See all FM4Bio models at https://huggingface.co/models?filter=fm4bio
+]
+
+if sys.platform != "darwin":
+    torch._C._jit_set_profiling_mode(False)
+    torch._C._jit_set_profiling_executor(False)
+    torch._C._jit_override_can_fuse_on_cpu(True)
+    torch._C._jit_override_can_fuse_on_gpu(True)
+
+logger = logging.get_logger(__name__)
+DeepNormCoefficients = namedtuple("DeepNormCoefficients", ["alpha", "beta"])
+
+# << Pan: checkpoint function
+def get_checkpoint_fn():
+    # import deepspeed
+    #if deepspeed.checkpointing.is_configured():
+    #    checkpoint = deepspeed.checkpointing.checkpoint
+    #else:
+    checkpoint = partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+    return checkpoint
+
+class FM4BioEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    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.position_embedding_type not in ("rope", "rope_2d"): # << Pan: add rope_2d
+            self.position_embeddings = nn.Embedding(
+                config.max_position_embeddings, config.hidden_size
+            )
+        # self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+
+        # << Pan: Structure embedding input dimension
+        if isinstance(config.str_embedding_in, str):
+            if config.str_embedding_in.isdigit():
+                self.str_embedding_in = int(config.str_embedding_in)
+            else:
+                self.str_embedding_in = None
+        else:
+            self.str_embedding_in = config.str_embedding_in
+
+        if self.str_embedding_in is not None and self.str_embedding_in > 0:
+            self.str_embeddings = nn.Linear(self.str_embedding_in, config.hidden_size, bias=False)
+
+        # In Megatron, layer-norm is applied after the 1st dropout.
+        # self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer(
+            "position_ids",
+            torch.arange(config.max_position_embeddings).expand((1, -1)),
+            persistent=False,
+        )
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "rope"
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        inputs_str_embeds: Optional[torch.LongTensor] = None, # << Pan: add structure embedding input
+        past_key_values_length: int = 0,
+    ) -> torch.Tensor:
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[
+                :, past_key_values_length : seq_length + past_key_values_length
+            ]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=self.position_ids.device
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        # token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        # << Pan: add structure embedding
+        if self.str_embedding_in is not None and self.str_embedding_in > 0:
+            if inputs_str_embeds is None:
+                print(f"Warning: str_embedding_in={self.str_embedding_in}, but inputs_str_embeds is None")
+            else:
+                # inputs_str_embeds: [B, L, D1]
+                # inputs_embeds: [B, L, D]
+                shape = f"inputs_str_embeds.shape={inputs_str_embeds.shape}, inputs_embeds.shape={inputs_embeds.shape}"
+                assert inputs_str_embeds.ndim == 3, shape
+                assert inputs_str_embeds.shape[0] == inputs_embeds.shape[0], shape
+                assert inputs_str_embeds.shape[1] == inputs_embeds.shape[1], shape
+                inputs_embeds = inputs_embeds + self.str_embeddings(inputs_str_embeds)
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        # embeddings = inputs_embeds + token_type_embeddings
+        embeddings = inputs_embeds
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+
+        # Megatron BERT moves that layer norm after the drop-out (and to each layer).
+        # embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->FM4Bio
+class FM4BioSelfAttention(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, bias=config.add_linear_bias
+        )
+        self.key = nn.Linear(
+            config.hidden_size, self.all_head_size, bias=config.add_linear_bias
+        )
+        self.value = nn.Linear(
+            config.hidden_size, self.all_head_size, bias=config.add_linear_bias
+        )
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        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
+            )
+
+        self.is_decoder = config.is_decoder
+
+    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,
+        rotary_pos_emb=None,
+    ) -> 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))
+
+        # [b, hn, sq, c]
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        if rotary_pos_emb is not None:
+            if isinstance(rotary_pos_emb, tuple):
+                rotary_pos_emb = rotary_pos_emb
+            else:
+                rotary_pos_emb = (rotary_pos_emb,) * 2
+            q_pos_emb, k_pos_emb = rotary_pos_emb
+
+            # [b, hn, sq, c] --> [sq, b, hn, c]
+            query_layer = query_layer.permute(2, 0, 1, 3).contiguous()
+            key_layer = key_layer.permute(2, 0, 1, 3).contiguous()
+
+            # debug_tensor = query_layer[:3, 0]
+            # query_layer = apply_rotary_pos_emb(
+            #     query_layer, q_pos_emb
+            # )  # debug query_layer[:,0]
+            # key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb)
+
+            # << Pan: add 2d rope
+            if q_pos_emb.ndim == 5: ## 2d rope
+                dim          = query_layer.shape[-1]
+                query_layer1 = apply_rotary_pos_emb(query_layer[..., :dim//2], q_pos_emb[0])
+                query_layer2 = apply_rotary_pos_emb(query_layer[..., dim//2:], q_pos_emb[1])
+                query_layer  = torch.cat([query_layer1, query_layer2], axis=-1)
+
+                dim         = key_layer.shape[-1]
+                key_layer1  = apply_rotary_pos_emb(key_layer[..., :dim//2], k_pos_emb[0])
+                key_layer2  = apply_rotary_pos_emb(key_layer[..., dim//2:], k_pos_emb[1])
+                key_layer   = torch.cat([key_layer1, key_layer2], axis=-1)
+            else: ## 1d rope
+                query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
+                key_layer   = apply_rotary_pos_emb(key_layer, k_pos_emb)
+
+            # [sq, b, hn, c] --> [b, hn, sq, c]
+            query_layer = query_layer.permute(1, 2, 0, 3).contiguous()
+            key_layer = key_layer.permute(1, 2, 0, 3).contiguous()
+
+        use_cache = past_key_value is not None
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        ######### << Pan: FlashAttention implementation
+        if output_attentions or head_mask is not None:
+            # Don't use FA
+
+            # 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))
+
+            attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+            if attention_mask is not None:
+                # Apply the attention mask is (precomputed for all layers in FM4BioModel forward() function)
+                attention_scores = attention_scores + attention_mask.to(attention_scores.dtype)
+
+            # Normalize the attention scores to probabilities.
+            attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+            no_prob_mask = attention_mask < -1e-5
+            attention_probs = attention_probs.masked_fill(no_prob_mask, 0.0)
+            # 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 attention_mask is not None:
+                if attention_mask.shape[0] == 1: # Batch size = 1
+                    attention_mask = None
+                else:
+                    attention_mask = attention_mask.clone()
+                    if torch.is_floating_point(attention_mask):
+                        attention_mask[attention_mask < -1e-5] = torch.finfo(attention_mask.dtype).min
+                    if torch.allclose(attention_mask, torch.zeros_like(attention_mask)):
+                        attention_mask = None
+
+            context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, attn_mask=attention_mask, dropout_p=self.dropout.p, is_causal=False, scale=None, enable_gqa=False)
+
+        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
+
+
+# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to FM4BioAttention below.
+class FM4BioSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(
+            config.hidden_size, config.hidden_size, bias=config.add_linear_bias
+        )
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, hidden_states: torch.Tensor, residual: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return residual + hidden_states
+
+
+# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.
+class FM4BioAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln = config.norm_cls(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.self = FM4BioSelfAttention(config)
+        self.output = FM4BioSelfOutput(config)
+        self.pruned_heads = set()
+
+    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: 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,
+        rotary_pos_emb=None,
+    ) -> Tuple[torch.Tensor]:
+        # debug_point1 = hidden_states[0]
+        ln_outputs = self.ln(hidden_states)
+        self_outputs = self.self(
+            ln_outputs,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+            rotary_pos_emb,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+def _config_to_kwargs(args):
+    # << Pan: support string input for torch_dtype
+    if isinstance(args.torch_dtype, str):
+        torch_dtype = eval(args.torch_dtype)
+    else:
+        torch_dtype = args.torch_dtype
+    common_kwargs = {
+        "dtype": torch_dtype,
+    }
+    return common_kwargs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FM4Bio
+class FM4BioMLP(nn.Module):
+    # def __init__(self, config: FM4BioConfig):
+    #     super().__init__()
+    #     assert config.hidden_act == "swiglu", "Only swiglu is supported."
+    #     self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.add_linear_bias)
+    #     self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.add_linear_bias)
+    #     self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.add_linear_bias)
+    #     self.intermediate_act_fn = ACT2FN['silu'] # swiglu use silu as part of its activation function
+
+    # def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+    #     down_proj = self.down_proj(self.intermediate_act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
+    #     return down_proj
+
+    def __init__(self, config: FM4BioConfig, device=None):
+        super(FM4BioMLP, self).__init__()
+
+        self.add_bias = config.add_linear_bias
+        self.moe = config.moe
+        self.num_experts = config.num_experts
+        self.experts_per_token = config.experts_per_token  # 2
+
+        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+        self.dense_h_to_4h = nn.Linear(
+            config.hidden_size,
+            config.intermediate_size * 2,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config),
+        )
+
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return x[0] * F.silu(x[1])
+
+        def geglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return x[0] * F.gelu(x[1])
+
+        if config.hidden_act == "geglu":
+            self.activation_func = geglu
+        elif config.hidden_act == "swiglu":
+            self.activation_func = swiglu
+        else:
+            assert RuntimeError(f"Unsupported glu_activation: {config.hidden_act}")
+
+        # Project back to h.
+        self.dense_4h_to_h = nn.Linear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config),
+        )
+
+        if self.moe:
+            assert self.num_experts > 1
+            del self.dense_h_to_4h
+            del self.dense_4h_to_h
+            self.router = nn.Linear(
+                config.hidden_size,
+                config.num_experts,
+                bias=False,
+                device=device,
+                dtype=torch.float32,
+            )
+            for i in range(0, self.num_experts):
+                self.register_module(
+                    f"dense_h_to_4h_{i}",
+                    nn.Linear(
+                        config.hidden_size,
+                        config.intermediate_size * 2,
+                        bias=self.add_bias,
+                        device=device,
+                        **_config_to_kwargs(config),
+                    ),
+                )
+                self.register_module(
+                    f"dense_4h_to_h_{i}",
+                    nn.Linear(
+                        config.intermediate_size,
+                        config.hidden_size,
+                        bias=self.add_bias,
+                        device=device,
+                        **_config_to_kwargs(config),
+                    ),
+                )
+
+    def moe_forward(self, hidden_states, expert_idx):
+        intermediate_parallel = getattr(self, f"dense_h_to_4h_{expert_idx}")(
+            hidden_states
+        )
+        intermediate_parallel = self.activation_func(intermediate_parallel)
+        output = getattr(self, f"dense_4h_to_h_{expert_idx}")(intermediate_parallel)
+        return output
+
+    def forward(self, hidden_states):
+        if self.moe:
+            # import pdb; pdb.set_trace();
+            s, b, n = hidden_states.shape
+            dtype = hidden_states.dtype
+            hidden_states = hidden_states.view(-1, hidden_states.size(2))  # [s*b h]
+            ## <<< Pan: router model dtype must be float32
+            self.router = self.router.float()
+            route = self.router(hidden_states.float()).to(dtype)
+
+            weights, selected_experts = torch.topk(route, self.experts_per_token)
+            weights = F.softmax(weights, dim=1, dtype=torch.float).to(
+                hidden_states.dtype
+            )
+            ## << Ning: trace moe weight and assignment
+            if getattr(self, "trace_moe", False):
+                # 保存本层最后一次前向的权重与索引（按需放CPU，避免显存占用）
+                self.last_moe_weights = weights.detach()         # 形状: [N, K] 或 [B, T, K]
+                self.last_moe_indices = selected_experts.detach()  # 形状: 同上
+
+            output = torch.zeros_like(
+                hidden_states, dtype=hidden_states.dtype, device=hidden_states.device
+            )
+            for expert_idx in range(self.num_experts):
+                batch_idx, nth_expert = torch.where(selected_experts == expert_idx)
+                if nth_expert.shape[0] == 0:
+                    continue
+                cur_out = self.moe_forward(hidden_states[batch_idx], expert_idx)
+                output[batch_idx] += weights[batch_idx, nth_expert, None] * cur_out
+            output = output.reshape(s, b, n)
+        else:
+            # [s, b, 4hp]
+            intermediate_parallel = self.dense_h_to_4h(hidden_states)
+            intermediate_parallel = self.activation_func(intermediate_parallel)
+            # [s, b, h]
+            output = self.dense_4h_to_h(intermediate_parallel)
+        return output
+
+
+# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to FM4BioLayer below.
+class FM4BioOutput(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: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        # hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return input_tensor + hidden_states
+
+
+# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.
+class FM4BioLayer(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 = FM4BioAttention(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 TypeError(
+                    f"{self} should be used as a decoder model if cross attention is added"
+                )
+            self.crossattention = FM4BioAttention(config)
+        self.ln = config.norm_cls(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = FM4BioMLP(config)
+        self.output = FM4BioOutput(config)
+
+    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,
+        rotary_pos_emb=None,
+    ) -> Tuple[torch.Tensor]:
+        # 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,
+            rotary_pos_emb=rotary_pos_emb,
+        )
+        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 = apply_chunking_to_forward(
+            self.feed_forward_chunk,
+            self.chunk_size_feed_forward,
+            self.seq_len_dim,
+            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):
+        # debug: attention_output[0]
+        ln_output = self.ln(attention_output)
+        mlp_output = self.mlp(ln_output)
+        layer_output = self.output(mlp_output, attention_output)
+        return layer_output
+
+
+class RnaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        same as LlamaRMSNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+
+ALL_LAYERNORM_LAYERS.append(RnaRMSNorm)
+
+
+class FM4BioEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList(
+            [FM4BioLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+
+        # The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
+        # is simply the final LN (Transformer's BERT has it attached to each hidden layer).
+        self.ln = config.norm_cls(config.hidden_size, eps=config.layer_norm_eps)
+        self.gradient_checkpointing = False
+
+    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_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        rotary_pos_emb: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. 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,
+                #     rotary_pos_emb,
+                # )
+                # <<< Pan: use gradient checkpoint
+                # If use the self._gradient_checkpointing_func version. program will abord with parameters used twice error for fold prediction task
+                layer_outputs = get_checkpoint_fn()(
+                    layer_module,
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    rotary_pos_emb,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    rotary_pos_emb,
+                )
+
+            # Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
+            # zed data here. If that's really needed, we must apply LN to match Transformer's BERT.
+
+            hidden_states = layer_outputs[0]
+            if use_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],)
+
+        # Finalize the hidden states.
+        hidden_states = self.ln(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,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->FM4Bio
+class FM4BioPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(
+            config.hidden_size, config.hidden_size, bias=config.add_linear_bias
+        )
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->FM4Bio
+class FM4BioPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(
+            config.hidden_size, config.hidden_size
+        )  # in megatron, this will always have bias
+
+        self.transform_act_fn = ACT2FN["gelu"]
+
+        if config.normalization_type == "RMSNorm":
+            self.LayerNorm = RnaRMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        else:
+            self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->FM4Bio
+class FM4BioLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = FM4BioPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->FM4Bio
+class FM4BioOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = FM4BioLMPredictionHead(config)
+
+    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->FM4Bio
+class FM4BioPreTrainingHeads(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = FM4BioLMPredictionHead(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class FM4BioPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = FM4BioConfig
+    # load_tf_weights = load_tf_weights_in_fm4bio
+    base_model_prefix = "bert"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "FM4BioLayer",
+        "FM4BioEmbeddings",
+        "FM4BioMLP",
+    ]  # should not be on different machines
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # 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)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, RnaRMSNorm):
+            module.weight.data.fill_(1.0)
+            # no bias
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+@dataclass
+# Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->FM4Bio
+class FM4BioForPreTrainingOutput(ModelOutput):
+    """
+    Output type of [`FM4BioForPreTraining`].
+
+    Args:
+        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+            Total loss as the sum of the masked language modeling loss and the next sequence prediction
+            (classification) loss.
+        prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
+            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+            before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    prediction_logits: torch.FloatTensor = None
+    seq_relationship_logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+FM4BIO_START_DOCSTRING = r"""
+
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`FM4BioConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+FM4BIO_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare FM4Bio Model transformer outputting raw hidden-states without any specific head on top.",
+    FM4BIO_START_DOCSTRING,
+)
+class FM4BioModel(FM4BioPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in [Attention is
+    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+    `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=False):
+        super().__init__(config)
+        self.config = config
+        if config.normalization_type == "RMSNorm":
+            self.config.norm_cls = RnaRMSNorm
+        else:
+            assert config.normalization_type == "LayerNorm"
+            self.config.norm_cls = nn.LayerNorm
+        self.embeddings = FM4BioEmbeddings(config)
+        self.encoder = FM4BioEncoder(config)
+
+        self.pooler = FM4BioPooler(config) if add_pooling_layer else None
+
+        # rotary position embeddings
+        if config.position_embedding_type == "rope":
+            rotary_dim = config.hidden_size // config.num_attention_heads
+
+            # partial rotary embeddings, which is better than full rotary
+            # Wang and Komatsuzaki et al
+            # https://github.com/kingoflolz/mesh-transformer-jax/
+            self.rotary_pos_emb = RotaryEmbedding(rotary_dim, config.rotary_percent)
+
+        # << Pan: add 2D rope
+        elif config.position_embedding_type == "rope_2d":
+            rotary_dim = config.hidden_size // config.num_attention_heads // 2
+            self.rotary_pos_emb = RotaryEmbedding(rotary_dim, config.rotary_percent)
+
+        # delete this from config so the config can be successfully saved
+        del self.config.norm_cls
+
+        # Initialize weights and apply final processing
+        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)
+
+    @add_start_docstrings_to_model_forward(
+        FM4BIO_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        inputs_str_embeds: Optional[torch.FloatTensor] = None, # << Pan: input structure embedding
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        """
+        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
+            )
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        # extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+        extended_attention_mask = bert_extended_attention_mask(
+            attention_mask
+        )  # True for pad, false for non-pad
+        extended_attention_mask = extended_attention_mask * torch.finfo(torch.float).min
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        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
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        # Rotary positional embeddings
+        rotary_pos_emb = None
+        if self.config.position_embedding_type == "rope":
+            rotary_pos_emb = self.rotary_pos_emb(input_ids.size(1))
+        # << Pan: rope_2d
+        elif self.config.position_embedding_type == 'rope_2d':
+            # input_ids: [1, 12800]
+            # position_ids: [B, 2, 12800]
+            # breakpoint()
+            rotary_pos_emb = self.rotary_pos_emb(input_ids.size(1)).squeeze(1) # [12800, 1, 1, D//H] -> [12800, 1, D//H//2]
+            rotary_pos_emb = rotary_pos_emb[ position_ids ] # [12800, 1, D//H] -> [B, 2, 12800, 1, D//H//2]
+            rotary_pos_emb = rotary_pos_emb.permute([1,2,0,3,4]) # [2, 12800, B, 1, D//H//2]
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            inputs_str_embeds=inputs_str_embeds, # << Pan: input structure embedding
+            past_key_values_length=past_key_values_length,
+        )
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        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,
+            rotary_pos_emb=rotary_pos_emb,
+        )
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        if os.environ.get("DEBUG", "FALSE") == "TRUE":
+            if torch.distributed.get_rank() == 0:
+                breakpoint()
+            torch.distributed.barrier()
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_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,
+        )
+
+
+@add_start_docstrings(
+    """
+    FM4Bio Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+    `next sentence prediction (classification)` head.
+    """,
+    FM4BIO_START_DOCSTRING,
+)
+class FM4BioForPreTraining(FM4BioPreTrainedModel):
+    # _tied_weights_keys = ["cls.predictions.decoder"]
+
+    def __init__(self, config, add_binary_head=True):
+        super().__init__(config)
+
+        self.bert = FM4BioModel(config)
+        self.cls = FM4BioPreTrainingHeads(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(
+        FM4BIO_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @replace_return_docstrings(
+        output_type=FM4BioForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        inputs_str_embeds: Optional[torch.FloatTensor] = None, # << Pan: input structure embedding
+        labels: Optional[torch.LongTensor] = None,
+        next_sentence_label: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, FM4BioForPreTrainingOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+            (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+            - 0 indicates sequence B is a continuation of sequence A,
+            - 1 indicates sequence B is a random sequence.
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, FM4BioForPreTraining
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("")
+        >>> model = FM4BioForPreTraining.from_pretrained("")
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> prediction_logits = outputs.prediction_logits
+        >>> seq_relationship_logits = outputs.seq_relationship_logits
+        ```"""
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            inputs_str_embeds=inputs_str_embeds, # << Pan: input structure embedding
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output, pooled_output = outputs[:2]
+        prediction_scores, seq_relationship_score = self.cls(
+            sequence_output, pooled_output
+        )
+
+        total_loss = None
+        if labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+            next_sentence_loss = loss_fct(
+                seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
+            )
+            total_loss = masked_lm_loss + next_sentence_loss
+
+        if not return_dict:
+            output = (prediction_scores, seq_relationship_score) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return FM4BioForPreTrainingOutput(
+            loss=total_loss,
+            prediction_logits=prediction_scores,
+            seq_relationship_logits=seq_relationship_score,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """FM4Bio Model with a `language modeling` head on top.""", FM4BIO_START_DOCSTRING
+)
+class FM4BioForMaskedLM(FM4BioPreTrainedModel):
+    # _tied_weights_keys = ["cls.predictions.decoder"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `FM4BioForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.bert = FM4BioModel(config, add_pooling_layer=False)
+        self.use_lm_head = config.use_lm_head
+        if config.use_lm_head:
+            self.cls = FM4BioOnlyMLMHead(config)
+        else:
+            if getattr(config, "output_vocab_size", None) is not None:
+                # used when the output uses a different vocab
+                # e.g., input vocab is amino acids, output vocab is structure tokens
+                self.output_embed = nn.Linear(
+                    config.hidden_size, config.output_vocab_size, bias=False
+                )
+            else:
+                self.output_embed = nn.Linear(
+                    config.hidden_size, config.vocab_size, bias=False
+                )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        if self.use_lm_head:
+            return self.cls.predictions.decoder
+        else:
+            return self.output_embed
+
+    def set_output_embeddings(self, new_embeddings):
+        if self.use_lm_head:
+            self.cls.predictions.decoder = new_embeddings
+        else:
+            raise NotImplementedError
+
+    @add_start_docstrings_to_model_forward(
+        FM4BIO_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        inputs_str_embeds: Optional[torch.FloatTensor] = None, # << Pan: input structure embedding
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = 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]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            inputs_str_embeds=inputs_str_embeds, # << Pan: input structure embedding
+            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]
+        if self.use_lm_head:
+            prediction_scores = self.cls(sequence_output)
+        else:
+            prediction_scores = self.output_embed(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            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,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, attention_mask=None, **model_kwargs
+    ):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        #  add a dummy token
+        if self.config.pad_token_id is None:
+            raise ValueError("The PAD token should be defined for generation")
+        attention_mask = torch.cat(
+            [attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))],
+            dim=-1,
+        )
+        dummy_token = torch.full(
+            (effective_batch_size, 1),
+            self.config.pad_token_id,
+            dtype=torch.long,
+            device=input_ids.device,
+        )
+        input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+from torch import Tensor, nn
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Embedding for language model.
+
+    Args:
+        kv_channels (int): Projection weights dimension in multi-head attention. Obtained from transformer config
+        rotary_percent (float): Percent of rotary dimension to use for rotary position embeddings.
+        seq_len_interpolation_factor (float, optional): scale of linearly interpolating RoPE for longer sequences. The value must be a float larger than 1.0. Defaults to None
+        rotary_base (int, optional): Base period for rotary position embeddings. Defaults to 10000.
+    """
+
+    def __init__(
+        self,
+        kv_channels: int,
+        rotary_percent: float,
+        seq_len_interpolation_factor: float = None,
+        rotary_base: int = 10000,
+    ) -> None:
+        super().__init__()
+
+        dim = kv_channels
+        if rotary_percent < 1.0:
+            dim = int(dim * rotary_percent)
+
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
+        device = (
+            torch.cuda.current_device()
+            if torch.cuda.is_available()
+            else torch.device("cpu")
+        )
+        self.inv_freq = 1.0 / (
+            rotary_base
+            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+        )
+
+    def forward(self, max_seq_len: int, offset: int = 0) -> Tensor:
+        """Forward pass of RoPE embedding.
+
+        Args:
+            max_seq_len (int): Maximum size of sequence
+            offset (int, optional): _description_. Defaults to 0.
+
+        Returns:
+            Tensor: Embeddings after applying RoPE.
+        """
+        seq = (
+            torch.arange(
+                max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype
+            )
+            + offset
+        )
+
+        if self.seq_len_interpolation_factor is not None:
+            seq *= 1 / self.seq_len_interpolation_factor
+
+        freqs = torch.outer(seq, self.inv_freq)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        emb = torch.cat((freqs, freqs), dim=-1)
+        # emb [seq_length, .., dim]
+        emb = emb[:, None, None, :]
+
+        return emb
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        state_dict.pop(f"{prefix}inv_freq", None)
+        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
+
+def _rotate_half(x: Tensor) -> Tensor:
+    """Change sign so the last dimension becomes [-odd, +even]
+
+    Args:
+        x (Tensor): Input tensor
+
+    Returns:
+        Tensor: Tensor rotated half
+    """
+
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t: Tensor, freqs: Tensor) -> Tensor:
+    """Apply rotary positional embedding to input tensor T.
+
+    check https://kexue.fm/archives/8265 for detailed formulas
+
+    Args:
+        t (Tensor): Input tensor T is of shape [seq_length, ... , dim]
+        freqs (Tensor): Rotary Positional embedding tensor freq is of shape [seq_length, ..., dim]
+
+    Returns:
+        Tensor: The input tensor after applying RoPE
+    """
+    rot_dim = freqs.shape[-1]
+
+    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
+    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]
+
+    # first part is cosine component
+    # second part is sine component, need to change signs with _rotate_half method
+    cos_ = torch.cos(freqs).to(t.dtype).to(t.device)
+    sin_ = torch.sin(freqs).to(t.dtype).to(t.device)
+
+    t = (t * cos_) + (_rotate_half(t) * sin_)
+    return torch.cat((t, t_pass), dim=-1)
+
+
+def bert_extended_attention_mask(attention_mask):
+    # We create a 3D attention mask from a 2D tensor mask.
+    # [b, 1, s]
+    attention_mask_b1s = attention_mask.unsqueeze(1)
+    # [b, s, 1]
+    attention_mask_bs1 = attention_mask.unsqueeze(2)
+    # [b, s, s]
+    attention_mask_bss = attention_mask_b1s * attention_mask_bs1
+    # [b, 1, s, s]
+    extended_attention_mask = attention_mask_bss.unsqueeze(1)
+
+    # Convert attention mask to binary:
+    extended_attention_mask = extended_attention_mask < 0.5
+
+    return extended_attention_mask
+
+
+class FM4BioForSequenceClassification(FM4BioPreTrainedModel):
+    def __init__(
+        self,
+        config,
+        arch="MLP",
+        pooling="mean_pooling",
+        conv_kernel_size=9,
+        dropout_prob=None,
+        augment_with_zeroshot=False,
+        inter_hidden_size=None,
+        activation_func="tanh",
+    ):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+        self.pooling = pooling
+        self.augment_with_zeroshot = augment_with_zeroshot
+        self.inter_hidden_size = inter_hidden_size
+
+        self.bert = FM4BioModel(config, add_pooling_layer=False)
+        self.classifier = FM4BioClassificationHead(
+            config,
+            arch,
+            pooling,
+            conv_kernel_size,
+            dropout_prob,
+            inter_hidden_size,
+            augment_with_zeroshot,
+            activation_func,
+        )
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        FM4BIO_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,  # (bs, seq_len), 0 means masking
+        zero_shot_fitness_predictions: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        inputs_str_embeds: Optional[torch.FloatTensor] = None, # << Pan: input string embeddings
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            inputs_str_embeds=inputs_str_embeds, # << Pan: input string embeddings
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]  # (bs, seq_len, hidden_size)
+        logits = self.classifier(
+            sequence_output, attention_mask, zero_shot_fitness_predictions
+        )
+
+        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":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FM4BioForTokenClassification(FM4BioPreTrainedModel):
+    def __init__(
+        self,
+        config,
+        arch="MLP",
+        conv_kernel_size=9,
+        dropout_prob=None,
+        pairwise=False,
+        inter_hidden_size=128,
+    ):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.pairwise = pairwise
+
+        self.bert = FM4BioModel(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        if self.pairwise:
+            self.inter_hidden_size = [inter_hidden_size, self.num_labels]
+            self.classifier = FM4BioContactHead(config, self.inter_hidden_size)
+        else:
+            self.classifier = FM4BioClassificationHead(
+                config,
+                arch=arch,
+                pooling=None,
+                conv_kernel_size=conv_kernel_size,
+                dropout_prob=dropout_prob,
+                inter_hidden_size=inter_hidden_size,
+            )
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        FM4BIO_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    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,
+        inputs_str_embeds: Optional[torch.FloatTensor] = None, # << Pan: input structure embedding
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            inputs_str_embeds=inputs_str_embeds, # << Pan: input structure embedding
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]  # (bs, seq_len, hidden_size)
+
+        # remove padding and [eos]
+        mbs = input_ids.shape[0]
+        seq_len = attention_mask.sum(1)  # (bs,)
+        seq_len = seq_len - 1  # (bs,)
+        assert mbs == 1, "currently only support mbs=1"
+        sequence_output = sequence_output[:, : seq_len[0]]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+            else:
+                loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(logits.device)
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FM4BioClassificationHead(nn.Module):
+    """Head for classification tasks and regression tasks."""
+
+    def __init__(
+        self,
+        config,
+        arch="MLP",
+        pooling="mean_pooling",
+        conv_kernel_size=9,
+        dropout_prob=None,
+        inter_hidden_size=None,
+        augment_with_zeroshot=False,
+        activation_func="tanh",
+    ):
+        super().__init__()
+        self.arch = arch
+        self.pooling = pooling
+        self.conv_kernal_size = conv_kernel_size
+        self.augment_with_zeroshot = augment_with_zeroshot
+
+        if dropout_prob is not None:
+            self.dropout_prob = dropout_prob
+        else:
+            self.dropout_prob = config.hidden_dropout_prob
+
+        if self.arch == "MLP" and inter_hidden_size is None:
+            self.inter_hidden_size = config.hidden_size // 2
+        else:
+            self.inter_hidden_size = inter_hidden_size
+
+        if activation_func == "tanh":
+            self.activation_func = nn.Tanh()
+        else:
+            self.activation_func = nn.ReLU()
+
+        if self.augment_with_zeroshot:
+            input_hidden_size = config.hidden_size + 1
+        else:
+            input_hidden_size = config.hidden_size
+
+        assert self.pooling in ["mean_pooling", None]
+        if self.arch == "CNN":
+            self.conv = nn.Conv1d(
+                in_channels=config.hidden_size,
+                out_channels=config.hidden_size,
+                kernel_size=conv_kernel_size,
+                padding="same",
+            )
+            self.dropout = nn.Dropout(self.dropout_prob)
+            self.out_proj = nn.Linear(input_hidden_size, config.num_labels)
+        elif self.arch == "MLP":
+            self.ffn = nn.Linear(input_hidden_size, self.inter_hidden_size)
+            self.dropout = nn.Dropout(self.dropout_prob)
+            self.out_proj = nn.Linear(self.inter_hidden_size, config.num_labels)
+        else:
+            raise NotImplementedError
+
+    def forward(
+        self, hidden_states, attention_mask=None, zero_shot_fitness_predictions=None
+    ):
+        """
+        Args:
+            hidden_states: (bs, seq_len, hidden_size)
+            attention_mask: (bs, seq_len), 0 means masking
+        """
+        x = hidden_states
+        if self.arch == "CNN":
+            # Refer to ProteinNPT
+            x = self.dropout(x)
+            x = x.permute(0, 2, 1)  # (bs, hidden_size, seq_len)
+            x = self.conv(x)  # （bs, hidden_size, seq_len1)
+            x = self.dropout(x)
+            x = self.activation_func(x)
+            x = x.permute(0, 2, 1)  # (bs, seq_len, hidden_size)
+            # mean pooling
+            if self.pooling == "mean_pooling":
+                x = x.mean(dim=-2)  # (bs, hidden_size)
+                if self.augment_with_zeroshot:
+                    x = self._get_zero_shot_aug_feats(
+                        x, zero_shot_fitness_predictions
+                    )  # (bs, hidden_size+1)
+            x = self.out_proj(x)
+
+        elif self.arch == "MLP":
+            if self.pooling == "mean_pooling":
+                input_mask_expanded = (
+                    attention_mask.unsqueeze(-1).expand(x.size()).float()
+                )
+                x = torch.sum(x * input_mask_expanded, 1) / torch.clamp(
+                    input_mask_expanded.sum(1), min=1e-9
+                )
+                if self.augment_with_zeroshot:
+                    x = self._get_zero_shot_aug_feats(
+                        x, zero_shot_fitness_predictions
+                    )  # (bs, hidden_size+1)
+            x = self.dropout(x)
+            x = self.ffn(x)
+            x = self.activation_func(x)
+            x = self.dropout(x)
+            x = self.out_proj(x)
+        return x
+
+    def _get_zero_shot_aug_feats(self, x, zero_shot_fitness_predictions):
+        """
+        Add zero_shot_prediction to the beginning of x as the first feats
+            x: torch.tensor, of shape (bs, hidden_size)
+            zero_shot_fitness_predictions: torch.tensor, of shape (bs,) or (bs, 1)
+        """
+        assert zero_shot_fitness_predictions is not None
+        if len(zero_shot_fitness_predictions.shape) == 1:
+            zero_shot_fitness_predictions = zero_shot_fitness_predictions.unsqueeze(
+                -1
+            ).to(x.dtype)
+        x = torch.cat((zero_shot_fitness_predictions, x), 1)  # (bs, hidden_size+1)
+        return x
+
+
+class FM4BioContactHead(nn.Module):
+    """Head for contact prediction."""
+
+    def __init__(self, config, inter_hidden_size=[128, 2]):
+        super().__init__()
+        self.ffn_0 = nn.Linear(config.hidden_size * 2, inter_hidden_size[0])
+        self.ffn_1 = nn.Linear(inter_hidden_size[0], inter_hidden_size[1])
+
+    def outer_concat(self, x):
+        batch_size, seq_len, features = x.shape
+
+        # Permute to [batch_size, features, seq_len]
+        x = x.permute(0, 2, 1)
+
+        # Introduce new dimensions for broadcasting
+        x_1 = x[:, None, :, :, None]  # [batch_size, 1, features, seq_len, 1]
+        x_2 = x[:, None, :, None, :]  # [batch_size, 1, features, 1, seq_len]
+
+        # Repeat along new dimensions
+        x_1 = x_1.repeat(
+            1, 1, 1, 1, seq_len
+        )  # [batch_size, 1, features, seq_len, seq_len]
+        x_2 = x_2.repeat(
+            1, 1, 1, seq_len, 1
+        )  # [batch_size, 1, features, seq_len, seq_len]
+
+        # Concatenate along the second dimension
+        x = torch.cat((x_1, x_2), dim=1)  # [batch_size, 2, features, seq_len, seq_len]
+
+        # Get lower triangular indices
+        I, J = torch.tril_indices(seq_len, seq_len, -1)
+
+        # Symmetrize
+        x[:, :, :, I, J] = x[:, :, :, J, I]
+
+        # Permute to desired shape and make contiguous
+        x = x.permute(
+            0, 3, 4, 2, 1
+        ).contiguous()  # [batch_size, seq_len, seq_len, features, 2]
+
+        # Reshape to combine the last two dimensions
+        x = x.view(
+            batch_size, seq_len, seq_len, features * 2
+        )  # [batch_size, seq_len, seq_len, features * 2]
+
+        return x
+
+    def forward(self, hidden_states):
+        # remove [sep] token at the end
+        # x = hidden_states[:, :-1]     #(bs, seq_len, hidden_size)
+        x = self.outer_concat(hidden_states)
+        x = self.ffn_0(x)
+        x = nn.ReLU()(x)
+        x = self.ffn_1(x)
+        return x

tokenization_fm4bio.py

@@ -0,0 +1,168 @@
+"""Tokenization classes for FM4Bio
+Currently supports only protein
+"""
+
+import os
+from typing import List, Optional
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab_protein.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "fm4bio/proteinmoe": "https://huggingface.co/fm4bio/proteinmoe/resolve/main/vocab.txt",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"proteinmoe": 2048, "rnabert": 1024}
+
+
+def load_vocab_file(vocab_file):
+    with open(vocab_file, "r") as f:
+        lines = f.read().splitlines()
+        return [l.strip() for l in lines]
+
+
+class FM4BioTokenizer(PreTrainedTokenizer):
+    """
+    Constructs an FM4Bio tokenizer.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        biotype="protein",
+        unk_token="-",
+        pad_token="[PAD]",
+        mask_token="[MASK]",
+        sep_token="[SEP]",
+        cls_token=None,
+        bos_token=None,
+        eos_token=None,
+        **kwargs,
+    ):
+        """
+        Args:
+            biotype: str, could be protein/rna/dna
+            the input is like ...[SEP]
+        """
+        self.biotype = biotype
+        if self.biotype != "protein":
+            raise NotImplementedError
+
+        self.all_tokens = load_vocab_file(vocab_file)
+        self._id_to_token = dict(enumerate(self.all_tokens))
+        self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
+
+        super().__init__(
+            unk_token=unk_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            sep_token=sep_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            **kwargs,
+        )
+
+        # TODO, all the tokens are added? But they are also part of the vocab... bit strange.
+        # none of them are special, but they all need special splitting.
+
+        self.unique_no_split_tokens = self.all_tokens
+        self._update_trie(self.unique_no_split_tokens)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._id_to_token.get(index, self.unk_token)
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
+
+    def _tokenize(self, text, **kwargs):
+        """
+        Hack for multiple chains (seperated by |)
+        Args:
+            text: str, eg. CALVSGGNYKPTF|CASSWGGAPLF|ELAGIGILTV
+        """
+        return text.replace("|", self.sep_token).split()
+
+    def get_vocab(self):
+        base_vocab = self._token_to_id.copy()
+        base_vocab.update(self.added_tokens_encoder)
+        return base_vocab
+
+    def token_to_id(self, token: str) -> int:
+        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
+
+    def id_to_token(self, index: int) -> str:
+        return self._id_to_token.get(index, self.unk_token)
+
+    def build_inputs_with_special_tokens(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+    ) -> List[int]:
+        if self.biotype == "protein":
+            sep = [self.sep_token_id]
+            if token_ids_1 is None:
+                return token_ids_0 + sep
+            else:
+                return token_ids_0 + sep + token_ids_1 + sep
+        else:
+            raise NotImplementedError
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List,
+        token_ids_1: Optional[List] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids of the first sequence.
+            token_ids_1 (`List[int]`, *optional*):
+                List of ids of the second sequence.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+
+            return [1 if token in self.all_special_ids else 0 for token in token_ids_0]
+        mask = ([0] * len(token_ids_0)) + [1]
+        if token_ids_1 is not None:
+            mask += [0] * len(token_ids_1) + [1]
+        return mask
+
+    def save_vocabulary(self, save_directory, filename_prefix):
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
+        )
+        with open(vocab_file, "w") as f:
+            f.write("\n".join(self.all_tokens))
+        return (vocab_file,)
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.all_tokens)

vocab_protein.txt

@@ -0,0 +1,44 @@
+[PAD]
+L
+A
+G
+V
+S
+E
+R
+T
+I
+D
+P
+K
+Q
+N
+F
+Y
+M
+H
+W
+C
+X
+B
+U
+Z
+O
+.
+-
+[MASK]
+[gMASK]
+[sMASK]
+[eod]
+[sop]
+[eop]
+[SEP]
+[HC]
+[LC]
+[HUMAN]
+<cdr1>
+</cdr1>
+<cdr2>
+</cdr2>
+<cdr3>
+</cdr3>