Upload custom code for molformer models

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "MolformerForMaskedLM"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_molformer.MolformerConfig",
+    "AutoModelForMaskedLM": "modeling_molformer.MolformerForMaskedLM"
+  },
   "classifier_dropout_prob": null,
   "classifier_skip_connection": true,
   "deterministic_eval": false,

configuration_molformer.py

@@ -0,0 +1,158 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+""" Molformer model configuration"""
+
+from collections import OrderedDict
+from typing import Mapping
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/config.json",
+}
+
+
+class MolformerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MolformerModel`]. It is used to instantiate an
+    Molformer model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the Molformer
+    [ibm/MoLFormer-XL-both-10pct](https://huggingface.co/ibm/MoLFormer-XL-both-10pct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 2362):
+            Vocabulary size of the Molformer model. Defines the number of different tokens that can be represented by
+            the `inputs_ids` passed when calling [`MolformerModel`] or [`TFMolformerModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 768):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        embedding_dropout_prob (`float`, *optional*, defaults to 0.2):
+            The dropout probability for the word embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 202):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 1536).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        linear_attention_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the linear attention layers normalization step.
+        num_random_features (`int`, *optional*, defaults to 32):
+            Random feature map dimension used in linear attention.
+        feature_map_kernel (`str` or `function`, *optional*, defaults to `"relu"`):
+            The non-linear activation function (function or string) in the generalized random features. If string,
+            `"gelu"`, `"relu"`, `"selu"`, and `"gelu_new"` ar supported.
+        deterministic_eval (`bool`, *optional*, defaults to `False`):
+            Whether the random features should only be redrawn when training or not. If `True` and `model.training` is
+            `False`, linear attention random feature weights will be constant, i.e., deterministic.
+        classifier_dropout_prob (`float`, *optional*):
+            The dropout probability for the classification head. If `None`, use `hidden_dropout_prob`.
+        classifier_skip_connection (`bool`, *optional*, defaults to `True`):
+            Whether a skip connection should be made between the layers of the classification head or not.
+        pad_token_id (`int`, *optional*, defaults to 2):
+            The id of the _padding_ token.
+
+    Example:
+
+    ```python
+    >>> from transformers import MolformerModel, MolformerConfig
+
+    >>> # Initializing a Molformer ibm/MoLFormer-XL-both-10pct style configuration
+    >>> configuration = MolformerConfig()
+
+    >>> # Initializing a model from the ibm/MoLFormer-XL-both-10pct style configuration
+    >>> model = MolformerModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "molformer"
+
+    def __init__(
+        self,
+        vocab_size=2362,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=768,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        embedding_dropout_prob=0.2,
+        max_position_embeddings=202,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        linear_attention_eps=1e-6,
+        num_random_features=32,
+        feature_map_kernel="relu",
+        deterministic_eval=False,
+        classifier_dropout_prob=None,
+        classifier_skip_connection=True,
+        pad_token_id=2,
+        **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.embedding_dropout_prob = embedding_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.linear_attention_eps = linear_attention_eps
+        self.num_random_features = num_random_features
+        self.feature_map_kernel = feature_map_kernel
+        self.deterministic_eval = deterministic_eval
+        self.classifier_dropout_prob = classifier_dropout_prob
+        self.classifier_skip_connection = classifier_skip_connection
+
+
+# Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Molformer
+class MolformerOnnxConfig(OnnxConfig):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
+        return OrderedDict(
+            [
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
+            ]
+        )

convert_molformer_original_checkpoint_to_pytorch.py

@@ -0,0 +1,87 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+"""Convert Molformer checkpoint."""
+
+
+import argparse
+import re
+
+import torch
+
+from transformers import MolformerConfig, MolformerForMaskedLM
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+RULES = [
+    (r"tok_emb", r"molformer.embeddings.word_embeddings"),
+    (
+        r"blocks\.layers\.(\d+)\.attention\.inner_attention\.feature_map\.omega",
+        r"molformer.encoder.layer.\1.attention.self.feature_map.weight",
+    ),
+    (
+        r"blocks\.layers\.(\d+)\.attention\.(query|key|value)_projection",
+        r"molformer.encoder.layer.\1.attention.self.\2",
+    ),
+    (r"blocks\.layers\.(\d+)\.attention\.out_projection", r"molformer.encoder.layer.\1.attention.output.dense"),
+    (r"blocks\.layers\.(\d+)\.norm1", r"molformer.encoder.layer.\1.attention.output.LayerNorm"),
+    (r"blocks\.layers\.(\d+)\.linear1", r"molformer.encoder.layer.\1.intermediate.dense"),
+    (r"blocks\.layers\.(\d+)\.linear2", r"molformer.encoder.layer.\1.output.dense"),
+    (r"blocks\.layers\.(\d+)\.norm2", r"molformer.encoder.layer.\1.output.LayerNorm"),
+    (r"blocks\.norm", r"molformer.LayerNorm"),
+    (r"lang_model\.embed", r"lm_head.transform.dense"),
+    (r"lang_model\.ln_f", r"lm_head.transform.LayerNorm"),
+    (r"lang_model\.head", r"lm_head.decoder"),
+]
+for i, (find, replace) in enumerate(RULES):
+    RULES[i] = (re.compile(find), replace)
+
+
+def convert_lightning_checkpoint_to_pytorch(lightning_checkpoint_path, pytorch_dump_path, config=None):
+    # Initialise PyTorch model
+    config = MolformerConfig(tie_word_embeddings=False) if config is None else MolformerConfig.from_pretrained(config)
+    print(f"Building PyTorch model from configuration: {config}")
+    model = MolformerForMaskedLM(config)
+
+    # Load weights from lightning checkpoint
+    checkpoint = torch.load(lightning_checkpoint_path, map_location="cpu")
+
+    state_dict = checkpoint["state_dict"]
+    new_state_dict = {}
+    for key, val in state_dict.items():
+        for find, replace in RULES:
+            if find.search(key) is not None:
+                new_state_dict[find.sub(replace, key)] = val
+                break
+    model.load_state_dict(new_state_dict)
+
+    # Save pytorch-model
+    print(f"Save PyTorch model to {pytorch_dump_path}")
+    torch.save(model.state_dict(), pytorch_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--lightning_checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint path."
+    )
+    parser.add_argument(
+        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+    )
+    parser.add_argument("--config", default=None, type=str, help="Path to config.json")
+    args = parser.parse_args()
+    convert_lightning_checkpoint_to_pytorch(args.lightning_checkpoint_path, args.pytorch_dump_path, config=args.config)

modeling_molformer.py

@@ -0,0 +1,921 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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 Molformer model."""
+
+
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+)
+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 (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+)
+from .configuration_molformer import MolformerConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "ibm/MoLFormer-XL-both-10pct"
+_CONFIG_FOR_DOC = "MolformerConfig"
+
+MOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "ibm/MoLFormer-XL-both-10pct",
+    # See all MoLFormer models at https://huggingface.co/models?filter=molformer
+]
+
+
+# Copied from transformers.models.esm.modeling_esm.rotate_half
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    cos = cos[position_ids].unsqueeze(1)  # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim]
+    sin = sin[position_ids].unsqueeze(1)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Molformer
+class MolformerRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+class MolformerEmbeddings(nn.Module):
+    """Construct the embeddings from word embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.dropout = nn.Dropout(config.embedding_dropout_prob)
+
+    def forward(
+        self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        embeddings = inputs_embeds
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class MolformerFeatureMap(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.query_size = config.hidden_size // config.num_attention_heads
+        self.num_components = config.num_random_features
+        self.orthogonal_random_weights()
+        if isinstance(config.feature_map_kernel, str):
+            self.kernel = ACT2FN[config.feature_map_kernel]
+        else:
+            self.kernel = config.feature_map_kernel
+        self.deterministic = config.deterministic_eval
+
+    def orthogonal_random_weights(self, device=None):
+        # make sure query size evenly divides feature size (round up)
+        num_batches = math.ceil(self.num_components / self.query_size)
+
+        def orthogonal_batch(size):
+            block = torch.randn(size, size, device=device)
+            norms = torch.linalg.norm(block, dim=1).unsqueeze(0)
+            Q, _ = torch.linalg.qr(block)
+            return Q * norms
+
+        random_weights = torch.cat([orthogonal_batch(self.query_size) for _ in range(num_batches)], dim=1)
+        random_weights = random_weights[:, : self.num_components]
+        self.register_buffer("weight", random_weights)
+
+    def forward(self, query, key):
+        if not self.deterministic or self.training:
+            self.orthogonal_random_weights(query.device)
+        # generalized random fourier features
+        query = torch.matmul(query, self.weight)
+        key = torch.matmul(key, self.weight)
+        return self.kernel(query), self.kernel(key)
+
+
+class MolformerSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.eps = config.linear_attention_eps
+
+        self.rotary_embeddings = MolformerRotaryEmbedding(
+            dim=self.attention_head_size, max_position_embeddings=config.max_position_embeddings
+        )
+        self.feature_map = MolformerFeatureMap(config)
+
+    # Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
+    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,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        query_layer = self.transpose_for_scores(self.query(hidden_states))
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        kv_seq_len = key_layer.shape[-2]
+        cos, sin = self.rotary_embeddings(value_layer, seq_len=kv_seq_len)
+        query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)
+        # Apply the feature map to the queries and keys
+        query_layer, key_layer = self.feature_map(query_layer, key_layer)
+
+        if attention_mask is not None:
+            # since we don't use softmax, we need to reconvert this mask to 1/0
+            attention_mask = (attention_mask == 0).to(attention_mask.dtype)
+            # separate original mask from causal mask
+            per_query_attn = attention_mask[:, 0, -1]
+            per_query_extended = per_query_attn[:, None, None, :]
+            if not torch.equal(attention_mask, per_query_extended):
+                raise ValueError(
+                    "MolformerSelfAttention does not support arbitrary 3D attention. attention_mask must be 2D (i.e., [batch size, sequence length])"
+                )
+
+            key_layer = key_layer * per_query_attn[:, None, -kv_seq_len:, None]
+
+        # linear attention
+        key_value = torch.matmul(key_layer.transpose(-1, -2), value_layer)
+        norm = torch.matmul(query_layer, key_layer.sum(dim=-2).unsqueeze(-1)).clamp(min=self.eps)
+        context_layer = torch.matmul(query_layer, key_value) / norm
+
+        if head_mask is not None:
+            context_layer = context_layer * head_mask
+
+        if output_attentions:
+            logger.warning(
+                "Outputting attentions in linear attention negates the efficiency gains! Only use for visualization/debugging."
+            )
+            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+            if attention_mask is not None:
+                attention_scores = attention_scores * attention_mask
+            attention_probs = nn.functional.normalize(attention_scores, p=1, dim=-1, eps=self.eps)
+            if head_mask is not None:
+                attention_probs = attention_probs * head_mask
+            # recompute context_layer for grad
+            context_layer = torch.matmul(attention_probs, value_layer)
+
+        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,)
+
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class MolformerSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        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)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class MolformerAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = MolformerSelfAttention(config)
+        self.output = MolformerSelfOutput(config)
+        self.pruned_heads = set()
+
+    # Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
+    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,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            position_ids,
+            head_mask,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class MolformerIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class MolformerOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        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)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class MolformerLayer(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 = MolformerAttention(config)
+        self.intermediate = MolformerIntermediate(config)
+        self.output = MolformerOutput(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            position_ids,
+            head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        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
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class MolformerEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([MolformerLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions 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
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    layer_head_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    layer_head_mask,
+                    output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    all_hidden_states,
+                    all_self_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform
+class MolformerPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        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
+
+
+class MolformerLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = MolformerPredictionHeadTransform(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaPreTrainedModel with Roberta->Molformer,roberta->molformer
+class MolformerPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = MolformerConfig
+    base_model_prefix = "molformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MolformerEmbeddings", "MolformerSelfAttention"]
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, MolformerEncoder):
+            module.gradient_checkpointing = value
+
+
+def masked_avg_pool1d(hidden_states, attention_mask, eps=1e-9):
+    attention_mask = attention_mask.unsqueeze(-1).expand_as(hidden_states).float()
+    sum_embeddings = torch.sum(hidden_states * attention_mask, dim=1)
+    sum_mask = torch.clamp(attention_mask.sum(dim=1), min=eps)
+    embedding = sum_embeddings / sum_mask
+    return embedding
+
+
+MOLFORMER_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
+    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`MolformerConfig`]): 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.
+"""
+
+MOLFORMER_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)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 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 Molformer Model transformer outputting raw hidden-states without any specific head on top.",
+    MOLFORMER_START_DOCSTRING,
+    """
+        add_pooling_layer (`bool`, *optional*, defaults to `True`):
+            Whether or not to apply pooling layer.
+    """,
+)
+class MolformerModel(MolformerPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention).
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = MolformerEmbeddings(config)
+        self.encoder = MolformerEncoder(config)
+
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.pooler = masked_avg_pool1d if add_pooling_layer else None
+
+        # 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(MOLFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[BaseModelOutputWithPooling, Tuple[torch.Tensor]]:
+        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 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
+
+        if position_ids is None:
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length), 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)
+
+        # 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)
+
+        embedding_output = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.LayerNorm(sequence_output)
+        pooled_output = self.pooler(sequence_output, attention_mask) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings("""Molformer Model with a `language modeling` head on top.""", MOLFORMER_START_DOCSTRING)
+class MolformerForMaskedLM(MolformerPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with Roberta->Molformer,roberta->molformer,LMHead->LMPredictionHead
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `MolformerForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.molformer = MolformerModel(config, add_pooling_layer=False)
+        self.lm_head = MolformerLMPredictionHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(MOLFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+        mask="P<mask>",  # add extra token so labels line up
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        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[MaskedLMOutput, Tuple[torch.Tensor]]:
+        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.molformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(prediction_scores.device)
+            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,
+        )
+
+
+class MolformerClassificationHead(nn.Module):
+    """Head for sequence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dense2 = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(
+            config.classifier_dropout_prob
+            if config.classifier_dropout_prob is not None
+            else config.hidden_dropout_prob
+        )
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+        if isinstance(config.hidden_act, str):
+            self.classifier_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.classifier_act_fn = config.hidden_act
+        self.skip_connection = config.classifier_skip_connection
+
+    def forward(self, pooled_output):
+        hidden_state = self.dense(pooled_output)
+        hidden_state = self.dropout(hidden_state)
+        hidden_state = self.classifier_act_fn(hidden_state)
+        if self.skip_connection:
+            hidden_state = residual = hidden_state + pooled_output
+        hidden_state = self.dense2(hidden_state)
+        hidden_state = self.dropout(hidden_state)
+        hidden_state = self.classifier_act_fn(hidden_state)
+        if self.skip_connection:
+            hidden_state = hidden_state + residual
+        logits = self.out_proj(hidden_state)
+        return logits
+
+
+@add_start_docstrings(
+    """
+    Molformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+    pooled output) e.g. for MoleculeNet tasks.
+    """,
+    MOLFORMER_START_DOCSTRING,
+)
+class MolformerForSequenceClassification(MolformerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.molformer = MolformerModel(config, add_pooling_layer=True)
+        self.classifier = MolformerClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(MOLFORMER_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.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: 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[torch.Tensor], 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.molformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            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,
+        )

tokenization_molformer.py

@@ -0,0 +1,226 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+"""Tokenization classes for Molformer."""
+
+import collections
+import json
+import os
+import re
+from typing import List, Optional, Tuple
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/vocab.json",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "ibm/MoLFormer-XL-both-10pct": 202,
+}
+
+
+class MolformerTokenizer(PreTrainedTokenizer):
+    r"""
+    Construct a Molformer tokenizer. Based on regex.
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            File containing the vocabulary.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (`str`, *optional*, defaults to `"<eos>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (`str`, *optional*, defaults to `"<bos>"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (`str`, *optional*, defaults to `"<mask>"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+    """
+
+    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,
+        unk_token="<unk>",
+        sep_token="<eos>",
+        pad_token="<pad>",
+        cls_token="<bos>",
+        mask_token="<mask>",
+        **kwargs,
+    ):
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from an IBM pretrained"
+                " model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+        with open(vocab_file, encoding="utf-8") as vocab_handle:
+            self.vocab = json.load(vocab_handle)
+        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+        self.pattern = (
+            r"(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9])"
+        )
+        self.regex_tokenizer = re.compile(self.pattern)
+
+        super().__init__(
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text):
+        split_tokens = self.regex_tokenizer.findall(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        out_string = "".join(tokens).strip()
+        return out_string
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve 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` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            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:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
+        pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+        return (vocab_file,)

tokenization_molformer_fast.py

@@ -0,0 +1,153 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+"""Tokenization classes for Molformer."""
+from typing import List, Optional, Tuple
+
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import logging
+from .tokenization_molformer import MolformerTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "tokenizer_file": "tokenizer.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/vocab.json",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "ibm/MoLFormer-XL-both-10pct": 202,
+}
+
+
+class MolformerTokenizerFast(PreTrainedTokenizerFast):
+    r"""
+    Construct a "fast" Molformer tokenizer.
+
+    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+    refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`, *optional*):
+            File containing the vocabulary.
+        tokenizer_file (`str`, *optional*):
+            The path to a tokenizer file to use instead of the vocab file.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (`str`, *optional*, defaults to `"<eos>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (`str`, *optional*, defaults to `"<bos>"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (`str`, *optional*, defaults to `"<mask>"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+    """
+
+    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"]
+    slow_tokenizer_class = MolformerTokenizer
+
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        unk_token="<unk>",
+        sep_token="<eos>",
+        pad_token="<pad>",
+        cls_token="<bos>",
+        mask_token="<mask>",
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file,
+            tokenizer_file=tokenizer_file,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+        if token_ids_1 is not None:
+            output += token_ids_1 + [self.sep_token_id]
+
+        return output
+
+    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.create_token_type_ids_from_sequences
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
+        pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.save_vocabulary
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+        return tuple(files)

tokenizer.json

@@ -0,0 +1,2520 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<bos>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "<eos>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "<pad>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 3,
+      "content": "<mask>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2361,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "Sequence",
+    "pretokenizers": [
+      {
+        "type": "Split",
+        "pattern": {
+          "Regex": "(\\[[^\\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\\(|\\)|\\.|=|#|-|\\+|\\\\|\\/|:|~|@|\\?|>|\\*|\\$|\\%[0-9]{2}|[0-9])"
+        },
+        "behavior": "Removed",
+        "invert": true
+      },
+      {
+        "type": "Split",
+        "pattern": {
+          "Regex": "(\\[[^\\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\\(|\\)|\\.|=|#|-|\\+|\\\\|\\/|:|~|@|\\?|>|\\*|\\$|\\%[0-9]{2}|[0-9])"
+        },
+        "behavior": "Isolated",
+        "invert": false
+      }
+    ]
+  },
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "<bos>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<eos>",
+          "type_id": 0
+        }
+      }
+    ],
+    "pair": [
+      {
+        "SpecialToken": {
+          "id": "<bos>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<eos>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "B",
+          "type_id": 1
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<eos>",
+          "type_id": 1
+        }
+      }
+    ],
+    "special_tokens": {
+      "<bos>": {
+        "id": "<bos>",
+        "ids": [
+          0
+        ],
+        "tokens": [
+          "<bos>"
+        ]
+      },
+      "<eos>": {
+        "id": "<eos>",
+        "ids": [
+          1
+        ],
+        "tokens": [
+          "<eos>"
+        ]
+      }
+    }
+  },
+  "decoder": {
+    "type": "Fuse"
+  },
+  "model": {
+    "type": "WordLevel",
+    "vocab": {
+      "<bos>": 0,
+      "<eos>": 1,
+      "<pad>": 2,
+      "<mask>": 3,
+      "C": 4,
+      "c": 5,
+      "(": 6,
+      ")": 7,
+      "1": 8,
+      "O": 9,
+      "N": 10,
+      "2": 11,
+      "=": 12,
+      "n": 13,
+      "3": 14,
+      "[C@H]": 15,
+      "[C@@H]": 16,
+      "F": 17,
+      "S": 18,
+      "4": 19,
+      "Cl": 20,
+      "-": 21,
+      "o": 22,
+      "s": 23,
+      "[nH]": 24,
+      "#": 25,
+      "/": 26,
+      "Br": 27,
+      "[C@]": 28,
+      "[C@@]": 29,
+      "[N+]": 30,
+      "[O-]": 31,
+      "5": 32,
+      "\\": 33,
+      ".": 34,
+      "I": 35,
+      "6": 36,
+      "[S@]": 37,
+      "[S@@]": 38,
+      "P": 39,
+      "[N-]": 40,
+      "[Si]": 41,
+      "7": 42,
+      "[n+]": 43,
+      "[2H]": 44,
+      "8": 45,
+      "[NH+]": 46,
+      "B": 47,
+      "9": 48,
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+      "[126Sb]": 2074,
+      "[128Sb]": 2075,
+      "[130Sb]": 2076,
+      "[67Ge]": 2077,
+      "[68Ge]": 2078,
+      "[78Ge]": 2079,
+      "[66Ge]": 2080,
+      "[223Fr]": 2081,
+      "[132Cs]": 2082,
+      "[125Cs]": 2083,
+      "[138Cs]": 2084,
+      "[133Te]": 2085,
+      "[84Rb]": 2086,
+      "[83Rb]": 2087,
+      "[81Rb]": 2088,
+      "[142Ba]": 2089,
+      "[200Bi]": 2090,
+      "[115Sb]": 2091,
+      "[194Tl]": 2092,
+      "[70Se]": 2093,
+      "[112In]": 2094,
+      "[118Sb]": 2095,
+      "[70Ga]": 2096,
+      "[27Mg]": 2097,
+      "[202Bi]": 2098,
+      "[83Se]": 2099,
+      "[9Li]": 2100,
+      "[69As]": 2101,
+      "[79Rb]": 2102,
+      "[81Sr]": 2103,
+      "[83Sr]": 2104,
+      "[78Se]": 2105,
+      "[109In]": 2106,
+      "[29Al]": 2107,
+      "[118Sn]": 2108,
+      "[117In]": 2109,
+      "[119Sb]": 2110,
+      "[114Sn]": 2111,
+      "[138Ba]": 2112,
+      "[69Ge]": 2113,
+      "[73Ga]": 2114,
+      "[74Ge]": 2115,
+      "[206Tl]": 2116,
+      "[199Tl]": 2117,
+      "[130Cs]": 2118,
+      "[28Mg]": 2119,
+      "[116Te]": 2120,
+      "[112Sn]": 2121,
+      "[126Ba]": 2122,
+      "[211Bi]": 2123,
+      "[81Se]": 2124,
+      "[127Sn]": 2125,
+      "[143Cs]": 2126,
+      "[134Te]": 2127,
+      "[80Sr]": 2128,
+      "[45K]": 2129,
+      "[215Po]": 2130,
+      "[207Po]": 2131,
+      "[111Sn]": 2132,
+      "[211Po]": 2133,
+      "[128Ba]": 2134,
+      "[198Tl]": 2135,
+      "[227Ra]": 2136,
+      "[213Po]": 2137,
+      "[220Ra]": 2138,
+      "[128Sn]": 2139,
+      "[203Po]": 2140,
+      "[205Po]": 2141,
+      "[65Ga]": 2142,
+      "[197Tl]": 2143,
+      "[88Sr]": 2144,
+      "[110In]": 2145,
+      "[31Si]": 2146,
+      "[201Bi]": 2147,
+      "[121Te]": 2148,
+      "[205Bi]": 2149,
+      "[203Bi]": 2150,
+      "[195Tl]": 2151,
+      "[209Tl]": 2152,
+      "[110Sn]": 2153,
+      "[222Fr]": 2154,
+      "[207At]": 2155,
+      "[119In]": 2156,
+      "[As@]": 2157,
+      "[129IH]": 2158,
+      "[157Dy]": 2159,
+      "[111IH]": 2160,
+      "[230Ra]": 2161,
+      "[144Pr+3]": 2162,
+      "[SiH3+]": 2163,
+      "[3He]": 2164,
+      "[AsH5]": 2165,
+      "[72Se]": 2166,
+      "[95Tc]": 2167,
+      "[103Pd]": 2168,
+      "[121Sn+2]": 2169,
+      "[211Rn]": 2170,
+      "[38SH2]": 2171,
+      "[127IH]": 2172,
+      "[74Br-]": 2173,
+      "[133I-]": 2174,
+      "[100Tc+4]": 2175,
+      "[100Tc]": 2176,
+      "[36Cl-]": 2177,
+      "[89Y+3]": 2178,
+      "[104Rh]": 2179,
+      "[152Sm]": 2180,
+      "[226Ra]": 2181,
+      "[19FH]": 2182,
+      "[104Pd]": 2183,
+      "[148Gd]": 2184,
+      "[157Lu]": 2185,
+      "[33SH2]": 2186,
+      "[121I-]": 2187,
+      "[17FH]": 2188,
+      "[71Se]": 2189,
+      "[157Sm]": 2190,
+      "[148Tb]": 2191,
+      "[164Dy]": 2192,
+      "[15OH2]": 2193,
+      "[15O+]": 2194,
+      "[39K]": 2195,
+      "[40Ar]": 2196,
+      "[50Cr+3]": 2197,
+      "[50Cr]": 2198,
+      "[52Ti]": 2199,
+      "[103Pd+2]": 2200,
+      "[130Ba]": 2201,
+      "[142Pm]": 2202,
+      "[153Gd+3]": 2203,
+      "[151Eu]": 2204,
+      "[103Rh]": 2205,
+      "[124Xe]": 2206,
+      "[152Tb]": 2207,
+      "[17OH2]": 2208,
+      "[20Ne]": 2209,
+      "[52Fe]": 2210,
+      "[94Zr+4]": 2211,
+      "[94Zr]": 2212,
+      "[149Pr]": 2213,
+      "[16OH2]": 2214,
+      "[53Cr+6]": 2215,
+      "[53Cr]": 2216,
+      "[81Br-]": 2217,
+      "[112Pd]": 2218,
+      "[125Xe]": 2219,
+      "[155Gd]": 2220,
+      "[157Gd]": 2221,
+      "[168Yb]": 2222,
+      "[184Os]": 2223,
+      "[166Tb]": 2224,
+      "[221Fr]": 2225,
+      "[212Ra]": 2226,
+      "[75Br-]": 2227,
+      "[79Br-]": 2228,
+      "[113Ag]": 2229,
+      "[23Na]": 2230,
+      "[34Cl-]": 2231,
+      "[34ClH]": 2232,
+      "[38Cl-]": 2233,
+      "[56Fe]": 2234,
+      "[68Cu]": 2235,
+      "[77Br-]": 2236,
+      "[90Zr+4]": 2237,
+      "[90Zr]": 2238,
+      "[102Pd]": 2239,
+      "[154Eu+3]": 2240,
+      "[57Mn]": 2241,
+      "[165Tm]": 2242,
+      "[152Dy]": 2243,
+      "[217At]": 2244,
+      "[77se]": 2245,
+      "[13cH-]": 2246,
+      "[122Te]": 2247,
+      "[156Gd]": 2248,
+      "[124Te]": 2249,
+      "[53Ni]": 2250,
+      "[131Xe]": 2251,
+      "[174Hf+4]": 2252,
+      "[174Hf]": 2253,
+      "[76Se]": 2254,
+      "[168Tm]": 2255,
+      "[167Dy]": 2256,
+      "[154Gd]": 2257,
+      "[95Ru]": 2258,
+      "[210At]": 2259,
+      "[85Br]": 2260,
+      "[59Co]": 2261,
+      "[122Xe]": 2262,
+      "[27Al]": 2263,
+      "[54Cr]": 2264,
+      "[198Hg]": 2265,
+      "[85Rb+]": 2266,
+      "[214Tl]": 2267,
+      "[229Rn]": 2268,
+      "[218Pb]": 2269,
+      "[218Bi]": 2270,
+      "[167Tm+3]": 2271,
+      "[18o+]": 2272,
+      "[P@@H+]": 2273,
+      "[P@H+]": 2274,
+      "[13N+]": 2275,
+      "[212Pb+2]": 2276,
+      "[217Bi]": 2277,
+      "[249Cf+2]": 2278,
+      "[18OH3+]": 2279,
+      "[90Sr-]": 2280,
+      "[Cf+3]": 2281,
+      "[200Hg]": 2282,
+      "[86Tc]": 2283,
+      "[141Pr+3]": 2284,
+      "[141Pr]": 2285,
+      "[16nH]": 2286,
+      "[14NH4+]": 2287,
+      "[132Xe]": 2288,
+      "[83Kr]": 2289,
+      "[70Zn+2]": 2290,
+      "[137Ba+2]": 2291,
+      "[36Ar]": 2292,
+      "[38Ar]": 2293,
+      "[21Ne]": 2294,
+      "[126Xe]": 2295,
+      "[136Xe]": 2296,
+      "[128Xe]": 2297,
+      "[134Xe]": 2298,
+      "[84Kr]": 2299,
+      "[86Kr]": 2300,
+      "[78Kr]": 2301,
+      "[80Kr]": 2302,
+      "[82Kr]": 2303,
+      "[67Zn+2]": 2304,
+      "[65Cu+2]": 2305,
+      "[110Te]": 2306,
+      "[58Fe+3]": 2307,
+      "[142Nd]": 2308,
+      "[38K]": 2309,
+      "[198Au+3]": 2310,
+      "[122IH]": 2311,
+      "[38PH3]": 2312,
+      "[130I-]": 2313,
+      "[40K+]": 2314,
+      "[38K+]": 2315,
+      "[28Mg+2]": 2316,
+      "[208Tl+]": 2317,
+      "[13OH2]": 2318,
+      "[198Bi]": 2319,
+      "[192Bi]": 2320,
+      "[194Bi]": 2321,
+      "[196Bi]": 2322,
+      "[132I-]": 2323,
+      "[83Sr+2]": 2324,
+      "[169Er+3]": 2325,
+      "[122I-]": 2326,
+      "[120I-]": 2327,
+      "[92Sr+2]": 2328,
+      "[126I-]": 2329,
+      "[24Mg]": 2330,
+      "[84Sr]": 2331,
+      "[118Pd+2]": 2332,
+      "[118Pd]": 2333,
+      "[AsH4]": 2334,
+      "[127I-]": 2335,
+      "[9C-]": 2336,
+      "[11CH3+]": 2337,
+      "[17B]": 2338,
+      "[7B]": 2339,
+      "[4HH]": 2340,
+      "[18C-]": 2341,
+      "[22CH3-]": 2342,
+      "[22CH4]": 2343,
+      "[17C-]": 2344,
+      "[15CH3]": 2345,
+      "[16CH3]": 2346,
+      "[11NH3]": 2347,
+      "[21NH3]": 2348,
+      "[11N-]": 2349,
+      "[11NH]": 2350,
+      "[16CH]": 2351,
+      "[17CH2]": 2352,
+      "[99Ru+2]": 2353,
+      "[181Ta+2]": 2354,
+      "[181Ta]": 2355,
+      "[20CH]": 2356,
+      "[32PH2]": 2357,
+      "[55Fe+2]": 2358,
+      "[SH3]": 2359,
+      "[S@H]": 2360,
+      "<unk>": 2361
+    },
+    "unk_token": "<unk>"
+  }
+}

tokenizer_config.json

@@ -41,6 +41,12 @@
       "special": true
     }
   },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_molformer.MolformerTokenizer",
+      "tokenization_molformer_fast.MolformerTokenizerFast"
+    ]
+  },
   "clean_up_tokenization_spaces": true,
   "cls_token": "<bos>",
   "mask_token": "<mask>",