MoLFormer-XL-both-10pct / modeling_molformer.py

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	# 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,
	)