from collections.abc import Sequence 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, MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel from .configuration_prosst import ProSSTConfig import torch.nn.functional as F def build_relative_position(query_size, key_size, device): """ Build relative position according to the query and key We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - P_k\\) Args: query_size (int): the length of query key_size (int): the length of key Return: `torch.LongTensor`: A tensor with shape [1, query_size, key_size] """ q_ids = torch.arange(query_size, dtype=torch.long, device=device) k_ids = torch.arange(key_size, dtype=torch.long, device=device) rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1) rel_pos_ids = rel_pos_ids[:query_size, :] rel_pos_ids = rel_pos_ids.unsqueeze(0) return rel_pos_ids @torch.jit.script def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): return c2p_pos.expand( [ query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1), ] ) @torch.jit.script def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): return c2p_pos.expand( [ query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2), ] ) @torch.jit.script def pos_dynamic_expand(pos_index, p2c_att, key_layer): return pos_index.expand( p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)) ) def rotate_half(x): x1, x2 = x.chunk(2, dim=-1) return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(x, cos, sin): cos = cos[:, :, : x.shape[-2], :] sin = sin[:, :, : x.shape[-2], :] return (x * cos) + (rotate_half(x) * sin) class RotaryEmbedding(torch.nn.Module): """ Rotary position embeddings based on those in [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation matrices which depend on their relative positions. """ def __init__(self, dim: int): super().__init__() # Generate and save the inverse frequency buffer (non trainable) inv_freq = 1.0 / ( 10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim) ) inv_freq = inv_freq self.register_buffer("inv_freq", inv_freq) self._seq_len_cached = None self._cos_cached = None self._sin_cached = None def _update_cos_sin_tables(self, x, seq_dimension=2): seq_len = x.shape[seq_dimension] # Reset the tables if the sequence length has changed, # or if we're on a new device (possibly due to tracing for instance) if seq_len != self._seq_len_cached or self._cos_cached.device != x.device: self._seq_len_cached = seq_len t = torch.arange(x.shape[seq_dimension], device=x.device).type_as( self.inv_freq ) freqs = torch.outer(t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self._cos_cached = emb.cos()[None, None, :, :] self._sin_cached = emb.sin()[None, None, :, :] return self._cos_cached, self._sin_cached def forward( self, q: torch.Tensor, k: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: self._cos_cached, self._sin_cached = self._update_cos_sin_tables( k, seq_dimension=-2 ) return ( apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached), apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached), ) class MaskedConv1d(nn.Conv1d): """A masked 1-dimensional convolution layer. Takes the same arguments as torch.nn.Conv1D, except that the padding is set automatically. Shape: Input: (N, L, in_channels) input_mask: (N, L, 1), optional Output: (N, L, out_channels) """ def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, dilation: int = 1, groups: int = 1, bias: bool = True, ): """ :param in_channels: input channels :param out_channels: output channels :param kernel_size: the kernel width :param stride: filter shift :param dilation: dilation factor :param groups: perform depth-wise convolutions :param bias: adds learnable bias to output """ padding = dilation * (kernel_size - 1) // 2 super().__init__( in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, groups=groups, bias=bias, padding=padding, ) def forward(self, x, input_mask=None): if input_mask is not None: x = x * input_mask return super().forward(x.transpose(1, 2)).transpose(1, 2) class Attention1dPooling(nn.Module): def __init__(self, config): super().__init__() self.layer = MaskedConv1d(config.hidden_size, 1, 1) def forward(self, x, input_mask=None): batch_szie = x.shape[0] attn = self.layer(x) attn = attn.view(batch_szie, -1) if input_mask is not None: attn = attn.masked_fill_( ~input_mask.view(batch_szie, -1).bool(), float("-inf") ) attn = F.softmax(attn, dim=-1).view(batch_szie, -1, 1) out = (attn * x).sum(dim=1) return out class MeanPooling(nn.Module): """Mean Pooling for sentence-level classification tasks.""" def __init__(self): super().__init__() def forward(self, features, input_mask=None): if input_mask is not None: # Applying input_mask to zero out masked values masked_features = features * input_mask.unsqueeze(2) sum_features = torch.sum(masked_features, dim=1) mean_pooled_features = sum_features / input_mask.sum(dim=1, keepdim=True) else: mean_pooled_features = torch.mean(features, dim=1) return mean_pooled_features class ContextPooler(nn.Module): def __init__(self, config): super().__init__() scale_hidden = getattr(config, "scale_hidden", 1) if config.pooling_head == "mean": self.mean_pooling = MeanPooling() elif config.pooling_head == "attention": self.mean_pooling = Attention1dPooling(config) self.dense = nn.Linear( config.pooler_hidden_size, scale_hidden * config.pooler_hidden_size ) self.dropout = nn.Dropout(config.pooler_dropout) self.config = config def forward(self, hidden_states, input_mask=None): # We "pool" the model by simply taking the hidden state corresponding # to the first token. context_token = self.mean_pooling(hidden_states, input_mask) context_token = self.dropout(context_token) pooled_output = self.dense(context_token) pooled_output = torch.tanh(pooled_output) return pooled_output @property def output_dim(self): return self.config.hidden_size class ProSSTLayerNorm(nn.Module): """LayerNorm module in the TF style (epsilon inside the square root).""" def __init__(self, size, eps=1e-12): super().__init__() self.weight = nn.Parameter(torch.ones(size)) self.bias = nn.Parameter(torch.zeros(size)) self.variance_epsilon = eps def forward(self, hidden_states): input_type = hidden_states.dtype hidden_states = hidden_states.float() mean = hidden_states.mean(-1, keepdim=True) variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) hidden_states = (hidden_states - mean) / torch.sqrt( variance + self.variance_epsilon ) hidden_states = hidden_states.to(input_type) y = self.weight * hidden_states + self.bias return y class DisentangledSelfAttention(nn.Module): def __init__(self, config: ProSSTConfig): super().__init__() if config.hidden_size % config.num_attention_heads != 0: 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 # Q, K, V projection layers 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) # AA->SS, AA->POS, SS->AA, POS->AA and AA->AA attention layers self.pos_att_type = ( config.pos_att_type if config.pos_att_type is not None else [] ) self.relative_attention = getattr(config, "relative_attention", False) self.position_embedding_type = getattr( config, "position_embedding_type", "relative" ) if self.position_embedding_type == "rotary": self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size) if self.relative_attention: if "aa2ss" in self.pos_att_type: self.ss_proj = nn.Linear( config.hidden_size, self.all_head_size, bias=False ) if "ss2aa" in self.pos_att_type: self.ss_q_proj = nn.Linear(config.hidden_size, self.all_head_size) elif self.position_embedding_type == "relative": if self.relative_attention: self.max_relative_positions = getattr( config, "max_relative_positions", -1 ) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.pos_dropout = nn.Dropout(config.hidden_dropout_prob) # amino acid to position if "aa2pos" in self.pos_att_type: self.pos_proj = nn.Linear( config.hidden_size, self.all_head_size, bias=False ) # Key if "pos2aa" in self.pos_att_type: self.pos_q_proj = nn.Linear( config.hidden_size, self.all_head_size ) # Query if "aa2ss" in self.pos_att_type: self.ss_proj = nn.Linear( config.hidden_size, self.all_head_size, bias=False ) if "ss2aa" in self.pos_att_type: self.ss_q_proj = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): # x [batch_size, seq_len, all_head_size] new_x_shape = x.size()[:-1] + (self.num_attention_heads, -1) # x [batch_size, seq_len, num_attention_heads, attention_head_size] x = x.view(new_x_shape) # x [batch_size, num_attention_heads, seq_len, attention_head_size] return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ss_hidden_states=None, ): 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)) if self.position_embedding_type == "rotary": query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer) rel_att = None scale_factor = 1 + len(self.pos_att_type) scale = torch.sqrt( torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor ) query_layer = query_layer / scale.to(dtype=query_layer.dtype) # [batch_size, num_attention_heads, seq_len, seq_len] attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.relative_attention: if self.position_embedding_type == "relative": rel_embeddings = self.pos_dropout(rel_embeddings) rel_att = self.disentangled_att_bias( query_layer, key_layer, relative_pos, rel_embeddings, scale_factor, ss_hidden_states, ) if rel_att is not None: attention_scores = attention_scores + rel_att rmask = ~(attention_mask.to(torch.bool)) attention_probs = attention_scores.masked_fill(rmask, float("-inf")) attention_probs = torch.softmax(attention_probs, -1) attention_probs = attention_probs.masked_fill(rmask, 0.0) # attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) attention_probs = self.dropout(attention_probs) 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] + (-1,) context_layer = context_layer.view(new_context_layer_shape) if output_attentions: return (context_layer, attention_probs) else: return context_layer def disentangled_att_bias( self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor, ss_hidden_states, ): if self.position_embedding_type == "relative": if relative_pos is None: q = query_layer.size(-2) relative_pos = build_relative_position( q, key_layer.size(-2), query_layer.device ) if relative_pos.dim() == 2: relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) elif relative_pos.dim() == 3: relative_pos = relative_pos.unsqueeze(1) # bxhxqxk elif relative_pos.dim() != 4: raise ValueError( f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}" ) att_span = min( max(query_layer.size(-2), key_layer.size(-2)), self.max_relative_positions, ) relative_pos = relative_pos.long().to(query_layer.device) rel_embeddings = rel_embeddings[ self.max_relative_positions - att_span : self.max_relative_positions + att_span, :, ].unsqueeze(0) score = 0 if "aa2pos" in self.pos_att_type: pos_key_layer = self.pos_proj(rel_embeddings) pos_key_layer = self.transpose_for_scores(pos_key_layer) aa2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2)) aa2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1) aa2p_att = torch.gather( aa2p_att, dim=-1, index=c2p_dynamic_expand(aa2p_pos, query_layer, relative_pos), ) score += aa2p_att if "pos2aa" in self.pos_att_type: pos_query_layer = self.pos_q_proj(rel_embeddings) pos_query_layer = self.transpose_for_scores(pos_query_layer) pos_query_layer /= torch.sqrt( torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor ) if query_layer.size(-2) != key_layer.size(-2): r_pos = build_relative_position( key_layer.size(-2), key_layer.size(-2), query_layer.device ) else: r_pos = relative_pos p2aa_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) p2aa_att = torch.matmul( key_layer, pos_query_layer.transpose(-1, -2).to(dtype=key_layer.dtype), ) p2aa_att = torch.gather( p2aa_att, dim=-1, index=p2c_dynamic_expand(p2aa_pos, query_layer, key_layer), ).transpose(-1, -2) if query_layer.size(-2) != key_layer.size(-2): pos_index = relative_pos[:, :, :, 0].unsqueeze(-1) p2aa_att = torch.gather( p2aa_att, dim=-2, index=pos_dynamic_expand(pos_index, p2aa_att, key_layer), ) score += p2aa_att # content -> structure if "aa2ss" in self.pos_att_type: assert ss_hidden_states is not None ss_key_layer = self.ss_proj(ss_hidden_states) ss_key_layer = self.transpose_for_scores(ss_key_layer) # [batch_size, num_attention_heads, seq_len, seq_len] aa2ss_att = torch.matmul(query_layer, ss_key_layer.transpose(-1, -2)) score += aa2ss_att if "ss2aa" in self.pos_att_type: assert ss_hidden_states is not None ss_query_layer = self.ss_q_proj(ss_hidden_states) ss_query_layer = self.transpose_for_scores(ss_query_layer) ss_query_layer /= torch.sqrt( torch.tensor(ss_query_layer.size(-1), dtype=torch.float) * scale_factor ) ss2aa_att = torch.matmul( key_layer, query_layer.transpose(-1, -2).to(dtype=key_layer.dtype) ) score += ss2aa_att return score elif self.position_embedding_type == "rotary": score = 0 if "aa2ss" in self.pos_att_type: assert ss_hidden_states is not None ss_key_layer = self.ss_proj(ss_hidden_states) ss_key_layer = self.transpose_for_scores(ss_key_layer) aa2ss_att = torch.matmul(query_layer, ss_key_layer.transpose(-1, -2)) score += aa2ss_att if "ss2aa" in self.pos_att_type: assert ss_hidden_states is not None ss_query_layer = self.ss_q_proj(ss_hidden_states) ss_query_layer = self.transpose_for_scores(ss_query_layer) ss_query_layer /= torch.sqrt( torch.tensor(ss_query_layer.size(-1), dtype=torch.float) * scale_factor ) ss2aa_att = torch.matmul( key_layer, query_layer.transpose(-1, -2).to(dtype=key_layer.dtype) ) score += ss2aa_att return score class ProSSTSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = ProSSTLayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class ProSSTAttention(nn.Module): def __init__(self, config): super().__init__() self.self = DisentangledSelfAttention(config) self.output = ProSSTSelfOutput(config) self.config = config def forward( self, hidden_states, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ss_hidden_states=None, ): self_output = self.self( hidden_states, attention_mask, output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ss_hidden_states=ss_hidden_states, ) if output_attentions: self_output, att_matrix = self_output if query_states is None: query_states = hidden_states attention_output = self.output(self_output, query_states) if output_attentions: return (attention_output, att_matrix) else: return attention_output class ProSSTIntermediate(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 class ProSSTOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = ProSSTLayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def forward(self, hidden_states, input_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 ProSSTLayer(nn.Module): def __init__(self, config): super().__init__() self.attention = ProSSTAttention(config) self.intermediate = ProSSTIntermediate(config) self.output = ProSSTOutput(config) def forward( self, hidden_states, attention_mask, query_states=None, relative_pos=None, rel_embeddings=None, output_attentions=False, ss_hidden_states=None, ): attention_output = self.attention( hidden_states, attention_mask, output_attentions=output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ss_hidden_states=ss_hidden_states, ) if output_attentions: attention_output, att_matrix = attention_output intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) if output_attentions: return (layer_output, att_matrix) else: return layer_output class ProSSTEncoder(nn.Module): """Modified BertEncoder with relative position bias support""" def __init__(self, config): super().__init__() self.layer = nn.ModuleList( [ProSSTLayer(config) for _ in range(config.num_hidden_layers)] ) self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.rel_embeddings = nn.Embedding( self.max_relative_positions * 2, config.hidden_size ) self.gradient_checkpointing = False def get_rel_embedding(self): rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None return rel_embeddings def get_attention_mask(self, attention_mask): if attention_mask.dim() <= 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attention_mask = extended_attention_mask * extended_attention_mask.squeeze( -2 ).unsqueeze(-1) elif attention_mask.dim() == 3: attention_mask = attention_mask.unsqueeze(1) return attention_mask def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): if self.relative_attention and relative_pos is None: q = ( query_states.size(-2) if query_states is not None else hidden_states.size(-2) ) relative_pos = build_relative_position( q, hidden_states.size(-2), hidden_states.device ) return relative_pos def forward( self, hidden_states, attention_mask, output_hidden_states=True, output_attentions=False, query_states=None, relative_pos=None, ss_hidden_states=None, return_dict=True, ): attention_mask = self.get_attention_mask(attention_mask) relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None if isinstance(hidden_states, Sequence): next_kv = hidden_states[0] else: next_kv = hidden_states rel_embeddings = self.get_rel_embedding() for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), next_kv, attention_mask, query_states, relative_pos, rel_embeddings, ss_hidden_states, ) else: hidden_states = layer_module( next_kv, attention_mask, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, output_attentions=output_attentions, ss_hidden_states=ss_hidden_states, ) if output_attentions: hidden_states, att_m = hidden_states if query_states is not None: query_states = hidden_states if isinstance(hidden_states, Sequence): next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None else: next_kv = hidden_states if output_attentions: all_attentions = all_attentions + (att_m,) 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_attentions] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions, ) class ProSSTEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() pad_token_id = getattr(config, "pad_token_id", 0) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.word_embeddings = nn.Embedding( config.vocab_size, self.embedding_size, padding_idx=pad_token_id ) self.position_biased_input = getattr(config, "position_biased_input", False) if not self.position_biased_input: self.position_embeddings = None else: # assert getattr(config, "position_embedding_type", "relative") == "absolute" self.position_embeddings = nn.Embedding( config.max_position_embeddings, self.embedding_size ) if config.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding( config.type_vocab_size, self.embedding_size ) if config.ss_vocab_size > 0: self.ss_embeddings = nn.Embedding(config.ss_vocab_size, self.embedding_size) self.ss_layer_norm = ProSSTLayerNorm( config.hidden_size, config.layer_norm_eps ) if self.embedding_size != config.hidden_size: self.embed_proj = nn.Linear( self.embedding_size, config.hidden_size, bias=False ) self.LayerNorm = ProSSTLayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config # position_ids (1, len position emb) is contiguous in memory and exported when serialized if self.position_biased_input: self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False, ) def forward( self, input_ids=None, ss_input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None, ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None and self.position_biased_input: position_ids = self.position_ids[:, :seq_length] if seq_length > position_ids.size(1): zero_padding = ( torch.zeros( (input_shape[0], seq_length - position_ids.size(1)), dtype=torch.long, device=position_ids.device, ) + 2047 ) position_ids = torch.cat([position_ids, zero_padding], dim=1) if token_type_ids is None: token_type_ids = torch.zeros( input_shape, dtype=torch.long, device=self.position_ids.device ) if inputs_embeds is None: if self.config.token_dropout: inputs_embeds = self.word_embeddings(input_ids) inputs_embeds.masked_fill_( (input_ids == self.config.mask_token_id).unsqueeze(-1), 0.0 ) mask_ratio_train = self.config.mlm_probability * 0.8 src_lengths = mask.sum(dim=-1) mask_ratio_observed = (input_ids == self.config.mask_token_id).sum( -1 ).to(inputs_embeds.dtype) / src_lengths inputs_embeds = ( inputs_embeds * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None] ) else: inputs_embeds = self.word_embeddings(input_ids) if self.position_embeddings is not None and self.position_biased_input: position_embeddings = self.position_embeddings(position_ids.long()) else: position_embeddings = torch.zeros_like(inputs_embeds) embeddings = inputs_embeds if self.position_biased_input: embeddings += position_embeddings if self.config.type_vocab_size > 0: token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings += token_type_embeddings if self.embedding_size != self.config.hidden_size: embeddings = self.embed_proj(embeddings) embeddings = self.LayerNorm(embeddings) if mask is not None: if mask.dim() != embeddings.dim(): if mask.dim() == 4: mask = mask.squeeze(1).squeeze(1) mask = mask.unsqueeze(2) mask = mask.to(embeddings.dtype) embeddings = embeddings * mask embeddings = self.dropout(embeddings) if self.config.ss_vocab_size > 0: ss_embeddings = self.ss_embeddings(ss_input_ids) ss_embeddings = self.ss_layer_norm(ss_embeddings) if mask is not None: if mask.dim() != ss_embeddings.dim(): if mask.dim() == 4: mask = mask.squeeze(1).squeeze(1) mask = mask.unsqueeze(2) mask = mask.to(ss_embeddings.dtype) ss_embeddings = ss_embeddings * mask ss_embeddings = self.dropout(ss_embeddings) return embeddings, ss_embeddings return embeddings, None class ProSSTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ProSSTConfig base_model_prefix = "ProSST" _keys_to_ignore_on_load_unexpected = ["position_embeddings"] supports_gradient_checkpointing = True 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_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, ProSSTEncoder): module.gradient_checkpointing = value class ProSSTModel(ProSSTPreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = ProSSTEmbeddings(config) self.encoder = ProSSTEncoder(config) self.config = config # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, new_embeddings): self.embeddings.word_embeddings = new_embeddings 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 """ raise NotImplementedError( "The prune function is not implemented in DeBERTa model." ) def forward( self, input_ids: Optional[torch.Tensor] = None, ss_input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) embedding_output, ss_embeddings = self.embeddings( input_ids=input_ids, ss_input_ids=ss_input_ids, token_type_ids=token_type_ids, position_ids=position_ids, mask=attention_mask, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict, ss_hidden_states=ss_embeddings, ) encoded_layers = encoder_outputs[1] sequence_output = encoded_layers[-1] if not return_dict: return (sequence_output,) + encoder_outputs[ (1 if output_hidden_states else 2) : ] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=( encoder_outputs.hidden_states if output_hidden_states else None ), attentions=encoder_outputs.attentions, ) class ProSSTPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.dense = nn.Linear(config.hidden_size, self.embedding_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(self.embedding_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class ProSSTLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = ProSSTPredictionHeadTransform(config) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(self.embedding_size, config.vocab_size, bias=False) # self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` # self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class ProSSTOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = ProSSTLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class ProSSTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ProSSTConfig base_model_prefix = "ProSST" _keys_to_ignore_on_load_unexpected = ["position_embeddings"] supports_gradient_checkpointing = True 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_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, ProSSTEncoder): module.gradient_checkpointing = value class ProSSTForMaskedLM(ProSSTPreTrainedModel): _tied_weights_keys = [ "cls.predictions.decoder.weight", "cls.predictions.decoder.bias", ] def __init__(self, config): super().__init__(config) self.prosst = ProSSTModel(config) self.cls = ProSSTOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.prosst.embeddings.word_embeddings def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings def forward( self, input_ids: Optional[torch.Tensor] = None, ss_input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) outputs = self.prosst( input_ids, ss_input_ids=ss_input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, 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.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct( prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) ) if not return_dict: output = (prediction_scores,) + outputs[1:] 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 ProSSTForSequenceClassification(ProSSTPreTrainedModel): def __init__(self, config): super().__init__(config) num_labels = getattr(config, "num_labels", 2) self.num_labels = num_labels self.scale_hidden = getattr(config, "scale_hidden", 1) self.prosst = ProSSTModel(config) self.pooler = ContextPooler(config) output_dim = self.pooler.output_dim * self.scale_hidden self.classifier = nn.Linear(output_dim, num_labels) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out self.dropout = nn.Dropout(drop_out) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.prosst.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.prosst.set_input_embeddings(new_embeddings) def forward( self, input_ids: Optional[torch.Tensor] = None, ss_input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) outputs = self.prosst( input_ids, ss_input_ids=ss_input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pooler(encoder_layer, attention_mask) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: # regression task loss_fn = nn.MSELoss() logits = logits.view(-1).to(labels.dtype) loss = loss_fn(logits, labels.view(-1)) elif labels.dim() == 1 or labels.size(-1) == 1: label_index = (labels >= 0).nonzero() labels = labels.long() if label_index.size(0) > 0: labeled_logits = torch.gather( logits, 0, label_index.expand(label_index.size(0), logits.size(1)), ) labels = torch.gather(labels, 0, label_index.view(-1)) loss_fct = CrossEntropyLoss() loss = loss_fct( labeled_logits.view(-1, self.num_labels).float(), labels.view(-1), ) else: loss = torch.tensor(0).to(logits) else: log_softmax = nn.LogSoftmax(-1) loss = -((log_softmax(logits) * labels).sum(-1)).mean() elif 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 == "binary_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits.squeeze(), labels.squeeze().to(logits.dtype)) 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.to(logits.dtype)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ProSSTForTokenClassification(ProSSTPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.prosst = ProSSTModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) outputs = self.prosst( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) ProSSTModel.register_for_auto_class("AutoModel") ProSSTForMaskedLM.register_for_auto_class("AutoModelForMaskedLM") ProSSTForSequenceClassification.register_for_auto_class( "AutoModelForSequenceClassification" ) ProSSTForTokenClassification.register_for_auto_class("AutoModelForTokenClassification")