from logging import warn from transformers.models.distilbert.modeling_distilbert import * import torch import torch.nn as nn from transformers.models.distilbert.configuration_distilbert import DistilBertConfig import sys AUTO_MAP = { "AutoModel": "modeling_lsg_distilbert.LSGDistilBertModel", "AutoModelForMaskedLM": "modeling_lsg_distilbert.LSGDistilBertForMaskedLM", "AutoModelForMultipleChoice": "modeling_lsg_distilbert.LSGDistilBertForMultipleChoice", "AutoModelForQuestionAnswering": "modeling_lsg_distilbert.LSGDistilBertForQuestionAnswering", "AutoModelForSequenceClassification": "modeling_lsg_distilbert.LSGDistilBertForSequenceClassification", "AutoModelForTokenClassification": "modeling_lsg_distilbert.LSGDistilBertForTokenClassification" } class LSGDistilBertConfig(DistilBertConfig): base_model_prefix = "lsg" model_type = "distilbert" def __init__( self, adaptive=True, base_model_prefix="lsg", block_size=128, lsh_num_pre_rounds=1, mask_first_token=False, num_global_tokens=1, pool_with_global=True, sparse_block_size=128, sparsity_factor=2, sparsity_type="norm", **kwargs ): """Constructs LSGDistilBertConfig.""" super().__init__(**kwargs) self.adaptive = adaptive self.auto_map = AUTO_MAP self.base_model_prefix = base_model_prefix self.block_size = block_size self.lsh_num_pre_rounds = lsh_num_pre_rounds self.mask_first_token = mask_first_token self.num_global_tokens = num_global_tokens self.pool_with_global = pool_with_global self.sparse_block_size = sparse_block_size self.sparsity_factor = sparsity_factor self.sparsity_type = sparsity_type if sparsity_type not in [None, "none", "norm", "lsh", "pooling", "stride", "block_stride"]: logger.warning( "[WARNING CONFIG]: sparsity_mode not in [None, 'none', 'norm', 'lsh', 'pooling', 'stride', 'block_stride'], \ setting sparsity_type=None, computation will skip sparse attention") self.sparsity_type = None if self.sparsity_type in ["stride", "block_stride"]: if self.sparsity_factor > self.encoder_attention_heads: logger.warning( "[WARNING CONFIG]: sparsity_factor > encoder_attention_heads is not recommended for stride/block_stride sparsity" ) if self.num_global_tokens < 1: logger.warning( "[WARNING CONFIG]: num_global_tokens < 1 is not compatible, setting num_global_tokens=1" ) self.num_global_tokens = 1 elif self.num_global_tokens > 512: logger.warning( "[WARNING CONFIG]: num_global_tokens > 512 is not allowed, setting num_global_tokens=512" ) self.num_global_tokens = 512 if self.sparsity_factor > 0: assert self.block_size % self.sparsity_factor == 0, "[ERROR CONFIG]: block_size must be divisible by sparsity_factor" assert self.block_size//self.sparsity_factor >= 1, "[ERROR CONFIG]: make sure block_size >= sparsity_factor" if self.mask_first_token and not pool_with_global: logger.warning( "[WARNING CONFIG]: pool_with_global==False is not compatible with mask_first_token==True. Setting pool_with_global to True.") self.pool_with_global = True if hasattr(self, "position_embedding_type"): if self.position_embedding_type != "absolute": logger.warning( "[WARNING CONFIG]: LSG Attention is not compatible with relative positional embedding and will skip its computation. Set position_embedding_type='absolute' to remove this warning.") class LSGEmbeddings(Embeddings): def __init__(self, config): super().__init__(config) self.num_global_tokens = config.num_global_tokens # Hardcoded but partially trained self.global_embeddings = nn.Embedding(512, embedding_dim=config.dim, ) self.block_size = config.block_size def forward(self, input_ids, inputs_embeds=None): """ Parameters: input_ids: torch.tensor(bs, max_seq_length) The token ids to embed. Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type embeddings) """ bs, seq_length = input_ids.shape[:2] if input_ids is not None else inputs_embeds.shape[:2] # Setting the position-ids to the registered buffer in constructor, it helps # when tracing the model without passing position-ids, solves # isues similar to issue #5664 if hasattr(self, "position_ids"): position_ids = self.position_ids[:, :seq_length] else: position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) # (max_seq_length) position_ids = position_ids.unsqueeze(0).expand(bs, seq_length) # (bs, max_seq_length) word_embeddings = self.word_embeddings(input_ids) if input_ids is not None else inputs_embeds position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim) word_embeddings = word_embeddings + position_embeddings # (bs, max_seq_length, dim) #if self.num_global_tokens < 0: n, t, d = word_embeddings.size() # Add global_tokens indexes = torch.arange(self.num_global_tokens, device=word_embeddings.device).reshape(1, -1) global_embeddings = self.global_embeddings(indexes) word_embeddings = torch.cat([global_embeddings.expand(n, -1, d), word_embeddings], dim=-2) word_embeddings = self.LayerNorm(word_embeddings) # (bs, max_seq_length, dim) word_embeddings = self.dropout(word_embeddings) # (bs, max_seq_length, dim) return word_embeddings class BaseSelfAttention(nn.Module): def init_modules(self, config): if config.dim % config.n_heads != 0 and not hasattr( config, "embedding_size" ): raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.dim, config.n_heads) ) self.n_heads = config.n_heads self.attention_head_size = int(config.dim / config.n_heads) self.all_head_size = self.n_heads * self.attention_head_size self.q_lin = nn.Linear(in_features=config.dim, out_features=config.dim) self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim) self.v_lin = nn.Linear(in_features=config.dim, out_features=config.dim) self.out_lin = nn.Linear(in_features=config.dim, out_features=config.dim) self.dropout = nn.Dropout(config.attention_dropout) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + ( self.n_heads, self.attention_head_size, ) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def reshape_output(self, context_layer): context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) return context_layer.view(*new_context_layer_shape) def project_QKV(self, hidden_states): query_layer = self.transpose_for_scores(self.q_lin(hidden_states)) key_layer = self.transpose_for_scores(self.k_lin(hidden_states)) value_layer = self.transpose_for_scores(self.v_lin(hidden_states)) return query_layer, key_layer, value_layer class BaseAttentionProduct(nn.Module): def __init__(self, config): """ Compute attention: softmax(Q @ K.T) @ V """ super().__init__() self.dropout = nn.Dropout(config.attention_dropout) def forward(self, query_layer, key_layer, value_layer, attention_mask=None): d = query_layer.shape[-1] # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = query_layer @ key_layer.transpose(-1, -2) / math.sqrt(d) del query_layer del key_layer if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) attention_scores = attention_scores + attention_mask del attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. context_layer = self.dropout(attention_probs) @ value_layer return context_layer class CausalAttentionProduct(nn.Module): def __init__(self, config): """ Compute attention: softmax(Q @ K.T) @ V """ super().__init__() self.dropout = nn.Dropout(config.attention_dropout) self.block_size = config.block_size def forward(self, query_layer, key_layer, value_layer, attention_mask=None, causal_shape=None): d = query_layer.shape[-1] # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = query_layer @ key_layer.transpose(-1, -2) / math.sqrt(d) del query_layer del key_layer if attention_mask is not None: # Add causal mask causal_shape = (self.block_size, self.block_size) if causal_shape is None else causal_shape causal_mask = torch.tril( torch.ones(*causal_shape, device=attention_mask.device, dtype=attention_scores.dtype), diagonal=-1 ) # Min value dtype_min = torch.tensor( torch.finfo(attention_scores.dtype).min, device=attention_scores.device, dtype=attention_scores.dtype ) # Build causal + attention_mask causal_mask = torch.nn.functional.pad(causal_mask.T * dtype_min, (attention_mask.size()[-1] - self.block_size, 0), value=0) attention_mask = torch.max(attention_mask + causal_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0), dtype_min) attention_scores = attention_scores + attention_mask del attention_mask del causal_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. context_layer = self.dropout(attention_probs) @ value_layer return context_layer class LSGAttentionProduct(nn.Module): def __init__(self, config, block_size=None, sparse_block_size=None, sparsity_factor=4, is_causal=False): """ Compute block or overlapping blocks attention products """ super().__init__() self.block_size = block_size self.sparse_block_size = sparse_block_size self.sparsity_factor = sparsity_factor self.is_causal = is_causal if self.block_size is None: self.block_size = config.block_size if self.sparse_block_size is None: self.sparse_block_size = config.sparse_block_size # Shape of blocks self.local_shapes = (self.block_size*3, self.block_size) if self.sparse_block_size and self.sparsity_factor > 0: self.sparse_shapes = (self.sparse_block_size*3, self.block_size//self.sparsity_factor) if is_causal: self.attention = CausalAttentionProduct(config) else: self.attention = BaseAttentionProduct(config) def build_lsg_inputs(self, hidden_states, sparse_hidden_states, global_hidden_states, is_attn_mask=False): # Build local tokens local_hidden_states = self.reshape_to_local_block(hidden_states, is_attn_mask) del hidden_states # Build sparse tokens if sparse_hidden_states is not None: sparse_hidden_states = self.reshape_to_sparse_block(sparse_hidden_states, is_attn_mask) return self.cat_global_sparse_local_tokens(global_hidden_states, sparse_hidden_states, local_hidden_states) def forward( self, query_layer, key_layer, value_layer, attention_mask=None, sparse_key=None, sparse_value=None, sparse_mask=None, global_key=None, global_value=None, global_mask=None ): # Input batch, heads, length, dim n, h, t, d = query_layer.size() n_blocks = t // self.block_size assert t % self.block_size == 0 key_layer = self.build_lsg_inputs( key_layer, sparse_key, global_key ) del sparse_key del global_key value_layer = self.build_lsg_inputs( value_layer, sparse_value, global_value ) del sparse_value del global_value attention_mask = self.build_lsg_inputs( attention_mask, sparse_mask, global_mask.transpose(-1, -2), is_attn_mask=True ).transpose(-1, -2) del sparse_mask del global_mask # expect (..., t, d) shape # Compute attention context_layer = self.attention( query_layer=self.chunk(query_layer, n_blocks), key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask ) return context_layer.reshape(n, h, -1, d) def reshape_to_local_block(self, hidden_states, is_attn_mask=False): size, step = self.local_shapes s = (size - step) // 2 # Pad before block reshaping if is_attn_mask: pad_value = torch.finfo(hidden_states.dtype).min hidden_states = hidden_states.transpose(-1, -2) else: pad_value = 0 hidden_states = torch.nn.functional.pad( hidden_states.transpose(-1, -2), pad=(s, s), value=pad_value ).transpose(-1, -2) # Make blocks hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2) # Skip third block if causal if self.is_causal: return hidden_states[..., :size*2//3, :] return hidden_states def reshape_to_sparse_block(self, hidden_states, is_attn_mask=False): size, step = self.sparse_shapes # In case of odd case odd_offset = (step % 2) # n, h, t, d*2 + 1 size = size*2 s = (size - step) // 2 + odd_offset # Pad before block reshaping if is_attn_mask: pad_value = torch.finfo(hidden_states.dtype).min hidden_states = hidden_states.transpose(-1, -2) else: pad_value = 0 hidden_states = torch.nn.functional.pad( hidden_states.transpose(-1, -2), pad=(s, s), value=pad_value ).transpose(-1, -2) # Make blocks hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2) # Fix case where block_size == sparsify_factor if odd_offset: hidden_states = hidden_states[..., :-1, :, :] # Indexes for selection u = (size - self.block_size * 3 // self.sparsity_factor) // 2 + odd_offset s = self.sparse_block_size # Skip right block if causal if self.is_causal: return hidden_states[..., u-s:u, :] u_ = u + odd_offset return torch.cat([hidden_states[..., u-s:u, :], hidden_states[..., -u_:-u_+s, :]], dim=-2) def cat_global_sparse_local_tokens(self, x_global, x_sparse=None, x_local=None, dim=-2): n, h, b, t, d = x_local.size() x_global = x_global.unsqueeze(-3).expand(-1, -1, b, -1, -1) if x_sparse is not None: return torch.cat([x_global, x_sparse, x_local], dim=dim) return torch.cat([x_global, x_local], dim=dim) def chunk(self, x, n_blocks): t, d = x.size()[-2:] return x.reshape(*x.size()[:-2], n_blocks, -1, d) class LSGSelfAttention(BaseSelfAttention): ''' Compute local attention with overlapping blocs Use global attention for tokens with highest norm ''' def __init__(self, config): super().__init__() self.init_modules(config) self.block_size = config.block_size self.sparse_block_size = config.sparse_block_size self.num_global_tokens = config.num_global_tokens self.sparsity_factor = config.sparsity_factor self.is_causal = config.is_decoder self.is_decoder = config.is_decoder self.attention = LSGAttentionProduct( config, block_size=config.block_size, sparse_block_size=config.sparse_block_size, sparsity_factor=self.sparsity_factor, is_causal=self.is_causal ) if self.is_causal: self.causal_attention = CausalAttentionProduct(config) self.full_attention = BaseAttentionProduct(config) sparse_functions = { "norm": self.get_sparse_tokens_with_norm, "pooling": self.get_sparse_tokens_with_pooling, "lsh": self.get_sparse_tokens_with_lsh, "stride": self.get_sparse_tokens_with_stride, "block_stride": self.get_sparse_tokens_with_block_stride, } self.sparsity_type = config.sparsity_type self.get_sparse_elements = sparse_functions.get(self.sparsity_type, lambda x, y, z: (None, None, None)) if config.sparsity_type == "lsh": self.lsh_num_pre_rounds = config.lsh_num_pre_rounds def get_sparse_tokens_with_norm(self, keys, values, mask): if self.sparsity_factor == 1: return keys, values, mask.expand(-1, keys.size()[1], -1, -1) with torch.no_grad(): block_size = min(self.block_size, self.sparse_block_size) key_norm = keys.detach().norm(dim=-1, keepdim=True) key_norm = key_norm * ~mask.transpose(-1, -2).bool() key_norm = self.chunk(key_norm, block_size) n, h, b, t, d = key_norm.size() idx = key_norm.argsort(dim=-2) del key_norm idx += (torch.arange(b, device=keys.device)*t).reshape(1, 1, b, 1, 1) split = (t - block_size // self.sparsity_factor, block_size // self.sparsity_factor) sparse_idx = idx.split(split, -2)[-1].reshape(n, h, -1, 1) d = keys.size()[-1] keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2) return keys, values, mask def get_sparse_tokens_with_pooling(self, keys, values, mask): if self.sparsity_factor == 1: return keys, values, mask.expand(-1, keys.size()[1], -1, -1) keys = self.chunk(keys, self.sparsity_factor) values = self.chunk(values, self.sparsity_factor) n, h, b, t, d = keys.size() mask = mask.reshape(n, 1, b, 1, t) mask = ~mask.transpose(-1, -2).bool() keys = keys * mask values = values * mask mask = mask.sum(dim=-2) keys = keys.sum(dim=-2) / (mask + 1e-6) values = values.sum(dim=-2) / (mask + 1e-6) mask = (1. - mask.clamp(0, 1)) mask *= torch.finfo(mask.dtype).min return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.expand(-1, h, -1, -1).transpose(-1, -2) def get_sparse_tokens_with_stride(self, keys, values, mask): if self.sparsity_factor == 1: return keys, values, mask.expand(-1, keys.size()[1], -1, -1) n, h, t, d = keys.size() sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device) * self.sparsity_factor sparse_idx = sparse_idx.reshape(1, 1, -1, 1) + (torch.arange(h, device=keys.device) % self.sparsity_factor).reshape(1, h, 1, 1) sparse_idx = sparse_idx.expand(n, h, -1, 1) keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2) return keys, values, mask def get_sparse_tokens_with_block_stride(self, keys, values, mask): if self.sparsity_factor == 1: return keys, values, mask.expand(-1, keys.size()[1], -1, -1) n, h, t, d = keys.size() t, b = self.block_size, t // self.block_size sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device) sparse_idx = sparse_idx.reshape(1, 1, 1, -1, 1) + torch.arange(h, device=keys.device).reshape(1, h, 1, 1, 1) * (t // self.sparsity_factor) sparse_idx = (sparse_idx % t) sparse_idx = sparse_idx + torch.arange(b, device=keys.device).reshape(1, 1, -1, 1, 1) * t sparse_idx = sparse_idx.reshape(1, h, -1, 1).expand(n, h, -1, 1) keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d)) mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2) return keys, values, mask def get_sparse_tokens_with_lsh(self, keys, values, mask): if self.sparsity_factor == 1: return keys, values, mask.expand(-1, keys.size()[1], -1, -1) block_size = min(self.block_size, self.sparse_block_size) keys = self.chunk(keys, block_size) values = self.chunk(values, block_size) n, h, b, t, d = keys.size() mask = mask.reshape(n, 1, b, 1, t) mask = ~mask.transpose(-1, -2).bool() keys = keys * mask values = values * mask mask = mask.expand(-1, h, -1, -1, -1).float() extra_factor = 1 for _ in range(self.lsh_num_pre_rounds): keys, values, mask = self.lsh_round(keys, values, mask, t*extra_factor) keys, values, mask = self.lsh_round(keys, values, mask, t//self.sparsity_factor) keys /= mask + 1e-8 values /= mask + 1e-8 mask = (1. - mask.clamp(0, 1)) mask *= torch.finfo(mask.dtype).min return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.transpose(-1, -2).reshape(n, h, 1, -1) def lsh_round(self, keys, values, mask, output_size): with torch.no_grad(): n_hashes = output_size // 2 n, h, b, t, d = keys.size() binary_mask = mask.clamp(0, 1) indexes = (torch.nn.functional.normalize(keys, dim=-1) * binary_mask) @ torch.randn(1, h, 1, d, n_hashes, device=keys.device) indexes = torch.cat([indexes, -indexes], dim=-1).argmax(dim=-1, keepdim=True) n, h, b, t, d = keys.size() x_ = torch.zeros(n, h, b, output_size, d, device=keys.device) mask_ = torch.zeros(n, h, b, output_size, 1, device=keys.device) keys = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=keys) values = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=values) mask = torch.scatter_add(mask_, dim=-2, index=indexes, src=mask) return keys[..., :output_size, :], values[..., :output_size, :], mask[..., :output_size, :] def forward( self, query, key, value, mask=None, head_mask=None, output_attentions=None, ): key_layer = self.transpose_for_scores(self.k_lin(key)) value_layer = self.transpose_for_scores(self.v_lin(value)) query_layer = self.transpose_for_scores(self.q_lin(query)) outputs = self.not_causal_forward( query_layer, key_layer, value_layer, attention_mask=mask, output_attentions=output_attentions ) return (self.out_lin(outputs[0]),) + outputs[1:] def not_causal_forward( self, query_layer, key_layer, value_layer, attention_mask=None, output_attentions=False, ): n, h, t, d = query_layer.size() # Cat global mask attention_mask = torch.nn.functional.pad(attention_mask, (self.num_global_tokens, 0), value=0) # Use normal attention if local attention covers every tokens if t <= 2 * self.block_size + self.num_global_tokens: context_layer = self.full_attention( query_layer=query_layer, key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask ) return (self.reshape_output(context_layer), ) # Split input into global tokens and other tokens split = (self.num_global_tokens, t - self.num_global_tokens) global_query, query_layer = query_layer.split(split, dim=-2) # Get global_attention bos = self.full_attention( query_layer=global_query, key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask ) # Split K Q M on global and non global global_key, key_layer = key_layer.split(split, dim=-2) global_value, value_layer = value_layer.split(split, dim=-2) global_mask, attention_mask = attention_mask.split(split, dim=-1) n, h, t, d = key_layer.size() # Get sparse idx sparse_key, sparse_value, sparse_mask = (None, None, None) if self.sparse_block_size and self.sparsity_factor > 0: sparse_key, sparse_value, sparse_mask = self.get_sparse_elements(key_layer, value_layer, attention_mask) # Expand masks on heads attention_mask = attention_mask.expand(-1, h, -1, -1) global_mask = global_mask.expand(-1, h, -1, -1) # Compute dot product attention context_layer = self.attention( query_layer, key_layer, value_layer, attention_mask, sparse_key=sparse_key, sparse_value=sparse_value, sparse_mask=sparse_mask, global_key=global_key, global_value=global_value, global_mask=global_mask ) # Merge global and local-sparse tokens context_layer = torch.cat([bos, context_layer], dim=-2) context_layer = self.reshape_output(context_layer) return (context_layer,) def chunk(self, x, chunk_size): n, h, t, d = x.size() return x.reshape(n, h, -1, chunk_size, d) class LSGTransformerBlock(TransformerBlock): def __init__(self, config): super().__init__(config) assert config.dim % config.n_heads == 0 self.attention = LSGSelfAttention(config) class LSGTransformer(Transformer): def __init__(self, config): super().__init__(config) self.layer = nn.ModuleList([LSGTransformerBlock(config) for _ in range(config.n_layers)]) assert hasattr(config, "num_global_tokens") self.num_global_tokens = config.num_global_tokens self.pad_idx = config.pad_token_id assert hasattr(config, "block_size") and hasattr(config, "adaptive") self.block_size = config.block_size self.adaptive = config.adaptive self.mask_first_token = config.mask_first_token self.pool_with_global = config.pool_with_global def forward( self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: Optional[bool] = None, ) -> Union[BaseModelOutput, Tuple[torch.Tensor, ...]]: # docstyle-ignore attn_mask = attn_mask.float() mask_value = 0 n, t = attn_mask.size() b = self.block_size * 2 pad = t % self.block_size # Check if t is multiple of block_size and pad if self.adaptive and t > b and pad > 0: pad_length = self.block_size - pad x = torch.nn.functional.pad(x.transpose(-1, -2), (0, pad_length), value=0.).transpose(-1, -2) attn_mask = torch.nn.functional.pad(attn_mask, (0, pad_length), value=mask_value) if self.mask_first_token: attn_mask[..., 0] = mask_value attn_mask = torch.finfo(x.dtype).min*(1 - attn_mask).unsqueeze(1).unsqueeze(1) encoder_outputs = super().forward( x=x, attn_mask=attn_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) sequence_output = encoder_outputs[0] if self.pool_with_global: sequence_output[:, self.num_global_tokens] = sequence_output[:, 0] # Adapt sequence to initial shape sequence_output = sequence_output[..., self.num_global_tokens: t + self.num_global_tokens, :] if not return_dict: return (sequence_output, ) + encoder_outputs[1:] encoder_outputs.last_hidden_state = sequence_output return encoder_outputs class LSGDistilBertPreTrainedModel(DistilBertPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LSGDistilBertConfig class LSGDistilBertModel(LSGDistilBertPreTrainedModel, DistilBertModel): def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.embeddings = LSGEmbeddings(config) # Embeddings self.transformer = LSGTransformer(config) # Encoder self.num_global_tokens = config.num_global_tokens # Initialize weights and apply final processing self.post_init() class LSGDistilBertForMaskedLM(LSGDistilBertPreTrainedModel, DistilBertForMaskedLM): _keys_to_ignore_on_load_missing = ["vocab_projector.weight"] _tied_weights_keys = ["vocab_projector.weight"] def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.activation = get_activation(config.activation) self.distilbert = LSGDistilBertModel(config) self.vocab_transform = nn.Linear(config.dim, config.dim) self.vocab_layer_norm = nn.LayerNorm(config.dim, eps=1e-12) self.vocab_projector = nn.Linear(config.dim, config.vocab_size) # Initialize weights and apply final processing self.post_init() self.mlm_loss_fct = nn.CrossEntropyLoss() class LSGDistilBertForSequenceClassification(LSGDistilBertPreTrainedModel, DistilBertForSequenceClassification): def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.num_labels = config.num_labels self.config = config self.distilbert = LSGDistilBertModel(config) self.pre_classifier = nn.Linear(config.dim, config.dim) self.classifier = nn.Linear(config.dim, config.num_labels) self.dropout = nn.Dropout(config.seq_classif_dropout) # Initialize weights and apply final processing self.post_init() class LSGDistilBertForQuestionAnswering(LSGDistilBertPreTrainedModel, DistilBertForQuestionAnswering): def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.distilbert = LSGDistilBertModel(config) self.qa_outputs = nn.Linear(config.dim, config.num_labels) assert config.num_labels == 2 self.dropout = nn.Dropout(config.qa_dropout) # Initialize weights and apply final processing self.post_init() class LSGDistilBertForTokenClassification(LSGDistilBertPreTrainedModel, DistilBertForTokenClassification): def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.num_labels = config.num_labels self.distilbert = LSGDistilBertModel(config) self.dropout = nn.Dropout(config.dropout) self.classifier = nn.Linear(config.dim, config.num_labels) # Initialize weights and apply final processing self.post_init() class LSGDistilBertForMultipleChoice(LSGDistilBertPreTrainedModel, DistilBertForMultipleChoice): def __init__(self, config): LSGDistilBertPreTrainedModel.__init__(self, config) self.distilbert = LSGDistilBertModel(config) self.pre_classifier = nn.Linear(config.dim, config.dim) self.classifier = nn.Linear(config.dim, 1) self.dropout = nn.Dropout(config.seq_classif_dropout) # Initialize weights and apply final processing self.post_init() def str_to_class(classname): return getattr(sys.modules[__name__], classname) # Register model in Auto API try: LSGDistilBertConfig.register_for_auto_class() for key, value in AUTO_MAP.items(): str_to_class(value.split(".")[-1]).register_for_auto_class(key) except: warn("AutoRegister isn't available, you'll have to manually copy modeling.py after .save_pretrained(...).") warn("Update to transformers >= 4.23.1 to fix.")