from logging import warn from transformers.models.electra.modeling_electra import * import torch import torch.nn as nn from transformers.models.electra.configuration_electra import ElectraConfig import sys AUTO_MAP = { "AutoModel": "modeling_lsg_electra.LSGElectraModel", "AutoModelForCausalLM": "modeling_lsg_electra.LSGElectraForCausalLM", "AutoModelForMaskedLM": "modeling_lsg_electra.LSGElectraForMaskedLM", "AutoModelForPreTraining": "modeling_lsg_electra.LSGElectraForPreTraining", "AutoModelForMultipleChoice": "modeling_lsg_electra.LSGElectraForMultipleChoice", "AutoModelForQuestionAnswering": "modeling_lsg_electra.LSGElectraForQuestionAnswering", "AutoModelForSequenceClassification": "modeling_lsg_electra.LSGElectraForSequenceClassification", "AutoModelForTokenClassification": "modeling_lsg_electra.LSGElectraForTokenClassification" } class LSGElectraConfig(ElectraConfig): """ This class overrides :class:`~transformers.ElectraConfig`. Please check the superclass for the appropriate documentation alongside usage examples. """ base_model_prefix = "lsg" model_type = "electra" 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 LSGElectraConfig.""" 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 BaseSelfAttention(nn.Module): def init_modules(self, config): if config.hidden_size % config.num_attention_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.hidden_size, 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.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): 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 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.query(hidden_states)) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(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_probs_dropout_prob) 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 ElectraModel 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_probs_dropout_prob) 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: # Apply the attention mask is (precomputed for all layers in ElectraModel forward() function) attention_scores = attention_scores + attention_mask # 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 ) causal_mask = causal_mask.T * torch.finfo(attention_scores.dtype).min attention_scores[..., -causal_shape[0]:, -causal_shape[1] + 1:] = causal_mask[:, 1:] 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 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, hidden_size 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 LSGElectraEmbeddings(ElectraEmbeddings): 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.embedding_size, ) self.block_size = config.block_size def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids[:, :seq_length]) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids[:, :seq_length]) embeddings += position_embeddings #if self.num_global_tokens < 0: n, t, d = embeddings.size() # Add global_tokens indexes = torch.arange(self.num_global_tokens, device=embeddings.device).reshape(1, -1) global_embeddings = self.global_embeddings(indexes) embeddings = torch.cat([global_embeddings.expand(n, -1, d), embeddings], dim=-2) embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings 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, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(query_layer) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) if is_cross_attention: outputs = self.cross_attention_forward( query_layer=query_layer, key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask, output_attentions=output_attentions ) else: outputs = self.causal_forward( query_layer, key_layer, value_layer, attention_mask=attention_mask, output_attentions=output_attentions, ) outputs = outputs + ((key_layer, value_layer),) else: outputs = self.not_causal_forward( query_layer, key_layer, value_layer, attention_mask=attention_mask, output_attentions=output_attentions ) return outputs def causal_forward( self, query_layer, key_layer, value_layer, attention_mask=None, output_attentions=False, ): n, h, t, d = key_layer.size() # Cat global mask attention_mask = torch.nn.functional.pad(attention_mask, (self.num_global_tokens, 0), value=0) # 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) # Use normal causal attention if local attention covers every tokens if t <= 2 * self.block_size + self.num_global_tokens: context_layer = self.causal_attention( query_layer=query_layer, key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask, causal_shape=(t - self.num_global_tokens, t - self.num_global_tokens) ) context_layer = torch.cat([global_query, context_layer], dim=-2) return (self.reshape_output(context_layer), ) # 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 pseudo global (causal) and local-sparse tokens context_layer = torch.cat([global_query, context_layer], dim=-2) context_layer = self.reshape_output(context_layer) return (context_layer,) 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 cross_attention_forward( self, query_layer, key_layer, value_layer, attention_mask=None, output_attentions=False, ): 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), ) def chunk(self, x, chunk_size): n, h, t, d = x.size() return x.reshape(n, h, -1, chunk_size, d) class LSGAttention(ElectraAttention): def __init__(self, config): nn.Module.__init__(self) self.self = LSGSelfAttention(config) self.output = ElectraSelfOutput(config) self.pruned_heads = set() class LSGElectraLayer(ElectraLayer): def __init__(self, config): super().__init__(config) self.attention = LSGAttention(config) if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = LSGAttention(config, position_embedding_type="absolute") class LSGElectraEncoder(ElectraEncoder): def __init__(self, config): super().__init__(config) self.layer = nn.ModuleList([LSGElectraLayer(config) for _ in range(config.num_hidden_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, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: mask_value = torch.finfo(attention_mask.dtype).min n, _, __, t = attention_mask.size() if not (self.config.is_decoder and encoder_hidden_states is not None): 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 hidden_states = torch.nn.functional.pad(hidden_states.transpose(-1, -2), (0, pad_length), value=0.).transpose(-1, -2) attention_mask = torch.nn.functional.pad(attention_mask, (0, pad_length), value=mask_value) if self.mask_first_token: attention_mask[..., 0] = mask_value encoder_outputs = super().forward( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) 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 LSGElectraPreTrainedModel(ElectraPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LSGElectraConfig load_tf_weights = load_tf_weights_in_electra base_model_prefix = "electra" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] _keys_to_ignore_on_load_unexpected = [r"electra.embeddings_project.weight", r"electra.embeddings_project.bias"] def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (ElectraEncoder, LSGElectraEncoder)): module.gradient_checkpointing = value class LSGElectraModel(LSGElectraPreTrainedModel, ElectraModel): """ This class overrides :class:`~transformers.ElectraModel`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.embeddings = LSGElectraEmbeddings(config) if config.embedding_size != config.hidden_size: self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size) self.encoder = LSGElectraEncoder(config) self.config = config # Initialize weights and apply final processing self.post_init() def get_extended_attention_mask(self, attention_mask, input_shape, device=None): # Do not rely on original triangular mask from BERT/RoBERTa for causalLM if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] elif attention_mask.dim() == 2: extended_attention_mask = attention_mask[:, None, None, :] else: raise ValueError( f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})" ) extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(extended_attention_mask.dtype).min return extended_attention_mask class LSGElectraForPreTraining(LSGElectraPreTrainedModel, ElectraForPreTraining): config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.electra = LSGElectraModel(config) self.discriminator_predictions = ElectraDiscriminatorPredictions(config) # Initialize weights and apply final processing self.post_init() class LSGElectraForMaskedLM(LSGElectraPreTrainedModel, ElectraForMaskedLM): """ This class overrides :class:`~transformers.ElectraForMaskedLM`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.electra = LSGElectraModel(config) self.generator_predictions = ElectraGeneratorPredictions(config) self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size) # Initialize weights and apply final processing self.post_init() class LSGElectraForSequenceClassification(LSGElectraPreTrainedModel, ElectraForSequenceClassification): """ This class overrides :class:`~transformers.ElectraForSequenceClassification`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.num_labels = config.num_labels self.config = config self.electra = LSGElectraModel(config) self.classifier = ElectraClassificationHead(config) # Initialize weights and apply final processing self.post_init() class LSGElectraForMultipleChoice(LSGElectraPreTrainedModel, ElectraForMultipleChoice): """ This class overrides :class:`~transformers.ElectraForMultipleChoice`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.electra = LSGElectraModel(config) self.sequence_summary = SequenceSummary(config) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() class LSGElectraForCausalLM(LSGElectraPreTrainedModel, ElectraForCausalLM): def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) if not config.is_decoder: logger.warning("If you want to use `ElectraForCausalLM` as a standalone, add `is_decoder=True.`") self.electra = LSGElectraModel(config) self.generator_predictions = ElectraGeneratorPredictions(config) self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size) self.init_weights() class LSGElectraForTokenClassification(LSGElectraPreTrainedModel, ElectraForTokenClassification): """ This class overrides :class:`~transformers.ElectraForTokenClassification`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.num_labels = config.num_labels self.electra = LSGElectraModel(config) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() class LSGElectraForQuestionAnswering(LSGElectraPreTrainedModel, ElectraForQuestionAnswering): """ This class overrides :class:`~transformers.ElectraForQuestionAnswering`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = LSGElectraConfig base_model_prefix = "electra" def __init__(self, config): LSGElectraPreTrainedModel.__init__(self, config) self.num_labels = config.num_labels self.electra = LSGElectraModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # 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: LSGElectraConfig.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 >= 4.17.0 to fix.")