from logging import warn import torch from transformers.models.bart.modeling_bart import * from transformers.models.bart.modeling_bart import _expand_mask import torch.nn as nn from torch.nn import BCEWithLogitsLoss import sys AUTO_MAP = { "AutoModel": "modeling_lsg_bart.LSGBartModel", "AutoModelForCausalLM": "modeling_lsg_bart.LSGBartForCausalLM", "AutoModelForQuestionAnswering": "modeling_lsg_bart.LSGBartForQuestionAnswering", "AutoModelForSequenceClassification": "modeling_lsg_bart.LSGBartForSequenceClassification", "AutoModelForSeq2SeqLM": "modeling_lsg_bart.LSGBartForConditionalGeneration" } class LSGBartConfig(BartConfig): """ This class overrides :class:`~transformers.RobertaConfig`. Please check the superclass for the appropriate documentation alongside usage examples. """ base_model_prefix = "lsg" model_type = "bart" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, adaptive=True, base_model_prefix="lsg", block_size=128, lsh_num_pre_rounds=1, num_global_tokens=1, pass_global_tokens_to_decoder=True, pool_with_global=True, sparse_block_size=128, sparsity_factor=2, sparsity_type="norm", **kwargs ): """Constructs LSGConfig.""" 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.num_global_tokens = num_global_tokens self.pass_global_tokens_to_decoder = pass_global_tokens_to_decoder 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 compatible, 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" def shift_tokens_right(input_ids, pad_token_id, decoder_start_token_id): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids def _make_causal_mask(input_ids_shape, dtype, past_key_values_length=0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), float("-inf")) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) def _expand_mask(mask, dtype, tgt_len=None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) class BaseSelfAttention(nn.Module): def __init__( self, embed_dim, num_heads, dropout=0.0, is_decoder=False, bias=True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim ** -0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + ( self.num_heads, self.head_dim, ) 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.embed_dim,) return context_layer.view(*new_context_layer_shape) def project_QKV(self, hidden_states): query_layer = self.transpose_for_scores(self.q_proj(hidden_states)) key_layer = self.transpose_for_scores(self.k_proj(hidden_states)) value_layer = self.transpose_for_scores(self.v_proj(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 LSGAttentionProduct(nn.Module): def __init__(self, config, block_size=None, sparse_block_size=None, sparsity_factor=4): """ 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 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) 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 = -10000 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) 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 = -10000 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 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 LSGBartEncoderAttention(BaseSelfAttention): ''' Compute local attention with overlapping blocs Use global attention for tokens with highest norm ''' def __init__( self, config, embed_dim, num_heads, dropout ): super().__init__(embed_dim, num_heads, dropout) 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.attention = LSGAttentionProduct( config, block_size=config.block_size, sparse_block_size=config.sparse_block_size, sparsity_factor=self.sparsity_factor, ) 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)) * 1e4 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 = -10000 * (1. - mask.clamp(0, 1)) 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, layer_head_mask=None, output_attentions=False ): query_layer, key_layer, value_layer = self.project_QKV(hidden_states) outputs = self.not_causal_forward( query_layer, key_layer, value_layer, attention_mask=attention_mask[:, :, :1, :], head_mask=layer_head_mask, output_attentions=output_attentions ) return self.out_proj(outputs), None, None def not_causal_forward( self, query_layer, key_layer, value_layer, attention_mask=None, head_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 ) if head_mask is not None: context_layer = context_layer * head_mask[:, :, :1, :1] 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) if head_mask is not None: context_layer = context_layer * head_mask[:, :, :1, :1] 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 LSGBartDecoderAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim, num_heads, dropout=0.0, is_decoder=False, bias=True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim ** -0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor, seq_len, bsz): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states, key_value_states=None, past_key_value=None, attention_mask=None, layer_head_mask=None, output_attentions=False, ): # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) 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_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class LSGBartLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings, embedding_dim): # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, input_ids_shape, past_key_values_length=0): """`input_ids_shape` is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids_shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ) return super().forward(positions + self.offset) class LSGBartEncoderLayer(nn.Module): def __init__(self, config): super().__init__() self.embed_dim = config.d_model self.self_attn = LSGBartEncoderAttention( config=config, embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states, attention_mask, layer_head_mask, output_attentions=False, ): """ Args: hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` attention_mask (:obj:`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class LSGBartDecoderLayer(nn.Module): def __init__(self, config): super().__init__() self.embed_dim = config.d_model self.self_attn = LSGBartDecoderAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = LSGBartDecoderAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, output_attentions=False, use_cache=True, ): """ Args: hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (:obj:`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (:obj:`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (:obj:`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class LSGBartClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__( self, input_dim, inner_dim, num_classes, pooler_dropout, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states): hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class LSGBartPretrainedModel(PreTrainedModel): config_class = LSGBartConfig base_model_prefix = "model" supports_gradient_checkpointing = True _keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"] def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) 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, (LSGBartDecoder, LSGBartEncoder)): module.gradient_checkpointing = value @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, } return dummy_inputs class PretrainedLSGBartModel(LSGBartPretrainedModel): def __init_subclass__(self): warnings.warn( "The class `PretrainedBartModel` has been depreciated, please use `LSGBartPretrainedModel` instead.", FutureWarning, ) class LSGBartEncoder(LSGBartPretrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a :class:`BartEncoderLayer`. Args: config: BartConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config, embed_tokens=None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) self.embed_positions = LSGBartLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([LSGBartEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) # 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.pool_with_global = config.pool_with_global self.pass_global_tokens_to_decoder = config.pass_global_tokens_to_decoder self.global_embeddings = nn.Embedding(512, embedding_dim=config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None ): inputs_ = input_ids if input_ids is not None else inputs_embeds n, t = inputs_.size()[:2] if attention_mask is None: attention_mask = torch.ones(n, t, device=inputs_.device) 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 if input_ids is not None: input_ids = torch.nn.functional.pad(input_ids, (0, pad_length), value=self.pad_idx) else: inputs_embeds = torch.nn.functional.pad(inputs_embeds.transpose(-1, -2), (0, pad_length), value=0.).transpose(-1, -2) attention_mask = torch.nn.functional.pad(attention_mask, (0, pad_length), value=0) n, t_ = attention_mask.size() encoder_outputs = self.forward_with_adaptive( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) context = encoder_outputs[0] diff = t - t_ if self.pass_global_tokens_to_decoder: offset = self.num_global_tokens else: if self.pool_with_global: context[:, self.num_global_tokens] = context[:, 0] context = context[..., self.num_global_tokens:, :] offset = 0 # Adapt sequence to initial shape if diff < 0: context = context[:, :t + offset] if return_dict: encoder_outputs.last_hidden_state = context else: encoder_outputs = (context, ) + encoder_outputs[1:] return encoder_outputs def forward_with_adaptive( self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): 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 # retrieve input_ids and inputs_embeds 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: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) 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") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs_embeds + embed_pos # Add global tokens n, t, d = hidden_states.size() global_idx = torch.arange(self.num_global_tokens, device=hidden_states.device).reshape(1, -1) hidden_states = torch.cat([self.global_embeddings(global_idx).expand(n, -1, -1), hidden_states], dim=-2) hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class LSGBartDecoder(BartDecoder, LSGBartPretrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`LSGBartDecoderLayer` Args: config: BartConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config, embed_tokens=None): LSGBartPretrainedModel.__init__(self, config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.adaptive = config.adaptive if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.embed_positions = LSGBartLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([LSGBartDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() class LSGBartModel(LSGBartPretrainedModel): def __init__(self, config): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) self.pass_global_tokens_to_decoder = config.pass_global_tokens_to_decoder self.num_global_tokens = config.num_global_tokens self.encoder = LSGBartEncoder(config, self.shared) self.decoder = LSGBartDecoder(config, self.shared) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, value): self.shared = value self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def forward( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): # different to other models, Bart automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, self.config.decoder_start_token_id ) 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Pad mask for global tokens if self.pass_global_tokens_to_decoder: attention_mask = torch.nn.functional.pad(attention_mask, pad=(self.num_global_tokens, 0), value=1) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class LSGBartForConditionalGeneration(BartForConditionalGeneration, LSGBartPretrainedModel): base_model_prefix = "model" _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"lm_head\.weight"] def __init__(self, config): LSGBartPretrainedModel.__init__(self, config) self.model = LSGBartModel(config) self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing self.post_init() class LSGBartForSequenceClassification(BartForSequenceClassification, LSGBartPretrainedModel): def __init__(self, config: LSGBartConfig, **kwargs): LSGBartPretrainedModel.__init__(self, config, **kwargs) self.model = LSGBartModel(config) self.classification_head = LSGBartClassificationHead( config.d_model, config.d_model, config.num_labels, config.classifier_dropout, ) self.model._init_weights(self.classification_head.dense) self.model._init_weights(self.classification_head.out_proj) class LSGBartForQuestionAnswering(BartForQuestionAnswering, LSGBartPretrainedModel): def __init__(self, config: LSGBartConfig): LSGBartPretrainedModel.__init__(self, config) config.num_labels = 2 self.num_labels = config.num_labels self.model = LSGBartModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.model._init_weights(self.qa_outputs) class LSGBartDecoderWrapper(LSGBartPretrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the :class:`~transformers.EncoderDecoderModel` framework. """ def __init__(self, config: LSGBartConfig): super().__init__(config) self.decoder = LSGBartDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) class LSGBartForCausalLM(BartForCausalLM, LSGBartPretrainedModel): def __init__(self, config: LSGBartConfig): config = copy.deepcopy(config) config.is_decoder = True config.is_encoder_decoder = False LSGBartPretrainedModel.__init__(self, config) self.model = LSGBartDecoderWrapper(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # 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: LSGBartConfig.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.")