from logging import warn import torch from transformers.models.mbart.modeling_mbart import * from transformers.models.mbart.modeling_mbart import _expand_mask import torch.nn as nn import sys AUTO_MAP = { "AutoModel": "modeling_lsg_mbart.LSGMBartModel", "AutoModelForCausalLM": "modeling_lsg_mbart.LSGMBartForCausalLM", "AutoModelForQuestionAnswering": "modeling_lsg_mbart.LSGMBartForQuestionAnswering", "AutoModelForSequenceClassification": "modeling_lsg_mbart.LSGMBartForSequenceClassification", "AutoModelForSeq2SeqLM": "modeling_lsg_mbart.LSGMBartForConditionalGeneration" } class LSGMBartConfig(MBartConfig): """ This class overrides :class:`~transformers.MBartConfig`. Please check the superclass for the appropriate documentation alongside usage examples. """ base_model_prefix = "lsg" model_type = "mbart" 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, mask_first_token=False, 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.mask_first_token = mask_first_token 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 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__( 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 = 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) 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 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 LSGMBartEncoderAttention(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)) 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, 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 ) 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 LSGMBartEncoderLayer(MBartEncoderLayer): def __init__(self, config): super().__init__(config) self.self_attn = LSGMBartEncoderAttention( config=config, embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) class LSGMBartPretrainedModel(MBartPreTrainedModel): config_class = LSGMBartConfig base_model_prefix = "model" supports_gradient_checkpointing = True def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (MBartDecoder, MBartEncoder, LSGMBartEncoder)): module.gradient_checkpointing = value class LSGMBartEncoder(LSGMBartPretrainedModel, MBartEncoder): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`MBartEncoderLayer`]. Args: config: MBartConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config, embed_tokens=None): LSGMBartPretrainedModel.__init__(self, 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 = MBartLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([LSGMBartEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.layer_norm = nn.LayerNorm(config.d_model) # 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 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 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, dtype=inputs_.dtype) if self.mask_first_token: attention_mask[:, 0] = 0 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(inputs_embeds) 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],) hidden_states = self.layer_norm(hidden_states) 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 LSGMBartModel(LSGMBartPretrainedModel, MBartModel): def __init__(self, config): LSGMBartPretrainedModel.__init__(self, 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 = LSGMBartEncoder(config, self.shared) self.decoder = MBartDecoder(config, self.shared) # Initialize weights and apply final processing self.post_init() 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, ): 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 # different to other models, MBart automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) 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 and attention_mask is not None: 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 LSGMBartForConditionalGeneration(LSGMBartPretrainedModel, MBartForConditionalGeneration): base_model_prefix = "model" _keys_to_ignore_on_load_missing = [ r"final_logits_bias", r"encoder.version", r"decoder.version", r"lm_head.weight", ] def __init__(self, config): LSGMBartPretrainedModel.__init__(self, config) self.model = LSGMBartModel(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 LSGMBartForSequenceClassification(LSGMBartPretrainedModel, MBartForSequenceClassification): def __init__(self, config, **kwargs): LSGMBartPretrainedModel.__init__(self, config, **kwargs) self.model = LSGMBartModel(config) self.classification_head = MBartClassificationHead( 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 LSGMBartForQuestionAnswering(LSGMBartPretrainedModel, MBartForQuestionAnswering): def __init__(self, config): LSGMBartPretrainedModel.__init__(self, config) config.num_labels = 2 self.num_labels = config.num_labels self.model = LSGMBartModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.model._init_weights(self.qa_outputs) class LSGMBartForCausalLM(LSGMBartPretrainedModel, MBartForCausalLM): def __init__(self, config): LSGMBartPretrainedModel.__init__(self, config) MBartForCausalLM.__init__(self, config) def str_to_class(classname): return getattr(sys.modules[__name__], classname) # Register model in Auto API try: LSGMBartConfig.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.")