import math from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers.pytorch_utils import softmax_backward_data from torch.utils import checkpoint from .configuration_nort5 import NorT5Config from transformers.modeling_utils import PreTrainedModel from transformers.activations import gelu_new from transformers.modeling_outputs import ( Seq2SeqModelOutput, Seq2SeqLMOutput, BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions ) class Encoder(nn.Module): def __init__(self, config, activation_checkpointing=False): super().__init__() self.main_input_name = "input_ids" self.relative_embedding = RelativeEmbedding(config) self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)]) for i, layer in enumerate(self.layers): layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) self.activation_checkpointing = activation_checkpointing def forward(self, hidden_states, attention_mask): relative_embedding = self.relative_embedding() hidden_states, attention_probs = [hidden_states], [] for layer in self.layers: if self.activation_checkpointing: hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding) else: hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding) hidden_states.append(hidden_state) attention_probs.append(attention_p) return hidden_states, attention_probs class Decoder(nn.Module): def __init__(self, config, activation_checkpointing=False): super().__init__() self.self_relative_embedding = RelativeEmbedding(config) self.cross_relative_embedding = RelativeEmbedding(config) self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)]) for i, layer in enumerate(self.layers): layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) self.activation_checkpointing = activation_checkpointing def forward(self, x, encoder_output, encoder_padding_mask, past_key_values=None): self_relative_embedding = self.self_relative_embedding() cross_relative_embedding = self.cross_relative_embedding() if past_key_values is None: autoreg_mask = torch.triu( torch.full((x.size(0), x.size(0)), True, device=x.device), diagonal=1 ) else: autoreg_mask = None # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.layers) hidden_states, self_attention_probs, cross_attention_probs, key_value_states = [x], [], [], [] for layer, past_key_value in zip(self.layers, past_key_values): if self.activation_checkpointing: hidden_state, self_attention_p, cross_attention_p, key_value_state = checkpoint.checkpoint(layer, hidden_states[-1], autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=None) else: hidden_state, self_attention_p, cross_attention_p, key_value_state = layer(hidden_states[-1], autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=past_key_value) hidden_states.append(hidden_state) self_attention_probs.append(self_attention_p) cross_attention_probs.append(cross_attention_p) key_value_states.append(key_value_state) return hidden_states, self_attention_probs, cross_attention_probs, key_value_states class MaskClassifier(nn.Module): def __init__(self, config): super().__init__() self.nonlinearity = nn.Sequential( nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False), nn.Dropout(config.hidden_dropout_prob), nn.Linear(config.hidden_size, config.vocab_size) ) self.initialize(config.hidden_size) def initialize(self, hidden_size): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std) self.nonlinearity[-1].bias.data.zero_() def forward(self, x): x = self.nonlinearity(x) return x class EncoderLayer(nn.Module): def __init__(self, config): super().__init__() self.attention = Attention(config, is_cross_attention=False) self.mlp = FeedForward(config) def forward(self, x, padding_mask, relative_embedding): attention_output, attention_probs, _ = self.attention(x, x, padding_mask, relative_embedding) x = x + attention_output x = x + self.mlp(x) return x, attention_probs class DecoderLayer(nn.Module): def __init__(self, config): super().__init__() self.self_attention = Attention(config, is_cross_attention=False) self.cross_attention = Attention(config, is_cross_attention=True) self.mlp = FeedForward(config) def forward(self, x, autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding, past_key_value=None): query_offset = 0 if past_key_value is not None: self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] query_offset = self_attn_past_key_value[0].size(2) else: self_attn_past_key_value, cross_attn_past_key_value = None, None x_, self_attention_probs, self_key_value_state = self.self_attention(x, x, autoreg_mask, self_relative_embedding, past_key_value=self_attn_past_key_value, query_offset=query_offset) x = x + x_ x_, cross_attention_probs, cross_key_value_state = self.cross_attention(x, encoder_output, encoder_padding_mask, cross_relative_embedding, past_key_value=cross_attn_past_key_value, query_offset=query_offset) x = x + x_ x = x + self.mlp(x) return x, self_attention_probs, cross_attention_probs, self_key_value_state + cross_key_value_state class GeGLU(nn.Module): def forward(self, x): x, gate = x.chunk(2, dim=-1) x = x * gelu_new(gate) return x class FeedForward(nn.Module): def __init__(self, config): super().__init__() self.mlp = nn.Sequential( nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False), GeGLU(), nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.intermediate_size, config.hidden_size, bias=False), nn.Dropout(config.hidden_dropout_prob) ) self.initialize(config.hidden_size) def initialize(self, hidden_size): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std) def forward(self, x): return self.mlp(x) class MaskedSoftmax(torch.autograd.Function): @staticmethod def forward(self, x, mask, dim): self.dim = dim if mask is not None: x.masked_fill_(mask, float('-inf')) x = torch.softmax(x, self.dim) if mask is not None: x.masked_fill_(mask, 0.0) self.save_for_backward(x) return x @staticmethod def backward(self, grad_output): output, = self.saved_tensors input_grad = softmax_backward_data(self, grad_output, output, self.dim, output) return input_grad, None, None class Attention(nn.Module): def __init__(self, config, is_cross_attention=False): super().__init__() self.config = config self.is_cross_attention = is_cross_attention if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}") self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_size = config.hidden_size // config.num_attention_heads self.in_proj_q = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.in_proj_k = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False) self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) position_indices = torch.arange(512, dtype=torch.long).unsqueeze(1) \ - torch.arange(512, dtype=torch.long).unsqueeze(0) position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, 512) position_indices = config.position_bucket_size - 1 + position_indices self.register_buffer("position_indices", position_indices, persistent=True) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.scale = 1.0 / math.sqrt(3 * self.head_size) self.initialize() def make_log_bucket_position(self, relative_pos, bucket_size, max_position): sign = torch.sign(relative_pos) mid = bucket_size // 2 abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1)) log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long() return bucket_pos def initialize(self): std = math.sqrt(2.0 / (5.0 * self.hidden_size)) nn.init.trunc_normal_(self.in_proj_q.weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.in_proj_k.weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std) self.in_proj_q.bias.data.zero_() self.in_proj_k.bias.data.zero_() self.in_proj_v.bias.data.zero_() self.out_proj.bias.data.zero_() def forward(self, q, kv, attention_mask, relative_embedding, past_key_value=None, query_offset=0): key_len, batch_size, _ = kv.size() query_len, _, _ = q.size() if not self.is_cross_attention or past_key_value is None or past_key_value[0].size(1) != kv.size(0): kv = self.pre_layer_norm(kv) key = self.in_proj_k(kv) # shape: [T, B, D] value = self.in_proj_v(kv) # shape: [T, B, D] key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) # shape: [BxH, T, D] value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) # shape: [BxH, T, D] if past_key_value is not None: if not self.is_cross_attention: key = torch.cat([past_key_value[0].flatten(0, 1), key], dim=1) value = torch.cat([past_key_value[1].flatten(0, 1), value], dim=1) key_len = key.size(1) elif past_key_value[0].size(1) == kv.size(0): key = past_key_value[0].flatten(0, 1) value = past_key_value[1].flatten(0, 1) if self.position_indices.size(0) < max(query_len, key_len): position_indices = torch.arange(max(query_len, key_len), dtype=torch.long).unsqueeze(1) \ - torch.arange(max(query_len, key_len), dtype=torch.long).unsqueeze(0) position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512) position_indices = self.config.position_bucket_size - 1 + position_indices self.register_buffer("position_indices", position_indices.to(q.device), persistent=True) q = self.pre_layer_norm(q) query = self.in_proj_q(q) # shape: [T, B, D] query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale) query_pos = self.in_proj_q(self.dropout(relative_embedding)) # shape: [2T-1, D] query_pos = query_pos.view(-1, self.num_heads, self.head_size) # shape: [2T-1, H, D] key_pos = self.in_proj_k(self.dropout(relative_embedding)) # shape: [2T-1, D] key_pos = key_pos.view(-1, self.num_heads, self.head_size) # shape: [2T-1, H, D] query_ = query.view(batch_size, self.num_heads, query_len, self.head_size) key_ = key.view(batch_size, self.num_heads, key_len, self.head_size) attention_c_p = torch.einsum("bhqd,khd->bhqk", query_, key_pos.squeeze(1) * self.scale) attention_p_c = torch.einsum("bhkd,qhd->bhqk", key_ * self.scale, query_pos.squeeze(1)) position_indices = self.position_indices[query_offset:query_offset+query_len, :key_len].expand(batch_size, self.num_heads, -1, -1) attention_c_p = attention_c_p.gather(3, position_indices) attention_p_c = attention_p_c.gather(2, position_indices) attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len) attention_scores.add_(attention_c_p) attention_scores.add_(attention_p_c) attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1) attention_probs = self.dropout(attention_probs) context = torch.bmm(attention_probs.flatten(0, 1), value) # shape: [B*H, Q, D] context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size) # shape: [Q, B, H*D] context = self.out_proj(context) context = self.post_layer_norm(context) context = self.dropout(context) key = key.detach().unflatten(0, (-1, self.num_heads)) value = value.detach().unflatten(0, (-1, self.num_heads)) return context, attention_probs.detach(), (key, value) class WordEmbedding(nn.Module): def __init__(self, config): super().__init__() self.hidden_size = config.hidden_size self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size) self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.initialize() def initialize(self): std = math.sqrt(2.0 / (5.0 * self.hidden_size)) nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std) def forward(self, input_ids): return self.dropout(self.word_layer_norm(self.word_embedding(input_ids))) class RelativeEmbedding(nn.Module): def __init__(self, config): super().__init__() self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size)) self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.initialize(config.hidden_size) def initialize(self, hidden_size): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std) def forward(self): return self.relative_layer_norm(self.relative_embedding) # # HuggingFace wrappers # class NorT5PreTrainedModel(PreTrainedModel): config_class = NorT5Config base_model_prefix = "norT5" supports_gradient_checkpointing = True def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, Encoder): module.activation_checkpointing = value def _init_weights(self, module): pass # everything is already initialized class NorT5Model(NorT5PreTrainedModel): def __init__(self, config, add_lm_layer=False, add_decoder=True): super().__init__(config) self.config = config self.cls_token_id = config.cls_token_id self.sep_token_id = config.sep_token_id self.bos_token_id = config.bos_token_id self.eos_token_id = config.eos_token_id self.pad_token_id = config.pad_token_id self.embedding = WordEmbedding(config) self.encoder = Encoder(config, activation_checkpointing=False) self.decoder = Decoder(config, activation_checkpointing=False) if add_decoder else None self.classifier = MaskClassifier(config) if add_lm_layer else None def get_input_embeddings(self): return self.embedding.word_embedding def set_input_embeddings(self, value): self.embedding.word_embedding = value def get_encoder(self): class EncoderWrapper: def __call__(cls, *args, **kwargs): return cls.forward(*args, **kwargs) def forward( cls, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.get_encoder_output( input_ids, attention_mask, output_hidden_states, output_attentions, return_dict=return_dict ) return EncoderWrapper() def get_decoder(self): return self.get_decoder_output def set_decoder_special_tokens(self, target_id): target_id.masked_fill_(target_id == self.cls_token_id, self.bos_token_id) target_id.masked_fill_(target_id == self.sep_token_id, self.eos_token_id) return target_id def _shift_right(self, input_ids): shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = self.bos_token_id shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id) return shifted_input_ids def get_encoder_output( self, input_ids: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict = False ): if input_ids is not None: input_shape = input_ids.size() else: raise ValueError("You have to specify input_ids") batch_size, seq_length = input_shape device = input_ids.device if attention_mask is None: attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device) else: attention_mask = ~attention_mask.bool() attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) static_embeddings = self.embedding(input_ids.t()) contextualized_embeddings, attention_probs = self.encoder(static_embeddings, attention_mask) contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings] last_layer = contextualized_embeddings[-1] contextualized_embeddings = [contextualized_embeddings[0]] + [ contextualized_embeddings[i] - contextualized_embeddings[i - 1] for i in range(1, len(contextualized_embeddings)) ] if not return_dict: return ( last_layer, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return BaseModelOutput( last_hidden_state=last_layer, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) def get_decoder_output( self, target_ids: torch.Tensor = None, encoder_output: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict = False ): batch_size, seq_length, _ = encoder_output.shape device = target_ids.device if attention_mask is None: attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device) else: attention_mask = ~attention_mask.bool() attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) hidden_states, self_attention_p, cross_attention_p, key_value_states = self.decoder( self.embedding(target_ids.t()), encoder_output.transpose(0, 1), attention_mask, past_key_values ) hidden_states = [e.transpose(0, 1) for e in hidden_states] last_layer = hidden_states[-1] hidden_states = [hidden_states[0]] + [ hidden_states[i] - hidden_states[i - 1] for i in range(1, len(hidden_states)) ] if not return_dict: return ( last_layer, *([key_value_states] if use_cache else []), *([hidden_states] if output_hidden_states else []), *([self_attention_p] if output_attentions else []), *([cross_attention_p] if output_attentions else []), ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=last_layer, past_key_values=key_value_states if use_cache else None, hidden_states=hidden_states if output_hidden_states else None, attentions=self_attention_p if output_attentions else None, cross_attentions=cross_attention_p if output_attentions else None ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict decoder_input_ids = self.set_decoder_special_tokens(decoder_input_ids) if encoder_outputs is None: encoder_outputs = self.get_encoder_output( input_ids, attention_mask, output_hidden_states, output_attentions, return_dict ) 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, ) decoder_outputs = self.get_decoder_output( decoder_input_ids, encoder_outputs[0], attention_mask, past_key_values, use_cache, output_hidden_states, output_attentions, 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 NorT5ForConditionalGeneration(NorT5Model): def __init__(self, config): super().__init__(config, add_lm_layer=True) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): use_cache = use_cache if use_cache is not None else getattr(self.config, "use_cache", False) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.get_encoder_output( input_ids, attention_mask, output_hidden_states, output_attentions, return_dict ) 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, ) if labels is not None: labels = self.set_decoder_special_tokens(labels) if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = self._shift_right(labels) elif decoder_input_ids is not None: decoder_input_ids = self.set_decoder_special_tokens(decoder_input_ids) decoder_outputs = self.get_decoder_output( decoder_input_ids, encoder_outputs[0], attention_mask, past_key_values, use_cache, output_hidden_states, output_attentions, return_dict ) lm_logits = self.classifier(decoder_outputs[0]) loss = None if labels is not None: labels.masked_fill_(labels == self.pad_token_id, -100) loss_fct = nn.CrossEntropyLoss(ignore_index=-100) loss = loss_fct(lm_logits.flatten(0, 1), labels.flatten()) if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, 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, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): if past_key_values is not None: input_ids = input_ids[:, -1:] return { "decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past_key_values, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past_key_values is None: print("You might want to consider setting `use_cache=True` to speed up decoding") return past_key_values reordered_decoder_past = () for layer_past_states in past_key_values: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states layer_past_state = layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)) reordered_layer_past_states = reordered_layer_past_states + (layer_past_state,) assert reordered_layer_past_states[0].shape == layer_past_states[0].shape assert len(reordered_layer_past_states) == len(layer_past_states) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past class NorT5Encoder(NorT5Model): def __init__(self, config): super().__init__(config, add_lm_layer=False, add_decoder=True) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.get_encoder_output( input_ids, attention_mask, output_hidden_states, output_attentions, return_dict=return_dict )