from __future__ import absolute_import, division, print_function, unicode_literals import math from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch import _softmax_backward_data as _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 ) 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))) def forward(self, x, encoder_output, encoder_padding_mask): self_relative_embedding = self.self_relative_embedding() cross_relative_embedding = self.cross_relative_embedding() autoreg_mask = torch.triu( torch.full((x.size(0), x.size(0)), True, device=x.device), diagonal=1 ) for layer in self.layers: x = layer(x, autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding) return x 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) 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) self.cross_attention = Attention(config) self.mlp = FeedForward(config) def forward(self, x, autoreg_mask, encoder_output, encoder_padding_mask, self_relative_embedding, cross_relative_embedding): x = x + self.self_attention(x, x, autoreg_mask, self_relative_embedding)[0] x = x + self.cross_attention(x, encoder_output, encoder_padding_mask, cross_relative_embedding)[0] x = x + self.mlp(x) return x 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 x.masked_fill_(mask, float('-inf')) x = torch.softmax(x, self.dim) x.masked_fill_(mask, 0.0) self.save_for_backward(x) return x @staticmethod def backward(self, grad_output): output, = self.saved_tensors inputGrad = _softmax_backward_data(grad_output, output, self.dim, output.dtype) return inputGrad, None, None class Attention(nn.Module): def __init__(self, config): super().__init__() self.config = config 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(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \ - torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0) position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings) 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 compute_attention_scores(self, q, kv, relative_embedding): key_len, batch_size, _ = kv.size() query_len, _, _ = q.size() if self.position_indices.size(0) < query_len: position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \ - torch.arange(query_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) kv = self.pre_layer_norm(kv) q = self.pre_layer_norm(q) query = self.in_proj_q(q) # shape: [T, B, D] key = self.in_proj_k(kv) # shape: [T, B, D] value = self.in_proj_v(kv) # shape: [T, B, D] query_pos = self.in_proj_q(self.dropout(relative_embedding)) # shape: [2T-1, 2D] query_pos = F.embedding(self.position_indices[:query_len, :key_len], query_pos) # shape: [T, T, 2D] query_pos = query_pos.view(query_len, key_len, self.num_heads, self.head_size) key_pos = self.in_proj_k(self.dropout(relative_embedding)) # shape: [2T-1, 2D] key_pos = F.embedding(self.position_indices[:query_len, :key_len], key_pos) # shape: [T, T, 2D] key_pos = key_pos.view(query_len, key_len, self.num_heads, self.head_size) query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale) 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_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len) attention_scores.add_(torch.einsum("bhqd,qkhd->bhqk", query, key_pos * self.scale)) attention_scores.add_(torch.einsum("bhkd,qkhd->bhqk", key * self.scale, query_pos)) return attention_scores, value def compute_output(self, attention_probs, value): 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) return context def forward(self, q, kv, attention_mask, relative_embedding): attention_scores, value = self.compute_attention_scores(q, kv, relative_embedding) attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1) return self.compute_output(attention_probs, value), attention_probs.detach() 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): return self.get_encoder_output def get_decoder(self): return self.decoder 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 return shifted_input_ids def get_encoder_output( 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 = 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, attention_probs return BaseModelOutput( last_hidden_state=last_layer, hidden_states=contextualized_embeddings, attentions=attention_probs ) def get_decoder_output( self, target_ids, encoder_output, attention_mask ): batch_size, seq_length = target_ids.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) return self.decoder( self.embedding(target_ids.t()), encoder_output.transpose(0, 1), attention_mask ).transpose(0, 1) 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, 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) encoder_outputs, encoder_contextualized_embeddings, encoder_attention_probs = self.get_encoder_output(input_ids, attention_mask) decoder_outputs = self.get_decoder_output(decoder_input_ids, encoder_outputs, attention_mask) if not return_dict: return (decoder_outputs, encoder_outputs) return Seq2SeqModelOutput( last_hidden_state=decoder_outputs, past_key_values=None, decoder_hidden_states=None, decoder_attentions=None, cross_attentions=None, encoder_last_hidden_state=encoder_outputs, encoder_hidden_states=encoder_contextualized_embeddings, encoder_attentions=encoder_attention_probs, ) 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 = 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, return_dict=True) 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.last_hidden_state, attention_mask) lm_logits = self.classifier(decoder_outputs) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss(ignore_index=self.pad_token_id) loss = loss_fct(lm_logits.flatten(0, 1), labels.flatten()) if not return_dict: output = (lm_logits,) + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, decoder_hidden_states=decoder_outputs, decoder_attentions=None, cross_attentions=None, 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, ): 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 reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) 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, return_dict=return_dict)