nort5-small / modeling_nort5.py
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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 not 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(1)
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], key], dim=1)
value = torch.cat([past_key_value[1], value], dim=1)
key_len = key.size(1)
elif past_key_value[0].size(1) == kv.size(0):
key = past_key_value[0]
value = past_key_value[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)
return context, attention_probs.detach(), (key.detach(), value.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.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 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.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.unflatten(0, (-1, self.config.num_attention_heads))
layer_past_state = layer_past_state.index_select(0, beam_idx.to(layer_past_state.device))
layer_past_state = layer_past_state.flatten(0, 1)
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
)