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# coding=utf-8
# Copyright 2022 IDEA-CCNL The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Della model. """
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Optional, Tuple
from transformers.modeling_outputs import ModelOutput
from transformers.modeling_utils import PreTrainedModel
from fengshen.models.deepVAE.configuration_della import DellaModelConfig
from fengshen.models.deepVAE.latent_connector import GPT2ForDecoderLatentConnector, GPT2ForEncoderLatentConnector
from fengshen.models.deepVAE.utils import connect, compute_kl_loss, top_k_top_p_filtering, enforce_repetition_penalty
_CHECKPOINT_FOR_DOC = "della-226M-base"
_CONFIG_FOR_DOC = "DellaModelConfig"
_TOKENIZER_FOR_DOC = "BertTokenizer"
Della_model_PRETRAINED_MODEL_ARCHIVE_LIST = [
"della-226M-base"
]
@dataclass
class DellaModelOutput(ModelOutput):
logits: torch.FloatTensor = None
posterior_latents: Optional[Tuple[torch.FloatTensor]] = None
prior_latent: Optional[Tuple[torch.FloatTensor]] = None
class latent_layer(nn.Module):
def __init__(self, input_dim) -> None:
super().__init__()
self.W_hh = nn.Linear(input_dim, input_dim, bias=False)
self.W_ih = nn.Linear(input_dim, input_dim, bias=False)
self.tanh = nn.Tanh()
def forward(self, z_lt_lm1, z_lm1):
# inputs are z_<l-1 and z_l-1
return self.tanh(self.W_hh(z_lt_lm1) + self.W_ih(z_lm1))
class AverageSelfAttention(nn.Module):
def __init__(self, hidden_dim):
super(AverageSelfAttention, self).__init__()
w = torch.empty(hidden_dim)
nn.init.normal_(w, std=0.02)
self.attention_weights = nn.Parameter(w)
self.softmax = nn.Softmax(dim=-1)
self.non_linearity = torch.tanh
def forward(self, inputs, attention_mask=None):
scores = self.non_linearity(inputs.matmul(self.attention_weights))
if attention_mask is not None:
scores = scores + attention_mask
scores = self.softmax(scores)
weighted = torch.mul(inputs, scores.unsqueeze(-1).expand_as(inputs))
representations = weighted.sum(1).squeeze(1)
return representations, scores
class DeepVAE(nn.Module):
"""DeepVAE with recursive latent z extracted from every layer of encoder and applied on every layer of decoder """
def __init__(self, encoder, decoder, latent_dim, hidden_dim, layer_num, pad_token_id, bos_token_id, eos_token_id, CVAE):
super(DeepVAE, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.latent_dim = latent_dim
self.layer_num = layer_num
self.CVAE = CVAE
# the first layer of latent net depends on zero vectors and therefore can be ignored
self.latent_nets = nn.ModuleList([latent_layer(latent_dim) for _ in range(layer_num-1)])
post_input_dim = hidden_dim+latent_dim if not CVAE else 2*hidden_dim+latent_dim
prior_input_dim = latent_dim if not CVAE else hidden_dim+latent_dim
self.posterior_nets = nn.ModuleList([nn.Linear(post_input_dim, 2*latent_dim, bias=False) for _ in range(layer_num)])
self.prior_nets = nn.ModuleList([nn.Linear(prior_input_dim, 2*latent_dim, bias=False) for _ in range(layer_num)])
# pooling because we are not using hidden states of BOS token
self.pooling = nn.ModuleList([AverageSelfAttention(hidden_dim) for _ in range(layer_num)])
def get_decoder_loss(self, inputs, layer_latent_vecs, cond_inputs):
loss_mask = None
dec_inputs = inputs
if self.CVAE:
loss_mask = torch.concat((torch.zeros_like(cond_inputs), torch.ones_like(inputs)), dim=1)
dec_inputs = torch.concat((cond_inputs, inputs), dim=1)
rec_loss = self.decoder(input_ids=dec_inputs, layer_latent_vecs=layer_latent_vecs,
labels=dec_inputs, label_ignore=self.pad_token_id, loss_mask=loss_mask).loss
rec_loss = rec_loss / torch.sum(inputs != self.pad_token_id, dim=1) # ignore both the pad token id and the cond inputs
return rec_loss.mean()
def get_latent_vecs(self, layer_hidden_states, sample=True, beta_logvar=1., cond_inputs=None):
prior_z_list, posterior_z_list = [], []
prior_output_list, posterior_output_list = [], []
batch_size = layer_hidden_states[0].shape[0]
z = torch.zeros((batch_size, self.latent_dim), dtype=layer_hidden_states[0].dtype, device=layer_hidden_states[0].device)
for layer_idx in range(self.layer_num):
# TODO be more specific about the pooling range, ignore the pad_token_ids could improve the repr of sent or cond inputs
if self.CVAE:
cond_length = cond_inputs.shape[-1]
cond_repr, _ = self.pooling[layer_idx](layer_hidden_states[layer_idx][:, :cond_length, :])
sent_repr, _ = self.pooling[layer_idx](layer_hidden_states[layer_idx][:, cond_length:, :])
prior_input = torch.cat([cond_repr, z], dim=1)
posterior_input = torch.cat([cond_repr, sent_repr, z], dim=1)
else:
sent_repr, _ = self.pooling[layer_idx](layer_hidden_states[layer_idx])
prior_input = z
posterior_input = torch.cat([sent_repr, z], dim=1)
prior_net_output = self.prior_nets[layer_idx](prior_input)
posterior_net_output = self.posterior_nets[layer_idx](posterior_input).squeeze(dim=1)
prior_z = connect(mean=prior_net_output[:, :self.latent_dim], logvar=prior_net_output[:, self.latent_dim:], sample=sample)
posterior_z = connect(mean=posterior_net_output[:, :self.latent_dim], logvar=posterior_net_output[:, self.latent_dim:],
sample=sample, beta_logvar=beta_logvar)
if layer_idx != self.layer_num - 1:
z = self.latent_nets[layer_idx](z, posterior_z) # we skip than last iteration
# save the outputs for decoder and kl loss calculations
prior_z_list.append(prior_z)
posterior_z_list.append(posterior_z)
prior_output_list.append(prior_net_output)
posterior_output_list.append(posterior_net_output)
return prior_z_list, posterior_z_list, prior_output_list, posterior_output_list
def get_kl_loss(self, prior_output_list, posterior_output_list, beta_kl_constraints):
total_kl_loss = None
layer_kl_loss = []
for prior_output, posterior_output in zip(prior_output_list, posterior_output_list):
kl_loss = compute_kl_loss(posterior_output[:, :self.latent_dim], posterior_output[:, self.latent_dim:],
prior_output[:, :self.latent_dim], prior_output[:, self.latent_dim:])
# incase of overflow and nan value we shall clip the loss here
# kl_loss = torch.clip(kl_loss, max=1e4)
total_kl_loss = kl_loss if total_kl_loss is None else total_kl_loss+kl_loss
layer_kl_loss.append(kl_loss)
return total_kl_loss.mean() * beta_kl_constraints, layer_kl_loss
def forward(self, inputs, beta_kl_constraints, cond_inputs=None):
# handle cond_inputs differently
enc_inputs = torch.concat((cond_inputs, inputs), dim=1) if self.CVAE else inputs
encoder_outputs = self.encoder(input_ids=enc_inputs)
# hidden_states are tuples with length layer_num+1 and each tensor has shape (batch_size, sequence_length, hidden_size), embedding layer is ignored
prior_z_list, posterior_z_list, prior_output_list, posterior_output_list = self.get_latent_vecs(
encoder_outputs.hidden_states[1:], cond_inputs=cond_inputs)
total_kl_loss, layer_kl_loss = self.get_kl_loss(prior_output_list, posterior_output_list, beta_kl_constraints)
# pass the posterior to decoder for layer-wise low rank tensor product
rec_loss = self.get_decoder_loss(inputs, posterior_z_list, cond_inputs)
return total_kl_loss+rec_loss, rec_loss, total_kl_loss, layer_kl_loss
def get_cond_prior_vecs(self, layer_hidden_states, cond_inputs, sample=True, beta_logvar=1.):
prior_z_list, prior_output_list = [], []
batch_size = layer_hidden_states[0].shape[0]
z = torch.zeros((batch_size, self.latent_dim), dtype=layer_hidden_states[0].dtype, device=layer_hidden_states[0].device)
for layer_idx in range(self.layer_num):
# TODO be more specific about the pooling range, ignore the pad_token_ids could improve the repr of sent or cond inputs
cond_length = cond_inputs.shape[-1]
cond_repr, _ = self.pooling[layer_idx](layer_hidden_states[layer_idx][:, :cond_length, :])
prior_input = torch.cat([cond_repr, z], dim=1)
prior_net_output = self.prior_nets[layer_idx](prior_input)
prior_z = connect(mean=prior_net_output[:, :self.latent_dim], logvar=prior_net_output[:, self.latent_dim:],
sample=sample, beta_logvar=beta_logvar)
if layer_idx != self.layer_num - 1:
z = self.latent_nets[layer_idx](z, prior_z) # we skip than last iteration
# save the outputs for decoder and kl loss calculations
prior_z_list.append(prior_z)
prior_output_list.append(prior_net_output)
return prior_z_list, prior_output_list
def inference(self, inputs, top_p, max_length, top_k=0., temperature=1., repetition_penalty=1., sample=False, beta_logvar=1.):
# NOTE: if we want to use BOS hidden states for x repr then we need to change the causal mask in attention block.
encoder_outputs = self.encoder(input_ids=inputs)
# hidden_states are tuples with length layer_num+1 and each tensor has shape (batch_size, sequence_length, hidden_size), embedding layer is ignored
if self.CVAE:
prior_z_list, prior_output_list = self.get_cond_prior_vecs(encoder_outputs.hidden_states[1:], inputs, sample=sample, beta_logvar=beta_logvar)
latent_vecs = prior_z_list
generated = inputs
else:
prior_z_list, posterior_z_list, prior_output_list, posterior_output_list = self.get_latent_vecs(encoder_outputs.hidden_states[1:], sample=sample, beta_logvar=beta_logvar)
latent_vecs = posterior_z_list
generated = [[self.bos_token_id] for _ in range(inputs.shape[0])]
generated = torch.tensor(generated, dtype=torch.long, device=inputs.device)
# start generation
with torch.no_grad():
for _ in range(max_length):
outputs = self.decoder(input_ids=generated, layer_latent_vecs=latent_vecs, labels=None,
label_ignore=self.pad_token_id)
next_token_logits = outputs.logits[:, -1, :] / temperature
filtered_logits = top_k_top_p_filtering(next_token_logits, top_p=top_p, top_k=top_k)
log_probs = F.softmax(filtered_logits, dim=-1)
if repetition_penalty != 1.0:
enforce_repetition_penalty(log_probs, generated, repetition_penalty)
next_token = torch.multinomial(log_probs, num_samples=1)
generated = torch.cat((generated, next_token), dim=1)
if all(next_token[idx, 0].item() == self.eos_token_id for idx in range(next_token.shape[0])):
break # if all samples predict eos in the batch.
return generated
class DellaPretrainedModel(PreTrainedModel):
def _init_weights(self, module):
""" Initialize the weights """
pass # to bypass the not implement error
class Della(DellaPretrainedModel):
'''This class is only implemented to suit huggingface interface, use vae_pl_module to initialize the VAE for training'''
config_class = DellaModelConfig
base_model_prefix = "della"
supports_gradient_checkpointing = True
def __init__(self, config: DellaModelConfig):
super().__init__(config)
self.config = config
encoder_model = GPT2ForEncoderLatentConnector(config=self.config)
decoder_model = GPT2ForDecoderLatentConnector(config=self.config, latent_dim=self.config.latent_dim)
vae_model = DeepVAE(encoder_model, decoder_model, latent_dim=self.config.latent_dim,
hidden_dim=self.config.hidden_size, layer_num=self.config.num_hidden_layers,
pad_token_id=self.config.pad_token_id, bos_token_id=self.config.bos_token_id,
eos_token_id=self.config.eos_token_id, CVAE=self.config.CVAE)
self.model = vae_model
def forward(self, inputs, cond_inputs=None, sample_latent=True):
# handle cond_inputs differently
enc_inputs = torch.concat((cond_inputs, inputs), dim=1) if self.model.CVAE else inputs
encoder_outputs = self.model.encoder(input_ids=enc_inputs)
# hidden_states are tuples with length layer_num+1 and each tensor has shape (batch_size, sequence_length, hidden_size), embedding layer is ignored
prior_z_list, posterior_z_list, prior_output_list, posterior_output_list = self.model.get_latent_vecs(
encoder_outputs.hidden_states[1:], cond_inputs=cond_inputs, sample=sample_latent)
loss_mask, dec_inputs = None, inputs
if self.model.CVAE:
loss_mask = torch.concat((torch.zeros_like(cond_inputs), torch.ones_like(inputs)), dim=1)
dec_inputs = torch.concat((cond_inputs, inputs), dim=1)
logits = self.model.decoder(input_ids=dec_inputs, layer_latent_vecs=posterior_z_list,
labels=dec_inputs, label_ignore=self.model.pad_token_id, loss_mask=loss_mask).logits
return DellaModelOutput(
logits=logits,
posterior_latents=posterior_z_list,
prior_latent=prior_z_list
)
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