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# import sonnet as snt
# from tensor2tensor.layers import common_attention
# from tensor2tensor.layers import common_layers
# import tensorflow.compat.v1 as tf
# from tensorflow.python.framework import function
# import tensorflow_probability as tfp
import numpy as np
import torch.nn as nn
# import layer_utils
import torch
# import data_utils_torch as data_utils
import math ##
# from options.options import opt
###
class TransformerEncoder(nn.Module):
def __init__(self,
hidden_size=256,
fc_size=1024,
num_heads=4,
layer_norm=True,
num_layers=8,
dropout_rate=0.2,
re_zero=True,
memory_efficient=False,
):
super(TransformerEncoder, self).__init__()
## hidden size, fc size, ##
## hidden size, fc size, num heads, layer_norm, num_layers, dropout_rate,
self.hidden_size = hidden_size
self.fc_size = fc_size ## fc_size ##
self.num_heads = num_heads ## num_heads ##
# self.num_heads = 1
self.layer_norm = layer_norm
self.num_layers = num_layers ## num_layers ##
self.dropout_rate = dropout_rate
self.re_zero = re_zero
self.memory_efficient = memory_efficient
### Attention layer and related modules ###
self.attention_layers = nn.ModuleList()
if self.layer_norm:
self.layer_norm_layers = nn.ModuleList()
if self.re_zero:
self.re_zero_vars = nn.ParameterList()
if self.dropout_rate: # dropout rate
self.dropout_layers = nn.ModuleList()
for i in range(self.num_layers): ## dropout rate, kdim, vdim,
cur_atten_layer = nn.MultiheadAttention( ## hidden_size, hidden_size ##
self.hidden_size, self.num_heads, dropout=0.0, bias=True, kdim=self.hidden_size, vdim=self.hidden_size, batch_first=True)
self.attention_layers.append(cur_atten_layer)
if self.layer_norm: ## layernorm ##
cur_layer_norm = nn.LayerNorm(self.hidden_size)
self.layer_norm_layers.append(cur_layer_norm)
if self.re_zero:
cur_re_zero_var = torch.nn.Parameter(torch.zeros(size=(1,), dtype=torch.float32, requires_grad=True), requires_grad=True)
self.re_zero_vars.append(cur_re_zero_var)
if self.dropout_rate:
cur_dropout_layer = nn.Dropout(p=self.dropout_rate)
self.dropout_layers.append(cur_dropout_layer)
### Attention layer and related modules ###
self.fc_layers = nn.ModuleList()
if self.layer_norm:
self.fc_layer_norm_layers = nn.ModuleList()
if self.re_zero:
self.fc_re_zero_vars = nn.ParameterList()
if self.dropout_rate:
self.fc_dropout_layers = nn.ModuleList() # dropout layers
for i in range(self.num_layers):
cur_fc_layer = nn.Linear(in_features=self.hidden_size, out_features=self.fc_size, bias=True)
cur_fc_layer_2 = nn.Linear(in_features=self.fc_size, out_features=self.hidden_size, bias=True)
self.fc_layers.append(nn.Sequential(*[cur_fc_layer, cur_fc_layer_2]))
if self.layer_norm: # layer norm
cur_layer_norm = nn.LayerNorm(self.hidden_size)
self.fc_layer_norm_layers.append(cur_layer_norm)
if self.re_zero: # re_zero_var
cur_re_zero_var = torch.nn.Parameter(
torch.zeros(size=(1,), dtype=torch.float32, requires_grad=True), requires_grad=True)
self.fc_re_zero_vars.append(cur_re_zero_var)
if self.dropout_rate:
cur_dropout_layer = nn.Dropout(p=self.dropout_rate)
self.fc_dropout_layers.append(cur_dropout_layer)
if self.layer_norm:
self.out_layer_norm = nn.LayerNorm(self.hidden_size)
def forward(self, inputs, set_attn_to_none=False):
### padding
# bsz x seq_length x embedding_dim #
bsz, seq_length = inputs.size(0), inputs.size(1)
if set_attn_to_none:
atten_mask = None
else:
atten_mask = np.tri(seq_length, seq_length, -1.0, dtype=np.float32).T # tri ### elements in the upper triangle are set to 1.0 ###
atten_mask = torch.from_numpy(atten_mask).float() # .cuda()
atten_mask = atten_mask.to(inputs.device)
atten_mask = atten_mask > 0.5 ## the bool format
# if inputs_mask is None:
# encoder_padding = layer_utils.embedding_to_padding(inputs) # bsz x n_vertices
# else:
# encoder_padding = inputs_mask # inputs_mask: bsz x n_vertices
# bsz = inputs.size(0)
# seq_length = inputs.size(1)
# ## attention masksingle direction ## need
# # encoder_self_attention_bias = layer_utils.attention_bias_ignore_padding(encoder_padding)
# # encoder_self_attention_mask = layer_utils.attention_mask(encoder_padding)
# encoder_self_attention_mask = layer_utils.attention_mask_single_direction(encoder_padding)
# # print(f"in vertex model forwarding function, encoder_self_attention_mask: {encoder_self_attention_mask.size()}, inputs: {inputs.size()}")
# encoder_self_attention_mask = encoder_self_attention_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
# encoder_self_attention_mask = encoder_self_attention_mask.contiguous().view(bsz * self.num_heads, seq_length, seq_length).contiguous()
# seq_length = inputs.size(1)
x = inputs ## bsz x seq_length x # bsz x seq x seq for the mask #
# zeros padding layer ## remember to add that! # zero padding layer #
# atten_mask = np.tri(seq_length, seq_length, -1.0, dtype=np.float32).T # mask;
# atten_mask = torch.from_numpy(atten_mask).float().cuda() ## mask single direction
## encode for each
for i in range(self.num_layers):
res = x.clone()
if self.layer_norm:
res = self.layer_norm_layers[i](res) ## res, res ## ## layernorm layers ##
# print(f"before attention {i}/{self.num_layers}, res: {res.size()}")
# res, _ = self.attention_layers[i](res, res, res, attn_mask=atten_mask)
## attentiion layers ### ## self-attention ## ## memory, q, k, v ## bsz x seq x latnetdim --> bsz x seq x seq for frame-frame weights ###
### bsz x seq x seq --> weights ### initialize something to zero for modeling controls ###
res, _ = self.attention_layers[i](res, res, res, attn_mask=atten_mask)
# print(f"after attention {i}/{self.num_layers}, res: {res.size()}")
if self.re_zero:
res = res * self.re_zero_vars[i]
if self.dropout_rate:
res = self.dropout_layers[i](res)
x = x + res
res = x.clone()
if self.layer_norm:
res = self.fc_layer_norm_layers[i](res) # fc norm #
res = self.fc_layers[i](res)
if self.re_zero:
res = res * self.fc_re_zero_vars[i]
if self.dropout_rate:
res = self.fc_dropout_layers[i](res)
x = x + res
if self.layer_norm:
x = self.out_layer_norm(x)
return x
class TransformerDecoder(nn.Module):
def __init__(self,
hidden_size=256,
fc_size=1024,
num_heads=4,
layer_norm=True,
num_layers=8,
dropout_rate=0.2,
re_zero=True,
with_seq_context=False
):
super(TransformerDecoder, self).__init__()
self.hidden_size = hidden_size
self.fc_size = fc_size
self.num_heads = num_heads
self.layer_norm = layer_norm
self.num_layers = num_layers
self.dropout_rate = dropout_rate
self.re_zero = re_zero
self.with_seq_context = with_seq_context
# self.context_window = opt.model.context_window
self.atten_mask = None
self.context_atten_mask = None
# self.prefix_key_len = opt.model.prefix_key_len ## can add prefix key values for prefix queries ##
# self.prefix_value_len = opt.model.prefix_value_len ## can add prefix key values for prefix queries ##
# self.prefix_value_len = value length #
### Attention layer and related modules ###
self.attention_layers = nn.ModuleList()
if self.layer_norm:
self.layer_norm_layers = nn.ModuleList()
if self.re_zero:
self.re_zero_vars = nn.ParameterList()
if self.dropout_rate:
self.dropout_layers = nn.ModuleList()
for i in range(self.num_layers):
cur_atten_layer = nn.MultiheadAttention(
self.hidden_size, self.num_heads, dropout=0.0, bias=True, kdim=self.hidden_size, vdim=self.hidden_size,
batch_first=True)
self.attention_layers.append(cur_atten_layer)
if self.layer_norm:
cur_layer_norm = nn.LayerNorm(self.hidden_size)
self.layer_norm_layers.append(cur_layer_norm)
if self.re_zero:
cur_re_zero_var = torch.nn.Parameter(torch.zeros(size=(1,), dtype=torch.float32, requires_grad=True), requires_grad=True)
self.re_zero_vars.append(cur_re_zero_var)
if self.dropout_rate: ## dropout
cur_dropout_layer = nn.Dropout(p=self.dropout_rate)
self.dropout_layers.append(cur_dropout_layer)
if self.with_seq_context:
##### attention, re_zero, dropout layers for the context attention layers #####
self.context_attention_layers = nn.ModuleList()
if self.layer_norm:
self.context_norm_layers = nn.ModuleList()
if self.re_zero:
self.context_re_zero_vars = nn.ParameterList()
if self.dropout_rate:
self.context_dropout_layers = nn.ModuleList()
for i in range(self.num_layers):
cur_atten_layer = nn.MultiheadAttention(
self.hidden_size, self.num_heads, dropout=0.0, bias=True, kdim=self.hidden_size, vdim=self.hidden_size,
batch_first=True)
self.context_attention_layers.append(cur_atten_layer)
if self.layer_norm:
cur_layer_norm = nn.LayerNorm(self.hidden_size)
self.context_norm_layers.append(cur_layer_norm)
if self.re_zero:
cur_re_zero_var = torch.nn.Parameter(torch.zeros(size=(1,), dtype=torch.float32, requires_grad=True), requires_grad=True)
self.context_re_zero_vars.append(cur_re_zero_var)
if self.dropout_rate:
cur_dropout_layer = nn.Dropout(p=self.dropout_rate)
# dropout layers
self.context_dropout_layers.append(cur_dropout_layer)
### Attention layer and related modules ###
self.fc_layers = nn.ModuleList()
if self.layer_norm:
self.fc_layer_norm_layers = nn.ModuleList()
if self.re_zero:
self.fc_re_zero_vars = nn.ParameterList()
# self.fc_re_zero_vars = nn.ModuleList()
if self.dropout_rate:
self.fc_dropout_layers = nn.ModuleList()
for i in range(self.num_layers):
cur_fc_layer = nn.Linear(in_features=self.hidden_size, out_features=self.fc_size, bias=True)
cur_fc_layer_2 = nn.Linear(in_features=self.fc_size, out_features=self.hidden_size, bias=True)
self.fc_layers.append(nn.Sequential(*[cur_fc_layer, cur_fc_layer_2]))
if self.layer_norm:
cur_layer_norm = nn.LayerNorm(self.hidden_size)
self.fc_layer_norm_layers.append(cur_layer_norm)
if self.re_zero:
cur_re_zero_var = torch.nn.Parameter(
torch.zeros(size=(1,), dtype=torch.float32, requires_grad=True), requires_grad=True)
self.fc_re_zero_vars.append(cur_re_zero_var)
if self.dropout_rate: ## dropout rate ##
cur_dropout_layer = nn.Dropout(p=self.dropout_rate)
self.fc_dropout_layers.append(cur_dropout_layer)
if self.layer_norm:
self.out_layer_norm = nn.LayerNorm(self.hidden_size)
def forward(self, inputs, sequential_context_embeddings=None):
seq_length = inputs.size(1)
bsz = inputs.size(0)
# #### ## sequential context embeddings for the embedding --> bsz x seq_length x feat_dim ####
# TODO: mask for inputs can be set to 1) None, then a fully-attention setting, 2) self-mask setting #
# ### sequential context mask should be set to a self-mask setting -> each self element can attend to self and before information ### #
atten_mask = None ## mask for inputs
if sequential_context_embeddings is not None:
# sequential_context_mask = np.tri(seq_length, seq_length, -1.0, dtype=np.float32).T # tri ## triangle mask ##
##
# sequential_context_mask = np.tri(inputs.size(1), sequential_context_embeddings.size(1), -1.0, dtype=np.float32).T # tri
# 1 x 30 --> no mask !
sequential_context_mask = np.tri(sequential_context_embeddings.size(1), inputs.size(1), -1.0, dtype=np.float32).T # tri
sequential_context_mask = torch.from_numpy(sequential_context_mask).float() # .cuda()
sequential_context_mask = sequential_context_mask.to(inputs.device)
sequential_context_mask = sequential_context_mask > 0.5
# # print(f"inputs: {inputs.size()}") ####
# if self.training:
# if self.atten_mask is None: ## seq length ##
# atten_mask = np.tri(seq_length, seq_length, -1.0, dtype=np.float32).T # tri
# # atten_mask = np.tri(seq_length, seq_length, 0.0, dtype=np.float32)
# atten_mask = torch.from_numpy(atten_mask).float().cuda()
# self.atten_mask = atten_mask
# else:
# atten_mask = self.atten_mask
# else: ### atten_mask
# atten_mask = np.tri(seq_length, seq_length, -1.0, dtype=np.float32).T # tri
# # atten_mask = np.tri(seq_length, seq_length, 0.0, dtype=np.float32)
# atten_mask = torch.from_numpy(atten_mask).float().cuda()
# context_window
# if self.context_window > 0 and sequential_context_embeddings is None:
# # ##### add global context embeddings to embedding vectors ##### #
# # inputs = inputs[:, 0:1] + inputs # add the contextual information to inputs # not add...
# # if opt.model.debug:
# # print(f"Using context window {self.context_window} for decoding...")
# if self.training:
# if self.context_atten_mask is None:
# context_atten_mask = np.tri(seq_length, seq_length, -1.0 * float(self.context_window), dtype=np.float32)
# context_atten_mask = torch.from_numpy(context_atten_mask).float().cuda()
# self.context_atten_mask = context_atten_mask
# else:
# context_atten_mask = self.context_atten_mask
# else:
# context_atten_mask = np.tri(seq_length, seq_length, -1.0 * float(self.context_window), dtype=np.float32)
# context_atten_mask = torch.from_numpy(context_atten_mask).float().cuda()
# atten_mask = context_atten_mask + atten_mask
# # context attention mask
# atten_mask = (atten_mask > 0.5)
# if len(atten_mask.size()) == 2:
# atten_mask[: self.prefix_key_len, ] = False
# else:
# atten_mask[:, : self.prefix_key_len] = False
# print(atten_mask)
# if sequential_context_embeddings is not None:
# context_length = sequential_context_embeddings.size(1)
# # sequential_context_padding = layer_utils.embedding_to_padding(sequential_context_embeddings)
# if sequential_context_mask is None:
# sequential_context_padding = layer_utils.embedding_to_padding(sequential_context_embeddings)
# else: #
# sequential_context_padding = 1. - sequential_context_mask.float() # sequential context mask?
# # sequential_context_padding = layer_utils.embedding_to_padding(sequential_context_embeddings)
# # j
# sequential_context_atten_mask = layer_utils.attention_mask_single_direction(sequential_context_padding, other_len=seq_length)
# # print(f"in decoder's forward function, sequential_context_padding: {sequential_context_padding.size()}, sequential_context_atten_mask: {sequential_context_atten_mask.size()}")
# sequential_context_atten_mask = sequential_context_atten_mask.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
# sequential_context_atten_mask = sequential_context_atten_mask.contiguous().view(bsz * self.num_heads, seq_length, context_length).contiguous()
x = inputs
for i in range(self.num_layers):
res = x.clone()
if self.layer_norm:
res = self.layer_norm_layers[i](res) # # self attention; all self attention; sequential
res, _ = self.attention_layers[i](res, res, res, attn_mask=atten_mask)
if self.re_zero:
res = res * self.re_zero_vars[i].unsqueeze(0).unsqueeze(0)
if self.dropout_rate:
res = self.dropout_layers[i](res)
x = x + res
# if we use sequential context embeddings
if sequential_context_embeddings is not None:
# for sequential context embedding
res = x.clone()
# then layer_norm, attention layer, re_zero layer and the dropout layer
if self.layer_norm:
res = self.context_norm_layers[i](res) ## need sequential masks! res can only attent to former sequential contexts ## ##
res, _ = self.context_attention_layers[i](res, sequential_context_embeddings, sequential_context_embeddings, attn_mask=sequential_context_mask)
if self.re_zero:
res = res * self.context_re_zero_vars[i].unsqueeze(0).unsqueeze(0)
if self.dropout_rate:
res = self.context_dropout_layers[i](res)
x = x + res
res = x.clone()
if self.layer_norm:
res = self.fc_layer_norm_layers[i](res)
res = self.fc_layers[i](res)
if self.re_zero:
res = res * self.fc_re_zero_vars[i]
if self.dropout_rate: # dropout layers # fc_dropout_layers
res = self.fc_dropout_layers[i](res)
x = x + res
if self.layer_norm:
x = self.out_layer_norm(x)
# x = x[:, self.prefix_key_len - 1: ]
return x
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