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import torch
import numpy as np
from models.transformer import BasicTransformerModel
from models import BaseModel
from models.flowplusplus import FlowPlusPlus
import ast
from torch import nn
import math
from .util.generation import autoregressive_generation_multimodal
from models.cdvae import ConditionalDiscreteVAE
class MowgliModel(BaseModel):
def __init__(self, opt):
super().__init__(opt)
input_mods = self.input_mods
output_mods = self.output_mods
dins = self.dins
douts = self.douts
input_lengths = self.input_lengths
output_lengths = self.output_lengths
self.input_mod_nets = []
self.output_mod_nets = []
self.output_mod_mean_nets = []
self.output_mod_vaes = []
self.conditioning_seq_lens = []
self.module_names = []
self.vae_temp = opt.vae_temp
if opt.cond_vae or opt.stage2:
for i, mod in enumerate(input_mods):
net = BasicTransformerModel(opt.dhid, dins[i], opt.nhead, opt.dhid, 2, opt.dropout, self.device, use_pos_emb=True, input_length=input_lengths[i], use_x_transformers=opt.use_x_transformers, opt=opt)
if opt.cond_vae and opt.stage2:
for param in net.parameters():
param.requires_grad = False
name = "_input_"+mod
setattr(self,"net"+name, net)
self.input_mod_nets.append(net)
self.module_names.append(name)
for i, mod in enumerate(output_mods):
# import pdb;pdb.set_trace()
vae = ConditionalDiscreteVAE(
input_shape = (output_lengths[i], 1),
channels = douts[i],
num_layers = opt.vae_num_layers, # number of downsamples - ex. 256 / (2 ** 3) = (32 x 32 feature map)
num_tokens = opt.vae_num_tokens, # number of visual tokens. in the paper, they used 8192, but could be smaller for downsized projects
codebook_dim = opt.vae_codebook_dim, # codebook dimension
hidden_dim = opt.vae_dhid, # hidden dimension
num_resnet_blocks = opt.vae_num_resnet_blocks, # number of resnet blocks
temperature = opt.vae_temp, # gumbel softmax temperature, the lower this is, the harder the discretization
straight_through = opt.vae_hard, # straight-through for gumbel softmax. unclear if it is better one way or the other
cond_dim = opt.dhid,
cond_vae = opt.cond_vae,
prior_nhead = opt.prior_nhead,
prior_dhid = opt.prior_dhid,
prior_nlayers = opt.prior_nlayers,
prior_dropout = opt.prior_dropout,
prior_use_pos_emb = not opt.prior_no_use_pos_emb,
prior_use_x_transformers = opt.prior_use_x_transformers,
opt = opt
)
if opt.stage2:
for param in vae.encoder.parameters():
param.requires_grad = False
for param in vae.decoder.parameters():
param.requires_grad = False
name = "_output_vae_"+mod
setattr(self, "net"+name, vae)
self.output_mod_vaes.append(vae)
self.conditioning_seq_lens.append(np.prod(vae.codebook_layer_shape))
if opt.cond_vae or opt.stage2:
net = BasicTransformerModel(opt.dhid, opt.dhid, opt.nhead, opt.dhid, opt.nlayers, opt.dropout, self.device, use_pos_emb=opt.use_pos_emb_output, input_length=sum(input_lengths), use_x_transformers=opt.use_x_transformers, opt=opt)
name = "_output_"+mod
setattr(self, "net"+name, net)
self.output_mod_nets.append(net)
self.module_names.append(name)
if opt.cond_vae and opt.stage2:
for param in net.parameters():
param.requires_grad = False
if opt.residual:
if self.opt.cond_concat_dims:
net = nn.Linear(opt.dhid,douts[i])
else:
net = nn.Linear(opt.dhid,opt.douts[i])
if opt.cond_vae and opt.stage2:
for param in net.parameters():
param.requires_grad = False
name="_output_mean_encoder"
setattr(self, "net"+name, net)
self.output_mod_mean_nets.append(net)
self.mean_loss = nn.MSELoss()
self.inputs = []
self.targets = []
self.mse_loss = 0
self.nll_loss = 0
self.prior_loss_weight = opt.prior_loss_weight_initial
def name(self):
return "mowgli"
@staticmethod
def modify_commandline_options(parser, opt):
parser.add_argument('--dhid', type=int, default=512)
# parser.add_argument('--conditioning_seq_lens', type=str, default=None, help="the number of outputs of the conditioning transformers to feed (meaning the number of elements along the sequence dimension)")
parser.add_argument('--nlayers', type=int, default=6)
parser.add_argument('--nhead', type=int, default=8)
parser.add_argument('--vae_num_layers', type=int, default=3)
parser.add_argument('--vae_num_tokens', type=int, default=2048)
parser.add_argument('--vae_codebook_dim', type=int, default=512)
parser.add_argument('--vae_dhid', type=int, default=64)
parser.add_argument('--prior_dhid', type=int, default=512)
parser.add_argument('--prior_nhead', type=int, default=8)
parser.add_argument('--prior_nlayers', type=int, default=8)
parser.add_argument('--prior_dropout', type=float, default=0)
parser.add_argument('--prior_loss_weight_initial', type=float, default=0)
parser.add_argument('--prior_loss_weight_warmup_epochs', type=float, default=100)
parser.add_argument('--max_prior_loss_weight', type=float, default=0, help="max value of prior loss weight during stage 1 (e.g. 0.01 is a good value)")
parser.add_argument('--vae_num_resnet_blocks', type=int, default=1)
parser.add_argument('--vae_temp', type=float, default=1.0)
parser.add_argument('--anneal_rate', type=float, default=1e-6)
parser.add_argument('--temp_min', type=float, default=0.5)
parser.add_argument('--vae_hard', action='store_true', help="whether to use the hard one-hot vector as output and use the straight through gradient estimator, for discrete latents")
parser.add_argument('--dropout', type=float, default=0.1)
parser.add_argument('--scales', type=str, default="[[10,0]]")
parser.add_argument('--residual', action='store_true', help="whether to use the vae to predict the residual around a determnisitic mean")
parser.add_argument('--use_pos_emb_output', action='store_true', help="whether to use positional embeddings for output modality transformers")
parser.add_argument('--use_rotary_pos_emb', action='store_true', help="whether to use rotary position embeddings")
parser.add_argument('--use_x_transformers', action='store_true', help="whether to use rotary position embeddings")
parser.add_argument('--prior_use_x_transformers', action='store_true', help="whether to use rotary position embeddings")
parser.add_argument('--prior_no_use_pos_emb', action='store_true', help="dont use positional embeddings for the prior transformer")
parser.add_argument('--stage2', action='store_true', help="stage2: train the prior, rather than the VAE")
parser.add_argument('--cond_vae', action='store_true', help="whether to use a conditional vae")
return parser
def forward(self, data, temp=1.0):
# in lightning, forward defines the prediction/inference actions
opt=self.opt
latents = []
for i, mod in enumerate(self.input_mods):
latents.append(self.input_mod_nets[i].forward(data[i]))
latent = torch.cat(latents)
outputs = []
if self.opt.residual:
for i, mod in enumerate(self.output_mods):
trans_output = self.output_mod_nets[i].forward(latent)[:self.conditioning_seq_lens[i]]
trans_predicted_mean_latents = self.output_mod_nets[i].forward(latent)[self.conditioning_seq_lens[i]:self.conditioning_seq_lens[i]+self.output_lengths[i]]
predicted_mean = self.output_mod_mean_nets[i](trans_predicted_mean_latents)
# residual, _ = self.output_mod_glows[i](x=None, cond=trans_output.permute(1,0,2), reverse=True)
residual = self.output_mod_vaes[i].generate(trans_output.permute(1,2,0), temp=temp)
residual = residual.squeeze(-1)
output = predicted_mean + residual.permute(2,0,1)
outputs.append(output)
else:
for i, mod in enumerate(self.output_mods):
trans_output = self.output_mod_nets[i].forward(latent)[:self.conditioning_seq_lens[i]]
output = self.output_mod_vaes[i].generate(trans_output.permute(1,2,0), temp=temp)
# import pdb;pdb.set_trace()
output = output.squeeze(-1)
outputs.append(output.permute(2,0,1))
return outputs
def on_train_epoch_start(self):
self.prior_loss_weight = self.opt.max_prior_loss_weight * min(self.current_epoch/self.opt.prior_loss_weight_warmup_epochs, 1)
self.vae_temp = max(self.vae_temp * math.exp(-self.opt.anneal_rate * self.global_step), self.opt.temp_min)
def training_step(self, batch, batch_idx):
opt = self.opt
self.set_inputs(batch)
# print(self.input_mod_nets[0].encoder1.weight.data)
# print(self.targets[0])
if opt.cond_vae or opt.stage2:
latents = []
for i, mod in enumerate(self.input_mods):
latents.append(self.input_mod_nets[i].forward(self.inputs[i]))
latent = torch.cat(latents)
# print(latent)
if self.opt.residual:
nll_loss = 0
mse_loss = 0
accuracies = []
for i, mod in enumerate(self.output_mods):
trans_output = self.output_mod_nets[i].forward(latent)
latents1 = trans_output[:self.conditioning_seq_lens[i]]
latents2 = latents1
trans_predicted_mean_latents = trans_output[self.conditioning_seq_lens[i]:self.conditioning_seq_lens[i]+self.output_lengths[i]]
predicted_mean = self.output_mod_mean_nets[i](trans_predicted_mean_latents)
vae = self.output_mod_vaes[i]
if not self.opt.stage2:
nll_loss += vae((self.targets[i] - predicted_mean).permute(1,2,0), cond=latents1.permute(1,2,0), return_loss=True, temp=self.vae_temp) #time, batch, features -> batch, features, time
if self.opt.max_prior_loss_weight > 0:
prior_loss, accuracy = vae.prior_logp((self.targets[i] - predicted_mean).permute(1,2,0), cond=latents2.permute(1,2,0), return_accuracy=True)
accuracies.append(accuracy)
nll_loss += self.prior_loss_weight * prior_loss
else:
prior_loss, accuracy = vae.prior_logp((self.targets[i] - predicted_mean).permute(1,2,0), cond=latents2.permute(1,2,0), return_accuracy=True, detach_cond=True)
nll_loss += prior_loss
accuracies.append(accuracy)
mse_loss += 100*self.mean_loss(predicted_mean[i], self.targets[i])
loss = nll_loss + mse_loss
self.mse_loss = mse_loss
self.nll_loss = nll_loss
self.log('mse_loss', mse_loss)
self.log('nll_loss', nll_loss)
if len(accuracies) > 0:
self.log('accuracy', torch.mean(torch.stack(accuracies)))
else:
loss = 0
accuracies = []
# import pdb;pdb.set_trace()
for i, mod in enumerate(self.output_mods):
output1 = self.output_mod_nets[i].forward(latent)[:self.conditioning_seq_lens[i]]
output2 = output1
vae = self.output_mod_vaes[i]
if not self.opt.stage2:
loss += vae(self.targets[i].permute(1,2,0), cond=output1.permute(1,2,0), return_loss=True, temp=self.vae_temp) #time, batch, features -> batch, features, time
if self.opt.max_prior_loss_weight > 0:
prior_loss, accuracy = vae.prior_logp(self.targets[i].permute(1,2,0), cond=output2.permute(1,2,0), return_accuracy=True)
loss += self.prior_loss_weight * prior_loss
accuracies.append(accuracy)
else:
prior_loss, accuracy = vae.prior_logp(self.targets[i].permute(1,2,0), cond=output2.permute(1,2,0), return_accuracy=True, detach_cond=True)
##prior_loss, accuracy = vae.prior_logp(self.targets[i].permute(1,2,0), return_accuracy=True, detach_cond=True)
loss += prior_loss
accuracies.append(accuracy)
else:
loss = 0
for i, mod in enumerate(self.output_mods):
vae = self.output_mod_vaes[i]
loss += vae(self.targets[i].permute(1,2,0), cond=None, return_loss=True, temp=self.vae_temp) #time, batch, features -> batch, features, time
self.log('loss', loss)
if opt.cond_vae or opt.stage2:
if len(accuracies) > 0:
self.log('accuracy', torch.mean(torch.stack(accuracies)))
# print(loss)
# for p in self.output_mod_nets[0].parameters():
# print(p.norm())
return loss
def test_step(self, batch, batch_idx):
if self.opt.residual:
self.eval()
loss = self.training_step(batch, batch_idx)
# print(loss)
return {"test_loss": loss, "test_mse_loss": self.mse_loss, "test_nll_loss": self.nll_loss}
else:
return super().test_step(batch, batch_idx)
def test_epoch_end(self, outputs):
if self.opt.residual:
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
avg_mse_loss = torch.stack([x['test_mse_loss'] for x in outputs]).mean()
avg_nll_loss = torch.stack([x['test_nll_loss'] for x in outputs]).mean()
logs = {'test_loss': avg_loss, 'test_mse_loss': avg_mse_loss, 'test_nll_loss': avg_nll_loss}
return {'log': logs}
else:
return super().test_epoch_end(outputs)
#to help debug XLA stuff, like missing ops, or data loading/compiling bottlenecks
# see https://youtu.be/iwtpwQRdb3Y?t=1056
# def on_epoch_end(self):
# import torch_xla.core.xla_model as xm
# import torch_xla.debug.metrics as met
# xm.master_print(met.metrics_report())
#def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
# optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
# optimizer.zero_grad()
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