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import numpy as np
import sys
import itertools
import time
import torch
from torch import Tensor
import math
import torch.nn.functional as F
import numpy as np
import random as rd
import lightning as L
from torch.distributions.categorical import Categorical
import torchmetrics
from dataclasses import dataclass
import gc
import pickle
import utils.utils as utils
import dataset as dataloader
import models.helmgpt as helmgpt
import models.peptideclm as peptideclm
from tokenizer.my_tokenizers import SMILES_SPE_Tokenizer
import noise_schedule
from torch.optim.lr_scheduler import _LRScheduler
import models.roformer as roformer
from utils.filter import PeptideAnalyzer
@dataclass
class Loss:
loss: torch.FloatTensor
nlls: torch.FloatTensor
attn_mask: torch.FloatTensor
class NLL(torchmetrics.aggregation.MeanMetric):
pass
class BPD(NLL):
def compute(self) -> Tensor:
"""Computes the bits per dimension.
Returns:
bpd
"""
return self.mean_value / self.weight / math.log(2)
class Perplexity(NLL):
def compute(self) -> Tensor:
"""Computes the Perplexity.
Returns:
Perplexity
"""
return torch.exp(self.mean_value / self.weight)
class Diffusion(L.LightningModule):
def __init__(self, config, tokenizer):
super().__init__()
self.config = config
#self.save_hyperparameters()
# PeptideCLM tokenizer
self.tokenizer = tokenizer
self.vocab_size = self.tokenizer.vocab_size
self.mask_token_id = self.tokenizer.mask_token_id
self.sampler = self.config.sampling.predictor
self.analyzer = PeptideAnalyzer()
# backbone LM PeptideCLM model
if self.config.backbone == 'peptideclm':
self.backbone = peptideclm.EncoderWrapper(self.tokenizer)
self.backbone.unfreeze_all_layers()
self.backbone = torch.compile(self.backbone)
elif self.config.backbone == 'helmgpt':
self.backbone = helmgpt.GPT(self.config, self.tokenizer)
#self.backbone = torch.compile(self.backbone)
elif self.config.backbone == 'roformer':
self.backbone = roformer.Roformer(self.config, self.tokenizer)
self.backbone.unfreeze_all_layers()
elif self.config.backbone == 'finetune_roformer':
self.backbone = roformer.Roformer(self.config, self.tokenizer)
self.backbone.freeze_model()
self.backbone.unfreeze_n_layers(n=8)
else:
Exception('invalid backbone config')
self.neg_infinity = -1000000.0
self.T = config.T
# noise schedule for non-peptide bond tokens (default to log-linear)
self.noise = noise_schedule.get_noise(config)
# noise schedule for peptide bonds (log-polynomial)
self.bond_noise = noise_schedule.LogPolyNoise()
self.time_conditioning = self.config.time_conditioning
self.fast_forward_epochs = None
self.fast_forward_batches = None
self.gen_ppl_eval_model_name_or_path = self.config.eval.gen_ppl_eval_model_name_or_path
self.gen_ppl_metric = Perplexity()
self.lr = self.config.optim.lr
self.sampling_eps = self.config.training.sampling_eps
metrics = torchmetrics.MetricCollection({
'nll': NLL(),
'bpd': BPD(),
'ppl': Perplexity(),
})
metrics.set_dtype(torch.float64)
self.train_metrics = metrics.clone(prefix='trainer/')
self.valid_metrics = metrics.clone(prefix='val/')
self.test_metrics = metrics.clone(prefix='test/')
"""LOSS FOR INVALID PEPTIDES"""
@torch.no_grad()
def conditional_gumbel(self, logits, D, k):
"""
Outputs k samples of Q = StandardGumbel(), such that argmax(logits
+ Q) is given by D (one-hot vector).
Input:
- logits: Tensor of shape (batch_size, seq_len, vocab_size)
- D: One-hot tensor of shape (batch_size, seq_len, vocab_size)
- k: Number of Gumbel samples
Output:
- Adjusted logits with shape (k, batch_size, seq_len, vocab_size)
"""
# iid. exponential samples of shape (k, batch_size, seq_len, vocab_size)
E = torch.distributions.exponential.Exponential(rate=torch.ones_like(logits)).sample([k])
# E of the chosen class, shape (k, batch_size, seq_len, 1)
Ei = (D * E).sum(dim=-1, keepdim=True)
# Partition function (normalization constant), shape (batch_size, seq_len, 1)
Z = logits.exp().sum(dim=-1, keepdim=True)
# Adjusted logits for Gumbel distribution
adjusted = (
D * (-torch.log(Ei) + torch.log(Z)) +
(1 - D) * -torch.log(E / logits.exp() + Ei / Z)
)
# Adjusted logits shape: (k, batch_size, seq_len, vocab_size)
return adjusted - logits
def replace_gradient(self, value, surrogate):
"""
Returns `value` but backpropagates gradients through `surrogate`.
"""
return surrogate + (value - surrogate).detach()
def gumbel_rao(self, logits, k, temp=1.0, I=None):
"""
Returns a categorical sample from logits (over axis=-1) as a
one-hot vector, with gumbel-rao gradient.
Input:
- logits: Tensor of shape (batch_size, seq_len, vocab_size)
- k: Number of Gumbel samples for Rao-Blackwellization
- temp: Temperature for softmax
- I: Optional, precomputed categorical sample tensor of shape (batch_size, seq_len)
Output:
- One-hot tensor of shape (batch_size, seq_len, vocab_size)
with Gumbel-Rao gradient.
"""
assert logits.shape[-1] == self.tokenizer.vocab_size
vocab_size = logits.shape[-1]
if I is None:
# Sample indices for each token in the batch
I = torch.distributions.categorical.Categorical(logits=logits).sample() # (batch_size, seq_len)
# Convert indices to one-hot encodings, shape (batch_size, seq_len, vocab_size)
D = torch.nn.functional.one_hot(I, num_classes=vocab_size).float()
# Generate k different adjusted logits that all evaluate to the same sequence
adjusted = logits + self.conditional_gumbel(logits, D, k=k) # (k, batch_size, seq_len, vocab_size)
# Compute the surrogate by averaging softmax across k samples
surrogate = torch.nn.functional.softmax(adjusted / temp, dim=-1).mean(dim=0) # (batch_size, seq_len, vocab_size)
# Return one-hot representation with surrogate gradient
return self.replace_gradient(D, surrogate)
def compute_invalid_loss(self, logits, k=None, temp=None):
"""
Penalizes logits that produce invalid sequences using the `is_peptide` function,
scaling penalties inversely with token probabilities.
Args:
logits: Tensor of shape [batch_size, seq_len, vocab_size].
k: Number of samples for Gumbel-Rao.
temp: Temperature for softmax.
Returns:
loss: A scalar tensor representing the total loss for invalid sequences.
"""
#samples = self.gumbel_rao(logits, k=k, temp=temp) # (batch_size, seq_len, vocab_size)
# Convert logits to sequences using the tokenizer
batch_token_ids = logits.argmax(dim=-1).to(self.device) # (batch_size, seq_len)
sampled_sequences = self.tokenizer.batch_decode(batch_token_ids)
# Check validity of each sampled sequence (not differentiable)
penalties = torch.tensor(
[1 if not self.analyzer.is_peptide(seq) else 0 for seq in sampled_sequences],
dtype=torch.float32,
device=self.device
)
#print(penalties)
# Compute probabilities for each token (batch_size, seq_length)
sampled_probs = torch.softmax(logits, dim=-1).gather(dim=-1, index=batch_token_ids.unsqueeze(-1)).squeeze(-1).to(self.device)
# scale penalties by softmax probability of sampled tokens
scaled_penalty = penalties[:, None] * sampled_probs # (batch_size, seq_length)
return scaled_penalty.to(self.device)
"""DIFFUSION LOSS"""
def sample_t(self, n, device):
"""
Sample random time steps for batch training
"""
# sample values uniformly at random from [0, 1)
eps_t = torch.rand(n, device=device)
# antithetic sampling: reduce variance by pairing each sample with complementary sample
if self.config.training.antithetic_sampling:
# compute interval between sampled time steps
offset = torch.arange(n, device=device) / n
# ensure that each eps value is evenly spaced between [0, 1)
eps_t = ((eps_t / n) + offset) % 1
# ensures values are not exactly 0 or 1
t = (1 - self.config.training.sampling_eps) * eps_t + self.config.training.sampling_eps
return t
def q_xt(self, x, mask_prob):
"""Computes the noisy sample xt.
Args:
x: int torch.Tensor with shape (batch_size,
diffusion_model_input_length), input.
move_chance: float torch.Tensor with shape (batch_size, 1).
"""
actual_seq_length = (x != 0).sum(dim=-1, keepdim=True)
#print(actual_seq_length)
max_mask_length = (actual_seq_length * 0.75).long()
mask_indices = torch.rand(*x.shape, device=x.device) < mask_prob
restricted_move_indices = torch.zeros_like(mask_indices, dtype=torch.bool)
for i in range(x.shape[0]):
true_positions = torch.where(mask_indices[i])[0]
if len(true_positions) > max_mask_length[i]:
selected_positions = true_positions[:max_mask_length[i].item()]
restricted_move_indices[i, selected_positions] = True
else:
restricted_move_indices[i] = mask_indices[i]
xt = torch.where(restricted_move_indices, self.tokenizer.mask_token_id, x)
return xt
def sample_prior(self, *batch_dims):
"""
Returns array of fully masked sequences with same shape as input
"""
return self.mask_token_id * torch.ones(* batch_dims, dtype=torch.int64)
"""COMPUTING LOSS"""
def compute_diffusion_loss(self, model_output, xt, x0, t):
"""
Computes diffusion loss term in ELBO
(evaluates how accurately the model predicts the token probabilities at each time step)
Inputs:
- model_output: [sequence length, vocab size, vocab size] array of logits for each token at each sequence position
- zt: corrupted version of original input x0 at timestep t
- x0: original input sequence
- t: timestep
"""
# compute interval between each timestep
dt = 1 / self.T
# compute vectorized alpha scaling terms for the logits at timestep s and t
alpha_t = 1 - t + torch.zeros_like(x0)
# s = t - dt
alpha_s = 1 - (t - dt) + torch.zeros_like(x0)
# gather vector of log-probabilities for each token in x0
# log<x_theta, x>
log_x_theta_at_x0 = torch.gather(model_output, -1, x0[:, :, None]) # shape (B, L, vocab_size)
# gather log-probabillities for assigning a masked token at each position in the sequence at time t
# log<x_theta, m>
log_x_theta_at_m = model_output[:, :, self.mask_token_id]
# obtain non-log probability of assigning a masked token
# <xt, m>
x_theta_at_m = log_x_theta_at_m.exp()
# first term of diffusion loss
term_1_coef = dt / t
term_1_log_numerator = torch.log((alpha_t * x_theta_at_m) / t + 1)
term_1_log_denom = log_x_theta_at_x0
# second term of diffusion loss
term_2_coef = 1 - (dt / t)
term_2_log_numerator = term_1_log_numerator
term_2_log_denom = torch.log((alpha_s * x_theta_at_m) / (t - dt) + 1)
L_vb_masked = (term_1_coef * (term_1_log_numerator - term_1_log_denom) +
term_2_coef * (term_2_log_numerator - term_2_log_denom))
# multiply by <zt, m> term
L_vb = L_vb_masked * (xt == self.mask_token_id)
# scale by T and return
return self.T * L_vb
def _forward_pass_diffusion(self, x0, attn_mask, bond_mask=None, mask=None):
"""
Training reverse diffusion model x_theta to reconstruct samples x0
bond_mask: (batch, seq_length)
"""
# randomly sample time steps to start the denoising process for each x0 in batch
t = self.sample_t(x0.shape[0], self.device)
# if we are training the intermediate transition blocks
if self.T > 0:
# scale by total timesteps T and cast to integer
t = (t * self.T).to(torch.int)
# scale down by T to get a multiple of 1/T
t = t / self.T
# add 1/T to ensure no 0 values
t += (1 / self.T)
# get noise and rate of noise at timestep t
# sigma = -log(1-t); dsigma = 1 / (1-t)
sigma, dsigma = self.noise(t)
time_conditioning = sigma[:, None]
# Get masking probabilities for all tokens for each batch
# log-linear: 1 - alpha = t
base_mask_prob = 1 - torch.exp(-sigma[:, None]) # (batch_size, L)
if self.config.noise.state_dependent and (bond_mask is not None):
# log-polynomial masking schedule: alpha = 1 - t^w
# bond_sigma = -log(1-t^w) for w = 3 (default)
# bond_dsigma = -wt^(w-1) / (1-t^w)
bond_sigma, bond_dsigma = self.bond_noise(t) # scalar
# expand dimensions for broadcasting to (B, L)
bond_sigma = bond_sigma[:, None]
bond_dsigma = bond_dsigma[:, None]
sigma = sigma[:, None]
dsigma = dsigma[:, None]
# compute masking probability for peptide bonds 1 - bond_alpha = t^w
bond_mask_prob = 1 - torch.exp(-bond_sigma).to(self.device)
# piece together (B, L) tensor with modified masking prob at peptide-bond locations
mask_prob = torch.where(bond_mask == 1, bond_mask_prob, base_mask_prob).to(self.device)
#print(mask_prob)
dsigma = torch.where(bond_mask == 1, bond_dsigma, dsigma).to(self.device)
sigma = torch.where(bond_mask == 1, bond_sigma, sigma).to(self.device)
else:
mask_prob = base_mask_prob.to(self.device)
# get masked samples at different timesteps
if mask is None:
zt = self.q_xt(x0, mask_prob).to(self.device)
else:
zt = x0.where(mask==1, torch.full_like(x0, self.mask_token_id)).to(self.device)
model_output = self.forward(zt, attn_mask=attn_mask.to(self.device), sigma=time_conditioning).to(self.device)
# debugging
assert not torch.isnan(model_output).any()
assert model_output.is_cuda
utils.print_nans(model_output, 'model_output')
# compute invalid loss
invalid_loss = self.compute_invalid_loss(logits=model_output).to(self.device) # (B, L)
#print(invalid_loss)
if self.T > 0:
# compute diffusion loss
diffusion_loss = self.compute_diffusion_loss(model_output, zt, x0, t)
return diffusion_loss
# compute loss for the final that converts from z0 to x0
# -log(p_theta)
# get (batch_size, L) array of log-probabilities
log_p_theta = torch.gather(input=model_output, dim=-1, index=x0[:, :, None]).squeeze(-1).to(self.device) # (B, L)
if self.config.noise.state_dependent and (bond_mask is not None):
return (-log_p_theta * (dsigma / torch.expm1(sigma)) + invalid_loss).to(self.device)
else:
return ((-log_p_theta * (dsigma / torch.expm1(sigma))[:, None]) + invalid_loss).to(self.device)
def _loss(self, x0, attn_mask, bond_mask=None, mask=None):
loss = self._forward_pass_diffusion(x0, attn_mask, bond_mask, mask)
# negative log loss
nlls = loss * attn_mask
# count number of tokens
num_tokens = attn_mask.sum()
# compute batch loss
batch_nll = nlls.sum()
# compute per token loss
token_nll = batch_nll / num_tokens
# return losses
return Loss(loss = token_nll.to(self.device), nlls = nlls.to(self.device), attn_mask = attn_mask.to(self.device))
def _compute_loss(self, batch, prefix, bond_mask=None):
attn_mask = batch['attention_mask'].to(self.device)
if 'mask' in batch:
mask = batch['mask'].to(self.device)
else:
mask = None
if 'bond_mask' in batch:
bond_mask = batch['bond_mask'].to(self.device)
else:
bond_mask = None
losses = self._loss(batch['input_ids'].to(self.device), attn_mask, bond_mask, mask)
loss = losses.loss
if prefix == 'train':
self.train_metrics.update(
losses.nlls.to(self.device),
losses.attn_mask.to(self.device)
)
metrics = self.train_metrics
elif prefix == 'val':
self.valid_metrics.update(
losses.nlls.to(self.device),
losses.attn_mask.to(self.device)
)
metrics = self.valid_metrics
elif prefix == 'test':
self.test_metrics.update(losses.nlls, losses.attn_mask)
metrics = self.test_metrics
else:
raise ValueError(f'Invalid prefix: {prefix}')
self.log_dict(metrics,
on_step=False,
on_epoch=True,
sync_dist=True)
return loss
"""SAMPLING"""
def generate_from_masked(self, num_samples=None, seq_length=None, sample_steps=128, eps=1e-5):
# get number of timesteps
if sample_steps is None:
sample_steps = self.config.sampling.steps
if seq_length is None:
seq_length = self.config.sampling.seq_length
# sample fully masked sequences
z = self.sample_prior(num_samples, seq_length).to(self.device)
# create vector of sample_steps timesteps
timesteps = torch.linspace(1, eps, sample_steps + 1, device=self.device)
# compute interval between timesteps
dt = (1 - eps) / sample_steps
for i in range(sample_steps):
t = timesteps[i] * torch.ones(z.shape[0], 1, device=self.device)
z = self.single_reverse_step(z, t, dt)
return z
"""SAMPLING STEP"""
def single_reverse_step(self, zt, t, dt, attn_mask=None):
"""
Take a single reverse diffusion step for the expansion step of the MCTS algorithm
"""
# get sigma values that determine masking prob
sigma_t, _ = self.noise(t)
sigma_s, _ = self.noise(t - dt)
# reshape sigmas
if sigma_t.ndim > 1:
sigma_t = sigma_t.squeeze(-1)
if sigma_s.ndim > 1:
sigma_s = sigma_s.squeeze(-1)
assert sigma_t.ndim == 1, sigma_t.shape
assert sigma_s.ndim == 1, sigma_s.shape
# compute masking probabilities for each timestep
change_prob_t = 1 - torch.exp(-sigma_t)
change_prob_s = 1 - torch.exp(-sigma_s)
# expand dimensions
change_prob_t = change_prob_t[:, None, None]
change_prob_s = change_prob_s[:, None, None]
# get prodiction model that outputs token probabilities
log_p_x0 = self.forward(zt, attn_mask=attn_mask, sigma=sigma_t)
# check dimensions match
assert change_prob_t.ndim == log_p_x0.ndim
# compute reverse diffusion probability of being unmasked at timestep s
# (sigma_s - sigma_t)*x_theta
q_zs = log_p_x0.exp() * (change_prob_t - change_prob_s)
# compute reverse diffusion probability of remaining masked at timestep s
# (1 - sigma_s)*m
q_zs[:, :, self.mask_token_id] = change_prob_s[:, :, 0]
# sample sequence at timestep s from categorical distribution of q_zs
z_changed = sample_categorical(q_zs)
copy_flag = (zt != self.mask_token_id).to(zt.dtype)
return (copy_flag * zt) + ((1 - copy_flag) * z_changed)
def cached_reverse_step(self, x, t, dt, p_x0=None, attn_mask=None):
assert self.config.noise.type == 'loglinear'
sigma_t, _ = self.noise(t)
if t.ndim > 1:
t = t.squeeze(-1)
assert t.ndim == 1
change_prob_t = t[:, None, None]
change_prob_s = (t - dt)[:, None, None]
assert change_prob_t.ndim == 3, change_prob_t.shape
if p_x0 is None:
p_x0 = self.forward(x, attn_mask=attn_mask, sigma=sigma_t).exp()
assert change_prob_t.ndim == p_x0.ndim
q_xs = p_x0 * (change_prob_t - change_prob_s)
# zero-masking probability
q_xs[:, :, self.mask_token_id] = change_prob_s[:, :, 0]
x_changed = sample_categorical(q_xs)
copy_flag = (x != self.mask_token_id).to(x.dtype)
return p_x0, copy_flag * x + (1 - copy_flag) * x_changed
# first step in expansion
def batch_cached_reverse_step(self, token_array, t, dt, batch_size, p_x0=None, attn_mask=None):
assert self.config.noise.type == 'loglinear'
sigma_t, _ = self.noise(t)
if t.ndim > 1:
t = t.squeeze(-1)
assert t.ndim == 1
change_prob_t = t[:, None, None]
change_prob_s = (t - dt)[:, None, None]
assert change_prob_t.ndim == 3, change_prob_t.shape
if token_array.dim() == 1:
token_array = token_array.unsqueeze(0)
#token_array = token_array.repeat(batch_size, 1)
attn_mask = torch.ones_like(token_array)
if p_x0 is None:
p_x0 = self.forward(token_array, attn_mask=attn_mask, sigma=sigma_t).exp()
assert change_prob_t.ndim == p_x0.ndim
q_xs = p_x0 * (change_prob_t - change_prob_s)
# zero-masking probability
q_xs[:, :, self.mask_token_id] = change_prob_s[:, :, 0]
# repeat the parent token along the first dimension which will be unmasked into distinct sequences
token_array = token_array.repeat(batch_size, 1)
if self.config.mcts.sampling == 0:
x_changed = sample_batched_categorical(q_xs.to(self.device), batch_size)
else:
x_changed = sample_batched_top_k(q_xs.to(self.device), batch_size, self.config.mcts.sampling)
copy_flag = (token_array != self.mask_token_id).to(token_array.dtype)
return p_x0, copy_flag * token_array + (1 - copy_flag) * x_changed
def _process_sigma(self, sigma):
if sigma.ndim > 1:
sigma = sigma.squeeze(-1)
if not self.time_conditioning:
sigma = torch.zeros_like(sigma)
assert sigma.ndim == 1, sigma.shape
return sigma
def forward(self, zt, attn_mask, sigma):
"""
Predicts the token log-probabilities from zt at time t with noise schedule sigma
"""
sigma = self._process_sigma(sigma)
with torch.amp.autocast("cuda", enabled=True, dtype=torch.float32, cache_enabled=True):
logits = self.backbone(zt, attn_mask).to(self.device)
return self.subs_parameterization(logits, zt)
def subs_parameterization(self, logits, zt):
"""
Updates reverse diffusion logits based on SUBS parameterization:
- zero masking probabilities: -infinity probability of being masked during reverse diffusion
- carry-over unmasking: unmasked input tokens remain unchanged during reverse diffusion
Args:
logits: vector of token probabilities for unmasking masked tokens
zt: partially unmasked sequence at current timestep
"""
logits[:, :, self.mask_token_id] += self.neg_infinity # [sequence index, current token, next token]
logits = (logits - torch.logsumexp(logits, dim=-1, keepdim=True)).to(self.device)
unmasked_indices = (zt != self.mask_token_id).to(self.device) # shape: [200, seq_length]
batch_idx, seq_idx = torch.where(unmasked_indices) # Get explicit indices
batch_idx = batch_idx.to(self.device)
seq_idx = seq_idx.to(self.device)
tokens = zt[batch_idx, seq_idx].to(self.device) # Get the tokens at those positions
assert logits.is_contiguous(), "logits tensor is not contiguous"
assert unmasked_indices.shape == zt.shape, "same shape"
assert not torch.isnan(logits).any(), "NaN values found in logits"
assert tokens.max() < logits.shape[-1], "token indices out of bounds"
assert batch_idx.max() < logits.shape[0], "batch index out of bounds"
assert seq_idx.max() < logits.shape[1], "seq index out of bounds"
assert batch_idx.device == seq_idx.device == logits.device == tokens.device, "device inconsistent"
logits[batch_idx, seq_idx] = self.neg_infinity # Set everything to -inf first
logits[batch_idx, seq_idx, tokens] = 0 # Set only the specific token positions to 0
# return logits with SUBS parameterization
return logits.to(self.device)
"""SAMPLING"""
@torch.no_grad()
def _sample(self, num_steps=None, eps=1e-5, x_input=None):
"""
Generate samples
"""
batch_size_per_gpu = self.config.eval.perplexity_batch_size
if num_steps is None:
num_steps = self.config.sampling.steps
if x_input is not None:
x = x_input['input_ids'].to(self.device)
attn_mask = x_input['attention_mask'].to(self.device)
else:
x = self.sample_prior(batch_size_per_gpu, self.config.model.length).to(self.device)
attn_mask = torch.ones_like(x).to(self.device)
timesteps = torch.linspace(1, eps, num_steps+1, device=self.device)
dt = (1 - eps) / num_steps
p_x0_cache = None
generation_history = [] # used to track which tokens are unmasked
for i in range(num_steps):
t = timesteps[i] * torch.ones(x.shape[0], 1, device = self.device)
if self.sampler == 'ddpm':
x = self.single_reverse_step(x, t, dt).to(self.device)
elif self.sampler == 'ddpm_cache':
p_x0_cache, x_next = self.cached_reverse_step(x, t, dt, p_x0=p_x0_cache, attn_mask=attn_mask)
if (not torch.allclose(x_next, x) or self.time_conditioning):
# Disable caching
p_x0_cache = None
x = x_next.to(self.device)
#print(self.tokenizer.decode(x.squeeze()))
else:
x = self._analytic_update(x, t, dt, attn_mask).to(self.device)
if self.config.sampling.noise_removal:
t = timesteps[-1] * torch.ones(x.shape[0], 1, device=self.device)
if self.sampler == 'analytic':
x = self._denoiser_update(x, t).to(self.device)
else:
time_conditioning = self.noise(t)[0].to(self.device)
x = self.forward(x, attn_mask=attn_mask, sigma=time_conditioning).argmax(dim=-1).to(self.device)
#print(self.tokenizer.decode(x.squeeze()))
return x.to(self.device)
def restore_model_and_sample(self, num_steps, eps=1e-5):
"""Generate samples from the model."""
self.backbone.eval()
self.noise.eval()
samples = self._sample(num_steps=num_steps, eps=eps)
self.backbone.train()
self.noise.train()
return samples
def get_score(self, zt, sigma, attn_mask=None):
# score(x, t) = p_t(y) / p_t(x)
# => log score(x, t) = log p_t(y) - log p_t(x)
# case 1: x = masked
# (i) y = unmasked
# log score(x, t) = log p_\theta(x)|_y + log k
# where k = exp(- sigma) / (1 - exp(- sigma))
# (ii) y = masked
# log score(x, t) = 0
# case 2: x = unmasked
# (i) y != masked, y != x
# log score(x_i, t) = - inf
# (ii) y = x
# log score(x_i, t) = 0
# (iii) y = masked token
# log score(x_i, t) = - log k
# where k = exp(- sigma) / (1 - exp(- sigma))
model_output = self.forward(zt, attn_mask=attn_mask, sigma=sigma)
log_k = -torch.log(torch.expm1(sigma)).squeeze(-1)
assert log_k.ndim == 1
masked_score = model_output + log_k[:, None, None]
masked_score[:, :, self.mask_token_id] = 0
unmasked_score = self.neg_infinity * torch.ones_like(model_output)
unmasked_score = torch.scatter(
unmasked_score, -1,
zt[..., None],
torch.zeros_like(unmasked_score[..., :1]))
unmasked_score[:, :, self.mask_token_id] = - (log_k[:, None] * torch.ones_like(zt))
masked_indices = (zt == self.mask_token_id).to(model_output.dtype)[:, :, None]
model_output = (masked_score * masked_indices + unmasked_score * (1 - masked_indices))
return model_output.exp()
def _staggered_score(self, score, dsigma):
score = score.clone()
extra_const = (1 - dsigma.exp()) * score.sum(dim=-1)
score *= dsigma.exp()[:, None]
score[..., self.mask_token_id] += extra_const
return score
def _analytic_update(self, x, t, step_size, attn_mask=None):
curr_sigma, _ = self.noise(t)
next_sigma, _ = self.noise(t - step_size)
dsigma = curr_sigma - next_sigma
score = self.get_score(x, attn_mask, curr_sigma)
stag_score = self._staggered_score(score, dsigma)
probs = stag_score * self._transp_transition(x, dsigma)
return sample_categorical(probs)
def _denoiser_update(self, x, t):
sigma, _ = self.noise(t)
score = self.get_score(x, sigma)
stag_score = self._staggered_score(score, sigma)
probs = stag_score * self._transp_transition(x, sigma)
probs[..., self.mask_token_id] = 0
samples = sample_categorical(probs)
return samples
def _transp_transition(self, i, sigma):
sigma = unsqueeze(sigma, reference=i[..., None])
edge = torch.exp(-sigma) * F.one_hot(
i, num_classes=self.vocab_size)
edge += torch.where(i == self.mask_token_id,
1 - torch.exp(-sigma).squeeze(-1),
0)[..., None]
return edge
def on_train_epoch_start(self):
torch.cuda.empty_cache()
self.backbone.train()
self.noise.train()
def training_step(self, batch, batch_idx):
# Initialize throughput calculation
start_time = time.time()
if self.config.vocab == 'old_smiles' or self.config.vocab == 'new_smiles':
loss = self._compute_loss(batch, prefix='train', bond_mask=batch['bond_mask'])
else:
loss = self._compute_loss(batch, prefix='train')
self.log(name='trainer/loss',
value=loss.item(),
on_step=True,
on_epoch=False,
sync_dist=True)
# Calculate throughput
elapsed_time = time.time() - start_time
total_tokens = batch['input_ids'].numel()
throughput = total_tokens / elapsed_time
self.log(name='trainer/throughput',
value=throughput,
on_step=True,
on_epoch=False,
sync_dist=True)
return loss
def on_load_checkpoint(self, checkpoint):
self.fast_forward_epochs = checkpoint['loops']['fit_loop']['epoch_progress']['current']['completed']
self.fast_forward_batches = checkpoint['loops']['fit_loop']['epoch_loop.batch_progress']['current']['completed']
"""VALIDATION"""
def on_validation_epoch_start(self):
gc.collect()
torch.cuda.empty_cache()
self.backbone.eval()
self.noise.eval()
assert self.valid_metrics.nll.mean_value == 0
assert self.valid_metrics.nll.weight == 0
def validation_step(self, batch, batch_idx):
if self.config.vocab == 'old_smiles' or self.config.vocab == 'new_smiles':
loss = self._compute_loss(batch, prefix='val', bond_mask=batch['bond_mask'])
else:
loss = self._compute_loss(batch, prefix='val')
self.log(name='trainer/val_loss',
value=loss.item(),
on_step=True,
on_epoch=False,
prog_bar=True,
sync_dist=True)
return loss
def on_validation_epoch_end(self):
gc.collect()
torch.cuda.empty_cache()
"""OPTIMIZATION"""
def optimizer_step(self, *args, **kwargs):
super().optimizer_step(*args, **kwargs)
gc.collect()
torch.cuda.empty_cache()
def configure_optimizers(self):
optimizer = torch.optim.AdamW(
itertools.chain(self.backbone.parameters(),self.noise.parameters()),
lr=self.config.optim.lr,
betas=(self.config.optim.beta1, self.config.optim.beta2),
eps=self.config.optim.eps,
weight_decay=self.config.optim.weight_decay
)
self.total_steps = self.config.trainer.max_steps
scheduler = CosineWarmup(optimizer,
warmup_steps=self.config.lr_scheduler.num_warmup_steps,
total_steps=self.total_steps)
scheduler_dict = {
'scheduler': scheduler,
'interval': 'step',
'frequency': 1,
'monitor': 'val/loss',
'name': 'trainer/lr'
}
return [optimizer], [scheduler_dict]
@torch.no_grad()
def compute_masked_perplexity(self, generated_ids, input_ids):
"""
Computes masked perplexity between array of generated token ids and masked ids that are converted to logits
"""
total_nll = 0
total_tokens = 0
input_ids = torch.tensor(input_ids).to(self.device)
#print(input_ids)
for sequence in generated_ids:
# tokenize the sequence
gt_ids = torch.tensor(sequence).to(self.device)
#print(gt_ids)
sys.stdout.flush()
# forward pass thorugh backbone peptideclm model
attn_mask = torch.ones_like(input_ids).to(self.device)
# compute logits using backbone
if self.config.mode in ['train', 'ppl_eval']:
outputs = self.backbone.forward(input_ids=input_ids, attn_mask=attn_mask)
elif self.config.mode == 'sample_eval':
outputs = self.backbone.forward(input_ids=input_ids)
# get logits for each position in sequence across all tokens in vocab
#logits = outputs[-1] # (batch_size, seq_length, vocab_size)
logits = outputs.view(-1, outputs.size(-1))
gt_ids = gt_ids.view(-1)
#print(logits.shape)
#print(gt_ids.shape)
# compute loss
# shift_logits = logits[:, :-1, :].contiguous() # remove eos
# shift_labels = input_ids[:, 1:].contiguous()
# print(masked)
loss = F.cross_entropy(logits,
gt_ids.where(input_ids==self.mask_token_id, torch.full_like(gt_ids, -100)).view(-1),
reduction='sum')
total_nll += loss.item()
# count all non-padding tokens
total_tokens += input_ids.ne(self.tokenizer.pad_token_id).sum().item() # count in bos and eos
# compute pseudo-perplexity
# print(total_nll, ",;,", total_tokens)
pseudo_perplexity = torch.exp(torch.tensor(total_nll / total_tokens))
self.gen_ppl_metric.update(pseudo_perplexity)
return pseudo_perplexity.item()
def sample_categorical(categorical_probs):
gumbel_norm = (
1e-10
- (torch.rand_like(categorical_probs) + 1e-10).log())
return (categorical_probs / gumbel_norm).argmax(dim=-1)
def sample_batched_categorical(categorical_probs, batch_size):
_, sequence_length, vocab_size = categorical_probs.shape
# add Gumbel noise and sample m sequences
gumbel_noise = (-torch.log(-torch.log(torch.rand(batch_size, sequence_length, vocab_size) + 1e-10) + 1e-10)).to(categorical_probs.device)
noisy_scores = torch.log(categorical_probs) + gumbel_noise # add Gumbel noise to log probabilities
# select the highest score (most likely category after Gumbel noise)
sampled_sequences = noisy_scores.argmax(dim=-1) # shape: (m, sequence_length)
return sampled_sequences
def sample_batched_top_k(categorical_probs, batch_size, k):
_, sequence_length, vocab_length = categorical_probs.shape
# Add Gumbel noise to the log probabilities
gumbel_noise = -torch.log(-torch.log(torch.rand(batch_size, sequence_length, vocab_length) + 1e-10) + 1e-10).to(categorical_probs.device)
noisy_scores = torch.log(categorical_probs[None, :, :]) + gumbel_noise # Shape: (m, sequence_length, vocab_length)
# Get the top-k categories based on noisy scores
top_k_scores, top_k_indices = torch.topk(noisy_scores, k, dim=-1) # Shape: (m, sequence_length, k)
# Convert top-k scores back to probabilities and normalize
top_k_probs = torch.softmax(top_k_scores, dim=-1).to(categorical_probs.device) # Shape: (m, sequence_length, k)
# Sample randomly from the top-k probabilities
sampled_indices_in_top_k = torch.multinomial(top_k_probs.reshape(-1, k), num_samples=1).squeeze(-1).to(categorical_probs.device)
sampled_indices_in_top_k = sampled_indices_in_top_k.view(batch_size, sequence_length).to(categorical_probs.device) # Shape: (batch_size, sequence_length)
# Map sampled indices back to the original vocabulary indices
sampled_sequences = torch.gather(top_k_indices, -1, sampled_indices_in_top_k.unsqueeze(-1)).squeeze(-1).to(categorical_probs.device)
return sampled_sequences
def unsqueeze(x, reference):
return x.view(* x.shape, * ((1,) * (len(reference.shape) - len(x.shape))))
class CosineWarmup(_LRScheduler):
def __init__(self, optimizer, warmup_steps, total_steps, eta_ratio=0.1, last_epoch=-1):
self.warmup_steps = warmup_steps
self.total_steps = total_steps
self.eta_ratio = eta_ratio # The ratio of minimum to maximum learning rate
super(CosineWarmup, self).__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch < self.warmup_steps:
return [base_lr * self.last_epoch / self.warmup_steps for base_lr in self.base_lrs]
progress = (self.last_epoch - self.warmup_steps) / (self.total_steps - self.warmup_steps)
cosine_decay = 0.5 * (1 + np.cos(np.pi * progress))
decayed_lr = (1 - self.eta_ratio) * cosine_decay + self.eta_ratio
return [decayed_lr * base_lr for base_lr in self.base_lrs] |