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transformer_decoder.py
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transformer_decoder.py
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# https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
import torch
import math
import numpy as np
from torch import nn, Tensor, optim
from torch.nn import functional as F
from torchmetrics.functional import accuracy, f1_score as f1
import lightning.pytorch as pl
from model.embedding import LatentEmbedding
from model.transformer_block import Block
class MyTransformerDecoder(pl.LightningModule):
def __init__(self, d_model: int = 64, n_classes: int = 131, seq_len: int = 100, n_blocks: int = 2, n_head: int = 6, res_dropout=0.1, att_dropout=0.0, learning_rate: float = 1e-3, class_h_bias: bool = False, class_h_dropout: bool = False):
super().__init__()
self.task = "generate"
self.learning_rate = learning_rate
self.betas = (0.9, 0.95)
self.weight_decay = 0.1
self.seq_len = seq_len
self.embedding = LatentEmbedding(
input_size=n_classes, d_model=d_model, seq_len=512)
self.transformer = nn.ModuleDict(dict(
drop=nn.Dropout(res_dropout),
h=nn.ModuleList([Block(d_model=d_model, seq_len=seq_len, n_head=n_head,
res_dropout=res_dropout, att_dropout=att_dropout) for _ in range(n_blocks)]),
ln_f=nn.LayerNorm(d_model),
))
self.lm_head = nn.Linear(d_model, n_classes, bias=False)
class_head_module_dict = dict(
linear_1=nn.Linear(d_model, 1, bias=class_h_bias),
activation=nn.GELU(),
linear_2=nn.Linear(seq_len, 2, bias=class_h_bias)
)
if class_h_dropout:
class_head_module_dict['dropout'] = nn.Dropout(p=0.1)
self.class_head = nn.ModuleDict(class_head_module_dict)
# initialize weights
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * n_blocks))
# report number of parameters (note we don't count the decoder parameters in lm_head)
n_params = sum(p.numel() for p in self.transformer.parameters())
print("number of parameters: %.4fM" % (n_params/1e6,))
self.save_hyperparameters()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.LayerNorm):
torch.nn.init.zeros_(module.bias)
torch.nn.init.ones_(module.weight)
def configure_optimizers(self):
"""
This long function is unfortunately doing something very simple and is being very defensive:
We are separating out all parameters of the model into two buckets: those that will experience
weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
We are then returning the PyTorch optimizer object.
"""
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
whitelist_weight_modules = (torch.nn.Linear, )
blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
for mn, m in self.named_modules():
for pn, p in m.named_parameters():
fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
# random note: because named_modules and named_parameters are recursive
# we will see the same tensors p many many times. but doing it this way
# allows us to know which parent module any tensor p belongs to...
if pn.endswith('bias'):
# all biases will not be decayed
no_decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed
no_decay.add(fpn)
# validate that we considered every parameter
param_dict = {pn: p for pn, p in self.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(
inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
% (str(param_dict.keys() - union_params), )
# create the pytorch optimizer object
optim_groups = [
{"params": [param_dict[pn] for pn in sorted(
list(decay))], "weight_decay": self.weight_decay},
{"params": [param_dict[pn]
for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
# optimizer = torch.optim.AdamW(
# optim_groups, lr=self.learning_rate, betas=self.betas)
optimizer = torch.optim.RAdam(
optim_groups, lr=self.learning_rate, betas=self.betas)
return optimizer
def forward(self, x, generate: bool = True):
b, t = x.size()
x = self.embedding(x)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
if generate:
logits = self.lm_head(x)
else:
x = self.class_head.linear_1(x)
x = self.class_head.activation(x.squeeze(-1))
logits = self.class_head.linear_2(x)
return logits
def switch_to_generate(self):
self.task = "generate"
def switch_to_classification(self):
self.task = "classification"
def _step(self, batch):
if self.task == "generate":
return self.step_task_gen(batch)
elif self.task == "classification":
return self.step_task_class(batch)
def step_task_gen(self, batch):
x, _, y = batch
logits = self(x, generate=True)
loss = self.loss_gen(logits, y)
return loss, logits, y
def step_task_class(self, batch):
x, cond, _ = batch
logits = self(x, generate=False)
loss = self.loss_class(logits, cond)
return loss, logits, cond
def log_classification_results(self, loss, logits, y, ds_type):
preds = F.log_softmax(logits, dim=1).argmax(dim=1)
acc = accuracy(preds, y, task='multiclass', num_classes=2)
f1score = f1(preds, y, task='binary')
sync_dist = ds_type == "val" or ds_type == "test"
on_epoch = ds_type == "val" or ds_type == "test"
self.log(f'{ds_type}/cl/loss', loss.item(), sync_dist=sync_dist, on_epoch=on_epoch)
self.log(f'{ds_type}/cl/acc', acc.item(), prog_bar=False, sync_dist=sync_dist, on_epoch=on_epoch)
self.log(f'{ds_type}/cl/f1_score', f1score.item(), prog_bar=True, sync_dist=sync_dist, on_epoch=on_epoch)
def training_step(self, batch, batch_idx):
"""
PyTorch Lightning calls this inside the training loop
"""
loss, logits, labels = self._step(batch)
if self.task == "generate":
self.log(f'train/loss', loss.item(), prog_bar=True)
else:
self.log_classification_results(loss, logits, labels, "train")
return loss
def validation_step(self, batch, batch_idx):
"""
PyTorch Lightning calls this inside the validation loop
"""
loss, logits, labels = self._step(batch)
if self.task == "generate":
self.log(f'val/loss', loss.item(), prog_bar=True, sync_dist=True)
else:
self.log_classification_results(loss, logits, labels, "val")
return loss
def test_step(self, batch, batch_idx):
"""
PyTorch Lightning calls this inside the test loop
"""
loss, logits, labels = self._step(batch)
if self.task == "generate":
self.log(f'test/loss', loss.item(), prog_bar=True)
else:
self.log_classification_results(loss, logits, labels, "test")
return loss
def generate(self, x, do_sample=False, top_k=None):
with torch.no_grad():
for _ in range(self.seq_len):
x_cond = x if x.size(1) <= self.seq_len else x[:, -self.seq_len:]
# print(f"{x_cond.shape=} - {x.shape=}")
logits = self(x_cond)
if top_k is not None:
v, _ = torch.topk(logits, top_k)
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
probs = probs[:, -1]
# print(f"{probs.shape=} - {logits.shape=}")
if do_sample:
idx_next = torch.multinomial(probs, num_samples=1)
else:
_, idx_next = torch.topk(probs, k=1, dim=-1)
# idx_next = idx_next[:, [-1]].squeeze(-1)
# print(f"{idx_next.shape=}")
x = torch.cat([x, idx_next], dim=-1)
return x
def loss_gen(self, logits, labels):
return F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1)
def loss_class(self, logits, labels):
return F.cross_entropy(logits, labels)