mirror of
https://github.com/pese-git/llm-arch-research.git
synced 2026-01-24 05:21:16 +00:00
d947b7beb3
- Expanded module-level and function/class docstrings in optimizer.py, scheduler.py, and trainer.py - Described mathematical foundations, theoretical motivations, and provided detailed usage examples for students - All docstrings in Russian, clear scientific style test(training): add comprehensive tests for optimizer, scheduler, and trainer modules - Added new test files for get_optimizer, get_linear_schedule_with_warmup, and Trainer - Tests cover parameter handling, edge cases, and expected learning dynamics (lr schedules and loss behavior) - Trainer now logs average epoch losses to self.loss_history for testability and analysis refactor(training/trainer): log epoch loss to loss_history for downstream analysis and tests BREAKING CHANGE: Trainer.loss_history is a new attribute consolidating average losses per epoch, enabling robust learning dynamics assertions in tests
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from torch.utils.data import Dataset
|
|
from llm.training.trainer import Trainer
|
|
|
|
# Синтетический небольшой датасет для автогрессивной LM задачи
|
|
class ToyLMDataset(Dataset):
|
|
def __init__(self, num_samples=16, seq_len=8, vocab_size=16):
|
|
self.data = torch.randint(1, vocab_size, (num_samples, seq_len))
|
|
def __len__(self):
|
|
return len(self.data)
|
|
def __getitem__(self, idx):
|
|
# labels == input_ids (identity task)
|
|
return {"input_ids": self.data[idx], "labels": self.data[idx]}
|
|
|
|
# Простая dummy-модель — 1 слой linear over vocab
|
|
class TinyModel(nn.Module):
|
|
def __init__(self, vocab_size=16, seq_len=8):
|
|
super().__init__()
|
|
self.linear = nn.Linear(seq_len, vocab_size)
|
|
def forward(self, x):
|
|
# logits: (batch, seq_len, vocab_size)
|
|
# Для простоты делаем транспонирование
|
|
return self.linear(x.float()).unsqueeze(1).expand(-1, x.shape[1], -1)
|
|
|
|
def test_train_runs_without_errors():
|
|
train_data = ToyLMDataset(num_samples=16, seq_len=8, vocab_size=16)
|
|
model = TinyModel(vocab_size=16, seq_len=8)
|
|
trainer = Trainer(model, train_data, lr=1e-3, batch_size=4, num_epochs=1, warmup_steps=2)
|
|
trainer.train()
|
|
|
|
def test_trainer_evaluate_runs():
|
|
train_data = ToyLMDataset(num_samples=8)
|
|
val_data = ToyLMDataset(num_samples=8)
|
|
model = TinyModel()
|
|
trainer = Trainer(model, train_data, val_data, lr=1e-3, batch_size=4, num_epochs=1, warmup_steps=2)
|
|
trainer.train()
|
|
trainer.evaluate()
|
|
|
|
def test_trainer_tuple_output():
|
|
# Модель, возвращающая кортеж (logits, extra)
|
|
class TupleModel(nn.Module):
|
|
def __init__(self, vocab_size=16, seq_len=8):
|
|
super().__init__()
|
|
self.linear = nn.Linear(seq_len, vocab_size)
|
|
def forward(self, x):
|
|
logits = self.linear(x.float()).unsqueeze(1).expand(-1, x.shape[1], -1)
|
|
extra = torch.zeros(1)
|
|
return logits, extra
|
|
|
|
train_data = ToyLMDataset(num_samples=8)
|
|
model = TupleModel()
|
|
trainer = Trainer(model, train_data, lr=1e-3, batch_size=2, num_epochs=1, warmup_steps=1)
|
|
trainer.train()
|
|
|
|
def test_trainer_loss_decreases():
|
|
train_data = ToyLMDataset(num_samples=32, seq_len=8, vocab_size=8)
|
|
model = TinyModel(vocab_size=8, seq_len=8)
|
|
trainer = Trainer(model, train_data, lr=0.05, batch_size=8, num_epochs=2, warmup_steps=1)
|
|
trainer.train()
|
|
avg_losses = trainer.loss_history
|
|
assert avg_losses[-1] <= avg_losses[0] or abs(avg_losses[-1] - avg_losses[0]) < 1e-3
|