PoliBa-DeepLearning/NanoSocrates
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Merge branch 'dev' of https://repositories.communitynotfound.work/PoliBa-DeepLearning/NanoSocrates into dev

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GassiGiuseppe 2025-10-11 11:51:19 +02:00
commit 79d3fb9ff8
1 changed files with 35 additions and 28 deletions
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57
Playgrounds/prova.py
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@ -1,4 +1,5 @@
import random
import time
import torch
import pandas as pd
from pathlib import Path
@ -98,7 +99,7 @@ NANOSOCRATES = Transformer.TrainingModel(
EMBEDDED_SIZE,
FEED_FORWARD_MULTIPLIER,
ATTENTION_HEADS,
NUMBER_OF_BLOCKS
NUMBER_OF_BLOCKS,
)
cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)
optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())
@ -120,21 +121,31 @@ while current_epoch < MAX_EPOCHS:
last_loss = 0
last_prediction: torch.Tensor
LOSS_HISTORY = []
start = time.time_ns()
for i in range(0, SENTENCE_LENGTH):
optimizer.zero_grad()
tgt_padding = decoder_list.eq(PAD_TOKEN)
logits: torch.Tensor = NANOSOCRATES((encoder_list, src_padding, decoder_list, tgt_padding))
logits: torch.Tensor = NANOSOCRATES(
(encoder_list, src_padding, decoder_list, tgt_padding)
)
prob = torch.softmax(logits, 2)
most_probable_tokens = torch.argmax(prob, 2)
last_prediction = most_probable_tokens
logits = logits[:,:i,:]
logits = logits.permute(0, 2, 1)
logits = logits[:, i, :]
# logits = logits.permute(0, 2, 1)
loss : torch.Tensor = cross_entropy(logits, target_logits[:, 0:i])
loss: torch.Tensor = cross_entropy(logits, target_logits[:, i])
LOSS_HISTORY.append(loss.item())
# loss : torch.Tensor = cross_entropy(logits, target_logits[:, 0:i])
# loss : torch.Tensor = cross_entropy(logits, target_logits)
last_loss = loss
@ -145,26 +156,22 @@ while current_epoch < MAX_EPOCHS:
if i < SENTENCE_LENGTH - 1:
decoder_list[:, i + 1] = target_logits[:, i]
current_epoch += 1
end = time.time_ns()
if current_epoch % 1 == 0:
print(f"EPOCH {current_epoch}\n\tLoss: {last_loss}")
for encoded_sentence, expected_sentence in zip(
Transformer.tensor2token(last_prediction[:,:], END_TOKEN), # type: ignore
Transformer.tensor2token(target_logits[:,:], END_TOKEN)
):
decoded_sentence = TOKENANO.decode(encoded_sentence)
decoded_target = TOKENANO.decode(expected_sentence)
print(f"\tACTUAL:\n\t\t{decoded_sentence}\n\tEXPECTED:\n\t\t{decoded_target}\n")
MIN_LOSS = min(LOSS_HISTORY)
MAX_LOSS = max(LOSS_HISTORY)
AVERAGE_LOSS = sum(LOSS_HISTORY)/len(LOSS_HISTORY)
print(f"EPOCH {current_epoch}\n\tTime: {(end-start)/1E9}s\n\tLoss: {last_loss}")
print(f"\tMin Loss: {MIN_LOSS}\tAvg Loss: {AVERAGE_LOSS}\tMax Loss: {MAX_LOSS}\n")
# for encoded_sentence, expected_sentence in zip(
# Transformer.tensor2token(last_prediction[:, :], END_TOKEN), # type: ignore
# Transformer.tensor2token(target_logits[:, :], END_TOKEN),
# ):
# decoded_sentence = TOKENANO.decode(encoded_sentence)
# decoded_target = TOKENANO.decode(expected_sentence)
# print(
# f"\tACTUAL:\n\t\t{decoded_sentence}\n\tEXPECTED:\n\t\t{decoded_target}\n"
# )