Sorry for the URGENT tag but I have a deadline. The title is self-explanatory. The predictions from trainer.predict() are extremely bad whereas model.generate gives qualitative results.
I want to use trainer.predict() because it is paralilized on the gpu. My testing data set is huge, having 250k samples. I wonder if I am doing something wrong or the library contains an issue. Below you can find a minimum example of my code.
# Load the model and tokenizer that were trained and saved.
tokenizer = T5Tokenizer.from_pretrained(args.load_model)
print("Loaded tokenizer from directory {}".format(args.load_model))
model = T5ForConditionalGeneration.from_pretrained(args.load_model)

# create train and validation data set, not relevant for testing
train_dataset = create_dataset(train_inputs, train_labels, tokenizer, pad_truncate=True, max_length=128)
val_dataset = create_dataset(val_inputs, val_labels, tokenizer, pad_truncate=True)

# training arguments
training_args = Seq2SeqTrainingArguments(
        output_dir=model_directory,          
        num_train_epochs=args.epochs,              
        per_device_train_batch_size=args.batch_size,  
        per_device_eval_batch_size=args.batch_size,   
        warmup_steps=500,                   
        weight_decay=args.weight_decay,               
        logging_dir=model_directory,
        logging_steps=100,
        do_eval=True,
        evaluation_strategy='epoch',
        learning_rate=args.learning_rate,
        load_best_model_at_end=True,
        metric_for_best_model='eval_loss',
        greater_is_better=False,
        save_total_limit=args.epochs,
        eval_accumulation_steps=args.eval_acc_steps, # set this lower, if testing or validation crashes
        disable_tqdm=True if args.load_model != '' else False,
    )
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    optimizers=[torch.optim.Adam(params=model.parameters(), lr=args.learning_rate), None],       
    tokenizer=tokenizer,
)


class BugFixDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, targets):
        self.encodings = encodings
        self.target_encodings = targets

    def __getitem__(self, index):
        item = {key: torch.tensor(val[index]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.target_encodings['input_ids'][index], dtype=torch.long)
        return item

    def __len__(self):
        return len(self.encodings['input_ids'])




# create the dataset
test_warning_dataset = create_dataset(test_warning, test_warning_labels, tokenizer, pad_truncate=True, max_length=target_max_length)
# the take models output --> logits --> argmax to obtain prediction ids.
output_ids = np.argmax(trainer.predict(test_dataset=test_warning_dataset, num_beams=5, max_length=target_max_length).predictions[0], axis=2)

trainer_outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
model_generate_outputs = []
for i, code in enumerate(test_warning):
    input_ids = tokenizer.encode(code, truncation=True, padding=True).to(model.device)
    beam_outputs = model.generate(input_ids, max_length=target_max_length, num_beams=5, early_stopping=False, num_return_sequences=1)
for pred in beam_outputs:
    x = tokenizer.decode(pred, skip_special_tokens)
    model_generate_outputs.append(x)

model_generate_outputs and trainer_outputs are different. What is the issue?
What am I doing wrong?