def _generative_step(self, batch: dict) -> dict:
start_time = time.time()
batch = BatchEncoding(batch).to(device=self.model.device)
generated_ids = self.model.generate(
batch["input_ids"],
attention_mask=batch["attention_mask"],
do_deduplication=False, # rag specific parameter
use_cache=True,
min_length=1,
max_length=self.target_lens["val"],
)
gen_time = (time.time() - start_time) / batch["input_ids"].shape[0]
preds: List[str] = self.ids_to_clean_text(generated_ids)
target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
# print(preds,target)
loss_tensors = self._step(batch)
base_metrics = {
name: loss
for name, loss in zip(self.loss_names, loss_tensors)
}
gen_metrics: Dict = self.calc_generative_metrics(preds, target)
summ_len = np.mean(lmap(len, generated_ids))
base_metrics.update(gen_time=gen_time,
gen_len=summ_len,
preds=preds,
target=target,
**gen_metrics)
return base_metrics
def _generative_step(self, batch: dict) -> dict:
# batch['decoder_input_ids'] = batch['labels']
# del batch['labels']
start_time = time.time()
batch = BatchEncoding(batch).to(device=self.model.device)
generated_ids = self.model.generate(
batch["input_ids"],
attention_mask=batch["attention_mask"],
use_cache=True,
min_length=1,
max_length=self.target_lens["val"],
)
gen_time = (time.time() - start_time) / batch["input_ids"].shape[0]
preds: List[str] = self.ids_to_clean_text(generated_ids)
label = batch["decoder_input_ids"].cpu().detach().numpy()
label = np.where(label != -100, label, self.tokenizer.pad_token_id)
target: List[str] = self.ids_to_clean_text(label)
loss_tensors = self._step(batch)
base_metrics = {
name: loss
for name, loss in zip(self.loss_names, loss_tensors)
}
gen_metrics: Dict = self.calc_generative_metrics(preds, target)
summ_len = np.mean(lmap(len, generated_ids))
base_metrics.update(gen_time=gen_time,
gen_len=summ_len,
preds=preds,
target=target,
**gen_metrics)
return base_metrics
def ids_to_clean_text(self, generated_ids: List[int]):
gen_text = self.tokenizer.batch_decode(
generated_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=True)
return lmap(str.strip, gen_text)
