(self, eval_dataloader, device)
| 38 | |
| 39 | |
| 40 | def perplexity_evaluation(self, eval_dataloader, device): |
| 41 | self.model.eval() |
| 42 | losses = 0 |
| 43 | for step, batch in enumerate(eval_dataloader): |
| 44 | # implementation, batch = {k: v.to(device) for k, v in batch.items()} |
| 45 | del batch['sources'] |
| 46 | batch = to_device(batch, device) |
| 47 | with torch.no_grad(): |
| 48 | outputs = self.model(**batch, use_cache=False) |
| 49 | loss = outputs.loss |
| 50 | losses += loss.float() |
| 51 | losses = losses / (step + 1) |
| 52 | try: |
| 53 | perplexity = torch.exp(losses) |
| 54 | except OverflowError: |
| 55 | perplexity = float("inf") |
| 56 | try: |
| 57 | perplexity = get_all_reduce_mean(perplexity).item() |
| 58 | except: |
| 59 | pass |
| 60 | return perplexity |
| 61 | |
| 62 | |
| 63 | def train_one_task(self, task, i_task, epochs): |
nothing calls this directly
no test coverage detected