Funasr1.0 (#1362)

zhifu gao

2024-02-06 d92cd5ae037ae85ab9730499d99e5c1bd475eed2

 funasr/train_utils/trainer.py

@@ -147,9 +147,18 @@
        for epoch in range(self.start_epoch, self.max_epoch + 1):
            
            self._train_epoch(epoch)


            
            if self.use_ddp or self.use_fsdp:
                dist.barrier()
                
            self._validate_epoch(epoch)
            

            if self.use_ddp or self.use_fsdp:
                dist.barrier()
           
           
            if self.rank == 0:
                self._save_checkpoint(epoch)
            
@@ -164,7 +173,10 @@
            
        if self.use_ddp or self.use_fsdp:
            dist.barrier()
        self.writer.close()


        if self.writer:
            self.writer.close()
        
    
    def _train_epoch(self, epoch):
@@ -192,7 +204,25 @@
            my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
            with my_context():
                time2 = time.perf_counter()
                print("before, GPU, memory: {:.1} MB, "
                      "{:.1} MB, "
                      "{:.1} MB, "
                      "{:.1} MB".format(torch.cuda.memory_allocated()/1024/1024/1024,
                                     torch.cuda.max_memory_allocated()/1024/1024/1024,
                                     torch.cuda.memory_reserved()/1024/1024/1024,
                                     torch.cuda.max_memory_reserved()/1024/1024/1024,
                                     ))

                retval = self.model(**batch)
                torch.cuda.empty_cache()
                print("after, GPU, memory: {:.1} MB, "
                      "{:.1} MB, "
                      "{:.1} MB, "
                      "{:.1} MB".format(torch.cuda.memory_allocated()/1024/1024/1024,
                                     torch.cuda.max_memory_allocated()/1024/1024/1024,
                                     torch.cuda.memory_reserved()/1024/1024/1024,
                                     torch.cuda.max_memory_reserved()/1024/1024/1024,
                                     ))
                time3 = time.perf_counter()
                speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
                loss, stats, weight = retval
@@ -230,6 +260,8 @@
                        continue
                
                # Execute an optimization step (update model parameters)
                if self.use_ddp or self.use_fsdp:
                    dist.barrier()
                self.optim.step()
                self.scheduler.step()
                # Clear gradients for the next accumulation stage
@@ -244,7 +276,7 @@
            pbar.update(1)
            if self.local_rank == 0:
                description = (
                    f"Epoch: {epoch}/{self.max_epoch}, "
                    f"Train epoch: {epoch}/{self.max_epoch}, "
                    f"step {batch_idx}/{len(self.dataloader_train)}, "
                    f"{speed_stats}, "
                    f"(loss: {loss.detach().cpu().item():.3f}), "
@@ -306,7 +338,7 @@
                pbar.update(1)
                if self.local_rank == 0:
                    description = (
                        f"validation: \nEpoch: {epoch}/{self.max_epoch}, "
                        f"validation epoch: {epoch}/{self.max_epoch}, "
                        f"step {batch_idx}/{len(self.dataloader_train)}, "
                        f"{speed_stats}, "
                        f"(loss: {loss.detach().cpu().item():.3f}), "