try to get the original perf
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@ -195,15 +195,18 @@ class Graph_DiT(pl.LightningModule):
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# print("Size of the input features Xdim {}, Edim {}, ydim {}".format(self.Xdim, self.Edim, self.ydim))
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def on_train_epoch_start(self) -> None:
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if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
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print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
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# if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
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if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
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# print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
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print("Starting train epoch {}/{}...".format(self.current_epoch, self.cfg.train.n_epochs))
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self.start_epoch_time = time.time()
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self.train_loss.reset()
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self.train_metrics.reset()
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def on_train_epoch_end(self) -> None:
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if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
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# if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
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if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
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log = True
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else:
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log = False
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@ -601,8 +604,8 @@ class Graph_DiT(pl.LightningModule):
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assert (E == torch.transpose(E, 1, 2)).all()
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# total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate,10], device=self.device)
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total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate,10], device=self.device)
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# total_log_probs = torch.zeros([self.cfg.general.samples_to_generate,10], device=self.device)
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# Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
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for s_int in reversed(range(0, self.T)):
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@ -161,7 +161,8 @@ def test(cfg: DictConfig):
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accelerator = Accelerator(
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mixed_precision='no',
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project_config=accelerator_config,
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gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
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# gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
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gradient_accumulation_steps=cfg.train.gradient_accumulation_steps,
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)
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# Debug: 确认可用设备
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@ -219,29 +220,34 @@ def test(cfg: DictConfig):
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for epoch in range(cfg.train.n_epochs):
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graph_dit_model.train() # 设置模型为训练模式
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print(f"Epoch {epoch}", end="\n")
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graph_dit_model.on_train_epoch_start()
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for data in train_dataloader: # 从数据加载器中获取一个批次的数据
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data.to(accelerator.device)
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data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
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data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
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dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
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dense_data = dense_data.mask(node_mask)
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X, E = dense_data.X, dense_data.E
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noisy_data = graph_dit_model.apply_noise(X, E, data.y, node_mask)
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pred = graph_dit_model.forward(noisy_data)
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loss = graph_dit_model.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
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true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
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log=epoch % graph_dit_model.log_every_steps == 0)
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# print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
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graph_dit_model.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
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log=epoch % graph_dit_model.log_every_steps == 0)
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graph_dit_model.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
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print(f"training loss: {loss}")
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with open("training-loss.csv", "a") as f:
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f.write(f"{loss}, {epoch}\n")
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# data.to(accelerator.device)
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# data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
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# data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
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# dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
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# dense_data = dense_data.mask(node_mask)
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# X, E = dense_data.X, dense_data.E
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# noisy_data = graph_dit_model.apply_noise(X, E, data.y, node_mask)
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# pred = graph_dit_model.forward(noisy_data)
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# loss = graph_dit_model.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
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# true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
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# log=epoch % graph_dit_model.log_every_steps == 0)
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# # print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
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# graph_dit_model.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
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# log=epoch % graph_dit_model.log_every_steps == 0)
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# graph_dit_model.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
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# print(f"training loss: {loss}")
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# with open("training-loss.csv", "a") as f:
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# f.write(f"{loss}, {epoch}\n")
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loss = graph_dit_model.training_step(data, epoch)
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loss = loss['loss']
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accelerator.backward(loss)
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optimizer.step()
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optimizer.zero_grad()
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# return {'loss': loss}
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graph_dit_model.on_train_epoch_end()
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if epoch % cfg.train.check_val_every_n_epoch == 0:
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print(f'print validation loss')
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graph_dit_model.eval()
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@ -253,126 +259,69 @@ def test(cfg: DictConfig):
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print("start testing")
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graph_dit_model.eval()
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test_dataloader = accelerator.prepare(datamodule.test_dataloader())
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graph_dit_model.on_test_epoch_start()
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for data in test_dataloader:
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data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
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data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
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nll = graph_dit_model.test_step(data, epoch)
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# data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
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# data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
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dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
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dense_data = dense_data.mask(node_mask)
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noisy_data = graph_dit_model.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
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pred = graph_dit_model.forward(noisy_data)
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nll = graph_dit_model.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
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graph_dit_model.test_y_collection.append(data.y)
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# dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
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# dense_data = dense_data.mask(node_mask)
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# noisy_data = graph_dit_model.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
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# pred = graph_dit_model.forward(noisy_data)
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# nll = graph_dit_model.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
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# graph_dit_model.test_y_collection.append(data.y)
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print(f'test loss: {nll}')
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graph_dit_model.on_test_epoch_end()
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# start sampling
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samples_left_to_generate = cfg.general.final_model_samples_to_generate
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samples_left_to_save = cfg.general.final_model_samples_to_save
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chains_left_to_save = cfg.general.final_model_chains_to_save
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# samples_left_to_generate = cfg.general.final_model_samples_to_generate
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# samples_left_to_save = cfg.general.final_model_samples_to_save
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# chains_left_to_save = cfg.general.final_model_chains_to_save
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samples, all_ys, batch_id = [], [], 0
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samples_with_log_probs = []
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test_y_collection = torch.cat(graph_dit_model.test_y_collection, dim=0)
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num_examples = test_y_collection.size(0)
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if cfg.general.final_model_samples_to_generate > num_examples:
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ratio = cfg.general.final_model_samples_to_generate // num_examples
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test_y_collection = test_y_collection.repeat(ratio+1, 1)
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num_examples = test_y_collection.size(0)
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# samples, all_ys, batch_id = [], [], 0
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# samples_with_log_probs = []
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# test_y_collection = torch.cat(graph_dit_model.test_y_collection, dim=0)
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# num_examples = test_y_collection.size(0)
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# if cfg.general.final_model_samples_to_generate > num_examples:
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# ratio = cfg.general.final_model_samples_to_generate // num_examples
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# test_y_collection = test_y_collection.repeat(ratio+1, 1)
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# num_examples = test_y_collection.size(0)
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# Normal reward function
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from nas_201_api import NASBench201API as API
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api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
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def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
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rewards = []
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if reward_model == 'swap':
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import csv
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with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
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reader = csv.reader(f)
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header = next(reader)
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data = [row for row in reader]
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swap_scores = [float(row[0]) for row in data]
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for graph in graphs:
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node_tensor = graph[0]
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node = node_tensor.cpu().numpy().tolist()
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def nodes_to_arch_str(nodes):
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num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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nodes_str = [num_to_op[node] for node in nodes]
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arch_str = '|' + nodes_str[1] + '~0|+' + \
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'|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
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'|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|'
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return arch_str
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arch_str = nodes_to_arch_str(node)
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reward = swap_scores[api.query_index_by_arch(arch_str)]
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rewards.append(reward)
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# from nas_201_api import NASBench201API as API
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# api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
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# def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
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# rewards = []
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# if reward_model == 'swap':
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# import csv
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# with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
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# reader = csv.reader(f)
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# header = next(reader)
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# data = [row for row in reader]
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# swap_scores = [float(row[0]) for row in data]
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# for graph in graphs:
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# reward = 1.0
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# node_tensor = graph[0]
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# node = node_tensor.cpu().numpy().tolist()
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# def nodes_to_arch_str(nodes):
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# num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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# nodes_str = [num_to_op[node] for node in nodes]
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# arch_str = '|' + nodes_str[1] + '~0|+' + \
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# '|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
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# '|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|'
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# return arch_str
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# arch_str = nodes_to_arch_str(node)
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# reward = swap_scores[api.query_index_by_arch(arch_str)]
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# rewards.append(reward)
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return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
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old_log_probs = None
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while samples_left_to_generate > 0:
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print(f'samples left to generate: {samples_left_to_generate}/'
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f'{cfg.general.final_model_samples_to_generate}', end='', flush=True)
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bs = 1 * cfg.train.batch_size
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to_generate = min(samples_left_to_generate, bs)
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to_save = min(samples_left_to_save, bs)
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chains_save = min(chains_left_to_save, bs)
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# batch_y = test_y_collection[batch_id : batch_id + to_generate]
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batch_y = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
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cur_sample, log_probs = graph_dit_model.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
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keep_chain=chains_save, number_chain_steps=graph_dit_model.number_chain_steps)
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samples = samples + cur_sample
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reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
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advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
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if old_log_probs is None:
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old_log_probs = log_probs.clone()
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ratio = torch.exp(log_probs - old_log_probs)
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unclipped_loss = -advantages * ratio
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clipped_loss = -advantages * torch.clamp(ratio, 1.0 - cfg.ppo.clip_param, 1.0 + cfg.ppo.clip_param)
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loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
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accelerator.backward(loss)
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optimizer.step()
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optimizer.zero_grad()
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samples_with_log_probs.append((cur_sample, log_probs, reward))
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all_ys.append(batch_y)
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batch_id += to_generate
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samples_left_to_save -= to_save
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samples_left_to_generate -= to_generate
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chains_left_to_save -= chains_save
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print(f"final Computing sampling metrics...")
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graph_dit_model.sampling_metrics.reset()
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graph_dit_model.sampling_metrics(samples, all_ys, graph_dit_model.name, graph_dit_model.current_epoch, graph_dit_model.val_counter, test=True)
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graph_dit_model.sampling_metrics.reset()
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print(f"Done.")
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# save samples
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print("Samples:")
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print(samples)
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# perm = torch.randperm(len(samples_with_log_probs), device=accelerator.device)
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# samples, log_probs, rewards = samples_with_log_probs[perm]
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# samples = list(samples)
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# log_probs = list(log_probs)
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# for i in range(len(log_probs)):
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# log_probs[i] = torch.sum(log_probs[i], dim=-1).unsqueeze(0)
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# print(f'log_probs: {log_probs[:5]}')
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# print(f'log_probs: {log_probs[0].shape}') # torch.Size([1])
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# rewards = list(rewards)
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# log_probs = torch.cat(log_probs, dim=0)
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# print(f'log_probs: {log_probs.shape}') # torch.Size([1000, 1])
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# old_log_probs = log_probs.clone()
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# ===
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# # for graph in graphs:
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# # reward = 1.0
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# # rewards.append(reward)
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# return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
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# old_log_probs = None
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# while samples_left_to_generate > 0:
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# print(f'samples left to generate: {samples_left_to_generate}/'
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@ -381,27 +330,28 @@ def test(cfg: DictConfig):
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# to_generate = min(samples_left_to_generate, bs)
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# to_save = min(samples_left_to_save, bs)
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# chains_save = min(chains_left_to_save, bs)
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# with accelerator.accumulate(graph_dit_model):
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# # batch_y = test_y_collection[batch_id : batch_id + to_generate]
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# batch_y = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
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# new_samples, log_probs = graph_dit_model.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,keep_chain=chains_save, number_chain_steps=graph_dit_model.number_chain_steps)
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# samples = samples + new_samples
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# reward = graph_reward_fn(new_samples, device=graph_dit_model.device)
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# cur_sample, log_probs = graph_dit_model.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
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# keep_chain=chains_save, number_chain_steps=graph_dit_model.number_chain_steps)
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# log_probs = torch.sum(log_probs, dim=-1).unsqueeze(1)
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# samples = samples + cur_sample
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# reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
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# advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
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# print(f'reward: {reward.shape}, advantages: {advantages.shape}, log_probs: {log_probs.shape}, cur_sample: {len(cur_sample)}')
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# if old_log_probs is None:
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# old_log_probs = log_probs.clone()
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# ratio = torch.exp(log_probs - old_log_probs)
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# unclipped_loss = -advantages * ratio
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# clipped_loss = -advantages * torch.clamp(ratio,
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# 1.0 - cfg.ppo.clip_param,
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# 1.0 + cfg.ppo.clip_param)
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# clipped_loss = -advantages * torch.clamp(ratio, 1.0 - cfg.ppo.clip_param, 1.0 + cfg.ppo.clip_param)
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# loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
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# accelerator.backward(loss)
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# optimizer.step()
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# optimizer.zero_grad()
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# samples_with_log_probs.append((new_samples, log_probs, reward))
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||||
# samples_with_log_probs.append((cur_sample, log_probs, reward))
|
||||
|
||||
# all_ys.append(batch_y)
|
||||
# batch_id += to_generate
|
||||
@ -409,7 +359,6 @@ def test(cfg: DictConfig):
|
||||
# samples_left_to_save -= to_save
|
||||
# samples_left_to_generate -= to_generate
|
||||
# chains_left_to_save -= chains_save
|
||||
# # break
|
||||
|
||||
# print(f"final Computing sampling metrics...")
|
||||
# graph_dit_model.sampling_metrics.reset()
|
||||
@ -421,46 +370,10 @@ def test(cfg: DictConfig):
|
||||
# print("Samples:")
|
||||
# print(samples)
|
||||
|
||||
# perm = torch.randperm(len(samples_with_log_probs), device=accelerator.device)
|
||||
# samples, log_probs, rewards = samples_with_log_probs[perm]
|
||||
# samples = list(samples)
|
||||
# log_probs = list(log_probs)
|
||||
# for i in range(len(log_probs)):
|
||||
# log_probs[i] = torch.sum(log_probs[i], dim=-1).unsqueeze(0)
|
||||
# print(f'log_probs: {log_probs[:5]}')
|
||||
# print(f'log_probs: {log_probs[0].shape}') # torch.Size([1])
|
||||
# rewards = list(rewards)
|
||||
# log_probs = torch.cat(log_probs, dim=0)
|
||||
# print(f'log_probs: {log_probs.shape}') # torch.Size([1000, 1])
|
||||
# old_log_probs = log_probs.clone()
|
||||
# # multi metrics range
|
||||
# # reward hacking hiking
|
||||
# for inner_epoch in range(cfg.train.n_epochs):
|
||||
# # print(f'rewards: {rewards.shape}') # torch.Size([1000])
|
||||
# print(f'rewards: {rewards[:5]}')
|
||||
# print(f'len rewards: {len(rewards)}')
|
||||
# print(f'type rewards: {type(rewards)}')
|
||||
# if len(rewards) > 1 and isinstance(rewards, list):
|
||||
# rewards = torch.cat(rewards, dim=0)
|
||||
# elif len(rewards) == 1 and isinstance(rewards, list):
|
||||
# rewards = rewards[0]
|
||||
# # print(f'rewards: {rewards.shape}')
|
||||
# advantages = (rewards - torch.mean(rewards)) / (torch.std(rewards) + 1e-6)
|
||||
# print(f'advantages: {advantages.shape}')
|
||||
# with accelerator.accumulate(graph_dit_model):
|
||||
# ratio = torch.exp(log_probs - old_log_probs)
|
||||
# unclipped_loss = -advantages * ratio
|
||||
# # z-score normalization
|
||||
# clipped_loss = -advantages * torch.clamp(ratio,
|
||||
# 1.0 - cfg.ppo.clip_param,
|
||||
# 1.0 + cfg.ppo.clip_param)
|
||||
# loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
|
||||
# accelerator.backward(loss)
|
||||
# optimizer.step()
|
||||
# optimizer.zero_grad()
|
||||
# ========================
|
||||
|
||||
|
||||
|
||||
# accelerator.log({"loss": loss.item(), "epoch": inner_epoch})
|
||||
# print(f"loss: {loss.item()}, epoch: {inner_epoch}")
|
||||
|
||||
|
||||
# trainer = Trainer(
|
||||
|
||||
Loading…
Reference in New Issue
Block a user