483 lines
21 KiB
Python
483 lines
21 KiB
Python
# These imports are tricky because they use c++, do not move them
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from tqdm import tqdm
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import os, shutil
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import warnings
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import torch
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import hydra
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from omegaconf import DictConfig
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from pytorch_lightning import Trainer
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import utils
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from datasets import dataset
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from diffusion_model import Graph_DiT
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from metrics.molecular_metrics_train import TrainMolecularMetricsDiscrete
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from metrics.molecular_metrics_train import TrainGraphMetricsDiscrete
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from metrics.molecular_metrics_sampling import SamplingMolecularMetrics
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from metrics.molecular_metrics_sampling import SamplingGraphMetrics
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from analysis.visualization import MolecularVisualization
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from analysis.visualization import GraphVisualization
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warnings.filterwarnings("ignore", category=UserWarning)
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torch.set_float32_matmul_precision("medium")
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def remove_folder(folder):
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for filename in os.listdir(folder):
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file_path = os.path.join(folder, filename)
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try:
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if os.path.isfile(file_path) or os.path.islink(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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except Exception as e:
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print("Failed to delete %s. Reason: %s" % (file_path, e))
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def get_resume(cfg, model_kwargs):
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"""Resumes a run. It loads previous config without allowing to update keys (used for testing)."""
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saved_cfg = cfg.copy()
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name = cfg.general.name + "_resume"
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resume = cfg.general.test_only
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batch_size = cfg.train.batch_size
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model = Graph_DiT.load_from_checkpoint(resume, **model_kwargs)
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cfg = model.cfg
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cfg.general.test_only = resume
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cfg.general.name = name
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cfg.train.batch_size = batch_size
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cfg = utils.update_config_with_new_keys(cfg, saved_cfg)
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return cfg, model
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def get_resume_adaptive(cfg, model_kwargs):
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"""Resumes a run. It loads previous config but allows to make some changes (used for resuming training)."""
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saved_cfg = cfg.copy()
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# Fetch path to this file to get base path
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current_path = os.path.dirname(os.path.realpath(__file__))
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root_dir = current_path.split("outputs")[0]
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resume_path = os.path.join(root_dir, cfg.general.resume)
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if cfg.model.type == "discrete":
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model = Graph_DiT.load_from_checkpoint(
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resume_path, **model_kwargs
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)
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else:
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raise NotImplementedError("Unknown model")
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new_cfg = model.cfg
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for category in cfg:
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for arg in cfg[category]:
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new_cfg[category][arg] = cfg[category][arg]
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new_cfg.general.resume = resume_path
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new_cfg.general.name = new_cfg.general.name + "_resume"
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new_cfg = utils.update_config_with_new_keys(new_cfg, saved_cfg)
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return new_cfg, model
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@hydra.main(
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version_base="1.1", config_path="../configs", config_name="config"
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)
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def main(cfg: DictConfig):
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datamodule = dataset.DataModule(cfg)
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datamodule.prepare_data()
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dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
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train_smiles, reference_smiles = datamodule.get_train_smiles()
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# train_graphs, reference_graphs = datamodule.get_train_graphs()
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# get input output dimensions
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dataset_infos.compute_input_output_dims(datamodule=datamodule)
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train_metrics = TrainMolecularMetricsDiscrete(dataset_infos)
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# train_metrics = TrainGraphMetricsDiscrete(dataset_infos)
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sampling_metrics = SamplingMolecularMetrics(
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dataset_infos, train_smiles, reference_smiles
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)
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# sampling_metrics = SamplingGraphMetrics(
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# dataset_infos, train_graphs, reference_graphs
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# )
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visualization_tools = MolecularVisualization(dataset_infos)
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model_kwargs = {
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"dataset_infos": dataset_infos,
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# "train_metrics": train_metrics,
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# "sampling_metrics": sampling_metrics,
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"visualization_tools": visualization_tools,
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}
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if cfg.general.test_only:
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# When testing, previous configuration is fully loaded
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cfg, _ = get_resume(cfg, model_kwargs)
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os.chdir(cfg.general.test_only.split("checkpoints")[0])
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elif cfg.general.resume is not None:
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# When resuming, we can override some parts of previous configuration
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cfg, _ = get_resume_adaptive(cfg, model_kwargs)
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os.chdir(cfg.general.resume.split("checkpoints")[0])
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model = Graph_DiT(cfg=cfg, **model_kwargs)
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trainer = Trainer(
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gradient_clip_val=cfg.train.clip_grad,
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# accelerator="gpu"
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# if torch.cuda.is_available() and cfg.general.gpus > 0
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# else "cpu",
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accelerator="cpu",
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devices=cfg.general.gpus
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if torch.cuda.is_available() and cfg.general.gpus > 0
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else None,
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max_epochs=cfg.train.n_epochs,
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enable_checkpointing=False,
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check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
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val_check_interval=cfg.train.val_check_interval,
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strategy="ddp" if cfg.general.gpus > 1 else "auto",
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enable_progress_bar=cfg.general.enable_progress_bar,
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callbacks=[],
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reload_dataloaders_every_n_epochs=0,
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logger=[],
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)
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if not cfg.general.test_only:
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trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
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if cfg.general.save_model:
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trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
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trainer.test(model, datamodule=datamodule)
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else:
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trainer.test(model, datamodule=datamodule, ckpt_path=cfg.general.test_only)
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from accelerate import Accelerator
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from accelerate.utils import set_seed, ProjectConfiguration
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@hydra.main(
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version_base="1.1", config_path="../configs", config_name="config"
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)
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def test(cfg: DictConfig):
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os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
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accelerator_config = ProjectConfiguration(
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project_dir=os.path.join(cfg.general.log_dir, cfg.general.name),
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automatic_checkpoint_naming=True,
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total_limit=cfg.general.number_checkpoint_limit,
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)
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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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)
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# Debug: 确认可用设备
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print(f"Available GPUs: {torch.cuda.device_count()}")
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print(f"Using device: {accelerator.device}")
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set_seed(cfg.train.seed, device_specific=True)
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datamodule = dataset.DataModule(cfg)
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datamodule.prepare_data()
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dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
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train_graphs, reference_graphs = datamodule.get_train_graphs()
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dataset_infos.compute_input_output_dims(datamodule=datamodule)
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train_metrics = TrainGraphMetricsDiscrete(dataset_infos)
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sampling_metrics = SamplingGraphMetrics(
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dataset_infos, train_graphs, reference_graphs
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)
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visulization_tools = GraphVisualization(dataset_infos)
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model_kwargs = {
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"dataset_infos": dataset_infos,
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"train_metrics": train_metrics,
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"sampling_metrics": sampling_metrics,
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"visualization_tools": visulization_tools,
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}
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# Debug: 确认可用设备
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print(f"Available GPUs: {torch.cuda.device_count()}")
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print(f"Using device: {accelerator.device}")
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if cfg.general.test_only:
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cfg, _ = get_resume(cfg, model_kwargs)
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os.chdir(cfg.general.test_only.split("checkpoints")[0])
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elif cfg.general.resume is not None:
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cfg, _ = get_resume_adaptive(cfg, model_kwargs)
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os.chdir(cfg.general.resume.split("checkpoints")[0])
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model = Graph_DiT(cfg=cfg, **model_kwargs)
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graph_dit_model = model
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inference_dtype = torch.float32
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graph_dit_model.to(accelerator.device, dtype=inference_dtype)
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# optional: freeze the model
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# graph_dit_model.model.requires_grad_(True)
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import torch.nn.functional as F
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optimizer = graph_dit_model.configure_optimizers()
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train_dataloader = accelerator.prepare(datamodule.train_dataloader())
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optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
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# start training
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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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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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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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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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graph_dit_model.on_validation_epoch_start()
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graph_dit_model.validation_step(data, epoch)
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graph_dit_model.on_validation_epoch_end()
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# start testing
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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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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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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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# 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, 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/nasbench201/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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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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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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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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# 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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# with accelerator.accumulate(graph_dit_model):
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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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# 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,
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# 1.0 - cfg.ppo.clip_param,
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# 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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# 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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# # break
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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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|
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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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# # multi metrics range
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# # reward hacking hiking
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# for inner_epoch in range(cfg.train.n_epochs):
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# # print(f'rewards: {rewards.shape}') # torch.Size([1000])
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# print(f'rewards: {rewards[:5]}')
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# print(f'len rewards: {len(rewards)}')
|
|
# print(f'type rewards: {type(rewards)}')
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# if len(rewards) > 1 and isinstance(rewards, list):
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# rewards = torch.cat(rewards, dim=0)
|
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# elif len(rewards) == 1 and isinstance(rewards, list):
|
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# rewards = rewards[0]
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# # print(f'rewards: {rewards.shape}')
|
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# advantages = (rewards - torch.mean(rewards)) / (torch.std(rewards) + 1e-6)
|
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# print(f'advantages: {advantages.shape}')
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# with accelerator.accumulate(graph_dit_model):
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# ratio = torch.exp(log_probs - old_log_probs)
|
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# unclipped_loss = -advantages * ratio
|
|
# # z-score normalization
|
|
# 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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# 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(
|
|
# gradient_clip_val=cfg.train.clip_grad,
|
|
# # accelerator="cpu",
|
|
# accelerator="gpu"
|
|
# if torch.cuda.is_available() and cfg.general.gpus > 0
|
|
# else "cpu",
|
|
# devices=[cfg.general.gpu_number]
|
|
# if torch.cuda.is_available() and cfg.general.gpus > 0
|
|
# else None,
|
|
# max_epochs=cfg.train.n_epochs,
|
|
# enable_checkpointing=False,
|
|
# check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
|
|
# val_check_interval=cfg.train.val_check_interval,
|
|
# strategy="ddp" if cfg.general.gpus > 1 else "auto",
|
|
# enable_progress_bar=cfg.general.enable_progress_bar,
|
|
# callbacks=[],
|
|
# reload_dataloaders_every_n_epochs=0,
|
|
# logger=[],
|
|
# )
|
|
|
|
# if not cfg.general.test_only:
|
|
# print("start testing fit method")
|
|
# trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
|
|
# if cfg.general.save_model:
|
|
# trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
|
|
# trainer.test(model, datamodule=datamodule)
|
|
|
|
if __name__ == "__main__":
|
|
test()
|