add swap cifar10 results property_metric path update

README.md

@@ -1,34 +1,14 @@
 Graph Diffusion Transformer for Multi-Conditional Molecular Generation
 ================================================================
 
-## Initial Setup
-
-Please download NASBench201 dataset(NAS-Bench-201-v1_1-096897.pth) from
-https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view
-
-and put it in the `/path/to/repo/graph_dit` folder.
-
-## Running the code
-
-start command:
-``` bash
-python main.py --config-name=config.yaml \
-model.ensure_connected=True \
-dataset.task_name='nasbench201' \
-dataset.guidance_target='regression'
-```
-
-This repository contains the code for the paper "Inverse Molecular Design with Multi-Conditional Diffusion Guidance" by Gang Liu, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
-
-
 Paper: https://arxiv.org/abs/2401.13858
 
-<!-- This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
+This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
 
 <div style="display: flex;" markdown="1">
       <img src="asset/reverse.png" style="width: 45%;" alt="Description of the first image">
       <img src="asset/arch.png" style="width: 45%;" alt="Description of the second image">
-</div> -->
+</div>
 
 
 ## Requirements

configs/config.yaml

@@ -2,20 +2,26 @@ general:
     name: 'graph_dit'
     wandb: 'disabled' 
     gpus: 1
-    gpu_number: 2
+    gpu_number: 0
     resume: null
     test_only: null
     sample_every_val: 2500
-    samples_to_generate: 512      
+    samples_to_generate: 1000
     samples_to_save: 3
     chains_to_save: 1
     log_every_steps: 50
     number_chain_steps: 8
-    final_model_samples_to_generate: 100
+    final_model_samples_to_generate: 1000
     final_model_samples_to_save: 20
     final_model_chains_to_save: 1
     enable_progress_bar: False
     save_model: True
+    log_dir: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT'
+    number_checkpoint_limit: 3
+    type: 'Trainer'
+    nas_201: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+    swap_result: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/swap_results.csv'
+    root: '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/'
 model:
     type: 'discrete'
     transition: 'marginal'                  
@@ -32,7 +38,7 @@ model:
     ensure_connected: True
 train:
     # n_epochs: 5000
-    n_epochs: 500
+    n_epochs: 10
     batch_size: 1200
     lr: 0.0002
     clip_grad: null
@@ -41,8 +47,11 @@ train:
     seed: 0
     val_check_interval: null
     check_val_every_n_epoch: 1
+    gradient_accumulation_steps: 1
 dataset:
     datadir: 'data/'
     task_name: 'nasbench-201'
     guidance_target: 'nasbench-201'
     pin_memory: False
+ppo:
+    clip_param: 1

environment.yaml

@@ -0,0 +1,228 @@
+name: graphdit
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - asttokens=2.4.1=pyhd8ed1ab_0
+  - blas=1.0=mkl
+  - brotli-python=1.0.9=py39h6a678d5_8
+  - bzip2=1.0.8=h5eee18b_6
+  - ca-certificates=2024.7.2=h06a4308_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - debugpy=1.6.7=py39h6a678d5_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - exceptiongroup=1.2.0=pyhd8ed1ab_2
+  - executing=2.0.1=pyhd8ed1ab_0
+  - ffmpeg=4.3=hf484d3e_0
+  - freetype=2.12.1=h4a9f257_0
+  - gmp=6.2.1=h295c915_3
+  - gmpy2=2.1.2=py39heeb90bb_0
+  - gnutls=3.6.15=he1e5248_0
+  - idna=3.7=py39h06a4308_0
+  - importlib-metadata=7.1.0=pyha770c72_0
+  - importlib_metadata=7.1.0=hd8ed1ab_0
+  - intel-openmp=2023.1.0=hdb19cb5_46306
+  - ipykernel=6.29.4=pyh3099207_0
+  - ipython=8.18.1=pyh707e725_3
+  - jedi=0.19.1=pyhd8ed1ab_0
+  - jinja2=3.1.4=py39h06a4308_0
+  - jpeg=9e=h5eee18b_1
+  - jupyter_client=8.6.2=pyhd8ed1ab_0
+  - jupyter_core=5.7.2=py39hf3d152e_0
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libdeflate=1.17=h5eee18b_1
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc-ng=13.2.0=h77fa898_7
+  - libgomp=13.2.0=h77fa898_7
+  - libiconv=1.16=h5eee18b_3
+  - libidn2=2.3.4=h5eee18b_0
+  - libpng=1.6.39=h5eee18b_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtasn1=4.19.0=h5eee18b_0
+  - libtiff=4.5.1=h6a678d5_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libwebp-base=1.3.2=h5eee18b_0
+  - lz4-c=1.9.4=h6a678d5_1
+  - matplotlib-inline=0.1.7=pyhd8ed1ab_0
+  - mkl=2023.1.0=h213fc3f_46344
+  - mkl-service=2.4.0=py39h5eee18b_1
+  - mkl_fft=1.3.8=py39h5eee18b_0
+  - mkl_random=1.2.4=py39hdb19cb5_0
+  - mpc=1.1.0=h10f8cd9_1
+  - mpfr=4.0.2=hb69a4c5_1
+  - mpmath=1.3.0=py39h06a4308_0
+  - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.6.0=pyhd8ed1ab_0
+  - nettle=3.7.3=hbbd107a_1
+  - numpy-base=1.26.4=py39hb5e798b_0
+  - openh264=2.1.1=h4ff587b_0
+  - openjpeg=2.4.0=h9ca470c_2
+  - openssl=3.3.1=h4ab18f5_0
+  - packaging=24.0=pyhd8ed1ab_0
+  - parso=0.8.4=pyhd8ed1ab_0
+  - pexpect=4.9.0=pyhd8ed1ab_0
+  - pickleshare=0.7.5=py_1003
+  - pip=24.0=py39h06a4308_0
+  - platformdirs=4.2.2=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.46=pyha770c72_0
+  - psutil=5.9.8=py39hd1e30aa_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pygments=2.18.0=pyhd8ed1ab_0
+  - pysocks=1.7.1=py39h06a4308_0
+  - python=3.9.19=h955ad1f_1
+  - python_abi=3.9=2_cp39
+  - pytorch-mutex=1.0=cpu
+  - pyzmq=25.1.2=py39h6a678d5_0
+  - readline=8.2=h5eee18b_0
+  - setuptools=69.5.1=py39h06a4308_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sqlite=3.45.3=h5eee18b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - sympy=1.12=py39h06a4308_0
+  - tbb=2021.8.0=hdb19cb5_0
+  - tk=8.6.14=h39e8969_0
+  - tornado=6.4.1=py39hd3abc70_0
+  - traitlets=5.14.3=pyhd8ed1ab_0
+  - typing_extensions=4.12.2=pyha770c72_0
+  - wcwidth=0.2.13=pyhd8ed1ab_0
+  - wheel=0.43.0=py39h06a4308_0
+  - xz=5.4.6=h5eee18b_1
+  - zeromq=4.3.5=h6a678d5_0
+  - zlib=1.2.13=h5eee18b_1
+  - zstd=1.5.5=hc292b87_2
+  - pip:
+      - absl-py==2.1.0
+      - accelerate==0.34.2
+      - aiohttp==3.9.5
+      - aiosignal==1.3.1
+      - antlr4-python3-runtime==4.9.3
+      - astunparse==1.6.3
+      - async-timeout==4.0.3
+      - attrs==23.2.0
+      - beautifulsoup4==4.12.3
+      - bleach==6.1.0
+      - certifi==2024.2.2
+      - charset-normalizer==3.1.0
+      - cmake==3.29.3
+      - contourpy==1.2.1
+      - cycler==0.12.1
+      - defusedxml==0.7.1
+      - fastjsonschema==2.19.1
+      - fcd-torch==1.0.7
+      - filelock==3.14.0
+      - flatbuffers==24.3.25
+      - fonttools==4.52.4
+      - frozenlist==1.4.1
+      - fsspec==2024.5.0
+      - gast==0.5.4
+      - google-pasta==0.2.0
+      - grpcio==1.64.1
+      - h5py==3.11.0
+      - huggingface-hub==0.24.6
+      - hydra-core==1.3.2
+      - imageio==2.26.0
+      - importlib-resources==6.4.0
+      - joblib==1.2.0
+      - jsonschema==4.22.0
+      - jsonschema-specifications==2023.12.1
+      - jupyterlab-pygments==0.3.0
+      - keras==3.3.3
+      - kiwisolver==1.4.5
+      - libclang==18.1.1
+      - lightning-utilities==0.11.2
+      - lit==18.1.6
+      - markdown==3.6
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - matplotlib==3.7.0
+      - mdurl==0.1.2
+      - mini-moses==1.0
+      - mistune==3.0.2
+      - ml-dtypes==0.3.2
+      - multidict==6.0.5
+      - namex==0.0.8
+      - nas-bench-201==2.1
+      - nasbench==1.0
+      - nbclient==0.10.0
+      - nbconvert==7.16.4
+      - nbformat==5.10.4
+      - networkx==3.0
+      - numpy==1.24.2
+      - nvidia-cublas-cu11==11.10.3.66
+      - nvidia-cublas-cu12==12.1.3.1
+      - nvidia-cuda-cupti-cu11==11.7.101
+      - nvidia-cuda-cupti-cu12==12.1.105
+      - nvidia-cuda-nvrtc-cu11==11.7.99
+      - nvidia-cuda-nvrtc-cu12==12.1.105
+      - nvidia-cuda-runtime-cu11==11.7.99
+      - nvidia-cuda-runtime-cu12==12.1.105
+      - nvidia-cudnn-cu11==8.5.0.96
+      - nvidia-cudnn-cu12==8.9.2.26
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-cufft-cu12==11.0.2.54
+      - nvidia-curand-cu11==10.2.10.91
+      - nvidia-curand-cu12==10.3.2.106
+      - nvidia-cusolver-cu11==11.4.0.1
+      - nvidia-cusolver-cu12==11.4.5.107
+      - nvidia-cusparse-cu11==11.7.4.91
+      - nvidia-cusparse-cu12==12.1.0.106
+      - nvidia-nccl-cu11==2.14.3
+      - nvidia-nccl-cu12==2.20.5
+      - nvidia-nvjitlink-cu12==12.5.40
+      - nvidia-nvtx-cu11==11.7.91
+      - nvidia-nvtx-cu12==12.1.105
+      - omegaconf==2.3.0
+      - opt-einsum==3.3.0
+      - optree==0.11.0
+      - pandas==1.5.3
+      - pandocfilters==1.5.1
+      - pillow==10.3.0
+      - protobuf==3.20.3
+      - pyparsing==3.1.2
+      - python-dateutil==2.9.0.post0
+      - pytorch-lightning==2.0.1
+      - pytz==2024.1
+      - pyyaml==6.0.1
+      - rdkit==2023.9.4
+      - referencing==0.35.1
+      - requests==2.32.2
+      - rich==13.7.1
+      - rpds-py==0.18.1
+      - safetensors==0.4.5
+      - scikit-learn==1.2.1
+      - scipy==1.13.1
+      - seaborn==0.13.2
+      - simplejson==3.19.2
+      - soupsieve==2.5
+      - tensorboard==2.16.2
+      - tensorboard-data-server==0.7.2
+      - tensorflow==2.16.1
+      - tensorflow-io-gcs-filesystem==0.37.0
+      - termcolor==2.4.0
+      - threadpoolctl==3.5.0
+      - tinycss2==1.3.0
+      - torch==2.0.0
+      - torch-geometric==2.3.0
+      - torchaudio==2.0.1+rocm5.4.2
+      - torchmetrics==0.11.4
+      - torchvision==0.15.1
+      - tqdm==4.64.1
+      - triton==2.0.0
+      - typing-extensions==4.12.0
+      - tzdata==2024.1
+      - urllib3==2.2.1
+      - webencodings==0.5.1
+      - werkzeug==3.0.3
+      - wrapt==1.16.0
+      - yacs==0.1.8
+      - yarl==1.9.4
+      - zipp==3.19.0
+prefix: /home/stud/hanzhang/anaconda3/envs/graphdit

graph_dit/analysis/rdkit_functions.py

@@ -54,7 +54,9 @@ class BasicGraphMetrics(object):
         covered_nodes = set()
         direct_valid_count = 0
         print(f"generated number: {len(generated)}")
+        print(f"generated: {generated}")
         for graph in generated:
+            print(f"graph: {graph}")
             node_types, edge_types = graph
             direct_valid_flag = True
             direct_valid_count += 1

graph_dit/datasets/dataset.py

@@ -25,7 +25,6 @@ from sklearn.model_selection import train_test_split
 import utils as utils
 from datasets.abstract_dataset import AbstractDatasetInfos, AbstractDataModule
 from diffusion.distributions import DistributionNodes
-from naswot.score_networks import get_nasbench201_idx_score
 from naswot import nasspace
 from naswot import datasets as dt
 
@@ -72,7 +71,9 @@ class DataModule(AbstractDataModule):
         #     base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
         # except NameError:
         # base_path = pathlib.Path(os.getcwd()).parent[2]
-        base_path = '/nfs/data3/hanzhang/nasbenchDiT'
+        # base_path = '/nfs/data3/hanzhang/nasbenchDiT'
+        base_path = os.path.join(self.cfg.general.root, "..")
+
         root_path = os.path.join(base_path, self.datadir)
         self.root_path = root_path
 
@@ -84,7 +85,7 @@ class DataModule(AbstractDataModule):
         # Load the dataset to the memory
         # Dataset has target property, root path, and transform
         source = './NAS-Bench-201-v1_1-096897.pth'
-        dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None)
+        dataset = Dataset(source=source, root=root_path, target_prop=target, transform=None, cfg=self.cfg)
         self.dataset = dataset
         # self.api = dataset.api
 
@@ -384,7 +385,7 @@ class DataModule_original(AbstractDataModule):
     def test_dataloader(self):
         return self.test_loader
 
-def new_graphs_to_json(graphs, filename):
+def new_graphs_to_json(graphs, filename, cfg):
     source_name = "nasbench-201"
     num_graph = len(graphs)
 
@@ -491,8 +492,9 @@ def new_graphs_to_json(graphs, filename):
         'num_active_nodes': len(active_nodes),
         'transition_E': transition_E.tolist(),
     }
-
+    import os
-    with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
+    # with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
+    with open(os.path.join(cfg.general.root,'nasbench-201-meta.json'), 'w') as f:
         json.dump(meta_dict, f)
     
     return meta_dict
@@ -656,9 +658,11 @@ def graphs_to_json(graphs, filename):
         json.dump(meta_dict, f)
     return meta_dict
 class Dataset(InMemoryDataset):
-    def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
+    def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None, cfg=None):
         self.target_prop = target_prop
-        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.cfg = cfg
+        # source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        source = os.path.join(self.cfg.general.root, 'NAS-Bench-201-v1_1-096897.pth')
         self.source = source
         # self.api = API(source)  # Initialize NAS-Bench-201 API
         # print('API loaded')
@@ -679,7 +683,8 @@ class Dataset(InMemoryDataset):
         return [f'{self.source}.pt']
 
     def process(self):
-        source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        # source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        source = self.cfg.general.nas_201
         # self.api = API(source)
 
         data_list = []
@@ -748,7 +753,8 @@ class Dataset(InMemoryDataset):
             return  edges,nodes
         
 
-        def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
+        # def graph_to_graph_data(graph, idx, train_loader, searchspace, args, device):
+        def graph_to_graph_data(graph, idx,  args, device):
         # def graph_to_graph_data(graph):
             ops = graph[1]
             adj = graph[0]
@@ -797,7 +803,7 @@ class Dataset(InMemoryDataset):
         args.batch_size = 128
         args.GPU = '0'
         args.dataset = 'cifar10'
-        args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        args.api_loc = self.cfg.general.nas_201 
         args.data_loc = '../cifardata/'
         args.seed = 777
         args.init = ''
@@ -812,11 +818,12 @@ class Dataset(InMemoryDataset):
         args.num_modules_per_stack = 3
         args.num_labels = 1
         searchspace = nasspace.get_search_space(args)
-        train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
+        # train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
         self.swap_scores = []
         import csv
         # with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
-        with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results_cifar100.csv', 'r') as f:
+        with open(self.cfg.general.swap_result, 'r') as f:
+        # with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results_cifar100.csv', 'r') as f:
             reader = csv.reader(f)
             header = next(reader)
             data = [row for row in reader]
@@ -824,12 +831,15 @@ class Dataset(InMemoryDataset):
         device = torch.device('cuda:2')
         with tqdm(total = len_data) as pbar:
             active_nodes = set()
-            file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            import os
+            # file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+            file_path = os.path.join(self.cfg.general.root, 'nasbench-201-graph.json')
             with open(file_path, 'r') as f:
                 graph_list = json.load(f)
             i = 0
             flex_graph_list = []
-            flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
+            # flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
+            flex_graph_path = os.path.join(self.cfg.general.root,'flex-nasbench201-graph.json')
             for graph in graph_list:
                 print(f'iterate every graph in graph_list, here is {i}')
                 arch_info = graph['arch_str']
@@ -837,7 +847,8 @@ class Dataset(InMemoryDataset):
                 for op in ops:
                     if op not in active_nodes:
                         active_nodes.add(op)
-                data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device) 
+                # data = graph_to_graph_data((adj_matrix, ops),idx=i, train_loader=train_loader, searchspace=searchspace, args=args, device=device) 
+                data = graph_to_graph_data((adj_matrix, ops),idx=i, args=args, device=device) 
                 i += 1
                 if data is None:
                     pbar.update(1)
@@ -1140,6 +1151,7 @@ class DataInfos(AbstractDatasetInfos):
         self.task = task_name
         self.task_type = tasktype_dict.get(task_name, "regression")
         self.ensure_connected = cfg.model.ensure_connected
+        self.cfg = cfg
         # self.api = dataset.api
 
         datadir = cfg.dataset.datadir
@@ -1182,14 +1194,15 @@ class DataInfos(AbstractDatasetInfos):
             # len_ops.add(len(ops))
             # graphs.append((adj_matrix, ops))
         # graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json')
-        graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
+        # graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
+        graphs = read_adj_ops_from_json(os.path.join(self.cfg.general.root, 'nasbench-201-graph.json'))
 
         # check first five graphs
         for i in range(5):
             print(f'graph {i} : {graphs[i]}')
         # print(f'ops_type: {ops_type}')
 
-        meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
+        meta_dict = new_graphs_to_json(graphs, 'nasbench-201', self.cfg)
         self.base_path = base_path
         self.active_nodes = meta_dict['active_nodes']
         self.max_n_nodes = meta_dict['max_n_nodes']
@@ -1396,11 +1409,12 @@ def compute_meta(root, source_name, train_index, test_index):
         'transition_E': tansition_E.tolist(),
         }
 
-    with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
+    # with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
+    with open(os.path.join(self.cfg.general.root, 'nasbench201.meta.json'), "w") as f:
         json.dump(meta_dict, f)
     
     return meta_dict
 
 
 if __name__ == "__main__":
-    dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
+    dataset = Dataset(source='nasbench', root='/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/', target_prop='Class', transform=None)

graph_dit/diffusion_model.py

@@ -23,6 +23,9 @@ class Graph_DiT(pl.LightningModule):
         self.test_only = cfg.general.test_only
         self.guidance_target = getattr(cfg.dataset, 'guidance_target', None)
 
+        from nas_201_api import NASBench201API as API
+        self.api = API(cfg.general.nas_201)
+
         input_dims = dataset_infos.input_dims
         output_dims = dataset_infos.output_dims
         nodes_dist = dataset_infos.nodes_dist
@@ -41,7 +44,7 @@ class Graph_DiT(pl.LightningModule):
         self.args.batch_size = 128
         self.args.GPU = '0'
         self.args.dataset = 'cifar10-valid'
-        self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+        self.args.api_loc = cfg.general.nas_201
         self.args.data_loc = '../cifardata/'
         self.args.seed = 777
         self.args.init = ''
@@ -79,6 +82,7 @@ class Graph_DiT(pl.LightningModule):
         self.node_dist = nodes_dist
         self.active_index = active_index
         self.dataset_info = dataset_infos
+        self.cur_epoch = 0
 
         self.train_loss = TrainLossDiscrete(self.cfg.model.lambda_train)
 
@@ -162,25 +166,81 @@ class Graph_DiT(pl.LightningModule):
         return pred
         
     def training_step(self, data, i):
-        data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
+        if self.cfg.general.type != 'accelerator' and self.current_epoch > self.cfg.train.n_epochs / 5 * 4:
-        data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+            samples_left_to_generate = self.cfg.general.samples_to_generate
+            samples_left_to_save = self.cfg.general.samples_to_save
+            chains_left_to_save = self.cfg.general.chains_to_save
 
-        dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
+            samples, all_ys, batch_id = [], [], 0
-        dense_data = dense_data.mask(node_mask)
+
-        X, E = dense_data.X, dense_data.E
+            def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
-        noisy_data = self.apply_noise(X, E, data.y, node_mask)
+                rewards = []
-        pred = self.forward(noisy_data)
+                if reward_model == 'swap':
-        loss = self.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
+                    import csv
-                            true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
+                    with open(self.cfg.general.swap_result, 'r') as f:
+                        reader = csv.reader(f)
+                        header = next(reader)
+                        data = [row for row in reader]
+                        swap_scores = [float(row[0]) for row in data]
+                        for graph in graphs:
+                            node_tensor = graph[0]
+                            node = node_tensor.cpu().numpy().tolist()
+
+                            def nodes_to_arch_str(nodes):
+                                num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
+                                nodes_str = [num_to_op[node] for node in nodes]
+                                arch_str = '|' + nodes_str[1] + '~0|+' + \
+                                        '|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
+                                        '|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|' 
+                                return arch_str
+                            
+                            arch_str = nodes_to_arch_str(node)
+                            reward = swap_scores[self.api.query_index_by_arch(arch_str)]
+                            rewards.append(reward)
+                return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
+            old_log_probs = None
+
+            bs = 1 * self.cfg.train.batch_size
+            to_generate = min(samples_left_to_generate, bs)
+            to_save = min(samples_left_to_save, bs)
+            chains_save = min(chains_left_to_save, bs)
+            # batch_y = test_y_collection[batch_id : batch_id + to_generate]
+            batch_y = torch.ones(to_generate, self.ydim_output, device=self.device)
+
+            cur_sample, log_probs = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
+                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
+            # samples = samples + cur_sample
+            samples.append(cur_sample) 
+            reward = graph_reward_fn(cur_sample, device=self.device)
+            advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6) # 
+            if old_log_probs is None:
+                old_log_probs = log_probs.clone()
+            ratio = torch.exp(log_probs - old_log_probs)
+            print(f"ratio: {ratio.shape}, advantages: {advantages.shape}")
+            unclipped_loss = -advantages * ratio
+            clipped_loss = -advantages * torch.clamp(ratio, 1.0 - self.cfg.ppo.clip_param, 1.0 + self.cfg.ppo.clip_param)
+            loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
+            return {'loss': loss}
+        else:
+            data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
+            data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+
+            dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
+            dense_data = dense_data.mask(node_mask)
+            X, E = dense_data.X, dense_data.E
+            noisy_data = self.apply_noise(X, E, data.y, node_mask)
+            pred = self.forward(noisy_data)
+            loss = self.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
+                                true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
+                                log=i % self.log_every_steps == 0)
+            # 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}')
+            self.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
                             log=i % self.log_every_steps == 0)
-        # 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}')
+            self.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
-        self.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
+            print(f"training loss: {loss}")
-                        log=i % self.log_every_steps == 0)
+            with open("training-loss.csv", "a") as f:
-        self.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
+                f.write(f"{loss}, {i}\n")
-        print(f"training loss: {loss}")
+            return {'loss': loss}
-        with open("training-loss.csv", "a") as f:
-            f.write(f"{loss}, {i}\n")
-        return {'loss': loss}
 
 
     def configure_optimizers(self):
@@ -195,17 +255,15 @@ class Graph_DiT(pl.LightningModule):
         # print("Size of the input features Xdim {}, Edim {}, ydim {}".format(self.Xdim, self.Edim, self.ydim))
 
     def on_train_epoch_start(self) -> None:
-        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-        if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
+        # if self.cur_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
-            # print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
+            print("Starting train epoch {}/{}...".format(self.cur_epoch, self.cfg.train.n_epochs))
-            print("Starting train epoch {}/{}...".format(self.current_epoch, self.cfg.train.n_epochs))
         self.start_epoch_time = time.time()
         self.train_loss.reset()
         self.train_metrics.reset()
 
     def on_train_epoch_end(self) -> None:
 
-        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
         if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
             log = True
         else:
@@ -242,8 +300,9 @@ class Graph_DiT(pl.LightningModule):
         
                    self.val_X_logp.compute(), self.val_E_logp.compute()]
         
-        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-        print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
+        # if self.cur_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
+            print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
                 f"Val Edge type KL: {metrics[2] :.2f}", 'Val loss: %.2f \t Best :  %.2f\n' % (metrics[0], self.best_val_nll))
         with open("validation-metrics.csv", "a") as f:
             # save the metrics as csv file
@@ -286,10 +345,15 @@ class Graph_DiT(pl.LightningModule):
                     num_examples = self.val_y_collection.size(0)
                 batch_y = self.val_y_collection[start_index:start_index + to_generate]                
                 all_ys.append(batch_y)
-                samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
+                cur_sample, logprobs = self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
                                                 save_final=to_save,
                                                 keep_chain=chains_save,
-                                                number_chain_steps=self.number_chain_steps)[0])
+                                                number_chain_steps=self.number_chain_steps)
+                samples.extend(cur_sample)
+                # samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
+                #                                 save_final=to_save,
+                #                                 keep_chain=chains_save,
+                #                                 number_chain_steps=self.number_chain_steps))
                 ident += to_generate
                 start_index += to_generate
 
@@ -339,7 +403,7 @@ class Graph_DiT(pl.LightningModule):
         print(f"Epoch {self.current_epoch}: Test NLL {metrics[0] :.2f} -- Test Atom type KL {metrics[1] :.2f} -- ",
               f"Test Edge type KL: {metrics[2] :.2f}")
 
-        ## final epcoh
+        ## final epoch
         samples_left_to_generate = self.cfg.general.final_model_samples_to_generate
         samples_left_to_save = self.cfg.general.final_model_samples_to_save
         chains_left_to_save = self.cfg.general.final_model_chains_to_save
@@ -362,9 +426,9 @@ class Graph_DiT(pl.LightningModule):
             # batch_y = test_y_collection[batch_id : batch_id + to_generate]
             batch_y = torch.ones(to_generate, self.ydim_output, device=self.device)
 
-            cur_sample = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
+            cur_sample, log_probs = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
-                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)[0]
+                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
-            samples = samples + cur_sample
+            samples.extend(cur_sample)  
             
             all_ys.append(batch_y)
             batch_id += to_generate
@@ -604,8 +668,11 @@ class Graph_DiT(pl.LightningModule):
 
         assert (E == torch.transpose(E, 1, 2)).all()
 
-        total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate,10], device=self.device)
+        if self.cfg.general.type != 'accelerator':
-        # total_log_probs = torch.zeros([self.cfg.general.samples_to_generate,10], device=self.device)
+            if self.trainer.training or self.trainer.validating:
+                total_log_probs = torch.zeros([self.cfg.general.samples_to_generate, 10], device=self.device)
+            elif self.trainer.testing:
+                total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate, 10], device=self.device)
 
         # Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
         for s_int in reversed(range(0, self.T)):
@@ -615,10 +682,8 @@ class Graph_DiT(pl.LightningModule):
             t_norm = t_array / self.T
 
             # Sample z_s
-            sampled_s, discrete_sampled_s, log_probs= self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
+            sampled_s, discrete_sampled_s, log_probs = self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
             X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
-            print(f'sampled_s.X shape: {sampled_s.X.shape}, sampled_s.E shape: {sampled_s.E.shape}')
-            print(f'log_probs shape: {log_probs.shape}')
             total_log_probs += log_probs
 
         # Sample
@@ -630,7 +695,9 @@ class Graph_DiT(pl.LightningModule):
             n = n_nodes[i]
             node_types = X[i, :n].cpu()
             edge_types = E[i, :n, :n].cpu()
-            graph_list.append([node_types, edge_types])
+            graph_list.append((node_types , edge_types))
+        
+        total_log_probs = torch.sum(total_log_probs, dim=-1)
         
         return graph_list, total_log_probs
 
@@ -644,7 +711,6 @@ class Graph_DiT(pl.LightningModule):
 
         # Neural net predictions
         noisy_data = {'X_t': X_t, 'E_t': E_t, 'y_t': y_t, 't': t, 'node_mask': node_mask}
-        print(f"sample p zs given zt X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}, node_mask shape: {node_mask.shape}")
         
         def get_prob(noisy_data, unconditioned=False):
             pred = self.forward(noisy_data, unconditioned=unconditioned)
@@ -684,19 +750,15 @@ class Graph_DiT(pl.LightningModule):
         # with condition = P_t(G_{t-1} |G_t, C)
         # with condition = P_t(A_{t-1} |A_t, y)
         prob_X, prob_E, pred = get_prob(noisy_data)
-        print(f'prob_X shape: {prob_X.shape}, prob_E shape: {prob_E.shape}')
+
-        print(f'X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}')
-        print(f'X_t: {X_t}')
         log_prob_X = torch.log(torch.gather(prob_X, -1, X_t.long()).squeeze(-1))  # bs, n
         log_prob_E = torch.log(torch.gather(prob_E, -1, E_t.long()).squeeze(-1))  # bs, n, n
 
         # Sum the log_prob across dimensions for total log_prob
         log_prob_X = log_prob_X.sum(dim=-1)
         log_prob_E = log_prob_E.sum(dim=(1, 2))
-        print(f'log_prob_X shape: {log_prob_X.shape}, log_prob_E shape: {log_prob_E.shape}')
+
-        # log_probs = log_prob_E + log_prob_X
+        log_probs = torch.cat([log_prob_X, log_prob_E], dim=-1)
-        log_probs = torch.cat([log_prob_X, log_prob_E], dim=-1)  # (batch_size, 2)
-        print(f'log_probs shape: {log_probs.shape}')
         ### Guidance
         if self.guidance_target is not None and self.guide_scale is not None and self.guide_scale != 1:
             uncon_prob_X, uncon_prob_E, pred = get_prob(noisy_data, unconditioned=True)

graph_dit/main.py

@@ -1,5 +1,4 @@
 # These imports are tricky because they use c++, do not move them
-from tqdm import tqdm
 import os, shutil
 import warnings
 
@@ -145,32 +144,10 @@ def main(cfg: DictConfig):
     else:
         trainer.test(model, datamodule=datamodule, ckpt_path=cfg.general.test_only)
 
-from accelerate import Accelerator
-from accelerate.utils import set_seed, ProjectConfiguration
-
 @hydra.main(
     version_base="1.1", config_path="../configs", config_name="config"
 )
 def test(cfg: DictConfig):
-    os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
-    accelerator_config = ProjectConfiguration(
-        project_dir=os.path.join(cfg.general.log_dir, cfg.general.name),
-        automatic_checkpoint_naming=True,
-        total_limit=cfg.general.number_checkpoint_limit,
-    )
-    accelerator = Accelerator(
-        mixed_precision='no',
-        project_config=accelerator_config,
-        # gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
-        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps, 
-    )
-
-    # Debug: 确认可用设备
-    print(f"Available GPUs: {torch.cuda.device_count()}")
-    print(f"Using device: {accelerator.device}")
-
-    set_seed(cfg.train.seed, device_specific=True)
-
     datamodule = dataset.DataModule(cfg)
     datamodule.prepare_data()
     dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
@@ -192,216 +169,100 @@ def test(cfg: DictConfig):
         "visualization_tools": visulization_tools,
     }
 
-    # Debug: 确认可用设备
-    print(f"Available GPUs: {torch.cuda.device_count()}")
-    print(f"Using device: {accelerator.device}")
-
     if cfg.general.test_only:
         cfg, _ = get_resume(cfg, model_kwargs)
         os.chdir(cfg.general.test_only.split("checkpoints")[0])
     elif cfg.general.resume is not None:
         cfg, _ = get_resume_adaptive(cfg, model_kwargs)
         os.chdir(cfg.general.resume.split("checkpoints")[0])
+    # os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
     model = Graph_DiT(cfg=cfg, **model_kwargs)
-    graph_dit_model = model
 
-    inference_dtype = torch.float32
+    if cfg.general.type == "accelerator":
-    graph_dit_model.to(accelerator.device, dtype=inference_dtype)
+        graph_dit_model = model
 
+        from accelerate import Accelerator
+        from accelerate.utils import set_seed, ProjectConfiguration
 
-    # optional: freeze the model
+        accelerator_config = ProjectConfiguration(
-    # graph_dit_model.model.requires_grad_(True)
+            project_dir=os.path.join(cfg.general.log_dir, cfg.general.name),
+            automatic_checkpoint_naming=True,
+            total_limit=cfg.general.number_checkpoint_limit,
+        )
+        accelerator = Accelerator(
+            mixed_precision='no',
+            project_config=accelerator_config,
+            # gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
+            gradient_accumulation_steps=cfg.train.gradient_accumulation_steps, 
+        )
 
-    import torch.nn.functional as F
+        optimizer = graph_dit_model.configure_optimizers()
-    optimizer = graph_dit_model.configure_optimizers()
-    train_dataloader = accelerator.prepare(datamodule.train_dataloader())
-    optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
-    # start training
-    for epoch in range(cfg.train.n_epochs):
-        graph_dit_model.train()  # 设置模型为训练模式
-        print(f"Epoch {epoch}", end="\n")
-        graph_dit_model.on_train_epoch_start()
-        for data in train_dataloader:  # 从数据加载器中获取一个批次的数据
-            # data.to(accelerator.device)
-            # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
-            # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
-            # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
-            # dense_data = dense_data.mask(node_mask)
-            # X, E = dense_data.X, dense_data.E
-            # noisy_data = graph_dit_model.apply_noise(X, E, data.y, node_mask)
-            # pred = graph_dit_model.forward(noisy_data)
-            # loss = graph_dit_model.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
-            #                     true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
-            #                     log=epoch % graph_dit_model.log_every_steps == 0)
-            # # 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}')
-            # graph_dit_model.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
-            #                 log=epoch % graph_dit_model.log_every_steps == 0)
-            # graph_dit_model.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
-            # print(f"training loss: {loss}")
-            # with open("training-loss.csv", "a") as f:
-            #     f.write(f"{loss}, {epoch}\n")
-            loss = graph_dit_model.training_step(data, epoch)
-            loss = loss['loss']
 
-            accelerator.backward(loss)
+        train_dataloader = datamodule.train_dataloader()
-            optimizer.step()
+        train_dataloader = accelerator.prepare(train_dataloader)
-            optimizer.zero_grad()
+        val_dataloader = datamodule.val_dataloader()
-            # return {'loss': loss}
+        val_dataloader = accelerator.prepare(val_dataloader)
-        graph_dit_model.on_train_epoch_end()
+        test_dataloader = datamodule.test_dataloader()
-        if epoch % cfg.train.check_val_every_n_epoch == 0:
+        test_dataloader = accelerator.prepare(test_dataloader)
-            print(f'print validation loss')
-            graph_dit_model.eval()
-            graph_dit_model.on_validation_epoch_start()
-            graph_dit_model.validation_step(data, epoch)
-            graph_dit_model.on_validation_epoch_end()
 
-    # start testing
+        optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
-    print("start testing")
-    graph_dit_model.eval()
-    test_dataloader = accelerator.prepare(datamodule.test_dataloader())
-    graph_dit_model.on_test_epoch_start()
-    for data in test_dataloader:
-        nll = graph_dit_model.test_step(data, epoch)
-        # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
-        # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
 
-        # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
+        # train_epoch
-        # dense_data = dense_data.mask(node_mask)
+        from pytorch_lightning import seed_everything
-        # noisy_data = graph_dit_model.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
+        seed_everything(cfg.train.seed)
-        # pred = graph_dit_model.forward(noisy_data)
+        for epoch in range(cfg.train.n_epochs):
-        # nll = graph_dit_model.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
+            print(f"Epoch {epoch}")
-        # graph_dit_model.test_y_collection.append(data.y)
+            graph_dit_model.train()
-        print(f'test loss: {nll}')
+            graph_dit_model.cur_epoch = epoch
+            graph_dit_model.on_train_epoch_start()
+            for batch in train_dataloader:
+                optimizer.zero_grad()
+                loss = graph_dit_model.training_step(batch, epoch)['loss']
+                accelerator.backward(loss)
+                optimizer.step()
+            graph_dit_model.on_train_epoch_end()
+            for batch in val_dataloader:
+                if epoch % cfg.train.check_val_every_n_epoch == 0:
+                    graph_dit_model.eval()
+                    graph_dit_model.on_validation_epoch_start()
+                    graph_dit_model.validation_step(batch, epoch)
+                    graph_dit_model.on_validation_epoch_end()
         
-    graph_dit_model.on_test_epoch_end()
+        # test_epoch
 
-    # start sampling
+        graph_dit_model.test()
+        graph_dit_model.on_test_epoch_start()
+        for batch in test_dataloader:
+            graph_dit_model.test_step(batch, epoch)
+        graph_dit_model.on_test_epoch_end()
     
-    # samples_left_to_generate = cfg.general.final_model_samples_to_generate
+    elif cfg.general.type == "Trainer": 
-    # samples_left_to_save = cfg.general.final_model_samples_to_save
+        trainer = Trainer(
-    # chains_left_to_save = cfg.general.final_model_chains_to_save
+            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=[],
+        )
 
-    # samples, all_ys, batch_id = [], [], 0
+        if not cfg.general.test_only:
-    # samples_with_log_probs = []
+            print("start testing fit method")
-    # test_y_collection = torch.cat(graph_dit_model.test_y_collection, dim=0)
+            trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
-    # num_examples = test_y_collection.size(0)
+            if cfg.general.save_model:
-    # if cfg.general.final_model_samples_to_generate > num_examples:
+                trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
-    #     ratio = cfg.general.final_model_samples_to_generate // num_examples
+            trainer.test(model, datamodule=datamodule)
-    #     test_y_collection = test_y_collection.repeat(ratio+1, 1)
-    #     num_examples = test_y_collection.size(0)
-    
-    # Normal reward function
-    # from nas_201_api import NASBench201API as API
-    # api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
-    # def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
-    #     rewards = []
-    #     if reward_model == 'swap':
-    #         import csv
-    #         with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
-    #             reader = csv.reader(f)
-    #             header = next(reader)
-    #             data = [row for row in reader]
-    #             swap_scores = [float(row[0]) for row in data]
-    #             for graph in graphs:
-    #                 node_tensor = graph[0]
-    #                 node = node_tensor.cpu().numpy().tolist()
-
-    #                 def nodes_to_arch_str(nodes):
-    #                     num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
-    #                     nodes_str = [num_to_op[node] for node in nodes]
-    #                     arch_str = '|' + nodes_str[1] + '~0|+' + \
-    #                             '|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
-    #                             '|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|' 
-    #                     return arch_str
-                    
-    #                 arch_str = nodes_to_arch_str(node)
-    #                 reward = swap_scores[api.query_index_by_arch(arch_str)]
-    #                 rewards.append(reward)
-                
-    #     # for graph in graphs:
-    #     #     reward = 1.0
-    #     #     rewards.append(reward)
-    #     return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
-    # old_log_probs = None
-    # while samples_left_to_generate > 0:
-    #     print(f'samples left to generate: {samples_left_to_generate}/'
-    #         f'{cfg.general.final_model_samples_to_generate}', end='', flush=True)
-    #     bs = 1 * cfg.train.batch_size
-    #     to_generate = min(samples_left_to_generate, bs)
-    #     to_save = min(samples_left_to_save, bs)
-    #     chains_save = min(chains_left_to_save, bs)
-    #     # batch_y = test_y_collection[batch_id : batch_id + to_generate]
-    #     batch_y = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
-
-    #     cur_sample, 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)
-    #     log_probs = torch.sum(log_probs, dim=-1).unsqueeze(1)
-    #     samples = samples + cur_sample
-    #     reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
-    #     advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
-    #     print(f'reward: {reward.shape}, advantages: {advantages.shape}, log_probs: {log_probs.shape}, cur_sample: {len(cur_sample)}')
-    #     if old_log_probs is None:
-    #         old_log_probs = log_probs.clone()
-    #     ratio = torch.exp(log_probs - old_log_probs)
-    #     unclipped_loss = -advantages * ratio
-    #     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()
-
-
-    #     samples_with_log_probs.append((cur_sample, log_probs, reward))
-        
-    #     all_ys.append(batch_y)
-    #     batch_id += to_generate
-
-    #     samples_left_to_save -= to_save
-    #     samples_left_to_generate -= to_generate
-    #     chains_left_to_save -= chains_save
-        
-    # print(f"final Computing sampling metrics...")
-    # graph_dit_model.sampling_metrics.reset()
-    # graph_dit_model.sampling_metrics(samples, all_ys, graph_dit_model.name, graph_dit_model.current_epoch, graph_dit_model.val_counter, test=True)
-    # graph_dit_model.sampling_metrics.reset()
-    # print(f"Done.")
-
-    # # save samples
-    # print("Samples:")
-    # print(samples)
-
-    # ========================
-    
-
-    
-
-
-    # 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()

graph_dit/metrics/property_metric.py

@@ -83,7 +83,8 @@ class TaskModel():
             return adj_ops_pairs
         def feature_from_adj_and_ops(adj, ops):
             return np.concatenate([adj.flatten(), ops])
-        filename = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+        # filename = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
+        filename = '/zhome/academic/HLRS/xmu/xmuhanma/nasbenchDiT/graph_dit/nasbench-201-graph.json'
         graphs = read_adj_ops_from_json(filename)
         adjs = []
         opss = []

graph_dit/models/conditions.py

@@ -76,8 +76,6 @@ class CategoricalEmbedder(nn.Module):
             embeddings = embeddings + noise
         return embeddings
     
-# 相似的condition cluster起来
-# size 
 class ClusterContinuousEmbedder(nn.Module):
     def __init__(self, input_size, hidden_size, dropout_prob):
         super().__init__()
@@ -110,8 +108,6 @@ class ClusterContinuousEmbedder(nn.Module):
         
         if drop_ids is not None:
             embeddings = torch.zeros((labels.shape[0], self.hidden_size), device=labels.device)
-            # print(labels[~drop_ids].shape)
-            # torch.Size([1200])
             embeddings[~drop_ids] = self.mlp(labels[~drop_ids])
             embeddings[drop_ids] += self.embedding_drop.weight[0]
         else:

graph_dit/models/transformer.py

@@ -17,22 +17,20 @@ class Denoiser(nn.Module):
         num_heads=16,
         mlp_ratio=4.0,
         drop_condition=0.1,
-        Xdim=7,
+        Xdim=118,
-        Edim=2,
+        Edim=5,
-        ydim=1,
+        ydim=3,
         task_type='regression',
     ):
         super().__init__()
-        print(f"Denoiser, xdim: {Xdim}, edim: {Edim}, ydim: {ydim}, hidden_size: {hidden_size}, depth: {depth}, num_heads: {num_heads}, mlp_ratio: {mlp_ratio}, drop_condition: {drop_condition}")
         self.num_heads = num_heads
         self.ydim = ydim
         self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
 
         self.t_embedder = TimestepEmbedder(hidden_size)
-        # 
         self.y_embedding_list = torch.nn.ModuleList()
 
-        self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
+        self.y_embedding_list.append(ClusterContinuousEmbedder(2, hidden_size, drop_condition))
         for i in range(ydim - 2):
             if task_type == 'regression':
                 self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
@@ -90,8 +88,6 @@ class Denoiser(nn.Module):
         
         # print("Denoiser Forward")
         # print(x.shape, e.shape, y.shape, t.shape, unconditioned)
-        # torch.Size([1200, 8, 7]) torch.Size([1200, 8, 8, 2]) torch.Size([1200, 2]) torch.Size([1200, 1]) False
-        # print(y)
         force_drop_id = torch.zeros_like(y.sum(-1))
         # drop the nan values
         force_drop_id[torch.isnan(y.sum(-1))] = 1
@@ -113,12 +109,11 @@ class Denoiser(nn.Module):
         c1 = self.t_embedder(t)
         # print("C1 after t_embedder")
         # print(c1.shape)
-        c2 = self.y_embedding_list[0](y[:,0].unsqueeze(-1), self.training, force_drop_id, t)
+        for i in range(1, self.ydim):
-        # for i in range(1, self.ydim):
+            if i == 1:
-        #     if i == 1:
+                c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
-        #         c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
+            else:
-        #     else:
+                c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
-                # c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
         # print("C2 after y_embedding_list")
         # print(c2.shape)
         # print("C1 + C2")

graph_dit/test_nasbench.ipynb

@@ -11,9 +11,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/stud/hanzhang/anaconda3/envs/graphdit/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import sys\n",
     "sys.path.append('../') \n",
@@ -34,6 +43,89 @@
     "from sklearn.model_selection import train_test_split\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([100, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensor1 = torch.randn(100,10)\n",
+    "sums_tensor1 = torch.sum(tensor1, dim=-1).unsqueeze(1)\n",
+    "print(sums_tensor1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 7.1564e-01, -3.5812e-02, -2.5924e-01],\n",
+      "        [ 1.5302e-03,  1.0105e+00,  2.3484e+00]])\n",
+      "tensor([[-0.3566, -0.9514, -0.3267],\n",
+      "        [-1.0915, -0.2063,  0.1615]])\n",
+      "torch.Size([4, 3])\n",
+      "tensor([[ 7.1564e-01, -3.5812e-02, -2.5924e-01],\n",
+      "        [ 1.5302e-03,  1.0105e+00,  2.3484e+00],\n",
+      "        [-3.5660e-01, -9.5144e-01, -3.2673e-01],\n",
+      "        [-1.0915e+00, -2.0631e-01,  1.6153e-01]])\n",
+      "torch.Size([2, 6])\n",
+      "tensor([[ 7.1564e-01, -3.5812e-02, -2.5924e-01, -3.5660e-01, -9.5144e-01,\n",
+      "         -3.2673e-01],\n",
+      "        [ 1.5302e-03,  1.0105e+00,  2.3484e+00, -1.0915e+00, -2.0631e-01,\n",
+      "          1.6153e-01]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensor1 = torch.randn(2, 3)\n",
+    "tensor2 = torch.randn(2, 3)\n",
+    "print(tensor1)\n",
+    "print(tensor2)\n",
+    "result1 = torch.cat([tensor1, tensor2], dim=0)\n",
+    "print(result1.shape)\n",
+    "print(result1)\n",
+    "\n",
+    "result2 = torch.cat([tensor1, tensor2], dim=1)\n",
+    "print(result2.shape)\n",
+    "print(result2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[tensor([[0.4206, 3.3604]]), tensor([[-1.6348, -1.1363]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tensors = [tensor1, tensor2]\n",
+    "for i in range(len(tensors)):\n",
+    "    tensors[i] = torch.sum(tensors[i], dim=-1).unsqueeze(0)\n",
+    "print(tensors)\n",
+    "# for tensor in tensors:\n",
+    "#     tensor = torch.sum(tensor).unsqueeze(0)\n",
+    "#     print(tensor)\n",
+    "# print(tensors)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,