I am training HetGNN model for node classification. when i try to run the script for training. I get the following error. Please help me AttributeError: 'dict' object has no attribute 'srcdata'

line 168, in assign_features_to_blocks assign_simple_node_features(blocks[0].srcdata, g, ntypes) AttributeError: 'dict' object has no attribute 'srcdata'

通过HetGNN跑提供的academic4HetGNN.zip 数据集。emb结果有完全相同的情况发生，原因未知。请问是否是符合预期的？

如下测试：

`import numpy as np

emb = np.load('emb50.npy') list = emb[:,0]

for i in np.unique(list): idx = np.argwhere(list == i) r = idx.reshape(1, -1).squeeze(0) if len(r) > 1: print('index for {}:\n'.format(i), r) for j in r: print(emb[j]) `

File "C:\Users\XyZ\OpenHGNN\openhgnn\models\GTN_sparse.py", line 220, in forward sum_g = dgl.adj_sum_graph(A, 'w_sum') AttributeError: module 'dgl' has no attribute 'adj_sum_graph'

This Issue came-up while I ran the command= python main.py -m GTN -d imdb4GTN -t node_classification -g -1 --use_best_config

Can someone say me where I went wrong?

python main.py -m KGCN -d LastFM4KGCN -t recommendation -g 0 --use_best_config

RuntimeError: Tensor for argument #2 'mat1' is on CPU, but expected it to be on GPU (while checking arguments for addmm)

for ex : https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/dataset#academic4HetGNN in this dataset

when I extract_archive , A bin file about g。

but where I can find ， how to build this dataset use dgl standalone program ?

Thank you very much for being able to provide this tool. I get an error when I run fastGTN using:

python main.py -m fastGTN -t node_classification -d acm4GTN -g 0 --use_best_config

The error is as follows：

Traceback (most recent call last): File "D:/github/OpenHGNN/main.py", line 30, in OpenHGNN(args=config) File "D:\github\OpenHGNN\openhgnn\start.py", line 19, in OpenHGNN result = flow.train() File "D:\github\OpenHGNN\openhgnn\trainerflow\node_classification.py", line 112, in train train_loss = self._full_train_step() File "D:\github\OpenHGNN\openhgnn\trainerflow\node_classification.py", line 152, in _full_train_step logits = self.model(self.hg, h_dict)[self.category] File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "D:\github\OpenHGNN\openhgnn\models\fastGTN.py", line 119, in forward hat_A = self.layersi File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "D:\github\OpenHGNN\openhgnn\models\fastGTN.py", line 180, in forward sum_g = dgl.adj_sum_graph(A, 'w_sum') File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl\transforms\functional.py", line 2766, in adj_sum_graph C_gidx, C_weights = F.csrsum(gidxs, weights) File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl\backend\pytorch\sparse.py", line 817, in csrsum nrows, ncols, C_indptr, C_indices, C_eids, C_weights = CSRSum.apply(gidxs, *weights) File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl\backend\pytorch\sparse.py", line 668, in forward gidxC, C_weights = _csrsum(gidxs, weights) File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl\sparse.py", line 776, in _csrsum C, C_weights = _CAPI_DGLCSRSum(As, [F.to_dgl_nd(w) for w in A_weights]) File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl_ffi_ctypes\function.py", line 188, in call check_call(_LIB.DGLFuncCall( File "D:\Program Files (x86)\anaconda\envs\OpenHGNN\lib\site-packages\dgl_ffi\base.py", line 65, in check_call raise DGLError(py_str(_LIB.DGLGetLastError())) dgl._ffi.base.DGLError: [15:31:21] C:\Users\Administrator\dgl-0.5\src\array\kernel.cc:471: Check failed: A[i].indptr->dtype == idtype (int64 vs. int32) : The ID types of all graphs must be equal.

I use the following software versions:

python = 3.8 cudatoolkit = 11.3.1 torch = 1.11.0+cu113 dgl-cu113 = 0.8.1 & 0.8.0

Then I ran the same version of the software on my ubuntu server with no errors.

🐛 Bug

To Reproduce

error occurred in _mini_train_step function in trainerflow/node_classification.py when use mini_batch_flag in node_classification task and SimpleHGN model

import argparse
from openhgnn.experiment import Experiment

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', '-m', default='SimpleHGN', type=str, help='name of models')
    parser.add_argument('--task', '-t', default='node_classification', type=str, help='name of task')
    # link_prediction / node_classification
    parser.add_argument('--dataset', '-d', default='imdb4MAGNN', type=str, help='name of datasets')
    parser.add_argument('--gpu', '-g', default='0', type=int, help='-1 means cpu')
    parser.add_argument('--use_best_config', action='store_true', help='will load utils.best_config')
    parser.add_argument('--load_from_pretrained', action='store_true', help='load model from the checkpoint')
    args = parser.parse_args()

    experiment = Experiment(model=args.model, dataset=args.dataset, task=args.task, gpu=args.gpu,
                            use_best_config=args.use_best_config, load_from_pretrained=args.load_from_pretrained, mini_batch_flag = True, batch_size=64)
    experiment.run()

Expected behavior

Minibatch training on a large heterograph

Environment

• torch==1.12.1
• dgl-cu113==0.9.0 # for CUDA support
• openhgnn==0.3.0
• Linux
• Python 3.8.13

Additional context

• the default minibatch sampler is MultiLayerFullNeighborSampler
• the blocks is a list (line 164) and the expected input in the forward function of the model (e.g. SimpleHGN) is a hg(line 159)

for i, (input_nodes, seeds, blocks) in enumerate(loader_tqdm):
    blocks = [blk.to(self.device) for blk in blocks]
    ...
    logits = self.model(blocks, emb)[self.category]

def forward(self, hg, h_dict):
    with hg.local_scope():
        hg.ndata['h'] = h_dict

请教下 在 x = self.model(blocks[0], input_features) 中返回的x 是dict 。 他里面每种node_type 的emb 和blocks[0] 的入参的点的顺序如何对应？

我核对了以后 发现并不是 blocks[0].srcnodes[node_type].data[dgl.NID] 所代表的节点顺序。

\OpenHGNN-main\openhgnn\tasks\link_prediction.py", line 32, in init self.train_hg, self.val_hg, self.test_hg = self.dataset.get_idx() AttributeError: 'HIN_LinkPrediction' object has no attribute 'get_idx'

运行 python main.py -m GTN -t node_classification -d acm4GTN -g 0 --use_best_config

报错信息 Using backend: pytorch Use the best config. Done saving data into cached files. Modify the out_dim with num_classes 0%| | 0/50 [00:00<?, ?it/s] Traceback (most recent call last): File "main.py", line 24, in OpenHGNN(args=config) File "/home/special/user/lihaoran/OpenHGNN_clone_from_github/openhgnn/start.py", line 17, in OpenHGNN result = flow.train() File "/home/special/user/lihaoran/OpenHGNN_clone_from_github/openhgnn/trainerflow/node_classification.py", line 77, in train loss = self._full_train_step() File "/home/special/user/lihaoran/OpenHGNN_clone_from_github/openhgnn/trainerflow/node_classification.py", line 109, in _full_train_step loss.backward() File "/opt/miniconda3/lib/python3.7/site-packages/torch/_tensor.py", line 255, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File "/opt/miniconda3/lib/python3.7/site-packages/torch/autograd/init.py", line 149, in backward allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag File "/opt/miniconda3/lib/python3.7/site-packages/torch/autograd/function.py", line 87, in apply return self._forward_cls.backward(self, *args) # type: ignore[attr-defined] File "/opt/miniconda3/lib/python3.7/site-packages/dgl/backend/pytorch/sparse.py", line 544, in backward gidxA.reverse(), A_weights, gidxC, dC_weights, gidxB.number_of_ntypes()) File "/opt/miniconda3/lib/python3.7/site-packages/dgl/backend/pytorch/sparse.py", line 638, in csrmm CSRMM.apply(gidxA, A_weights, gidxB, B_weights, num_vtypes) File "/opt/miniconda3/lib/python3.7/site-packages/dgl/backend/pytorch/sparse.py", line 528, in forward gidxC, C_weights = _csrmm(gidxA, A_weights, gidxB, B_weights, num_vtypes) File "/opt/miniconda3/lib/python3.7/site-packages/dgl/sparse.py", line 548, in _csrmm A, F.to_dgl_nd(A_weights), B, F.to_dgl_nd(B_weights), num_vtypes) File "dgl/_ffi/_cython/./function.pxi", line 287, in dgl._ffi._cy3.core.FunctionBase.call File "dgl/_ffi/_cython/./function.pxi", line 232, in dgl._ffi._cy3.core.FuncCall File "dgl/_ffi/_cython/./base.pxi", line 155, in dgl._ffi._cy3.core.CALL dgl._ffi.base.DGLError: [17:18:53] /opt/dgl/src/array/cuda/csr_mm.cu:87: Check failed: e == CUSPARSE_STATUS_SUCCESS: CUSPARSE ERROR: 11 Stack trace: [bt] (0) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x4f) [0x7fd13c2565df] [bt] (1) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(std::pair<dgl::aten::CSRMatrix, dgl::runtime::NDArray> dgl::aten::cusparse::CusparseSpgemm<float, int>(dgl::aten::CSRMatrix const&, dgl::runtime::NDArray, dgl::aten::CSRMatrix const&, dgl::runtime::NDArray)+0x625) [0x7fd13c6accd5] [bt] (2) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(std::pair<dgl::aten::CSRMatrix, dgl::runtime::NDArray> dgl::aten::CSRMM<2, long, float>(dgl::aten::CSRMatrix const&, dgl::runtime::NDArray, dgl::aten::CSRMatrix const&, dgl::runtime::NDArray)+0x59e) [0x7fd13c6af81e] [bt] (3) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(dgl::aten::CSRMM(dgl::aten::CSRMatrix, dgl::runtime::NDArray, dgl::aten::CSRMatrix, dgl::runtime::NDArray)+0x10d6) [0x7fd13c493466] [bt] (4) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(+0x48cfa8) [0x7fd13c493fa8] [bt] (5) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(+0x48d724) [0x7fd13c494724] [bt] (6) /opt/miniconda3/lib/python3.7/site-packages/dgl/libdgl.so(DGLFuncCall+0x48) [0x7fd13c4d5c78] [bt] (7) /opt/miniconda3/lib/python3.7/site-packages/dgl/_ffi/_cy3/core.cpython-37m-x86_64-linux-gnu.so(+0x163ea) [0x7fd1136f03ea] [bt] (8) /opt/miniconda3/lib/python3.7/site-packages/dgl/_ffi/_cy3/core.cpython-37m-x86_64-linux-gnu.so(+0x1695b) [0x7fd1136f095b]

GPU：A100-PCIE DGL版本：dgl-cu111-0.8a211008 看起来可以获得logits，但是无法进行反向传播

很奇怪的是运行IMDB4GTN数据集时没有任何问题 使用MHNF运行ACM4GTN也报了同样的错误

我看到GTN有两个，一个是叫GTN_spare.py一个GTN.py，默认是用的GTN_spare。用GTN.py可以运行ACM4GTN，但是准确率只有60%上下

Hi, I am trying to create a new trainer flow, as well as a new task. I am struggling a bit and have a few questions: When I register them with @register_flow(str_flow) and @register_task(str_task), must str_taskand str_flowbe identical?
Because as my flow is not specific to a model, it is not in the specific_trainerflowdictionnary defined in the Experiment class. So the line 92 in experiment.py( trainerflow = self.specific_trainerflow.get(self.config.model, self.config.task) ) returns the key of the task as the trainerflow_key. Is this the wanted behavior?

Thanks!

🐛 Bug

when i run the suggest command :

python main.py -m HGSL -d acm4GTN -t node_classification -g 0 --use_best_config

this raise an error like:

Traceback (most recent call last): File "main.py", line 21, in experiment.run() File "/workspace/OpenHGNN/openhgnn/experiment.py", line 97, in run flow = build_flow(self.config, trainerflow) File "/workspace/OpenHGNN/openhgnn/trainerflow/init.py", line 46, in build_flow return FLOW_REGISTRYflow_name File "/workspace/OpenHGNN/openhgnn/trainerflow/node_classification.py", line 42, in init self.model = build_model(self.model).build_model_from_args(self.args, self.hg).to(self.device) File "/workspace/OpenHGNN/openhgnn/models/HGSL.py", line 106, in build_model_from_args mp_emb_dim = hg.nodes["paper"].data["pap_m2v_emb"].shape[1] File "/opt/conda/lib/python3.7/site-packages/dgl/view.py", line 73, in getitem return self._graph._get_n_repr(self._ntid, self._nodes)[key] File "/opt/conda/lib/python3.7/site-packages/dgl/frame.py", line 622, in getitem return self._columns[name].data KeyError: 'pap_m2v_emb'

it seems like there is no pap_m2v_emb key in paper nodes data， so how to fix it?

more error update: when I just make mp_emb_dim=0 to jump this line, more errors raise, such as no hidden_dim、mini_batch_flag ... defined in config, besides, when I successfully run this model, another exception was raised:

Do you have an updated version of the model？

Sincere thanks.

To Reproduce

Steps to reproduce the behavior:

1.cd OpenHGNN 2.python main.py -m HGSL -d acm4GTN -t node_classification -g 0 --use_best_config

Expected behavior

Environment

• OpenHGNN Version (e.g., 1.0):
• PyTorch latest, DGL latest
• Linux
• python main.py -m HGSL -d acm4GTN -t node_classification -g 0 --use_best_config
• best_config for recommend

❓ Questions and Help

I want to train my own HNN data, could you tell me how to edit this code? the data in ./openhgnn/dataset are download from https://s3.cn-north-1.amazonaws.com.cn/dgl-data/ and is .bin file. So how could I change this dataset? 救救孩子

Description

Checklist

Please feel free to remove inapplicable items for your PR.

• [x] The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
• [x] Changes are complete (i.e. I finished coding on this PR)
• [x] All changes have test coverage
• [x] Code is well-documented
• [x] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
• [x] Related issue is referred in this PR
• [x] If the PR is for a new model/paper, I've updated the example index here.

Changes

Description

Add the Mg2vec Model and add the EdgeClassification Task

Checklist

Please feel free to remove inapplicable items for your PR.

• [ ] The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
• [ ] Changes are complete (i.e. I finished coding on this PR)
• [ ] All changes have test coverage
• [ ] Code is well-documented
• [ ] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
• [ ] Related issue is referred in this PR
• [ ] If the PR is for a new model/paper, I've updated the example index here.

Changes

• [ ] Add configs for Mg2vec in config.ini and config.py
• [ ] Add Mg2vec.py, which contains the model part
• [ ] Add mg2vec_sampler.py for reading data
• [ ] Add mg2vec_trainer.py for training
• [ ] Add EdgeClassificationDataset.py for EdgeClassification Task, which is a modified version of NodeClassificationDataset.py
• [ ] Add mg2vec_dataset.py for download/read mg2vec dataset
• [ ] Add edge_classification.py, which is a modified version of node_classification.py
• [ ] Add ec_with_SVC function in evaluator.py for edge_classification task
• [ ] Add readme.md for Mg2vec model
• [ ] Modify corresponding init.py and experiment.py