Update README.md

README.md

 # Permutation code README
 
-## Overview:
+## Overview: before running training script
 
 NB: before running the test codes,please download the procrustes package first:
 from https://github.com/theochem/procrustes
 
-To test the permutation codes:
+Make sure that the product of running ```scripts/generate_mmcif_cache.py``` is ready and available in ```tests/test_data```
+I have uploaded the json file to owncloud [here](https://oc.embl.de/index.php/s/wVUwc1IHiJUt9sP)
+
+To test the train multimer codes:
 ```bash
-python -m unittest tests/test_permutation.py
+python3 train_openfold.py /g/alphafold/AlphaFold_DBs/pdb_mmcif/mmcif_files/ \
+    tests/test_data/alignments/ \
+    /g/alphafold/AlphaFold_DBs/pdb_mmcif/mmcif_files/ \
+    /scratch/gyu/train_openfold_output \
+    2500-01-01 \
+    --train_mmcif_data_cache_path=/tests/test_data/train_mmcifs_cache.json \
+    --template_release_dates_cache_path=tests/test_data/mmcif_cache.json \
+    --config_preset=model_1_multimer_v3 --seed=42 --gpus=1
 ```
-The files that has been changed is:
-[```openfold/utils/loss.py```](https://github.com/dingquanyu/openfold/blob/permutation/openfold/utils/loss.py), in which the forward function is modified in 
-original ```AlphaFoldLoss``` class; 
-
-create a child class called ```AlphaFoldMultimerLoss``` that not only inherited all the loss calculations but also 
-has multi-chain permutation codes; 
 
-some loss calculations have to be modified e.g. in ```fape``` loss, ```tm``` loss calculations, an extra validation was added to check if the input tensor belongs to tensor_7 or tensor_4*4 for example : https://github.com/dingquanyu/openfold/blob/02b008dc4b8c2e9e680826444c605297eeb9ffb4/openfold/utils/loss.py#L190-L193
+Unlike training monomer, chain_cache_data is not required but the train_mmcifs_cache is required. In this case, I selected these 9 mmcifs that are already in the previous test_data folder as a training set. ```./tests/test_data/train_mmcifs_cache.json``` in the command above record the information of these 9 structures and is needed to run the training code. 
 
-[```openfold/config.py```](https://github.com/dingquanyu/openfold/blob/permutation/openfold/config.py) has seen a couple of modifications as well. Some namings were wrong and previous script forgot to update  config.loss with multimer_model_config_update
+## Issues
+Testing the codes on cpu works fine but when running it on a gpu, it causes ```RuntimeError: CUDA error: device-side assert triggered``` at unexpected steps.
 
+For example, this error was raised while calculating the best rotation matrix that aligns selected anchors during multi-chain permutation steps, I have to use 
+```torch.masked_select``` and ```torch.index_select``` in https://github.com/dingquanyu/openfold/blob/a1ef4c8fa99da5cff9501051de71be440ca3cedf/openfold/utils/loss.py#L2043 and https://github.com/dingquanyu/openfold/blob/a1ef4c8fa99da5cff9501051de71be440ca3cedf/openfold/utils/loss.py#L2060 instead of simply slicing the matrix like ```matrix[index]```.
 
-These files are newly added:
-[```tests/test_permutation.py```](https://github.com/dingquanyu/openfold/blob/permutation/tests/test_permutation.py): A unittest script 
-that tests permutation functions.
+Later on the same ```CUDA error: device-side assert triggered``` error was raised while adding dimensions to the ```atom_pred_positions``` in https://github.com/dingquanyu/openfold/blob/a1ef4c8fa99da5cff9501051de71be440ca3cedf/openfold/utils/loss.py#L989
 
-[```tests/test_data/label_1.pkl```](https://github.com/dingquanyu/openfold/blob/permutation/tests/test_data/label_1.pkl) 
-and [```tests/test_data/label_2.pkl```](https://github.com/dingquanyu/openfold/blob/permutation/tests/test_data/label_2.pkl) are 2 fake ground truth structures.
-```label_1.pkl``` has 9 residues and ```label_2.pkl``` has 13 residues
-### Notes
-29/06/23 Fill NaN in the lddt scores with the matrix mean for now because the test data are randomly generated and it gives NaN in the lddt score somehow.
-**Delete** this step before merging to Multimer branch
+I've dumped the matrices in a pickle and load them individually outside the programme to a GPU then the indexing steps worked without the CUDA error.