Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

用的模型是helixfold-single/user_data/model_data/helixfold-single.pdparams

出错的代码在data_utils.py中，： def aatype_to_sequence(aatype):

return ''.join([
    residue_constants.restypes_with_x[aatype[i]] 
    for i in range(len(aatype))
])

Traceback (most recent call last): File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 121, in main(args) File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 103, in main args.fasta_file, af2_model_config) File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 56, in sequence_to_batch sequence, description = read_fasta_file(fasta_file) File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 42, in read_fasta_file with open(fasta_file, 'r') as f: TypeError: expected str, bytes or os.PathLike object, not NoneType (base) /mnt/workspace> /home/pai/bin/python /mnt/workspace/helixfold-single_original/helixfold_single_inference.py /home/pai/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:22: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp /home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:8: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release. from cryptography import utils, x509 W1202 15:20:51.727890 26523 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.0, Driver API Version: 11.4, Runtime API Version: 10.2 W1202 15:20:51.730804 26523 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6. [RunTapeModel] freeze_tape: False model size: 1187148024 Load model from helixfold-single/user_data/model_data/helixfold-single.pdparams 2022-12-02 15:21:01.499896: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Traceback (most recent call last): File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 121, in main(args) File "/mnt/workspace/helixfold-single_original/helixfold_single_inference.py", line 106, in main results = model(batch, compute_loss=False) File "/home/pai/lib/python3.6/site-packages/paddle/fluid/dygraph/layers.py", line 948, in call return self.forward(*inputs, **kwargs) File "/mnt/workspace/helixfold-single_original/utils/model_tape.py", line 115, in forward batch = self._forward_tape(batch) File "/mnt/workspace/helixfold-single_original/utils/model_tape.py", line 95, in _forward_tape tape_input = self._create_tape_input(batch) File "/mnt/workspace/helixfold-single_original/utils/model_tape.py", line 80, in _create_tape_input text = aatype_to_sequence(aatype[:seq_len]) File "/mnt/workspace/helixfold-single_original/alphafold_paddle/data/data_utils.py", line 96, in aatype_to_sequence for i in range(len(aatype)) File "/mnt/workspace/helixfold-single_original/alphafold_paddle/data/data_utils.py", line 96, in for i in range(len(aatype))

I use th 2.3version paddle cuda 11.2 linux docker. I solved the dependency according to the readme. And I download the official init model. But when I run the code , I got the valueError. The code is PaddleHelix/apps/protein_folding/helixfold-single/helixfold_single_inference.py

like robin said, what's the problem?

2022-09-26 09:24:25.062647: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2500000000 Hz
/usr/local/lib/python3.7/dist-packages/paddle/fluid/framework.py:3623: DeprecationWarning: Op `slice` is executed through `append_op` under the dynamic mode, the corresponding API implementation needs to be upgraded to using `_C_ops` method.
  "using `_C_ops` method." % type, DeprecationWarning)
Traceback (most recent call last):
  File "helixfold_single_inference.py", line 121, in <module>
    main(args)
  File "helixfold_single_inference.py", line 106, in main
    results = model(batch, compute_loss=False)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 929, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 914, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/tmp/helix/utils/model_tape.py", line 115, in forward
    batch = self._forward_tape(batch)
  File "/tmp/helix/utils/model_tape.py", line 98, in _forward_tape
    return_representations=True, return_last_n_weight=self.model_config.last_n_weight)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 929, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 914, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/tmp/helix/tape/others/protein_sequence_model_dynamic.py", line 218, in forward
    return_last_n_weight=return_last_n_weight)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 929, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 914, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/tmp/helix/tape/others/transformer_block.py", line 530, in forward
    is_recompute=self.training)
  File "/tmp/helix/tape/others/transformer_block.py", line 26, in recompute_wrapper
    return func(*args)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 929, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 914, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/tmp/helix/tape/others/transformer_block.py", line 480, in forward
    attn_results = self.self_attn(src, src, src, src_mask, relative_pos, rel_embeddings)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 929, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 914, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/tmp/helix/tape/others/transformer_block.py", line 398, in forward
    rel_att = self.disentangled_attention_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor)
  File "/tmp/helix/tape/others/transformer_block.py", line 367, in disentangled_attention_bias
    c2p_att = self.gather_4d(c2p_att, index=c2p_gather_idx)
  File "/tmp/helix/tape/others/transformer_block.py", line 343, in gather_4d
    stack_0 = paddle.tile(paddle.arange(start=0, end=a, step=1, dtype="float32").reshape([a, 1]), [b * c * d]).reshape([a, b, c, d]).cast(index.dtype)
  File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/manipulation.py", line 3243, in reshape
    out, _ = _C_ops.reshape2(x, None, 'shape', shape)
ValueError: (InvalidArgument) The 'shape' in ReshapeOp is invalid. The input tensor X'size must be equal to the capacity of 'shape'. But received X's shape = [1, 1067237297], X's size = 1067237297, 'shape' is [1, 16, 2, 2], the capacity of 'shape' is 64.
  [Hint: Expected capacity == in_size, but received capacity:64 != in_size:1067237297.] (at /root/paddlejob/workspace/env_run/Paddle/paddle/fluid/operators/reshape_op.cc:204)

An error raised when I am runing GEM I am using single GPU which is GeForce RTX 2080 Ti with 11G memory my code is the same as that in github: `### build model init_model = '/home/outdo/PaddleHelix/apps/pretrained_compound/ChemRL/GEM/pretrain_models-chemrl_gem/regr.pdparams'

compound_encoder = GeoGNNModel(compound_encoder_config) model = DownstreamModel(model_config, compound_encoder) if metric == 'square': criterion = nn.MSELoss() else: criterion = nn.L1Loss() encoder_params = compound_encoder.parameters() head_params = exempt_parameters(model.parameters(), encoder_params) encoder_opt = paddle.optimizer.Adam(args.encoder_lr, parameters=encoder_params) head_opt = paddle.optimizer.Adam(args.head_lr, parameters=head_params) print('Total param num: %s' % (len(model.parameters()))) print('Encoder param num: %s' % (len(encoder_params))) print('Head param num: %s' % (len(head_params))) for i, param in enumerate(model.named_parameters()): print(i, param[0], param[1].name)

if not init_model is None and not args.init_model == "": compound_encoder.set_state_dict(paddle.load(args.init_model)) print('Load state_dict from %s' % args.init_model)`

error information: `--------------------------------------------------------------------------- OSError Traceback (most recent call last) Input In [25], in <cell line: 5>() 4 import paddle.fluid as fluid 5 with fluid.device_guard("cpu"): ----> 6 compound_encoder = GeoGNNModel(compound_encoder_config) 7 model = DownstreamModel(model_config, compound_encoder) 8 if metric == 'square':

File ~/PaddleHelix/pahelix/model_zoo/gem_model.py:81, in GeoGNNModel.init(self, model_config) 78 self.bond_float_names = model_config['bond_float_names'] 79 self.bond_angle_float_names = model_config['bond_angle_float_names'] ---> 81 self.init_atom_embedding = AtomEmbedding(self.atom_names, self.embed_dim) 82 self.init_bond_embedding = BondEmbedding(self.bond_names, self.embed_dim) 83 self.init_bond_float_rbf = BondFloatRBF(self.bond_float_names, self.embed_dim)

File ~/PaddleHelix/pahelix/networks/compound_encoder.py:38, in AtomEmbedding.init(self, atom_names, embed_dim) 36 self.embed_list = nn.LayerList() 37 for name in self.atom_names: ---> 38 embed = nn.Embedding( 39 CompoundKit.get_atom_feature_size(name) + 5, 40 embed_dim, 41 weight_attr=nn.initializer.XavierUniform()) 42 self.embed_list.append(embed)

File ~/anaconda3/envs/paddlehelix/lib/python3.8/site-packages/paddle/nn/layer/common.py:1453, in Embedding.init(self, num_embeddings, embedding_dim, padding_idx, sparse, weight_attr, name) 1451 self._remote_prefetch = False 1452 self._name = name -> 1453 self.weight = self.create_parameter( 1454 attr=self._weight_attr, 1455 shape=self._size, 1456 dtype=self._dtype, 1457 is_bias=False) 1459 if in_dynamic_mode() and padding_idx != -1: 1460 with paddle.no_grad():

File ~/anaconda3/envs/paddlehelix/lib/python3.8/site-packages/paddle/fluid/dygraph/layers.py:423, in Layer.create_parameter(self, shape, attr, dtype, is_bias, default_initializer) 421 if isinstance(temp_attr, six.string_types) and temp_attr == "": 422 temp_attr = None --> 423 return self._helper.create_parameter(temp_attr, shape, dtype, is_bias, 424 default_initializer)

File ~/anaconda3/envs/paddlehelix/lib/python3.8/site-packages/paddle/fluid/layer_helper_base.py:376, in LayerHelperBase.create_parameter(self, attr, shape, dtype, is_bias, default_initializer, stop_gradient, type) 370 if is_used: 371 raise ValueError( 372 "parameter name [{}] have be been used. " 373 "In dygraph mode, the name of parameter can't be same." 374 "Please check the parameter attr value passed to self.create_parameter or " 375 "constructor of dygraph Layers".format(attr.name)) --> 376 return self.main_program.global_block().create_parameter( 377 dtype=dtype, 378 shape=shape, 379 type=type, 380 stop_gradient=stop_gradient, 381 **attr._to_kwargs(with_initializer=True)) 382 else: 383 self.startup_program.global_block().create_parameter( 384 dtype=dtype, 385 shape=shape, 386 type=type, 387 **attr._to_kwargs(with_initializer=True))

File ~/anaconda3/envs/paddlehelix/lib/python3.8/site-packages/paddle/fluid/framework.py:3572, in Block.create_parameter(self, *args, **kwargs) 3570 pass 3571 else: -> 3572 initializer(param, self) 3573 return param

File ~/anaconda3/envs/paddlehelix/lib/python3.8/site-packages/paddle/fluid/initializer.py:605, in XavierInitializer.call(self, var, block) 603 if self._uniform: 604 limit = np.sqrt(6.0 / float(fan_in + fan_out)) --> 605 out_var = _C_ops.uniform_random('shape', out_var.shape, 'min', 606 -limit, 'max', limit, 'seed', 607 self._seed, 'dtype', out_dtype) 608 else: 609 std = math.sqrt(2.0 / float(fan_in + fan_out))

OSError: [operator < uniform_random > error]`

Could anyone help me? Thank you so much!

1. setxor错误：举例输入 setxor(a=[1, 0], b=[0, 2])，将会得到 [0, 1, 0, 2], []， 实际上按照bond_graph_base的生成方式，只需要取a[0]和 b[1]即可 https://github.com/PaddlePaddle/PaddleHelix/blob/e5578f72c2a203a27d9df7da111f1ced826c1429/apps/drug_target_interaction/sign/dataset.py#L149

2. 这里输出的atoms使用的是atom在特征矩阵的维度， 与后面的atom_type不符, 提供的处理好的数据是没有问题的(https://www.dropbox.com/sh/68vc7j5cvqo4p39/AAB_96TpzJWXw6N0zxHdsppEa) https://github.com/PaddlePaddle/PaddleHelix/blob/e5578f72c2a203a27d9df7da111f1ced826c1429/apps/drug_target_interaction/sign/preprocess_pdbbind.py#L280

存在疑惑的地方： 3. 如果 a边:[0, 1 ], b边[1, 0], 则c边为[0, 0], 如果取dist_mat[0, 0],则c边长度为inf，计算可得夹角为180度（encode为5）但按照其它的边的夹角构造方式，则夹角应该为0度（encode为0） https://github.com/PaddlePaddle/PaddleHelix/blob/e5578f72c2a203a27d9df7da111f1ced826c1429/apps/drug_target_interaction/sign/dataset.py#L152

Hi I recently read your paper "GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions" and I'm quite impressed by the performance of GEM2 on PCQM4Mv2. However, I have some difficulties in understanding its implementation. In the method call of class OptimusTransformerFn from https://github.com/PaddlePaddle/PaddleHelix/blob/dev/apps/pretrained_compound/ChemRL/GEM-2/src/featurizer.py I see two methods to compute 3d coordiantes for each molecule: (1) raw3d and (2) rdkit3d The first method seems to load the 3d information provided by PCQM4Mv2 dataset, so it only applies for training set. The second method seems to use some built-in algorithm of rdkit to compute 3d information and can apply for both training and valid, test set. So here are my questions: (1) For the result reported in your paper, which method you use to compute 3d information? raw3d or rdkit3d? (2) Does GEM2 use 3d information during inference on valid and test set? Or it just turn off 3d information? (3) If possible, can you provide the pretrained weight for the GEM2 model reported in the paper? Thank you!

Add instructions on how to create a new environment on conda, and change the paddlepaddle version requirement from only 2.0.0rc0 to equal or bigger than 2.0.0rc0.

Stepped through this issue and I've found that <simtk.openmm.app.internal.pdbstructure.Model object at 0x7f424e6f6750> is passed in to this method within openmm that expects a file object. It is possible this is an openmm issue but this is currently blocking my usage of HelixFold. I have verified that I have the latest versions of both openmm and pdbfixer and also have recently pulled the updated setup_env file that made changes to the linking of openmm into simtek. Traceback (most recent call last): File "run_helixfold.py", line 375, in <module> main(args) File "run_helixfold.py", line 280, in main random_seed=random_seed) File "run_helixfold.py", line 160, in predict_structure output_dir, 0, timings) File "/home/common/proj/FoldingBenchMarks/HelixFold/apps/protein_folding/helixfold/alphafold_paddle/model/model.py", line 283, in postprocess relaxed_pdb_str = relaxer.process(prot=prot)[0] File "/home/common/proj/FoldingBenchMarks/HelixFold/apps/protein_folding/helixfold/alphafold_paddle/relax/relax.py", line 63, in process max_outer_iterations=self._max_outer_iterations) File "/home/common/proj/FoldingBenchMarks/HelixFold/apps/protein_folding/helixfold/alphafold_paddle/relax/amber_minimize.py", line 939, in run_pipeline pdb_string = clean_protein(prot, checks=checks) File "/home/common/proj/FoldingBenchMarks/HelixFold/apps/protein_folding/helixfold/alphafold_paddle/relax/amber_minimize.py", line 187, in clean_protein as_file = openmm_app.PDBFile(pdb_structure) File "/home/grads/bernardm/.conda/envs/helixfold/lib/python3.7/site-packages/simtk/openmm/app/pdbfile.py", line 96, in __init__ pdb = PdbStructure(inputfile, load_all_models=True, extraParticleIdentifier=extraParticleIdentifier) File "/home/grads/bernardm/.conda/envs/helixfold/lib/python3.7/site-packages/openmm/app/internal/pdbstructure.py", line 153, in __init__ self._load(input_stream) File "/home/grads/bernardm/.conda/envs/helixfold/lib/python3.7/site-packages/openmm/app/internal/pdbstructure.py", line 161, in _load if not isinstance(pdb_line, str): AttributeError: 'Model' object has no attribute 'decode'

Hi

I mention that you introduce branch parallelism in your arxiv paper. I wonder that is the model structure implemented by BP is identical to the one in Alphafold2 paper. It appears to me that computations are sequential in the paper.

Thanks!

The setup_env script pins OpenMM to 7.5.1, which is an old release that isn't supported anymore. Could that be updated to the current release, or alternatively could the pin just be removed? As far as I can tell nothing in the code requires the old version.

我按照helixfold的README_inference.md文件运行run_helixfold.py模型时遇到了显存溢出的问题，我使用的是一张12GB的3080Ti。我尝试降低batch的大小，但是看代码中batch似乎是要预测的蛋白质fasta文件的特征文件。所以有什么好的方法能够降低模型占用的显存容量吗，或者有其它能够帮助该模型在12G的显存上运行的建议吗？十分感谢您的帮助！！！！

在运行KDD 2021 paper: "Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity".这篇文章的代码数据处理部分命令行时 python preprocess_pdbbind.py --data_path_core YOUR_DATASET_PATH --data_path_refined YOUR_DATASET_PATH --dataset_name pdbbind2016 --output_path YOUR_OUTPUT_PATH --cutoff 5 出现了下图中的错误，不知该怎样解决，求助大佬

使用的版本如下 Python 3.8.13 paddlepaddle-gpu 2.3.1.post112

Hi, I have a question regarding GEM-2 model.

How did you measure the performance of validation set and test set on PCQM4Mv2 dataset?

Because I knew that they do not provide 3D coordinates.

Thank you.

针对PR中的代码修改，我写了单测比较精度：https://gist.github.com/Xreki/f451fcb6c3dfe7d83d137b3f7c0ca3f1

收集了模型中rots_mul_rots和rots_mul_vecs输入输出的shape，发现主要存在2种配置。

• rots_mul_rots

| | a.shape | b.shape | out.shape | 说明 | |---|---|---|---|---| | 不需要广播 | [2, 256, 8, 3, 3] | [2, 256, 8, 3, 3] | [2, 256, 8, 3, 3] | 原始实现需要107个算子，PR修改后只需要1个算子 | | 需要广播 | [2, 256, 1, 3, 3] | [2, 256, 8, 3, 3] | [2, 256, 8, 3, 3] | 原始实现需要107个算子，PR修改后只需要3个算子 |

• rots_mul_vecs

| | m.shape | v.shape | out.shape | 说明 | |---|---|---|---|---| | 不需要广播 | [2, 256, 14, 3, 3] | [2, 256, 14, 3] | [2, 256, 14, 3] | PR修改后只需要3个算子 | | 需要广播 | [2, 256, 1, 3, 3] | [2, 256, 8, 3] | [2, 256, 8, 3] | PR修改后只需要5个算子 |