mindsponge.cell.MSARowAttentionWithPairBias

class mindsponge.cell.MSARowAttentionWithPairBias(num_head, key_dim, gating, msa_act_dim, pair_act_dim, batch_size=None, slice_num=0)[source]
MSA row attention. Information from pair action value is made as the bias of the matrix of MSARowAttention,

in order to update the state of MSA using pair information.

Reference:

Jumper et al. (2021) Suppl. Alg. 7 ‘MSARowAttentionWithPairBias’.

Parameters

  • num_head (int) – The number of the attention head.

  • key_dim (int) – The dimension of the attention hidden layer.

  • gating (bool) – Indicator of if the attention is gated.

  • msa_act_dim (int) – The dimension of the msa_act.

  • pair_act_dim (int) – The dimension of the pair_act.

  • batch_size (int) – The batch size of parameters in MSA row attention, used in while control flow. Default: None.

  • slice_num (int) – The number of slices to be made to reduce memory. Default: 0.

Inputs:

  • msa_act (Tensor) - Tensor of msa_act with shape \((N_{seqs}, N_{res}, msa\_act\_dim)\) .

  • msa_mask (Tensor) - The mask for MSA row attention matrix with shape \((N_{seqs}, N_{res})\) .

  • pair_act (Tensor) - Tensor of pair_act with shape \((N_{res}, N_{res}, pair\_act\_dim)\) . Data type is float.

  • index (Tensor) - The index of while loop, only used in case of while control flow. Default: “None”.

  • norm_msa_mask (Tensor) - The mask of msa_act when to do layernorm with shape \((N_{seqs}, N_{res})\), Default: “None”.

  • norm_pair_mask (Tensor) - The mask of pair_act when to do layernorm with shape \((N_{res}, N_{res})\), Default: “None”.

  • res_idx (Tensor) - The residue index used to perform ROPE with shape \((N_{res})\), Default: “None”.

Outputs:

Tensor, the float tensor of the msa_act of the layer with shape \((N_{seqs}, N_{res}, msa\_act\_dim)\) .

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindsponge.cell import MSARowAttentionWithPairBias
>>> from mindspore import dtype as mstype
>>> from mindspore import Tensor
>>> model = MSARowAttentionWithPairBias(num_head=4, key_dim=4, gating=True,
...                                     msa_act_dim=64, pair_act_dim=128,
...                                     batch_size=None)
>>> msa_act = Tensor(np.ones((4, 256, 64)), mstype.float32)
>>> msa_mask = Tensor(np.ones((4, 256)), mstype.float16)
>>> pair_act = Tensor(np.ones((256, 256, 128)), mstype.float32)
>>> index = None
>>> msa_out = model(msa_act, msa_mask, pair_act, index)
>>> print(msa_out.shape)
(4, 256, 64)