ó Ñ‘®i[ãó„•SSKJr SSKJr /rSSKJr SSKJr \(aSSKJ r SS Sjjr g) é)Ú annotations)Ú TYPE_CHECKING)Ú_C_ops)Úin_dynamic_or_pir_mode)ÚTensorNcó^•[5(dS5e[R"XX#XE5$)aŽ Note: This API is only used from ``CUDA 11.8`` . SparseCsrTensor is used to store the intermediate result of Attention matrix in Transformer module, which can reduce memory usage and improve performance. ``sparse_mask`` express the sparse layout in CSR format. The calculation equation is: .. math:: result = softmax(\frac{ Q * K^T }{\sqrt{d}}) * V where : ``Q``, ``K``, and ``V`` represent the three input parameters of the attention module. The shape of the three parameters are: `[batch_size, num_heads, seq_len, head_dim]`, and ``d`` represents ``head_dim`` . Args: query (DenseTensor): `query` in the Attention module. 4D Tensor with float32 or float64. key (DenseTensor): `key` in the Attention module. 4D Tensor with float32 or float64. value (DenseTensor): `value` in the Attention module. 4D Tensor with float32 or float64. sparse_mask (SparseCsrTensor): The sparse layout in the Attention module. Its dense shape is `[batch_size*num_heads, seq_len, seq_len]`. `nnz` of each batch must be the same. dtype of `crows` and `cols` must be int64, dtype of `values` can be float32 or float64. key_padding_mask (DenseTensor|None, optional): The key padding mask tensor in the Attention module. 2D tensor with shape: [batch_size, seq_len]. dtype can be float32 or float64. Default: None. attn_mask (DenseTensor|None, optional): The attention mask tensor in the Attention module. 2D tensor with shape: [seq_len, seq_len]. dtype can be float32 or float64. Default: None. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: 4D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. dtype is same with input. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> batch_size = 16 >>> num_heads = 16 >>> seq_len = 512 >>> head_dim = 32 >>> query = paddle.rand([batch_size, num_heads, seq_len, head_dim]) >>> key = paddle.rand([batch_size, num_heads, seq_len, head_dim]) >>> value = paddle.rand([batch_size, num_heads, seq_len, head_dim]) >>> query.stop_gradient = False >>> key.stop_gradient = False >>> value.stop_gradient = False >>> mask = paddle.nn.functional.dropout(paddle.ones([seq_len, seq_len])).expand([batch_size, num_heads, seq_len, seq_len]) >>> sp_mask = mask.reshape([-1, seq_len, seq_len]).to_sparse_csr() >>> kp_mask = paddle.randint(0, 2, [batch_size, seq_len]).astype(paddle.float32) >>> attn_mask = paddle.randint(0, 2, [seq_len, seq_len]).astype(paddle.float32) >>> output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask) >>> output.backward() zrsƒðõ#å à €åÝ8æÝð'+Ø#ØðMØ ðMà ðMð ðMðð Mð $ð Mð ð Mð ðMð öMr