parameters: (query,
                       key,
                       value,
                       sparse_csr_offset,
                       sparse_csr_columns,
                       key_padding_mask=None,
                       attn_mask=None,
                       name=None)
return_statement: return out
docstring: r"""
      This operator sparsify the Attention matrix in Transformer module
      to achieve the effect of reducing memory consumption and computation. 
      The sparse layout is expressed in CSR format and contains two parameters, 
      ``offset`` and ``columns``. The 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 dimensions of the three parameters are the same. 
      ``d`` represents the size of the last dimension of the three parameters.
  
      Warning:    
          This API is only used in ``CUDA 11.3`` and above versions.
  
      Args:
          query(Tensor): The query tensor in the Attention module. 
                          4-D tensor with shape: 
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          key(Tensor): The key tensor in the Attention module. 
                          4-D tensor with shape: 
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          value(Tensor): The value tensor in the Attention module. 
                          4-D tensor with shape:  
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          sparse_csr_offset(Tensor): The sparsity feature in the Attention module 
                          is expressed in the CSR format, and the offset represents 
                          the number of non-zero elements in each row of the matrix.
                          3-D tensor with shape:   
                          [batch_size, num_heads, seq_len + 1]. 
                          The dtype should be int32.
          sparse_csr_columns(Tensor): The sparsity feature in the Attention module 
                          is expressed in the CSR format, and the columns represent 
                          the column index values of non-zero elements in the matrix.
                          3-D tensor with shape:  
                          [batch_size, num_heads, sparse_nnz]. 
                          The dtype should be int32.
          key_padding_mask(Tensor, optional):The key padding mask tensor in the Attention module. 
                          2-D tensor with shape: [batch_size, seq_len]. 
                          The dtype can be float32 and float64.
                          A value of 0 means that the position is masked.
          attn_mask(Tensor, optional):The attention mask tensor in the Attention module. 
                          2-D tensor with shape: [seq_len, seq_len]. 
                          The dtype can be float32 and float64.
                          A value of 0 means that the position is masked.
          name(str, 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:
          4-D tensor with shape:
          [batch_size, num_heads, seq_len, head_dim]. 
          The dtype can be float32 or float64.
  
      Examples:
          .. code-block:: python
  
              # required: skiptest
              import paddle
              import numpy as np
  
              query_data = np.array([[[[0, 1,], [2, 3],
                      [ 0, 1], [2, 3]]]]).astype("float32")
              key_data = np.array([[[[0, 1,], [2, 3],
                              [ 0, 1], [2, 3]]]]).astype("float32")
              value_data = np.array([[[[0, 1,], [2, 3],
                              [ 0, 1], [2, 3]]]]).astype("float32")
              sparse_csr_offset_data = np.array([[[0, 2,
                              4, 6, 8]]]).astype("int32")
              sparse_csr_columns_data = np.array([[[0, 1,
                              0, 1, 2, 3, 2, 3]]]).astype("int32")
              key_padding_mask_data = np.array([[1,1,1,0]]).astype("float32")
              attention_mask_data = np.array([[1,0,1,1],[1,1,1,1],[1,1,1,1],[1,1,1,1]]).astype("float32")
              print(query_data.shape)
              # (1, 1, 4, 2)
              print(sparse_csr_offset_data.shape)
              # (1, 1, 5)
              print(sparse_csr_columns_data.shape)
              # (1, 1, 8)
              paddle.disable_static()
              query = paddle.to_tensor(query_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              key = paddle.to_tensor(key_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              value = paddle.to_tensor(value_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              offset = paddle.to_tensor(sparse_csr_offset_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              columns = paddle.to_tensor(sparse_csr_columns_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              key_padding_mask = paddle.to_tensor(key_padding_mask_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              attention_mask = paddle.to_tensor(attention_mask_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              output_mask = paddle.nn.functional.sparse_attention(query, key, 
                              value, offset, columns, 
                              key_padding_mask=key_padding_mask, attn_mask=attention_mask)
              print(output_mask)
              # [[[[0.        , 1.        ],
              #    [1.99830270, 2.99830270],
              #    [0.        , 1.        ],
              #    [0.        , 1.        ]]]]
              output = paddle.nn.functional.sparse_attention(query, key, 
                              value, offset, columns)
              print(output) 
              # [[[[1.60885942, 2.60885954],
              #       [1.99830270, 2.99830270],
              #       [1.60885942, 2.60885954],
              #       [1.99830270, 2.99830270]]]]
docstring_summary: r"""
      This operator sparsify the Attention matrix in Transformer module
      to achieve the effect of reducing memory consumption and computation. 
      The sparse layout is expressed in CSR format and contains two parameters, 
      ``offset`` and ``columns``. The equation is:
docstring_tokens: r This operator sparsify the Attention matrix in Transformer module to achieve the effect of reducing memory consumption and computation . The sparse layout is expressed in CSR format and contains two parameters offset and columns . The equation is :
function: def sparse_attention(query,
                       key,
                       value,
                       sparse_csr_offset,
                       sparse_csr_columns,
                       key_padding_mask=None,
                       attn_mask=None,
                       name=None):
      r"""
      This operator sparsify the Attention matrix in Transformer module
      to achieve the effect of reducing memory consumption and computation. 
      The sparse layout is expressed in CSR format and contains two parameters, 
      ``offset`` and ``columns``. The 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 dimensions of the three parameters are the same. 
      ``d`` represents the size of the last dimension of the three parameters.
  
      Warning:    
          This API is only used in ``CUDA 11.3`` and above versions.
  
      Args:
          query(Tensor): The query tensor in the Attention module. 
                          4-D tensor with shape: 
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          key(Tensor): The key tensor in the Attention module. 
                          4-D tensor with shape: 
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          value(Tensor): The value tensor in the Attention module. 
                          4-D tensor with shape:  
                          [batch_size, num_heads, seq_len, head_dim]. 
                          The dtype can be float32 and float64.
          sparse_csr_offset(Tensor): The sparsity feature in the Attention module 
                          is expressed in the CSR format, and the offset represents 
                          the number of non-zero elements in each row of the matrix.
                          3-D tensor with shape:   
                          [batch_size, num_heads, seq_len + 1]. 
                          The dtype should be int32.
          sparse_csr_columns(Tensor): The sparsity feature in the Attention module 
                          is expressed in the CSR format, and the columns represent 
                          the column index values of non-zero elements in the matrix.
                          3-D tensor with shape:  
                          [batch_size, num_heads, sparse_nnz]. 
                          The dtype should be int32.
          key_padding_mask(Tensor, optional):The key padding mask tensor in the Attention module. 
                          2-D tensor with shape: [batch_size, seq_len]. 
                          The dtype can be float32 and float64.
                          A value of 0 means that the position is masked.
          attn_mask(Tensor, optional):The attention mask tensor in the Attention module. 
                          2-D tensor with shape: [seq_len, seq_len]. 
                          The dtype can be float32 and float64.
                          A value of 0 means that the position is masked.
          name(str, 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:
          4-D tensor with shape:
          [batch_size, num_heads, seq_len, head_dim]. 
          The dtype can be float32 or float64.
  
      Examples:
          .. code-block:: python
  
              # required: skiptest
              import paddle
              import numpy as np
  
              query_data = np.array([[[[0, 1,], [2, 3],
                      [ 0, 1], [2, 3]]]]).astype("float32")
              key_data = np.array([[[[0, 1,], [2, 3],
                              [ 0, 1], [2, 3]]]]).astype("float32")
              value_data = np.array([[[[0, 1,], [2, 3],
                              [ 0, 1], [2, 3]]]]).astype("float32")
              sparse_csr_offset_data = np.array([[[0, 2,
                              4, 6, 8]]]).astype("int32")
              sparse_csr_columns_data = np.array([[[0, 1,
                              0, 1, 2, 3, 2, 3]]]).astype("int32")
              key_padding_mask_data = np.array([[1,1,1,0]]).astype("float32")
              attention_mask_data = np.array([[1,0,1,1],[1,1,1,1],[1,1,1,1],[1,1,1,1]]).astype("float32")
              print(query_data.shape)
              # (1, 1, 4, 2)
              print(sparse_csr_offset_data.shape)
              # (1, 1, 5)
              print(sparse_csr_columns_data.shape)
              # (1, 1, 8)
              paddle.disable_static()
              query = paddle.to_tensor(query_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              key = paddle.to_tensor(key_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              value = paddle.to_tensor(value_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              offset = paddle.to_tensor(sparse_csr_offset_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              columns = paddle.to_tensor(sparse_csr_columns_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              key_padding_mask = paddle.to_tensor(key_padding_mask_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              attention_mask = paddle.to_tensor(attention_mask_data, stop_gradient=False, 
                              place=paddle.CUDAPlace(0))
              output_mask = paddle.nn.functional.sparse_attention(query, key, 
                              value, offset, columns, 
                              key_padding_mask=key_padding_mask, attn_mask=attention_mask)
              print(output_mask)
              # [[[[0.        , 1.        ],
              #    [1.99830270, 2.99830270],
              #    [0.        , 1.        ],
              #    [0.        , 1.        ]]]]
              output = paddle.nn.functional.sparse_attention(query, key, 
                              value, offset, columns)
              print(output) 
              # [[[[1.60885942, 2.60885954],
              #       [1.99830270, 2.99830270],
              #       [1.60885942, 2.60885954],
              #       [1.99830270, 2.99830270]]]]
      """
      if in_dygraph_mode():
          result_attention, result_sdd, result_softmax = _C_ops.sparse_attention(
              query, key, value, sparse_csr_offset, sparse_csr_columns,
              key_padding_mask, attn_mask)
          return result_attention
  
      helper = LayerHelper('sparse_attention', **locals())
      dtype = helper.input_dtype(input_param_name='Q')
      out = helper.create_variable_for_type_inference(dtype)
      result_sdd = helper.create_variable_for_type_inference(dtype)
      result_softmax = helper.create_variable_for_type_inference(dtype)
      inputs = {
          'Q': query,
          'K': key,
          'V': value,
          'Offset': sparse_csr_offset,
          'Columns': sparse_csr_columns,
          'KeyPaddingMask': key_padding_mask,
          'AttnMask': attn_mask,
      }
      outputs = {
          'Out': out,
          'SparseDotSdd': result_sdd,
          'Softmax': result_softmax
      }
      helper.append_op(type='sparse_attention', inputs=inputs, outputs=outputs)
      return out
function_tokens: def sparse_attention ( query , key , value , sparse_csr_offset , sparse_csr_columns , key_padding_mask = None , attn_mask = None , name = None ) : if in_dygraph_mode ( ) : result_attention , result_sdd , result_softmax = _C_ops . sparse_attention ( query , key , value , sparse_csr_offset , sparse_csr_columns , key_padding_mask , attn_mask ) return result_attention helper = LayerHelper ( 'sparse_attention' , * * locals ( ) ) dtype = helper . input_dtype ( input_param_name = 'Q' ) out = helper . create_variable_for_type_inference ( dtype ) result_sdd = helper . create_variable_for_type_inference ( dtype ) result_softmax = helper . create_variable_for_type_inference ( dtype ) inputs = { 'Q' : query , 'K' : key , 'V' : value , 'Offset' : sparse_csr_offset , 'Columns' : sparse_csr_columns , 'KeyPaddingMask' : key_padding_mask , 'AttnMask' : attn_mask , } outputs = { 'Out' : out , 'SparseDotSdd' : result_sdd , 'Softmax' : result_softmax } helper . append_op ( type = 'sparse_attention' , inputs = inputs , outputs = outputs ) return out