attention¶

paddle.sparse.nn.functional. attention ( query, key, value, sparse_mask, key_padding_mask=None, attn_mask=None, name=None ) [source]

Note:

This API is only used from CUDA 11.7 .

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:

\[result = softmax(\]

System Message: WARNING/2 (/usr/local/lib/python3.8/site-packages/paddle/sparse/nn/functional/transformer.py:docstring of paddle.sparse.nn.functional.transformer.attention, line 12)

Block quote ends without a blank line; unexpected unindent.

rac{ 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

System Message: ERROR/3 (/usr/local/lib/python3.8/site-packages/paddle/sparse/nn/functional/transformer.py:docstring of paddle.sparse.nn.functional.transformer.attention, line 23)

Unexpected indentation.

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.

System Message: WARNING/2 (/usr/local/lib/python3.8/site-packages/paddle/sparse/nn/functional/transformer.py:docstring of paddle.sparse.nn.functional.transformer.attention, line 25)

Block quote ends without a blank line; unexpected unindent.

key_padding_mask(DenseTensor, 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, 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, optional): The default value is None. Normally there is no need for user

to set this property. For more information, please refer to Name.

Returns:

4D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. dtype is same with input.

Examples:

# required: gpu
import paddle

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])
attn_mask = paddle.randint(0, 2, [seq_len, seq_len])

output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask)
output.backward()