# tensorplay.nn.functional.scaled_dot_product_attention

Source: https://www.tensorplay.cn/docs/generated/tensorplay.nn.functional.scaled_dot_product_attention.html

```python
tensorplay.nn.functional.scaled_dot_product_attention(query: Tensor, key: Tensor, value: Tensor, attn_mask=None, dropout_p: float = 0.0, is_causal: bool = False, scale=None, enable_gqa: bool = False, backend: str | None = None) → Tensor
```

scaled_dot_product_attention(query, key, value, attn_mask=None,
dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False,
backend=None) -> Tensor

Computes scaled dot product attention on query, key and value. Routes to
reference:

$$\text{Attention}(Q, K, V) = \text{softmax}(\frac{Q K^T}{\sqrt{E}}) V$$

Parameters:

backend ([str](https://docs.python.org/3/builtins/stdtypes.html#str), optional) – 'flash' | 'mem_efficient',
'math', or None to pick automatically. 'flash'
selects the fused flash-attention kernel, 'math' forces the
composed reference path. When None, the routing candidate
order is governed by [tensorplay.nn.attention.sdpa_kernel()](/docs/generated/tensorplay.nn.attention.sdpa_kernel.html#tensorplay.nn.attention.sdpa_kernel).