ΑiSSKJr SSKJr SSKJs Jr \(aSSKJ r SSSjjr g)) annotations) TYPE_CHECKINGN)Tensorc U(aUb [S5eURSS5URSS5URSS5p!n[R"UUUUUUSSUUS5 nURSS5$)a 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 only verifies inputs with dtype float16 and bfloat16, other dtypes may fall back to math implementation, which is less optimized. Note: This API differs from :ref:`api_paddle_nn_functional_scaled_dot_product_attention` in that: The QKV layout of this API is [batch_size, num_heads, seq_len, head_dim] or [num_heads, seq_len, head_dim]. Args: query(Tensor): The query tensor in the Attention module. 4-D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. 3-D tensor with shape: [num_heads, seq_len, head_dim]. The dtype can be float16 or bfloat16. key(Tensor): The key tensor in the Attention module. 4-D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. 3-D tensor with shape: [num_heads, seq_len, head_dim]. The dtype can be float16 or bfloat16. value(Tensor): The value tensor in the Attention module. 4-D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. 3-D tensor with shape: [num_heads, seq_len, head_dim]. The dtype can be float16 or bfloat16. attn_mask(Tensor, optional): The attention mask tensor. The shape should be broadcastable to [batch_size, num_heads, seq_len_key, seq_len_query]. The dtype can be bool or same type of query. The bool mask indicates the positions should take part in attention. The non-bool mask will be added to attention score. is_causal(bool, optional): Whether enable causal mode. If True, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias when the mask is a non-square matrix. An error is thrown if both attn_mask and is_causal are set. scale(float, optional): The scaling factor used in the calculation of attention weights. If None, scale = 1 / sqrt(head_dim). enable_gqa(bool, optional): Whether enable GQA mode. Default False. Returns: out(Tensor): The attention tensor. 4-D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. 3-D tensor with shape: [num_heads, seq_len, head_dim]. The dtype can be float16 or bfloat16. Examples: .. code-block:: python >>> # doctest: +SKIP('bfloat need V100 compile') >>> import paddle >>> q = paddle.rand((1, 2, 128, 16), dtype=paddle.bfloat16) >>> output = paddle.compat.nn.functional.scaled_dot_product_attention(q, q, q, None, 0.9, False) >>> print(output) >>> # doctest: -SKIP Nz8Explicit attn_mask should not be set when is_causal=TrueT) RuntimeErrorswapaxesFscaled_dot_product_attention) querykeyvalue attn_mask dropout_p is_causalscale enable_gqaouts `/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/compat/nn/functional/sdpa.pyr r s\Y* F  r2 R r2E ( (        C <<B )NgFNF)r rrrrrrz Tensor | Nonerfloatrboolrz float | Nonerrreturnr) __future__rtypingrpaddle.nn.functionalnn functionalr paddlerr rrr"s#  $e e e  e  e  e  e  e e  e r