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auto build linux release (#2341)
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@ -492,6 +492,16 @@ workflows:
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branches:
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ignore: /.*/
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upload-docs: true
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- build_linux_release:
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filters:
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tags:
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only: /^v.*/
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branches:
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ignore: /.*/
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matrix:
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parameters:
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python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
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extra_env: ["PYPI_RELEASE=1"]
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prb:
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when:
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@ -175,11 +175,12 @@ void init_fast(nb::module_& parent_module) {
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* `Grouped Query Attention <https://arxiv.org/abs/2305.13245>`_
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* `Multi-Query Attention <https://arxiv.org/abs/1911.02150>`_
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Note: The softmax operation is performed in ``float32`` regardless of
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the input precision.
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.. note::
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Note: For Grouped Query Attention and Multi-Query Attention, the ``k``
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and ``v`` inputs should not be pre-tiled to match ``q``.
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* The softmax operation is performed in ``float32`` regardless of
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the input precision.
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* For Grouped Query Attention and Multi-Query Attention, the ``k``
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and ``v`` inputs should not be pre-tiled to match ``q``.
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In the following the dimensions are given by:
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@ -195,13 +196,30 @@ void init_fast(nb::module_& parent_module) {
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k (array): Keys with shape ``[B, N_kv, T_kv, D]``.
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v (array): Values with shape ``[B, N_kv, T_kv, D]``.
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scale (float): Scale for queries (typically ``1.0 / sqrt(q.shape(-1)``)
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mask (Union[None, str, array], optional): A causal, boolean or additive
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mask to apply to the query-key scores. The mask can have at most 4
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dimensions and must be broadcast-compatible with the shape
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``[B, N, T_q, T_kv]``. If an additive mask is given its type must
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promote to the promoted type of ``q``, ``k``, and ``v``.
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mask (Union[None, str, array], optional): The mask to apply to the
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query-key scores. The mask can be an array or a string indicating
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the mask type. The only supported string type is ``"causal"``. If
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the mask is an array it can be a boolean or additive mask. The mask
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can have at most 4 dimensions and must be broadcast-compatible with
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the shape ``[B, N, T_q, T_kv]``. If an additive mask is given its
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type must promote to the promoted type of ``q``, ``k``, and ``v``.
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Returns:
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array: The output array.
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Example:
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.. code-block:: python
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B = 2
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N_q = N_kv = 32
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T_q = T_kv = 1000
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D = 128
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q = mx.random.normal(shape=(B, N_q, T_q, D))
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k = mx.random.normal(shape=(B, N_kv, T_kv, D))
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v = mx.random.normal(shape=(B, N_kv, T_kv, D))
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scale = D ** -0.5
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out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask="causal")
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)pbdoc");
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m.def(
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