baddbmm

→Tensor

baddbmm(input: Tensor, batch1: Tensor, batch2: Tensor, beta: float = ..., alpha: float = ...)

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Batched GEMM with a batched accumulator.

Computes $\beta \cdot \text{input} + \alpha \cdot \operatorname{bmm}(\text{batch1}, \text{batch2})$ where the batch axis is preserved (unlike addbmm which reduces it).

Parameters

inputTensor

Accumulator of shape (B, M, N).

batch1Tensor

Batched left matrices of shape (B, M, K).

batch2Tensor

Batched right matrices of shape (B, K, N).

betafloat

Scalar multiplier on input. Defaults to 1.0.

alphafloat

Scalar multiplier on bmm(batch1, batch2). Defaults to 1.0.

Returns

Tensor

Tensor of shape (B, M, N).

Notes

Per-batch definition:

\text{out}[k] = \beta \cdot \text{input}[k] + \alpha \cdot (\text{batch1}[k] \cdot \text{batch2}[k]).

The implementation defers to lucid.bmm, which dispatches to Accelerate-batched GEMM on CPU and MLX on GPU.

Examples

>>> import lucid
>>> b1 = lucid.ones((3, 2, 4))
>>> b2 = lucid.ones((3, 4, 2))
>>> M = lucid.zeros((3, 2, 2))
>>> lucid.baddbmm(M, b1, b2).shape
(3, 2, 2)