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fn gemm(
A: Tensor<T>,
B: Tensor<T>,
C: Option<Tensor<T>>,
alpha: Option<T>,
beta: Option<T>,
transA: bool,
transB: bool
) -> Tensor<T>;
Performs General Matrix multiplication:
  • A' = transpose(A) if transA else A
  • B' = transpose(B) if transB else B
Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is true, same for B and transB.


  • A(Tensor<T>) - Input tensor A. The shape of A should be (M, K) if transA is false, or (K, M) if transA is true.
  • B(Tensor<T>) - Input tensor B. The shape of B should be (K, N) if transB is false, or (N, K) if transB is true.
  • C(Option<Tensor<T>>) - Optional input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
  • alpha(Option<T>) - Optional scalar multiplier for the product of input tensors A * B.
  • beta(Option<T>) - Optional scalar multiplier for input tensor C.
  • transA(bool) - Whether A should be transposed.
  • transB(bool) - Whether B should be transposed.


A Tensor<T> of shape (M, N).


mod input_0;
mod input_1;
mod input_2;
use orion::operators::nn::NNTrait;
use orion::numbers::FixedTrait;
use orion::operators::nn::FP16x16NN;
use orion::operators::tensor::FP16x16TensorPartialEq;
fn gemm_all_attributes_example() -> Tensor<FP16x16> {
let input_0 = input_0::input_0(); // shape [4;3]
let input_1 = input_1::input_1(); // shape [5;4]
let input_2 = input_2::input_2(); // shape [1;5]
let y = NNTrait::gemm(
Option::Some(FixedTrait::new(16384, false)), // 0.25
Option::Some(FixedTrait::new(22938, false)), // 0.35
return y;
>>> tensor of shape [3;5]