Torch.Indef.Dynamic.Tensor.Math.Blas

Description

Blas functions.

Synopsis

# Documentation

Arguments

 :: HsReal v1 -> Dynamic vec1 -> HsReal v2 -> Dynamic mat -> Dynamic vec2 -> Dynamic res

Performs a matrix-vector multiplication between mat (2D Tensor) and vec2 (1D Tensor) and add it to vec1.

Values v1 and v2 are scalars that multiply vec1 and vec2 respectively. They are optional in C and we may be able to add this to the API in the future.

In other words,

  res = (v1 * vec1) + (v2 * (mat * vec2))


Sizes must respect the matrix-multiplication operation: if mat is a n × m matrix, vec2 must be vector of size m and vec1 must be a vector of size n.

Arguments

 :: HsReal v1 -> Dynamic M -> HsReal v2 -> Dynamic mat1 -> Dynamic mat2 -> Dynamic res

Performs a matrix-matrix multiplication between mat1 (2D Tensor) and mat2 (2D Tensor).

Values v1 and v2 are scalars that multiply M and mat1 * mat2 respectively. They are optional in C and we may be able to add this to the API in the future.

In other words,

  res = (v1 * M) + (v2 * mat1 * mat2)


If mat1 is a n × m matrix, mat2 a m × p matrix, M must be a n × p matrix.

Arguments

 :: HsReal v1 -> Dynamic mat_ij -> HsReal v2 -> Dynamic vec1_i -> Dynamic vec2_j -> Dynamic res_ij

Performs the outer-product between vec1 (1D Tensor) and vec2 (1D Tensor).

Values v1 and v2 are scalars that multiply mat_ij and vec1_i [out] vec2_j respectively. They are optional in C and we may be able to add this to the API in the future.

Thus:

  res_ij = (v1 * mat_ij) + (v2 * vec1_i * vec2_j)


If vec1_ is a vector of size i and vec2_j is a vector of size j, then mat_ij must be a matrix of size i × j.

Arguments

 :: HsReal v1 -> Dynamic M -> HsReal v2 -> Dynamic batch1_i -> Dynamic batch2_i -> Dynamic res

Batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced add step (all matrix multiplications get accumulated in a single place).

batch1 and batch2 must be 3D Tensors each containing the same number of matrices. If batch1 is a b × n × m Tensor, batch2 a b × m × p Tensor, res will be a n × p Tensor.

In other words,

  res = (v1 * M) + (v2 * sum(batch1_i * batch2_i, i = 1, b))


Arguments

 :: HsReal v1 -> Dynamic M_i -> HsReal v2 -> Dynamic batch1_i -> Dynamic batch2_i -> Dynamic res_i

Batch matrix matrix product of matrices stored in batch1 and batch2, with batch add.

batch1 and batch2 must be 3D Tensors each containing the same number of matrices. If batch1 is a b × n × m Tensor, batch2 a b × m × p Tensor, res will be a b × n × p Tensor.

In other words,

  res_i = (v1 * M_i) + (v2 * batch1_i * batch2_i)


Arguments

 :: HsReal v1 -> Dynamic vec1 -> HsReal v2 -> Dynamic mat -> Dynamic vec2 -> IO ()

Inline version of addmv, mutating vec1 inplace.

Arguments

 :: HsReal v1 -> Dynamic M -> HsReal v2 -> Dynamic mat1 -> Dynamic mat2 -> IO ()

Inline version of addmm, mutating M inplace.

Arguments

 :: HsReal v1 -> Dynamic mat_ij -- mutated inplace -> HsReal v2 -> Dynamic vec1_i -> Dynamic vec2_j -> IO ()

Inline version of addr, mutating mat_ij in-place.

Arguments

 :: HsReal v1 -> Dynamic M -> HsReal v2 -> Dynamic batch1_i -> Dynamic batch2_i -> IO ()

Inline version of addbmm, mutating M in-place.

Arguments

 :: HsReal v1 -> Dynamic M_i -> HsReal v2 -> Dynamic batch1_i -> Dynamic batch2_i -> IO ()

Inline version of baddbmm, mutating M_i in-place.

Performs the dot product between two tensors. The number of elements must match: both tensors are seen as a 1D vector.

inline alias of dot