create_contraction_trinary#

nvmath.bindings.cutensor.create_contraction_trinary( intptr_t handle, intptr_t desc_a, mode_a, int op_a, intptr_t desc_b, mode_b, int op_b, intptr_t desc_c, mode_c, int op_c, intptr_t desc_d, mode_d, int op_d, intptr_t desc_e, mode_e, intptr_t desc_compute, ) → intptr_t[source]#

This function allocates a cutensorOperationDescriptor_t object that encodes a tensor contraction of the form .

Parameters:

handle (intptr_t) – Opaque handle holding cuTENSOR’s library context.
desc_a (intptr_t) – The descriptor that holds the information about the data type, modes and strides of A.
mode_a (object) –
Array with ‘nmode_a’ entries that represent the modes of A. The mode_a[i] corresponds to extent[i] and stride[i] w.r.t. the arguments provided to cutensorInitTensorDescriptor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
op_a (Operator) – Unary operator that will be applied to each element of A before it is further processed. The original data of this tensor remains unchanged.
desc_b (intptr_t) – The descriptor that holds information about the data type, modes, and strides of B.
mode_b (object) –
Array with ‘nmode_b’ entries that represent the modes of B. The mode_b[i] corresponds to extent[i] and stride[i] w.r.t. the arguments provided to cutensorInitTensorDescriptor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
op_b (Operator) – Unary operator that will be applied to each element of B before it is further processed. The original data of this tensor remains unchanged.
desc_c (intptr_t) – The escriptor that holds information about the data type, modes, and strides of C.
mode_c (object) –
Array with ‘nmode_c’ entries that represent the modes of C. The mode_c[i] corresponds to extent[i] and stride[i] w.r.t. the arguments provided to cutensorInitTensorDescriptor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
op_c (Operator) – Unary operator that will be applied to each element of C before it is further processed. The original data of this tensor remains unchanged.
desc_d (intptr_t) – The escriptor that holds information about the data type, modes, and strides of D.
mode_d (object) –
Array with ‘nmode_d’ entries that represent the modes of D. The mode_d[i] corresponds to extent[i] and stride[i] w.r.t. the arguments provided to cutensorInitTensorDescriptor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
op_d (Operator) – Unary operator that will be applied to each element of D before it is further processed. The original data of this tensor remains unchanged.
desc_e (intptr_t) – Array with ‘nmode_e’ entries that represent the modes of E (must be identical to mode_d for now). The mode_e[i] corresponds to extent[i] and stride[i] w.r.t. the arguments provided to cutensorInitTensorDescriptor.
mode_e (object) –
The descriptor that holds information about the data type, modes, and strides of E (must be identical to desc_d for now). It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
desc_compute (intptr_t) – Determines the precision in which this operations is performed.

Returns:

This opaque struct gets allocated and filled with the information that encodes the tensor contraction operation.

Return type:

intptr_t