#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
BLAS functions (matmul, dot, etc)
"""
from .library import *
from .array import *
[docs]def matmul(lhs, rhs, lhs_opts=MATPROP.NONE, rhs_opts=MATPROP.NONE):
"""
Generalized matrix multiplication for two matrices.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
lhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `lhs`.
- af.MATPROP.TRANS - If `lhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `lhs` has to be hermitian transposed before multiplying.
rhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `rhs`.
- af.MATPROP.TRANS - If `rhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `rhs` has to be hermitian transposed before multiplying.
Returns
-------
out : af.Array
Output of the matrix multiplication on `lhs` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(c_pointer(out.arr), lhs.arr, rhs.arr,
lhs_opts.value, rhs_opts.value))
return out
[docs]def matmulTN(lhs, rhs):
"""
Matrix multiplication after transposing the first matrix.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `transpose(lhs)` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(c_pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.TRANS.value, MATPROP.NONE.value))
return out
[docs]def matmulNT(lhs, rhs):
"""
Matrix multiplication after transposing the second matrix.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `lhs` and `transpose(rhs)`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(c_pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.NONE.value, MATPROP.TRANS.value))
return out
[docs]def matmulTT(lhs, rhs):
"""
Matrix multiplication after transposing both inputs.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
Returns
-------
out : af.Array
Output of the matrix multiplication on `transpose(lhs)` and `transpose(rhs)`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
out = Array()
safe_call(backend.get().af_matmul(c_pointer(out.arr), lhs.arr, rhs.arr,
MATPROP.TRANS.value, MATPROP.TRANS.value))
return out
[docs]def dot(lhs, rhs, lhs_opts=MATPROP.NONE, rhs_opts=MATPROP.NONE, return_scalar = False):
"""
Dot product of two input vectors.
Parameters
----------
lhs : af.Array
A 1 dimensional, real or complex arrayfire array.
rhs : af.Array
A 1 dimensional, real or complex arrayfire array.
lhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `lhs`.
- No other options are currently supported.
rhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `rhs`.
- No other options are currently supported.
return_scalar: optional: bool. default: False.
- When set to true, the input arrays are flattened and the output is a scalar
Returns
-------
out : af.Array or scalar
Output of dot product of `lhs` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
if return_scalar:
real = c_double_t(0)
imag = c_double_t(0)
safe_call(backend.get().af_dot_all(c_pointer(real), c_pointer(imag),
lhs.arr, rhs.arr, lhs_opts.value, rhs_opts.value))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
else:
out = Array()
safe_call(backend.get().af_dot(c_pointer(out.arr), lhs.arr, rhs.arr,
lhs_opts.value, rhs_opts.value))
return out
[docs]def gemm(lhs, rhs, alpha=1.0, beta=0.0, lhs_opts=MATPROP.NONE, rhs_opts=MATPROP.NONE, C=None):
"""
BLAS general matrix multiply (GEMM) of two af_array objects.
This provides a general interface to the BLAS level 3 general matrix multiply (GEMM), which is generally defined as:
C = alpha * opA(A) opB(B) + beta * C
where alpha and beta are both scalars; A and B are the matrix multiply operands;
and opA and opB are noop (if AF_MAT_NONE) or transpose (if AF_MAT_TRANS) operations
on A or B before the actual GEMM operation.
Batched GEMM is supported if at least either A or B have more than two dimensions
(see af::matmul for more details on broadcasting).
However, only one alpha and one beta can be used for all of the batched matrix operands.
Parameters
----------
lhs : af.Array
A 2 dimensional, real or complex arrayfire array.
rhs : af.Array
A 2 dimensional, real or complex arrayfire array.
alpha : scalar
beta : scalar
lhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `lhs`.
- af.MATPROP.TRANS - If `lhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `lhs` has to be hermitian transposed before multiplying.
rhs_opts: optional: af.MATPROP. default: af.MATPROP.NONE.
Can be one of
- af.MATPROP.NONE - If no op should be done on `rhs`.
- af.MATPROP.TRANS - If `rhs` has to be transposed before multiplying.
- af.MATPROP.CTRANS - If `rhs` has to be hermitian transposed before multiplying.
Returns
-------
out : af.Array
Output of the matrix multiplication on `lhs` and `rhs`.
Note
-----
- The data types of `lhs` and `rhs` should be the same.
- Batches are not supported.
"""
if C is None:
out = Array()
else:
out = C
ltype = lhs.dtype()
if ltype == Dtype.f32:
aptr = c_cast(c_pointer(c_float_t(alpha)),c_void_ptr_t)
bptr = c_cast(c_pointer(c_float_t(beta)), c_void_ptr_t)
elif ltype == Dtype.c32:
if isinstance(alpha, af_cfloat_t):
aptr = c_cast(c_pointer(alpha), c_void_ptr_t)
elif isinstance(alpha, tuple):
aptr = c_cast(c_pointer(af_cfloat_t(alpha[0], alpha[1])), c_void_ptr_t)
else:
aptr = c_cast(c_pointer(af_cfloat_t(alpha)), c_void_ptr_t)
if isinstance(beta, af_cfloat_t):
bptr = c_cast(c_pointer(beta), c_void_ptr_t)
elif isinstance(beta, tuple):
bptr = c_cast(c_pointer(af_cfloat_t(beta[0], beta[1])), c_void_ptr_t)
else:
bptr = c_cast(c_pointer(af_cfloat_t(beta)), c_void_ptr_t)
elif ltype == Dtype.f64:
aptr = c_cast(c_pointer(c_double_t(alpha)),c_void_ptr_t)
bptr = c_cast(c_pointer(c_double_t(beta)), c_void_ptr_t)
elif ltype == Dtype.c64:
if isinstance(alpha, af_cdouble_t):
aptr = c_cast(c_pointer(alpha), c_void_ptr_t)
elif isinstance(alpha, tuple):
aptr = c_cast(c_pointer(af_cdouble_t(alpha[0], alpha[1])), c_void_ptr_t)
else:
aptr = c_cast(c_pointer(af_cdouble_t(alpha)), c_void_ptr_t)
if isinstance(beta, af_cdouble_t):
bptr = c_cast(c_pointer(beta), c_void_ptr_t)
elif isinstance(beta, tuple):
bptr = c_cast(c_pointer(af_cdouble_t(beta[0], beta[1])), c_void_ptr_t)
else:
bptr = c_cast(c_pointer(af_cdouble_t(beta)), c_void_ptr_t)
elif ltype == Dtype.f16:
raise TypeError("fp16 currently unsupported gemm() input type")
else:
raise TypeError("unsupported input type")
safe_call(backend.get().af_gemm(c_pointer(out.arr),
lhs_opts.value, rhs_opts.value,
aptr, lhs.arr, rhs.arr, bptr))
return out
