import cuv_python as cp C = cp.dev_tensor_float_cm([2048,2048]) # column major tensor A = cp.dev_tensor_float_cm([2048,2048]) B = cp.dev_tensor_float_cm([2048,2048]) cp.fill(C,0) # fill with some defined values, not really necessary here cp.sequence(A) cp.sequence(B) cp.apply_binary_functor(B,A,cp.binary_functor.MULT) # elementwise multiplication B *= A # operators also work (elementwise) cp.prod(C,A,B,'n','t') # matrix multiplication C = cp.prod(A, B.T) # numpy-like form, allocates new matrix for result
def testTensorToNpyCmTrans(self):
""" convert a tensor to a numpy matrix (column major, transposed) """
t = cp.dev_tensor_float(self.shape)
cp.sequence(t)
n = t.np
self.cmp3d(t, n)
def testTensorToNpy(self):
""" convert a tensor to a numpy matrix """
t = cp.dev_tensor_float(self.shape)
cp.sequence(t)
n = t.np
self.cmp3d(t, n)
import cuv_python as cp
C = cp.dev_tensor_float_cm([2048, 2048]) # column major tensor
A = cp.dev_tensor_float_cm([2048, 2048])
B = cp.dev_tensor_float_cm([2048, 2048])
cp.fill(C, 0) # fill with some defined values, not really necessary here
cp.sequence(A)
cp.sequence(B)
cp.apply_binary_functor(B, A,
cp.binary_functor.MULT) # elementwise multiplication
B *= A # operators also work (elementwise)
cp.prod(C, A, B, 'n', 't') # matrix multiplication
C = cp.prod(A, B.T) # numpy-like form, allocates new matrix for result
def testTensorToNpyCmTrans(self):
""" convert a tensor to a numpy matrix (column major, transposed) """
t = cp.dev_tensor_float(self.shape)
cp.sequence(t)
n = t.np
self.cmp3d(t,n)
def testTensorToNpy(self):
""" convert a tensor to a numpy matrix """
t = cp.dev_tensor_float(self.shape)
cp.sequence(t)
n = t.np
self.cmp3d(t,n)
