def local_structured_add_s_v(node):
if node.op == sparse.structured_add_s_v:
x, y = node.inputs
x_is_sparse_variable = _is_sparse_variable(x)
#y_is_sparse_variable = _is_sparse_variable(y)
if x_is_sparse_variable:
svar = x
dvar = y
else:
svar = y
dvar = x
if dvar.type.ndim != 1:
return False
elif svar.type.format == 'csr':
CSx = sparse.CSR
structured_add_s_v_csx = sparse.structured_add_s_v_csr
else:
return False
s_val, s_ind, s_ptr, s_shape = sparse.csm_properties(svar)
c_data = structured_add_s_v_csx(s_val, s_ind, s_ptr, dvar)
return [CSx(c_data, s_ind, s_ptr, s_shape)]
return False
def local_structured_add_s_v(node):
if node.op == structured_add_s_v:
x, y = node.inputs
x_is_sparse_variable = _is_sparse_variable(x)
#y_is_sparse_variable = _is_sparse_variable(y)
if x_is_sparse_variable:
svar = x
dvar = y
else:
svar = y
dvar = x
if dvar.type.ndim != 1:
return False
elif svar.type.format == 'csr':
CSx = CSR
structured_add_s_v_csx = structured_add_s_v_csr
else:
return False
s_val, s_ind, s_ptr, s_shape = csm_properties(svar)
c_data = structured_add_s_v_csx(s_val, s_ind, s_ptr, dvar)
return [CSx(c_data, s_ind, s_ptr, s_shape)]
return False
def wrapper(*args):
x = as_sparse_variable(args[0])
xs = [scalar.as_scalar(arg) for arg in args[1:]]
data, ind, ptr, shape = csm_properties(x)
data = tensor_op(data, *xs)
return CSM(x.format)(data, ind, ptr, shape)
def wrapper(*args):
x = as_sparse_variable(args[0])
xs = [scalar.as_scalar(arg) for arg in args[1:]]
data, ind, ptr, shape = csm_properties(x)
data = tensor_op(data, *xs)
return CSM(x.format)(data, ind, ptr, shape)
def local_sampling_dot_csr(node):
if node.op == sparse.sampling_dot:
x, y, p = node.inputs
if p.type.format == 'csr':
p_data, p_ind, p_ptr, p_shape = sparse.csm_properties(p)
z_data, z_ind, z_ptr = sparse.sampling_dot_csr(x, y, p_data,
p_ind, p_ptr, p_shape[1])
return [sparse.CSR(z_data, z_ind, z_ptr, p_shape)]
return False
def structured_exp(x):
"""
Element-wise exponential function to the non-zero elements.
"""
x = as_sparse_variable(x)
x_data, x_ind, x_ptr, x_shape = csm_properties(x)
x_data = tensor.exp(x_data)
return CSR(x_data, x_ind, x_ptr, x_shape)
def structured_sigmoid(x):
"""
Element-wise sigmoid function only to the non-zero elements.
"""
x = as_sparse_variable(x)
x_data, x_ind, x_ptr, x_shape = csm_properties(x)
x_data = tensor.nnet.sigmoid(x_data)
return CSR(x_data, x_ind, x_ptr, x_shape)
def local_sampling_dot_csr(node):
if node.op == sampling_dot:
x, y, p = node.inputs
if p.type.format == 'csr':
p_data, p_ind, p_ptr, p_shape = csm_properties(p)
z_data, z_ind, z_ptr = sampling_dot_csr(x, y, p_data, p_ind, p_ptr,
p_shape[1])
return [CSR(z_data, z_ind, z_ptr, p_shape)]
return False
def structured_minimum(x, y):
"""
Element-wise minimum function only to non-zero elements.
"""
x = as_sparse_variable(x)
y = tensor.as_tensor_variable(y)
x_data, x_ind, x_ptr, x_shape = csm_properties(x)
x_data = tensor.minimum(x_data, y)
return CSR(x_data, x_ind, x_ptr, x_shape)
