def test_create_ops_arg_function(self, read): u = OpsAccessible('u', dtype=np.float32, read_only=read) dat = OpsDat('u_dat') stencil = OpsStencil('stencil') info = AccessibleInfo(u, None, None) ops_arg = create_ops_arg(u, {'u': info}, {'u': dat}, {u: stencil}) assert ops_arg.ops_type == namespace['ops_arg_dat'] assert ops_arg.ops_name == OpsDat('u_dat') assert ops_arg.elements_per_point == 1 assert ops_arg.dtype == Literal('"%s"' % dtype_to_cstr(u.dtype)) assert ops_arg.rw_flag == \ namespace['ops_read'] if read else namespace['ops_write']
def test_create_ops_arg_function(self, read): u = OpsAccessible('u', np.float32, read) dat = OpsDat('u_dat') stencil = OpsStencil('stencil') info = AccessibleInfo(u, None, None) res = create_ops_arg(u, {'u': info}, {'u': dat}, {u: stencil}) assert type(res) == namespace['ops_arg_dat'] assert res.args == (dat, 1, stencil, Literal('"%s"' % dtype_to_cstr(u.dtype)), namespace['ops_read'] if read else namespace['ops_write'])
def create_ops_dat(f, name_to_ops_dat, block): ndim = f.ndim - (1 if f.is_TimeFunction else 0) dim = Array(name=namespace['ops_dat_dim'](f.name), dimensions=(DefaultDimension(name='dim', default_value=ndim), ), dtype=np.int32, scope='stack') base = Array(name=namespace['ops_dat_base'](f.name), dimensions=(DefaultDimension(name='base', default_value=ndim), ), dtype=np.int32, scope='stack') d_p = Array(name=namespace['ops_dat_d_p'](f.name), dimensions=(DefaultDimension(name='d_p', default_value=ndim), ), dtype=np.int32, scope='stack') d_m = Array(name=namespace['ops_dat_d_m'](f.name), dimensions=(DefaultDimension(name='d_m', default_value=ndim), ), dtype=np.int32, scope='stack') base_val = [Zero() for i in range(ndim)] # If f is a TimeFunction we need to create a ops_dat for each time stepping # variable (eg: t1, t2) if f.is_TimeFunction: time_pos = f._time_position time_index = f.indices[time_pos] time_dims = f.shape[time_pos] dim_val = f.shape[:time_pos] + f.shape[time_pos + 1:] d_p_val = f._size_nodomain.left[time_pos + 1:] d_m_val = [-i for i in f._size_nodomain.right[time_pos + 1:]] ops_dat_array = Array(name=namespace['ops_dat_name'](f.name), dimensions=(DefaultDimension( name='dat', default_value=time_dims), ), dtype=namespace['ops_dat_type'], scope='stack') dat_decls = [] for i in range(time_dims): name = '%s%s%s' % (f.name, time_index, i) dat_decls.append(namespace['ops_decl_dat'](block, 1, Symbol(dim.name), Symbol(base.name), Symbol(d_m.name), Symbol(d_p.name), Byref(f.indexify([i])), Literal('"%s"' % f._C_typedata), Literal('"%s"' % name))) ops_decl_dat = Expression( ClusterizedEq(Eq(ops_dat_array, ListInitializer(dat_decls)))) # Inserting the ops_dat array in case of TimeFunction. name_to_ops_dat[f.name] = ops_dat_array else: ops_dat = OpsDat("%s_dat" % f.name) name_to_ops_dat[f.name] = ops_dat dim_val = f.shape d_p_val = f._size_nodomain.left d_m_val = [-i for i in f._size_nodomain.right] ops_decl_dat = Expression( ClusterizedEq( Eq( ops_dat, namespace['ops_decl_dat'](block, 1, Symbol(dim.name), Symbol(base.name), Symbol(d_m.name), Symbol(d_p.name), Byref(f.indexify([0])), Literal('"%s"' % f._C_typedata), Literal('"%s"' % f.name))))) dim_val = Expression(ClusterizedEq(Eq(dim, ListInitializer(dim_val)))) base_val = Expression(ClusterizedEq(Eq(base, ListInitializer(base_val)))) d_p_val = Expression(ClusterizedEq(Eq(d_p, ListInitializer(d_p_val)))) d_m_val = Expression(ClusterizedEq(Eq(d_m, ListInitializer(d_m_val)))) return OpsDatDecl(dim_val=dim_val, base_val=base_val, d_p_val=d_p_val, d_m_val=d_m_val, ops_decl_dat=ops_decl_dat)
def create_ops_dat(f, name_to_ops_dat, block): ndim = f.ndim - (1 if f.is_TimeFunction else 0) dim = Array(name=namespace['ops_dat_dim'](f.name), dimensions=(DefaultDimension(name='dim', default_value=ndim), ), dtype=np.int32, scope='stack') base = Array(name=namespace['ops_dat_base'](f.name), dimensions=(DefaultDimension(name='base', default_value=ndim), ), dtype=np.int32, scope='stack') d_p = Array(name=namespace['ops_dat_d_p'](f.name), dimensions=(DefaultDimension(name='d_p', default_value=ndim), ), dtype=np.int32, scope='stack') d_m = Array(name=namespace['ops_dat_d_m'](f.name), dimensions=(DefaultDimension(name='d_m', default_value=ndim), ), dtype=np.int32, scope='stack') res = [] base_val = [Zero() for i in range(ndim)] # If f is a TimeFunction we need to create a ops_dat for each time stepping # variable (eg: t1, t2) if f.is_TimeFunction: time_pos = f._time_position time_index = f.indices[time_pos] time_dims = f.shape[time_pos] dim_shape = sympify(f.shape[:time_pos] + f.shape[time_pos + 1:]) padding = f.padding[:time_pos] + f.padding[time_pos + 1:] halo = f.halo[:time_pos] + f.halo[time_pos + 1:] d_p_val = tuple(sympify([p[0] + h[0] for p, h in zip(padding, halo)])) d_m_val = tuple( sympify([-(p[1] + h[1]) for p, h in zip(padding, halo)])) ops_dat_array = Array(name=namespace['ops_dat_name'](f.name), dimensions=(DefaultDimension( name='dat', default_value=time_dims), ), dtype='ops_dat', scope='stack') dat_decls = [] for i in range(time_dims): name = '%s%s%s' % (f.name, time_index, i) name_to_ops_dat[name] = ops_dat_array.indexify( [Symbol('%s%s' % (time_index, i))]) dat_decls.append(namespace['ops_decl_dat'](block, 1, Symbol(dim.name), Symbol(base.name), Symbol(d_m.name), Symbol(d_p.name), Byref(f.indexify([i])), Literal('"%s"' % f._C_typedata), Literal('"%s"' % name))) ops_decl_dat = Expression( ClusterizedEq(Eq(ops_dat_array, ListInitializer(dat_decls)))) else: ops_dat = OpsDat("%s_dat" % f.name) name_to_ops_dat[f.name] = ops_dat d_p_val = tuple( sympify([p[0] + h[0] for p, h in zip(f.padding, f.halo)])) d_m_val = tuple( sympify([-(p[1] + h[1]) for p, h in zip(f.padding, f.halo)])) dim_shape = sympify(f.shape) ops_decl_dat = Expression( ClusterizedEq( Eq( ops_dat, namespace['ops_decl_dat'](block, 1, Symbol(dim.name), Symbol(base.name), Symbol(d_m.name), Symbol(d_p.name), Byref(f.indexify([0])), Literal('"%s"' % f._C_typedata), Literal('"%s"' % f.name))))) res.append(Expression(ClusterizedEq(Eq(dim, ListInitializer(dim_shape))))) res.append(Expression(ClusterizedEq(Eq(base, ListInitializer(base_val))))) res.append(Expression(ClusterizedEq(Eq(d_p, ListInitializer(d_p_val))))) res.append(Expression(ClusterizedEq(Eq(d_m, ListInitializer(d_m_val))))) res.append(ops_decl_dat) return res