Source code for openmdao.vectors.default_transfer

"""Define the default Transfer class."""

from __future__ import division

from itertools import product, chain
from collections import defaultdict
from six import iteritems, itervalues

import numpy as np

from openmdao.vectors.vector import INT_DTYPE
from openmdao.vectors.transfer import Transfer
from openmdao.utils.array_utils import convert_neg, _global2local_offsets, _flatten_src_indices

_empty_idx_array = np.array([], dtype=INT_DTYPE)

[docs]class DefaultTransfer(Transfer): """ Default NumPy transfer. """ @staticmethod def _setup_transfers(group, recurse=True): """ Compute all transfers that are owned by our parent group. Parameters ---------- group : <Group> Parent group. recurse : bool Whether to call this method in subsystems. """ iproc = group.comm.rank rev = group._mode == 'rev' or group._mode == 'auto' def merge(indices_list): if len(indices_list) > 0: return np.concatenate(indices_list) else: return _empty_idx_array if recurse: for subsys in group._subgroups_myproc: subsys._setup_transfers(recurse) # Pre-compute map from abs_names to the index of the containing subsystem abs2isub = {} for subsys, isub in zip(group._subsystems_myproc, group._subsystems_myproc_inds): for type_ in ['input', 'output']: for abs_name in subsys._var_allprocs_abs_names[type_]: abs2isub[abs_name] = isub abs2meta = group._var_abs2meta allprocs_abs2meta = group._var_allprocs_abs2meta group._transfers = transfers = {} vectors = group._vectors offsets = _global2local_offsets(group._get_var_offsets()) vec_names = group._lin_rel_vec_name_list if group._use_derivatives else group._vec_names for vec_name in vec_names: relvars, _ = group._relevant[vec_name]['@all'] relvars_in = relvars['input'] relvars_out = relvars['output'] # Initialize empty lists for the transfer indices nsub_allprocs = len(group._subsystems_allprocs) xfer_in = [] xfer_out = [] fwd_xfer_in = [[] for i in range(nsub_allprocs)] fwd_xfer_out = [[] for i in range(nsub_allprocs)] if rev: rev_xfer_in = [[] for i in range(nsub_allprocs)] rev_xfer_out = [[] for i in range(nsub_allprocs)] allprocs_abs2idx = group._var_allprocs_abs2idx[vec_name] sizes_in = group._var_sizes[vec_name]['input'] sizes_out = group._var_sizes[vec_name]['output'] offsets_in = offsets[vec_name]['input'] offsets_out = offsets[vec_name]['output'] # Loop through all explicit / implicit connections owned by this system for abs_in, abs_out in iteritems(group._conn_abs_in2out): if abs_out not in relvars_out or abs_in not in relvars_in: continue # Only continue if the input exists on this processor if abs_in in abs2meta and abs_in in relvars['input']: # Get meta meta_in = abs2meta[abs_in] meta_out = allprocs_abs2meta[abs_out] idx_in = allprocs_abs2idx[abs_in] idx_out = allprocs_abs2idx[abs_out] # Read in and process src_indices src_indices = meta_in['src_indices'] if src_indices is None: pass elif src_indices.ndim == 1: src_indices = convert_neg(src_indices, meta_out['global_size']) else: src_indices = _flatten_src_indices(src_indices, meta_in['shape'], meta_out['global_shape'], meta_out['global_size']) # 1. Compute the output indices offset = offsets_out[iproc, idx_out] if src_indices is None: output_inds = np.arange(offset, offset + meta_in['size'], dtype=INT_DTYPE) else: output_inds = src_indices + offset # 2. Compute the input indices input_inds = np.arange(offsets_in[iproc, idx_in], offsets_in[iproc, idx_in] + sizes_in[iproc, idx_in], dtype=INT_DTYPE) # Now the indices are ready - input_inds, output_inds xfer_in.append(input_inds) xfer_out.append(output_inds) isub = abs2isub[abs_in] fwd_xfer_in[isub].append(input_inds) fwd_xfer_out[isub].append(output_inds) if rev and abs_out in abs2isub: isub = abs2isub[abs_out] rev_xfer_in[isub].append(input_inds) rev_xfer_out[isub].append(output_inds) xfer_in = merge(xfer_in) xfer_out = merge(xfer_out) for isub in range(nsub_allprocs): fwd_xfer_in[isub] = merge(fwd_xfer_in[isub]) fwd_xfer_out[isub] = merge(fwd_xfer_out[isub]) if rev: rev_xfer_in[isub] = merge(rev_xfer_in[isub]) rev_xfer_out[isub] = merge(rev_xfer_out[isub]) out_vec = vectors['output'][vec_name] transfers[vec_name] = {} xfer_all = DefaultTransfer(vectors['input'][vec_name], out_vec, xfer_in, xfer_out, group.comm) transfers[vec_name]['fwd', None] = xfer_all if rev: transfers[vec_name]['rev', None] = xfer_all for isub in range(nsub_allprocs): if fwd_xfer_in[isub].size > 0: transfers[vec_name]['fwd', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], fwd_xfer_in[isub], fwd_xfer_out[isub], group.comm) else: transfers[vec_name]['fwd', isub] = None if rev: if rev_xfer_out[isub].size > 0: transfers[vec_name]['rev', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], rev_xfer_in[isub], rev_xfer_out[isub], group.comm) else: transfers[vec_name]['rev', isub] = None if group._use_derivatives: transfers['nonlinear'] = transfers['linear'] @staticmethod def _setup_discrete_transfers(group, recurse=True): """ Compute all transfers that are owned by our parent group. Parameters ---------- group : <Group> Parent group. recurse : bool Whether to call this method in subsystems. """ group._discrete_transfers = transfers = defaultdict(list) offset = len(group.pathname) + 1 if group.pathname else 0 for tgt, src in iteritems(group._conn_discrete_in2out): src_sys, src_var = src[offset:].split('.', 1) tgt_sys, tgt_var = tgt[offset:].split('.', 1) xfer = (src_sys, src_var, tgt_sys, tgt_var) transfers[tgt_sys].append(xfer) transfers[None].append(xfer) def _transfer(self, in_vec, out_vec, mode='fwd'): """ Perform transfer. Parameters ---------- in_vec : <Vector> pointer to the input vector. out_vec : <Vector> pointer to the output vector. mode : str 'fwd' or 'rev'. """ if mode == 'fwd': # this works whether the vecs have multi columns or not due to broadcasting in_vec._data[self._in_inds] = out_vec._data[self._out_inds] else: # rev if out_vec._ncol == 1: out_vec._data[:] += np.bincount(self._out_inds, in_vec._data[self._in_inds], minlength=out_vec._data.size) else: # matrix-matrix (bincount only works with 1d arrays), self._out_inds, in_vec._data[self._in_inds])