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. """ group._transfers = {} 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 transfers = group._transfers vectors = group._vectors offsets = _global2local_offsets(group._get_var_offsets()) for vec_name in group._lin_rel_vec_name_list: 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 shape_in = meta_in['shape'] shape_out = meta_out['shape'] global_size_out = meta_out['global_size'] src_indices = meta_in['src_indices'] if src_indices is None: pass elif src_indices.ndim == 1: src_indices = convert_neg(src_indices, global_size_out) else: if len(shape_out ) == 1 or shape_in == src_indices.shape: src_indices = src_indices.flatten() src_indices = convert_neg(src_indices, global_size_out) else: # TODO: this duplicates code found # in System._setup_scaling. entries = [list(range(x)) for x in shape_in] cols = np.vstack(src_indices[i] for i in product(*entries)) dimidxs = [ convert_neg(cols[:, i], shape_out[i]) for i in range(cols.shape[1]) ] src_indices = np.ravel_multi_index( dimidxs, shape_out) # 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): transfers[vec_name]['fwd', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], fwd_xfer_in[isub], fwd_xfer_out[isub], group.comm) if rev: transfers[vec_name]['rev', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], rev_xfer_in[isub], rev_xfer_out[isub], group.comm) transfers['nonlinear'] = transfers['linear']
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. """ group._transfers = {} 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 transfers = group._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): transfers[vec_name]['fwd', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], fwd_xfer_in[isub], fwd_xfer_out[isub], group.comm) if rev: transfers[vec_name]['rev', isub] = DefaultTransfer( vectors['input'][vec_name], vectors['output'][vec_name], rev_xfer_in[isub], rev_xfer_out[isub], group.comm) if group._use_derivatives: transfers['nonlinear'] = transfers['linear']
def _setup_transfers(group): """ Compute all transfers that are owned by our parent group. Parameters ---------- group : <Group> Parent group. """ iproc = group.comm.rank rev = group._mode == 'rev' or group._mode == 'auto' for subsys in group._subgroups_myproc: subsys._setup_transfers() abs2meta = group._var_abs2meta allprocs_abs2meta_out = group._var_allprocs_abs2meta['output'] 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 allsubs = group._subsystems_allprocs mypathlen = len(group.pathname + '.' if group.pathname else '') 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(allsubs) xfer_in = [] xfer_out = [] fwd_xfer_in = defaultdict(list) fwd_xfer_out = defaultdict(list) if rev: rev_xfer_in = defaultdict(list) rev_xfer_out = defaultdict(list) 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 connections owned by this group for abs_in, abs_out in group._conn_abs_in2out.items(): 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['input']: indices = None # Get meta meta_in = abs2meta['input'][abs_in] meta_out = allprocs_abs2meta_out[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 not None: if src_indices.ndim == 1: src_indices = convert_neg(src_indices, 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) if indices is not None: input_inds = input_inds.reshape(indices.shape) # Now the indices are ready - input_inds, output_inds sub_in = abs_in[mypathlen:].split('.', 1)[0] fwd_xfer_in[sub_in].append(input_inds) fwd_xfer_out[sub_in].append(output_inds) if rev and abs_out in abs2meta['output']: sub_out = abs_out[mypathlen:].split('.', 1)[0] rev_xfer_in[sub_out].append(input_inds) rev_xfer_out[sub_out].append(output_inds) tot_size = 0 for sname, inds in fwd_xfer_in.items(): fwd_xfer_in[sname] = arr = _merge(inds) fwd_xfer_out[sname] = _merge(fwd_xfer_out[sname]) tot_size += arr.size if rev: for sname, inds in rev_xfer_in.items(): rev_xfer_in[sname] = _merge(inds) rev_xfer_out[sname] = _merge(rev_xfer_out[sname]) transfers[vec_name] = {} if tot_size > 0: try: xfer_in = np.concatenate(list(fwd_xfer_in.values())) xfer_out = np.concatenate(list(fwd_xfer_out.values())) except ValueError: xfer_in = xfer_out = np.zeros(0, dtype=INT_DTYPE) out_vec = vectors['output'][vec_name] xfer_all = DefaultTransfer(vectors['input'][vec_name], out_vec, xfer_in, xfer_out, group.comm) else: xfer_all = None transfers[vec_name]['fwd'] = xfwd = {} xfwd[None] = xfer_all if rev: transfers[vec_name]['rev'] = xrev = {} xrev[None] = xfer_all for sname, inds in fwd_xfer_in.items(): if inds.size > 0: xfwd[sname] = DefaultTransfer(vectors['input'][vec_name], vectors['output'][vec_name], inds, fwd_xfer_out[sname], group.comm) else: xfwd[sname] = None if rev: for sname, inds in rev_xfer_out.items(): if inds.size > 0: xrev[sname] = DefaultTransfer(vectors['input'][vec_name], vectors['output'][vec_name], rev_xfer_in[sname], inds, group.comm) else: xrev[sname] = None if group._use_derivatives: transfers['nonlinear'] = transfers['linear']
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' if recurse: for subsys in group._subgroups_myproc: subsys._setup_transfers(recurse) 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 mypathlen = len(group.pathname + '.' if group.pathname else '') sub_inds = group._subsystems_inds 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 s in group._subsystems_allprocs] fwd_xfer_out = [[] for s in group._subsystems_allprocs] if rev: rev_xfer_in = [[] for s in group._subsystems_allprocs] rev_xfer_out = [[] for s in group._subsystems_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 connections owned by this group for abs_in, abs_out in group._conn_abs_in2out.items(): 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: # 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 sub_in = abs_in[mypathlen:].split('.', 1)[0] isub = sub_inds[sub_in] fwd_xfer_in[isub].append(input_inds) fwd_xfer_out[isub].append(output_inds) if rev and abs_out in abs2meta: sub_out = abs_out[mypathlen:].split('.', 1)[0] isub = sub_inds[sub_out] rev_xfer_in[isub].append(input_inds) rev_xfer_out[isub].append(output_inds) tot_size = 0 for isub in range(nsub_allprocs): fwd_xfer_in[isub] = _merge(fwd_xfer_in[isub]) fwd_xfer_out[isub] = _merge(fwd_xfer_out[isub]) tot_size += fwd_xfer_in[isub].size if rev: rev_xfer_in[isub] = _merge(rev_xfer_in[isub]) rev_xfer_out[isub] = _merge(rev_xfer_out[isub]) transfers[vec_name] = {} if tot_size > 0: xfer_in = np.concatenate(fwd_xfer_in) xfer_out = np.concatenate(fwd_xfer_out) out_vec = vectors['output'][vec_name] xfer_all = DefaultTransfer(vectors['input'][vec_name], out_vec, xfer_in, xfer_out, group.comm) else: xfer_all = None 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']
def _setup_transfers(group): """ Compute all transfers that are owned by our parent group. Parameters ---------- group : <Group> Parent group. """ iproc = group.comm.rank rev = group._mode == 'rev' or group._mode == 'auto' for subsys in group._subgroups_myproc: subsys._setup_transfers() abs2meta = group._var_abs2meta group._transfers = transfers = {} vectors = group._vectors offsets = _global2local_offsets(group._get_var_offsets()) mypathlen = len(group.pathname + '.' if group.pathname else '') # Initialize empty lists for the transfer indices xfer_in = [] xfer_out = [] fwd_xfer_in = defaultdict(list) fwd_xfer_out = defaultdict(list) if rev: rev_xfer_in = defaultdict(list) rev_xfer_out = defaultdict(list) allprocs_abs2idx = group._var_allprocs_abs2idx sizes_in = group._var_sizes['input'] offsets_in = offsets['input'] if sizes_in.size > 0: sizes_in = sizes_in[iproc] offsets_in = offsets_in[iproc] offsets_out = offsets['output'] if offsets_out.size > 0: offsets_out = offsets_out[iproc] # Loop through all connections owned by this group for abs_in, abs_out in group._conn_abs_in2out.items(): # This weeds out discrete vars (all vars are local if using this Transfer) if abs_in in abs2meta['input']: indices = None # Get meta meta_in = abs2meta['input'][abs_in] 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 not None: src_indices = src_indices.shaped_array() # 1. Compute the output indices offset = offsets_out[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[idx_in], offsets_in[idx_in] + sizes_in[idx_in], dtype=INT_DTYPE) if indices is not None: input_inds = input_inds.reshape(indices.shape) # Now the indices are ready - input_inds, output_inds sub_in = abs_in[mypathlen:].split('.', 1)[0] fwd_xfer_in[sub_in].append(input_inds) fwd_xfer_out[sub_in].append(output_inds) if rev and abs_out in abs2meta['output']: sub_out = abs_out[mypathlen:].split('.', 1)[0] rev_xfer_in[sub_out].append(input_inds) rev_xfer_out[sub_out].append(output_inds) tot_size = 0 for sname, inds in fwd_xfer_in.items(): fwd_xfer_in[sname] = arr = _merge(inds) fwd_xfer_out[sname] = _merge(fwd_xfer_out[sname]) tot_size += arr.size if rev: for sname, inds in rev_xfer_in.items(): rev_xfer_in[sname] = _merge(inds) rev_xfer_out[sname] = _merge(rev_xfer_out[sname]) if tot_size > 0: try: xfer_in = np.concatenate(list(fwd_xfer_in.values())) xfer_out = np.concatenate(list(fwd_xfer_out.values())) except ValueError: xfer_in = xfer_out = np.zeros(0, dtype=INT_DTYPE) xfer_all = DefaultTransfer(vectors['input']['nonlinear'], vectors['output']['nonlinear'], xfer_in, xfer_out, group.comm) else: xfer_all = None transfers['fwd'] = xfwd = {} xfwd[None] = xfer_all if rev: transfers['rev'] = xrev = {} xrev[None] = xfer_all for sname, inds in fwd_xfer_in.items(): if inds.size > 0: xfwd[sname] = DefaultTransfer(vectors['input']['nonlinear'], vectors['output']['nonlinear'], inds, fwd_xfer_out[sname], group.comm) else: xfwd[sname] = None if rev: for sname, inds in rev_xfer_out.items(): if inds.size > 0: xrev[sname] = DefaultTransfer(vectors['input']['nonlinear'], vectors['output']['nonlinear'], rev_xfer_in[sname], inds, group.comm) else: xrev[sname] = None