remove old manual operator

hysop/operator/inter_task_param_comm.py

-# Copyright (c) HySoP 2011-2024
-#
-# This file is part of HySoP software.
-# See "https://particle_methods.gricad-pages.univ-grenoble-alpes.fr/hysop-doc/"
-# for further info.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-Inter-task parameters exchange.
-"""
-import numpy as np
-from hysop.core.graph.computational_node_frontend import ComputationalGraphNodeFrontend
-from hysop.constants import Implementation, HYSOP_DIM
-from hysop.backend.host.host_operator import HostOperator
-from hysop.tools.htypes import check_instance
-from hysop.parameters.scalar_parameter import ScalarParameter, TensorParameter
-from hysop.tools.decorators import debug
-from hysop.core.graph.graph import op_apply
-from hysop import MPIParams, MPI
-
-
-class PythonInterTaskParamComm(HostOperator):
-    def __new__(cls, parameter, source_task, dest_task, domain, **kwds):
-        return super().__new__(cls, **kwds)
-
-    @debug
-    def __init__(self, parameter, source_task, dest_task, domain, **kwds):
-        """
-        Communicate parameter through tasks
-
-        parameter
-        ----------
-        parameter: tuple of ScalarParameter or TensorParameter
-            parameters to communicate
-        source_task: integer
-            task id to be used as source for communication
-        parent_task: integer
-            task id to be used as context for inter-communication
-
-        """
-        check_instance(parameter, tuple, values=(ScalarParameter, TensorParameter))
-        check_instance(source_task, (int, HYSOP_DIM))
-        check_instance(dest_task, (int, HYSOP_DIM))
-        input_fields, output_fields = {}, {}
-        super().__init__(
-            input_params={_.name: _ for _ in parameter},
-            output_params={_.name: _ for _ in parameter},
-            input_fields=input_fields,
-            output_fields=output_fields,
-            **kwds,
-        )
-        self.domain = domain
-        self.source_task = source_task
-        self.dest_task = dest_task
-        self.task_is_source = domain.is_on_task(source_task)
-        self.task_is_dest = domain.is_on_task(dest_task)
-        self.inter_comm = domain.task_intercomm(
-            dest_task if self.task_is_source else source_task
-        )
-        if self.inter_comm.is_inter:
-            # Disjoint tasks with real inter-communicator
-            self._the_apply = self._apply_intercomm
-        elif self.inter_comm.is_intra:
-            # Overlapping tasks using an intra-communicator fron union of tasks procs
-            self._the_apply = self._apply_intracomm
-
-        self._all_params_by_type = {}
-        for p in sorted(self.parameters, key=lambda _: _.name):
-            if not p.dtype in self._all_params_by_type:
-                self._all_params_by_type[p.dtype] = []
-            self._all_params_by_type[p.dtype].append(p)
-        self._send_temp_by_type = {
-            t: np.zeros((len(self._all_params_by_type[t]),), dtype=t)
-            for t in self._all_params_by_type.keys()
-        }
-        self._recv_temp_by_type = {
-            t: np.zeros((len(self._all_params_by_type[t]),), dtype=t)
-            for t in self._all_params_by_type.keys()
-        }
-
-    @op_apply
-    def apply(self, **kwds):
-        self._the_apply(**kwds)
-
-    def _apply_intercomm(self, **kwds):
-        """Disjoint tasks so inter-comm bcast is needed."""
-        for t in self._all_params_by_type.keys():
-            if self.task_is_source:
-                self._send_temp_by_type[t][...] = [
-                    p() for p in self._all_params_by_type[t]
-                ]
-                self.inter_comm.bcast(
-                    self._send_temp_by_type[t],
-                    root=MPI.ROOT if self.domain.task_rank() == 0 else MPI.PROC_NULL,
-                )
-            if self.task_is_dest:
-                self._recv_temp_by_type[t] = self.inter_comm.bcast(
-                    self._send_temp_by_type[t], root=0
-                )
-                for p, v in zip(
-                    self._all_params_by_type[t], self._recv_temp_by_type[t]
-                ):
-                    p.value = v
-
-    def _apply_intracomm(self, **kwds):
-        """Communicator is an intra-communicator defined as tasks' comm union.
-        Single broadcast is enough.
-        """
-        for t in self._all_params_by_type.keys():
-            if self.task_is_source and self.domain.task_rank() == 0:
-                self._send_temp_by_type[t][...] = [
-                    p() for p in self._all_params_by_type[t]
-                ]
-            self._recv_temp_by_type[t] = self.inter_comm.bcast(
-                self._send_temp_by_type[t],
-                self.domain.task_root_in_parent(self.source_task),
-            )
-            if self.task_is_dest:
-                for p, v in zip(
-                    self._all_params_by_type[t], self._recv_temp_by_type[t]
-                ):
-                    p.value = v
-
-    @classmethod
-    def supports_mpi(cls):
-        return True
-
-
-class InterTaskParamComm(ComputationalGraphNodeFrontend):
-
-    __implementations = {Implementation.PYTHON: PythonInterTaskParamComm}
-
-    @classmethod
-    def implementations(cls):
-        return cls.__implementations
-
-    @classmethod
-    def default_implementation(cls):
-        return Implementation.PYTHON

hysop/operator/meson.build

@@ -16,7 +16,6 @@ src_python  = [
   'gradient.py',
   'hdf_io.py',
   'integrate.py',
-  'inter_task_param_comm.py',
   'kinetic_energy.py',
   'mean_field.py',
   'memory_reordering.py',