From 36ed9794ec8a9dac4f61a9417db8df71c5c7f865 Mon Sep 17 00:00:00 2001
From: Jean-Matthieu Etancelin <jean-matthieu.etancelin@univ-pau.fr>
Date: Mon, 21 Oct 2024 14:46:35 +0200
Subject: [PATCH] remove old manual operator
---
hysop/operator/inter_task_param_comm.py | 151 ------------------------
hysop/operator/meson.build | 1 -
2 files changed, 152 deletions(-)
delete mode 100644 hysop/operator/inter_task_param_comm.py
diff --git a/hysop/operator/inter_task_param_comm.py b/hysop/operator/inter_task_param_comm.py
deleted file mode 100644
index 907c9ac9c..000000000
--- a/hysop/operator/inter_task_param_comm.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# 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
diff --git a/hysop/operator/meson.build b/hysop/operator/meson.build
index c466dd37a..725effa38 100644
--- a/hysop/operator/meson.build
+++ b/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',
--
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