chore: import upstream snapshot with attribution

python/paddle/distributed/auto_parallel/static/planner_v2.py

@@ -0,0 +1,180 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import logging
+import os
+import sys
+
+import numpy as np
+
+from paddle.distributed.auto_parallel.process_mesh import ProcessMesh
+from paddle.distributed.auto_parallel.static.dist_attribute import (
+    OperatorDistAttr,
+    TensorDistAttr,
+)
+from paddle.distributed.auto_parallel.static.dist_op import DistributedOperator
+from paddle.distributed.auto_parallel.static.dist_tensor import (
+    DistributedTensor,
+)
+
+from ...utils.log_utils import get_logger
+from .completion import Completer
+from .dist_context import get_default_distributed_context
+from .tuner.parallel_tuner import ParallelTuner
+from .tuner.rule_based_tuner import RuleBasedTuner
+from .utils import is_naive_data_parallel
+
+
+class Planner:
+    def __init__(self, mode, dist_context):
+        self._mode = mode
+        self._dist_context = dist_context
+        self._load = False  # load dist_attr from file
+
+        # NOTE: [HighOrderGrad]. There are grad ops in forward phase, and it need
+        # dependency of backward-forward ops in forward completion.
+        default_ctx = get_default_distributed_context()
+        self._dist_context._dist_op_context = default_ctx.dist_op_context
+        self._dist_context.data_parallel = default_ctx.data_parallel
+        if not is_naive_data_parallel(self._dist_context):
+            # Use SSA graph for complex parallelism
+            self._dist_context.initialize(with_graph=True)
+        else:
+            # Use program for data parallel parallelism
+            self._dist_context.initialize(with_graph=False)
+
+        self._completer = Completer(self._dist_context)
+
+        self._strategy = dist_context.strategy
+        # set parallel tuner for auto search
+        if self._strategy.auto_mode == "full_random":
+            self._parallel_tuner = ParallelTuner(
+                self._dist_context, mode=self._mode
+            )
+        elif self._strategy.auto_mode == "full_rule_based":
+            self._parallel_tuner = RuleBasedTuner(
+                self._dist_context, mode=self._mode
+            )
+
+    @property
+    def completer(self):
+        return self._completer
+
+    def plan(self):
+        logger = get_logger(logging.INFO)
+        path = None
+        if self._dist_context._json_config:
+            try:
+                path = self._dist_context._json_config["tuner_load_path"]
+            except:
+                path = None
+        if path and os.path.exists(path):
+            try:
+                with open(path, "rb") as f:
+                    from paddle.framework.restricted_unpickler import (
+                        safe_load_pickle,
+                    )
+
+                    dist_attrs = safe_load_pickle(f)
+                tensor_dist_attrs = dist_attrs["tensor"]
+                op_dist_attrs = dist_attrs["op"]
+                process_meshes = dist_attrs["process_meshes"]
+                cluster = dist_attrs["cluster"]
+                last_gpu_model = cluster.machines[0].devices[0].model
+                last_gpu_memory = cluster.machines[0].devices[0].memory
+                last_node_count = len(cluster.machines)
+                last_device_count = len(cluster.get_all_devices("GPU"))
+
+                gpu_model = (
+                    self._dist_context.cluster.machines[0].devices[0].model
+                )
+                gpu_memory = (
+                    self._dist_context.cluster.machines[0].devices[0].memory
+                )
+                node_count = len(self._dist_context.cluster.machines)
+                device_count = len(
+                    self._dist_context.cluster.get_all_devices("GPU")
+                )
+                if (
+                    gpu_model != last_gpu_model
+                    or gpu_memory != last_gpu_memory
+                    or last_node_count != node_count
+                    or device_count != last_device_count
+                ):
+                    logger.info(
+                        f"The cluster {node_count} nodes {device_count} {gpu_model} devices is different from the saved last cluster {last_node_count} nodes {last_device_count} {last_gpu_model} devices, so we run the planner again."
+                    )
+                    need_set_dist_attr = False
+                else:
+                    need_set_dist_attr = True
+            except:
+                need_set_dist_attr = False
+
+            if need_set_dist_attr:
+                for key in op_dist_attrs:
+                    serial_op = self._dist_context._dist_ops_for_program[
+                        key
+                    ].serial_op
+                    # clear dist attr
+                    serial_op.dist_attr = OperatorDistAttr(serial_op.desc)
+                    serial_op.dist_attr.parse_from_string(op_dist_attrs[key])
+                    self._dist_context._dist_ops_for_program[key] = (
+                        DistributedOperator(serial_op)
+                    )
+
+                for key in tensor_dist_attrs:
+                    serial_tensor = (
+                        self._dist_context._dist_tensors_for_program[
+                            key
+                        ].serial_tensor
+                    )
+                    # clear dist attr
+                    serial_tensor.dist_attr = TensorDistAttr(serial_tensor.desc)
+                    serial_tensor.dist_attr.parse_from_string(
+                        tensor_dist_attrs[key]
+                    )
+                    self._dist_context._dist_tensors_for_program[key] = (
+                        DistributedTensor(serial_tensor)
+                    )
+
+                process_meshes = []
+                for item in dist_attrs["process_meshes"]:
+                    process_ids = item[0]
+                    shape = item[1]
+                    process_meshes.append(
+                        ProcessMesh(
+                            np.array(process_ids).reshape(shape).tolist()
+                        )
+                    )
+
+                self._dist_context.process_meshes = process_meshes
+                self._load = True
+
+                logger.info(
+                    f"The parallel strategy has been loaded from {path}"
+                )
+
+        if not self._load:
+            if self._strategy.auto_mode != "semi":
+                self._parallel_tuner.tune()
+            else:
+                self._completer.complete_forward_annotation()
+
+        if os.getenv("PADDLE_AUTO_PARALLEL_STAGE", "run") != "run":
+            sys.exit()
+
+        # parse forward sub block
+        self._dist_context.block_state.parse_forward_blocks(
+            self._dist_context.serial_main_program
+        )