wehub-resource-sync
1113 lines
44 KiB
Python
Executable File
1113 lines
44 KiB
Python
Executable File
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import random
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import time
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from collections import OrderedDict
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from functools import reduce
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from itertools import chain, product
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import numpy as np
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import paddle
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from paddle.distributed.fleet import auto
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from .cost_model import estimate_cost
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from .dist_attribute import OperatorDistAttr, TensorDistAttr
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from .dist_context import DistributedContext, DistributedOperatorContext
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from .dist_op import DistributedOperator
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from .operators.common import (
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get_distributed_operator_impl_container,
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is_elementwise_op,
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)
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from .process_group import get_process_group
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from .utils import (
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get_all_distributed_main_program,
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update_op_dims_mapping_by_default_dist_impl,
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update_op_dims_mapping_by_elementwise_like_dist_impl,
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)
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paddle.seed(123)
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random.seed(123)
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np.random.seed(123)
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class PlanFilter:
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@staticmethod
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def check_dims_mapping_for_tensor(
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process_mesh_topology, tensor_shape, dims_mapping
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):
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valid = True
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assert len(tensor_shape) == len(dims_mapping)
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for idx, dim_mapping in enumerate(dims_mapping):
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if dim_mapping != -1:
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if (
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tensor_shape[idx] % process_mesh_topology[dim_mapping] != 0
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or dims_mapping.count(dim_mapping) > 1
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):
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valid = False
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if dim_mapping != -1 and process_mesh_topology[0] == 1:
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valid = False
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return valid
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@staticmethod
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def check_dims_mapping_for_op(op, op_dist_attr, vars):
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process_mesh = op_dist_attr.process_mesh
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assert process_mesh is not None, "The process mesh should not be None."
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for var_name in op.input_arg_names:
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dims_mapping = op_dist_attr.get_input_dims_mapping(var_name)
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if not PlanFilter.check_dims_mapping_for_tensor(
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process_mesh.shape, vars[var_name].shape, dims_mapping
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):
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return False
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if vars[var_name].is_data and len(dims_mapping) > 1:
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for dim in dims_mapping[1:]:
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if dim != -1:
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return False
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for var_name in op.output_arg_names:
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dims_mapping = op_dist_attr.get_output_dims_mapping(var_name)
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if not PlanFilter.check_dims_mapping_for_tensor(
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process_mesh.shape, vars[var_name].shape, dims_mapping
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):
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return False
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return True
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@staticmethod
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def check_dims_mapping_for_special_op(op, op_dist_attr, vars):
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# NOTE: Those ops has some partition limits, and will be solved when corresponding dist op implemented in the future.
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if (
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op.type == "elementwise_add"
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or op.type == 'layer_norm'
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or op.type == "softmax_with_cross_entropy"
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):
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for name in op.input_arg_names:
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for item in op_dist_attr.get_input_dims_mapping(name):
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if item != -1:
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return False
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for name in op.output_arg_names:
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for item in op_dist_attr.get_output_dims_mapping(name):
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if item != -1:
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return False
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if op.type == "lookup_table_v2":
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for name in op.input_arg_names:
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if name == 'pos_embeddings':
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for item in op_dist_attr.get_input_dims_mapping(name):
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if item != -1:
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return False
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return True
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class PlanSpace:
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not_enum_ops = ["create_py_reader", "create_double_buffer_reader", "read"]
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special_vars = [
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"lod_tensor_blocking_queue_0",
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"create_py_reader_0",
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"double_buffer_0",
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]
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@staticmethod
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def _enum_dims_mapping(
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process_mesh_topology, visited, path, depth, res, tensor_shape
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):
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"""Enumerate dims mapping of tensor by the given process_mesh_topology"""
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nums = list(range(-1, len(process_mesh_topology)))
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if depth == len(tensor_shape):
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valid = True
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for idx, item in enumerate(path):
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if item != -1:
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if (
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tensor_shape[idx] % process_mesh_topology[item] != 0
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or path.count(item) > 1
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):
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valid = False
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if valid:
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res.append(copy.deepcopy(path))
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return
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for i in range(len(nums)):
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if not visited[i]:
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if i != 0:
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visited[i] = True
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path.append(nums[i])
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PlanSpace._enum_dims_mapping(
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process_mesh_topology,
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visited,
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path,
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depth + 1,
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res,
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tensor_shape,
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)
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visited[i] = False
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path.pop()
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@staticmethod
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def enum_process_mesh_topology(processes):
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"""Enumerate all process meshes with the given processes."""
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assert processes >= 1, (
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"The processes must be number and greater than 0."
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)
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# compute divisors
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divisors = []
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for i in range(1, processes + 1):
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if processes % i == 0:
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divisors.append(i)
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# compute valid process mesh
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results = []
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for i in range(len(divisors) - 1, 0, -1):
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result = []
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result.append(divisors[i])
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if i == len(divisors) - 1:
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results.append(copy.deepcopy(result))
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continue
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j = 1
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while j < len(divisors):
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if len(result) == 1:
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result.append(divisors[j])
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elif len(result) == 2:
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if processes % (result[0] * result[1]) == 0:
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if processes // (result[0] * result[1]) == 1:
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results.append(copy.deepcopy(result))
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break
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else:
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result.append(processes // (result[0] * result[1]))
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results.append(copy.deepcopy(result))
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result.pop(-1)
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result.pop(-1)
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j += 1
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else:
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if result[0] * result[1] < processes:
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result.pop(-1)
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j += 1
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else:
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break
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return results
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@staticmethod
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def _enum_valid_dist_attr_for_op(program, op, process_mesh):
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"""Enumerate the valid distributed attribute for op based on the given process mesh."""
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vars = program.global_block().vars
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dims_mapping_dict = OrderedDict()
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op_valid_dist_attrs = []
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dist_op_impl_container = get_distributed_operator_impl_container(
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op.type
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)
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# enumerate all valid dims mapping of tensor when process mesh given
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for var_name in chain(op.input_arg_names, op.output_arg_names):
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visited = [
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False
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for _ in range(len(list(range(-1, len(process_mesh.shape)))))
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]
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depth = 0
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path = []
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dims_mapping_list = []
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PlanSpace._enum_dims_mapping(
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process_mesh.shape,
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visited,
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path,
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depth,
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dims_mapping_list,
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vars[var_name].shape,
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)
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dims_mapping_dict[var_name] = copy.deepcopy(dims_mapping_list)
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# compose dims mapping
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composed_dims_mapping_list = list(
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product(
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*[dims_mapping_dict[key] for key in dims_mapping_dict.keys()]
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)
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)
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for composed_dims_mapping in composed_dims_mapping_list:
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op_dist_attr = OperatorDistAttr()
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op_dist_attr.process_mesh = process_mesh
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var_names = list(dims_mapping_dict.keys())
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for idx, dims_mapping in enumerate(composed_dims_mapping):
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if var_names[idx] in op.input_arg_names:
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op_dist_attr.set_input_dims_mapping(
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var_names[idx], dims_mapping
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)
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elif var_names[idx] in op.output_arg_names:
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op_dist_attr.set_output_dims_mapping(
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var_names[idx], dims_mapping
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)
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else:
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raise ValueError(
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"The {varname} is not input or output of op {op}.".format(
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varname='var_names[idx]', op='op'
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)
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)
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dist_op = DistributedOperator(op, op_dist_attr)
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if dist_op_impl_container is None:
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if is_elementwise_op(op.type):
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changed = True
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valid = True
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try:
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changed = update_op_dims_mapping_by_elementwise_like_dist_impl(
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dist_op
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)
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except Exception as e:
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valid = False
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if valid and not changed:
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if PlanFilter.check_dims_mapping_for_op(
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op, dist_op.dist_attr, vars
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) and PlanFilter.check_dims_mapping_for_special_op(
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op, dist_op.dist_attr, vars
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):
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dist_op.dist_attr.impl_type = "elementwise"
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dist_op.dist_attr.impl_idx = 0
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op_valid_dist_attrs.append(dist_op.dist_attr)
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continue
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else:
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changed = True
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valid = True
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try:
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changed = update_op_dims_mapping_by_default_dist_impl(
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dist_op
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)
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except Exception as e:
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valid = False
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if valid and not changed:
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if PlanFilter.check_dims_mapping_for_op(
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op, dist_op.dist_attr, vars
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) and PlanFilter.check_dims_mapping_for_special_op(
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op, dist_op.dist_attr, vars
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):
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dist_op.dist_attr.impl_type = "default"
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dist_op.dist_attr.impl_idx = 0
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op_valid_dist_attrs.append(dist_op.dist_attr)
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continue
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# if op has distributed implements, find all valid dist attr of this op
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impls = dist_op_impl_container.impls
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for idx, impl in enumerate(impls):
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if impl.is_auto_compatible(dist_op):
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if PlanFilter.check_dims_mapping_for_op(
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op, dist_op.dist_attr, vars
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):
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dist_op.dist_attr.impl_type = dist_op.serial_op.type
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dist_op.dist_attr.impl_idx = idx
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op_valid_dist_attrs.append(dist_op.dist_attr)
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# set default dist attr for some special ops whose distributed attributes can not be enumerated
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if not op_valid_dist_attrs:
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op_dist_attr = OperatorDistAttr()
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op_dist_attr.process_mesh = process_mesh
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for var_name in op.input_arg_names:
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op_dist_attr.set_input_dims_mapping(
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vars[var_name].name, [-1 for i in vars[var_name].shape]
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)
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for var_name in op.output_arg_names:
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op_dist_attr.set_output_dims_mapping(
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vars[var_name].name, [-1 for i in vars[var_name].shape]
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)
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# The dist op must be built after the dist attr has been completely constructed
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dist_op = DistributedOperator(op, op_dist_attr)
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dist_op.dist_attr.impl_type = "default"
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dist_op.dist_attr.impl_idx = 0
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op_valid_dist_attrs.append(dist_op.dist_attr)
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return op_valid_dist_attrs
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@staticmethod
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def enum_valid_dist_attr_for_program(
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program, process_mesh_topology, is_pipeline=False
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):
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"""Enumerate valid distributed attributes for all ops in program."""
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valid_dist_attr_dict = OrderedDict()
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ops = program.global_block().ops
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vars = program.global_block().vars
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processes = reduce(lambda x, y: x * y, process_mesh_topology, 1)
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global_group = list(range(processes))
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global_process_mesh = None
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pipeline_process_meshes = None
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# in the pipeline mode, there are some process meshes
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if is_pipeline:
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pipeline_stages = process_mesh_topology[-1]
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op_count_per_stage = len(ops) // pipeline_stages
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if len(process_mesh_topology) > 1:
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process_mesh_shape = process_mesh_topology[:-1]
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per_process_mesh_group = processes // pipeline_stages
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pipeline_process_meshes = [
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auto.ProcessMesh(
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mesh=np.array(
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global_group[
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i * per_process_mesh_group : (i + 1)
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* per_process_mesh_group
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]
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)
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.reshape(process_mesh_shape)
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.tolist()
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)
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for i in range(pipeline_stages)
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]
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elif len(process_mesh_topology) == 1:
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pipeline_process_meshes = [
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auto.ProcessMesh(mesh=[i]) for i in range(pipeline_stages)
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]
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else:
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if len(process_mesh_topology) > 1:
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global_process_mesh = auto.ProcessMesh(
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mesh=np.array(global_group)
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.reshape(process_mesh_topology)
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.tolist()
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)
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else:
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global_process_mesh = auto.ProcessMesh(mesh=global_group)
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# enumerate valid distributed attribute for each op in the program
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for idx, op in enumerate(ops):
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op_valid_dist_attrs = None
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op_process_mesh = global_process_mesh
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pipeline_stage = -1
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if pipeline_process_meshes is not None:
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pipeline_stage = (
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idx // op_count_per_stage
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if idx // op_count_per_stage < len(pipeline_process_meshes)
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else idx // op_count_per_stage - 1
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)
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if pipeline_stage >= len(pipeline_process_meshes):
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pipeline_stage = len(pipeline_process_meshes) - 1
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op_process_mesh = pipeline_process_meshes[pipeline_stage]
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if op.type in PlanSpace.not_enum_ops:
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op_dist_attr = OperatorDistAttr()
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op_dist_attr.process_mesh = op_process_mesh
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for var_name in op.input_arg_names:
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if var_name in PlanSpace.special_vars:
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op_dist_attr.set_input_dims_mapping(var_name, [])
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else:
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dims_mapping = [-1 for i in vars[var_name].shape]
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op_dist_attr.set_input_dims_mapping(
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var_name, dims_mapping
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)
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for var_name in op.output_arg_names:
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if var_name in PlanSpace.special_vars:
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op_dist_attr.set_output_dims_mapping(var_name, [])
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else:
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dims_mapping = [-1 for i in vars[var_name].shape]
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op_dist_attr.set_output_dims_mapping(
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var_name, dims_mapping
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)
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op_valid_dist_attrs = [op_dist_attr]
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pipeline_stage = 0 if pipeline_stage != -1 else pipeline_stage
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else:
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op_valid_dist_attrs = PlanSpace._enum_valid_dist_attr_for_op(
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program, op, op_process_mesh
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)
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assert op_valid_dist_attrs is not None, (
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f"Enumerate {op} valid distributed attribute failed."
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)
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valid_dist_attr_dict[op.desc.id()] = [
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op_valid_dist_attrs,
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pipeline_stage,
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]
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return (
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valid_dist_attr_dict,
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pipeline_process_meshes,
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global_process_mesh,
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)
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class SearchAlgorithm:
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def __init__(self, name):
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self._name = name
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@property
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def name(self):
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self.name = self._name
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def search(self):
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raise NotImplementedError("Please Implement this method in subclass.")
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|
|
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class MCMC(SearchAlgorithm):
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def __init__(self, serial_program_info, parallelizer, max_search_times=5):
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super().__init__("mcmc")
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self._serial_program_info = serial_program_info
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self._max_search_times = max_search_times
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self._parallelizer = parallelizer
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|
|
|
@property
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def serial_program_info(self):
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return self._serial_program_info
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|
|
@property
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def parallelizer(self):
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return self._parallelizer
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|
|
|
@property
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|
def max_search_times(self):
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return self._max_search_times
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|
|
def make_special_op_unshard(
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self, op, ops, vars, dist_context, valid_dist_attr_dict
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):
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if op.type == "softmax_with_cross_entropy":
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for var_name in op.input_arg_names:
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dims_mapping = dist_context.get_op_dist_attr_for_program(
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op
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).get_input_dims_mapping(var_name)
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if (
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dims_mapping
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!= dist_context.get_tensor_dist_attr_for_program(
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vars[var_name]
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).dims_mapping
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):
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has_changed = False
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for search_op in ops:
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if var_name in search_op.output_arg_names:
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op_dist_attr_list = valid_dist_attr_dict[
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search_op.desc.id()
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][0]
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for op_dist_attr in op_dist_attr_list:
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if (
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op_dist_attr.get_output_dims_mapping(
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var_name
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)
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== dims_mapping
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):
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dist_context.set_op_dist_attr_for_program(
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search_op, op_dist_attr
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)
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for name in search_op.output_arg_names:
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tensor_dist_attr = TensorDistAttr()
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tensor_dist_attr.process_mesh = (
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op_dist_attr.process_mesh
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)
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tensor_dist_attr.dims_mapping = op_dist_attr.get_output_dims_mapping(
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name
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)
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dist_context.set_tensor_dist_attr_for_program(
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vars[name], tensor_dist_attr
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)
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has_changed = True
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break
|
|
if has_changed:
|
|
break
|
|
if not has_changed:
|
|
raise ValueError(
|
|
"Change softmax_with_cross_entropy dist attr failed"
|
|
)
|
|
|
|
def init_program(
|
|
self,
|
|
valid_dist_attr_dict,
|
|
program,
|
|
pipeline_process_meshes,
|
|
global_process_mesh,
|
|
):
|
|
ops = program.global_block().ops
|
|
vars = program.global_block().vars
|
|
new_dist_context = DistributedContext()
|
|
|
|
for op in ops:
|
|
op_valid_dist_attr_list = valid_dist_attr_dict[op.desc.id()][0]
|
|
random_op_dist_attr = np.random.randint(
|
|
len(op_valid_dist_attr_list)
|
|
)
|
|
init_op_dist_attr = op_valid_dist_attr_list[random_op_dist_attr]
|
|
new_dist_context.set_op_dist_attr_for_program(op, init_op_dist_attr)
|
|
for var_name in op.input_arg_names:
|
|
if var_name == "lod_tensor_blocking_queue_0":
|
|
continue
|
|
if (
|
|
new_dist_context.get_tensor_dist_attr_for_program(
|
|
vars[var_name]
|
|
)
|
|
is None
|
|
):
|
|
tensor_dist_attr = TensorDistAttr()
|
|
tensor_dist_attr.process_mesh = (
|
|
init_op_dist_attr.process_mesh
|
|
)
|
|
tensor_dist_attr.dims_mapping = (
|
|
init_op_dist_attr.get_input_dims_mapping(var_name)
|
|
)
|
|
new_dist_context.set_tensor_dist_attr_for_program(
|
|
vars[var_name], tensor_dist_attr
|
|
)
|
|
|
|
for var_name in op.output_arg_names:
|
|
tensor_dist_attr = TensorDistAttr()
|
|
tensor_dist_attr.process_mesh = init_op_dist_attr.process_mesh
|
|
tensor_dist_attr.dims_mapping = (
|
|
init_op_dist_attr.get_output_dims_mapping(var_name)
|
|
)
|
|
new_dist_context.set_tensor_dist_attr_for_program(
|
|
vars[var_name], tensor_dist_attr
|
|
)
|
|
|
|
# NOTE: this is a temporary solution to make softmax_with_cross_entropy unshard
|
|
self.make_special_op_unshard(
|
|
op, ops, vars, new_dist_context, valid_dist_attr_dict
|
|
)
|
|
|
|
# add process meshes to distributed context
|
|
if global_process_mesh is not None:
|
|
new_dist_context.add_process_mesh(global_process_mesh)
|
|
elif pipeline_process_meshes is not None:
|
|
for process_mesh in pipeline_process_meshes:
|
|
new_dist_context.add_process_mesh(process_mesh)
|
|
|
|
return new_dist_context
|
|
|
|
def estimate_searched_strategy_cost(
|
|
self, dist_context, pipeline_process_meshes=None
|
|
):
|
|
cost = None
|
|
# get all distributed programs
|
|
all_dist_main_program = get_all_distributed_main_program(
|
|
self.serial_program_info, dist_context, self.parallelizer
|
|
)
|
|
pipeline_config = (
|
|
[
|
|
process_mesh.process_ids
|
|
for process_mesh in pipeline_process_meshes
|
|
]
|
|
if pipeline_process_meshes is not None
|
|
else None
|
|
)
|
|
microbatch_size = 1
|
|
for program in all_dist_main_program:
|
|
searched_batch_size = False
|
|
for var in program.list_vars():
|
|
if var.is_data and "@RESHARD" in var.name:
|
|
microbatch_size = var.shape[0]
|
|
searched_batch_size = True
|
|
break
|
|
if searched_batch_size:
|
|
break
|
|
|
|
from .utils import get_standalone_cost_data
|
|
|
|
standalone_cost_data = get_standalone_cost_data(all_dist_main_program)
|
|
|
|
# cost model does not support cluster argument
|
|
cost = estimate_cost(
|
|
all_dist_main_program,
|
|
cluster=None,
|
|
pipeline_config=pipeline_config,
|
|
standalone_cost_data=standalone_cost_data,
|
|
batch_size=microbatch_size,
|
|
)
|
|
|
|
return cost
|
|
|
|
def set_tensor_dist_attr(self, op, op_dist_attr, vars, dist_context):
|
|
# set output tensor distributed attribute
|
|
for var_name in op.output_arg_names:
|
|
process_mesh = op_dist_attr.process_mesh
|
|
tensor_dist_attr = TensorDistAttr()
|
|
tensor_dist_attr.process_mesh = process_mesh
|
|
tensor_dist_attr.dims_mapping = (
|
|
op_dist_attr.get_output_dims_mapping(var_name)
|
|
)
|
|
dist_context.set_tensor_dist_attr_for_program(
|
|
vars[var_name], tensor_dist_attr
|
|
)
|
|
|
|
# set input tensor distributed attribute if input is data or parameter
|
|
for var_name in op.input_arg_names:
|
|
if vars[var_name].is_parameter or vars[var_name].is_data:
|
|
process_mesh = op_dist_attr.process_mesh
|
|
tensor_dist_attr = TensorDistAttr()
|
|
tensor_dist_attr.process_mesh = process_mesh
|
|
tensor_dist_attr.dims_mapping = (
|
|
op_dist_attr.get_input_dims_mapping(var_name)
|
|
)
|
|
dist_context.set_tensor_dist_attr_for_program(
|
|
vars[var_name], tensor_dist_attr
|
|
)
|
|
|
|
def change_process_mesh(self, op, changed_process_mesh, vars, dist_context):
|
|
dist_context.get_op_dist_attr_for_program(
|
|
op
|
|
).process_mesh = changed_process_mesh
|
|
for var_name in op.output_arg_names:
|
|
dist_context.get_tensor_dist_attr_for_program(
|
|
vars[var_name]
|
|
).process_mesh = changed_process_mesh
|
|
for var_name in op.input_arg_names:
|
|
if vars[var_name].is_parameter or vars[var_name].is_data:
|
|
dist_context.get_tensor_dist_attr_for_program(
|
|
vars[var_name]
|
|
).process_mesh = changed_process_mesh
|
|
|
|
def search_once(
|
|
self,
|
|
program,
|
|
valid_dist_attr_dict,
|
|
dist_context,
|
|
pipeline_process_meshes=None,
|
|
):
|
|
raw_ops = program.global_block().ops
|
|
ops = []
|
|
for op in raw_ops:
|
|
if op.type not in PlanSpace.not_enum_ops:
|
|
ops.append(op)
|
|
assert ops, "The ops of program have no distributed attributes."
|
|
vars = program.global_block().vars
|
|
new_dist_context = copy.deepcopy(dist_context)
|
|
new_dist_context._dist_op_context = DistributedOperatorContext()
|
|
new_valid_dist_attr_dict = None
|
|
random_selected_op_idx = np.random.randint(len(ops))
|
|
selected_op = ops[random_selected_op_idx]
|
|
op_valid_dist_attr_list = valid_dist_attr_dict[selected_op.desc.id()][0]
|
|
pipeline_stage = valid_dist_attr_dict[selected_op.desc.id()][1]
|
|
random_selected_dist_attr_idx = np.random.randint(
|
|
len(op_valid_dist_attr_list)
|
|
)
|
|
selected_op_dist_attr = copy.deepcopy(
|
|
op_valid_dist_attr_list[random_selected_dist_attr_idx]
|
|
)
|
|
|
|
start_idx = ops[0].desc.id()
|
|
if pipeline_stage > -1:
|
|
# in pipeline mode, the above phase just select a dims mapping
|
|
# 0 represents not changed, 1 represents to be the same with before stage, 2 represents to be the same with the latter stage
|
|
new_valid_dist_attr_dict = copy.deepcopy(valid_dist_attr_dict)
|
|
changed_mode = np.random.randint(3)
|
|
if changed_mode == 0:
|
|
# not change the process mesh, just change dims mapping
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
self.set_tensor_dist_attr(
|
|
selected_op, selected_op_dist_attr, vars, new_dist_context
|
|
)
|
|
|
|
elif changed_mode == 1:
|
|
changed_stage = pipeline_stage - 1
|
|
if (
|
|
changed_stage == -1
|
|
or random_selected_op_idx == len(ops) - 1
|
|
or (
|
|
random_selected_op_idx + 1 == len(ops) - 1
|
|
and new_valid_dist_attr_dict[
|
|
ops[random_selected_op_idx + 1].desc.id()
|
|
][1]
|
|
== pipeline_stage + 1
|
|
)
|
|
):
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
self.set_tensor_dist_attr(
|
|
selected_op,
|
|
selected_op_dist_attr,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
else:
|
|
selected_op_process_mesh = pipeline_process_meshes[
|
|
pipeline_stage
|
|
]
|
|
next_op_id = ops[random_selected_op_idx + 1].desc.id()
|
|
if (
|
|
new_valid_dist_attr_dict[next_op_id][1]
|
|
== pipeline_stage + 1
|
|
and random_selected_op_idx + 1 != len(ops) - 1
|
|
):
|
|
new_valid_dist_attr_dict[next_op_id][1] = pipeline_stage
|
|
for op_dist_attr in new_valid_dist_attr_dict[
|
|
next_op_id
|
|
][0]:
|
|
op_dist_attr.process_mesh = selected_op_process_mesh
|
|
# set next op dist attr in the discontext and output/input tensor process mesh
|
|
self.change_process_mesh(
|
|
ops[random_selected_op_idx + 1],
|
|
selected_op_process_mesh,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
# change the selected op stage and output dist attr
|
|
new_valid_dist_attr_dict[selected_op.desc.id()][1] = (
|
|
changed_stage
|
|
)
|
|
new_process_mesh = pipeline_process_meshes[changed_stage]
|
|
selected_op_dist_attr.process_mesh = new_process_mesh
|
|
for op_dist_attr in new_valid_dist_attr_dict[
|
|
selected_op.desc.id()
|
|
][0]:
|
|
op_dist_attr.process_mesh = new_process_mesh
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
|
|
self.set_tensor_dist_attr(
|
|
selected_op,
|
|
selected_op_dist_attr,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
# change the pre op stage
|
|
for idx in range(random_selected_op_idx - 1, -1, -1):
|
|
stage = new_valid_dist_attr_dict[ops[idx].desc.id()][1]
|
|
valid_dist_attr_list = new_valid_dist_attr_dict[
|
|
ops[idx].desc.id()
|
|
][0]
|
|
new_process_mesh = pipeline_process_meshes[
|
|
changed_stage
|
|
]
|
|
if stage == changed_stage + 1:
|
|
new_valid_dist_attr_dict[ops[idx].desc.id()][1] = (
|
|
changed_stage
|
|
)
|
|
for op_dist_attr in valid_dist_attr_list:
|
|
op_dist_attr.process_mesh = new_process_mesh
|
|
new_dist_context.get_op_dist_attr_for_program(
|
|
ops[idx]
|
|
).process_mesh = new_process_mesh
|
|
# change process mesh of the output and input tensor
|
|
self.change_process_mesh(
|
|
ops[idx],
|
|
new_process_mesh,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
else:
|
|
break
|
|
|
|
else:
|
|
changed_stage = pipeline_stage + 1
|
|
if (
|
|
changed_stage == len(pipeline_process_meshes)
|
|
or random_selected_op_idx == 0
|
|
or (
|
|
new_valid_dist_attr_dict[
|
|
ops[random_selected_op_idx - 1].desc.id()
|
|
][1]
|
|
== pipeline_stage - 1
|
|
and (random_selected_op_idx == 1)
|
|
)
|
|
):
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
self.set_tensor_dist_attr(
|
|
selected_op,
|
|
selected_op_dist_attr,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
else:
|
|
selected_op_process_mesh = pipeline_process_meshes[
|
|
pipeline_stage
|
|
]
|
|
pre_op_id = ops[random_selected_op_idx - 1].desc.id()
|
|
if (
|
|
new_valid_dist_attr_dict[pre_op_id][1]
|
|
== pipeline_stage - 1
|
|
and random_selected_op_idx != 1
|
|
):
|
|
new_valid_dist_attr_dict[pre_op_id][1] = pipeline_stage
|
|
for op_dist_attr in new_valid_dist_attr_dict[pre_op_id][
|
|
0
|
|
]:
|
|
op_dist_attr.process_mesh = selected_op_process_mesh
|
|
# set pre op dist attr in the discontext and output tensor process mesh
|
|
self.change_process_mesh(
|
|
ops[random_selected_op_idx - 1],
|
|
selected_op_process_mesh,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
# change the selected op stage and output tensor dist attr
|
|
new_valid_dist_attr_dict[selected_op.desc.id()][1] = (
|
|
changed_stage
|
|
)
|
|
new_process_mesh = pipeline_process_meshes[changed_stage]
|
|
selected_op_dist_attr.process_mesh = new_process_mesh
|
|
for op_dist_attr in new_valid_dist_attr_dict[
|
|
selected_op.desc.id()
|
|
][0]:
|
|
op_dist_attr.process_mesh = new_process_mesh
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
self.set_tensor_dist_attr(
|
|
selected_op,
|
|
selected_op_dist_attr,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
|
|
# change the next op stage
|
|
for idx in range(random_selected_op_idx + 1, len(ops)):
|
|
stage = new_valid_dist_attr_dict[ops[idx].desc.id()][1]
|
|
valid_dist_attr_list = new_valid_dist_attr_dict[
|
|
ops[idx].desc.id()
|
|
][0]
|
|
new_process_mesh = pipeline_process_meshes[
|
|
changed_stage
|
|
]
|
|
if stage == changed_stage - 1:
|
|
new_valid_dist_attr_dict[ops[idx].desc.id()][1] = (
|
|
changed_stage
|
|
)
|
|
for op_dist_attr in valid_dist_attr_list:
|
|
op_dist_attr.process_mesh = new_process_mesh
|
|
|
|
new_dist_context.get_op_dist_attr_for_program(
|
|
ops[idx]
|
|
).process_mesh = new_process_mesh
|
|
# change the output tensor dist attr
|
|
self.change_process_mesh(
|
|
ops[idx],
|
|
new_process_mesh,
|
|
vars,
|
|
new_dist_context,
|
|
)
|
|
else:
|
|
break
|
|
else:
|
|
new_dist_context.set_op_dist_attr_for_program(
|
|
selected_op, selected_op_dist_attr
|
|
)
|
|
self.set_tensor_dist_attr(
|
|
selected_op, selected_op_dist_attr, vars, new_dist_context
|
|
)
|
|
|
|
for op in ops:
|
|
# make softmax_with_cross_entropy unshard
|
|
if op.type == "softmax_with_cross_entropy":
|
|
self.make_special_op_unshard(
|
|
op, ops, vars, new_dist_context, valid_dist_attr_dict
|
|
)
|
|
break
|
|
|
|
if new_valid_dist_attr_dict is None:
|
|
return valid_dist_attr_dict, new_dist_context
|
|
else:
|
|
return new_valid_dist_attr_dict, new_dist_context
|
|
|
|
def _search_core(
|
|
self,
|
|
valid_dist_attr_dict,
|
|
init_dist_context,
|
|
pipeline_process_meshes=None,
|
|
):
|
|
times = 0
|
|
best_dist_context = init_dist_context
|
|
cost = self.estimate_searched_strategy_cost(
|
|
init_dist_context, pipeline_process_meshes
|
|
).runtime
|
|
min_cost = cost
|
|
while times < self.max_search_times:
|
|
times += 1
|
|
new_dist_context = self.search_once(
|
|
self.serial_program_info.train_program,
|
|
valid_dist_attr_dict,
|
|
best_dist_context,
|
|
pipeline_process_meshes,
|
|
)[1]
|
|
cur_cost = self.estimate_searched_strategy_cost(
|
|
new_dist_context, pipeline_process_meshes
|
|
).runtime
|
|
if (min_cost - cur_cost) > 0:
|
|
best_dist_context = copy.deepcopy(new_dist_context)
|
|
min_cost = cur_cost
|
|
times = 0
|
|
return best_dist_context, min_cost
|
|
|
|
def search(self):
|
|
print("Start MCMC searching.")
|
|
start_time = time.time()
|
|
train_program = self.serial_program_info.train_program
|
|
cluster = self.serial_program_info.cluster
|
|
processes = (
|
|
paddle.distributed.get_world_size()
|
|
if cluster is None
|
|
else len(cluster.get_all_devices("GPU"))
|
|
)
|
|
assert processes > 0, "Get process failed."
|
|
|
|
process_mesh_topology_list = PlanSpace.enum_process_mesh_topology(
|
|
processes
|
|
)
|
|
searched_dist_context = None
|
|
min_cost = None
|
|
|
|
searched_pipeline_dist_context = None
|
|
pipeline_min_cost = None
|
|
for process_mesh_topology in process_mesh_topology_list:
|
|
print(
|
|
f"MCMC search: search process mesh {process_mesh_topology} with pipeline mode."
|
|
)
|
|
(
|
|
valid_dist_attr_dict,
|
|
pipeline_process_meshes,
|
|
global_process_mesh,
|
|
) = PlanSpace.enum_valid_dist_attr_for_program(
|
|
train_program, process_mesh_topology, True
|
|
)
|
|
init_dist_context = self.init_program(
|
|
valid_dist_attr_dict,
|
|
train_program,
|
|
pipeline_process_meshes,
|
|
global_process_mesh,
|
|
)
|
|
best_dist_context, cost = self._search_core(
|
|
valid_dist_attr_dict, init_dist_context, pipeline_process_meshes
|
|
)
|
|
print(
|
|
f"MCMC search: the min cost is {cost} in the process mesh {process_mesh_topology} with pipeline mode."
|
|
)
|
|
best_dist_context._dist_op_context = DistributedOperatorContext()
|
|
pipeline_min_cost = (
|
|
cost if pipeline_min_cost is None else pipeline_min_cost
|
|
)
|
|
searched_pipeline_dist_context = (
|
|
best_dist_context
|
|
if searched_pipeline_dist_context is None
|
|
else searched_pipeline_dist_context
|
|
)
|
|
if pipeline_min_cost > cost:
|
|
searched_pipeline_dist_context = best_dist_context
|
|
pipeline_min_cost = cost
|
|
|
|
searched_non_pipeline_dist_context = None
|
|
non_pipeline_min_cost = None
|
|
for process_mesh_topology in process_mesh_topology_list:
|
|
# if process_mesh_topology shape is 3, include pipeline mode by default
|
|
if len(process_mesh_topology) == 3:
|
|
continue
|
|
print(
|
|
f"MCMC search: search process mesh {process_mesh_topology} without pipeline mode."
|
|
)
|
|
(
|
|
valid_dist_attr_dict,
|
|
pipeline_process_meshes,
|
|
global_process_mesh,
|
|
) = PlanSpace.enum_valid_dist_attr_for_program(
|
|
train_program, process_mesh_topology, False
|
|
)
|
|
init_dist_context = self.init_program(
|
|
valid_dist_attr_dict,
|
|
train_program,
|
|
pipeline_process_meshes,
|
|
global_process_mesh,
|
|
)
|
|
best_dist_context, cost = self._search_core(
|
|
valid_dist_attr_dict, init_dist_context, pipeline_process_meshes
|
|
)
|
|
print(
|
|
f"MCMC search: the min cost is {cost} in the process mesh {process_mesh_topology} without pipeline mode."
|
|
)
|
|
best_dist_context._dist_op_context = DistributedOperatorContext()
|
|
non_pipeline_min_cost = (
|
|
cost if non_pipeline_min_cost is None else non_pipeline_min_cost
|
|
)
|
|
searched_non_pipeline_dist_context = (
|
|
best_dist_context
|
|
if searched_non_pipeline_dist_context is None
|
|
else searched_non_pipeline_dist_context
|
|
)
|
|
if non_pipeline_min_cost > cost:
|
|
searched_non_pipeline_dist_context = best_dist_context
|
|
non_pipeline_min_cost = cost
|
|
|
|
if non_pipeline_min_cost > pipeline_min_cost:
|
|
searched_dist_context = searched_pipeline_dist_context
|
|
min_cost = pipeline_min_cost
|
|
print(
|
|
"Better set FLAGS_benchmark=1 to avoid hang problem in the pipeline mode."
|
|
)
|
|
else:
|
|
searched_dist_context = searched_non_pipeline_dist_context
|
|
min_cost = non_pipeline_min_cost
|
|
|
|
# rebuild g_process_group
|
|
pg0 = get_process_group(0)
|
|
for process_mesh in searched_dist_context._process_meshes:
|
|
pg0.add_ranks(process_mesh.process_ids)
|
|
end_time = time.time()
|
|
print(
|
|
f"End MCMC searching: the min cost is {min_cost} and the search time is {end_time - start_time}s."
|
|
)
|
|
return searched_dist_context, min_cost
|
|
|
|
|
|
class Planner:
|
|
def __init__(
|
|
self, serial_program_info, parallelizer, algorithm_config=None
|
|
):
|
|
self._serial_program_info = serial_program_info
|
|
self._parallelizer = parallelizer
|
|
self._algorithm_config = algorithm_config
|
|
self._algorithm_searcher = self.create_algorithm_searcher(
|
|
algorithm_config
|
|
)
|
|
|
|
@property
|
|
def serial_program_info(self):
|
|
return self._serial_program_info
|
|
|
|
@property
|
|
def algorithm_config(self):
|
|
return self._algorithm_config
|
|
|
|
@property
|
|
def algorithm_searcher(self):
|
|
return self._algorithm_searcher
|
|
|
|
@property
|
|
def parallelizer(self):
|
|
return self._parallelizer
|
|
|
|
def create_algorithm_searcher(self, algorithm_config):
|
|
name = algorithm_config.get("name", None)
|
|
assert name is not None, "Invalid algorithm config."
|
|
|
|
algorithm_searcher = None
|
|
if name == "mcmc":
|
|
# NOTE: Only GPU clusters are supported now.
|
|
max_search_times = algorithm_config.get("max_search_times", None)
|
|
algorithm_searcher = (
|
|
MCMC(
|
|
self.serial_program_info,
|
|
self.parallelizer,
|
|
max_search_times,
|
|
)
|
|
if max_search_times is not None
|
|
else MCMC(self.serial_program_info, self.parallelizer)
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"Other search algorithms have not been supported now."
|
|
)
|
|
|
|
return algorithm_searcher
|
|
|
|
def search(self):
|
|
return self.algorithm_searcher.search()
|