mirror of https://github.com/hpcaitech/ColossalAI
445 lines
17 KiB
Python
445 lines
17 KiB
Python
import warnings
|
|
|
|
import time
|
|
import numpy as np
|
|
import multiprocessing
|
|
from torch.fx.node import Node
|
|
from torch.fx.graph import Graph
|
|
from . import GraphAnalyser
|
|
from colossalai.auto_parallel.solver.cost_graph import CostGraph
|
|
from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
|
|
from typing import Dict
|
|
from .constants import INFINITY_COST
|
|
try:
|
|
import pulp
|
|
from pulp import LpVariable, LpProblem, LpMinimize, lpSum, lpDot, LpStatus
|
|
except:
|
|
warnings.warn(f'please install the pulp')
|
|
|
|
__all___ = ['Solver']
|
|
|
|
|
|
class Solver:
|
|
|
|
def __init__(self,
|
|
graph: Graph,
|
|
strategies_constructor: StrategiesConstructor,
|
|
cost_graph: CostGraph,
|
|
graph_analyser: GraphAnalyser,
|
|
memory_budget: float = -1.0,
|
|
solution_numbers: int = 1,
|
|
memory_increasing_coefficient: float = 1.3):
|
|
'''
|
|
Solver class will integrate information provided by the components and use ILP solver to find a possible optimal strategies combination for target computing graph.
|
|
|
|
Argument:
|
|
graph: The computing graph to be optimized.
|
|
strategies_constructor: It will provide all the possible strategies for each node in the computing graph.
|
|
cost_graph: A graph data structure to simplify the edge cost graph.
|
|
graph_analyser: graph_analyser will analyse the graph to obtain the variable liveness information, which will be used to generate memory constraints.
|
|
memory_budget: Memory constraint for the solution.
|
|
solution_numbers: If solution_numbers is larger than one, solver will us a serious of solutions based on different memory budget.
|
|
memory_increasing_coefficient: If solution_numbers is larger than one, we will use this coefficient to generate new memory budget.
|
|
'''
|
|
self.graph = graph
|
|
self.strategies_constructor = strategies_constructor
|
|
self.cost_graph = cost_graph
|
|
self.graph_analyser = graph_analyser
|
|
self.nodes = list(self.graph.nodes)
|
|
self.leaf_strategies = self.strategies_constructor.leaf_strategies
|
|
self.strategy_map = self.strategies_constructor.strategy_map
|
|
self.memory_budget = memory_budget
|
|
self.solution_numbers = solution_numbers
|
|
if self.solution_numbers > 1:
|
|
self.memory_increasing_coefficient = memory_increasing_coefficient
|
|
else:
|
|
self.memory_increasing_coefficient = 1
|
|
self.liveness_list = self.graph_analyser.liveness_analysis()
|
|
self.node_index_dict = self._generate_node_index_dict()
|
|
# The last solution vector of auto sharding.
|
|
self.last_s_val = None
|
|
# The last objective value of the best ILP solution.
|
|
self.last_objective = None
|
|
|
|
def _generate_node_index_dict(self) -> Dict[Node, int]:
|
|
node_index_dict = {}
|
|
for index, strategies_vector in enumerate(self.leaf_strategies):
|
|
node_index_dict[strategies_vector.node] = index
|
|
return node_index_dict
|
|
|
|
def _prepare_data_for_solver(self):
|
|
'''
|
|
Extract information from components for solver.
|
|
'''
|
|
node_nums = len(self.leaf_strategies)
|
|
memory_budget = self.memory_budget
|
|
|
|
# prepare strategies_len
|
|
strategies_len = []
|
|
for node in self.nodes:
|
|
strategies_len.append(self.cost_graph.node_lens[node])
|
|
strategies_len = np.array(strategies_len)
|
|
|
|
# prepare following_nodes
|
|
following_nodes = self.cost_graph.following_dict
|
|
index_following_nodes = {}
|
|
for src, target in following_nodes.items():
|
|
src_index = self.node_index_dict[src]
|
|
target_index = self.node_index_dict[target]
|
|
index_following_nodes[src_index] = target_index
|
|
following_nodes = index_following_nodes
|
|
for index in range(node_nums):
|
|
if index not in following_nodes:
|
|
following_nodes[index] = -1
|
|
|
|
# prepare edge_pairs and resharding costs
|
|
edge_pairs = []
|
|
resharding_costs = []
|
|
for pairs, edge_cost in self.cost_graph.edge_costs.items():
|
|
src_node = pairs[0]
|
|
dst_node = pairs[1]
|
|
src_node_index = self.node_index_dict[src_node]
|
|
dst_node_index = self.node_index_dict[dst_node]
|
|
edge_pairs.append(src_node_index)
|
|
edge_pairs.append(dst_node_index)
|
|
|
|
for i in range(strategies_len[src_node_index]):
|
|
for j in range(strategies_len[dst_node_index]):
|
|
resharding_costs.append(edge_cost[(i, j)])
|
|
edge_pairs = np.array(edge_pairs)
|
|
resharding_costs = np.array(resharding_costs)
|
|
|
|
# prepare liveness_set
|
|
liveness_set = self.liveness_list
|
|
|
|
# omit alias_set now
|
|
alias_set = None
|
|
alias_convert_costs = None
|
|
|
|
# prepare compute_costs, communication_costs and memory_costs
|
|
compute_costs = []
|
|
communication_costs = []
|
|
memory_costs = []
|
|
extra_node_costs = self.cost_graph.extra_node_costs
|
|
for strategies_vector in self.leaf_strategies:
|
|
node = strategies_vector.node
|
|
for index, strategy in enumerate(strategies_vector):
|
|
compute_costs.append(strategy.compute_cost)
|
|
# node in extra_node_costs means it has some extra communication
|
|
# cost from node merging, so we need to add those extra communication
|
|
# cost into
|
|
if node in extra_node_costs:
|
|
origin_communication_cost = strategy.communication_cost
|
|
extra_node_cost = extra_node_costs[node][index]
|
|
communication_cost = origin_communication_cost + extra_node_cost
|
|
communication_costs.append(communication_cost)
|
|
else:
|
|
communication_costs.append(strategy.communication_cost)
|
|
# temporarily we just consider the forward memory cost
|
|
memory_cost = strategy.memory_cost
|
|
if isinstance(memory_cost, tuple):
|
|
memory_costs.append(memory_cost[0])
|
|
else:
|
|
memory_costs.append(memory_cost)
|
|
compute_costs = np.array(compute_costs)
|
|
communication_costs = np.array(communication_costs)
|
|
memory_costs = np.array(memory_costs)
|
|
|
|
# omit initial value for nodes
|
|
s_init_np = None
|
|
|
|
return node_nums, memory_budget, strategies_len, following_nodes, edge_pairs, alias_set, liveness_set, compute_costs, communication_costs, memory_costs, resharding_costs, alias_convert_costs, s_init_np
|
|
|
|
def _call_solver_serialized_args(self,
|
|
node_nums,
|
|
memory_budget,
|
|
strategies_len,
|
|
following_nodes,
|
|
edge_pairs,
|
|
alias_set,
|
|
liveness_set,
|
|
compute_costs,
|
|
communication_costs,
|
|
memory_costs,
|
|
resharding_costs,
|
|
alias_convert_costs,
|
|
s_init_np=None):
|
|
"""
|
|
Call the solver with serialized arguments.
|
|
"""
|
|
|
|
tic = time.time()
|
|
|
|
for x in [strategies_len, edge_pairs, compute_costs, communication_costs, memory_costs, resharding_costs]:
|
|
assert isinstance(x, np.ndarray)
|
|
assert len(strategies_len) == node_nums, "strategies_len"
|
|
|
|
def get_non_zero_index(binary_vector):
|
|
"""
|
|
Get the index of non-zero item in a vector.
|
|
"""
|
|
ct = 0
|
|
ret = None
|
|
for i, elem in enumerate(binary_vector):
|
|
if pulp.value(elem):
|
|
ret = i
|
|
ct += 1
|
|
|
|
assert ct == 1
|
|
return ret
|
|
|
|
# 0. Unpack flatten numpy arrays
|
|
s_follow = following_nodes
|
|
|
|
E = edge_pairs.reshape((-1, 2)) # noqa
|
|
r = []
|
|
pt = 0
|
|
edge_set = set()
|
|
for (i, j) in E:
|
|
prod_length = strategies_len[i] * strategies_len[j]
|
|
|
|
if (i, j) in edge_set:
|
|
raise ValueError(f"Duplicated edges: {(i, j)}")
|
|
|
|
edge_set.add((i, j))
|
|
r.append(resharding_costs[pt:pt + prod_length])
|
|
pt += prod_length
|
|
assert pt == len(resharding_costs)
|
|
|
|
######################
|
|
# omit alias set now #
|
|
######################
|
|
|
|
# A = alias_set.reshape((-1, 2)) # noqa
|
|
# for (i, j) in A:
|
|
# prod_length = strategies_len[i] * strategies_len[j]
|
|
# v.append(alias_convert_costs[pt:pt + prod_length])
|
|
# pt += prod_length
|
|
# assert pt == len(alias_convert_costs)
|
|
|
|
# L = [] # noqa
|
|
# pt = node_nums
|
|
# for i in range(node_nums):
|
|
# length = liveness_set[i]
|
|
# L.append(liveness_set[pt:pt + length])
|
|
# pt += length
|
|
# assert pt == len(liveness_set)
|
|
v = []
|
|
pt = 0
|
|
|
|
c = []
|
|
d = []
|
|
m = []
|
|
pt = 0
|
|
for i in range(node_nums):
|
|
length = strategies_len[i]
|
|
c.append(compute_costs[pt:pt + length])
|
|
d.append(communication_costs[pt:pt + length])
|
|
m.append(memory_costs[pt:pt + length])
|
|
pt += length
|
|
assert pt == len(compute_costs), f"{pt} == {len(compute_costs)}"
|
|
assert pt == len(communication_costs), f"{pt} == {len(communication_costs)}"
|
|
assert pt == len(memory_costs), f"{pt} == {len(memory_costs)}"
|
|
|
|
# 1. Create variables
|
|
|
|
#############################
|
|
# create variables for node #
|
|
#############################
|
|
s = []
|
|
num_nodes = 0
|
|
reverse_follow_backpatch = []
|
|
for i in range(node_nums):
|
|
if s_follow[i] < 0:
|
|
if strategies_len[i] == 1:
|
|
s.append([1])
|
|
else:
|
|
num_nodes += 1
|
|
s.append(LpVariable.matrix(f"s[{i}]", (range(strategies_len[i]),), cat="Binary"))
|
|
else:
|
|
if s_follow[i] < len(s):
|
|
s.append(s[s_follow[i]])
|
|
else:
|
|
s.append(None)
|
|
reverse_follow_backpatch.append(i)
|
|
|
|
for i in reverse_follow_backpatch:
|
|
s[i] = s[s_follow[i]]
|
|
|
|
#############################
|
|
# create variables for edge #
|
|
#############################
|
|
e = []
|
|
num_edges = 0
|
|
for (idx, (i, j)) in enumerate(E):
|
|
if len(s[i]) == 1:
|
|
e.append(s[j])
|
|
elif len(s[j]) == 1:
|
|
e.append(s[i])
|
|
else:
|
|
num_edges += 1
|
|
e.append(LpVariable.matrix(f"e[{i},{j}]", (range(len(s[i]) * len(s[j])),), cat="Binary"))
|
|
assert len(e[idx]) == len(r[idx])
|
|
|
|
# 2. Set initial value
|
|
######################################
|
|
# set a initial value for warm start #
|
|
######################################
|
|
if s_init_np is not None:
|
|
s_init = s_init_np.reshape((-1, 3))
|
|
for (idx, value, fix) in s_init:
|
|
for i in range(len(s[idx])):
|
|
s[idx][i].setInitialValue(i == value)
|
|
if fix:
|
|
s[idx][i].fixValue()
|
|
|
|
# 3. Objective
|
|
prob = LpProblem("myProblem", LpMinimize)
|
|
###################################################################
|
|
# computing the node cost(computing cost and communication cost) #
|
|
###################################################################
|
|
obj = 0
|
|
for i in range(node_nums):
|
|
obj += lpDot(s[i], c[i]) + lpDot(s[i], d[i])
|
|
|
|
#############################################
|
|
# computing the edge cost(resharding cost) #
|
|
#############################################
|
|
for i in range(len(E)):
|
|
obj += lpDot(e[i], r[i])
|
|
|
|
prob += obj
|
|
|
|
# 4. Constraints
|
|
# (a). specified by `cat="Binary"`
|
|
|
|
# (b)
|
|
#################################################
|
|
# make sure each node only choose one strategy #
|
|
#################################################
|
|
for i in range(node_nums):
|
|
if s_follow[i] < 0:
|
|
prob += lpSum(s[i]) == 1
|
|
|
|
# (c)
|
|
#################################################
|
|
# compute memory consumption with liveness set #
|
|
#################################################
|
|
if memory_budget > 0:
|
|
for liveness_stage in liveness_set:
|
|
mem = 0
|
|
for live_variable in liveness_stage.unique_live_vars:
|
|
node_index = self.node_index_dict[live_variable.node]
|
|
mem += lpSum(s[node_index][j] * m[node_index][j] for j in range(len(s[node_index])))
|
|
prob += mem <= memory_budget
|
|
|
|
# (d). specified by `cat="Binary"`
|
|
|
|
for (idx, (i, j)) in enumerate(E):
|
|
if strategies_len[i] == 1 or strategies_len[j] == 1:
|
|
continue
|
|
|
|
# (e)
|
|
prob += lpSum(e[idx]) == 1
|
|
|
|
# (f)
|
|
for row in range(len(s[i])):
|
|
C = len(s[j]) # noqa
|
|
prob += lpSum(e[idx][row * C + col] for col in range(0, C)) <= s[i][row]
|
|
|
|
# (g)
|
|
for col in range(len(s[j])):
|
|
R = len(s[i]) # noqa
|
|
C = len(s[j]) # noqa
|
|
prob += lpSum(e[idx][row * C + col] for row in range(0, R)) <= s[j][col]
|
|
|
|
# (h)
|
|
######################
|
|
# omit alias set now #
|
|
######################
|
|
|
|
# alias_set = set()
|
|
# for (idx, (i, j)) in enumerate(A):
|
|
# R = len(s[i]) # noqa
|
|
# C = len(s[j]) # noqa
|
|
# if (i, j) in alias_set:
|
|
# raise ValueError(f"Duplicated edges: {(i, j)}")
|
|
|
|
# alias_set.add((i, j))
|
|
# alias_set.add((j, i))
|
|
|
|
# for row in range(len(s[i])):
|
|
# for col in range(len(s[j])):
|
|
# if v[idx][row * C + col] > 0.5:
|
|
# prob += s[i][row] + s[j][col] <= 1
|
|
|
|
verbose = True
|
|
|
|
msg = verbose
|
|
time_limit = 600
|
|
assert "COIN_CMD" in pulp.listSolvers(
|
|
onlyAvailable=True), ("Please install ILP solvers by 'sudo apt install coinor-cbc'")
|
|
|
|
solver = pulp.COIN_CMD(mip=True, msg=msg, timeLimit=time_limit, threads=multiprocessing.cpu_count())
|
|
# solver = pulp.GLPK_CMD(mip=True, msg=msg, timeLimit=time_limit)
|
|
prob.solve(solver)
|
|
|
|
status = prob.status
|
|
objective = pulp.value(prob.objective)
|
|
objective = float(objective) if objective is not None else -1.0
|
|
if verbose:
|
|
print(f"ILP Status: {LpStatus[status]}\tObjective: {objective}\t"
|
|
f"Time: {time.time() - tic}")
|
|
print(f"#nodes: {num_nodes}, #edges: {num_edges}")
|
|
|
|
if prob.status in [pulp.LpStatusInfeasible]:
|
|
raise RuntimeError("Cannot run the function under the given memory budget. "
|
|
"Please increase the memory budget.")
|
|
|
|
# Get and check results
|
|
s_val = np.full((node_nums,), -1, dtype=np.int32)
|
|
for i in range(node_nums):
|
|
s_val[i] = get_non_zero_index(s[i])
|
|
|
|
e_val = np.full((len(E),), -1, dtype=np.int32)
|
|
for (idx, (i, j)) in enumerate(E):
|
|
e_val[idx] = get_non_zero_index(e[idx])
|
|
i_spec_index = e_val[idx] // len(s[j])
|
|
j_spec_index = e_val[idx] % len(s[j])
|
|
assert i_spec_index == s_val[i], f"e_val[{i}][{j}]"
|
|
assert j_spec_index == s_val[j], f"e_val[{i}][{j}]"
|
|
if verbose and r[idx][e_val[idx]] > 0:
|
|
print(f"Edge cost {(i, j)} : {r[idx][e_val[idx]]}")
|
|
|
|
self.last_s_val = s_val
|
|
self.last_objective = objective
|
|
|
|
if objective > INFINITY_COST:
|
|
warnings.warn("Detect unexpected behaviors in the auto-sharding pass.")
|
|
|
|
return s_val, e_val, objective, status
|
|
|
|
def call_solver_serialized_args(self):
|
|
"""
|
|
Call the solver with serialized arguments and handle python errors. Additionally,
|
|
we could give a serious of solutions with different memory budget.
|
|
"""
|
|
if self.solution_numbers == 1:
|
|
args = self._prepare_data_for_solver()
|
|
ret = self._call_solver_serialized_args(*args)
|
|
|
|
return ret
|
|
|
|
origin_memory_budget = self.memory_budget
|
|
memory_budget_list = [
|
|
origin_memory_budget * self.memory_increasing_coefficient**i for i in range(self.solution_numbers)
|
|
]
|
|
ret_list = []
|
|
for memory_budget in memory_budget_list:
|
|
self.memory_budget = memory_budget
|
|
args = self._prepare_data_for_solver()
|
|
ret = self._call_solver_serialized_args(*args)
|
|
ret_list.append(ret)
|
|
|
|
return ret_list
|