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import torch
from torch import nn
from collections import OrderedDict
from torch.utils.checkpoint import checkpoint
from .feature import *
from .pyenv import PyEnv
from .encode import Encode
from .decode import Decode
class Agent(nn.Module):
def __init__(self, nn_args):
super(Agent, self).__init__()
self.nn_args = nn_args
self.vars_dim = sum(nn_args['variable_dim'].values())
self.steps_ratio = nn_args.setdefault('decode_steps_ratio', 1.0);
logit_clips = nn_args.setdefault('decode_logit_clips', 10.0);
if isinstance(logit_clips, str):
self.logit_clips = [float(v) for v in logit_clips.split(',')]
else:
self.logit_clips = [float(logit_clips)]
self.nn_encode = Encode(nn_args)
self.nn_decode = Decode(nn_args)
def nn_args_dict(self):
return self.nn_args
def forward(self, problem, batch_size, greedy=False, solution=None, memopt=0):
X, K, V = self.nn_encode(problem.feats, problem.batch_size,
problem.worker_num, problem.task_num, memopt)
return self.interact(problem, X, K, V, batch_size, greedy, solution, memopt)
def interact(self, problem, X, K, V, batch_size, greedy, solution, memopt):
NP = problem.batch_size
NW = problem.worker_num
NT = problem.task_num
sample_num = batch_size // NP
assert sample_num > 0 and batch_size % NP == 0
MyEnv = problem.environment
if MyEnv is None:
env = PyEnv(problem, batch_size, sample_num, self.nn_args)
else:
env = MyEnv(str(problem.device), problem.feats, batch_size,
sample_num, problem.worker_num, problem.task_num)
query = X.new_zeros(batch_size, X.size(-1))
state1 = X.new_zeros(batch_size, X.size(-1))
state2 = X.new_zeros(batch_size, X.size(-1))
p_list = []
NULL = X.new_ones(0)
p_index = torch.div(torch.arange(batch_size, device=X.device), sample_num, rounding_mode='trunc') # torch.arange(batch_size, device=X.device) // sample_num
if solution is not None:
solution = solution[:, :, 0:2].to(torch.int64).permute(1, 0, 2)
assert torch.all(solution >= 0) and solution.size(1) == batch_size
offset = torch.tensor([0, NW, NW + NW, NW + NW + NT], device=X.device)
chosen_list = solution[:, :, 1] + offset[solution[:, :, 0]]
mode = 0
sample_p = torch.rand(batch_size, device=X.device)
for chosen in chosen_list:
env_time = env.time()
clip = self.logit_clips[min(env_time, len(self.logit_clips) - 1)]
varfeat = env.make_feat() if self.vars_dim > 0 else NULL
state1, state2, chosen_p = self.decode(X, K, V, query, state1, state2,
varfeat, env.mask(), chosen, sample_p, clip, mode, memopt)
query = X[p_index, chosen]
p_list.append(chosen_p)
env.step(chosen)
assert env.all_finished(), 'not all finished!'
else:
mode = 1 if greedy else 2
min_env_time = int(self.steps_ratio * NT)
R = torch.rand(NT * 2, batch_size, device=X.device)
while True:
env_time = env.time()
if env_time > min_env_time and env_time % 3 == 0 and env.all_finished():
break
clip = self.logit_clips[min(env_time, len(self.logit_clips) - 1)]
sample_p = R[env_time % R.size(0)]
chosen = X.new_empty(batch_size, dtype=torch.int64)
varfeat = env.make_feat() if self.vars_dim > 0 else NULL
state1, state2, chosen_p = self.decode(X, K, V, query, state1, state2,
varfeat, env.mask(), chosen, sample_p, clip, mode, memopt)
query = X[p_index, chosen]
p_list.append(chosen_p)
env.step(chosen)
env.finalize()
return env, torch.stack(p_list, 1)
def decode(self, X, K, V, query, state1, state2, varfeat, mask, chosen, sample_p, clip, mode, memopt):
run_fn = self.decode_fn(clip, mode, memopt)
if self.training and memopt > 3:
return checkpoint(run_fn, X, K, V, query, state1, state2, varfeat, mask, chosen, sample_p)
else:
return run_fn(X, K, V, query, state1, state2, varfeat, mask, chosen, sample_p)
def decode_fn(self, clip, mode, memopt):
memopt = 0 if memopt > 3 else memopt
def run_fn(X, K, V, query, state1, state2, varfeat, mask, chosen, sample_p):
return self.nn_decode(X, K, V, query, state1, state2,
varfeat, mask, chosen, sample_p, clip, mode, memopt)
return run_fn
def parse_nn_args(problem, nn_args):
worker_dim = OrderedDict()
task_dim = OrderedDict()
edge_dim = OrderedDict()
variable_dim = OrderedDict()
embed_dict = OrderedDict()
def set_dim_by_name(name, k, dim):
if name.startswith("worker_task_"):
edge_dim[k] = dim
elif name.startswith("worker_"):
worker_dim[k] = dim
elif name.startswith("task_"):
task_dim[k] = dim
elif name.endswith("_matrix"):
edge_dim[k] = dim
else:
raise Exception("attribute can't be feature: {}".format(k))
feature_dict = make_feat_dict(problem)
variables = [var(problem, problem.batch_size, 1) for var in problem.variables]
variable_dict = dict([(var.name, var) for var in variables])
for k, f in feature_dict.items():
if isinstance(f, VariableFeature):
var = variable_dict[f.name]
assert hasattr(var, 'make_feat'), \
"{} cann't be variable feature, name:{}".format(type(var).__name__, k)
v = var.make_feat()
if v.dim() == 2:
variable_dim[k] = 1
else:
variable_dim[k] = v.size(-1)
elif isinstance(f, SparseLocalFeature):
edge_dim[k] = 1
set_dim_by_name(f.value, k, 1)
elif isinstance(f, LocalFeature):
edge_dim[k] = 1
set_dim_by_name(f.name, k, 1)
elif isinstance(f, LocalCategory):
edge_dim[k] = 1
elif isinstance(f, GlobalCategory):
set_dim_by_name(f.name, k, nn_args.setdefault('encode_hidden_dim', 128))
embed_dict[k] = f.size
elif isinstance(f, ContinuousFeature):
v = problem.feats[k]
if k.startswith("worker_task_") or k.endswith("_matrix"):
simple_dim = 3
else:
simple_dim = 2
if v.dim() == simple_dim:
set_dim_by_name(f.name, k, 1)
else:
set_dim_by_name(f.name, k, v.size(-1))
else:
raise Exception("unsupported feature type: {}".format(type(f)))
nn_args['worker_dim'] = worker_dim
nn_args['task_dim'] = task_dim
nn_args['edge_dim'] = edge_dim
nn_args['variable_dim'] = variable_dim
nn_args['embed_dict'] = embed_dict
nn_args['feature_dict'] = feature_dict
return nn_args
def make_feat_dict(problem):
feature_dict = OrderedDict()
def add(k, f):
_f = feature_dict.get(k)
if _f is None or _f == f:
feature_dict[k] = f
else:
"duplicated feature, name: {}, feature1: {}, feature2: {}".format(k, _f, f)
for f in problem.features:
if isinstance(f, VariableFeature):
add(':'.join(['var', f.name]), f)
elif isinstance(f, SparseLocalFeature):
add(':'.join([f.index, f.value]), f)
else:
add(f.name, f)
return feature_dict
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