def test():
# create an empty task but has the correct key we want
task = Task("yolo1", None, (1, 3, 448, 448, 64, 7, 2, 3, 1, 1), "llvm", 0)
beg = time.time()
# s, bufs, configs = schedule(task.key)
end = time.time()
# print(tvm.lower(s, bufs, simple_mode=True))
# print("######################################")
# print("op schedules:")
# for config in configs.op_config_lst:
# print("----------------------------------")
# for name, value in config.items():
# if value:
# print(name, value)
# print("graph schedules:")
# for name, value in configs.graph_config.items():
# if value:
# print(name, value)
op_configs = [{
"spatial": [[1, 1, 1, 1], [1, 1, 1, 3], [454, 1, 1, 1], [1, 227, 2,
1]],
"unroll": [[1500, 1]]
}, {
"spatial": [[1, 1, 1, 1], [2, 4, 2, 4], [8, 1, 4, 7], [7, 1, 16, 2]],
"reduce": [[1, 3, 1], [7, 1, 1], [7, 1, 1]],
"unroll": [[1500, 1]]
}]
graph_config = {"inline": [[0, 0]]}
configs = Config(op_configs, graph_config)
s, bufs = schedule_with_config(task.key, configs)
time_cost = _evaluate(s, bufs, "llvm", 0, 10)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
trials=args.trials,
parallel=args.parallel,
method=args.method,
use_model=args.use_model,
rpc_info=rpc_info,
force_inline=args.force_inline,
logfile=flog,
)
else:
ret = optimize(
args.shapes,
args.from_,
shapes[args.from_:end],
target=args.target,
dev_id=args.device,
timeout=args.timeout,
trials=args.trials,
parallel=args.parallel,
method=args.method,
use_model=args.use_model,
rpc_info=rpc_info,
force_inline=args.force_inline,
logfile=sys.stdout,
)
if args.test != "":
with open(args.test, "r") as fin:
for line in fin:
name, string = line.split(":", 1)
obj = json.loads(string)
configs = Config(obj[0], obj[1])
test(name, configs, dev_id=args.device, rpc_info=rpc_info)
def gemm_config(M, N, K, logits_dict):
spatial_split_parts = 4
reduce_split_parts = 4
unroll_max_factor = 10
sy = any_factor_split(M, spatial_split_parts)
sx = any_factor_split(N, spatial_split_parts)
sk = any_factor_split(K, reduce_split_parts)
unroll = []
for i in range(1):
for j in range(unroll_max_factor + 1):
unroll.append([i, 2**j])
def _rational(lst, max_val):
return torch.FloatTensor([[y / float(max_val) for y in x]
for x in lst])
nsy = _rational(sy, M)
nsx = _rational(sx, N)
nsk = _rational(sk, K)
n_unroll = torch.FloatTensor([[x[0] / float(2) + 0.5,
math.log2(x[1]) / 1] for x in unroll])
# get logits
spatial_logits = logits_dict["spatial"]
reduce_logits = logits_dict["reduce"]
unroll_logits = logits_dict["unroll"]
# make choice
feature_size = len(logits_dict["spatial"][0])
split_classifier = model.MLP(feature_size + spatial_split_parts)
unroll_classifier = model.MLP(feature_size + 2)
cy = torch.argmax(
split_classifier(
torch.cat([
nsy,
torch.zeros([nsy.shape[0], feature_size]) + spatial_logits[0]
],
dim=1)))
cx = torch.argmax(
split_classifier(
torch.cat([
nsx,
torch.zeros([nsx.shape[0], feature_size]) + spatial_logits[1]
],
dim=1)))
ck = torch.argmax(
split_classifier(
torch.cat([
nsk,
torch.zeros([nsk.shape[0], feature_size]) + reduce_logits[0]
],
dim=1)))
cu = torch.argmax(
unroll_classifier(
torch.cat([
n_unroll,
torch.zeros([n_unroll.shape[0], feature_size]) + unroll_logits
],
dim=1)))
print(cy, cx, ck, cu)
# print choice
print("Print choice")
print("split y =", sy[cy])
print("split x =", sx[cx])
print("split k =", sk[ck])
print("unroll", unroll[cu])
# make config
op_config = [{
"spatial": [sy[cy], sx[cx]],
"reduce": [sk[ck]],
"inline": [],
"unroll": [unroll[cu]]
}]
graph_config = {"spatial": [], "reduce": [], "inline": [[0]], "unroll": []}
return Config(op_config, graph_config)
