def optimize(shapes,
slevel=4,
rlevel=3,
target="llvm",
dev_id=0,
timeout=4.0,
trials=100,
parallel=1,
method="searching",
use_model=False,
rpc_info=None,
logfile=sys.stdout):
ret = dict()
for i, shape in enumerate(shapes):
print("Optimize depthwise conv2d shape {}".format(shape), flush=True)
batch, in_channel, H, W, factor, k, _, stride, padding, dilation = shape
# create an empty task but has the correct key we want
task = Task(
"conv2d", "depthwise", None,
(batch, in_channel, H, W, factor, k, stride, padding, dilation),
target, dev_id)
beg = time.time()
s, bufs, configs = schedule(
task.key,
slevel=slevel,
rlevel=rlevel,
op_trial=trials,
timeout=timeout,
op_stop=trials // 2,
method=method,
use_model=use_model,
parallel=parallel,
rpc_info=rpc_info,
trials=[trials // 10, trials],
number=10,
)
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)
ret[task.key] = configs
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = evaluate(task.key, s, bufs, target, task.dev_id, 10,
rpc_info)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
print()
return ret
def optimize(prefix,
from_,
shapes,
target="llvm",
dev_id=0,
trials=100,
timeout=4.0,
parallel=1,
method="searching",
use_model=False,
logfile=sys.stdout):
ret = dict()
for i, shape in enumerate(shapes):
print("Optimize {} conv_transpose2d layer {} shape {}".format(
prefix, i + 1 + from_, shape),
flush=True)
batch, in_channel, height, width, out_channel, _, k_h, k_w, _, stride, padding, dilation, groups = shape
rout_channel = in_channel
rin_channel = out_channel
rheight = (height + 2 * padding - dilation *
(k_h - 1) - 1) // stride + 1
rwidth = (width + 2 * padding - dilation * (k_w - 1) - 1) // stride + 1
# create an empty task but has the correct key we want
task = Task("conv_transpose2d", prefix + str(i + from_), None,
(batch, rin_channel, rheight, rwidth, rout_channel, k_h,
stride, padding, dilation, groups), target, dev_id)
beg = time.time()
s, bufs, configs = schedule(
task.key,
op_trial=trials,
timeout=timeout,
op_stop=30,
parallel=parallel,
method=method,
use_model=use_model,
trials=[trials // 10, trials // 10, trials])
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)
ret[task.key] = configs
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = _evaluate(s, bufs, target, task.dev_id, 10)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
print()
return ret
def optimize(shapes,
slevel=4,
rlevel=3,
target="llvm",
dev_id=0,
timeout=4.0,
trials=100,
parallel=1,
method="searching",
use_model=False,
rpc_info=None,
logfile=sys.stdout):
ret = dict()
for i, shape in enumerate(shapes):
print("Optimize gemm shape %s [%.6f]" % (str(shape), time.time()),
flush=True)
N, K, M = shape
# create an empty task but has the correct key we want
task = Task("gemm", "gemm", None, (N, K, M), target, dev_id)
beg = time.time()
s, bufs, configs = schedule(task.key,
slevel=slevel,
rlevel=rlevel,
op_trial=trials,
timeout=timeout,
op_stop=trials // 2,
method=method,
use_model=use_model,
parallel=parallel,
rpc_info=rpc_info,
number=10)
end = time.time()
# print(tvm.lower(s, bufs, simple_mode=True))
print("###################################### [%.6f]" % time.time())
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)
ret[task.key] = configs
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = evaluate(task.key,
s,
bufs,
target,
dev_id,
rpc_info=rpc_info)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
print()
return ret
def optimize(prefix,
from_,
shapes,
target="llvm",
dev_id=0,
trials=100,
timeout=4.0,
parallel=1,
method="searching",
use_model=False,
rpc_info=None,
force_inline=False,
logfile=sys.stdout):
ret = dict()
for i, shape in enumerate(shapes):
print("Optimize {} pixelCNN layer {} shape {}".format(
prefix, i + 1 + from_, shape),
flush=True)
# create an empty task but has the correct key we want
task = Task("gatedpixelcnn", "gatedpixelcnn", None, shape, target,
dev_id)
beg = time.time()
s, bufs, configs = schedule(
task.key,
op_trial=trials,
timeout=timeout,
op_stop=30,
parallel=parallel,
method=method,
use_model=use_model,
# trials=[trials//10, trials],
force_inline=force_inline,
rpc_info=rpc_info,
)
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)
ret[task.key] = configs
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = evaluate(task.key, s, bufs, target, task.dev_id, 10,
rpc_info)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
print()
return ret
def optimize(shapes,
slevel=4,
rlevel=3,
target="llvm",
dev_id=0,
timeout=4.0,
trials=100,
parallel=1,
method="searching",
use_model=False,
logfile=sys.stdout):
ret = dict()
for i, shape in enumerate(shapes):
print("Optimize block_circulant_matrix shape {}".format(shape),
flush=True)
ROW, COL, FFT = shape
# create an empty task but has the correct key we want
task = Task("block_circulant_matrix", "block_circulant_matrix", None,
(ROW, COL, FFT), target, dev_id)
beg = time.time()
s, bufs, configs = schedule(task.key,
slevel=slevel,
rlevel=rlevel,
op_trial=trials,
timeout=timeout,
op_stop=30,
method=method,
use_model=use_model,
parallel=parallel)
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)
ret[task.key] = configs
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = _evaluate(s, bufs, target, task.dev_id, 10)
print("Use", time_cost, "ms")
print("Cost", end - beg, "s")
print()
return ret
args = (N, C, H, W, K, k, use_bias, st, pad, dilation, group)
task = Task("conv2d_nchwc",
"yolo_conv6",
conv2d_nchwc_compute_avx2,
args,
target,
dev_id=dev_id)
register_task(task, override=False)
beg = time.time()
s, bufs, configs = schedule(task.key,
op_trial=trials,
timeout=timeout,
op_stop=30,
parallel=parallel,
method=method,
use_model=use_model,
trials=[trials // 10, trials],
force_inline=force_inline,
rpc_info=rpc_info,
slevel=2,
rlevel=2)
end = time.time()
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:")
