def test_task_scheduler_round_robin():
tasks = []
for n in [2, 4, 8]:
tasks.append(
auto_scheduler.create_task(matmul_auto_scheduler_test, (n, n, n),
"llvm"))
def objective_func(costs):
return sum(costs)
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
num_trials_per_task = 2
# Tune all tasks
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=num_trials_per_task * len(tasks),
runner=measure_ctx.runner,
num_measures_per_round=1,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
task_scheduler = auto_scheduler.TaskScheduler(tasks,
objective_func,
strategy="round-robin")
task_scheduler.tune(tune_option, search_policy="sketch.random")
# Check the result of round robin
counters = {}
for task in tasks:
counters[task.workload_key] = 0
for inp, res in auto_scheduler.load_records(log_file):
counters[inp.task.workload_key] += 1
for task in tasks:
assert counters[task.workload_key] == num_trials_per_task
# test continuous tuning (restoring the status)
task_scheduler = auto_scheduler.TaskScheduler(tasks,
objective_func,
strategy="round-robin",
load_log_file=log_file)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=len(tasks),
num_measures_per_round=1,
)
task_scheduler.tune(tune_option, search_policy="sketch.random")
del measure_ctx
def local_auto_scheduler(self,
repeat=1,
min_repeat_ms=300,
timeout=10,
num_measure_trials=200):
# extract tasks
tasks, task_weights = auto_scheduler.extract_tasks(
self.mod["main"], self.params, self.target)
for idx, task in enumerate(tasks):
logger.debug("========== Task %d (workload key: %s) ==========" %
(idx, task.workload_key))
logger.debug(task.compute_dag)
# generate tuner
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
logging.info("Begin tuning...")
measure_ctx = auto_scheduler.LocalRPCMeasureContext(
repeat=repeat, min_repeat_ms=min_repeat_ms, timeout=timeout)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=num_measure_trials,
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(self.log_file)],
)
tuner.tune(tune_option)
# update self.lib
with auto_scheduler.ApplyHistoryBest(self.log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
self._lib = relay.build(self.mod,
target=self.target,
params=self.params)
logger.info(f"load optimized library from {self.log_file}")
def tune_network(network, target):
# Extract tasks
mod, params = get_network(network)
target = tvm.target.Target(target)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params,
target)
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tuning
measure_ctx = auto_scheduler.LocalRPCMeasureContext(timeout=60)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=100,
num_measures_per_round=2,
early_stopping=1,
runner=measure_ctx.runner,
builder=auto_scheduler.LocalBuilder(timeout=60),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
del measure_ctx
# Compile with the history best
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, params=params)
def schedule_tasks(
tasks, task_weights, tuning_options, tuning_records=None, log_estimated_latency=False
):
"""Generate the schedules for the different tasks (i.e., subgraphs) contained in the module.
Store the schedules in a json file that will be used later by the compiler.
Parameters
----------
tasks : list
A list of auto_scheduler.SearchTask to tune.
task_weights : list
The weight (i.e. the number of appearance) of extracted tasks
tuning_options: dict
The options of tuning
tuning_records : str, optional
The json file used to preload the autoscheduler
"""
if not log_estimated_latency:
callbacks = [auto_scheduler.task_scheduler.PrintTableInfo()]
else:
callbacks = [
auto_scheduler.task_scheduler.PrintTableInfo(),
auto_scheduler.task_scheduler.LogEstimatedLatency(("total_latency.tsv")),
]
# Create the scheduler
tuner = auto_scheduler.TaskScheduler(
tasks, task_weights, load_log_file=tuning_records, callbacks=callbacks
)
# Tune the tasks
tuner.tune(tuning_options)
def test_tuning_cuda():
auto_scheduler.enable_relay_integration()
# Extract tasks
mod, params = get_network("mlp")
target = tvm.target.Target("cuda")
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights))
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tuning
measure_ctx = auto_scheduler.LocalRPCMeasureContext(timeout=100)
tuner = auto_scheduler.TaskScheduler(tasks, objective)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=2,
num_measures_per_round=1,
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
del measure_ctx
# Compile with the history best
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
# Todo(merrymercy): compile without any history to test the fallback mechanism
auto_scheduler.enable_relay_integration(False)
def auto_scheduler_tune(network, target, input_name, log_file):
if os.path.exists(log_file):
os.remove(log_file)
mod, net_params, input_shape, output_shape = get_network(network)
if network not in ["bert"]:
# convert to NHWC layout
desired_layouts = {'nn.conv2d': ['NHWC', 'default']}
seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
relay.transform.ConvertLayout(desired_layouts)])
with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
if "cpu" in target.keys:
tuning_opt = auto_scheduler.TuningOptions(
num_measure_trials=20000, # change this to 20000 to achieve the best performance
runner=auto_scheduler.LocalRunner(repeat=10, enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
else:
measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=300, timeout=10)
tuning_opt = auto_scheduler.TuningOptions(
num_measure_trials=20000, # change this to 20000 to achieve the best performance
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], net_params, target)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tuner.tune(tuning_opt)
def run_tuning():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=
200, # change this to 20000 to achieve the best performance
runner=auto_scheduler.LocalRunner(repeat=10,
enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
if use_sparse:
from tvm.topi.sparse.utils import sparse_sketch_rules
search_policy = [
auto_scheduler.SketchPolicy(
task,
program_cost_model=auto_scheduler.XGBModel(),
init_search_callbacks=sparse_sketch_rules(),
) for task in tasks
]
tuner.tune(tune_option, search_policy=search_policy)
else:
tuner.tune(tune_option)
def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=
200, # change this to 20000 to achieve the best performance
builder=auto_scheduler.LocalBuilder(
build_func="ndk" if use_ndk else "default"),
runner=auto_scheduler.RPCRunner(
device_key,
host=rpc_host,
port=rpc_port,
timeout=30,
repeat=1,
min_repeat_ms=200,
enable_cpu_cache_flush=True,
),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
# Compile with the history best
print("Compile...")
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3, config={"relay.backend.use_auto_scheduler":
True}):
lib = relay.build(mod, target=target, params=params)
# Export library
tmp = tempdir()
if use_ndk:
from tvm.contrib import ndk
filename = "net.so"
lib.export_library(tmp.relpath(filename), ndk.create_shared)
else:
filename = "net.tar"
lib.export_library(tmp.relpath(filename))
# Upload module to device
print("Upload...")
remote = auto_scheduler.utils.request_remote(device_key,
rpc_host,
rpc_port,
timeout=10000)
remote.upload(tmp.relpath(filename))
rlib = remote.load_module(filename)
# Create graph executor
dev = remote.cpu()
module = graph_executor.GraphModule(rlib["default"](dev))
data_tvm = tvm.nd.array(
(np.random.uniform(size=input_shape)).astype(dtype))
module.set_input("data", data_tvm)
# Evaluate
print("Evaluate inference time cost...")
print(module.benchmark(dev, repeat=3, min_repeat_ms=500))
def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200,
builder=auto_scheduler.LocalBuilder(build_func="ndk"),
runner=auto_scheduler.RPCRunner(
device_key,
host=rpc_host,
port=rpc_port,
timeout=30,
repeat=1,
min_repeat_ms=200,
enable_cpu_cache_flush=True,
),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
# Compile with the history best
print("Compile...")
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, params=params)
# Export library
tmp = tempdir()
filename = "net.so"
lib.export_library(tmp.relpath(filename), ndk.create_shared)
# Upload module to device
print("Upload...")
remote = auto_scheduler.utils.request_remote(device_key,
rpc_host,
rpc_port,
timeout=10000)
remote.upload(tmp.relpath(filename))
rlib = remote.load_module(filename)
# Create graph executor
dev = remote.cpu()
module = graph_executor.GraphModule(rlib["default"](dev))
for key, value in shape_dict.items():
data_tvm = tvm.nd.array(
(np.random.uniform(size=value)).astype("float32"))
module.set_input(key, data_tvm)
# Evaluate
print("Evaluate inference time cost...")
ftimer = module.module.time_evaluator("run",
dev,
repeat=3,
min_repeat_ms=500)
prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
def tune_network(network, target):
# Extract tasks
mod, params = get_network(network)
target = tvm.target.Target(target)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params,
target)
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tuning
measure_ctx = auto_scheduler.LocalRPCMeasureContext(timeout=60)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=100,
num_measures_per_round=2,
early_stopping=1,
runner=measure_ctx.runner,
builder=auto_scheduler.LocalBuilder(timeout=60),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
del measure_ctx
# Compile with the history best
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, params=params)
# Compile without auto-scheduler and any other optimization for correctness check
with tvm.transform.PassContext(opt_level=0):
lib2 = relay.build(mod, target=target, params=params)
# Check the correctness
def get_output(data, lib):
ctx = tvm.gpu()
module = graph_runtime.GraphModule(lib["default"](ctx))
module.set_input("data", data)
module.run()
return module.get_output(0).asnumpy()
np.random.seed(0)
if network == "mlp":
data = np.random.uniform(size=(1, 32))
elif network == "winograd-test":
data = np.random.uniform(size=(1, 23, 40, 32))
else:
raise ValueError("Unknown network: " + network)
actual_output = get_output(data, lib)
expected_output = get_output(data, lib2)
tvm.testing.assert_allclose(actual_output,
expected_output,
rtol=1e-4,
atol=1e-4)
def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=
200, # change this to 20000 to achieve the best performance
builder=auto_scheduler.LocalBuilder(
build_func="ndk" if use_ndk else "default"),
runner=auto_scheduler.RPCRunner(device_key,
host="0.0.0.0",
port=9190,
repeat=3,
timeout=50),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
# Compile the whole network
print("Compile...")
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3, config={"relay.backend.use_auto_scheduler":
True}):
lib = relay.build(mod,
target=target,
target_host=target_host,
params=params)
# Create graph runtime
print("=============== Request Remote ===============")
from tvm.auto_scheduler.utils import request_remote
remote = request_remote(device_key, "0.0.0.0", 9190)
ctx = remote.cl()
from tvm.contrib import utils, ndk
temp = utils.tempdir()
filename = "deploy_lib.so"
path_lib = temp.relpath(filename)
lib.export_library(path_lib, ndk.create_shared)
remote.upload(path_lib)
loaded_lib = remote.load_module(filename)
module = graph_runtime.GraphModule(loaded_lib["default"](ctx))
data = (np.random.uniform(size=input_shape)).astype(dtype)
data_tvm = tvm.nd.array(data)
module.set_input("data", data_tvm)
# Evaluate
print("Evaluate inference time cost...")
ftimer = module.module.time_evaluator("run",
ctx,
repeat=3,
min_repeat_ms=500)
prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
def run_tuning():
print("Begin tuning...")
measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=300, timeout=10)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200, # change this to 20000 to achieve the best performance
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
def run_tuning():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=
200, # change this to 20000 to achieve the best performance
runner=auto_scheduler.LocalRunner(repeat=10,
enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
def tune_and_check(mod, data, weight):
# Extract tasks from a relay program
target = tvm.target.Target("llvm")
tasks, task_weights = auto_scheduler.extract_tasks(
mod, target=target, params={"weight": weight})
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tune tasks
tuner = auto_scheduler.TaskScheduler(tasks, task_weights, callbacks=[])
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=1,
num_measures_per_round=1,
builder=auto_scheduler.LocalBuilder(timeout=60),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
# Compile
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True},
):
lib = relay.build(mod,
target=target,
params={"weight": weight})
# Compile without auto-scheduler for correctness check
with tvm.transform.PassContext(opt_level=0):
lib2 = relay.build(mod, target=target, params={"weight": weight})
def get_output(data, lib):
dev = tvm.cpu()
module = graph_executor.GraphModule(lib["default"](dev))
module.set_input("data", data)
module.run()
return module.get_output(0).numpy()
# Check correctness
actual_output = get_output(data, lib)
expected_output = get_output(data, lib2)
tvm.testing.assert_allclose(actual_output,
expected_output,
rtol=1e-4,
atol=2e-4)
def run_tuning(tasks, task_weights, log_file):
print("Begin tuning...")
measure_runner = auto_scheduler.RPCRunner("m1",
"127.0.0.1",
9190,
min_repeat_ms=300,
timeout=30,
repeat=2)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=10000,
runner=measure_runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
verbose=2,
)
tuner.tune(tune_option)
def test_task_scheduler_gradient():
tasks = []
for n in [2, 4]:
tasks.append(
auto_scheduler.SearchTask(
func=matmul_auto_scheduler_test, args=(n, n, n), target="llvm"
)
)
def objective_func(costs):
return costs[0]
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
n_trials = 5
# Tune all tasks
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=n_trials,
runner=measure_ctx.runner,
num_measures_per_round=1,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
task_scheduler = auto_scheduler.TaskScheduler(
tasks, objective_func=objective_func, callbacks=[]
)
# Forcely rewrite the initial values.
# This can make this test more stable on the slow CI machines
task_scheduler.best_costs = np.array([1e2, 1e-8])
task_scheduler.tune(tune_option, search_policy="sketch.random")
# Check the allocation results
counters = {}
for task in tasks:
counters[task.workload_key] = 0
for inp, _ in auto_scheduler.load_records(log_file):
counters[inp.task.workload_key] += 1
assert counters[tasks[0].workload_key] == n_trials - 1
assert counters[tasks[1].workload_key] == 1
del measure_ctx
def test_check_auto_schedule_tuning(host, port): # pylint: disable=too-many-locals
log_file = TEMPORARY_DIRECTORY.relpath("ios_tuning_stat.log")
target = tvm.target.Target(target=f"llvm -mtriple={ARCH}-apple-darwin")
mod, params = relay.testing.mlp.get_workload(batch_size=4,
image_shape=(1, 4, 4))
try:
status_ok = True
measure_runner = auto_scheduler.RPCRunner(
DEVICE_KEY,
host,
port,
min_repeat_ms=1,
timeout=10,
n_parallel=multiprocessing.cpu_count(),
)
builder = auto_scheduler.LocalBuilder(timeout=10,
build_func=ios_create_dylib)
tune_option = auto_scheduler.TuningOptions(
builder=builder,
num_measure_trials=2,
num_measures_per_round=1,
runner=measure_runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
verbose=0,
)
tasks, task_weights = auto_scheduler.extract_tasks(
mod["main"], params, target)
tasks, task_weights = tasks[:2], task_weights[:2]
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tuner.tune(tune_option, search_policy="sketch.random")
# Check tuning log
tuning_statistic = list(load_records(log_file))
for _, measure_result in tuning_statistic:
if measure_result.error_no != MeasureErrorNo.NO_ERROR:
raise ValueError(
f"Error for MeasureResult. Error code: {measure_result.error_no},"
f" for details see MeasureErrorNO.")
except Exception as e: # pylint: disable=broad-except
status_ok = False
print(e)
assert status_ok, "Tuning failed, see logs."
def auto_scheduler_tune(network, batch_size, dtype, target, log_file):
os.makedirs(os.path.dirname(log_file), exist_ok=True)
#if os.path.exists(log_file):
# os.remove(log_file)
layout = "NHWC"
mod, params, input_name, input_shape, output_shape = get_network(
network, batch_size, dtype, layout)
n_trials = network_to_n_trials[(network, batch_size, dtype,
str(target.kind))]
if "cpu" in target.keys:
tuning_opt = auto_scheduler.TuningOptions(
num_measure_trials=n_trials,
runner=auto_scheduler.LocalRunner(repeat=10,
enable_cpu_cache_flush=True),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
else:
min_repeat_ms = 450 if network in ["bert"] else 300
measure_ctx = auto_scheduler.LocalRPCMeasureContext(
repeat=1, min_repeat_ms=min_repeat_ms, timeout=10)
tuning_opt = auto_scheduler.TuningOptions(
num_measure_trials=n_trials,
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params,
target)
print(log_file)
update_file(log_file, tasks)
return
for idx, task in enumerate(tasks):
print("========== Task %d (workload key: %s) ==========" %
(idx, task.workload_key))
print(task.compute_dag)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tuner.tune(tuning_opt)
def schedule_tasks(
tasks: List[auto_scheduler.SearchTask],
task_weights: List[float],
tuning_options: auto_scheduler.TuningOptions,
prior_records: Optional[str] = None,
log_estimated_latency: bool = False,
):
"""Generate the schedules for the different tasks (i.e., subgraphs) contained in the module.
Store the schedules in a json file that will be used later by the compiler.
Parameters
----------
tasks : list
A list of auto_scheduler.SearchTask to tune.
task_weights : list
The weight (i.e. the number of appearance) of extracted tasks
tuning_options: auto_scheduler.TuningOptions
The options of tuning
prior_records : str, optional
The json file used to preload the autoscheduler
log_estimated_latency : bool, optional
If true, writes the estimated runtime of the model during each step of tuning to file.
"""
if not log_estimated_latency:
callbacks = [auto_scheduler.task_scheduler.PrintTableInfo()]
else:
callbacks = [
auto_scheduler.task_scheduler.PrintTableInfo(),
auto_scheduler.task_scheduler.LogEstimatedLatency(
("total_latency.tsv")),
]
# Create the scheduler
tuner = auto_scheduler.TaskScheduler(tasks,
task_weights,
load_log_file=prior_records,
callbacks=callbacks)
# Tune the tasks
tuner.tune(tuning_options)
def remote_auto_scheduler(self, device_key, rpc_host, rpc_port):
# generate tasks
tasks, task_weights = auto_scheduler.extract_tasks(
self.mod["main"], self.params, self.target)
for idx, task in enumerate(tasks):
logger.debug("========== Task %d (workload key: %s) ==========" %
(idx, task.workload_key))
logger.debug(task.compute_dag)
# generate tuner
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=200,
builder=auto_scheduler.LocalBuilder(),
runner=auto_scheduler.RPCRunner(
device_key,
host=rpc_host,
port=rpc_port,
timeout=30,
repeat=1,
min_repeat_ms=200,
enable_cpu_cache_flush=True,
),
measure_callbacks=[auto_scheduler.RecordToFile(self.log_file)],
)
tuner.tune(tune_option)
# update self.lib
with auto_scheduler.ApplyHistoryBest(self.log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
self._lib = relay.build(self.mod,
target=self.target,
params=self.params)
logger.info(f"load optimized library from {self.log_file}")
def tune_network(network, target):
auto_scheduler.enable_relay_integration()
# Extract tasks
mod, params = get_network(network)
target = tvm.target.Target(target)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params,
target)
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tuning
measure_ctx = auto_scheduler.LocalRPCMeasureContext(timeout=60)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=100,
num_measures_per_round=2,
early_stopping=1,
runner=measure_ctx.runner,
builder=auto_scheduler.LocalBuilder(timeout=60),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
del measure_ctx
# Compile with the history best
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
# Todo(merrymercy): when the cpu backend is upstreamed, do the following things:
# 1. compile without history to test the fallback mechanism
# 2. check the correctness of layout rewrite / winograd pre-transform
auto_scheduler.enable_relay_integration(False)
"nico2_v100_32", # change the device key to your key
"0.0.0.0",
9190,
n_parallel=2,
number=5,
repeat=1,
timeout=20,
min_repeat_ms=300,
),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
verbose=2,
)
print("Begin tuning multiple convs...")
# measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=400, timeout=10)
tuner = auto_scheduler.TaskScheduler(tasks, strategy='round-robin')
tuner.tune(tune_option)
sys.exit()
######################################################################
# Run the search
# ^^^^^^^^^^^^^^
# Now we get all inputs ready. Pretty simple, isn't it?
# We can kick off the search and let the auto-scheduler do its magic.
# After some measurement trials, it will return the best schedule it found.
sch, args = auto_scheduler.auto_schedule(task, tuning_options=tune_option)
# Kill the process for measurement
del measure_ctx
def tune_network(network, target):
# Extract tasks
mod, params = get_network(network)
target = tvm.target.Target(target)
tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
with tempfile.NamedTemporaryFile() as fp:
log_file = fp.name
# Tuning
measure_ctx = auto_scheduler.LocalRPCMeasureContext(timeout=60, device=0)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights, callbacks=[])
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=100,
num_measures_per_round=2,
early_stopping=1,
runner=measure_ctx.runner,
builder=auto_scheduler.LocalBuilder(timeout=60),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option, search_policy="sketch.random")
del measure_ctx
# Compile with the history best
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3, config={"relay.backend.use_auto_scheduler": True}
):
lib = relay.build(mod, target=target, params=params)
# Also test that multiple log files can be loaded.
with auto_scheduler.ApplyHistoryBest([log_file, log_file]) as best:
assert isinstance(
best, auto_scheduler.dispatcher.ApplyHistoryBest
), "Unable to load multiple log files jointly."
# Confirm iterables can be directly loaded.
loaded_recs = auto_scheduler.dispatcher.load_records(log_file)
with auto_scheduler.ApplyHistoryBest(iter(loaded_recs)) as best:
assert isinstance(
best, auto_scheduler.dispatcher.ApplyHistoryBest
), "Unable to ingest logs from an interator."
# Sample a schedule when missing
with auto_scheduler.ApplyHistoryBestOrSample(None, num_measure=2):
with tvm.transform.PassContext(
opt_level=3, config={"relay.backend.use_auto_scheduler": True}
):
lib2 = relay.build(mod, target=target, params=params)
# Compile without auto-scheduler and any other optimization for correctness check
with tvm.transform.PassContext(opt_level=0):
ref_lib = relay.build(mod, target=target, params=params)
# Check the correctness
def get_output(data, lib):
dev = tvm.cuda()
module = graph_executor.GraphModule(lib["default"](dev))
module.set_input("data", data)
module.run()
return module.get_output(0).numpy()
np.random.seed(0)
if network == "mlp":
data = np.random.uniform(size=(1, 32))
elif network == "winograd-test":
data = np.random.uniform(size=(1, 23, 40, 32))
else:
raise ValueError("Unknown network: " + network)
actual_output1 = get_output(data, lib)
actual_output2 = get_output(data, lib2)
expected_output = get_output(data, ref_lib)
tvm.testing.assert_allclose(actual_output1, expected_output, rtol=1e-4, atol=1e-4)
tvm.testing.assert_allclose(actual_output2, expected_output, rtol=1e-4, atol=1e-4)
def _tvm_compile(fx_module,
example_inputs,
target=None,
tuning_logfile=None,
use_ansor_tuning=False):
import tvm
from tvm import relay, auto_scheduler
from tvm.contrib import graph_executor
import os
# Find the target and device for TVM.
dev = tvm.cpu(0)
if target is None:
raise ValueError("Setup the TVM target correctly.")
elif isinstance(target, str):
if "cuda" in target:
dev = tvm.cuda(0)
target = tvm.target.Target(target)
elif isinstance(target, tvm.target.target.Target):
if "cuda" in target.keys:
dev = tvm.cuda(0)
# JIT the model and pass it to Torchscript to Relay frontend parser. TVM
# tutorials suggest tracing instead of scripting. The main reason is to
# avoid Pythonic computation to show up in JIT module. However, with Python
# key tracing, AOT Autograd leads to simpler graphs. Therefore, we use
# scripting here to retrieve the JIT module.
jit_mod = torch.jit.script(fx_module)
shape_list = [(f"inp_{idx}", i.shape)
for idx, i in enumerate(example_inputs)]
mod, params = relay.frontend.from_pytorch(jit_mod, shape_list)
# TVM Autotuning
if use_ansor_tuning:
tasks, task_weights = auto_scheduler.extract_tasks(
mod["main"], params, target)
if tuning_logfile is None:
log_file = f"{time.time()}.json"
else:
log_file = f"{tuning_logfile}.json"
if len(tasks) != 0:
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=20000,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
# early_stopping=1000,
# verbose=2,
)
tuner.tune(tune_option)
elif tuning_logfile is not None:
log_file = f"{tuning_logfile}.json"
if use_ansor_tuning or tuning_logfile is not None:
assert os.path.exists(log_file)
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True}):
lib = relay.build(mod, target=target, params=params)
else:
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
# Get a graph executor graph module
m = graph_executor.GraphModule(lib["default"](dev))
def exec_tvm(*args):
for idx, arg in enumerate(args, 0):
if arg.dim() != 0:
m.set_input(
f"inp_{idx}",
tvm.nd.from_dlpack(
torch.utils.dlpack.to_dlpack(arg.contiguous())),
)
m.run()
outs = [
torch.utils.dlpack.from_dlpack(m.get_output(i).to_dlpack())
for i in range(m.get_num_outputs())
]
return outs
return exec_tvm
def main():
log_file = os.path.join(ARGS.work_dir, f"{ARGS.model_name}.json")
runner = auto_scheduler.RPCRunner(
key=ARGS.rpc_key,
host=ARGS.rpc_host,
port=ARGS.rpc_port,
n_parallel=cpu_count(logical=True),
number=ARGS.number,
repeat=ARGS.repeat,
min_repeat_ms=ARGS.min_repeat_ms,
enable_cpu_cache_flush=ARGS.cpu_flush,
)
if ARGS.target.kind.name == "llvm":
hardware_params = auto_scheduler.HardwareParams(
num_cores=int(ARGS.target.attrs["num-cores"]),
target=ARGS.target,
)
elif ARGS.target.kind.name == "cuda":
hardware_params = auto_scheduler.HardwareParams(
num_cores=-1,
vector_unit_bytes=16,
cache_line_bytes=64,
max_shared_memory_per_block=int(
ARGS.target.attrs["max_shared_memory_per_block"]),
max_threads_per_block=int(
ARGS.target.attrs["max_threads_per_block"]),
# The value `max_local_memory_per_block` is not used in AutoScheduler,
# but is required by the API.
max_local_memory_per_block=12345678,
max_vthread_extent=8,
warp_size=32,
)
else:
raise NotImplementedError(f"Unsupported target {ARGS.target}")
describe()
print(f"Workload: {ARGS.model_name}")
onnx_model = onnx.load(ARGS.onnx_path)
shape_dict = {}
for item in ARGS.input_shape:
print(f" input_name: {item['name']}")
print(f" input_shape: {item['shape']}")
print(f" input_dtype: {item['dtype']}")
shape_dict[item["name"]] = item["shape"]
mod, params = from_onnx(onnx_model, shape_dict, freeze_params=True)
tasks, task_weights = auto_scheduler.extract_tasks(
mod["main"],
params,
target=ARGS.target,
hardware_params=hardware_params,
)
for idx, (task, task_weight) in enumerate(zip(tasks, task_weights)):
print(
f"==== Task {idx}: {task.desc} (weight {task_weight} key: {task.workload_key}) ====="
)
print(task.compute_dag)
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tuner.tune(
auto_scheduler.TuningOptions(
num_measure_trials=ARGS.num_trials,
runner=runner,
measure_callbacks=[
auto_scheduler.RecordToFile(log_file),
],
))
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True},
):
lib = relay.build(
mod,
target=ARGS.target,
params=params,
)
graph, rt_mod, params = lib.graph_json, lib.lib, lib.params
input_data = {}
for item in ARGS.input_shape:
input_name, input_shape, input_dtype = item["name"], item[
"shape"], item["dtype"]
if input_dtype.startswith("float"):
input_data[input_name] = np.random.uniform(
size=input_shape).astype(input_dtype)
else:
input_data[input_name] = np.random.randint(low=0,
high=10000,
size=input_shape,
dtype=input_dtype)
def f_timer(rt_mod, dev, input_data):
# pylint: disable=import-outside-toplevel
from tvm.contrib.graph_executor import GraphModule
# pylint: enable=import-outside-toplevel
mod = GraphModule(rt_mod["default"](dev))
for input_name, input_value in input_data.items():
mod.set_input(input_name, input_value)
ftimer = mod.module.time_evaluator(
"run",
dev,
min_repeat_ms=500,
repeat=3,
)
results = list(np.array(ftimer().results) * 1000.0) # type: ignore
print("Running time in time_evaluator: ", results)
run_module_via_rpc(
rpc_config=ARGS.rpc_config,
lib=lib,
dev_type=ARGS.target.kind.name,
args=input_data,
continuation=f_timer,
)
def f_per_layer(rt_mod, dev, input_data):
# pylint: disable=import-outside-toplevel
from tvm.contrib.debugger.debug_executor import create
# pylint: enable=import-outside-toplevel
mod = create(graph, rt_mod, dev)
for input_name, input_value in input_data.items():
mod.set_input(input_name, input_value)
graph_nodes = [n["name"] for n in json.loads(graph)["nodes"]]
graph_time = mod.run_individual(number=10,
repeat=1,
min_repeat_ms=5000)
print("|graph_nodes| = ", len(graph_nodes))
print("|graph_time| = ", len(graph_time))
graph_nodes_time = {
k: float(v)
for k, v in zip(graph_nodes, graph_time)
}
for k, v in graph_nodes_time.items():
print(f"{k} : {v:.3f}")
run_module_via_rpc(
rpc_config=ARGS.rpc_config,
lib=rt_mod,
dev_type=ARGS.target.kind.name,
args=input_data,
continuation=f_per_layer,
)
def main():
log_file = os.path.join(ARGS.work_dir, f"{ARGS.model_name}.json")
runner = auto_scheduler.RPCRunner(
key=ARGS.rpc_key,
host=ARGS.rpc_host,
port=ARGS.rpc_port,
n_parallel=cpu_count(logical=True),
number=ARGS.number,
repeat=ARGS.repeat,
min_repeat_ms=ARGS.min_repeat_ms,
enable_cpu_cache_flush=ARGS.cpu_flush,
timeout=ARGS.rpc_config.session_timeout_sec,
)
if ARGS.target.kind.name == "llvm":
hardware_params = auto_scheduler.HardwareParams(
num_cores=int(ARGS.target.attrs["num-cores"]),
target=ARGS.target,
)
elif ARGS.target.kind.name == "cuda":
hardware_params = auto_scheduler.HardwareParams(
num_cores=-1,
vector_unit_bytes=16,
cache_line_bytes=64,
max_shared_memory_per_block=int(
ARGS.target.attrs["max_shared_memory_per_block"]),
max_threads_per_block=int(
ARGS.target.attrs["max_threads_per_block"]),
# The value `max_local_memory_per_block` is not used in AutoScheduler,
# but is required by the API.
max_local_memory_per_block=12345678,
max_vthread_extent=8,
warp_size=32,
)
else:
raise NotImplementedError(f"Unsupported target {ARGS.target}")
describe()
print(f"Workload: {ARGS.model_name}")
onnx_model = onnx.load(ARGS.onnx_path)
shape_dict = {}
for item in ARGS.input_shape:
print(f" input_name : {item['name']}")
print(f" input_shape: {item['shape']}")
print(f" input_dtype: {item['dtype']}")
shape_dict[item["name"]] = item["shape"]
mod, params = from_onnx(onnx_model, shape_dict, freeze_params=True)
input_data = {
item["name"]: generate_input_data(item["shape"], item["dtype"])
for item in ARGS.input_shape
}
with ms.Profiler() as profiler:
tasks, task_weights = auto_scheduler.extract_tasks(
mod["main"],
params,
target=ARGS.target,
hardware_params=hardware_params,
)
for idx, (task, task_weight) in enumerate(zip(tasks, task_weights)):
print(f"==== Task {idx}: {task.desc} "
f"(weight {task_weight} key: {task.workload_key}) =====")
print(task.compute_dag)
if ARGS.num_trials > 0:
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tuner.tune(
auto_scheduler.TuningOptions(
num_measure_trials=ARGS.num_trials,
runner=runner,
measure_callbacks=[
auto_scheduler.RecordToFile(log_file),
],
),
adaptive_training=ARGS.adaptive_training,
)
relay_build = {
"graph": relay.build,
"vm": relay.vm.compile
}[ARGS.backend]
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_auto_scheduler": True},
):
lib = relay_build(
mod,
target=ARGS.target,
params=params,
)
print("Tuning Time:")
print(profiler.table())
run_module_via_rpc(
rpc_config=ARGS.rpc_config,
lib=lib,
dev_type=ARGS.target.kind.name,
args=input_data,
continuation=create_timer(ARGS.backend),
backend=ARGS.backend,
)
def tune_and_evaluate():
print("Begin tuning...")
tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=
200, # change this to 20000 to achieve the best performance
runner=auto_scheduler.RPCRunner(
device_key,
host="0.0.0.0",
port=9191,
timeout=30,
repeat=1,
min_repeat_ms=200,
enable_cpu_cache_flush=True,
),
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
tuner.tune(tune_option)
# Compile with the history best
print("Compile...")
with auto_scheduler.ApplyHistoryBest(log_file):
with tvm.transform.PassContext(
opt_level=3, config={"relay.backend.use_auto_scheduler":
True}):
lib = relay.build(mod, target=target, params=params)
# Export library
tmp = tempdir()
if use_ndk:
from tvm.contrib import ndk
filename = "net.so"
lib.export_library(tmp.relpath(filename), ndk.create_shared)
else:
filename = "net.tar"
lib.export_library(tmp.relpath(filename))
# Upload module to device
print("Upload...")
remote = auto_scheduler.utils.request_remote(device_key,
"0.0.0.0",
9191,
timeout=10000)
remote.upload(tmp.relpath(filename))
rlib = remote.load_module(filename)
# Create graph runtime
dev = remote.cpu()
module = graph_runtime.GraphModule(rlib["default"](dev))
data_tvm = tvm.nd.array(
(np.random.uniform(size=input_shape)).astype(dtype))
module.set_input("data", data_tvm)
# Evaluate
print("Evaluate inference time cost...")
ftimer = module.module.time_evaluator("run",
dev,
repeat=3,
min_repeat_ms=500)
prof_res = np.array(ftimer().results) * 1e3 # convert to millisecond
print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
