def test_sketch_search_policy_basic():
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common,
kwargs={'seed': 944563397, 'search_policy': 'sketch'})
t.start()
t.join()
def test_sketch_search_policy_xgbmodel():
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common,
kwargs={'seed': 944563397, 'search_policy': 'sketch',
'cost_model': auto_scheduler.XGBModel()})
t.start()
t.join()
def test_sketch_search_policy_cuda_rpc_runner():
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common,
kwargs={'seed': 944563397, 'search_policy': 'sketch', 'target': 'cuda',
'runner': measure_ctx.runner})
t.start()
t.join()
def test_sketch_search_policy_cuda_xgbmodel_rpc_runner():
if not tvm.runtime.enabled("cuda"):
return
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common,
kwargs={'seed': 944563397, 'search_policy': 'sketch', 'target': 'cuda',
'runner': measure_ctx.runner, 'cost_model': auto_scheduler.XGBModel()})
t.start()
t.join()
def test_sketch_search_policy_cuda_rpc_runner():
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(
target=search_common,
kwargs={
"target": "cuda",
"runner": measure_ctx.runner,
},
)
t.start()
t.join()
def test_sketch_search_policy_cuda_xgbmodel_rpc_runner():
if not tvm.runtime.enabled("cuda"):
return
measure_ctx = auto_scheduler.LocalRPCMeasureContext()
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(
target=search_common,
kwargs={
"seed": 944563397,
"search_policy": "sketch",
"target": "cuda",
"runner": measure_ctx.runner,
"cost_model": auto_scheduler.XGBModel(),
},
)
t.start()
t.join()
def test_workload_registry_search_basic():
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common, kwargs={"seed": 944563397})
t.start()
t.join()
t = PropagatingThread(target=search_common,
kwargs={
"seed": 944563397,
"workload": "matmul_auto_scheduler_test"
})
t.start()
t.join()
t = PropagatingThread(
target=search_common,
kwargs={
"seed": 944563397,
"workload": "matmul_auto_scheduler_test_rename_1"
},
)
t.start()
t.join()
def test_workload_registry_search_basic():
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common,
kwargs={
"search_policy": "empty",
"num_measure_trials": 2
})
t.start()
t.join()
t = PropagatingThread(
target=search_common,
kwargs={
"workload": "matmul_auto_scheduler_test",
"num_measure_trials": 2,
"search_policy": "empty",
},
)
t.start()
t.join()
t = PropagatingThread(
target=search_common,
kwargs={
"workload": "matmul_auto_scheduler_test_rename_1",
"num_measure_trials": 2,
"search_policy": "empty",
},
)
t.start()
t.join()
def test_workload_registry_search_basic():
if not tvm.runtime.enabled("llvm"):
return
# wrap the search in a new thread to avoid the conflict
# between python's multiprocessing and tvm's thread pool
t = PropagatingThread(target=search_common, kwargs={'seed': 944563397})
t.start()
t.join()
t = PropagatingThread(target=search_common,
kwargs={
'seed': 944563397,
'workload': "matmul_auto_scheduler_test"
})
t.start()
t.join()
t = PropagatingThread(target=search_common,
kwargs={
'seed': 944563397,
'workload': "matmul_auto_scheduler_test_rename_1"
})
t.start()
t.join()
