def test_meta_schedule_runner_matmul_test():
"""Test meta schedule runner with add module"""
def _check_correct_matmul(
args_before: List[np.ndarray],
args_after: List[np.ndarray],
) -> None:
a_before, b_before, c_before = args_before
a_after, b_after, c_after = args_after
c_before = np.matmul(a_before, b_before)
assert (a_before == a_after).all()
assert (b_before == b_after).all()
tvm.testing.assert_allclose(c_before, c_after, rtol=1e-5)
def test_alloc_argument(
session: RPCSession,
device: Device,
args_info: Any,
alloc_repeat: int,
) -> List[Any]:
global repeated_args_before # pylint: disable=global-variable-undefined, invalid-name
repeated_args_before = [] # type: ignore
repeated_args = rpc_default_alloc_argument(session, device, args_info,
alloc_repeat)
for args in repeated_args:
repeated_args_before.append([arg.numpy()
for arg in args]) # type: ignore
return repeated_args
def test_run_evaluator(
session: RPCSession, # pylint: disable=unused-argument
rt_mod: Module,
device: Device,
evaluator_config: EvaluatorConfig,
repeated_args: List[Any],
) -> List[float]:
global repeated_args_before # pylint: disable=global-variable-undefined, invalid-name
repeated_args_after = []
evaluator = rt_mod.time_evaluator(
func_name=rt_mod.entry_name,
dev=device,
number=evaluator_config.number,
repeat=evaluator_config.repeat,
min_repeat_ms=evaluator_config.min_repeat_ms,
f_preproc="cache_flush_cpu_non_first_arg"
if evaluator_config.enable_cpu_cache_flush else "",
)
repeated_costs: List[List[float]] = []
for args in repeated_args:
device.sync()
profile_result = evaluator(*args)
repeated_costs.append(profile_result.results)
repeated_args_after.append([arg.numpy() for arg in args])
costs = [
float(cost)
for cost in itertools.chain.from_iterable(repeated_costs)
]
for args_before, args_after in zip(
repeated_args_before, # type: ignore
repeated_args_after,
):
_check_correct_matmul(args_before, args_after)
del repeated_args_before # type: ignore
return costs
# Build the module
mod = MatmulModule
builder = LocalBuilder()
(builder_result, ) = builder.build([BuilderInput(mod, Target("llvm"))])
assert builder_result.artifact_path is not None
assert builder_result.error_msg is None
runner_input = RunnerInput(
builder_result.artifact_path,
"llvm",
[
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
],
)
with LocalRPC() as rpc:
rpc_config = RPCConfig(
tracker_host=rpc.tracker_host,
tracker_port=rpc.tracker_port,
tracker_key=rpc.tracker_key,
session_priority=1,
session_timeout_sec=100,
)
evaluator_config = EvaluatorConfig(
number=1,
repeat=1,
min_repeat_ms=0,
enable_cpu_cache_flush=False,
)
runner = RPCRunner(
rpc_config,
evaluator_config,
f_alloc_argument=test_alloc_argument,
f_run_evaluator=test_run_evaluator,
)
# Run the module
(runner_future, ) = runner.run([runner_input])
runner_result = runner_future.result()
assert runner_result.error_msg is None
for result in runner_result.run_secs:
if isinstance(result, FloatImm):
result = result.value
assert isinstance(result, float)
assert result >= 0.0
_clean_build(builder_result.artifact_path)
def test_meta_schedule_local_runner_add_test():
"""Test meta schedule local runner with add module"""
def _check_correct_add(args_before: List[np.array],
args_after: List[np.array]) -> None:
a_before, b_before, c_before = args_before
a_after, b_after, c_after = args_after
c_before = a_before + b_before
assert (a_before == a_after).all()
assert (b_before == b_after).all()
assert (c_before == c_after).all()
def test_alloc_argument(
device: Device,
args_info: T_ARG_INFO_JSON_OBJ_LIST, # pylint: disable=unused-argument
alloc_repeat: int,
) -> List[T_ARGUMENT_LIST]:
global repeated_args_before # pylint: disable=global-variable-undefined, invalid-name
repeated_args_before = []
repeated_args = local_default_alloc_argument(device, args_info,
alloc_repeat)
for args in repeated_args:
repeated_args_before.append([arg.asnumpy() for arg in args])
return repeated_args
def test_run_evaluator(
rt_mod: Module,
device: Device,
evaluator_config: EvaluatorConfig,
repeated_args: List[Any],
) -> List[float]:
global repeated_args_before # pylint: disable=global-variable-undefined, invalid-name
repeated_args_after = []
evaluator = rt_mod.time_evaluator(
func_name=rt_mod.entry_name,
dev=device,
number=evaluator_config.number,
repeat=evaluator_config.repeat,
min_repeat_ms=evaluator_config.min_repeat_ms,
f_preproc="cache_flush_cpu_non_first_arg"
if evaluator_config.enable_cpu_cache_flush else "",
)
repeated_costs: List[List[float]] = []
for args in repeated_args:
device.sync()
profile_result = evaluator(*args)
repeated_costs.append(profile_result.results)
repeated_args_after.append([arg.asnumpy() for arg in args])
costs = [
float(cost)
for cost in itertools.chain.from_iterable(repeated_costs)
]
for args_before, args_after in zip(repeated_args_before,
repeated_args_after):
_check_correct_add(args_before, args_after)
del repeated_args_before
return costs
# Build the module
mod = AddModule
builder = LocalBuilder()
(builder_result, ) = builder.build([BuilderInput(mod, Target("llvm"))])
assert builder_result.artifact_path is not None
assert builder_result.error_msg is None
runner_input = RunnerInput(
builder_result.artifact_path,
"llvm",
[
TensorInfo("float32", [MATMUL_M]),
TensorInfo("float32", [MATMUL_M]),
TensorInfo("float32", [MATMUL_M]),
],
)
evaluator_config = EvaluatorConfig(
number=1,
repeat=1,
min_repeat_ms=0,
enable_cpu_cache_flush=False,
)
runner = LocalRunner(
timeout_sec=100,
evaluator_config=evaluator_config,
f_alloc_argument=test_alloc_argument,
f_run_evaluator=test_run_evaluator,
)
# Run the module
(runner_future, ) = runner.run([runner_input])
runner_result = runner_future.result()
assert runner_result.error_msg is None
for result in runner_result.run_secs:
if isinstance(result, FloatImm):
result = result.value
assert isinstance(result, float)
assert result >= 0.0
_clean_build(builder_result.artifact_path)
def test_meta_schedule_local_multiple_runs():
"""Test meta schedule local runner for multiple runs"""
# Build the module
mods = [
MatmulModule,
MatmulReluModule,
BatchMatmulModule,
]
builder = LocalBuilder()
builder_inputs = [BuilderInput(mod, Target("llvm")) for mod in mods]
builder_results = builder.build(builder_inputs)
for builder_result in builder_results:
assert builder_result.artifact_path is not None
assert builder_result.error_msg is None
args_infos = [
[
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
],
[
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
TensorInfo("float32", (MATMUL_N, MATMUL_N)),
],
[
TensorInfo("float32", [16, MATMUL_M, MATMUL_M]),
TensorInfo("float32", [16, MATMUL_M, MATMUL_M]),
TensorInfo("float32", [16, MATMUL_M, MATMUL_M]),
],
]
runner_inputs = [
RunnerInput(builder_results[i].artifact_path, "llvm", args_infos[i])
for i in range(len(mods))
]
evaluator_config = EvaluatorConfig(
number=1,
repeat=1,
min_repeat_ms=0,
enable_cpu_cache_flush=False,
)
runner = LocalRunner(timeout_sec=100, evaluator_config=evaluator_config)
# Run the module
runner_futures = runner.run(runner_inputs)
runner_results = [
runner_future.result() for runner_future in runner_futures
]
for runner_result in runner_results:
assert runner_result.error_msg is None
for result in runner_result.run_secs:
if isinstance(result, FloatImm):
result = result.value
assert isinstance(result, float)
assert result >= 0.0
for builder_result in builder_results:
_clean_build(builder_result.artifact_path)