def test_layout_transform():
in_shape = (1, 32, 8, 8)
A = tvm.placeholder(shape=in_shape, dtype="float32", name="A")
B = topi.layout_transform(A, "NCHW", "NCHW16c")
input = np.random.uniform(size=in_shape).astype(A.dtype)
output = np.transpose(input, axes=(0, 2, 3, 1))
output = np.reshape(output, newshape=(1, 8, 8, 2, 16))
output = np.transpose(output, axes=(0, 3, 1, 2, 4))
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
return
tvm_input = tvm.nd.array(input, ctx)
tvm_output = tvm.nd.empty(output.shape, ctx=ctx, dtype=B.dtype)
print("Running on target: %s" % device)
with tvm.target.create(device):
s = topi.generic.schedule_injective(B)
f = tvm.build(s, [A, B], device, name="layout_transform")
f(tvm_input, tvm_output)
tvm.testing.assert_allclose(tvm_output.asnumpy(), output)
for backend in get_all_backend():
check_device(backend)
def test_layout_transform():
in_shape = (1, 32, 8, 8)
A = tvm.placeholder(shape=in_shape, dtype="float32", name="A")
B = topi.layout_transform(A, "NCHW", "NCHW16c")
input = np.random.uniform(size=in_shape).astype(A.dtype)
output = np.transpose(input, axes=(0, 2, 3, 1))
output = np.reshape(output, newshape=(1, 8, 8, 2, 16))
output = np.transpose(output, axes=(0, 3, 1, 2, 4))
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
return
tvm_input = tvm.nd.array(input, ctx)
tvm_output = tvm.nd.empty(output.shape, ctx=ctx, dtype=B.dtype)
print("Running on target: %s" % device)
with tvm.target.create(device):
s = topi.generic.schedule_injective(B)
f = tvm.build(s, [A, B], device, name="layout_transform")
f(tvm_input, tvm_output)
tvm.testing.assert_allclose(tvm_output.asnumpy(), output)
for backend in get_all_backend():
check_device(backend)
def layout_transform(*args):
"""Autotvm layout transform template."""
cfg = get_config()
cfg.add_flop(-1)
data = args[0]
out = topi.layout_transform(*args)
sch = topi.generic.schedule_injective([out])
return sch, [data, out]
def benchmark_layout_transform(self,
min_exec_num=100,
timeout=10,
use_rpc=False,
device_key=None,
host="localhost",
port=9190,
n_parallel=1,
build_func='default',
layout_records=None,
target_host=None,
infer_layout=False):
"""Benchmark all possible layout transformation in the graph,
given a set of schedule candidates for each workload of target operator.
Parameters
----------
min_exec_num : int, optional
Minimum number of execution. Final execution time is the average of
all execution time.
timeout : int, optional
Time out for each execution.
use_rpc : boolean, optional
Whether to use rpc mode for benchmarking.
device_key : str, optional
Remote device key which can be queried by
python -m tvm.exec.query_rpc_tracker --host=0.0.0.0 --port=9190
host : str, optional
IP address used to create RPC tracker on host machine.
port : int, optional
Port number used to create RPC tracker on host machine.
n_parallel: int, optional
The number of measurement task that can run in parallel.
Set this according to the number of cpu cores (for compilation) and
the number of devices you have (for measuring generate code).
build_func: str or callable, optional
'default': call default builder. This works for normal target (llvm, cuda)
'ndk': use Android NDK to create shared library. Use this for android target.
callable: customized build function for other backends (e.g. VTA).
See autotvm/measure/measure_methods.py::default_build_func for example.
layout_records : str or iterator of (MeasureInput, MeasureResult). optional
Collection of layout_transform benchmarking records.
If is str, then it should be the filename of a records log file.
Each row of this file is an encoded record pair.
Otherwise, it is an iterator.
If this argument is set, graph tuner will first check whether layout_transform
workload already exists in records and skip benchmarking if possible.
target_host : str, optional
str or :any:`tvm.target.Target` optional
Host compilation target, if target is device.
When TVM compiles device specific program such as CUDA,
we also need host(CPU) side code to interact with the driver
setup the dimensions and parameters correctly.
target_host is used to specify the host side codegen target.
By default, llvm is used if it is enabled,
otherwise a stackvm intepreter is used.
infer_layout : bool, optional
Whether to infer layout transformation time if it doesn't exist in records, instead
of benchmarking on target device.
This might bring performance loss comparing to benchmarking layout transformation.
"""
self._logger.info("Start to benchmark layout transformation...")
if layout_records is None and infer_layout:
raise RuntimeError(
"Requires some records to infer layout transformation time.")
if isinstance(layout_records, str):
layout_records = load_from_file(layout_records)
if not layout_records and infer_layout:
raise RuntimeError(
"Records must be non-empty to infer layout transformation time."
)
if isinstance(layout_records, str):
layout_records = load_from_file(layout_records)
num_flops, total_time = 0, 0
if layout_records is not None:
for record in layout_records:
ltf_wkl = record[0].task.workload
self._layout_transform_perf_records[ltf_wkl] = record
input_shape = ltf_wkl[1][1]
flops = np.prod(input_shape)
num_flops += flops
total_time += record[1].costs[0]
avg_time = total_time / num_flops if num_flops > 0 else 0
args_list = []
def _fetch_args_callback(from_node_idx, to_node_idx, from_sch_idx,
to_sch_idx, args):
"""Callback function to fetch layout transform args"""
_, in_layout, out_layout = args
if in_layout != out_layout:
args_list.append(args)
self._iterate_layout_transform(_fetch_args_callback)
def _log_to_list(record_list):
"""Callback to log result to a list."""
def _callback(_, inputs, results):
"""Callback implementation"""
record_list.append((inputs[0], results[0]))
return _callback
builder = autotvm.LocalBuilder(n_parallel=n_parallel,
build_func=build_func)
runner = autotvm.LocalRunner(number=min_exec_num,
repeat=1,
timeout=timeout)
if use_rpc:
if device_key is None:
raise RuntimeError(
"device_key need to be set to use rpc tracker mode.")
runner = autotvm.measure.RPCRunner(device_key,
host,
port,
n_parallel=n_parallel,
number=min_exec_num,
repeat=1,
timeout=timeout)
measure_option = autotvm.measure_option(builder=builder, runner=runner)
for args in args_list:
data, in_layout, out_layout = args
args = serialize_args(args)
ltf_workload = (
'layout_transform', ) + autotvm.task.args_to_workload(args)
if ltf_workload in self._layout_transform_perf_records:
continue
if infer_layout:
input_shape = ltf_workload[1][1]
flops = 1
for i in input_shape:
flops *= i
# Rule out invalid layout transformations
out = topi.layout_transform(data, in_layout, out_layout)
out_flops = 1
for i in topi.util.get_const_tuple(out.shape):
out_flops *= i
if flops != out_flops:
inferred_time = INVALID_LAYOUT_TIME
else:
inferred_time = flops * avg_time
record_input = MeasureInput(target=self._target,
task=None,
config=None)
record_output = MeasureResult(costs=(inferred_time, ),
error_no=0,
all_cost=-1,
timestamp=-1)
self._layout_transform_perf_records[ltf_workload] = (
record_input, record_output)
continue
records = []
task = autotvm.task.create(layout_transform,
args=args,
target=self._target,
target_host=target_host)
task.workload = ltf_workload
tuner = autotvm.tuner.GridSearchTuner(task)
tuner.tune(n_trial=1,
measure_option=measure_option,
callbacks=[_log_to_list(records)])
if not isinstance(records[0][1].costs[0], float):
records[0] = (records[0][0], records[0][1]._replace(
costs=(INVALID_LAYOUT_TIME, )))
self._layout_transform_perf_records[ltf_workload] = records[0]
self._iterate_layout_transform(self._create_matrix_callback)
self._logger.info("Benchmarking layout transformation successful.")
