def test_tuple():
target = "llvm"
dtype = "float32"
dshape = (1, 5, 32, 32)
layout = "NCHW"
target_ops = [relay.nn.conv2d]
data = relay.var("data", shape=dshape, dtype=dtype)
w0 = relay.var("w0_weight")
conv0 = relay.nn.conv2d(data,
w0,
channels=2,
kernel_size=(3, 3),
padding=(1, 1))
w1 = relay.var("w1_weight")
conv1 = relay.nn.conv2d(data,
w1,
channels=3,
kernel_size=(3, 3),
padding=(1, 1))
out = relay.concatenate([conv0, conv1], axis=1)
net = relay.Function(relay.analysis.free_vars(out), out)
net, params = relay.testing.create_workload(net)
tasks = autotvm.task.extract_from_program(net["main"],
target=target,
params=params,
ops=(relay.op.nn.conv2d, ))
wkl_list = [
create_workload((1, 5, 32, 32), (2, 5, 3, 3), (1, 1), (1, 1), (1, 1),
layout, layout, dtype, dtype),
create_workload((1, 5, 32, 32), (3, 5, 3, 3), (1, 1), (1, 1), (1, 1),
layout, layout, dtype, dtype),
]
costs = [0.01, 0.012, 0.03, 0.04]
config_list = []
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [1, 5]], ["tile_oc", "sp", [1, 2]],
["tile_ow", "sp", [4, 8]], ["unroll_kw", "ot", True]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [1, 5]], ["tile_oc", "sp", [1, 3]],
["tile_ow", "sp", [2, 16]], ["unroll_kw", "ot", False]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [1, 5]], ["tile_oc", "sp", [2, 1]],
["tile_ow", "sp", [4, 8]], ["unroll_kw", "ot", True]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [1, 5]], ["tile_oc", "sp", [3, 1]],
["tile_ow", "sp", [2, 16]], ["unroll_kw", "ot", False]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
records = []
wkl_list = wkl_list + wkl_list
tasks = tasks + tasks
for wkl, cost, config, task in zip(wkl_list, costs, config_list, tasks):
task.workload = wkl
ms_input = MeasureInput(target=target, task=task, config=config)
ms_output = MeasureResult(costs=(cost, ),
error_no=0,
all_cost=-1,
timestamp=-1)
records.append((ms_input, ms_output))
ltf_records = []
ltf_arg = [
tvm.placeholder((1, 64, 16, 16, 8), dtype=dtype), "NCHW8c", "NCHW512c"
]
ltf_arg = autotvm.task.topi_integration.serialize_args(ltf_arg)
ltf_wkl = ('layout_transform', ) + autotvm.task.args_to_workload(ltf_arg)
ltf_task = copy.deepcopy(tasks[0])
ltf_task.workload = ltf_wkl
ms_input = MeasureInput(target=target, task=ltf_task, config=None)
ms_output = MeasureResult(costs=(1.91224744e-05, ),
error_no=0,
all_cost=-1,
timestamp=-1)
ltf_records.append((ms_input, ms_output))
executor = DPTuner(net, {"data": dshape}, records, target_ops, target)
executor.benchmark_layout_transform(layout_records=ltf_records,
infer_layout=True)
executor.run()
out = [record[0].config for record in executor.get_optimal_records()]
expected_out = [records[2][0].config, records[1][0].config]
assert expected_out == out, "Output mismatch: expecting %s but got %s" \
% (str(expected_out), str(out))
executor = PBQPTuner(net, {"data": dshape}, records, target_ops, target)
executor.benchmark_layout_transform(layout_records=ltf_records,
infer_layout=True)
executor.run()
out = [record[0].config for record in executor.get_optimal_records()]
expected_out = [records[2][0].config, records[1][0].config]
assert expected_out == out, "Output mismatch: expecting %s but got %s" \
% (str(expected_out), str(out))
def _create_data(target, dshape, dtype, layout):
data = relay.var("data", shape=dshape, dtype=dtype)
w0 = relay.var("w0_weight")
conv0 = relay.nn.conv2d(data,
w0,
channels=16,
kernel_size=(3, 3),
padding=(1, 1))
w1 = relay.var("w1_weight")
conv1 = relay.nn.conv2d(conv0, w1, channels=32, kernel_size=(1, 1))
w2 = relay.var("w2_weight")
conv2 = relay.nn.conv2d(conv1,
w2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
out = relay.add(conv1, conv2)
net = relay.Function(relay.analysis.free_vars(out), out)
mod, params = relay.testing.create_workload(net)
tasks = autotvm.task.extract_from_program(mod["main"],
target=target,
params=params,
ops=(relay.op.nn.conv2d, ))
wkl_list = [
create_workload((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1), (1, 1),
layout, layout, dtype, dtype),
create_workload((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0), (1, 1),
layout, layout, dtype, dtype),
create_workload((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1), (1, 1),
layout, layout, dtype, dtype),
]
costs = [0.04, 0.012, 0.03]
config_list = []
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [3, 1]], ["tile_oc", "sp", [4, 4]],
["tile_ow", "sp", [4, 2]], ["unroll_kw", "ot", True]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [2, 8]], ["tile_oc", "sp", [1, 32]],
["tile_oh", "ot", 1], ["tile_ow", "sp", [4, 2]]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"i":
-1,
"c":
None,
"e": [["tile_ic", "sp", [8, 4]], ["tile_oc", "sp", [4, 8]],
["tile_ow", "sp", [2, 4]], ["unroll_kw", "ot", False]],
"t":
""
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
records = []
for wkl, cost, config, task in zip(wkl_list, costs, config_list, tasks):
task.workload = wkl
ms_input = MeasureInput(target=target, task=task, config=config)
ms_output = MeasureResult(costs=(cost, ),
error_no=0,
all_cost=-1,
timestamp=-1)
records.append((ms_input, ms_output))
ltf_records = []
ltf_arg = [
tvm.placeholder((1, 64, 16, 16, 8), dtype=dtype), "NCHW8c", "NCHW512c"
]
ltf_arg = autotvm.task.topi_integration.serialize_args(ltf_arg)
ltf_wkl = ('layout_transform', ) + autotvm.task.args_to_workload(ltf_arg)
ltf_task = copy.deepcopy(tasks[0])
ltf_task.workload = ltf_wkl
ms_input = MeasureInput(target=target, task=ltf_task, config=None)
ms_output = MeasureResult(costs=(1.91224744e-05, ),
error_no=0,
all_cost=-1,
timestamp=-1)
ltf_records.append((ms_input, ms_output))
ltf_keys = []
ltf_arg = [
tvm.placeholder((1, 4, 8, 8, 4), dtype=dtype), "NCHW4c", "NCHW8c"
]
ltf_arg = autotvm.task.topi_integration.serialize_args(ltf_arg)
ltf_wkl = ('layout_transform', ) + autotvm.task.args_to_workload(ltf_arg)
ltf_keys.append(ltf_wkl)
ltf_arg = [
tvm.placeholder((1, 1, 8, 8, 32), dtype=dtype), "NCHW32c", "NCHW4c"
]
ltf_arg = autotvm.task.topi_integration.serialize_args(ltf_arg)
ltf_wkl = ('layout_transform', ) + autotvm.task.args_to_workload(ltf_arg)
ltf_keys.append(ltf_wkl)
ltf_arg = [
tvm.placeholder((1, 4, 8, 8, 8), dtype=dtype), "NCHW8c", "NCHW32c"
]
ltf_arg = autotvm.task.topi_integration.serialize_args(ltf_arg)
ltf_wkl = ('layout_transform', ) + autotvm.task.args_to_workload(ltf_arg)
ltf_keys.append(ltf_wkl)
return net, records, ltf_records, ltf_keys, tasks
