def test_graph_tuner_layout_transform():
log_file = "%s/test_tuner.log" % (os.getcwd())
target = "llvm"
dshape = (1, 3, 8, 8)
dtype = "float32"
layout = "NCHW"
target_ops = [relay.nn.conv2d]
g, records, ltf_records, ltf_keys, _ = _create_data(target, dshape, dtype, layout)
executor = DPTuner(g, {"data": dshape}, records, target_ops, target=target, log_file=log_file)
executor.benchmark_layout_transform(layout_records=ltf_records, infer_layout=True)
out = executor._layout_transform_perf_records
num_flops = 0
total_time = 0
for record in ltf_records:
ltf_wkl = record[0].task.workload
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
for ltf_workload in out:
input_shape = ltf_workload[1][1]
flops = 1
for i in input_shape:
flops *= i
expected_time = flops * avg_time
out_time = out[ltf_workload][1].costs[0]
assert expected_time == out_time, "Inferred layout transformation time mismatch for %s: " \
"expecting %f but got %f" % (str(ltf_workload), expected_time,
out_time)
def test_DPTuner_run():
log_file = "%s/test_tuner.log" % (os.getcwd())
target = "llvm"
dtype = "float32"
layout = "NCHW"
dshape = (1, 3, 8, 8)
target_ops = [relay.nn.conv2d]
g, records, ltf_records, ltf_keys, tasks = _create_data(target, dshape, dtype, layout)
mod = relay.module.Module()
mod["main"] = g
costs = [0.02, 0.02, 0.045]
config_list = []
cfg_dict = {"i": -1,
"c": None,
"e": [["tile_ic", "sp", [1, 3]],
["tile_oc", "sp", [2, 8]],
["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", [4, 4]],
["tile_oc", "sp", [2, 16]],
["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", [16, 2]],
["tile_oc", "sp", [8, 4]],
["tile_ow", "sp", [2, 4]],
["unroll_kw", "ot", False]],
"t": ""}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
for cost, config, task in zip(costs, config_list, tasks):
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))
executor = DPTuner(mod, {"data": dshape}, records, target_ops, target, log_file=log_file)
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[3][0].config, records[1][0].config, records[2][0].config]
assert expected_out == out, "Output mismatch: expecting %s but got %s" \
% (str(expected_out), str(out))
assert os.path.isfile(log_file), "No log file with name %s exists." % log_file
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 test_triangle_block():
target = "llvm"
dtype = "float32"
dshape = (1, 3, 8, 8)
layout = "NCHW"
conv2d = relay.op.get("nn.conv2d")
target_ops = [conv2d]
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(data,
w2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
out = relay.concatenate([conv0, conv1, conv2], 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=(conv2d, ))
costs = [0.04, 0.012, 0.03, 0.02, 0.02, 0.045]
config_list = []
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [3, 1]], ["tile_oc", "sp", [4, 4]],
["tile_ow", "sp", [4, 2]], ["unroll_kw", "ot", True]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [2, 8]], ["tile_oc", "sp", [1, 32]],
["tile_oh", "ot", 1], ["tile_ow", "sp", [4, 2]]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [8, 4]], ["tile_oc", "sp", [4, 8]],
["tile_ow", "sp", [2, 4]], ["unroll_kw", "ot", False]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [1, 3]], ["tile_oc", "sp", [2, 8]],
["tile_ow", "sp", [4, 2]], ["unroll_kw", "ot", True]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [4, 4]], ["tile_oc", "sp", [2, 16]],
["tile_oh", "ot", 1], ["tile_ow", "sp", [4, 2]]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
cfg_dict = {
"index":
-1,
"code_hash":
None,
"entity": [["tile_ic", "sp", [16, 2]], ["tile_oc", "sp", [8, 4]],
["tile_ow", "sp", [2, 4]], ["unroll_kw", "ot", False]]
}
config_list.append(ConfigEntity.from_json_dict(cfg_dict))
records = []
tasks = tasks + tasks
for cost, config, task in zip(costs, config_list, tasks):
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_task = autotvm.task.create('layout_transform', ltf_arg, target)
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[3][0].config, records[1][0].config, records[2][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[3][0].config, records[1][0].config, records[2][0].config
]
assert expected_out == out, "Output mismatch: expecting %s but got %s" \
% (str(expected_out), str(out))