def test_byoc_microtvm(merge_compiler_regions):
"""This is a simple test to check BYOC capabilities of AOT - with and without merging compiler regions to test for https://github.com/apache/tvm/issues/9036"""
use_unpacked_api = False
interface_api = "packed"
test_runner = AOTTestRunner(pass_config={"tir.usmp.enable": True})
x = relay.var("x", shape=(10, 10))
w0 = relay.var("w0", shape=(10, 10))
w1 = relay.var("w1", shape=(10, 10))
# z0 = x + w0
x_ = compiler_begin(x, "ccompiler")
w0_ = compiler_begin(w0, "ccompiler")
z0_ = relay.add(x_, w0_)
z0 = compiler_end(z0_, "ccompiler")
# z1 = z0 + w1
z0__ = compiler_begin(z0, "ccompiler")
w1_ = compiler_begin(w1, "ccompiler")
z1_ = relay.add(z0__, w1_)
z1 = compiler_end(z1_, "ccompiler")
# z2 = z0 + z1
z2 = relay.add(z0, z1)
f = relay.Function([x, w0, w1], z2)
mod = tvm.IRModule()
mod["main"] = f
if merge_compiler_regions:
mod = transform.MergeCompilerRegions()(mod)
mod = transform.PartitionGraph("mod_name")(mod)
mod = transform.InferType()(mod)
x_data = [("x", np.random.rand(10, 10).astype("float32"))]
w_data = [("w{}".format(i), np.random.rand(10, 10).astype("float32")) for i in range(2)]
map_inputs = OrderedDict(x_data + w_data)
output_list = generate_ref_data(mod, map_inputs)
compiled_test_mods = compile_models(
AOTTestModel(name="my_mod", module=mod, inputs=map_inputs, outputs=output_list),
interface_api=interface_api,
use_unpacked_api=use_unpacked_api,
pass_config=test_runner.pass_config,
)
for compiled_model in compiled_test_mods:
check_for_no_tvm_backendallocworkspace_calls(compiled_model.executor_factory.lib)
run_and_check(
models=compiled_test_mods,
runner=test_runner,
interface_api=interface_api,
)
def test_byoc_microtvm(merge_compiler_regions):
"""
This is a simple test to check BYOC capabilities of AOT
with and without merging compiler regions to test for https://github.com/apache/tvm/issues/9036
"""
use_unpacked_api = False
interface_api = "packed"
test_runner = AOT_DEFAULT_RUNNER
input_x = relay.var("x", shape=(10, 10))
input_w0 = relay.var("w0", shape=(10, 10))
input_w1 = relay.var("w1", shape=(10, 10))
# z0 = x + w0
marked_input_x = compiler_begin(input_x, "ccompiler")
marked_input_w0 = compiler_begin(input_w0, "ccompiler")
add_x_and_w0 = relay.add(marked_input_x, marked_input_w0)
end_inner_add = compiler_end(add_x_and_w0, "ccompiler")
# z1 = z0 + w1
marked_inner_add = compiler_begin(end_inner_add, "ccompiler")
marked_w1 = compiler_begin(input_w1, "ccompiler")
add_nested_and_w1 = relay.add(marked_inner_add, marked_w1)
end_outer_add = compiler_end(add_nested_and_w1, "ccompiler")
# z2 = z0 + z1
final_add = relay.add(end_inner_add, end_outer_add)
relay_func = relay.Function([input_x, input_w0, input_w1], final_add)
mod = tvm.IRModule()
mod["main"] = relay_func
if merge_compiler_regions:
mod = transform.MergeCompilerRegions()(mod)
mod = transform.PartitionGraph("mod_name")(mod)
mod = transform.InferType()(mod)
x_data = [("x", np.random.rand(10, 10).astype("float32"))]
w_data = [("w{}".format(i), np.random.rand(10, 10).astype("float32"))
for i in range(2)]
map_inputs = OrderedDict(x_data + w_data)
output_list = generate_ref_data(mod, map_inputs)
compile_and_run(
AOTTestModel(name="my_mod",
module=mod,
inputs=map_inputs,
outputs=output_list),
test_runner,
interface_api,
use_unpacked_api,
)
def test_region_set_creator_diamond():
data = relay.var('data', shape=(10, 10))
cb_1 = compiler_begin(data, 'test_target')
O_1 = relay.abs(cb_1)
ce_1 = compiler_end(O_1, 'test_target')
ce_2 = compiler_end(O_1, 'test_target')
cb_2 = compiler_begin(ce_1, 'test_target')
O_2 = relay.nn.relu(cb_2)
ce_3 = compiler_end(O_2, 'test_target')
cb_d = compiler_begin(ce_2, "default")
X = relay.tanh(cb_d)
ce_d = compiler_end(X, 'default')
cb_3 = compiler_begin(ce_3, 'test_target')
cb_4 = compiler_begin(ce_d, 'test_target')
O_3 = relay.add(cb_3, cb_4)
ce_4 = compiler_end(O_3, 'test_target')
diamond = relay.Function([data], ce_4)
region_set = relay.analysis.AnnotatedRegionSet(
diamond, relay.op.get("annotation.compiler_begin"),
relay.op.get("annotation.compiler_end"))
assert len(region_set) == 4
check_region(
region_set,
'test_target',
[cb_1],
[cb_1, O_1, ce_1, ce_2],
[ce_1, ce_2],
)
check_region(
region_set,
'test_target',
[cb_2],
[cb_2, O_2, ce_3],
[ce_3],
)
check_region(
region_set,
'default',
[cb_d],
[cb_d, X, ce_d],
[ce_d],
)
check_region(
region_set,
'test_target',
[cb_3, cb_4],
[cb_3, cb_4, O_3, ce_4],
[ce_4],
)
def test_byoc_microtvm(merge_compiler_regions):
"""This is a simple test to check BYOC capabilities of AOT - with and without merging compiler regions to test for https://github.com/apache/tvm/issues/9036"""
use_unpacked_api = False
interface_api = "packed"
test_runner = AOT_DEFAULT_RUNNER
x = relay.var("x", shape=(10, 10))
w0 = relay.var("w0", shape=(10, 10))
w1 = relay.var("w1", shape=(10, 10))
# z0 = x + w0
x_ = compiler_begin(x, "ccompiler")
w0_ = compiler_begin(w0, "ccompiler")
z0_ = relay.add(x_, w0_)
z0 = compiler_end(z0_, "ccompiler")
# z1 = z0 + w1
z0__ = compiler_begin(z0, "ccompiler")
w1_ = compiler_begin(w1, "ccompiler")
z1_ = relay.add(z0__, w1_)
z1 = compiler_end(z1_, "ccompiler")
# z2 = z0 + z1
z2 = relay.add(z0, z1)
f = relay.Function([x, w0, w1], z2)
mod = tvm.IRModule()
mod["main"] = f
if merge_compiler_regions:
mod = transform.MergeCompilerRegions()(mod)
mod = transform.PartitionGraph("mod_name")(mod)
mod = transform.InferType()(mod)
x_data = [("x", np.random.rand(10, 10).astype("float32"))]
w_data = [("w{}".format(i), np.random.rand(10, 10).astype("float32"))
for i in range(2)]
map_inputs = OrderedDict(x_data + w_data)
output_list = generate_ref_data(mod, map_inputs)
compile_and_run(
AOTTestModel(name="my_mod",
module=mod,
inputs=map_inputs,
outputs=output_list),
test_runner,
interface_api,
use_unpacked_api,
)
def visit_call(self, call):
new_args = []
for arg in call.args:
ann = compiler_begin(self.visit(arg), "ccompiler")
new_args.append(ann)
new_call = relay.Call(call.op, new_args)
return compiler_end(new_call, "ccompiler")
def expected():
data = relay.var('data', shape=(10, 10))
cb_1 = compiler_begin(data, "test")
O_1 = relay.abs(cb_1)
ce_2 = compiler_end(O_1, "test")
O_2 = relay.nn.relu(O_1)
ce_3 = compiler_end(O_2, "test")
X = relay.tanh(ce_2)
cb_3 = compiler_begin(ce_3, "test")
cb_4 = compiler_begin(X, "test")
O_3 = relay.add(cb_3, cb_4)
ce_4 = compiler_end(O_3, "test")
func = relay.Function([data], ce_4)
return func
def create_graph():
data = relay.var('data', shape=(10, 10))
cb_1 = compiler_begin(data, 'test_target')
O_1 = relay.abs(cb_1)
ce_2 = compiler_end(O_1, 'test_target')
O_2 = relay.nn.relu(O_1)
ce_3 = compiler_end(O_2, 'test_target')
X = relay.tanh(ce_2)
cb_3 = compiler_begin(ce_3, 'test_target')
cb_4 = compiler_begin(X, 'test_target')
O_3 = relay.add(cb_3, cb_4)
ce_4 = compiler_end(O_3, 'test_target')
func = relay.Function([data], ce_4)
return func
def visit_tuple_getitem(self, op):
"""Add compiler_begin and compiler_end annotations to TupleGetItem"""
if int(hash(op.tuple_value)) in annotator.relay_ids:
tuple_value = compiler_begin(super().visit(op.tuple_value),
annotator.compiler)
return compiler_end(TupleGetItem(tuple_value, op.index),
annotator.compiler)
else:
tuple_value = super().visit(op.tuple_value)
return TupleGetItem(tuple_value, op.index)
def test_region_set_creator_merged():
data = relay.var("data", shape=(10, 10))
cb_1 = compiler_begin(data, "test_target")
O_1 = relay.abs(cb_1)
ce_2 = compiler_end(O_1, "test_target")
O_2 = relay.nn.relu(O_1)
ce_3 = compiler_end(O_2, "test_target")
cb_d = compiler_begin(ce_2, "default")
X = relay.tanh(cb_d)
ce_d = compiler_end(X, "default")
cb_3 = compiler_begin(ce_3, "test_target")
cb_4 = compiler_begin(ce_d, "test_target")
O_3 = relay.add(cb_3, cb_4)
O_4 = relay.add(cb_3, cb_4)
O_5 = relay.Tuple([O_3, O_4])
ce_4 = compiler_end(O_5, "test_target")
merged = relay.Function([data], ce_4)
region_set = relay.analysis.AnnotatedRegionSet(
merged, relay.op.get("annotation.compiler_begin"),
relay.op.get("annotation.compiler_end"))
assert len(region_set) == 3
check_region(
region_set,
"test_target",
[cb_1],
[cb_1, O_1, O_2, ce_2, ce_3],
[ce_2, ce_3],
)
check_region(
region_set,
"default",
[cb_d],
[cb_d, X, ce_d],
[ce_d],
)
check_region(
region_set,
"test_target",
[cb_3, cb_4],
[cb_3, cb_4, O_3, O_4, O_5, ce_4],
[ce_4],
)
def diamond_graph_fanouts():
data = relay.var('data', shape=(10, 10))
cb_1 = compiler_begin(data, "test")
O_1 = relay.abs(cb_1)
ce_1 = compiler_end(O_1, "test")
ce_2 = compiler_end(O_1, "test")
cb_2 = compiler_begin(ce_1, "test")
O_2 = relay.nn.relu(cb_2)
ce_3 = compiler_end(O_2, "test")
X = relay.tanh(ce_2)
cb_3 = compiler_begin(ce_3, "test")
cb_4 = compiler_begin(X, "test")
O_3 = relay.add(cb_3, cb_4)
ce_4 = compiler_end(O_3, "test")
diamond = relay.Function([data], ce_4)
return diamond
def visit_call(self, call):
op_name = call.op.name
if op_name in annotator.op_list:
new_args = []
for arg in call.args:
ann = compiler_begin(super().visit(arg), annotator.compiler)
new_args.append(ann)
new_call = relay.Call(call.op, new_args, call.attrs, call.type_args)
return compiler_end(new_call, annotator.compiler)
else:
return super().visit_call(call)
def expected():
in_1 = relay.var('in_1', shape=(10, 10), dtype='float32')
in_2 = relay.var('in_2', shape=(10, 10), dtype='float32')
in_3 = relay.var('in_3', shape=(10, 10), dtype='float32')
in_4 = relay.var('in_4', shape=(10, 10), dtype='float32')
in_5 = relay.var('in_5', shape=(10, 10), dtype='float32')
in_6 = relay.var('in_6', shape=(10, 10), dtype='float32')
in_7 = relay.var('in_7', shape=(10, 10), dtype='float32')
in_8 = relay.var('in_8', shape=(10, 10), dtype='float32')
in_9 = relay.var('in_9', shape=(10, 10), dtype='float32')
in_10 = relay.var('in_10', shape=(10, 10), dtype='float32')
begin0 = compiler_begin(in_1, "test")
begin1 = compiler_begin(in_2, "test")
begin2 = compiler_begin(in_3, "test")
begin3 = compiler_begin(in_4, "test")
node0 = relay.add(begin0, begin1)
node1 = relay.add(begin2, begin3)
node2 = relay.add(node0, node1)
node3 = relay.subtract(in_5, in_6)
node4 = relay.subtract(in_7, node3)
begin4 = compiler_begin(node4, "test")
begin5 = compiler_begin(in_9, "test")
node5 = relay.add(node2, begin4)
end1 = compiler_end(node5, "test")
node6 = relay.subtract(in_8, end1)
node7 = relay.add(begin5, node5)
end2 = compiler_end(node7, "test")
begin6 = compiler_begin(end2, "test")
begin7 = compiler_begin(node6, "test")
node8 = relay.add(begin7, begin6)
begin8 = compiler_begin(in_10, "test")
node9 = relay.add(begin8, node8)
end3 = compiler_end(node9, "test")
f = relay.Function(
[in_1, in_2, in_3, in_4, in_5, in_6, in_7, in_8, in_9, in_10],
end3)
mod = tvm.IRModule.from_expr(f)
return mod
def test_load_params_with_constants_in_ext_codegen():
# After binding params and partitioning graph_module.get_params()
# might contain parameters that are not an graph runtime input but
# for example constants in external function.
y_in = np.ones((1,)).astype("float32")
params = {"y": y_in}
mod = tvm.IRModule()
x = relay.var("x", shape=(1, 10))
y = relay.var("y", shape=(1,))
xcb = compiler_begin(x, "ccompiler")
ycb = compiler_begin(y, "ccompiler")
z = relay.add(xcb, ycb)
zce = compiler_end(z, "ccompiler")
mod["main"] = relay.Function([x, y], zce)
mod["main"] = bind_params_by_name(mod["main"], params)
mod = transform.PartitionGraph()(mod)
graph_module = relay.build(mod, target="llvm", params=params)
lib = update_lib(graph_module.get_lib())
rt_mod = tvm.contrib.graph_runtime.create(graph_module.get_json(), lib, tvm.cpu(0))
rt_mod.load_params(runtime.save_param_dict(graph_module.get_params()))
def torchop(script_fn, *params):
"""Insert an Operation executed in the PyTorch JIT
The operation includes backend annotation
Currently, only tensors are supported. The shape inferrence
assumes that input shapes (and not values) determine output shapes."""
return compiler_end(
relay.op._make.torchop([compiler_begin(p, "torch") for p in params],
script_fn.save_to_buffer()),
"torch",
)
def create_graph():
data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))
weight = relay.var("weight", relay.TensorType((16, 3, 3, 3), "float32"))
bn_gamma = relay.var("bn_gamma", relay.TensorType((16,), "float32"))
bn_beta = relay.var("bn_beta", relay.TensorType((16,), "float32"))
bn_mean = relay.var("bn_mean", relay.TensorType((16,), "float32"))
bn_var = relay.var("bn_var", relay.TensorType((16,), "float32"))
data_cb = compiler_begin(data, "test_target")
weight_cb = compiler_begin(weight, "test_target")
bn_gamma_cb = compiler_begin(bn_gamma, "test_target")
bn_beta_cb = compiler_begin(bn_beta, "test_target")
bn_mean_cb = compiler_begin(bn_mean, "test_target")
bn_var_cb = compiler_begin(bn_var, "test_target")
conv_o = relay.nn.conv2d(
data=data_cb, weight=weight_cb, kernel_size=(3, 3), channels=16, padding=(1, 1)
)
bn_o = relay.nn.batch_norm(conv_o, bn_gamma_cb, bn_beta_cb, bn_mean_cb, bn_var_cb)
relu_o = relay.nn.relu(bn_o[0])
relu_o_ce = compiler_end(relu_o, "test_target")
bn_omean = bn_o[1]
rebn_omean_ce = compiler_end(bn_omean, "test_target")
bn_ovar = bn_o[2]
bn_ovar_ce = compiler_end(bn_ovar, "test_target")
dummy_mean_abs = relay.abs(rebn_omean_ce)
dummy_ovar_abs = relay.abs(bn_ovar_ce)
dummy_tuple = relay.Tuple((relu_o_ce, dummy_mean_abs, dummy_ovar_abs))
func = relay.Function([data, weight, bn_gamma, bn_beta, bn_mean, bn_var], dummy_tuple)
return func
def visit_call(self, call):
if call.op.name == "add": # Annotate begin at args
if self.in_compiler == 1:
lhs = compiler_begin(super().visit(call.args[0]), "ccompiler")
rhs = compiler_begin(super().visit(call.args[1]), "ccompiler")
op = relay.add(lhs, rhs)
self.in_compiler = 2
return op
elif call.op.name == "subtract":
if self.in_compiler == 1:
lhs = super().visit(call.args[0])
rhs = super().visit(call.args[1])
if isinstance(lhs, relay.expr.Var):
lhs = compiler_begin(lhs, "ccompiler")
if isinstance(rhs, relay.expr.Var):
rhs = compiler_begin(rhs, "ccompiler")
return relay.subtract(lhs, rhs)
elif call.op.name == "multiply": # Annotate end at output
self.in_compiler = 1
lhs = super().visit(call.args[0])
rhs = super().visit(call.args[1])
if isinstance(lhs, relay.expr.Var):
lhs = compiler_begin(lhs, "ccompiler")
if isinstance(rhs, relay.expr.Var):
rhs = compiler_begin(rhs, "ccompiler")
op = relay.multiply(lhs, rhs)
if self.in_compiler == 2:
op = compiler_end(op, "ccompiler")
self.in_compiler = 0
return op
return super().visit_call(call)
def visit_call(self, call):
"""Add compiler_begin and compiler_end annotations to the Call expr"""
if int(hash(call)) in annotator.relay_ids:
new_args = []
for arg in call.args:
ann = compiler_begin(super().visit(arg),
annotator.compiler)
new_args.append(ann)
new_call = relay.Call(call.op, new_args, call.attrs,
call.type_args)
return compiler_end(new_call, annotator.compiler)
else:
return super().visit_call(call)
def visit_call(self, call):
curr_last = self.last_call
self.last_call = False
params = []
for arg in call.args:
param = super().visit(arg)
if isinstance(param, relay.expr.Var):
param = compiler_begin(param, self.compiler)
params.append(param)
new_call = relay.Call(call.op, params, call.attrs)
if curr_last:
new_call = compiler_end(new_call, self.compiler)
return new_call
def visit_tuple(self, tup):
"""Add compiler_begin and compiler_end annotations to Tuple"""
field_list = []
cond = int(hash(tup))
for field in tup.fields:
if cond in annotator.relay_ids:
field_list.append(
compiler_begin(super().visit(field),
annotator.compiler))
else:
field_list.append(super().visit(field))
if cond in annotator.relay_ids:
return compiler_end(Tuple(field_list), annotator.compiler)
else:
return Tuple(field_list)
def visit_call(self, call):
if call.op.name == 'nn.global_avg_pool2d':
self.compiler_open = True
compiler_open = self.compiler_open
params = []
for arg in call.args:
param = super().visit(arg)
if call.op.name == 'nn.global_avg_pool2d':
param = compiler_end(param, self.compiler)
if compiler_open and isinstance(param, relay.expr.Var):
param = compiler_begin(param, self.compiler)
params.append(param)
new_call = relay.Call(call.op, params, call.attrs)
return new_call
def visit_constant(self, constant):
new_constant = compiler_begin(constant, self.compiler)
return new_constant
def test_partition():
in_1 = relay.var("in_1", shape=(10, 10), dtype="float32")
in_2 = relay.var("in_2", shape=(10, 10), dtype="float32")
in_3 = relay.var("in_3", shape=(10, 10), dtype="float32")
in_4 = relay.var("in_4", shape=(10, 10), dtype="float32")
in_5 = relay.var("in_5", shape=(10, 10), dtype="float32")
in_6 = relay.var("in_6", shape=(10, 10), dtype="float32")
in_7 = relay.var("in_7", shape=(10, 10), dtype="float32")
in_8 = relay.var("in_8", shape=(10, 10), dtype="float32")
in_9 = relay.var("in_9", shape=(10, 10), dtype="float32")
in_10 = relay.var("in_10", shape=(10, 10), dtype="float32")
begin0 = compiler_begin(in_1, "onnx")
begin1 = compiler_begin(in_2, "onnx")
begin2 = compiler_begin(in_3, "onnx")
begin3 = compiler_begin(in_4, "onnx")
node0 = relay.add(begin0, begin1)
node1 = relay.add(begin2, begin3)
end0 = compiler_end(node0, "onnx")
end1 = compiler_end(node1, "onnx")
begin4 = compiler_begin(end0, "onnx")
begin5 = compiler_begin(end1, "onnx")
node2 = relay.add(begin4, begin5)
end2 = compiler_end(node2, "onnx")
dbegin0 = compiler_begin(in_5, "default")
dbegin1 = compiler_begin(in_6, "default")
node3 = relay.subtract(dbegin0, dbegin1)
dbegin2 = compiler_begin(in_7, "default")
dend1 = compiler_end(node3, "default")
dbegin3 = compiler_begin(dend1, "default")
node4 = relay.subtract(dbegin2, dbegin3)
dend2 = compiler_end(node4, "default")
begin6 = compiler_begin(end2, "onnx")
begin7 = compiler_begin(dend2, "onnx")
node5 = relay.add(begin6, begin7)
end3 = compiler_end(node5, "onnx")
end4 = compiler_end(node5, "onnx")
dbegin4 = compiler_begin(in_8, "default")
dbegin5 = compiler_begin(end3, "default")
node6 = relay.subtract(dbegin4, dbegin5)
begin8 = compiler_begin(in_9, "onnx")
begin9 = compiler_begin(end4, "onnx")
node7 = relay.multiply(begin8, begin9)
end5 = compiler_end(node7, "onnx")
dend3 = compiler_end(node6, "default")
begin10 = compiler_begin(dend3, "onnx")
begin11 = compiler_begin(end5, "onnx")
node8 = relay.add(begin10, begin11)
end6 = compiler_end(node8, "onnx")
begin12 = compiler_begin(in_10, "onnx")
begin13 = compiler_begin(end6, "onnx")
node9 = relay.add(begin12, begin13)
end7 = compiler_end(node9, "onnx")
func = relay.Function(
[in_1, in_2, in_3, in_4, in_5, in_6, in_7, in_8, in_9, in_10], end7)
target = "llvm"
mod = IRModule.from_expr(func)
mod = transform.PartitionGraph()(mod)
with tvm.transform.PassContext(opt_level=3, disabled_pass=["FuseOps"]):
graph_json, mod1, params = relay.build(mod, target)
assert mod1.type_key == "metadata"
assert mod1.imported_modules[0].type_key == "llvm"
assert mod1.imported_modules[0].get_source()
assert mod1.imported_modules[1].type_key == "onnx"
assert mod1.imported_modules[1].get_source()
def annotated():
in_1 = relay.var("in_1", shape=(10, 10), dtype="float32")
in_2 = relay.var("in_2", shape=(10, 10), dtype="float32")
in_3 = relay.var("in_3", shape=(10, 10), dtype="float32")
in_4 = relay.var("in_4", shape=(10, 10), dtype="float32")
in_5 = relay.var("in_5", shape=(10, 10), dtype="float32")
in_6 = relay.var("in_6", shape=(10, 10), dtype="float32")
in_7 = relay.var("in_7", shape=(10, 10), dtype="float32")
in_8 = relay.var("in_8", shape=(10, 10), dtype="float32")
in_9 = relay.var("in_9", shape=(10, 10), dtype="float32")
in_10 = relay.var("in_10", shape=(10, 10), dtype="float32")
begin0 = compiler_begin(in_1, "test")
begin1 = compiler_begin(in_2, "test")
begin2 = compiler_begin(in_3, "test")
begin3 = compiler_begin(in_4, "test")
node0 = relay.add(begin0, begin1)
node1 = relay.add(begin2, begin3)
end0 = compiler_end(node0, "test")
end1 = compiler_end(node1, "test")
begin4 = compiler_begin(end0, "test")
begin5 = compiler_begin(end1, "test")
node2 = relay.add(begin4, begin5)
end2 = compiler_end(node2, "test")
dbegin0 = compiler_begin(in_5, "default")
dbegin1 = compiler_begin(in_6, "default")
node3 = relay.subtract(dbegin0, dbegin1)
dbegin2 = compiler_begin(in_7, "default")
dend1 = compiler_end(node3, "default")
dbegin3 = compiler_begin(dend1, "default")
node4 = relay.subtract(dbegin2, dbegin3)
dend2 = compiler_end(node4, "default")
begin6 = compiler_begin(end2, "test")
begin7 = compiler_begin(dend2, "test")
node5 = relay.add(begin6, begin7)
end3 = compiler_end(node5, "test")
end4 = compiler_end(node5, "test")
dbegin4 = compiler_begin(in_8, "default")
dbegin5 = compiler_begin(end3, "default")
node6 = relay.subtract(dbegin4, dbegin5)
begin8 = compiler_begin(in_9, "test")
begin9 = compiler_begin(end4, "test")
node7 = relay.add(begin8, begin9)
end5 = compiler_end(node7, "test")
dend3 = compiler_end(node6, "default")
begin10 = compiler_begin(dend3, "test")
begin11 = compiler_begin(end5, "test")
node8 = relay.add(begin10, begin11)
end6 = compiler_end(node8, "test")
begin12 = compiler_begin(in_10, "test")
begin13 = compiler_begin(end6, "test")
node9 = relay.add(begin12, begin13)
end7 = compiler_end(node9, "test")
f = relay.Function(
[in_1, in_2, in_3, in_4, in_5, in_6, in_7, in_8, in_9, in_10],
end7)
mod = tvm.IRModule.from_expr(f)
return mod