Skip to content

Commit

Permalink
Remove depreceated mointergration tests with mxnet zoo importers (#10772
Browse files Browse the repository at this point in the history 
)

Co-authored-by: Michalis Papapdimitriou <mpapapdimitriou@octoml.ai>
  • Loading branch information
@mikepapadim
mikepapadim and Michalis Papapdimitriou authored Mar 25, 2022
1 parent b56f9e5 commit 9de36f7
Showing 1 changed file with 0 additions and 212 deletions.
212 changes: 0 additions & 212 deletions tests/python/contrib/test_tensorrt.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -146,55 +146,6 @@ def run_and_verify_func(config, target="cuda", run_module=True, data_type="float
assert_result_dict_holds(result_dict, data_type)


def run_and_verify_model(model, run_module):
import mxnet as mx
from mxnet.gluon.model_zoo.vision import get_model

def check_trt_used(mod):
num_trt_subgraphs = sum(
[1 if gv.name_hint == "tensorrt_0" else 0 for gv in mod.get_global_vars()]
)
assert num_trt_subgraphs == 1

def compile_and_run(mod, params, i_data, mode="vm", use_trt=True):
assert mode in ["graph", "vm"]

if use_trt:
mod, config = tensorrt.partition_for_tensorrt(mod, params)
check_trt_used(mod)
with tvm.transform.PassContext(
opt_level=3, config={"relay.ext.tensorrt.options": config}
):
func = relay.create_executor(
mode, mod=mod, device=tvm.cuda(0), target="cuda"
).evaluate()
else:
with tvm.transform.PassContext(opt_level=3):
func = relay.create_executor(
mode, mod=mod, device=tvm.cuda(0), target="cuda"
).evaluate()

res = func(i_data, **params) if run_module else None
return res

dtype = "float32"
input_shape = (1, 3, 224, 224)
i_data = np.random.uniform(-1, 1, input_shape).astype(dtype)
block = get_model(model, pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)

result_dict = dict()
for mode in ["vm", "graph"]:
for use_trt in [True, False]:
result_key = mode + ("_trt" if use_trt else "")
result_dict[result_key] = compile_and_run(
mod, params, i_data, mode=mode, use_trt=use_trt
)

if run_module:
assert_result_dict_holds(result_dict)


def test_tensorrt_simple(run_module):
for dtype in SUPPORTED_DTYPES:
xshape = (1, 3, 2, 2)
Expand Down Expand Up @@ -278,113 +229,6 @@ def test_tensorrt_not_compatible(run_module):
results = func(x_data)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_tensorrt_serialize_graph_executor(run_module):
import mxnet as mx
from mxnet.gluon.model_zoo.vision import get_model

data_shape = (1, 3, 224, 224)
data_type = "float32"
i_data = np.random.uniform(0, 1, data_shape).astype(data_type)
block = get_model("resnet18_v1", pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={"data": data_shape}, dtype=data_type)
mod, config = tensorrt.partition_for_tensorrt(mod)
tmpdir = utils.tempdir()

def compile_graph(mod, params):
with tvm.transform.PassContext(opt_level=3, config={"relay.ext.tensorrt.options": config}):
graph, lib, params = relay.build(mod, params=params, target="cuda")
params = runtime.save_param_dict(params)
return graph, lib, params

def run_graph(graph, lib, params):
mod_ = graph_executor.create(graph, lib, device=tvm.cuda(0))
mod_.load_params(params)
mod_.run(data=i_data)
res = mod_.get_output(0)
return res

def save_graph(graph, lib, params):
# Serialize
with open(tmpdir.relpath("compiled.json"), "w") as f_graph_json:
f_graph_json.write(graph)
with open(tmpdir.relpath("compiled.params"), "wb") as f_params:
f_params.write(params)
lib.export_library(tmpdir.relpath("compiled.so"))

def load_graph():
# Deserialize
with open(tmpdir.relpath("compiled.json"), "r") as f_graph_json:
graph = f_graph_json.read()
with open(tmpdir.relpath("compiled.params"), "rb") as f_params:
params = bytearray(f_params.read())
lib = tvm.runtime.load_module(tmpdir.relpath("compiled.so"))
return graph, lib, params

# Test serialization with graph executor
graph, lib, graph_params = compile_graph(mod, params)
save_graph(graph, lib, graph_params)
loaded_graph, loaded_lib, loaded_params = load_graph()

if run_module:
result_dict = dict()
result_dict["graph"] = run_graph(graph, lib, graph_params)
result_dict["graph_ref"] = run_graph(loaded_graph, loaded_lib, loaded_params)
assert_result_dict_holds(result_dict)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_tensorrt_serialize_vm(run_module):
import mxnet as mx
from mxnet.gluon.model_zoo.vision import get_model

data_shape = (1, 3, 224, 224)
data_type = "float32"
i_data = np.random.uniform(0, 1, data_shape).astype(data_type)
block = get_model("resnet18_v1", pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={"data": data_shape}, dtype=data_type)
mod, config = tensorrt.partition_for_tensorrt(mod)
tmpdir = utils.tempdir()

def compile_vm(mod, params):
with tvm.transform.PassContext(opt_level=3, config={"relay.ext.tensorrt.options": config}):
vm_exec = relay.vm.compile(mod, target="cuda", params=params)
code, lib = vm_exec.save()
return code, lib

def run_vm(code, lib):
vm_exec = tvm.runtime.vm.Executable.load_exec(code, lib)
vm = VirtualMachine(vm_exec, tvm.cuda(0))
result = vm.invoke("main", data=i_data)
return result

def save_vm(code, lib):
# save and load the code and lib file.
lib.export_library(tmpdir.relpath("path_lib.so"))
with open(tmpdir.relpath("path_code.ro"), "wb") as fo:
fo.write(code)

def load_vm():
lib = tvm.runtime.load_module(tmpdir.relpath("path_lib.so"))
code = bytearray(open(tmpdir.relpath("path_code.ro"), "rb").read())
return lib, code

# Test serialization with VM
code_vm, lib_vm = compile_vm(mod, params)
save_vm(code_vm, lib_vm)
loaded_lib_vm, loaded_code_vm = load_vm()

if run_module:
result_dict = dict()
result_dict["vm"] = run_vm(code_vm, lib_vm)
result_dict["vm_ref"] = run_vm(loaded_code_vm, loaded_lib_vm)
assert_result_dict_holds(result_dict)


def test_conv1d(run_module):
def get_graph(
x_shape=((1, 3, 224)),
Expand Down Expand Up @@ -1302,62 +1146,6 @@ def get_graph(
)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_alexnet(run_module):
run_and_verify_model("alexnet", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_resnet18_v1(run_module):
run_and_verify_model("resnet18_v1", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_resnet18_v2(run_module):
run_and_verify_model("resnet18_v2", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_squeezenet(run_module):
run_and_verify_model("squeezenet1.0", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_mobilenet(run_module):
run_and_verify_model("mobilenet0.25", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_mobilenet_v2(run_module):
run_and_verify_model("mobilenetv2_0.25", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_vgg11(run_module):
run_and_verify_model("vgg11", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
def test_densenet121(run_module):
run_and_verify_model("densenet121", run_module)


@pytest.mark.xfail(
reason=("Currently failing test. See tracking issue https://github.com/apache/tvm/issues/8901")
)
Expand Down

0 comments on commit 9de36f7

Please sign in to comment.