def _generate_project(temp_dir, board, west_cmd, lowered, build_config, sample,
output_shape):
with tempfile.NamedTemporaryFile() as tar_temp_file:
with tarfile.open(tar_temp_file.name, "w:gz") as tf:
with tempfile.TemporaryDirectory() as tar_temp_dir:
model_files_path = os.path.join(tar_temp_dir, "include")
os.mkdir(model_files_path)
test_utils.loadCMSIS(model_files_path)
tf.add(model_files_path,
arcname=os.path.relpath(model_files_path, tar_temp_dir))
header_path = generate_c_interface_header(
lowered.libmod_name, ["input_1"], ["output"], [],
model_files_path)
tf.add(header_path,
arcname=os.path.relpath(header_path, tar_temp_dir))
test_utils.create_header_file("input_data", sample, "include", tf)
test_utils.create_header_file(
"output_data", np.zeros(shape=output_shape, dtype="float32"),
"include", tf)
project, _ = test_utils.build_project(
temp_dir,
board,
west_cmd,
lowered,
build_config,
extra_files_tar=tar_temp_file.name,
)
return project
def test_qemu_make_fail(temp_dir, board, west_cmd, tvm_debug):
"""Testing QEMU make fail."""
if board not in ["qemu_x86", "mps2_an521"]:
pytest.skip(msg="Only for QEMU targets.")
model = test_utils.ZEPHYR_BOARDS[board]
build_config = {"debug": tvm_debug}
shape = (10,)
dtype = "float32"
# Construct Relay program.
x = relay.var("x", relay.TensorType(shape=shape, dtype=dtype))
xx = relay.multiply(x, x)
z = relay.add(xx, relay.const(np.ones(shape=shape, dtype=dtype)))
func = relay.Function([x], z)
ir_mod = tvm.IRModule.from_expr(func)
target = tvm.target.target.micro(model)
executor = Executor("aot")
runtime = Runtime("crt")
with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
lowered = relay.build(ir_mod, target, executor=executor, runtime=runtime)
# Generate input/output header files
with tempfile.NamedTemporaryFile() as tar_temp_file:
with tarfile.open(tar_temp_file.name, "w:gz") as tf:
with tempfile.TemporaryDirectory() as tar_temp_dir:
model_files_path = os.path.join(tar_temp_dir, "include")
os.mkdir(model_files_path)
header_path = generate_c_interface_header(
lowered.libmod_name, ["input_1"], ["output"], [], 0, model_files_path
)
tf.add(header_path, arcname=os.path.relpath(header_path, tar_temp_dir))
test_utils.create_header_file(
"input_data", np.zeros(shape=shape, dtype=dtype), "include", tf
)
test_utils.create_header_file(
"output_data", np.zeros(shape=shape, dtype=dtype), "include", tf
)
project, project_dir = test_utils.build_project(
temp_dir,
board,
west_cmd,
lowered,
build_config,
extra_files_tar=tar_temp_file.name,
)
file_path = (
pathlib.Path(project_dir) / "build" / "zephyr" / "CMakeFiles" / "run.dir" / "build.make"
)
assert file_path.is_file(), f"[{file_path}] does not exist."
# Remove a file to create make failure.
os.remove(file_path)
project.flash()
with pytest.raises(server.JSONRPCError) as excinfo:
project.transport().open()
assert "QEMU setup failed" in str(excinfo.value)
def test_tflite(temp_dir, board, west_cmd, tvm_debug):
"""Testing a TFLite model."""
model = test_utils.ZEPHYR_BOARDS[board]
input_shape = (1, 49, 10, 1)
output_shape = (1, 12)
build_config = {"debug": tvm_debug}
model_url = "https://github.com/tlc-pack/web-data/raw/25fe99fb00329a26bd37d3dca723da94316fd34c/testdata/microTVM/model/keyword_spotting_quant.tflite"
model_path = download_testdata(model_url, "keyword_spotting_quant.tflite", module="model")
# Import TFLite model
tflite_model_buf = open(model_path, "rb").read()
try:
import tflite
tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
import tflite.Model
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
# Load TFLite model and convert to Relay
relay_mod, params = relay.frontend.from_tflite(
tflite_model, shape_dict={"input_1": input_shape}, dtype_dict={"input_1 ": "int8"}
)
target = tvm.target.target.micro(model)
executor = Executor(
"aot", {"unpacked-api": True, "interface-api": "c", "workspace-byte-alignment": 4}
)
runtime = Runtime("crt")
with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
lowered = relay.build(relay_mod, target, params=params, runtime=runtime, executor=executor)
sample_url = "https://github.com/tlc-pack/web-data/raw/967fc387dadb272c5a7f8c3461d34c060100dbf1/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy"
sample_path = download_testdata(sample_url, "keyword_spotting_int8_6.pyc.npy", module="data")
sample = np.load(sample_path)
with tempfile.NamedTemporaryFile() as tar_temp_file:
with tarfile.open(tar_temp_file.name, "w:gz") as tf:
with tempfile.TemporaryDirectory() as tar_temp_dir:
model_files_path = os.path.join(tar_temp_dir, "include")
os.mkdir(model_files_path)
header_path = generate_c_interface_header(
lowered.libmod_name, ["input_1"], ["output"], [], 0, model_files_path
)
tf.add(header_path, arcname=os.path.relpath(header_path, tar_temp_dir))
test_utils.create_header_file("input_data", sample, "include", tf)
test_utils.create_header_file(
"output_data", np.zeros(shape=output_shape, dtype="int8"), "include", tf
)
project, _ = test_utils.build_project(
temp_dir,
board,
west_cmd,
lowered,
build_config,
extra_files_tar=tar_temp_file.name,
)
project.flash()
with project.transport() as transport:
aot_transport_init_wait(transport)
transport.write(b"infer%", timeout_sec=5)
result_line = aot_transport_find_message(transport, "result", timeout_sec=60)
result_line = result_line.strip("\n")
result_line = result_line.split(":")
result = int(result_line[1])
time = int(result_line[2])
logging.info(f"Result: {result}\ttime: {time} ms")
assert result == 6
def test_tflite(temp_dir, board, west_cmd, tvm_debug):
"""Testing a TFLite model."""
if board not in [
"qemu_x86",
"mps2_an521",
"nrf5340dk_nrf5340_cpuapp",
"nucleo_l4r5zi",
"qemu_cortex_r5",
]:
pytest.skip(msg="Model does not fit.")
model = test_utils.ZEPHYR_BOARDS[board]
input_shape = (1, 32, 32, 3)
output_shape = (1, 10)
build_config = {"debug": tvm_debug}
model_url = "https://github.com/eembc/ulpmark-ml/raw/fc1499c7cc83681a02820d5ddf5d97fe75d4f663/base_models/ic01/ic01_fp32.tflite"
model_path = download_testdata(model_url,
"ic01_fp32.tflite",
module="model")
# Import TFLite model
tflite_model_buf = open(model_path, "rb").read()
try:
import tflite
tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
import tflite.Model
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
# Load TFLite model and convert to Relay
relay_mod, params = relay.frontend.from_tflite(
tflite_model,
shape_dict={"input_1": input_shape},
dtype_dict={"input_1 ": "float32"})
target = tvm.target.target.micro(model,
options=[
"-link-params=1", "--executor=aot",
"--unpacked-api=1",
"--interface-api=c"
])
with tvm.transform.PassContext(opt_level=3,
config={"tir.disable_vectorize": True}):
lowered = relay.build(relay_mod, target, params=params)
# Load sample and generate input/output header files
sample_url = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/testdata_image_classification_fp32_8.npy"
sample_path = download_testdata(sample_url,
"testdata_image_classification_fp32_8.npy",
module="data")
sample = np.load(sample_path)
with tempfile.NamedTemporaryFile() as tar_temp_file:
with tarfile.open(tar_temp_file.name, "w:gz") as tf:
with tempfile.TemporaryDirectory() as tar_temp_dir:
model_files_path = os.path.join(tar_temp_dir, "include")
os.mkdir(model_files_path)
header_path = generate_c_interface_header(
lowered.libmod_name, ["input_1"], ["output"],
model_files_path)
tf.add(header_path,
arcname=os.path.relpath(header_path, tar_temp_dir))
test_utils.create_header_file("input_data", sample, "include", tf)
test_utils.create_header_file(
"output_data", np.zeros(shape=output_shape, dtype="float32"),
"include", tf)
project, _ = test_utils.build_project(
temp_dir,
board,
west_cmd,
lowered,
build_config,
extra_files_tar=tar_temp_file.name,
)
project.flash()
with project.transport() as transport:
timeout_read = 60
test_utils.get_message(transport, "#wakeup", timeout_sec=timeout_read)
transport.write(b"start\n", timeout_sec=5)
result_line = test_utils.get_message(transport,
"#result",
timeout_sec=timeout_read)
result_line = result_line.strip("\n")
result_line = result_line.split(":")
result = int(result_line[1])
time = int(result_line[2])
logging.info(f"Result: {result}\ttime: {time} ms")
assert result == 8