prepare_test_libs.py

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"""Script to prepare test_addone.so"""
import tvm
import numpy as np
from tvm import te
from tvm import relay
import os
import onnx

img_data = np.random.rand(1, 3, 640, 640).astype("float32")/255
input_name = "images"
shape_dict = {input_name:img_data.shape}
input_shape = img_data.shape
print("input shape",img_data.shape)
model_path = "/yolov5s.onnx"
onnx_model = onnx.load(model_path)
np.random.seed(0)
dtype = "float32"


def prepare_test_libs(base_path):
    n = te.var("n")
    A = te.placeholder((n,), name="A")
    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
    s = te.create_schedule(B.op)
    # Compile library as dynamic library
    fadd_dylib = tvm.build(s, [A, B], "llvm", name="addone")
    dylib_path = os.path.join(base_path, "test_addone_dll.so")
    fadd_dylib.export_library(dylib_path)

    # Compile library in system library mode
    fadd_syslib = tvm.build(s, [A, B], "llvm", name="addonesys")
    syslib_path = os.path.join(base_path, "test_addone_sys.o")
    fadd_syslib.save(syslib_path)


def prepare_graph_lib(base_path):
    #x = relay.var("x", shape=(2, 2), dtype="float32")
    #y = relay.var("y", shape=(2, 2), dtype="float32")
    #params = {"y": np.ones((2, 2), dtype="float32")}
    #mod = tvm.IRModule.from_expr(relay.Function([x, y], x + y))
    # build a module
    mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, dtype=dtype)

    compiled_lib = relay.build(mod, tvm.target.Target("rocm -libs=miopen,rocblas"), params=params)
    # export it as a shared library
    # If you are running cross compilation, you can also consider export
    # to tar and invoke host compiler later.
    dylib_path = os.path.join(base_path, "yolov5s_miopen_rocblas.so")
    compiled_lib.export_library(dylib_path)
    
def model_test():
    from tvm.contrib import graph_executor
    ctx = tvm.rocm()
    #compile_lib = tvm.runtime.load_module("lib/vgg16_test_relay_add.so")
    compile_lib:tvm.runtime.Module = tvm.runtime.load_module("lib/vgg16_test_relay_add.so")
    module = graph_executor.GraphModule(compile_lib["default"](ctx))
    module.set_input(onnx_model.graph.input[0].name, img_data)
    module.run()
    output = module.get_output(0).asnumpy()
    print(output.shape)

if __name__ == "__main__":
    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
    #prepare_test_libs(os.path.join(curr_path, "lib"))
    prepare_graph_lib(os.path.join(curr_path, "lib"))
    #model_test()