# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """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(1,3,224,224).astype("float32")/255 input_name = "data" shape_dict = {input_name: img_data.shape} input_shape = img_data.shape print("input shape",img_data.shape) #mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, dtype=dtype) model_path = "/resnet50-v2-7.onnx" onnx_model = onnx.load(model_path) #target = "rocm -libs=miopen,rocblas" np.random.seed(0) dtype = "float32" #img_data = np.random.rand(1,3,224,224).astype("float32")/255 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 mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, dtype=dtype) fadd_dylib = tvm.build(mod, params, "rocm -libs=miopen,rocblas", name="addone") dylib_path = os.path.join(base_path, "resnet50_addone_dll.so") fadd_dylib.export_library(dylib_path) # Compile library in system library mode fadd_syslib = relay.build(mod, params, "rocm -libs=miopen,rocblas", name="add_resnet50") syslib_path = os.path.join(base_path, "resnet50_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)) mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, dtype=dtype) # build a module #with tvm.transform.PassContext(opt_level=3): 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, "resnet50-v2-7.so") compiled_lib.export_library(dylib_path) 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"))