prepare_test_libs.py 3.06 KB
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# 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 = "/mobilenetv2-7.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, "MobileNet_V2_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, "MobileNet_V2_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, "MobileNet_V2.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"))