jtuyls commented on a change in pull request #6343: URL: https://github.com/apache/incubator-tvm/pull/6343#discussion_r484547245
########## File path: tests/python/contrib/test_vitis_ai_codegen.py ########## @@ -0,0 +1,203 @@ +# 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. +# pylint: disable=no-else-return, unidiomatic-typecheck, invalid-name, W0611 +"""Vitis-AI codegen tests.""" + +import numpy as np + +import tvm +from tvm import relay +from tvm.relay import transform +from tvm.relay.op.contrib.vitis_ai import annotation +from tvm.contrib.target import vitis_ai + +import pyxir +import pyxir.contrib.target.DPUCADX8G + +def set_func_attr(func, compile_name, symbol_name): + func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1)) + func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1)) + func = func.with_attr("Compiler", compile_name) + func = func.with_attr("global_symbol", symbol_name) + return func + +def _create_graph(): + shape = (10, 10) + mod = tvm.IRModule() + x = relay.var('x', shape=shape) + y = relay.var('y', shape=shape) + z = x + x + p = y * y + func = relay.Function([x, y], p - z) + mod["main"] = func + params = {} + params["x"] = np.random.rand(10, 10).astype('float32') + params["y"] = np.random.rand(10, 10).astype('float32') + return mod, params + + +def _construct_model(func, params=None): + mod = tvm.IRModule() + mod["main"] = func + if params is None: + params = {} + mod = annotation(mod, params, "DPUCADX8G") + mod = transform.MergeCompilerRegions()(mod) + mod = transform.PartitionGraph()(mod) + fcompile = tvm._ffi.get_global_func("relay.ext.vai") + subgraph_mod = tvm.IRModule() + for _, funcnode in mod.functions.items(): + if funcnode.attrs and 'Compiler' in funcnode.attrs and \ + funcnode.attrs['Compiler'] == 'vai': + subgraph_mod["main"] = funcnode + with tvm.transform.PassContext(opt_level=3, config={'target_':'DPUCADX8G'}): + fcompile(subgraph_mod["main"]) + + +def test_add(): Review comment: Depthwise convolution should indeed be tested here as it's supported on our edge DPU target. General broadcast addition is not supported however. I think we are missing a few cases here that we should cover. We are also adding more checks for testing correctness of the partitions that are being generated. We intend to test all individual operations we can handle and that will hopefully give you a better idea of the scope of what is being offloaded. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
