jtuyls commented on a change in pull request #6343: URL: https://github.com/apache/incubator-tvm/pull/6343#discussion_r479611862
########## 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(): + shape = (10, 10) + x = relay.var('x', shape=shape) + y = x + x + func = relay.Function([x], y) + _construct_model(func) + +def test_relu(): + shape = (10, 10) + x = relay.var('x', shape=shape) + y = relay.nn.relu(x) + func = relay.Function([x], y) + _construct_model(func) + +def test_conv2d(): + x = relay.var('x', shape=(1, 3, 224, 224)) + w = relay.const(np.zeros((16, 3, 3, 3), dtype='float32')) + y = relay.nn.conv2d(x, w, strides=[2, 2], padding=[1, 1, 1, 1], kernel_size=[3, 3]) + func = relay.Function([x], y) + params = {} + params["x"] = np.zeros((16, 3, 3, 3), dtype='float32') + _construct_model(func, params) + + +def test_global_avg_pool2d(): + shape = (10, 10, 10, 10) + x = relay.var('x', shape=shape) + y = relay.nn.global_avg_pool2d(x) + func = relay.Function([x], y) + _construct_model(func) + +def test_annotate(): + """Test annotation with Vitis-AI DP (DPUCADX8G)""" + def partition(): + data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32")) + weight = relay.var("weight", relay.TensorType((16, 3, 3, 3), "float32")) + bn_gamma = relay.var("bn_gamma", relay.TensorType((16, ), "float32")) + bn_beta = relay.var("bn_beta", relay.TensorType((16, ), "float32")) + bn_mmean = relay.var("bn_mean", relay.TensorType((16, ), "float32")) + bn_mvar = relay.var("bn_var", relay.TensorType((16, ), "float32")) + + conv = relay.nn.conv2d( + data=data, + weight=weight, + kernel_size=(3, 3), + channels=16, + padding=(1, 1)) + bn_output = relay.nn.batch_norm(conv, bn_gamma, bn_beta, bn_mmean, + bn_mvar) + + func = relay.Function([data, weight, bn_gamma, bn_beta, bn_mmean, + bn_mvar], bn_output.astuple()) + mod = tvm.IRModule() + mod["main"] = func + params = {} + params["weight"] = np.random.rand(16, 3, 3, 3).astype('float32') + params["bn_gamma"] = np.random.rand(16).astype('float32') + params["bn_beta"] = np.random.rand(16).astype('float32') + params["bn_mean"] = np.random.rand(16).astype('float32') + params["bn_var"] = np.random.rand(16).astype('float32') + mod = annotation(mod, params, "DPUCADX8G") + + opt_pass = tvm.transform.Sequential([ + transform.InferType(), + transform.PartitionGraph(), + transform.SimplifyInference(), + transform.FoldConstant(), + transform.AlterOpLayout(), + ]) + + with tvm.transform.PassContext(opt_level=3): + mod = opt_pass(mod) + + return mod + + def expected(): + # function for batch_norm + data0 = relay.var("data0", relay.TensorType((1, 16, 224, 224), + "float32")) + mod = tvm.IRModule() + bn_gamma = relay.var("bn_gamma1", relay.TensorType((16, ), "float32")) + bn_beta = relay.var("bn_beta1", relay.TensorType((16, ), "float32")) + bn_mmean = relay.var("bn_mean1", relay.TensorType((16, ), "float32")) + bn_mvar = relay.var("bn_var1", relay.TensorType((16, ), "float32")) + + bn = relay.nn.batch_norm(data0, bn_gamma, bn_beta, bn_mmean, bn_mvar) + func0 = relay.Function([data0, bn_gamma, bn_beta, bn_mmean, bn_mvar], Review comment: Yes, indeed, we will add a test case for batch norm. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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