areusch commented on code in PR #11557: URL: https://github.com/apache/tvm/pull/11557#discussion_r893913372
########## gallery/how_to/work_with_relay/using_with_pipeline_executor.py: ########## @@ -0,0 +1,182 @@ +# 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. +""" +Using Pipeline Executor in Relay +================================= +**Author**: `Hua Jiang <https://https://github.com/huajsj>`_ + +This is a short tutorial on how to use the Pipeline Executor with Relay. +""" +import tvm +from tvm import te +import numpy as np +from tvm.contrib import graph_executor as runtime +from tvm import relay +from tvm.relay import testing +import tvm.testing +import time + +####################################################################### +# Create a simple network, this network can be a pre-trained model too. +# --------------------------------------------------------------------- +# Let's create a very simple network for demonstration. +# It consists of convolution, batch normalization, and ReLU activation. +def get_network(): + out_channels = 16 + batch_size = 1 + data = relay.var("data", relay.TensorType((batch_size, 3, 224, 224), "float32")) + weight = relay.var("weight") + second_weight = relay.var("second_weight") + bn_gamma = relay.var("bn_gamma") + bn_beta = relay.var("bn_beta") + bn_mmean = relay.var("bn_mean") + bn_mvar = relay.var("bn_var") + simple_net = relay.nn.conv2d( + data=data, weight=weight, kernel_size=(3, 3), channels=out_channels, padding=(1, 1) + ) + simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0] + simple_net = relay.nn.relu(simple_net) + simple_net = relay.nn.conv2d( + data=simple_net, + weight=second_weight, + kernel_size=(3, 3), + channels=out_channels, + padding=(1, 1), + ) + simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) + data_shape = (batch_size, 3, 224, 224) + net, params = testing.create_workload(simple_net) + return net, params, data_shape + + +net, params, data_shape = get_network() +############################################# +# Apply a customer graph splitting function. +# ------------------------------------------ +# We use an testing linear graph splitting function as a example. User also can create their +# own splitting function logic. +import os + +os.sys.path.append(os.path.abspath(os.environ["TVM_HOME"] + "/tests/python/relay")) Review Comment: it should have whatever is enabled in ci_gpu, and that's determined partly by Dockerfile.ci_gpu and by tests/scripts/task_config_build_gpu.sh. you could propose a change there if you need something for your tutorial (just add to this PR). -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
