huajsj commented on code in PR #11557: URL: https://github.com/apache/tvm/pull/11557#discussion_r926773867
########## gallery/how_to/work_with_relay/using_pipeline_executor.py: ########## @@ -0,0 +1,251 @@ +# 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 "Pipeline Executor" with Relay. +""" +import tvm +from tvm import te +import numpy as np +from tvm.contrib import graph_executor as runtime +from tvm.relay.op.contrib.cutlass import partition_for_cutlass +from tvm import relay +from tvm.relay import testing +import tvm.testing +from tvm.contrib.cutlass import ( + has_cutlass, + num_cutlass_partitions, + finalize_modules, + finalize_modules_vm, +) + +img_size = 8 +####################################################################### +# 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, dense, and ReLU activation. +def get_network(): + out_channels = 16 + batch_size = 1 + data = relay.var("data", relay.TensorType((batch_size, 3, img_size, img_size), "float16")) + dense_weight = relay.var( + "dweight", relay.TensorType((batch_size, 16 * img_size * img_size), "float16") + ) + 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.batch_flatten(simple_net) + simple_net = relay.nn.dense(simple_net, dense_weight) + simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) + data_shape = (batch_size, 3, img_size, img_size) + net, params = testing.create_workload(simple_net) + return net, params, data_shape + + +net, params, data_shape = get_network() +########################################### +# Splitting the network into two subgraphs. +# ----------------------------------------- +# It is an example that the graph splitting function comes from a unit test. User can create a +# customized function to split the graph. +import inspect +import os + +test_path = os.path.dirname(inspect.getfile(lambda: None)) +os.sys.path.append(os.path.join(test_path, "../../../tests/python/relay")) +from test_pipeline_executor import graph_split + +########################################### +# Splitting the network into two subgraphs. +split_config = [{"op_name": "nn.relu", "op_index": 0}] +subgraphs = graph_split(net["main"], split_config, params) +########################################################### +# The generated subgraphs should look something like below. + +""" +#subgraphs[0]) + + def @main(%data: Tensor[(1, 3, img_size, img_size), float16]) { + %0 = nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(16, 3, 3, 3), float16] */, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* ty=Tensor[(1, 16, img_size, img_size), float16] */; + %1 = nn.batch_norm(%0, meta[relay.Constant][1] /* ty=Tensor[(16), float16] */, meta[relay.Constant][2] /* ty=Tensor[(16), float16]*/, meta[relay.Constant][3] /* ty=Tensor[(16), float16] */, meta[relay.Constant][4] /* ty=Tensor[(16), float16] */) /* ty=(Tensor[(1,16, img_size, img_size), float16], Tensor[(16), float16], Tensor[(16), float16]) */; + %2 = %1.0; + nn.relu(%2) /* ty=Tensor[(1, 16, img_size, img_size), float16] */ + } + +#subgraphs[1] + + def @main(%data_n_0: Tensor[(1, 16, 8, 8), float16] /* ty=Tensor[(1, 16, 8, 8), float16] */) { + %0 = nn.batch_flatten(%data_n_0) /* ty=Tensor[(1, 1024), float16] */; + nn.dense(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 1024), float16] */, units=None) /* ty=Tensor[(1, 1), float16] */ + } + +""" + +# sphinx_gallery_start_ignore +from tvm import testing + +testing.utils.install_request_hook(depth=3) +# sphinx_gallery_end_ignore + +######################################### +# Build the subgraph with cutlass target. +# --------------------------------------- + +cutlass = tvm.target.Target( + { + "kind": "cutlass", + "sm": int(tvm.target.Target("cuda").arch.split("_")[1]), + "use_3xtf32": True, + "split_k_slices": [1], + "profile_all_alignments": False, + "find_first_valid": True, + "use_multiprocessing": True, + "use_fast_math": False, + "tmp_dir": "./tmp", + }, + host=tvm.target.Target("llvm"), +) + + +def cutlass_build(mod, target, params=None, target_host=None, mod_name="default"): + target = [target, cutlass] + lib = relay.build_module.build( + mod, target=target, params=params, target_host=target_host, mod_name=mod_name + ) + return lib + + +########################################################### +# Run the two subgraphs in pipeline with pipeline executor. +# --------------------------------------------------------- +# Set 'USE_PIPELINE_EXECUTOR' as ON, and set USE_CUTLASS' as ON in cmake. +from tvm.contrib import graph_executor, pipeline_executor, pipeline_executor_build + +######################################### +# Create subgraph pipeline configuration. +# Associate a subgraph module with a target. +# Use CUTLASS BYOC to build the second subgraph module. +mod0, mod1 = subgraphs[0], subgraphs[1] +# Use cutlass as the codegen. +mod1 = partition_for_cutlass(mod1) +################################################# +# Get the pipeline executor configuration object. +pipe_config = pipeline_executor_build.PipelineConfig() +########################################################################### +# Set the compile target of the subgraph module. +pipe_config[mod0].target = "llvm" +pipe_config[mod0].dev = tvm.cpu(0) +############################################################################### +# Set the cpu afinity for control flow, for example using cpu 0 for control flow. +pipe_config[mod1].cpu_affinity = "0" +pipe_config[mod1].export_cc = None Review Comment: removed. -- This is an automated message from the Apache Git Service. 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