mbaret commented on a change in pull request #6882: URL: https://github.com/apache/incubator-tvm/pull/6882#discussion_r519691947
########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks +# +# * :code:`measure_ctx` launches a different process for measurement to +# provide isolation. It can protect the master process from GPU crashes +# happened during measurement and avoid other runtime conflicts. +# * :code:`min_repeat_ms` defines the minimum duration of one "repeat" in every measurement. +# This can warmup the GPU, which is necessary to get accurate measurement results. +# Typically, we recommend a value > 300 ms. +# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning. +# You can set it to a small number (e.g., 200) for a fast demonstrative run. +# In practice, we recommend setting it round :code:`1000 * len(tasks)`, +# which is typically enough for the search to converge. +# For example, there are 21 tasks in resnet-18, so we can set it as 20000 for renset-18. +# You can adjust this parameter according to your time budget. +# * In addition, we use :code:`RecordToFile` to dump measurement records into the log file, +# The measurement records can be used to query the history best, resume the search, +# and do more analyses later. +# * see :any:`auto_scheduler.TuningOptions`, +# :any:`auto_scheduler.LocalRPCMeasureContext` for more parameters. +# + + +def run_tuning(): + print("Begin tuning...") + measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=400, timeout=10) + + tuner = auto_scheduler.TaskScheduler(tasks, objective) + tune_option = auto_scheduler.TuningOptions( + num_measure_trials=200, # change this to 20000 to achieve the best performance + runner=measure_ctx.runner, + measure_callbacks=[auto_scheduler.RecordToFile(log_file)], + ) + + tuner.tune(tune_option) + + +# We do not run the tuning in our webpage server since it takes too long. +# Uncomment the following line to run it by yourself. + +# run_tuning() + + +###################################################################### +# .. note:: Explain the printed information during tuning +# +# During the tuning, a lot of information will be printed on the screen. +# They are used for debugging purposes. The most important info is the output +# of the task scheduler. The following table is a sample output. +# +# .. code-block:: c +# +# ---------------------------------------------------------------------- +# ------------------------------ [ Task Scheduler ] +# ---------------------------------------------------------------------- +# | ID | Latency (ms) | Speed (GFLOPS) | Trials | +# ------------------------------------------------- +# | 0 | 0.014 | 72.07 | 64 | +# | 1 | 0.185 | 1250.68 | 128 | +# | 2 | 0.142 | 1626.36 | 192 | +# | 3 | 0.137 | 1689.42 | 128 | +# | 4 | 0.097 | 1189.75 | 128 | +# | 5 | 0.092 | 2505.25 | 128 | +# | 6 | 0.080 | 2893.08 | 128 | +# | 7 | 0.119 | 1947.84 | 128 | +# | 8 | 0.090 | 1292.62 | 64 | +# | 9 | 0.107 | 2172.30 | 64 | +# | 10 | 0.095 | 2439.36 | 64 | +# | 11 | 0.077 | 3003.22 | 64 | +# | 12 | 0.068 | 1695.13 | 64 | +# | 13 | 0.058 | 3979.29 | 64 | +# | 14 | 0.048 | 4859.95 | 128 | +# | 15 | 0.073 | 3151.76 | 64 | +# | 16 | 0.056 | 4265.94 | 64 | +# | 17 | 0.009 | 2754.90 | 64 | +# | 18 | 0.011 | 1156.08 | 64 | +# | 19 | 0.013 | 955.80 | 64 | +# | 20 | 0.029 | 437.71 | 64 | +# ------------------------------------------------- +# Total latency: 1.649 ms Trials: 1920 Used time : 3598 s Next ID: 9 +# +# This table lists the latency and speed of all tasks. +# It also lists the allocation of measurement trials for all tasks. +# The last line prints the total weighted latency of these tasks, +# which can be a rough estimation of the end-to-end execution time +# of the network. +# The last line also prints the total number of measurement trials, +# total time spent on auto-tuning and the id of the next task to tune. +# +# There will also be some "dmlc::Error"s and CUDA errors. You can safely +# ignore them if the tuning can continue. + +###################################################################### +# .. note:: Terminate the tuning earlier +# +# You can terminate the tuning earlier by forcely killing this process. Review comment: ```suggestion # You can terminate the tuning earlier by forcibly killing this process. ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) Review comment: As someone not very familiar with the auto-scheduler, it seems a bit strange to me that this is exposed here. Could this not be a default objective? ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network Review comment: ```suggestion # Define the objective as the end-to-end execution time of the network ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks +# +# * :code:`measure_ctx` launches a different process for measurement to +# provide isolation. It can protect the master process from GPU crashes +# happened during measurement and avoid other runtime conflicts. +# * :code:`min_repeat_ms` defines the minimum duration of one "repeat" in every measurement. +# This can warmup the GPU, which is necessary to get accurate measurement results. +# Typically, we recommend a value > 300 ms. +# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning. +# You can set it to a small number (e.g., 200) for a fast demonstrative run. +# In practice, we recommend setting it round :code:`1000 * len(tasks)`, Review comment: ```suggestion # In practice, we recommend setting it around :code:`1000 * len(tasks)`, ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. Review comment: ```suggestion # First, we need to define the network using the relay frontend API. ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks +# +# * :code:`measure_ctx` launches a different process for measurement to +# provide isolation. It can protect the master process from GPU crashes +# happened during measurement and avoid other runtime conflicts. +# * :code:`min_repeat_ms` defines the minimum duration of one "repeat" in every measurement. +# This can warmup the GPU, which is necessary to get accurate measurement results. +# Typically, we recommend a value > 300 ms. +# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning. +# You can set it to a small number (e.g., 200) for a fast demonstrative run. +# In practice, we recommend setting it round :code:`1000 * len(tasks)`, +# which is typically enough for the search to converge. +# For example, there are 21 tasks in resnet-18, so we can set it as 20000 for renset-18. Review comment: ```suggestion # For example, there are 21 tasks in resnet-18, so we can set it as 20000. ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks +# +# * :code:`measure_ctx` launches a different process for measurement to +# provide isolation. It can protect the master process from GPU crashes +# happened during measurement and avoid other runtime conflicts. Review comment: ```suggestion # that happen during measurement and avoid other runtime conflicts. ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks +# +# * :code:`measure_ctx` launches a different process for measurement to +# provide isolation. It can protect the master process from GPU crashes +# happened during measurement and avoid other runtime conflicts. +# * :code:`min_repeat_ms` defines the minimum duration of one "repeat" in every measurement. +# This can warmup the GPU, which is necessary to get accurate measurement results. +# Typically, we recommend a value > 300 ms. +# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning. +# You can set it to a small number (e.g., 200) for a fast demonstrative run. +# In practice, we recommend setting it round :code:`1000 * len(tasks)`, +# which is typically enough for the search to converge. +# For example, there are 21 tasks in resnet-18, so we can set it as 20000 for renset-18. +# You can adjust this parameter according to your time budget. +# * In addition, we use :code:`RecordToFile` to dump measurement records into the log file, +# The measurement records can be used to query the history best, resume the search, +# and do more analyses later. +# * see :any:`auto_scheduler.TuningOptions`, +# :any:`auto_scheduler.LocalRPCMeasureContext` for more parameters. +# + + +def run_tuning(): + print("Begin tuning...") + measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=400, timeout=10) + + tuner = auto_scheduler.TaskScheduler(tasks, objective) + tune_option = auto_scheduler.TuningOptions( + num_measure_trials=200, # change this to 20000 to achieve the best performance + runner=measure_ctx.runner, + measure_callbacks=[auto_scheduler.RecordToFile(log_file)], + ) + + tuner.tune(tune_option) + + +# We do not run the tuning in our webpage server since it takes too long. +# Uncomment the following line to run it by yourself. + +# run_tuning() + + +###################################################################### +# .. note:: Explain the printed information during tuning +# +# During the tuning, a lot of information will be printed on the screen. +# They are used for debugging purposes. The most important info is the output +# of the task scheduler. The following table is a sample output. +# +# .. code-block:: c +# +# ---------------------------------------------------------------------- +# ------------------------------ [ Task Scheduler ] +# ---------------------------------------------------------------------- +# | ID | Latency (ms) | Speed (GFLOPS) | Trials | +# ------------------------------------------------- +# | 0 | 0.014 | 72.07 | 64 | +# | 1 | 0.185 | 1250.68 | 128 | +# | 2 | 0.142 | 1626.36 | 192 | +# | 3 | 0.137 | 1689.42 | 128 | +# | 4 | 0.097 | 1189.75 | 128 | +# | 5 | 0.092 | 2505.25 | 128 | +# | 6 | 0.080 | 2893.08 | 128 | +# | 7 | 0.119 | 1947.84 | 128 | +# | 8 | 0.090 | 1292.62 | 64 | +# | 9 | 0.107 | 2172.30 | 64 | +# | 10 | 0.095 | 2439.36 | 64 | +# | 11 | 0.077 | 3003.22 | 64 | +# | 12 | 0.068 | 1695.13 | 64 | +# | 13 | 0.058 | 3979.29 | 64 | +# | 14 | 0.048 | 4859.95 | 128 | +# | 15 | 0.073 | 3151.76 | 64 | +# | 16 | 0.056 | 4265.94 | 64 | +# | 17 | 0.009 | 2754.90 | 64 | +# | 18 | 0.011 | 1156.08 | 64 | +# | 19 | 0.013 | 955.80 | 64 | +# | 20 | 0.029 | 437.71 | 64 | +# ------------------------------------------------- +# Total latency: 1.649 ms Trials: 1920 Used time : 3598 s Next ID: 9 +# +# This table lists the latency and speed of all tasks. +# It also lists the allocation of measurement trials for all tasks. +# The last line prints the total weighted latency of these tasks, +# which can be a rough estimation of the end-to-end execution time +# of the network. +# The last line also prints the total number of measurement trials, +# total time spent on auto-tuning and the id of the next task to tune. +# +# There will also be some "dmlc::Error"s and CUDA errors. You can safely +# ignore them if the tuning can continue. + +###################################################################### +# .. note:: Terminate the tuning earlier +# +# You can terminate the tuning earlier by forcely killing this process. +# As long as you get at least one valid schedule for each task in the log file, +# you should be able to do the compilation (the secion below). +# + +################################################################# +# Compile and Evaluate +# -------------------- +# After auto-tuning, we can compile the network with the best schedules we found. +# All measurement records are dumpled into the log file during auto-tuning, Review comment: ```suggestion # All measurement records are dumped into the log file during auto-tuning, ``` ########## File path: tutorials/auto_scheduler/tune_network_cuda.py ########## @@ -0,0 +1,286 @@ +# 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. +""" +Auto-tuning a Neural Network for NVIDIA GPU +================================================== +**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_ + +Auto-tuning for specific devices and workloads is critical for getting the +best performance. This is a tutorial on how to tune a whole neural +network for NVIDIA GPU with the auto-scheduler. + +To auto-tune a neural network, we partition the network into small subgraphs and +tune them independently. Each subgraph is treated as one search task. +A task scheduler slices the time and dynamically allocates time resources to +these tasks. The task scheduler predicts the impact of each task on the end-to-end +execution time and prioritizes the one that can reduce the execution time fastest. + +For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to +get the computational DAG in the tensor expression form. +We then use the auto-scheduler to construct a search space of this DAG and search +for good schedules (low-level optimizations). + +Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on +manual templates to define the search space, the auto-scheduler does not require any +schedule templates. So the auto-scheduler only uses the compute declarations +in :code:`tvm/python/topi` while does not use existing schedule templates. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import numpy as np + +import tvm +from tvm import relay, auto_scheduler +import tvm.relay.testing +from tvm.contrib import graph_runtime + +################################################################# +# Define a Network +# ---------------- +# First, we need to define the network in relay frontend API. +# We can load some pre-defined network from :code:`tvm.relay.testing`. +# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow +# (see :ref:`front end tutorials<tutorial-frontend>`). +# +# Note that although auto-scheduler can work with any layouts, +# we found that the best performance is typically archived with NHWC layout +# for convolutional neural networks. +# + + +def get_network(name, batch_size, layout="NHWC", dtype="float32"): + """Get the symbol definition and random weight of a network""" + + # auto-scheduler prefers NHWC layout + if layout == "NHWC": + image_shape = (224, 224, 3) + elif layout == "NCHW": + image_shape = (3, 224, 224) + else: + raise ValueError("Invalid layout: " + layout) + + input_shape = (batch_size,) + image_shape + output_shape = (batch_size, 1000) + + if name.startswith("resnet-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name.startswith("resnet3d-"): + n_layer = int(name.split("-")[1]) + mod, params = relay.testing.resnet.get_workload( + num_layers=n_layer, + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "mobilenet": + mod, params = relay.testing.mobilenet.get_workload( + batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape + ) + elif name == "squeezenet_v1.1": + mod, params = relay.testing.squeezenet.get_workload( + version="1.1", + batch_size=batch_size, + layout=layout, + dtype=dtype, + image_shape=image_shape, + ) + elif name == "inception_v3": + input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3) + mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) + elif name == "mxnet": + # an example for mxnet model + from mxnet.gluon.model_zoo.vision import get_model + + assert layout == "NCHW" + + block = get_model("resnet18_v1", pretrained=True) + mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype) + net = mod["main"] + net = relay.Function( + net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs + ) + mod = tvm.IRModule.from_expr(net) + + return mod, params, input_shape, output_shape + + +# Define the neural network and compilation target +network = "resnet-18" +batch_size = 1 +layout = "NHWC" +target = tvm.target.Target("cuda") +dtype = "float32" +log_file = "%s-%s-B%d.json" % (network, layout, batch_size) + +################################################################# +# Extract Search Tasks +# -------------------- +# Next, we extract the search tasks and their weights from a network. +# The weight of a task is the number of appearances of the task's subgraph +# in the whole network. +# By using the weight, we can approximate the end-to-end latency of the network +# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the +# latency of a task and :code:`weight[t]` is the weight of the task. + +# Enable auto-scheduler in relay +auto_scheduler.enable_relay_integration() + +# Extract tasks from the network +print("Extract tasks...") +mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype) +tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target) + +# Define the objective as the end-to-end exeuction time of the network +objective = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) + +################################################################# +# Begin Tuning +# ------------ +# Now, we set some options of tuning and launch the search tasks Review comment: ```suggestion # Now, we set some options for tuning and launch the search tasks ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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