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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/09/27 21:58:37 UTC
[GitHub] [tvm] shingjan commented on a diff in pull request #12914: Add a script to tune and benchmark models from TorchBench
shingjan commented on code in PR #12914:
URL: https://github.com/apache/tvm/pull/12914#discussion_r981737528
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python/tvm/meta_schedule/testing/torchbench/run.py:
##########
@@ -0,0 +1,511 @@
+# 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.
+"""
+This script is for benchmarking TVM performance on models from TorchBench.
+It uses the TorchDynamo as the frontend to ingest models into TVM, and it also
+leverages the benchmark util from TorchDynamo.
+
+TorchDynamo (https://github.com/pytorch/torchdynamo) and TorchBench
+(https://github.com/pytorch/benchmark) need to be in the parent directory of TVM.
+We need a local clone of these repos because torchbench and the benchmark runner
+in TorchDynamo isn't designed to be used as a Python package.
+
+To setup the environment, run the following commands in the parent directory of TVM and with
+the appropriate Python environment:
+```bash
+# torchdynamo requires nightly pytorch. If it fails to find the specified version, try
+# installing the latest nightly pytorch.
+pip3 install --pre \
+ --extra-index-url https://download.pytorch.org/whl/nightly/cu116 \
+ torch==1.13.0.dev20220926 \
+ torchvision==0.14.0.dev20220926 \
+ torchtext==0.14.0.dev20220926
+
+git clone https://github.com/pytorch/torchdynamo
+pushd torchdynamo
+git checkout c537639f9712621dc04ca09908796dbbe86c354b
+pip install -e .
+popd
+
+sudo apt install git-lfs # git lfs is used for TorchBench
+git clone https://github.com/pytorch/benchmark
+pushd benchmark
+python install.py --continue_on_fail # fambench_xlmr might fail to install
+popd
+```
+
+To run a benchmark, the script can be run under 'tune' mode by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode tune \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 20000 \
+ --rpc-host <rpc tracker host for tuning> \
+ --rpc-port <rpc tracker port for tuning> \
+ --rpc-key <rpc key> \
+```
+
+All available target tags (like nvidia/geforce-rtx-3070) can be found at
+https://github.com/apache/tvm/blob/main/src/target/tag.cc
+
+Then the script can be run under 'eval' mode to actual benchmark the performance,
+using the tuning database under the work directory. This can be executed on a different
+machine than the one executes tuning.
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode eval \
Review Comment:
NIT: As no perf evaluation will be done with `--tuning`, I feel like we should combine `tuning` and `all`, if perf evaluation doesn't really take much time.
##########
python/tvm/meta_schedule/testing/torchbench/run.py:
##########
@@ -0,0 +1,511 @@
+# 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.
+"""
+This script is for benchmarking TVM performance on models from TorchBench.
+It uses the TorchDynamo as the frontend to ingest models into TVM, and it also
+leverages the benchmark util from TorchDynamo.
+
+TorchDynamo (https://github.com/pytorch/torchdynamo) and TorchBench
+(https://github.com/pytorch/benchmark) need to be in the parent directory of TVM.
+We need a local clone of these repos because torchbench and the benchmark runner
+in TorchDynamo isn't designed to be used as a Python package.
+
+To setup the environment, run the following commands in the parent directory of TVM and with
+the appropriate Python environment:
+```bash
+# torchdynamo requires nightly pytorch. If it fails to find the specified version, try
+# installing the latest nightly pytorch.
+pip3 install --pre \
+ --extra-index-url https://download.pytorch.org/whl/nightly/cu116 \
+ torch==1.13.0.dev20220926 \
+ torchvision==0.14.0.dev20220926 \
+ torchtext==0.14.0.dev20220926
+
+git clone https://github.com/pytorch/torchdynamo
+pushd torchdynamo
+git checkout c537639f9712621dc04ca09908796dbbe86c354b
+pip install -e .
+popd
+
+sudo apt install git-lfs # git lfs is used for TorchBench
+git clone https://github.com/pytorch/benchmark
+pushd benchmark
+python install.py --continue_on_fail # fambench_xlmr might fail to install
+popd
+```
+
+To run a benchmark, the script can be run under 'tune' mode by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode tune \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 20000 \
+ --rpc-host <rpc tracker host for tuning> \
+ --rpc-port <rpc tracker port for tuning> \
+ --rpc-key <rpc key> \
+```
+
+All available target tags (like nvidia/geforce-rtx-3070) can be found at
+https://github.com/apache/tvm/blob/main/src/target/tag.cc
+
+Then the script can be run under 'eval' mode to actual benchmark the performance,
+using the tuning database under the work directory. This can be executed on a different
+machine than the one executes tuning.
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode eval \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+
+Alternatively, both tuning and evaluation can be done in a single run on the same machine,
+by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --model resnet50 \
+ --target "llvm -num-cores 6" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+"""
+
+import argparse
+import functools
+import logging
+import warnings
+
+import numpy as np
+import torch
+from scipy.stats import ttest_ind
+
+import tvm
+import tvm.relay
+from tvm import meta_schedule as ms
+from tvm.contrib import graph_executor
+from tvm.meta_schedule.testing.torchbench.utils import (
+ load_torchdynamo_benchmark_runner,
+ same,
+ timed,
+)
+from tvm.runtime.vm import VirtualMachine
+from tvm.support import describe
+
+
+def parse_args():
+ args = argparse.ArgumentParser()
+
+ args.add_argument(
+ "--mode",
+ type=str,
+ choices=["tune", "eval", "all"],
+ default="all",
+ help="""
+ The running mode of this script. Available values are:
+ - tune: Only tune the model and create the tuning database.
+ - eval: Only benchmark model using pre-existing tuning database.
+ - all: Run both tuning and benchmark
+ """,
+ )
+ args.add_argument(
+ "--batch-size",
+ type=int,
+ default=None,
+ help="The batch size of model input. Use TorchBench's default value if not specified.",
+ )
+ args.add_argument(
+ "--cosine-similarity",
+ action="store_true",
+ help="""
+ Whether to use cosine similarity to determine whether the output is the same as
+ expected. By default torch.allclose is used.
+ """,
+ )
+ args.add_argument(
+ "--benchmark-repeat",
+ type=int,
+ default=10,
+ help="The number of times to repeat the benchmark measurement.",
+ )
+
+ # Model selection
+ args.add_argument(
+ "--model",
+ type=str,
+ required=True,
+ help="""
+ The name of model to run. It should a directory name under
+ https://github.com/pytorch/benchmark/tree/main/torchbenchmark/models.
+ """,
+ )
+
+ # Tuning-related config
+ args.add_argument(
+ "--target",
+ type=tvm.target.Target,
+ required=True,
+ help="The target to tune and run benchmark for.",
+ )
+ args.add_argument(
+ "--work-dir",
+ type=str,
+ required=True,
+ help="The working directory to save intermediate results.",
+ )
+ args.add_argument(
+ "--cache-dir",
+ type=str,
+ default=None,
+ help="""
+ The directory to cache the generated network.
+ If not specified, the cache will be disabled.
+ """,
+ )
+ args.add_argument(
+ "--num-trials",
+ type=int,
+ required=True,
+ help="The max number of trials to run MetaSchedule.",
+ )
+ args.add_argument(
+ "--max-trials-per-task",
+ type=int,
+ default=None,
+ help="""
+ The max number of trials to run per task extracted in MetaSchedule.
+ By default it's the same as --num-trials.
+ """,
+ )
+ args.add_argument(
+ "--backend",
+ type=str,
+ choices=["graph", "vm"],
+ default="graph",
+ help="The backend to use for relay compilation(graph / vm).",
+ )
+ # TODO(@yelite): Add a layout arg to transform the network after
+ # ingesting into Relay and before feeding into MetaSchedule.
+
+ # Evaluator-related config
+ args.add_argument(
+ "--number",
+ type=int,
+ default=3,
+ help="The number of times to run the model for taking average in a single measurement.",
+ )
+ args.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="The number of times to repeat the measurement.",
+ )
+ args.add_argument(
+ "--min-repeat-ms",
+ type=int,
+ default=100,
+ help="""
+ Minimum repeat time in ms. The number of runs will be increased if the actual
+ repeat time is lowered than this.
+ """,
+ )
+ args.add_argument(
+ "--adaptive-training",
+ action="store_true",
+ help="Whether to use adpative training for cost model.",
+ )
+ args.add_argument(
+ "--cpu-flush",
+ action="store_true",
+ help="Whether to perform CPU cache flush.",
+ )
+
+ # RPC-related args
+ args.add_argument(
+ "--rpc-host",
+ type=str,
+ help="Host of the RPC Tracker for tuning. Use LocalRunner if not provided",
+ )
+ args.add_argument(
+ "--rpc-port",
+ type=int,
+ help="Port of the RPC Tracker for tuning",
+ )
+ args.add_argument(
+ "--rpc-key",
+ type=str,
+ help="Key of the RPC Tracker for tuning",
+ )
+
+ parsed = args.parse_args()
+ return parsed
+
+
+logging.basicConfig(
+ format="%(asctime)s.%(msecs)03d %(levelname)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
+)
+logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG)
+ARGS = parse_args()
+IS_CUDA = ARGS.target.kind.name == "cuda"
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+runner = load_torchdynamo_benchmark_runner(IS_CUDA)
+import torchdynamo
+
+
+def get_metaschedule_runner():
+ if ARGS.rpc_host is not None:
+ return ms.runner.RPCRunner(
+ rpc_config=ms.runner.RPCConfig(
+ tracker_host=ARGS.rpc_host,
+ tracker_port=ARGS.rpc_port,
+ tracker_key=ARGS.rpc_key,
+ session_timeout_sec=600,
+ ),
+ evaluator_config=ms.runner.EvaluatorConfig(
+ number=ARGS.number,
+ repeat=ARGS.repeat,
+ min_repeat_ms=ARGS.min_repeat_ms,
+ enable_cpu_cache_flush=ARGS.cpu_flush,
+ ),
+ alloc_repeat=1,
+ )
+ else:
+ warnings.warn("Falling back to Metaschedule LocalRunner because --rpc-host isn't provided.")
+ return ms.runner.LocalRunner()
+
+
+def get_tune_config():
+ if ARGS.mode == "eval":
+ max_trials_per_task = 0
+ max_trials_global = 0
+ else:
+ max_trials_per_task = ARGS.max_trials_per_task
+ max_trials_global = ARGS.num_trials
+
+ if max_trials_per_task is None:
+ max_trials_per_task = max_trials_global
+
+ return ms.TuneConfig(
+ strategy="evolutionary",
+ num_trials_per_iter=64,
+ max_trials_per_task=max_trials_per_task,
+ max_trials_global=max_trials_global,
+ adaptive_training=ARGS.adaptive_training,
+ )
+
+
+def get_graph_executor_forward(mod, device):
+ def forward(*args):
+ if IS_CUDA:
+ torch.cuda.synchronize()
+ args = [arg.contiguous() for arg in args]
+ for idx, arg in enumerate(args, 0):
+ mod.set_input(
+ f"inp_{idx}",
+ tvm.nd.from_dlpack(arg),
Review Comment:
i think this could potentially be a problem and that is reason why in torchdynamo's TVM backend torch.Tensor is converted to numpy and then to TVM.NDarray. And if the arg is typed `torch.Tensor` you may need `torch.utils.dlpack.to_dlpack(arg)` for this approach as well.
##########
python/tvm/meta_schedule/testing/torchbench/run.py:
##########
@@ -0,0 +1,508 @@
+# 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.
+"""
+This script is for benchmarking TVM performance on models from TorchBench.
+It uses the TorchDynamo as the frontend to ingest models into TVM, and it also
+leverages the benchmark util from TorchDynamo.
+
+TorchDynamo (https://github.com/pytorch/torchdynamo) and TorchBench
+(https://github.com/pytorch/benchmark) need to be in the parent directory of TVM.
+We need a local clone of these repos because torchbench and the benchmark runner
+in TorchDynamo isn't designed to be used as a Python package.
+
+To setup the environment, run the following commands in the parent directory of TVM and with
+the appropriate Python environment:
+```bash
+# torchdynamo requires nightly pytorch. If it fails to find the specified version, try
+# installing the latest nightly pytorch.
+pip3 install --pre \
+ --extra-index-url https://download.pytorch.org/whl/nightly/cu116 \
+ torch==1.13.0.dev20220926 \
+ torchvision==0.14.0.dev20220926 \
+ torchtext==0.14.0.dev20220926
+
+git clone https://github.com/pytorch/torchdynamo
+pushd torchdynamo
+git checkout c537639f9712621dc04ca09908796dbbe86c354b
+pip install -e .
+popd
+
+sudo apt install git-lfs # git lfs is used for TorchBench
+git clone https://github.com/pytorch/benchmark
+pushd benchmark
+python install.py --continue_on_fail # fambench_xlmr might fail to install
+popd
+```
+
+To run a benchmark, the script can be run under 'tune' mode by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode tune \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 20000 \
+ --rpc-host <rpc tracker host for tuning> \
+ --rpc-port <rpc tracker port for tuning> \
+ --rpc-key <rpc key> \
+```
+
+Then the script can be run under 'eval' mode to actual benchmark the performance,
+using the tuning database under the work directory. This can be executed on a different
+machine than the one executes tuning.
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode eval \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+
+Alternatively, both tuning and evaluation can be done in a single run on the same machine,
+by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --model resnet50 \
+ --target "llvm -num-cores 6" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+"""
+
+import argparse
+import functools
+import logging
+
+import numpy as np
+import torch
+from scipy.stats import ttest_ind
+
+import tvm
+import tvm.relay
+from tvm import meta_schedule as ms
+from tvm.contrib import graph_executor
+from tvm.meta_schedule.testing.torchbench.utils import (
+ load_torchdynamo_benchmark_runner,
+ same,
+ timed,
+)
+from tvm.runtime.vm import VirtualMachine
+from tvm.support import describe
+
+
+def parse_args():
+ args = argparse.ArgumentParser()
+
+ args.add_argument(
+ "--mode",
+ type=str,
+ choices=["tune", "eval", "all"],
+ default="all",
+ help="""
+ The running mode of this script. Available values are:
+ - tune: Only tune the model and create the tuning database.
+ - eval: Only benchmark model using pre-existing tuning database.
+ - all: Run both tuning and benchmark
+ """,
+ )
+ args.add_argument(
+ "--batch-size",
+ type=int,
+ default=None,
+ help="The batch size of model input. Use TorchBench's default value if not specified.",
+ )
+ args.add_argument(
+ "--cosine-similarity",
+ action="store_true",
+ help="""
+ Whether to use cosine similarity to determine whether the output is the same as
+ expected. By default torch.allclose is used.
+ """,
+ )
+ args.add_argument(
+ "--benchmark-repeat",
+ type=int,
+ default=10,
+ help="The number of times to repeat the benchmark measurement.",
Review Comment:
In torchdynamo there is warm up rounds. Adding this could be an option here.
##########
python/tvm/meta_schedule/testing/torchbench/run.py:
##########
@@ -0,0 +1,504 @@
+# 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.
+"""
+This script is for benchmarking TVM performance on models from TorchBench.
+It uses the torchdynamo as the frontend to ingest models into TVM, and it also
+leverages the benchmark util from torchdynamo.
+
+torchdynamo (https://github.com/pytorch/torchdynamo) and TorchBench
+(https://github.com/pytorch/benchmark) need to be in the parent directory of TVM.
+We need a local clone of these repos because torchbench and the benchmark runner
+in torchdynamo isn't designed to be used as a Python package.
+
+To setup the environment, run the following commands in the parent directory of TVM and with
+the appropriate Python environment:
+```
+# torchdynamo requires nightly pytorch
+pip3 install --pre \
+ --extra-index-url https://download.pytorch.org/whl/nightly/cu116 \
+ torch==1.13.0.dev20220926 \
+ torchvision==0.14.0.dev20220926 \
+ torchtext==0.14.0.dev20220926
+
+git clone https://github.com/pytorch/torchdynamo
+pushd torchdynamo
+git checkout c537639f9712621dc04ca09908796dbbe86c354b
+pip install -e .
+popd
+
+sudo apt install git-lfs # git lfs is used for TorchBench
+git clone https://github.com/pytorch/benchmark
+pushd benchmark
+python install.py --continue_on_fail # fambench_xlmr might fail to install
+popd
+```
+
+To run a benchmark, the script can be run under 'tune' mode by
+```
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode tune \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 20000 \
+ --rpc-host <rpc tracker host for tuning> \
+ --rpc-port <rpc tracker port for tuning> \
+ --rpc-key <rpc key> \
+```
+
+Then the script can be run under 'eval' mode to actual benchmark the performance,
+using the tuning database under the work directory. This can be executed on a different
+machine than the one executes tuning.
+```
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode eval \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+
+Alternatively, both tuning and evaluation can be done in a single run on the same machine,
+by
+```
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --model resnet50 \
+ --target "llvm -num-cores 6" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+"""
+
+import argparse
+import functools
+import logging
+
+import numpy as np
+import torch
+from scipy.stats import ttest_ind
+
+import tvm
+import tvm.relay
+from tvm import meta_schedule as ms
+from tvm.contrib import graph_executor
+from tvm.meta_schedule.testing.torchbench.utils import (
+ load_torchdynamo_benchmark_runner,
+ same,
+ timed,
+)
+from tvm.runtime.vm import VirtualMachine
+from tvm.support import describe
+
+
+def parse_args():
+ args = argparse.ArgumentParser()
+
+ args.add_argument(
+ "--mode",
+ type=str,
+ choices=["tune", "eval", "all"],
+ default="all",
+ help="""
+ The running mode of this script. Available values are:
+ - tune: Only tune the model and create the tuning database.
+ - eval: Only benchmark model using pre-existing tuning database.
+ - all: Run both tuning and benchmark
+ """,
+ )
+ args.add_argument(
+ "--batch-size",
+ type=int,
+ default=None,
+ help="The batch size of model input. Use TorchBench's default value if not specified.",
+ )
+ args.add_argument(
+ "--cosine-similarity",
+ action="store_true",
+ help="""
+ Whether to use cosine similarity to determine whether the output is the same as
+ expected. By default torch.allclose is used.
+ """,
+ )
Review Comment:
I think the `--cosine-similarity` idea is from torchdynamo. I am okay with this argument as some models on CUDA will fail `allclose` anyway and we need to specify `--cosine` for those models.
##########
python/tvm/meta_schedule/testing/torchbench/run.py:
##########
@@ -0,0 +1,511 @@
+# 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.
+"""
+This script is for benchmarking TVM performance on models from TorchBench.
+It uses the TorchDynamo as the frontend to ingest models into TVM, and it also
+leverages the benchmark util from TorchDynamo.
+
+TorchDynamo (https://github.com/pytorch/torchdynamo) and TorchBench
+(https://github.com/pytorch/benchmark) need to be in the parent directory of TVM.
+We need a local clone of these repos because torchbench and the benchmark runner
+in TorchDynamo isn't designed to be used as a Python package.
+
+To setup the environment, run the following commands in the parent directory of TVM and with
+the appropriate Python environment:
+```bash
+# torchdynamo requires nightly pytorch. If it fails to find the specified version, try
+# installing the latest nightly pytorch.
+pip3 install --pre \
+ --extra-index-url https://download.pytorch.org/whl/nightly/cu116 \
+ torch==1.13.0.dev20220926 \
+ torchvision==0.14.0.dev20220926 \
+ torchtext==0.14.0.dev20220926
+
+git clone https://github.com/pytorch/torchdynamo
+pushd torchdynamo
+git checkout c537639f9712621dc04ca09908796dbbe86c354b
+pip install -e .
+popd
+
+sudo apt install git-lfs # git lfs is used for TorchBench
+git clone https://github.com/pytorch/benchmark
+pushd benchmark
+python install.py --continue_on_fail # fambench_xlmr might fail to install
+popd
+```
+
+To run a benchmark, the script can be run under 'tune' mode by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode tune \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 20000 \
+ --rpc-host <rpc tracker host for tuning> \
+ --rpc-port <rpc tracker port for tuning> \
+ --rpc-key <rpc key> \
+```
+
+All available target tags (like nvidia/geforce-rtx-3070) can be found at
+https://github.com/apache/tvm/blob/main/src/target/tag.cc
+
+Then the script can be run under 'eval' mode to actual benchmark the performance,
+using the tuning database under the work directory. This can be executed on a different
+machine than the one executes tuning.
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --mode eval \
+ --model resnet50 \
+ --target "nvidia/geforce-rtx-3070" \
+ --work-dir ../workdir \
+ --num-trials 0
+```
+
+Alternatively, both tuning and evaluation can be done in a single run on the same machine,
+by
+```bash
+python python/tvm/meta_schedule/testing/torchbench/run.py \
+ --model resnet50 \
Review Comment:
maybe better to specify the `--mode all` here.
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