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Chronos: update benchmark tool guide to demonstrate new functions (#6882)

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docs/readthedocs/source/doc/Chronos/Howto/how_to_use_benchmark_tool.md
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@ -28,10 +28,16 @@ benchmark-chronos -m lstm -l 96 -o 720
```
### Stage
Regarding a model, training and inference stages are most concerned. By setting `-s/--stage` parameter, users can obtain knowledge of throughput during training (`-s train`), throughput during inference (`-s throughput`) and latency of inference (`-s latency`). If not specified, train is used as the default.
Regarding a model, training and inference stages are most concerned. By setting `-s/--stage` parameter, users can obtain knowledge of throughput during training (`-s train`), accuracy after training(`-s accuracy`). throughput during inference (`-s throughput`) and latency of inference (`-s latency`). If not specified, train is used as the default.
```bash
benchmark-chronos -s latency -l 96 -o 720
```
```eval_rst
.. note::
**More About Accuracy Results**:
After setting ``-s accuracy``, the tool will load dataset and split it to train, validation and test set with ratio of 7:1:2. Then validation loss is monitored during training epoches and checkpoint of the epoch with smallest loss is loaded after training. With the trained forecaster, obtain evaluation results corresponding to ``--metrics``.
```
### Dataset
Several built-in datasets can be chosen, including nyc_taxi and tsinghua_electricity. If users are with poor Internet connection and hard to download dataset, run benchmark tool with `-d synthetic_dataset` to use synthetic dataset. Default to be tsinghua_electricity if `-d/--dataset` parameter is not specified.
@ -74,12 +80,26 @@ benchmark-chronos -l 96 -o 720
```
## Advanced Options
When `-s/--stage accuracy` is set, users can further specify evaluation metrics through `--metrics` which default to be mse and mae.
```bash
benchmark-chronos --stage accuracy --metrics mse rmse -l 96 -o 720
```
To improve model accuracy, the tool provides with normalization trick to alleviate distribution shift. Once enable `--normalization`, normalization trick will be applied to forecaster.
```bash
benchmark-chronos --stage accuracy --normalization -l 96 -o 720
```
```eval_rst
.. note::
Only TCNForecaster supports normalization trick now.
```
Besides, number of processes and epoches can be set by `--training_processes` and `--training_epochs`. Users can also tune batchsize during training and inference through `--training_batchsize` and `--inference_batchsize` respectively.
```bash
benchmark-chronos --training_processes 2 --training_epochs 3 --training_batchsize 32 --inference_batchsize 128 -l 96 -o 720
```
To speed up inference, accelerators like ONNXRuntime and OpenVINO are usually used. To benchmark inference performance with or without accelerator, run tool with `--inference_framework` to specify without accelerator (`--inference_framework torch`)or with ONNXRuntime (`--inference_framework onnx`) or with OpenVINO (`--inference_framework openvino`).
To speed up inference, accelerators like ONNXRuntime and OpenVINO are usually used. To benchmark inference performance with or without accelerator, run tool with `--inference_framework` to specify without accelerator (`--inference_framework torch`)or with ONNXRuntime (`--inference_framework onnx`) or with OpenVINO (`--inference_framework openvino`) or with jit (`--inference_framework jit`).
```bash
benchmark-chronos --inference_framework onnx -l 96 -o 720
```
@ -101,21 +121,21 @@ benchmark-chronos -h
```eval_rst
.. code-block:: python
usage: benchmark-chronos.py [-h] [-m] [-s] [-d] [-f] [-c] -l lookback -o
horizon [--training_processes]
[--training_batchsize] [--training_epochs]
[--inference_batchsize] [--quantize]
[--inference_framework [...]] [--ipex]
[--quantize_type] [--ckpt]
usage: benchmark-chronos [-h] [-m] [-s] [-d] [-f] [-c] -l lookback -o horizon
[--training_processes] [--training_batchsize]
[--training_epochs] [--inference_batchsize]
[--quantize] [--inference_framework [...]] [--ipex]
[--quantize_type] [--ckpt] [--metrics [...]]
[--normalization]
Benchmarking Parameters
Benchmarking Parameters
optional arguments:
optional arguments:
-h, --help show this help message and exit
-m, --model model name, choose from
tcn/lstm/seq2seq/nbeats/autoformer, default to "tcn".
-s, --stage stage name, choose from train/latency/throughput,
default to "train".
-s, --stage stage name, choose from
train/latency/throughput/accuracy, default to "train".
-d, --dataset dataset name, choose from
nyc_taxi/tsinghua_electricity/synthetic_dataset,
default to "tsinghua_electricity".
@ -137,7 +157,7 @@ benchmark-chronos -h
False.
--inference_framework [ ...]
predict without/with accelerator, choose from
torch/onnx/openvino, default to "torch" (i.e. predict
torch/onnx/openvino/jit, default to "torch" (i.e. predict
without accelerator).
--ipex if use ipex as accelerator for trainer, default to
False.
@ -146,5 +166,9 @@ benchmark-chronos -h
default to "pytorch_fx".
--ckpt checkpoint path of a trained model, e.g.
"checkpoints/tcn", default to "checkpoints/tcn".
--metrics [ ...] evaluation metrics of a trained model, e.g.
"mse"/"mae", default to "mse, mae".
--normalization if to use normalization trick to alleviate
distribution shift.
```