Nano : Update pytorch inference QuickStart with InferenceOptimizer

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_inference.md

@@ -1,21 +1,36 @@
 # BigDL-Nano PyTorch Inference Overview
 
-BigDL-Nano provides several APIs which can help users easily apply optimizations on inference pipelines to improve latency and throughput. Currently, performance accelerations are achieved by integrating extra runtimes as inference backend engines or using quantization methods on full-precision trained models to reduce computation during inference. Trainer (`bigdl.nano.pytorch.Trainer`) provides the APIs for all optimizations that you need for inference.
+BigDL-Nano provides several APIs which can help users easily apply optimizations on inference pipelines to improve latency and throughput. Currently, performance accelerations are achieved by integrating extra runtimes as inference backend engines or using quantization methods on full-precision trained models to reduce computation during inference. InferenceOptimizer (`bigdl.nano.pytorch.InferenceOptimizer`) provides the APIs for all optimizations that you need for inference.
 
-For runtime acceleration, BigDL-Nano has enabled two kinds of runtime for users in `Trainer.trace()`, ONNXRuntime and OpenVINO.
+For runtime acceleration, BigDL-Nano has enabled three kinds of runtime for users in `InferenceOptimizer.trace()`, ONNXRuntime, OpenVINO and jit.
 
-For quantization, BigDL-Nano provides only post-training quantization in `trainer.quantize()` for users to infer with models of 8-bit precision. Quantization-aware training is not available for now. Model conversion to 16-bit like BF16, and FP16 will be coming soon.
+```eval_rst
+.. warning::
+    ``bigdl.nano.pytorch.Trainer.trace`` will be deprecated in future release.
+
+    Please use ``bigdl.nano.pytorch.InferenceOptimizer.trace`` instead.
+```
+
+For quantization, BigDL-Nano provides only post-training quantization in `InferenceOptimizer.quantize()` for users to infer with models of 8-bit precision or 16-bit precision. Quantization-aware training is not available for now. Model conversion to 16-bit like BF16 is supported now.
+
+```eval_rst
+.. warning::
+    ``bigdl.nano.pytorch.Trainer.quantize`` will be deprecated in future release.
+
+    Please use ``bigdl.nano.pytorch.InferenceOptimizer.quantize`` instead.
+```
 
 Before you go ahead with these APIs, you have to make sure BigDL-Nano is correctly installed for PyTorch. If not, please follow [this](../Overview/nano.md) to set up your environment.
 
 ##  Runtime Acceleration
-All available runtime accelerations are integrated in `Trainer.trace(accelerator='onnxruntime'/'openvino')` with different accelerator values. Let's take mobilenetv3 as an example model and here is a short script that you might have before applying any BigDL-Nano's optimizations:
+All available runtime accelerations are integrated in `InferenceOptimizer.trace(accelerator='onnxruntime'/'openvino'/'jit')` with different accelerator values. Let's take mobilenetv3 as an example model and here is a short script that you might have before applying any BigDL-Nano's optimizations:
 ```python
 from torchvision.models.mobilenetv3 import mobilenet_v3_small
 import torch
 from torch.utils.data.dataset import TensorDataset
 from torch.utils.data.dataloader import DataLoader
-from bigdl.nano.pytorch import Trainer
+from bigdl.nano.pytorch import InferenceOptimizer, Trainer
+
 # step 1: create your model
 model = mobilenet_v3_small(num_classes=10)
 
@@ -45,7 +60,7 @@ When you're ready, you can simply append the following part to enable your ONNXR
 ```python
 # step 4: trace your model as an ONNXRuntime model
 # if you have run `trainer.fit` before trace, then argument `input_sample` is not required.
-ort_model = Trainer.trace(model, accelerator='onnruntime', input_sample=x)
+ort_model = InferenceOptimizer.trace(model, accelerator='onnruntime', input_sample=x)
 
 # step 5: use returned model for transparent acceleration
 # The usage is almost the same with any PyTorch module
@@ -67,7 +82,7 @@ The OpenVINO usage is quite similar to ONNXRuntime, the following usage is for O
 ```python
 # step 4: trace your model as a openvino model
 # if you have run `trainer.fit` before trace, then argument `input_sample` is not required.
-ov_model = Trainer.trace(model, accelerator='openvino', input_sample=x)
+ov_model = InferenceOptimizer.trace(model, accelerator='openvino', input_sample=x)
 
 # step 5: use returned model for transparent acceleration
 # The usage is almost the same with any PyTorch module
@@ -83,11 +98,11 @@ trainer.predict(ort_model, dataloader)
 ```
 
 ## Quantization
-Quantization is widely used to compress models to a lower precision, which not only reduces the model size but also accelerates inference. BigDL-Nano provides `Trainer.quantize()` API for users to quickly obtain a quantized model with accuracy control by specifying a few arguments. Intel Neural Compressor (INC) and Post-training Optimization Tools (POT) from OpenVINO toolkit are enabled as options. In the meantime, runtime acceleration is also included directly in the quantization pipeline when using `accelerator='onnxruntime'/'openvino'` so you don't have to run `Trainer.trace` before quantization.
+Quantization is widely used to compress models to a lower precision, which not only reduces the model size but also accelerates inference. BigDL-Nano provides `InferenceOptimizer.quantize()` API for users to quickly obtain a quantized model with accuracy control by specifying a few arguments. Intel Neural Compressor (INC) and Post-training Optimization Tools (POT) from OpenVINO toolkit are enabled as options. In the meantime, runtime acceleration is also included directly in the quantization pipeline when using `accelerator='onnxruntime'/'openvino'` so you don't have to run `InferenceOptimizer.trace` before quantization.
 
 To use INC as your quantization engine, you can choose accelerator as `None` or `'onnxruntime'`. Otherwise, `accelerator='openvino'` means using OpenVINO POT to do quantization.
 
-By default, `Trainer.quantize()` doesn't search the tuning space and returns the fully-quantized model without considering the accuracy drop. If you need to search quantization tuning space for a model with accuracy control, you'll have to specify a few arguments to define the tuning space. More instructions in [Quantization with Accuracy Control](#quantization-with-accuracy-control)
+By default, `InferenceOptimizer.quantize()` doesn't search the tuning space and returns the fully-quantized model without considering the accuracy drop. If you need to search quantization tuning space for a model with accuracy control, you'll have to specify a few arguments to define the tuning space. More instructions in [Quantization with Accuracy Control](#quantization-with-accuracy-control)
 
 ### Quantization using Intel Neural Compressor
 By default, Intel Neural Compressor is not installed with BigDL-Nano. So if you determine to use it as your quantization backend, you'll need to install it first:
@@ -96,9 +111,9 @@ pip install neural-compressor==1.11.0
 ```
 **Quantization without extra accelerator**
 
-Without extra accelerator, `Trainer.quantize()` returns a PyTorch module with desired precision and accuracy. Following the example in [Runtime Acceleration](#runtime-acceleration), you can add quantization as below:
+Without extra accelerator, `InferenceOptimizer.quantize()` returns a PyTorch module with desired precision and accuracy. Following the example in [Runtime Acceleration](#runtime-acceleration), you can add quantization as below:
 ```python
-q_model = trainer.quantize(model, calib_dataloader=dataloader)
+q_model = InferenceOptimizer.quantize(model, calib_dataloader=dataloader)
 # run simple prediction with transparent acceleration
 y_hat = q_model(x)
 
@@ -111,13 +126,13 @@ This is a most basic usage to quantize a model with defaults, INT8 precision, an
 
 **Quantization with ONNXRuntime accelerator**
 
-With the ONNXRuntime accelerator, `Trainer.quantize()` will return a model with compressed precision and running inference in the ONNXRuntime engine. It's also required to install onnxruntime-extensions as a dependency of INC when using ONNXRuntime as backend as well as the dependencies required in [ONNXRuntime Acceleration](#onnxruntime-acceleration):
+With the ONNXRuntime accelerator, `InferenceOptimizer.quantize()` will return a model with compressed precision and running inference in the ONNXRuntime engine. It's also required to install onnxruntime-extensions as a dependency of INC when using ONNXRuntime as backend as well as the dependencies required in [ONNXRuntime Acceleration](#onnxruntime-acceleration):
 ```shell
 pip install onnx onnxruntime onnxruntime-extensions
 ```
 Still taking the example in [Runtime Acceleration](pytorch_inference.md#runtime-acceleration), you can add quantization as below:
 ```python
-ort_q_model = trainer.quantize(model, accelerator='onnxruntime', calib_dataloader=dataloader)
+ort_q_model = InferenceOptimizer.quantize(model, accelerator='onnxruntime', calib_dataloader=dataloader)
 # run simple prediction with transparent acceleration
 y_hat = ort_q_model(x)
 
@@ -128,8 +143,8 @@ trainer.predict(ort_q_model, dataloader)
 ```
 Using `accelerator='onnxruntime'` actually equals to converting the model from PyTorch to ONNX firstly and then do quantization on the converted ONNX model:
 ```python
-ort_model = Trainer.trace(model, accelerator='onnruntime', input_sample=x):
-ort_q_model = trainer.quantize(ort_model, accelerator='onnxruntime', calib_dataloader=dataloader)
+ort_model = InferenceOptimizer.trace(model, accelerator='onnruntime', input_sample=x):
+ort_q_model = InferenceOptimizer.quantize(ort_model, accelerator='onnxruntime', calib_dataloader=dataloader)
 
 # run inference with transparent acceleration 
 y_hat = ort_q_model(x)
@@ -145,7 +160,7 @@ pip install openvino-dev
 ```
 Take the example in [Runtime Acceleration](#runtime-acceleration), and add quantization:
 ```python
-ov_q_model = trainer.quantize(model, accelerator='openvino', calib_dataloader=dataloader)
+ov_q_model = InferenceOptimizer.quantize(model, accelerator='openvino', calib_dataloader=dataloader)
 # run simple prediction with transparent acceleration
 y_hat = ov_q_model(x)
 
@@ -156,8 +171,8 @@ trainer.predict(ov_q_model, dataloader)
 ```
 Same as using ONNXRuntime accelerator, it equals to converting the model from PyTorch to OpenVINO firstly and then doing quantization on the converted OpenVINO model:
 ```python
-ov_model = Trainer.trace(model, accelerator='openvino', input_sample=x):
-ov_q_model = trainer.quantize(ov_model, accelerator='onnxruntime', calib_dataloader=dataloader)
+ov_model = InferenceOptimizer.trace(model, accelerator='openvino', input_sample=x):
+ov_q_model = InferenceOptimizer.quantize(ov_model, accelerator='onnxruntime', calib_dataloader=dataloader)
 
 # run inference with transparent acceleration 
 y_hat = ov_q_model(x)
@@ -186,30 +201,30 @@ There are a few arguments required only by INC, and you should not specify or mo
 Here is an example to use INC with accuracy control as below. It will search for a model within 1% accuracy drop with 10 trials.
 ```python
 from torchmetrics.classification import Accuracy
-trainer.quantize(model,
-                 precision='int8',
-                 accelerator=None,
-                 calib_dataloader= dataloader,
-                 metric=Accuracy()
-                 accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
-                 approach='static',
-                 method='fx',
-                 tuning_strategy='bayesian',
-                 timeout=0,
-                 max_trials=10,
-                ):
+InferenceOptimizer.quantize(model,
+                            precision='int8',
+                            accelerator=None,
+                            calib_dataloader= dataloader,
+                            metric=Accuracy()
+                            accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
+                            approach='static',
+                            method='fx',
+                            tuning_strategy='bayesian',
+                            timeout=0,
+                            max_trials=10,
+                            ):
 ```
 **Accuracy Control with POT**
 Similar to INC, we can run quantization like:
 ```python
 from torchmetrics.classification import Accuracy
-trainer.quantize(model,
-                 precision='int8',
-                 accelerator=`openvino`,
-                 calib_dataloader= dataloader,
-                 metric=Accuracy()
-                 accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
-                 approach='static',
-                 max_trials=10,
-                ):
+InferenceOptimizer.quantize(model,
+                            precision='int8',
+                            accelerator='openvino',
+                            calib_dataloader= dataloader,
+                            metric=Accuracy()
+                            accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
+                            approach='static',
+                            max_trials=10,
+                            ):
 ```