update runtime extension 1.12 document (#1012)

* fix the package name of Task Example

* change the API name space to ipex

docs/tutorials/features/runtime_extension.md

@@ -3,9 +3,9 @@ Runtime Extension
 
 Intel® Extension for PyTorch\* Runtime Extension provides a couple of PyTorch frontend APIs for users to get finer-grained control of the thread runtime. It provides:
 
-1. Multi-stream inference via the Python frontend module `intel_extension_for_pytorch.cpu.runtime.MultiStreamModule`.
-2. Spawn asynchronous tasks via the Python frontend module `intel_extension_for_pytorch.cpu.runtime.Task`.
-3. Program core bindings for OpenMP threads via the Python frontend `intel_extension_for_pytorch.cpu.runtime.pin`.
+1. Multi-stream inference via the Python frontend module `ipex.cpu.runtime.MultiStreamModule`.
+2. Spawn asynchronous tasks via the Python frontend module `ipex.cpu.runtime.Task`.
+3. Program core bindings for OpenMP threads via the Python frontend `ipex.cpu.runtime.pin`.
 
 **note**: Intel® Extension for PyTorch\* Runtime extension is in the **experimental** stage. The API is subject to change. More detailed descriptions are available at [API Documentation page](../api_doc.rst).
 
@@ -27,6 +27,9 @@ If the inputs' batchsize is larger than and divisible by ``num_streams``, the ba
 
 Let's create some ExampleNets that will be used by further examples:
 ```
+import torch
+import intel_extension_for_pytorch as ipex
+
 class ExampleNet1(torch.nn.Module):
     def __init__(self):
         super(ExampleNet1, self).__init__()
@@ -70,8 +73,8 @@ with torch.no_grad():
 Here is the example of a model with single tensor input/output. We create a CPUPool with all the cores available on numa node 0. And creating a `MultiStreamModule` with stream number of 2 to do inference.
 ```
 # Convert the model into multi_Stream_model
-cpu_pool = intel_extension_for_pytorch.cpu.runtime.CPUPool(node_id=0)
-multi_Stream_model = intel_extension_for_pytorch.cpu.runtime.MultiStreamModule(traced_model1, num_streams=2, cpu_pool=cpu_pool)
+cpu_pool = ipex.cpu.runtime.CPUPool(node_id=0)
+multi_Stream_model = ipex.cpu.runtime.MultiStreamModule(traced_model1, num_streams=2, cpu_pool=cpu_pool)
 
 with torch.no_grad():
     y = multi_Stream_model(x)
@@ -81,7 +84,7 @@ with torch.no_grad():
 When creating a `MultiStreamModule`, we have default settings for `num_streams` ("AUTO") and `cpu_pool` (with all the cores available on numa node 0). For the `num_streams` of "AUTO", there are limitations to use with int8 datatype as we mentioned in below performance receipts section.
 ```
 # Convert the model into multi_Stream_model
-multi_Stream_model = intel_extension_for_pytorch.cpu.runtime.MultiStreamModule(traced_model1)
+multi_Stream_model = ipex.cpu.runtime.MultiStreamModule(traced_model1)
 
 with torch.no_grad():
     y = multi_Stream_model(x)
@@ -91,17 +94,17 @@ with torch.no_grad():
 For module such as ExampleNet2 with structure input/output tensors, user needs to create `MultiStreamModuleHint` as input hint and output hint. `MultiStreamModuleHint` tells `MultiStreamModule` how to auto split the input into streams and concat the output from each steam.
 ```
 # Convert the model into multi_Stream_model
-cpu_pool = intel_extension_for_pytorch.cpu.runtime.CPUPool(node_id=0)
+cpu_pool = ipex.cpu.runtime.CPUPool(node_id=0)
 # Create the input hint object
-input_hint = intel_extension_for_pytorch.cpu.runtime.MultiStreamModuleHint(0, 0)
+input_hint = ipex.cpu.runtime.MultiStreamModuleHint(0, 0)
 # Create the output hint object
 # When Python module has multi output tensors, it will be auto pack into a tuple, So we pass a tuple(0, 0) to create the output_hint
-output_hint = intel_extension_for_pytorch.cpu.runtime.MultiStreamModuleHint((0, 0))
-multi_Stream_model = intel_extension_for_pytorch.cpu.runtime.MultiStreamModule(traced_model2,
-                                                                            num_streams=2,
-                                                                            cpu_pool=cpu_pool,
-                                                                            input_split_hint=input_hint,
-                                                                            output_concat_hint=output_hint)
+output_hint = ipex.cpu.runtime.MultiStreamModuleHint((0, 0))
+multi_Stream_model = ipex.cpu.runtime.MultiStreamModule(traced_model2,
+                                                        num_streams=2,
+                                                        cpu_pool=cpu_pool,
+                                                        input_split_hint=input_hint,
+                                                        output_concat_hint=output_hint)
 
 with torch.no_grad():
     y = multi_Stream_model(x, x2)
@@ -133,12 +136,11 @@ Here are some performance receipes that we recommend for better multi-stream per
 Here is an example for using asynchronous tasks. With the support of a runtime API, you can run 2 modules simultaneously. Each module runs on the corresponding cpu pool.
 
 ```
-# Create the cpu pool and numa aware memory allocator
-cpu_pool1 = ipex.runtime.CPUPool([0, 1, 2, 3])
-cpu_pool2 = ipex.runtime.CPUPool([4, 5, 6, 7])
+cpu_pool1 = ipex.cpu.runtime.CPUPool([0, 1, 2, 3])
+cpu_pool2 = ipex.cpu.runtime.CPUPool([4, 5, 6, 7])
 
-task1 = ipex.runtime.Task(traced_model1, cpu_pool1)
-task2 = ipex.runtime.Task(traced_model1, cpu_pool2)
+task1 = ipex.cpu.runtime.Task(traced_model1, cpu_pool1)
+task2 = ipex.cpu.runtime.Task(traced_model1, cpu_pool2)
 
 y1_future = task1(x)
 y2_future = task2(x)
@@ -149,11 +151,11 @@ y2 = y2_future.get()
 
 ### Example of configuring core binding
 
-Runtime Extension provides API of `intel_extension_for_pytorch.cpu.runtime.pin` to a CPU Pool for binding physical cores. We can use it without the async task feature. Here is the example to use `intel_extension_for_pytorch.cpu.runtime.pin` in the `with` context.
+Runtime Extension provides API of `ipex.cpu.runtime.pin` to a CPU Pool for binding physical cores. We can use it without the async task feature. Here is the example to use `ipex.cpu.runtime.pin` in the `with` context.
 
 ```
-cpu_pool = intel_extension_for_pytorch.cpu.runtime.CPUPool(node_id=0)
-with intel_extension_for_pytorch.cpu.runtime.pin(cpu_pool):
+cpu_pool = ipex.cpu.runtime.CPUPool(node_id=0)
+with ipex.cpu.runtime.pin(cpu_pool):
     y_runtime = traced_model1(x)
 ```