From 3cdeca6effcb656487b42c0b700669a547dd7e14 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 24 Jun 2020 23:36:34 +0800
Subject: [PATCH 01/28] Adding Pluggable Device For TensorFlow RFC

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+# **Pluggable device for TensorFlow**
+
+| Status        | Proposed                                             |
+:-------------- |:---------------------------------------------------- |
+| **RFC #**     | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)|
+| **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
+| **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
+| **Updated**   | 2020-06-24                                           |
+
+## **Objective**
+
+Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing most of the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
+
+This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plugin capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
+
+## **Motivation**
+
+When extending TensorFlow to support a new device, one needs to modify TensorFlow code and maintain a special TensorFlow build for the new device. Modular TensorFlow RFC design a plugin architecture for serveral TensorFlow components(`Networking`, `Filesystems`, `Kernel`, `Graph` and `Accelerator backends`). This RFC describes the Accelerator backends module in the Tensorflow proper side, by introducing pluggable device to the TensorFlow device classes.
+
+The pluggable device discovery and initialization is transparent to end users. As long as the device plugin libraries follow the design described in this RFC, it can be plugged to TensorFlow proper and enable TensorFlow to run existing TensorFlow programs on a new device. 
+
+## **User Benefit**
+
+This RFC allows TensorFlow to transparently run TensorFlow programs on new devices, as long as users set up the system properly installing the device plugin. 
+
+## **Design Proposal**
+
+### Design Overview
+
+This RFC extends the TensorFlow device class hierarchy to add a standardized pluggable device named `PluggableDevice` which is built on top of [StreamExecutor](https://github.com/tensorflow/tensorflow/blob/e5023a1738cce7efcdf9d87863b85c80ab2f8c9e/tensorflow/stream_executor/stream_executor_pimpl.h#L73), and all new third-party devices who want to integrate with current TensorFlow stack only need to implement StreamExecutor C API(shown in Diagram 1).
+
+<div align=center> 
+<img src=20200624-pluggable-device-for-tensorflow/design_overview.png>
+</div>
+
+* `PluggableDevice` is defined in TensorFlow proper which inherits from [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h).It is built on top of  StreamExecutor C++ interface to manage `PluggableDevice`’s key abstractions like StreamExecutor, stream, memory and event.
+
+* `PluggableDeviceExecutor` implements [StreamExecutor](https://github.com/tensorflow/tensorflow/blob/e5023a1738cce7efcdf9d87863b85c80ab2f8c9e/tensorflow/stream_executor/stream_executor_pimpl.h#L73) and is built on top of StreamExecutor C API (addressed in [RFC](https://github.com/tensorflow/community/pull/257)). 
+
+* `PluggableDevice Implementation` is inside the TensorFlow plugin, which provides those C functions implementation defined in the StreamExecutor C API.
+
+The pluggable device mechanism contains device discovery and creation process which creates a `PluggableDevice` object and `PluggableDeviceExecutor` object for each pluggable device. 
+
+With the RFC, existing TensorFlow GPU programs can run on a plugged device without the user changing the code. The Diagram 2 describes the workflow of TensorFlow with device plugin, it shows how a simple GPU program runs on the pluggable device.
+<div align="center">
+<img src=20200624-pluggable-device-for-tensorflow/gpu_example.png>
+</div>
+
+### Device Discovery
+
+Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
+
+During the plugin library initialization, it calls the `SE_ReigsterPlatform()` API to register the stream executor platform (`SE_Platform` struct) to TensorFlow proper. The `SE_ReigsterPlatform()` API is a callback API, part of StreamExecutor C API, which passes necessary information to TensorFlow proper to instantiate a stream executor platform ([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and register to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82). 
+The stream executor platform must be registered with the name "PluggableDevice".  
+See below code which is an example of registering a PluggableDevice platform with StreamExecutor C API:
+```cpp
+void RegisterPluggableDevicePlatform() {
+  static plugin_id_value = 123;
+  SE_PlatformId id;
+  id.id = &plugin_id_value;
+  int visible_device_count = get_plugin_device_count;
+  SE_Platform* custom_platform = SE_NewPlatform(
+     id, visible_device_count,
+     create_device, create_stream_executor,
+     delete_device, delete_stream_executor);
+  TF_Status* status = TF_NewStatus();
+  std::string name = "PluggableDevice";
+  SE_RegisterPlatform(
+     name.c_str(), name.size(),
+     custom_platform,
+     status);
+}
+
+```
+Use static initialization to register the new platform:
+```cpp
+static bool IsPluggableDevicePlatformRegistered = []() {
+ RegisterPluggablePlatform();
+ return true;
+}();
+
+```
+
+### Device Creation
+
+`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, PluggableDeviceFactory is registered as "GPU" name and given higher priority than the default GPU device.  
+&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("GPU",PluggableDeviceFactory, 220); // plugged GPU`  
+&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("GPU", GPUDeviceFactory, 210);//default GPU`  
+For those vendor who don't want to use "GPU" name, it's optional to register a new device name. For example:   
+&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("Third-party device",PluggableDeviceFactory, 230); // plugged third party device`
+
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find its `se::platform` through its platform name: "PluggableDevice”,  then stream executor platform (`se::platform`) further creates a StreamExecutor object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the StreamExecutor objects. 
+
+The section below shows some pseudo code to introduce some extension inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
+
+1. `PluggableDeviceFactory` creates and initializes a set of `PluggableDevice` instances when the session is created.  
+```cpp
+   PluggableDeviceFactory::CreateDevices(SessionOptions& options, const string& name_prefix, std::vector<std::unique_ptr<Device>>* devices) {
+     for (int i = 0; i < options.device_count(); i++) {
+      PluggableDevice pluggable_device = CreatePluggableDevice(options,i); //set allocator
+      pluggable_device->Init(options);
+      devices.push_back(std::move(pluggable_device));
+     }
+   }
+```
+
+2. `PluggableDevice` object binds a StreamExecutor and creates a set of Streams during the initialization.Streams include one compute stream and several memory copy streams.
+```cpp
+   void PluggableDevice::Init(SessionOption& options) {  
+     se::Platform* platform= se::MultiPlatformManager::PlatformWithName("PluggableDevice");
+     stream_executor_ = platform->ExecutorForDevice(pluggable_dev_id_);
+     compute_stream_ = new se::Stream(stream_executor_);
+     compute_stream_->Init();
+     host_to_device_stream_ = new se::Stream(stream_executor_);
+     host_to_device_stream_->Init();
+     ...
+   }  // create StreamExecutor
+```
+3. `PluggableDevicePlatform` is responsible for the StreamExecutor creation. It creates an `SE_StreamExecutor` and `SE_Device` object through create_stream_executor and create_device which are registered in the `SE_Platform`. Then `PluggableDeviceExecutor` is constructed with `SE_StreamExecutor` and `SE_Device` object.   
+```cpp
+   StreamExecutor* PluggableDevicePlaform::ExeutorForDevice(int device_id） {
+    auto config = get_plugin_config(device_id);
+    SE_Options* se_option = get_se_option(device_id);
+    SE_StreamExecutor* se= platform_->create_stream_executor();
+    SE_Device* sd = platform_->create_device(se_options)
+    auto executor = absl::make_unique<StreamExecutor>(this, absl::make_unique<PluggableDeviceExecutor>(config, se, sd));
+    return std::move(executor);
+   }
+```
+**TensorFlow Proper**
+
+TensorFlow proper needs to be extended to support a new class `PluggableDevice` to represent a set of new third-party devices and a new stream executor platform (`PluggableDevicePlatform`) to create the device and related resources with the information registered from plugin. 
+
+Two sets of classes need to be defined in TensorFlow proper. 
+* Set 1: `PluggableDevice` related classes 
+   * class `PluggableDevice`:  a class represents a set of new third-party devices, it has a new device type named "PluggableDevice"/DEVICE_PLUGGABLE.
+   * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
+   * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.
+   * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
+   * class `PluggableDeviceHostAllocator`: allocator for pinned CPU RAM that is made known to PluggableDevice for the purpose of efficient DMA with PluggableDevice.
+   * class `PluggableDeviceEventMgr`: an object to keep track of pending Events in the StreamExecutor streams.
+   * class `PluggableDeviceContext`: a wrapper of pluggable device specific context that can be passed to OpKernels.
+* Set 2: `PluggableDevicePlatform` related classes 
+   * class `PluggableDevicePlatform`: PluggableDevice-specific platform, its platform name is "PluggableDevice", it contains a C struct: SE_Platform* platform_ which is its internal implementation and as the C interface registered by device plugin.
+   * class `PluggableDeviceExecutor`: PluggableDevice-platform implementation of the platform-agnostic StreamExecutorInterface, it contains C structs: SE_StreamExecutor* executor_ and SE_Device* device_ whose member can be accessed in both TensorFlow proper and device plugins.
+   * class `PluggableDeviceStream`: wraps a StreamHandle in order to satisfy the platform-independent StreamInterface. It returns SE_Stream which is treated as an opaque type to TensorFlow,  whose structure is created by the device plugin.  
+   * class `PluggableDeviceTimer`: wraps an opaque handle: SE_Timer to satisfy the platform-independent TimerInterface.
+   * class `PluggableDeviceEvent`: wraps an opaque handle: SE_Event to satisfy the platform-independent EventInterface.
+
+**Tensorflow Plugin**
+
+Plugin authors need to provide those C functions implementation defined in StreamExecutor C API . 
+*  `SE_StreamExecutor` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_StreamExecutor and registers its C API implementations to the SE_StreamExecutor.  
+```cpp
+   SE_StreamExecutor* create_stream_executor() {
+     SE_StreamExecutor* se_nfs = new SE_StreamExecutor();
+     se->memcpy_from_host = my_device_memory_from_host_function;
+     se->allocate = my_allocate_function;
+     …
+   }//Init device
+```
+* `SE_Device` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_Device and fills its device opaque handle and device name to the SE_Device.
+```cpp
+  SE_Device* create_device(SE_Options* options, TF_Status* status) {
+    SE_Device* se = new SE_Device();
+    se->device_handle = get_my_device_handle();
+    ...
+    return se;
+  }
+```
+* `SE_Stream` is defined in plugin and treated as an opaque struct in TensorFlow proper. 
+```cpp
+  void create_stream(SE_Device* executor, SE_Stream* stream, TF_Status*) {
+    *stream = new SE_Stream_st();
+    (*stream)->stream_handle = create_my_stream_handle(executor);
+    ..
+  }
+```
+
+### PluggableDevice kernel registration
+
+This RFC shows an example of registering kernels for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
+
+TensorFlow proper defines a new device_type named DEVICE_PLUGGABLE for PluggableDevice.This device_type is used for the kernel registration and dispatch. Plugin needs to register its kernel implementation with DEVICE_PLUGGABLE type.
+```cpp
+void InitPlugin() {
+  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", DEVICE_PLUGGABLE,
+      &Conv_Create, &Conv_Compute, &Conv_Delete);
+  TF_Status* status = TF_NewStatus();
+  TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
+  if (TF_GetCode(status) != TF_OK) { /* handle errors */ }
+  TF_DeleteStatus(status);
+}
+```
+### Using stream inside PluggableDevice kernel
+
+The following code shows a convolution kernel implementation using the stream handle. The streams are created during the pluggable device creation. The placer decides which device to use for each OP in the graph. Then the streams associated with the device are used to construct the OpKernelContext for the op computation during the graph execution.
+```cpp
+void Conv_Compute(TF_OpKernelContext*) {
+  TF_GetInput(context, input_index, &input, &status);
+  TF_GetInput(context, filter_index, &filter, &status);
+  auto output = TF_AllocateOutput(context, output_index, TF_Float32, dims, num_dims, len, status);
+  SE_Stream se_stream = TF_GetStream(TF_OpKernelContext);
+  auto native_stream = static_cast<native_stream_type>(se_stream->stream_handle);
+  my_conv_impl(input, filter, output, native_stream);
+}
+```
+Kernel and op registration and implementation API [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md) needs to be extended to retrieve streams/device context from the TF_OpKernelContext, besides inputs and outputs. 
+
+### **Alternatives Considered**
+
+* Without this RFC, end users need to change the python code to import the third-party device plugin. 
+
+* Without this RFC, the third-party device vendor may implement the LocalDevice interface, which is not a C API interface and may interact with potential C++ ABI incompatibility issues.  
+
+### **Performance Implications**
+
+* We don’t expect performance impact due to this RFC. The functions described by this RFC are realized at the initialization stage. 
+
+### **Dependencies**
+
+* This RFC doesn’t add new dependencies to external libraries. 
+
+* It depends on three modular TensorFlow related RFC 
+
+    * Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77)
+
+    * StreamExecutor C interface [RFC](https://github.com/tensorflow/community/pull/257)
+
+    * Kernel and op registration and implementation API [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md) 
+
+### **Engineering Impact**
+
+* The impact to binary size / startup time / build time / test times are minimum. 
+
+* The TensorFlow team will maintain this code. 
+
+### **Platforms and Environments**
+
+* The pluggable device mechanism is based on `LoadLibrary()` so should work on all the platforms supported by `LoadLibrary`. The other enhancement to tensorflow proper is platform independent.
+
+### **Best Practices**
+
+* This works with Modular TensorFlow which will be the only way to integrate new third-party devices to the current TensorFlow stack. 
+
+### **Compatibility**
+
+The RFC promotes the current TensorFlow ecosystem as it supports plugging new devices to TensorFlow.  
+
+We don't expect this proposal to impact with other parts of the TensorFlow ecosystem. It doesn't support TFLite. It should not impede distribution strategies and would not interact with tf.fuction and SaveModel.  
+
diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/design_overview.png b/rfcs/20200624-pluggable-device-for-tensorflow/design_overview.png
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HcmV?d00001


From 97d805fb6c122ba69b239d468b80247a33350fd1 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 24 Jun 2020 23:51:31 +0800
Subject: [PATCH 02/28] Update RFC PR Number

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 89af0dccb..eda7c21ae 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -2,7 +2,7 @@
 
 | Status        | Proposed                                             |
 :-------------- |:---------------------------------------------------- |
-| **RFC #**     | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)|
+| **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
 | **Updated**   | 2020-06-24                                           |

From 72c45896ef9da8dcb448ccd99cfa0f155b02f0c1 Mon Sep 17 00:00:00 2001
From: jzhoulon <zhoulong.jiang@intel.com>
Date: Thu, 2 Jul 2020 14:46:47 +0800
Subject: [PATCH 03/28] Update 20200624-pluggable-device-for-tensorflow.md

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index eda7c21ae..3888afcb8 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -9,7 +9,7 @@
 
 ## **Objective**
 
-Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing most of the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
+Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
 
 This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plugin capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 

From 8fec775c9119fcacd476401c67ddb75eebfe6a26 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 8 Jul 2020 17:35:22 +0800
Subject: [PATCH 04/28] update StreamExecutor C API

---
 ...0200624-pluggable-device-for-tensorflow.md | 42 ++++++++++---------
 1 file changed, 22 insertions(+), 20 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 3888afcb8..0ac348a88 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -51,34 +51,36 @@ With the RFC, existing TensorFlow GPU programs can run on a plugged device witho
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
 During the plugin library initialization, it calls the `SE_ReigsterPlatform()` API to register the stream executor platform (`SE_Platform` struct) to TensorFlow proper. The `SE_ReigsterPlatform()` API is a callback API, part of StreamExecutor C API, which passes necessary information to TensorFlow proper to instantiate a stream executor platform ([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and register to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82). 
-The stream executor platform must be registered with the name "PluggableDevice".  
 See below code which is an example of registering a PluggableDevice platform with StreamExecutor C API:
 ```cpp
 void RegisterPluggableDevicePlatform() {
   static plugin_id_value = 123;
   SE_PlatformId id;
   id.id = &plugin_id_value;
-  int visible_device_count = get_plugin_device_count;
-  SE_Platform* custom_platform = SE_NewPlatform(
-     id, visible_device_count,
-     create_device, create_stream_executor,
-     delete_device, delete_stream_executor);
+  int visible_device_count = get_plugin_device_count();
+  
+  SE_PlatformParams platform_params {SE_PLATFORM_PARAMS_SIZE, id, 
+                                    create_device, destory_device, 
+                                    create_stream_executor, destory_stream_executor};
+  SE_Platform* custom_platform = SE_NewPlatform(platform_params);
+
+  string platform_name = "MyCustomPlatform";
+  string device_name = "MyCustomDevice";
+
+  SE_PlatformRegistartionParams platform_register_params {SE_PLATFORM_REGISTRATION_PARAMS_SIZE,
+                                                          platform_name.c_str(), platform_name.size(),
+                                                          device_name.c_str(), device_name.size(),
+                                                          custom_platform};
   TF_Status* status = TF_NewStatus();
-  std::string name = "PluggableDevice";
-  SE_RegisterPlatform(
-     name.c_str(), name.size(),
-     custom_platform,
-     status);
+  SE_RegisterPlatform(platform_register_params, status);
 }
 
 ```
-Use static initialization to register the new platform:
+Use dynamic initialization to register the new platform:
 ```cpp
-static bool IsPluggableDevicePlatformRegistered = []() {
- RegisterPluggablePlatform();
- return true;
-}();
-
+void TF_InitPlugin() {
+  RegisterPluggableDevicePlatform(); 
+}
 ```
 
 ### Device Creation
@@ -152,8 +154,8 @@ Two sets of classes need to be defined in TensorFlow proper.
 Plugin authors need to provide those C functions implementation defined in StreamExecutor C API . 
 *  `SE_StreamExecutor` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_StreamExecutor and registers its C API implementations to the SE_StreamExecutor.  
 ```cpp
-   SE_StreamExecutor* create_stream_executor() {
-     SE_StreamExecutor* se_nfs = new SE_StreamExecutor();
+   SE_StreamExecutor* create_stream_executor(TF_Status* status) {
+     SE_StreamExecutor* se_nfs = new SE_StreamExecutor{ SE_STREAMEXECUTOR_STRUCT_SIZE };
      se->memcpy_from_host = my_device_memory_from_host_function;
      se->allocate = my_allocate_function;
      …
@@ -162,7 +164,7 @@ Plugin authors need to provide those C functions implementation defined in Strea
 * `SE_Device` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_Device and fills its device opaque handle and device name to the SE_Device.
 ```cpp
   SE_Device* create_device(SE_Options* options, TF_Status* status) {
-    SE_Device* se = new SE_Device();
+    SE_Device* se = new SE_Device( SE_DEVICE_STRUCT_SIZE );
     se->device_handle = get_my_device_handle();
     ...
     return se;

From a60fb80c36e5fffa544c7b978de6b8422dd72ba2 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Sat, 11 Jul 2020 21:25:13 +0800
Subject: [PATCH 05/28] replace SE_RegisterPlatform->SE_InitializePlugin
 according to StreamExecutor C API RFC

---
 ...0200624-pluggable-device-for-tensorflow.md | 80 ++++++++++---------
 1 file changed, 43 insertions(+), 37 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 0ac348a88..4fbb12da1 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -15,7 +15,7 @@ This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow
 
 ## **Motivation**
 
-When extending TensorFlow to support a new device, one needs to modify TensorFlow code and maintain a special TensorFlow build for the new device. Modular TensorFlow RFC design a plugin architecture for serveral TensorFlow components(`Networking`, `Filesystems`, `Kernel`, `Graph` and `Accelerator backends`). This RFC describes the Accelerator backends module in the Tensorflow proper side, by introducing pluggable device to the TensorFlow device classes.
+When extending TensorFlow to support a new device, one needs to modify TensorFlow code and maintain a special TensorFlow build for the new device. Modular TensorFlow RFC design a plugin architecture for serveral TensorFlow components(`Networking`, `Filesystems`, `Kernel`, `Graph` and `Accelerator backends`). This RFC describes the Accelerator backends module in the TensorFlow proper side, by introducing pluggable device to the TensorFlow device classes.
 
 The pluggable device discovery and initialization is transparent to end users. As long as the device plugin libraries follow the design described in this RFC, it can be plugged to TensorFlow proper and enable TensorFlow to run existing TensorFlow programs on a new device. 
 
@@ -50,48 +50,54 @@ With the RFC, existing TensorFlow GPU programs can run on a plugged device witho
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, it calls the `SE_ReigsterPlatform()` API to register the stream executor platform (`SE_Platform` struct) to TensorFlow proper. The `SE_ReigsterPlatform()` API is a callback API, part of StreamExecutor C API, which passes necessary information to TensorFlow proper to instantiate a stream executor platform ([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and register to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82). 
-See below code which is an example of registering a PluggableDevice platform with StreamExecutor C API:
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and register to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper also gets the device type through `SE_InitializePlugin` and register the `PluggableDeviceFactory`with this type. The device type will be the device strings to be used to access pluggable device with tf.device() in python layer.
+Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:
 ```cpp
-void RegisterPluggableDevicePlatform() {
-  static plugin_id_value = 123;
-  SE_PlatformId id;
+void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
+  static const int32_t plugin_id_value = 123;
+  SE_PlatformId id{ SE_PLATFORMID_STRUCT_SIZE };
   id.id = &plugin_id_value;
-  int visible_device_count = get_plugin_device_count();
+  int32_t visible_device_count = get_plugin_device_count();
   
-  SE_PlatformParams platform_params {SE_PLATFORM_PARAMS_SIZE, id, 
-                                    create_device, destory_device, 
-                                    create_stream_executor, destory_stream_executor};
-  SE_Platform* custom_platform = SE_NewPlatform(platform_params);
-
-  string platform_name = "MyCustomPlatform";
-  string device_name = "MyCustomDevice";
-
-  SE_PlatformRegistartionParams platform_register_params {SE_PLATFORM_REGISTRATION_PARAMS_SIZE,
-                                                          platform_name.c_str(), platform_name.size(),
-                                                          device_name.c_str(), device_name.size(),
-                                                          custom_platform};
-  TF_Status* status = TF_NewStatus();
-  SE_RegisterPlatform(platform_register_params, status);
-}
-
-```
-Use dynamic initialization to register the new platform:
-```cpp
-void TF_InitPlugin() {
-  RegisterPluggableDevicePlatform(); 
+  std::string name = "MyDevicePlatform";
+  std::string type = "GPU";
+
+  params.params.id = id;
+  params.params.visible_device_count = visible_device_count;
+  params.params.create_device = create_device;
+  params.params.destroy_device = destroy_device;
+  params.params.create_stream_executor = create_stream_executor;
+  params.params.destroy_stream_executor = destroy_stream_executor;
+  params.params.name = name.c_str();
+  params.params.name_len = name.size();
+  params.params.type = type.c_str();
+  params.params.type_len = type.size();
 }
 ```
-
 ### Device Creation
 
-`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, PluggableDeviceFactory is registered as "GPU" name and given higher priority than the default GPU device.  
-&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("GPU",PluggableDeviceFactory, 220); // plugged GPU`  
-&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("GPU", GPUDeviceFactory, 210);//default GPU`  
-For those vendor who don't want to use "GPU" name, it's optional to register a new device name. For example:   
-&emsp;&emsp;`REGISTER_LOCAL_DEVICE_FACTORY("Third-party device",PluggableDeviceFactory, 230); // plugged third party device`
-
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find its `se::platform` through its platform name: "PluggableDevice”,  then stream executor platform (`se::platform`) further creates a StreamExecutor object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the StreamExecutor objects. 
+`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register "GPU" string as the device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDevice` as "GPU" name with higher priority than the default GPU device.    
+Plugin:
+```
+  SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
+    ...
+    std::string type = "GPU"
+    params.params.type = type.c_str()
+    ...
+  }
+```
+Proper:
+```
+  std::string platform_name_str(params.params.name, params.params.name_len);
+  std::string type_str(params.params.type, params.params.type_len);
+  DeviceFactory::Register(type_str, new PluggableDeviceFactory(platform_name_str), priority); 
+```  
+For those vendors who don't want to use "GPU" name, it's optional to register a new device name.  
+Limitation: when multiple devices registered, their device names should be different, or it will get conflict and the registration will fail. This can be enhanced in the future. A possible solutoin: python layer provides API to let user specify an alternative device name they prefer if there is a conflict, such as:  
+```
+  tf.load_plugin("CustomDeviceName", path_to_plugin_lib)
+```
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find its `se::platform` through its platform name registered from plugin: "MyDevicePlatform”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
 The section below shows some pseudo code to introduce some extension inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -149,7 +155,7 @@ Two sets of classes need to be defined in TensorFlow proper.
    * class `PluggableDeviceTimer`: wraps an opaque handle: SE_Timer to satisfy the platform-independent TimerInterface.
    * class `PluggableDeviceEvent`: wraps an opaque handle: SE_Event to satisfy the platform-independent EventInterface.
 
-**Tensorflow Plugin**
+**TensorFlow Plugin**
 
 Plugin authors need to provide those C functions implementation defined in StreamExecutor C API . 
 *  `SE_StreamExecutor` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_StreamExecutor and registers its C API implementations to the SE_StreamExecutor.  

From 3317b4b783033d160ff8e11ca66e7e86a601d6fd Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Sat, 11 Jul 2020 21:44:03 +0800
Subject: [PATCH 06/28] add user example

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 4fbb12da1..0b5dd8c03 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -96,6 +96,8 @@ For those vendors who don't want to use "GPU" name, it's optional to register a
 Limitation: when multiple devices registered, their device names should be different, or it will get conflict and the registration will fail. This can be enhanced in the future. A possible solutoin: python layer provides API to let user specify an alternative device name they prefer if there is a conflict, such as:  
 ```
   tf.load_plugin("CustomDeviceName", path_to_plugin_lib)
+  with tf.device("/CustomDeviceName:0"):
+    ...
 ```
 When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find its `se::platform` through its platform name registered from plugin: "MyDevicePlatform”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 

From a4b9120859eac80a84afff0463f077d2dcafd4ed Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Tue, 14 Jul 2020 09:14:47 +0800
Subject: [PATCH 07/28] update time

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 0b5dd8c03..3b12c0682 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-06-24                                           |
+| **Updated**   | 2020-07-14                                           |
 
 ## **Objective**
 

From ec2ff5153cce96462033c476993034cc5eaeb0a2 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Tue, 14 Jul 2020 21:17:19 +0800
Subject: [PATCH 08/28] device_type attribute of PluggableDevice

---
 ...0200624-pluggable-device-for-tensorflow.md | 63 ++++++++++++++++---
 1 file changed, 56 insertions(+), 7 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 3b12c0682..71fff422f 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -79,7 +79,7 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
 `PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register "GPU" string as the device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDevice` as "GPU" name with higher priority than the default GPU device.    
 Plugin:
 ```
-  SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
+void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
     ...
     std::string type = "GPU"
     params.params.type = type.c_str()
@@ -108,7 +108,7 @@ The section below shows some pseudo code to introduce some extension inside the
    PluggableDeviceFactory::CreateDevices(SessionOptions& options, const string& name_prefix, std::vector<std::unique_ptr<Device>>* devices) {
      for (int i = 0; i < options.device_count(); i++) {
       PluggableDevice pluggable_device = CreatePluggableDevice(options,i); //set allocator
-      pluggable_device->Init(options);
+      pluggable_device->Init(options, pluggable_device_platform_name_);
       devices.push_back(std::move(pluggable_device));
      }
    }
@@ -116,8 +116,8 @@ The section below shows some pseudo code to introduce some extension inside the
 
 2. `PluggableDevice` object binds a StreamExecutor and creates a set of Streams during the initialization.Streams include one compute stream and several memory copy streams.
 ```cpp
-   void PluggableDevice::Init(SessionOption& options) {  
-     se::Platform* platform= se::MultiPlatformManager::PlatformWithName("PluggableDevice");
+   void PluggableDevice::Init(SessionOption& options, const string& platform_name) {  
+     se::Platform* platform= se::MultiPlatformManager::PlatformWithName(platform_name);
      stream_executor_ = platform->ExecutorForDevice(pluggable_dev_id_);
      compute_stream_ = new se::Stream(stream_executor_);
      compute_stream_->Init();
@@ -143,7 +143,7 @@ TensorFlow proper needs to be extended to support a new class `PluggableDevice`
 
 Two sets of classes need to be defined in TensorFlow proper. 
 * Set 1: `PluggableDevice` related classes 
-   * class `PluggableDevice`:  a class represents a set of new third-party devices, it has a new device type named "PluggableDevice"/DEVICE_PLUGGABLE.
+   * class `PluggableDevice`:  a class represents a set of new third-party devices, its device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) should be seperated from front-end visible device type name("GPU") to avoid kernel registration conflict with exsiting GPU(CUDA) kernels. it can be an alternative string registered from plugin, or "PLUGGABLE_" + device type(front-end visible device type registered through `SE_InitializePlugin`) as the device_type attribute, depending on StreamExecutor C API and kernel registration  C API design. For the second option(adding "PLUGGABLE_" prefix), Kernel registration C API needs to add the "PLUGGABLE_" prefix to the registered device type so this device_type attribute can be transparently to the plugin authors. For example, plugin authors provide a "GPU" name through `SE_InitializePlugin` and register the kernels to the "GPU" name through `TF_NewKernelBuilder` in plugin side, and TensorFlow Proper takes the "GPU" as the name for Device registration and makes the "PLUGGABLE_GPU" as the device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) for PluggableDevice. 
    * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
    * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.
    * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
@@ -191,10 +191,25 @@ Plugin authors need to provide those C functions implementation defined in Strea
 
 This RFC shows an example of registering kernels for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
 
-TensorFlow proper defines a new device_type named DEVICE_PLUGGABLE for PluggableDevice.This device_type is used for the kernel registration and dispatch. Plugin needs to register its kernel implementation with DEVICE_PLUGGABLE type.
+To avoid kernel registration conflict with existing GPU(CUDA) kernels, the backend device_type for kernel registration should be seperated from the front-end visible device type ("GPU"). Two Options:  
+&emsp;option 1) The backend device_type can be an alternative string provided by plugin, and plugin authors use the string for kernel registration.   
+&emsp;option 2) Another option is that plugin authors only need to provide one device type, and Tensorflow proper takes it as the string name for Device registration and makes "PLUGGABLE_" + device type as the device_type attribute in PluggableDevice for kernel registration.  
+
+**Option 1:**  
+Plugin side:
+plugin author provides an alternative string(such as "CUDA") to TensorFlow proper, which seperates from the front-end device type("GPU") and uses this string for kernel registration.
 ```cpp
+void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
+  ...
+  std::string type = "GPU" // front-end visible device type
+  params.params.type = type.c_str();
+  std::string backend_device_type = "CUDA";
+  params.params.type = backend_device_type.c_str();
+  ...
+}
+
 void InitPlugin() {
-  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", DEVICE_PLUGGABLE,
+  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "CUDA", // seperate from front-end visible device type
       &Conv_Create, &Conv_Compute, &Conv_Delete);
   TF_Status* status = TF_NewStatus();
   TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
@@ -202,6 +217,40 @@ void InitPlugin() {
   TF_DeleteStatus(status);
 }
 ```
+**Option 2:**  
+Plugin side:  
+plugin author provides the device type("GPU") for Device registration, and also uses it for kernel registration in plugin side.
+```
+void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
+  ...
+  std::string type = "GPU" // front-end visible device type
+  params.params.type = type.c_str();
+  ...
+}
+
+void InitPlugin() {
+  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", // same type as front-end visible device type
+      &Conv_Create, &Conv_Compute, &Conv_Delete);
+  TF_Status* status = TF_NewStatus();
+  TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
+  if (TF_GetCode(status) != TF_OK) { /* handle errors */ }
+  TF_DeleteStatus(status);
+}
+```
+TensorFlow Proper side:  
+TensorFlow Proper uses this device type for Device registration and makes "PLUGGABLE_" + device_type("GPU") as the device_type attribute for kernel registration, this device_type attribute is transparently to the plugin authors.
+```
+TF_KernelBuilder* TF_NewKernelBuilder(
+    const char* op_name, const char* device_name,
+    void* (*create_func)(TF_OpKernelConstruction*),
+    void (*compute_func)(void*, TF_OpKernelContext*),
+    void (*delete_func)(void*)) {
+  ...
+  result->cc_builder->Device(strcat("PLUGGABLE_", device_name)); // "PLUGGABLE_GPU"
+  ...
+}
+```
+
 ### Using stream inside PluggableDevice kernel
 
 The following code shows a convolution kernel implementation using the stream handle. The streams are created during the pluggable device creation. The placer decides which device to use for each OP in the graph. Then the streams associated with the device are used to construct the OpKernelContext for the op computation during the graph execution.

From f42a102b8b751ef4a22d559c3109a1a14fad31a9 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 22 Jul 2020 21:56:59 +0800
Subject: [PATCH 09/28] update PluggableBFCAllocator description

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 71fff422f..7d0a2d769 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -145,7 +145,7 @@ Two sets of classes need to be defined in TensorFlow proper.
 * Set 1: `PluggableDevice` related classes 
    * class `PluggableDevice`:  a class represents a set of new third-party devices, its device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) should be seperated from front-end visible device type name("GPU") to avoid kernel registration conflict with exsiting GPU(CUDA) kernels. it can be an alternative string registered from plugin, or "PLUGGABLE_" + device type(front-end visible device type registered through `SE_InitializePlugin`) as the device_type attribute, depending on StreamExecutor C API and kernel registration  C API design. For the second option(adding "PLUGGABLE_" prefix), Kernel registration C API needs to add the "PLUGGABLE_" prefix to the registered device type so this device_type attribute can be transparently to the plugin authors. For example, plugin authors provide a "GPU" name through `SE_InitializePlugin` and register the kernels to the "GPU" name through `TF_NewKernelBuilder` in plugin side, and TensorFlow Proper takes the "GPU" as the name for Device registration and makes the "PLUGGABLE_GPU" as the device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) for PluggableDevice. 
    * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
-   * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.
+   * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.It extends the BFC algorithm, counter part of GPUBFCAllocator.
    * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
    * class `PluggableDeviceHostAllocator`: allocator for pinned CPU RAM that is made known to PluggableDevice for the purpose of efficient DMA with PluggableDevice.
    * class `PluggableDeviceEventMgr`: an object to keep track of pending Events in the StreamExecutor streams.

From 2bf7974c9e1ab0f8ad766c139229c414f2b6aa2a Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Fri, 31 Jul 2020 05:01:05 -0400
Subject: [PATCH 10/28] adding supported/unsupported scenero for pluggable
 device and add subdevice type for low-level specialization of GPU device

---
 ...0200624-pluggable-device-for-tensorflow.md | 120 +++++++++---------
 .../scenario1.png                             | Bin 0 -> 13752 bytes
 .../scenario2.png                             | Bin 0 -> 14671 bytes
 .../scenario3.png                             | Bin 0 -> 18939 bytes
 .../scenario4.png                             | Bin 0 -> 19668 bytes
 5 files changed, 63 insertions(+), 57 deletions(-)
 create mode 100644 rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png
 create mode 100644 rfcs/20200624-pluggable-device-for-tensorflow/scenario2.png
 create mode 100644 rfcs/20200624-pluggable-device-for-tensorflow/scenario3.png
 create mode 100644 rfcs/20200624-pluggable-device-for-tensorflow/scenario4.png

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 7d0a2d769..802c5f531 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -1,11 +1,11 @@
-# **Pluggable device for TensorFlow**
+ **Pluggable device for TensorFlow**
 
 | Status        | Proposed                                             |
 :-------------- |:---------------------------------------------------- |
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-07-14                                           |
+| **Updated**   | 2020-07-31                                           |
 
 ## **Objective**
 
@@ -46,12 +46,41 @@ With the RFC, existing TensorFlow GPU programs can run on a plugged device witho
 <img src=20200624-pluggable-device-for-tensorflow/gpu_example.png>
 </div>
 
+### Supported user scenarios of PluggableDevice 
+
+This topic describes the user scenarios that are supported/unsupported in PluggableDevice.  
+* **Supported scenario**: Single PluggableDevice registered as "GPU" device type  
+  In the case of installing one plugin that registers its PluggableDevice as "GPU" device type, the default GPUDevice will be invalid when the plugin is loaded. When user specifies the "GPU" device for ops under `with tf.device("gpu:0")`, PluggableDevice registered will be selected to run those ops.
+<div align="center">
+<img src=20200624-pluggable-device-for-tensorflow/scenario1.png>
+</div>  
+
+* **Supported scenario**: Single PluggableDevice registered as a new device type.  
+  In the case of installing one plugin that registers its PluggableDevice as a new device type, e.g., "XPU" device, user can speficies the "XPU" device for ops under `with tf.device("xpu:0")`, PluggableDevice registered will be selected to run those ops.
+<div align="center">
+<img src=20200624-pluggable-device-for-tensorflow/scenario2.png>
+</div>
+
+* **Supported scenario**: Multiple PluggableDevices registered as different device types.   
+  In the case of installing multiple plugins that registers PluggableDevice as different device types, e.g., one is registered as "GPU" device and another is registered as "XPU" device, these PluggableDevices can be registered successfully and user can specify the device type to run ops on different hardware.
+<div align="center">
+<img src=20200624-pluggable-device-for-tensorflow/scenario3.png>
+</div>
+
+* **Non-Supported scenario**: Multiple PluggableDevices registered as the same device type.  
+  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device, these plugins's initialization will fail due to conflict. User needs to select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
+<div align="center">
+<img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
+</div>
+
+
+
 ### Device Discovery
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and register to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper also gets the device type through `SE_InitializePlugin` and register the `PluggableDeviceFactory`with this type. The device type will be the device strings to be used to access pluggable device with tf.device() in python layer.
-Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the device type. The device type will be the device string to be used to access PluggableDevice with tf.device() in python layer. The subdevice type is for low-level specialization of GPU device. If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type.
+Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   static const int32_t plugin_id_value = 123;
@@ -61,6 +90,7 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
   
   std::string name = "MyDevicePlatform";
   std::string type = "GPU";
+  std::string sub_type = "MY_GPU"
 
   params.params.id = id;
   params.params.visible_device_count = visible_device_count;
@@ -72,11 +102,25 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
   params.params.name_len = name.size();
   params.params.type = type.c_str();
   params.params.type_len = type.size();
+  params.params.sub_type = sub_type.c_str();
+  params.params.sub_type_len = sub_type.size();
+}
+```
+`ListPhysicalDevice` will encode the subdevice type to the device name
+```cpp
+Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices) {
+  se::Platform* platform = se::MultiPlatformManager::PlatformWithName(platform_name_);
+  for(int i = 0; i < platform->VisibleDeviceCount(); i++) {
+    const string device_name = strcat("/physical_device:", device_type_, "/", sub_device_type_, ":", i);
+    devices->push_back(device_name);
+  }
+  return Status::OK();
 }
 ```
+
 ### Device Creation
 
-`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register "GPU" string as the device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDevice` as "GPU" name with higher priority than the default GPU device.    
+`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register "GPU" string as the device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDevice` as "GPU" type with higher priority than the default GPU device.    
 Plugin:
 ```
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
@@ -92,14 +136,15 @@ Proper:
   std::string type_str(params.params.type, params.params.type_len);
   DeviceFactory::Register(type_str, new PluggableDeviceFactory(platform_name_str), priority); 
 ```  
-For those vendors who don't want to use "GPU" name, it's optional to register a new device name.  
-Limitation: when multiple devices registered, their device names should be different, or it will get conflict and the registration will fail. This can be enhanced in the future. A possible solutoin: python layer provides API to let user specify an alternative device name they prefer if there is a conflict, such as:  
+For those vendors who don't want to use "GPU" type, it's optional to register a new device type.  
+**Limitation**: when multiple plugins are installed, their device types should be different, or it will get conflict and the device registration will fail. TensorFlow proper can provide a python API to let user select a plugin by specifing a higher priority.
 ```
-  tf.load_plugin("CustomDeviceName", path_to_plugin_lib)
-  with tf.device("/CustomDeviceName:0"):
+  tf.load_plugin_with_highest_priority(path_to_plugin_lib)
+  with tf.device("/GPU:0"):
     ...
 ```
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find its `se::platform` through its platform name registered from plugin: "MyDevicePlatform”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
+
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name registered from plugin: "MyDevicePlatform”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
 The section below shows some pseudo code to introduce some extension inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -114,7 +159,7 @@ The section below shows some pseudo code to introduce some extension inside the
    }
 ```
 
-2. `PluggableDevice` object binds a StreamExecutor and creates a set of Streams during the initialization.Streams include one compute stream and several memory copy streams.
+2. `PluggableDevice` object binds a StreamExecutor and creates a set of Streams during the initialization. Streams include one compute stream and several memory copy streams.
 ```cpp
    void PluggableDevice::Init(SessionOption& options, const string& platform_name) {  
      se::Platform* platform= se::MultiPlatformManager::PlatformWithName(platform_name);
@@ -143,7 +188,7 @@ TensorFlow proper needs to be extended to support a new class `PluggableDevice`
 
 Two sets of classes need to be defined in TensorFlow proper. 
 * Set 1: `PluggableDevice` related classes 
-   * class `PluggableDevice`:  a class represents a set of new third-party devices, its device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) should be seperated from front-end visible device type name("GPU") to avoid kernel registration conflict with exsiting GPU(CUDA) kernels. it can be an alternative string registered from plugin, or "PLUGGABLE_" + device type(front-end visible device type registered through `SE_InitializePlugin`) as the device_type attribute, depending on StreamExecutor C API and kernel registration  C API design. For the second option(adding "PLUGGABLE_" prefix), Kernel registration C API needs to add the "PLUGGABLE_" prefix to the registered device type so this device_type attribute can be transparently to the plugin authors. For example, plugin authors provide a "GPU" name through `SE_InitializePlugin` and register the kernels to the "GPU" name through `TF_NewKernelBuilder` in plugin side, and TensorFlow Proper takes the "GPU" as the name for Device registration and makes the "PLUGGABLE_GPU" as the device_type attribute (counter part of DEVICE_GPU, DEVICE_CPU) for PluggableDevice. 
+   * class `PluggableDevice`: a class represents a set of new third-party devices, its device_type attribute describes what kind of device this is. it can be "GPU" or other device type string. it also has an attribute: subdevice_type, subdevice_type is for low-level specialization of GPU device. It will be part of kernel dispatch key to avoid conflict issue with exiting GPU(CUDA) kernels. The subdevice_type is also used to check whether there is some CUDA specific logic code in grappler and common runtime when the device type is "GPU".
    * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
    * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.It extends the BFC algorithm, counter part of GPUBFCAllocator.
    * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
@@ -189,67 +234,28 @@ Plugin authors need to provide those C functions implementation defined in Strea
 
 ### PluggableDevice kernel registration
 
-This RFC shows an example of registering kernels for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
+This RFC shows an example of kernel registration for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
 
-To avoid kernel registration conflict with existing GPU(CUDA) kernels, the backend device_type for kernel registration should be seperated from the front-end visible device type ("GPU"). Two Options:  
-&emsp;option 1) The backend device_type can be an alternative string provided by plugin, and plugin authors use the string for kernel registration.   
-&emsp;option 2) Another option is that plugin authors only need to provide one device type, and Tensorflow proper takes it as the string name for Device registration and makes "PLUGGABLE_" + device type as the device_type attribute in PluggableDevice for kernel registration.  
-
-**Option 1:**  
-Plugin side:
-plugin author provides an alternative string(such as "CUDA") to TensorFlow proper, which seperates from the front-end device type("GPU") and uses this string for kernel registration.
+To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the GPU device.
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   ...
   std::string type = "GPU" // front-end visible device type
   params.params.type = type.c_str();
-  std::string backend_device_type = "CUDA";
+  std::string sub_device_type = "INTEL_GPU"; // low-level specialization device type
   params.params.type = backend_device_type.c_str();
   ...
 }
 
-void InitPlugin() {
-  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "CUDA", // seperate from front-end visible device type
-      &Conv_Create, &Conv_Compute, &Conv_Delete);
-  TF_Status* status = TF_NewStatus();
-  TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
-  if (TF_GetCode(status) != TF_OK) { /* handle errors */ }
-  TF_DeleteStatus(status);
-}
-```
-**Option 2:**  
-Plugin side:  
-plugin author provides the device type("GPU") for Device registration, and also uses it for kernel registration in plugin side.
-```
-void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
-  ...
-  std::string type = "GPU" // front-end visible device type
-  params.params.type = type.c_str();
-  ...
-}
-
-void InitPlugin() {
-  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", // same type as front-end visible device type
-      &Conv_Create, &Conv_Compute, &Conv_Delete);
+void InitKernelPlugin() {
+  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", "INTEL_GPU", //"GPU" is device type
+      "INTEL_GPU", &Conv_Create, &Conv_Compute, &Conv_Delete); // "INTEL_GPU" is sub device type
   TF_Status* status = TF_NewStatus();
   TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
   if (TF_GetCode(status) != TF_OK) { /* handle errors */ }
   TF_DeleteStatus(status);
 }
 ```
-TensorFlow Proper side:  
-TensorFlow Proper uses this device type for Device registration and makes "PLUGGABLE_" + device_type("GPU") as the device_type attribute for kernel registration, this device_type attribute is transparently to the plugin authors.
-```
-TF_KernelBuilder* TF_NewKernelBuilder(
-    const char* op_name, const char* device_name,
-    void* (*create_func)(TF_OpKernelConstruction*),
-    void (*compute_func)(void*, TF_OpKernelContext*),
-    void (*delete_func)(void*)) {
-  ...
-  result->cc_builder->Device(strcat("PLUGGABLE_", device_name)); // "PLUGGABLE_GPU"
-  ...
-}
-```
 
 ### Using stream inside PluggableDevice kernel
 
diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png
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From f1b0375a28f95413978b116cbdc4ade54df7cdcc Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Mon, 3 Aug 2020 05:34:04 -0400
Subject: [PATCH 11/28] fix subdevice description

---
 ...0200624-pluggable-device-for-tensorflow.md | 30 ++++++++-----------
 1 file changed, 13 insertions(+), 17 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 802c5f531..d9c840a6f 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -79,8 +79,7 @@ This topic describes the user scenarios that are supported/unsupported in Plugga
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the device type. The device type will be the device string to be used to access PluggableDevice with tf.device() in python layer. The subdevice type is for low-level specialization of GPU device. If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type.
-Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:  
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type string is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type of GPU to identify. Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   static const int32_t plugin_id_value = 123;
@@ -88,9 +87,8 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
   id.id = &plugin_id_value;
   int32_t visible_device_count = get_plugin_device_count();
   
-  std::string name = "MyDevicePlatform";
-  std::string type = "GPU";
-  std::string sub_type = "MY_GPU"
+  std::string name = "My_GPU"; //StreamExecutor platform name && subdevice type
+  std::string type = "GPU"; // device type
 
   params.params.id = id;
   params.params.visible_device_count = visible_device_count;
@@ -102,14 +100,12 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
   params.params.name_len = name.size();
   params.params.type = type.c_str();
   params.params.type_len = type.size();
-  params.params.sub_type = sub_type.c_str();
-  params.params.sub_type_len = sub_type.size();
 }
 ```
-`ListPhysicalDevice` will encode the subdevice type to the device name
+`ListPhysicalDevice` encodes the subdevice type string to the device type string.
 ```cpp
 Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices) {
-  se::Platform* platform = se::MultiPlatformManager::PlatformWithName(platform_name_);
+  se::Platform* platform = se::MultiPlatformManager::PlatformWithName(sub_device_type_);
   for(int i = 0; i < platform->VisibleDeviceCount(); i++) {
     const string device_name = strcat("/physical_device:", device_type_, "/", sub_device_type_, ":", i);
     devices->push_back(device_name);
@@ -120,13 +116,13 @@ Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices)
 
 ### Device Creation
 
-`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register "GPU" string as the device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDevice` as "GPU" type with higher priority than the default GPU device.    
+`PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register the "GPU" device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDeviceFactory` for "GPU" type with higher priority than the default GPU device.  
 Plugin:
 ```
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
     ...
-    std::string type = "GPU"
-    params.params.type = type.c_str()
+    std::string type = "GPU"; 
+    params.params.type = type.c_str();
     ...
   }
 ```
@@ -144,7 +140,7 @@ For those vendors who don't want to use "GPU" type, it's optional to register a
     ...
 ```
 
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name registered from plugin: "MyDevicePlatform”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin: "My_GPU”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
 The section below shows some pseudo code to introduce some extension inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -236,19 +232,19 @@ Plugin authors need to provide those C functions implementation defined in Strea
 
 This RFC shows an example of kernel registration for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
 
-To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the GPU device.
+To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the device.
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   ...
   std::string type = "GPU" // front-end visible device type
   params.params.type = type.c_str();
-  std::string sub_device_type = "INTEL_GPU"; // low-level specialization device type
-  params.params.type = backend_device_type.c_str();
+  std::string name = "INTEL_GPU"; // low-level specialization device type
+  params.params.type = name.c_str();
   ...
 }
 
 void InitKernelPlugin() {
-  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", "INTEL_GPU", //"GPU" is device type
+  TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", //"GPU" is device type
       "INTEL_GPU", &Conv_Create, &Conv_Compute, &Conv_Delete); // "INTEL_GPU" is sub device type
   TF_Status* status = TF_NewStatus();
   TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);

From 25ee309a29612966b706e1bca42c0b89a7043f37 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Mon, 3 Aug 2020 05:36:10 -0400
Subject: [PATCH 12/28] fix title

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index d9c840a6f..c4a992983 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -1,4 +1,4 @@
- **Pluggable device for TensorFlow**
+#Pluggable device for TensorFlow
 
 | Status        | Proposed                                             |
 :-------------- |:---------------------------------------------------- |

From f81cd14668b10003efa72708fc217da6ce56f72b Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Mon, 3 Aug 2020 05:39:21 -0400
Subject: [PATCH 13/28] fix title format

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index c4a992983..e32c0ae98 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -1,4 +1,4 @@
-#Pluggable device for TensorFlow
+# Pluggable device for TensorFlow
 
 | Status        | Proposed                                             |
 :-------------- |:---------------------------------------------------- |

From 28209ff2edfb5162e24f12ae2561a389a8f802d9 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Tue, 4 Aug 2020 05:14:54 -0400
Subject: [PATCH 14/28] fix format

---
 ...0200624-pluggable-device-for-tensorflow.md |  27 +++++++++---------
 .../scenario1.png                             | Bin 13752 -> 14441 bytes
 .../scenario2.png                             | Bin 14671 -> 14807 bytes
 .../scenario3.png                             | Bin 18939 -> 20494 bytes
 .../scenario4.png                             | Bin 19668 -> 19838 bytes
 5 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index e32c0ae98..b6b84f808 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -11,7 +11,7 @@
 
 Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
 
-This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plugin capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
+This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plug in capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
 ## **Motivation**
 
@@ -33,22 +33,22 @@ This RFC extends the TensorFlow device class hierarchy to add a standardized plu
 <img src=20200624-pluggable-device-for-tensorflow/design_overview.png>
 </div>
 
-* `PluggableDevice` is defined in TensorFlow proper which inherits from [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h).It is built on top of  StreamExecutor C++ interface to manage `PluggableDevice`’s key abstractions like StreamExecutor, stream, memory and event.
+* `PluggableDevice` is defined in TensorFlow proper which inherits from [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h). It is built on top of  StreamExecutor C++ interface and manages `PluggableDevice`’s key abstractions like StreamExecutor, stream, memory and event.
 
-* `PluggableDeviceExecutor` implements [StreamExecutor](https://github.com/tensorflow/tensorflow/blob/e5023a1738cce7efcdf9d87863b85c80ab2f8c9e/tensorflow/stream_executor/stream_executor_pimpl.h#L73) and is built on top of StreamExecutor C API (addressed in [RFC](https://github.com/tensorflow/community/pull/257)). 
+* `PluggableDeviceExecutor` is built on top of StreamExecutor C API (addressed in [RFC](https://github.com/tensorflow/community/pull/257)), implements [StreamExecutor](https://github.com/tensorflow/tensorflow/blob/e5023a1738cce7efcdf9d87863b85c80ab2f8c9e/tensorflow/stream_executor/stream_executor_pimpl.h#L73) . 
 
-* `PluggableDevice Implementation` is inside the TensorFlow plugin, which provides those C functions implementation defined in the StreamExecutor C API.
+* `PluggableDeviceExecutor Implementation` is inside the TensorFlow plugin, which provides those C functions implementation defined in the StreamExecutor C API.
 
 The pluggable device mechanism contains device discovery and creation process which creates a `PluggableDevice` object and `PluggableDeviceExecutor` object for each pluggable device. 
 
-With the RFC, existing TensorFlow GPU programs can run on a plugged device without the user changing the code. The Diagram 2 describes the workflow of TensorFlow with device plugin, it shows how a simple GPU program runs on the pluggable device.
+With the RFC, existing TensorFlow GPU programs can run on a plugged device without user changing the code. The Diagram 2 describes the workflow of TensorFlow with device plugin, it shows how a simple GPU program runs on the pluggable device.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/gpu_example.png>
 </div>
 
 ### Supported user scenarios of PluggableDevice 
 
-This topic describes the user scenarios that are supported/unsupported in PluggableDevice.  
+This section describes the user scenarios that are supported/unsupported for PluggableDevice.  
 * **Supported scenario**: Single PluggableDevice registered as "GPU" device type  
   In the case of installing one plugin that registers its PluggableDevice as "GPU" device type, the default GPUDevice will be invalid when the plugin is loaded. When user specifies the "GPU" device for ops under `with tf.device("gpu:0")`, PluggableDevice registered will be selected to run those ops.
 <div align="center">
@@ -56,19 +56,19 @@ This topic describes the user scenarios that are supported/unsupported in Plugga
 </div>  
 
 * **Supported scenario**: Single PluggableDevice registered as a new device type.  
-  In the case of installing one plugin that registers its PluggableDevice as a new device type, e.g., "XPU" device, user can speficies the "XPU" device for ops under `with tf.device("xpu:0")`, PluggableDevice registered will be selected to run those ops.
+  In the case of installing one plugin that registers its PluggableDevice as a new device type, e.g., "XPU" device type, user can speficies the "XPU" device for ops under `with tf.device("xpu:0")`, PluggableDevice registered will be selected to run those ops.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario2.png>
 </div>
 
 * **Supported scenario**: Multiple PluggableDevices registered as different device types.   
-  In the case of installing multiple plugins that registers PluggableDevice as different device types, e.g., one is registered as "GPU" device and another is registered as "XPU" device, these PluggableDevices can be registered successfully and user can specify the device type to run ops on different hardware.
+  In the case of installing multiple plugins that register PluggableDevices as different device types, e.g., one is registered as "GPU" device type and another is registered as "XPU" device type, these PluggableDevices can be registered successfully and user can specify the registered device type to run ops on different hardware.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario3.png>
 </div>
 
 * **Non-Supported scenario**: Multiple PluggableDevices registered as the same device type.  
-  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device, these plugins's initialization will fail due to conflict. User needs to select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
+  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device type, these plugins's initialization will fail due to registration conflict. User needs to select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
 </div>
@@ -79,7 +79,8 @@ This topic describes the user scenarios that are supported/unsupported in Plugga
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type string is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type of GPU to identify. Plugin authors needs to implement `SE_InitializePlugin` and provide the necessary informations:  
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type string is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type of GPU for identification.   
+Plugin authors need to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   static const int32_t plugin_id_value = 123;
@@ -133,16 +134,16 @@ Proper:
   DeviceFactory::Register(type_str, new PluggableDeviceFactory(platform_name_str), priority); 
 ```  
 For those vendors who don't want to use "GPU" type, it's optional to register a new device type.  
-**Limitation**: when multiple plugins are installed, their device types should be different, or it will get conflict and the device registration will fail. TensorFlow proper can provide a python API to let user select a plugin by specifing a higher priority.
+**Limitation**: when multiple plugins are installed, their registered device types should be different, or it will get conflict and the device registration will fail. TensorFlow proper can provide a python API to let user select a plugin by specifing a higher priority.
 ```
   tf.load_plugin_with_highest_priority(path_to_plugin_lib)
   with tf.device("/GPU:0"):
     ...
 ```
 
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin: "My_GPU”,  then stream executor platform (`se::platform`) further creates or find a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin, then stream executor platform (`se::platform`) further creates or finds a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
-The section below shows some pseudo code to introduce some extension inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
+The section below shows some pseudo code to introduce some extensions inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
 1. `PluggableDeviceFactory` creates and initializes a set of `PluggableDevice` instances when the session is created.  
 ```cpp
diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png
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From beb2fc52e14119039810995eb7baafc0af34f07e Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Thu, 6 Aug 2020 05:37:52 -0400
Subject: [PATCH 15/28] update with new StreamExecutor C API(SE_->SP_)

---
 ...0200624-pluggable-device-for-tensorflow.md | 57 ++++++++++---------
 1 file changed, 29 insertions(+), 28 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index b6b84f808..5be81a805 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -11,7 +11,7 @@
 
 Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
 
-This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plug in capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
+This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims at extending the TensorFlow design to plug in capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
 ## **Motivation**
 
@@ -56,7 +56,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 </div>  
 
 * **Supported scenario**: Single PluggableDevice registered as a new device type.  
-  In the case of installing one plugin that registers its PluggableDevice as a new device type, e.g., "XPU" device type, user can speficies the "XPU" device for ops under `with tf.device("xpu:0")`, PluggableDevice registered will be selected to run those ops.
+  In the case of installing one plugin that registers its PluggableDevice as a new device type, e.g., "XPU" device type, user can speficify the "XPU" device for ops under `with tf.device("xpu:0")`, PluggableDevice registered will be selected to run those ops.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario2.png>
 </div>
@@ -68,7 +68,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 </div>
 
 * **Non-Supported scenario**: Multiple PluggableDevices registered as the same device type.  
-  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device type, these plugins's initialization will fail due to registration conflict. User needs to select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
+  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device type, these plugins's initialization will fail due to registration conflict. User needs to manually select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
 </div>
@@ -79,7 +79,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the Plugin to instantiate a StreamExecutor Platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type string is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type of GPU for identification.   
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type for identification.   
 Plugin authors need to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
@@ -134,14 +134,15 @@ Proper:
   DeviceFactory::Register(type_str, new PluggableDeviceFactory(platform_name_str), priority); 
 ```  
 For those vendors who don't want to use "GPU" type, it's optional to register a new device type.  
-**Limitation**: when multiple plugins are installed, their registered device types should be different, or it will get conflict and the device registration will fail. TensorFlow proper can provide a python API to let user select a plugin by specifing a higher priority.
+**Limitation**: when multiple plugins are installed, their registered device types should be different, or it will get conflict and the device registration will fail. TensorFlow proper can provide a python API to let user select a plugin by specifing a higher priority.  
+For example:
 ```
   tf.load_plugin_with_highest_priority(path_to_plugin_lib)
   with tf.device("/GPU:0"):
     ...
 ```
 
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin, then stream executor platform (`se::platform`) further creates or finds a `StreamExecutor` object containing a `PluggableDeviceExecutor`, and multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin, then stream executor platform (`se::platform`) further creates or finds a `StreamExecutor` object containing a `PluggableDeviceExecutor`, as well as multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
 The section below shows some pseudo code to introduce some extensions inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -168,14 +169,17 @@ The section below shows some pseudo code to introduce some extensions inside the
      ...
    }  // create StreamExecutor
 ```
-3. `PluggableDevicePlatform` is responsible for the StreamExecutor creation. It creates an `SE_StreamExecutor` and `SE_Device` object through create_stream_executor and create_device which are registered in the `SE_Platform`. Then `PluggableDeviceExecutor` is constructed with `SE_StreamExecutor` and `SE_Device` object.   
+3. `PluggableDevicePlatform` is responsible for the StreamExecutor creation. It creates an `SP_StreamExecutor` and `SP_Device` object through create_stream_executor and create_device which are registered in the `SE_Platform`. Then `PluggableDeviceExecutor` is constructed with `SP_StreamExecutor` and `SP_Device` object.   
 ```cpp
    StreamExecutor* PluggableDevicePlaform::ExeutorForDevice(int device_id） {
     auto config = get_plugin_config(device_id);
     SE_Options* se_option = get_se_option(device_id);
-    SE_StreamExecutor* se= platform_->create_stream_executor();
-    SE_Device* sd = platform_->create_device(se_options)
-    auto executor = absl::make_unique<StreamExecutor>(this, absl::make_unique<PluggableDeviceExecutor>(config, se, sd));
+    TF_Status* status  = TF_NewStatus();
+    SP_StreamExecutor* se = new SP_StreamExecutor{ SP_STREAMEXECUTOR_STRUCT_SIZE };
+    platform_->create_stream_executor(se, status); // create SP_StreamExecutor
+    SP_Device* se_device = new SP_Device{ SP_DEVICE_STRUCT_SIZE };
+    platform_->create_device(se_device, se_options, status);//create SP_Device
+    auto executor = absl::make_unique<StreamExecutor>(this, absl::make_unique<PluggableDeviceExecutor>(config, se, se_device));
     return std::move(executor);
    }
 ```
@@ -194,44 +198,41 @@ Two sets of classes need to be defined in TensorFlow proper.
    * class `PluggableDeviceContext`: a wrapper of pluggable device specific context that can be passed to OpKernels.
 * Set 2: `PluggableDevicePlatform` related classes 
    * class `PluggableDevicePlatform`: PluggableDevice-specific platform, its platform name is "PluggableDevice", it contains a C struct: SE_Platform* platform_ which is its internal implementation and as the C interface registered by device plugin.
-   * class `PluggableDeviceExecutor`: PluggableDevice-platform implementation of the platform-agnostic StreamExecutorInterface, it contains C structs: SE_StreamExecutor* executor_ and SE_Device* device_ whose member can be accessed in both TensorFlow proper and device plugins.
-   * class `PluggableDeviceStream`: wraps a StreamHandle in order to satisfy the platform-independent StreamInterface. It returns SE_Stream which is treated as an opaque type to TensorFlow,  whose structure is created by the device plugin.  
+   * class `PluggableDeviceExecutor`: PluggableDevice-platform implementation of the platform-agnostic StreamExecutorInterface, it contains C structs: SP_StreamExecutor* executor_ and SP_Device* device_ whose member can be accessed in both TensorFlow proper and device plugins.
+   * class `PluggableDeviceStream`: wraps a StreamHandle in order to satisfy the platform-independent StreamInterface. It returns SP_Stream which is treated as an opaque type to TensorFlow,  whose structure is created by the device plugin.  
    * class `PluggableDeviceTimer`: wraps an opaque handle: SE_Timer to satisfy the platform-independent TimerInterface.
    * class `PluggableDeviceEvent`: wraps an opaque handle: SE_Event to satisfy the platform-independent EventInterface.
 
 **TensorFlow Plugin**
 
 Plugin authors need to provide those C functions implementation defined in StreamExecutor C API . 
-*  `SE_StreamExecutor` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_StreamExecutor and registers its C API implementations to the SE_StreamExecutor.  
+* `SP_StreamExecutor` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. TensorFlow proper creates a SP_StreamExecutor object and pass it to the plugin, then plugin fills it with its C API implementations.  
 ```cpp
-   SE_StreamExecutor* create_stream_executor(TF_Status* status) {
-     SE_StreamExecutor* se_nfs = new SE_StreamExecutor{ SE_STREAMEXECUTOR_STRUCT_SIZE };
+   void create_stream_executor(SP_StreamExecutor* se, TF_Status* status) {
      se->memcpy_from_host = my_device_memory_from_host_function;
      se->allocate = my_allocate_function;
      …
    }//Init device
 ```
-* `SE_Device` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. Plugin creates the SE_Device and fills its device opaque handle and device name to the SE_Device.
+* `SP_Device` is defined as struct in the C API, both sides(TensorFlow proper and plugins) can access its members. TensorFlow proper creates a SP_Device with device ordinal and plugin fills the corresponding device opaque handle and device name to the SP_Device.
 ```cpp
-  SE_Device* create_device(SE_Options* options, TF_Status* status) {
-    SE_Device* se = new SE_Device( SE_DEVICE_STRUCT_SIZE );
-    se->device_handle = get_my_device_handle();
+   create_device(SP_Device* device, SE_Options* options, TF_Status* status) {
+    device->device_handle = get_my_device_handle(SE_Options);
     ...
     return se;
-  }
+   }
 ```
-* `SE_Stream` is defined in plugin and treated as an opaque struct in TensorFlow proper. 
+* `SP_Stream` is defined in plugin and treated as an opaque struct in TensorFlow proper. 
 ```cpp
-  void create_stream(SE_Device* executor, SE_Stream* stream, TF_Status*) {
-    *stream = new SE_Stream_st();
-    (*stream)->stream_handle = create_my_stream_handle(executor);
+  void create_stream(SP_Device* device, SP_Stream* stream, TF_Status*) {
+    (stream)->stream_handle = create_my_stream_handle(device);
     ..
   }
 ```
 
 ### PluggableDevice kernel registration
 
-This RFC shows an example of kernel registration for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
+This section shows an example of kernel registration for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
 
 To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the device.
 ```cpp
@@ -256,13 +257,13 @@ void InitKernelPlugin() {
 
 ### Using stream inside PluggableDevice kernel
 
-The following code shows a convolution kernel implementation using the stream handle. The streams are created during the pluggable device creation. The placer decides which device to use for each OP in the graph. Then the streams associated with the device are used to construct the OpKernelContext for the op computation during the graph execution.
+The following code shows a convolution kernel implementation using the stream handle. The streams are created during the pluggable device creation. The placer decides which device to use for each OP in the graph. Then the streams associated with the device are used to construct the OpKernelContext for the op computations during the graph execution.
 ```cpp
 void Conv_Compute(TF_OpKernelContext*) {
-  TF_GetInput(context, input_index, &input, &status);
+  (context, input_index, &input, &status);
   TF_GetInput(context, filter_index, &filter, &status);
   auto output = TF_AllocateOutput(context, output_index, TF_Float32, dims, num_dims, len, status);
-  SE_Stream se_stream = TF_GetStream(TF_OpKernelContext);
+  SP_Stream* se_stream = TF_GetStream(TF_OpKernelContext);
   auto native_stream = static_cast<native_stream_type>(se_stream->stream_handle);
   my_conv_impl(input, filter, output, native_stream);
 }

From 81481f8fcc09f8c9b3444ee3e92a34dd4ed73bdc Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Mon, 10 Aug 2020 05:28:47 -0400
Subject: [PATCH 16/28] fix typo

---
 ...0200624-pluggable-device-for-tensorflow.md | 41 ++++++++++---------
 1 file changed, 21 insertions(+), 20 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 5be81a805..99a1c5d1f 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -9,7 +9,7 @@
 
 ## **Objective**
 
-Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
+Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install the plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin. 
 
 This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims at extending the TensorFlow design to plug in capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -39,7 +39,7 @@ This RFC extends the TensorFlow device class hierarchy to add a standardized plu
 
 * `PluggableDeviceExecutor Implementation` is inside the TensorFlow plugin, which provides those C functions implementation defined in the StreamExecutor C API.
 
-The pluggable device mechanism contains device discovery and creation process which creates a `PluggableDevice` object and `PluggableDeviceExecutor` object for each pluggable device. 
+The pluggable device mechanism contains device discovery and creation process. Device discovery process loads the plugin and registers the device type as well as [DeviceFactory](https://github.com/tensorflow/tensorflow/blob/24e203fa08feee48c766b15eaa3afcc912324437/tensorflow/core/common_runtime/device_factory.h#L30). Device creation process creates a `PluggableDevice` object and finds or creates a `PluggableDeviceExecutor` object for each pluggable device. 
 
 With the RFC, existing TensorFlow GPU programs can run on a plugged device without user changing the code. The Diagram 2 describes the workflow of TensorFlow with device plugin, it shows how a simple GPU program runs on the pluggable device.
 <div align="center">
@@ -77,21 +77,18 @@ This section describes the user scenarios that are supported/unsupported for Plu
 
 ### Device Discovery
 
-Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
+Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to the default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type for identification.   
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices` or `tf.config.experimental.get_device_details`) to get the subdevice type for identification.   
 Plugin authors need to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
-  static const int32_t plugin_id_value = 123;
-  SE_PlatformId id{ SE_PLATFORMID_STRUCT_SIZE };
-  id.id = &plugin_id_value;
   int32_t visible_device_count = get_plugin_device_count();
   
   std::string name = "My_GPU"; //StreamExecutor platform name && subdevice type
   std::string type = "GPU"; // device type
 
-  params.params.id = id;
+  params.params.id = plugin_id_value;
   params.params.visible_device_count = visible_device_count;
   params.params.create_device = create_device;
   params.params.destroy_device = destroy_device;
@@ -103,7 +100,7 @@ void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* statu
   params.params.type_len = type.size();
 }
 ```
-`ListPhysicalDevice` encodes the subdevice type string to the device type string.
+`ListPhysicalDevice` encodes the subdevice type string to the device type string.  
 ```cpp
 Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices) {
   se::Platform* platform = se::MultiPlatformManager::PlatformWithName(sub_device_type_);
@@ -114,6 +111,12 @@ Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices)
   return Status::OK();
 }
 ```
+`GetDeviceDetails` encodes the subdevice type string and it can be queried by `tf.config.experimental.get_device_details`.  
+Status PluggableDeviceFactory::GetDeviceDetails(int device_index, std::unordered_map<string, string>* details) {
+ ... 
+ (*details)["subdevice_type"] = sub_device_type_;
+ ...
+}
 
 ### Device Creation
 
@@ -141,8 +144,7 @@ For example:
   with tf.device("/GPU:0"):
     ...
 ```
-
-When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugin device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type registered from plugin, then stream executor platform (`se::platform`) further creates or finds a `StreamExecutor` object containing a `PluggableDeviceExecutor`, as well as multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
+When a session is created, `PluggableDeviceFactory` creates a `PluggableDevice` object for the plugged device. During the initialization of the `PluggableDevice`, a global object `se::MultiPlatformManager` will find the `se::platform` through its platform name / subdevice type which is registered from plugin, then stream executor platform (`se::platform`) further creates or finds a `StreamExecutor` object containing a `PluggableDeviceExecutor`, as well as multiple stream objects(a computation stream and several memory copy streams) supporting the `StreamExecutor` objects. 
 
 The section below shows some pseudo code to introduce some extensions inside the TensorFlow proper for the pluggable device creation. The implementation is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257). 
 
@@ -169,7 +171,7 @@ The section below shows some pseudo code to introduce some extensions inside the
      ...
    }  // create StreamExecutor
 ```
-3. `PluggableDevicePlatform` is responsible for the StreamExecutor creation. It creates an `SP_StreamExecutor` and `SP_Device` object through create_stream_executor and create_device which are registered in the `SE_Platform`. Then `PluggableDeviceExecutor` is constructed with `SP_StreamExecutor` and `SP_Device` object.   
+3. `PluggableDevicePlatform` is responsible for the StreamExecutor creation. It creates an `SP_StreamExecutor` and `SP_Device` object through create_stream_executor and create_device which are registered in the `SP_Platform`. Then `PluggableDeviceExecutor` is constructed with `SP_StreamExecutor` and `SP_Device` object.   
 ```cpp
    StreamExecutor* PluggableDevicePlaform::ExeutorForDevice(int device_id） {
     auto config = get_plugin_config(device_id);
@@ -191,17 +193,17 @@ Two sets of classes need to be defined in TensorFlow proper.
 * Set 1: `PluggableDevice` related classes 
    * class `PluggableDevice`: a class represents a set of new third-party devices, its device_type attribute describes what kind of device this is. it can be "GPU" or other device type string. it also has an attribute: subdevice_type, subdevice_type is for low-level specialization of GPU device. It will be part of kernel dispatch key to avoid conflict issue with exiting GPU(CUDA) kernels. The subdevice_type is also used to check whether there is some CUDA specific logic code in grappler and common runtime when the device type is "GPU".
    * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
-   * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm.It extends the BFC algorithm, counter part of GPUBFCAllocator.
+   * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm. It extends the BFC algorithm, counter part of GPUBFCAllocator.
    * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
    * class `PluggableDeviceHostAllocator`: allocator for pinned CPU RAM that is made known to PluggableDevice for the purpose of efficient DMA with PluggableDevice.
    * class `PluggableDeviceEventMgr`: an object to keep track of pending Events in the StreamExecutor streams.
    * class `PluggableDeviceContext`: a wrapper of pluggable device specific context that can be passed to OpKernels.
 * Set 2: `PluggableDevicePlatform` related classes 
-   * class `PluggableDevicePlatform`: PluggableDevice-specific platform, its platform name is "PluggableDevice", it contains a C struct: SE_Platform* platform_ which is its internal implementation and as the C interface registered by device plugin.
-   * class `PluggableDeviceExecutor`: PluggableDevice-platform implementation of the platform-agnostic StreamExecutorInterface, it contains C structs: SP_StreamExecutor* executor_ and SP_Device* device_ whose member can be accessed in both TensorFlow proper and device plugins.
-   * class `PluggableDeviceStream`: wraps a StreamHandle in order to satisfy the platform-independent StreamInterface. It returns SP_Stream which is treated as an opaque type to TensorFlow,  whose structure is created by the device plugin.  
-   * class `PluggableDeviceTimer`: wraps an opaque handle: SE_Timer to satisfy the platform-independent TimerInterface.
-   * class `PluggableDeviceEvent`: wraps an opaque handle: SE_Event to satisfy the platform-independent EventInterface.
+   * class `PluggableDevicePlatform`: PluggableDevice-specific platform, its platform name is registered from plugin through `SE_InitializePlugin`, the platform object contains a C struct: SP_Platform* platform_, which is its internal implementation and as the C interface registered by device plugin.
+   * class `PluggableDeviceExecutor`: The PluggableDevice implementation of the  StreamExecutorInterface functionality, it contains two C structs: SP_StreamExecutor* executor_ and SP_Device* device_ , which as the StreamExecutor C interface registered by device plugin. 
+   * class `PluggableDeviceStream`: wraps a StreamHandle in order to satisfy the platform-independent StreamInterface. It returns SP_Stream which is treated as an opaque type to TensorFlow,  whose structure is defined by the device plugin.  
+   * class `PluggableDeviceTimer`: wraps an opaque handle: SP_Timer to satisfy the platform-independent TimerInterface.
+   * class `PluggableDeviceEvent`: wraps an opaque handle: SP_Event to satisfy the platform-independent EventInterface.
 
 **TensorFlow Plugin**
 
@@ -232,8 +234,7 @@ Plugin authors need to provide those C functions implementation defined in Strea
 
 ### PluggableDevice kernel registration
 
-This section shows an example of kernel registration for PluggableDevice. Kernel and op registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
-
+This section shows an example of kernel registration for PluggableDevice. Kernel registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
 To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the device.
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {

From 6c0fc36fde5fbefe3989c28951d8b134d36feaa4 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Mon, 10 Aug 2020 22:36:37 -0400
Subject: [PATCH 17/28] update date

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 99a1c5d1f..7b8fba484 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-07-31                                           |
+| **Updated**   | 2020-08-11                                           |
 
 ## **Objective**
 
@@ -112,12 +112,13 @@ Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices)
 }
 ```
 `GetDeviceDetails` encodes the subdevice type string and it can be queried by `tf.config.experimental.get_device_details`.  
+```
 Status PluggableDeviceFactory::GetDeviceDetails(int device_index, std::unordered_map<string, string>* details) {
  ... 
  (*details)["subdevice_type"] = sub_device_type_;
  ...
 }
-
+```
 ### Device Creation
 
 `PluggableDeviceFactory` is introduced to create the `PluggableDevice`, following the [LocalDevice](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/local_device.h) design pattern. To support existing GPU programs running on a new device without user changing the code, plugin authors can register the "GPU" device type through `SE_InitializePlugin` and then TensorFlow proper will register the `PluggableDeviceFactory` for "GPU" type with higher priority than the default GPU device.  

From 8257245572d5496b98bf5d4f3ac3ac5a755c4924 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 05:57:14 -0400
Subject: [PATCH 18/28] add front-end mirroring mechanism

---
 ...0200624-pluggable-device-for-tensorflow.md | 43 ++++++++++++++++---
 1 file changed, 38 insertions(+), 5 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 7b8fba484..9145b1028 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -73,13 +73,45 @@ This section describes the user scenarios that are supported/unsupported for Plu
 <img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
 </div>
 
-
+### Front-end Mirroring mechanism
+This section describes the front-end mirroring mechanism for python users, pointing at previous user scenarios.
+* **device type**  
+   Device type is user visible and controllable. User can specify the device type for the ops. e.g, specify device type as "gpu", "xpu".
+   ```
+   >> with tf.device("/gpu:0"):
+      ...
+   >> with tf.device("/xpu:0"):
+      ...
+   ```
+   user need to manually select a platform when multiple plugins register the same device type, or the plugins initilaization will fail due to device type conflict. e.g, when multiple plugins register the "GPU" device type ,user need to set a higher priority for the plugin.
+   ```
+   >> tf.load_plugin_with_highest_priority(path_to_plugin_lib)
+   >> with tf.device("/gpu:0")
+      ...
+   ```
+* **subdevice type**  
+   Subdevice type is user visible but not controllable. User can query the subdevice type of the device if he wants to know whether the GPU device is NVIDIA_GPU or INTEL_GPU, through [tf.config.experimental.list_physical_devices()](https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices).
+
+   ```
+   >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
+   >> print(gpu_device)
+   [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`]
+   ```
+* **real device name**  
+   Real device name is user visible but not controllable. User can query the real device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
+   ```
+   >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
+   >> if gpu_device:
+         details = tf.config.experimental.get_device_details(gpu_device[0])
+  	 print(details.get(`device_name`)) 
+   "TITAN_V, XXX"     
+   ```
 
 ### Device Discovery
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to the default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices` or `tf.config.experimental.get_device_details`) to get the subdevice type for identification.   
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type for identification. user can also use `tf.config.get_device_details` to get the real device name(e.g. "TITAN V")for the specified device.  
 Plugin authors need to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
@@ -111,11 +143,12 @@ Status PluggableDeviceFactory::ListPhysicalDevices(std::vector<string>* devices)
   return Status::OK();
 }
 ```
-`GetDeviceDetails` encodes the subdevice type string and it can be queried by `tf.config.experimental.get_device_details`.  
+`GetDeviceDetails` retrieves the physical device name of the hardware from plugin.  
 ```
 Status PluggableDeviceFactory::GetDeviceDetails(int device_index, std::unordered_map<string, string>* details) {
- ... 
- (*details)["subdevice_type"] = sub_device_type_;
+ se::Platform* platfom = se::MultiPlatformManager::PlatformWithName(sub_device_type_);
+ auto desc = platform->DescriptionForDevice(device_index).ConsumeValueOrDie();
+ (*details)["device_name"] = desc->name(); // Titan V: XXX
  ...
 }
 ```

From 43be4ab6d490a4760bbf2544d37963f9f97fa1a7 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 05:58:03 -0400
Subject: [PATCH 19/28] update date

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 9145b1028..6bd66177d 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-08-11                                           |
+| **Updated**   | 2020-08-12                                           |
 
 ## **Objective**
 

From 27d5b275165c5cf91ba286160d32b9df03729728 Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 21:20:48 +0800
Subject: [PATCH 20/28] update scenario1 desc

---
 .../scenario1.png                             | Bin 14441 -> 15974 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png
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From 18258c01bdb8120c08053793581b893d47087c7a Mon Sep 17 00:00:00 2001
From: jiangzho <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 21:31:42 +0800
Subject: [PATCH 21/28] update scenario 3 desc

---
 ...0200624-pluggable-device-for-tensorflow.md |   4 ++--
 .../scenario3.png                             | Bin 20494 -> 20766 bytes
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 6bd66177d..5e62507bf 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-08-12                                           |
+| **Updated**   | 2020-08-11                                           |
 
 ## **Objective**
 
@@ -62,7 +62,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 </div>
 
 * **Supported scenario**: Multiple PluggableDevices registered as different device types.   
-  In the case of installing multiple plugins that register PluggableDevices as different device types, e.g., one is registered as "GPU" device type and another is registered as "XPU" device type, these PluggableDevices can be registered successfully and user can specify the registered device type to run ops on different hardware.
+  In the case of installing multiple plugins that register PluggableDevices as different device types, e.g., one is registered as "GPU" device type and another is registered as "XPU" device type, these PluggableDevices can be registered successfully and user can specify the registered device type to run ops on different hardware. Same as scenario1, when one PluggableDevice is registered as "GPU" device type, the default GPUDevice will be overrided.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario3.png>
 </div>
diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario3.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario3.png
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From eb83a479d91eb55e0b2e1d0ca25bba0a41e1d3bf Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 22:58:25 -0400
Subject: [PATCH 22/28] update front-end mirroring mechanisim

---
 ...0200624-pluggable-device-for-tensorflow.md | 44 +++++++++++--------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 6bd66177d..417c5ab86 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-08-12                                           |
+| **Updated**   | 2020-08-11                                           |
 
 ## **Objective**
 
@@ -62,7 +62,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 </div>
 
 * **Supported scenario**: Multiple PluggableDevices registered as different device types.   
-  In the case of installing multiple plugins that register PluggableDevices as different device types, e.g., one is registered as "GPU" device type and another is registered as "XPU" device type, these PluggableDevices can be registered successfully and user can specify the registered device type to run ops on different hardware.
+  In the case of installing multiple plugins that register PluggableDevices as different device types, e.g., one is registered as "GPU" device type and another is registered as "XPU" device type, these PluggableDevices can be registered successfully and user can specify the registered device type to run ops on different hardware. Same with scenario1, when one PluggableDevice is registered as "GPU" device type, the default GPUDevice will be overrided.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario3.png>
 </div>
@@ -75,30 +75,38 @@ This section describes the user scenarios that are supported/unsupported for Plu
 
 ### Front-end Mirroring mechanism
 This section describes the front-end mirroring mechanism for python users, pointing at previous user scenarios.
-* **device type**  
-   Device type is user visible and controllable. User can specify the device type for the ops. e.g, specify device type as "gpu", "xpu".
+* **device type && subdevice type**  
+   Device type is user visible and controllable. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user specify which subdevice to use for the device type(mirroring), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
    ```
    >> with tf.device("/gpu:0"):
       ...
    >> with tf.device("/xpu:0"):
       ...
    ```
-   user need to manually select a platform when multiple plugins register the same device type, or the plugins initilaization will fail due to device type conflict. e.g, when multiple plugins register the "GPU" device type ,user need to set a higher priority for the plugin.
+* **Front-end mirroring** 
+   In the case of two GPUs in the same system, e.g. NVIDIA GPU + INTEL GPU and installing the Intel GPU plugin.
+  * **Option 1**
+    Only plugged gpu device is visible, PluggableDevice overrides GPUDevice. If user want to use CUDA device, he need to uninstall the plugin
+    ```
+    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
+    >> print(gpu_device)
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`]
+    >> with tf.device("/gpu:0"):
+         .. // place ops on PluggableDevice(Intel GPU)
+    ```
+  * **Option 2**
+    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled, if user want to use CUDA device, he need to call mirroring API(set_sub_device_mapping()) to switch to CUDA device.
    ```
-   >> tf.load_plugin_with_highest_priority(path_to_plugin_lib)
-   >> with tf.device("/gpu:0")
-      ...
-   ```
-* **subdevice type**  
-   Subdevice type is user visible but not controllable. User can query the subdevice type of the device if he wants to know whether the GPU device is NVIDIA_GPU or INTEL_GPU, through [tf.config.experimental.list_physical_devices()](https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices).
-
-   ```
-   >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
-   >> print(gpu_device)
-   [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`]
+    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
+    >> print(gpu_device)
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`, enabled]
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, not-enabled]
+    >> tf.config.set_subdevice_mapping("NVIDIA_GPU")
+    >> with tf.device("/gpu:0"):
+         .. // place ops on GPUDevice(NVIDIA GPU)
    ```
-* **real device name**  
-   Real device name is user visible but not controllable. User can query the real device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
+* **physical device name**  
+   physical device name is user visible. User can query the physical device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
    ```
    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
    >> if gpu_device:

From c4542bf7d63028f195f954061994b5ece0a228f7 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 22:58:51 -0400
Subject: [PATCH 23/28] update date

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 417c5ab86..1a38e3922 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -5,7 +5,7 @@
 | **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
 | **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
 | **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
-| **Updated**   | 2020-08-11                                           |
+| **Updated**   | 2020-08-13                                           |
 
 ## **Objective**
 

From 5030d99853e1c105feb8d26862cce60a1d746492 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Wed, 12 Aug 2020 22:59:46 -0400
Subject: [PATCH 24/28] fix desc

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 1a38e3922..ccf17ba4a 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -76,7 +76,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 ### Front-end Mirroring mechanism
 This section describes the front-end mirroring mechanism for python users, pointing at previous user scenarios.
 * **device type && subdevice type**  
-   Device type is user visible and controllable. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user specify which subdevice to use for the device type(mirroring), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
+   Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user can specify which subdevice to use for the device type(mirroring), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
    ```
    >> with tf.device("/gpu:0"):
       ...

From e9ed21064e0ef80d47c8b47becb3edad89c90b5f Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Thu, 13 Aug 2020 00:09:25 -0400
Subject: [PATCH 25/28] update front-end mirroring description

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index e16b7933e..0364b0d29 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -75,7 +75,7 @@ This section describes the user scenarios that are supported/unsupported for Plu
 
 ### Front-end Mirroring mechanism
 This section describes the front-end mirroring mechanism for python users, pointing at previous user scenarios.
-* **device type && subdevice type**  
+* **Device type && Subdevice type**  
    Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user can specify which subdevice to use for the device type(mirroring), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
    ```
    >> with tf.device("/gpu:0"):
@@ -86,16 +86,16 @@ This section describes the front-end mirroring mechanism for python users, point
 * **Front-end mirroring** 
    In the case of two GPUs in the same system, e.g. NVIDIA GPU + INTEL GPU and installing the Intel GPU plugin.
   * **Option 1**
-    Only plugged gpu device is visible, PluggableDevice overrides GPUDevice. If user want to use CUDA device, he need to uninstall the plugin
+    Only plugged gpu device is visible, PluggableDevice(INTEL GPU) overrides the default GPUDevice(NVIDIA GPU). If user want to use NVIDIA GPU, he needs to manually uninstall the plugin.
     ```
     >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
     >> print(gpu_device)
     [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`]
     >> with tf.device("/gpu:0"):
-         .. // place ops on PluggableDevice(Intel GPU)
+    >>   .. // place ops on PluggableDevice(Intel GPU)
     ```
   * **Option 2**
-    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled, if user want to use CUDA device, he need to call mirroring API(set_sub_device_mapping()) to switch to CUDA device.
+    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled, if user want to use NVIDIA GPU, he need to call mirroring API(set_sub_device_mapping()) to switch to NVIDIA gpu device.
    ```
     >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
     >> print(gpu_device)
@@ -103,15 +103,15 @@ This section describes the front-end mirroring mechanism for python users, point
     [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, not-enabled]
     >> tf.config.set_subdevice_mapping("NVIDIA_GPU")
     >> with tf.device("/gpu:0"):
-         .. // place ops on GPUDevice(NVIDIA GPU)
+    >>   .. // place ops on GPUDevice(NVIDIA GPU)
    ```
-* **physical device name**  
+* **Physical device name**  
    physical device name is user visible. User can query the physical device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
    ```
    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
    >> if gpu_device:
-         details = tf.config.experimental.get_device_details(gpu_device[0])
-  	 print(details.get(`device_name`)) 
+   >>    details = tf.config.experimental.get_device_details(gpu_device[0])
+   >>    print(details.get(`device_name`)) 
    "TITAN_V, XXX"     
    ```
 

From 0b3feb369060e36606e65f0baf7d80b015d97d47 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Thu, 13 Aug 2020 03:00:56 -0400
Subject: [PATCH 26/28] front-end mirroring -> device mapping

---
 ...0200624-pluggable-device-for-tensorflow.md |  51 ++++++++++--------
 .../scenario1.png                             | Bin 15974 -> 14532 bytes
 .../scenario3.png                             | Bin 20766 -> 20671 bytes
 .../scenario4.png                             | Bin 19838 -> 19703 bytes
 4 files changed, 28 insertions(+), 23 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 0364b0d29..d49997458 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -50,7 +50,7 @@ With the RFC, existing TensorFlow GPU programs can run on a plugged device witho
 
 This section describes the user scenarios that are supported/unsupported for PluggableDevice.  
 * **Supported scenario**: Single PluggableDevice registered as "GPU" device type  
-  In the case of installing one plugin that registers its PluggableDevice as "GPU" device type, the default GPUDevice will be invalid when the plugin is loaded. When user specifies the "GPU" device for ops under `with tf.device("gpu:0")`, PluggableDevice registered will be selected to run those ops.
+  In the case of installing one plugin that registers its PluggableDevice as "GPU" device type, the default GPUDevice will be overrided by PluggableDevice when the plugin is loaded. When user specifies the "GPU" device for ops under `with tf.device("gpu:0")`, PluggableDevice registered will be selected to run those ops.
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario1.png>
 </div>  
@@ -68,43 +68,49 @@ This section describes the user scenarios that are supported/unsupported for Plu
 </div>
 
 * **Non-Supported scenario**: Multiple PluggableDevices registered as the same device type.  
-  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device type, these plugins's initialization will fail due to registration conflict. User needs to manually select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
+  In the case of installing multiple plugins that registers PluggableDevice as the same device type, e.g., more than one plugin registers its PluggableDevice as "GPU" device type, these plugins's initialization will fail due to registration conflict. Users need to manually select which platform they want to use(either unloads the conflicting plugin or reconfigures the plugin with python API).
 <div align="center">
 <img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
 </div>
 
-### Front-end Mirroring mechanism
-This section describes the front-end mirroring mechanism for python users, pointing at previous user scenarios.
+### Device mapping mechanism
+This section describes the device mapping mechanism for python users, pointing at previous user scenarios.
 * **Device type && Subdevice type**  
-   Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user can specify which subdevice to use for the device type(mirroring), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
+   Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user can specify which subdevice to use for the device type(device mapping), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
    ```
    >> with tf.device("/gpu:0"):
       ...
    >> with tf.device("/xpu:0"):
       ...
    ```
-* **Front-end mirroring** 
-   In the case of two GPUs in the same system, e.g. NVIDIA GPU + INTEL GPU and installing the Intel GPU plugin.
-  * **Option 1**
-    Only plugged gpu device is visible, PluggableDevice(INTEL GPU) overrides the default GPUDevice(NVIDIA GPU). If user want to use NVIDIA GPU, he needs to manually uninstall the plugin.
+* **Device mapping**   
+   In the case of two GPUs in the same system, e.g. NVIDIA GPU + X GPU and installing the X GPU plugin.
+  * **Option 1**: override CUDA device, only plugged gpu device visible  
+    Only plugged gpu device is visible, PluggableDevice(X GPU) overrides the default GPUDevice(CUDA GPU). If users want to use CUDA GPU, they need to manually uninstall the plugin.
     ```
     >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
     >> print(gpu_device)
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`]
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `X_GPU`]
     >> with tf.device("/gpu:0"):
-    >>   .. // place ops on PluggableDevice(Intel GPU)
+    >>   .. // place ops on PluggableDevice(X GPU)
+    ```
+  * **Option 2**: both visible, but plugged gpu is default, user can set the device mapping  
+    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled(with higher priority), if users want to use NVIDIA GPU, they need to call device mapping API(set_sub_device_mapping()) to switch to CUDA device.
     ```
-  * **Option 2**
-    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled, if user want to use NVIDIA GPU, he need to call mirroring API(set_sub_device_mapping()) to switch to NVIDIA gpu device.
-   ```
     >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
     >> print(gpu_device)
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `INTEL_GPU`, enabled]
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, not-enabled]
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `X_GPU`, enabled]
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, disabled]
+ 
     >> tf.config.set_subdevice_mapping("NVIDIA_GPU")
+    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
+    >> print(gpu_device)
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `X_GPU`, disabled]
+    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, enabled]
+    
     >> with tf.device("/gpu:0"):
     >>   .. // place ops on GPUDevice(NVIDIA GPU)
-   ```
+    ```
 * **Physical device name**  
    physical device name is user visible. User can query the physical device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
    ```
@@ -114,18 +120,17 @@ This section describes the front-end mirroring mechanism for python users, point
    >>    print(details.get(`device_name`)) 
    "TITAN_V, XXX"     
    ```
-
 ### Device Discovery
 
 Upon initialization of TensorFlow, it uses platform independent `LoadLibrary()` to load the dynamic library. The plugin library should be installed to the default plugin directory "…python_dir.../site-packages/tensorflow-plugins". The modular tensorflow [RFC](https://github.com/tensorflow/community/pull/77) describes the process of loading plugins. 
 
-During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the user cares whether he is running on Intel/NVIDIA GPU, he can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type for identification. user can also use `tf.config.get_device_details` to get the real device name(e.g. "TITAN V")for the specified device.  
+During the plugin library initialization, TensorFlow proper calls the `SE_InitializePlugin` API (part of StreamExecutor C API) to retrieve nescessary informations from the plugin to instantiate a StreamExecutor platform([se::platform](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/platform.h#L93) class) and registers the platform to a global object [se::MultiPlatformManager](https://github.com/tensorflow/tensorflow/blob/cb32cf0f0160d1f582787119d0480de3ba8b9b53/tensorflow/stream_executor/multi_platform_manager.h#L82), TensorFlow proper gets a device type and a subdevice type from plugin through `SE_InitializePlugin` and then registers the `PluggableDeviceFactory`with the registered device type. The device type string will be used to access PluggableDevice with tf.device() in python layer. The subdevice type is used for low-level specialization of GPU device(kernel, StreamExecutor, common runtime, grapper, placer..). If the users care whether they are running on NVIDIA GPU or X GPU(Intel GPU, Amd GPU..), they can call python API (such as `tf.config.list_physical_devices`) to get the subdevice type for identification. user can also use `tf.config.get_device_details` to get the real device name(e.g. "TITAN V")for the specified device.  
 Plugin authors need to implement `SE_InitializePlugin` and provide the necessary informations:  
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   int32_t visible_device_count = get_plugin_device_count();
   
-  std::string name = "My_GPU"; //StreamExecutor platform name && subdevice type
+  std::string name = "X_GPU"; //StreamExecutor platform name && subdevice type
   std::string type = "GPU"; // device type
 
   params.params.id = plugin_id_value;
@@ -277,20 +282,20 @@ Plugin authors need to provide those C functions implementation defined in Strea
 ### PluggableDevice kernel registration
 
 This section shows an example of kernel registration for PluggableDevice. Kernel registration and implementation API is addressed in a separate [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md). 
-To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "INTEL_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the device.
+To avoid kernel registration conflict with existing GPU(CUDA) kernels, plugin author needs to provide a device type(such as "GPU") as well as a subdevice type(such as "X_GPU") to TensorFlow proper for kernel registration and dispatch. The device type indicates the device the kernel runs on, the subdevice type is for low-level specialization of the device.
 ```cpp
 void SE_InitializePlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {
   ...
   std::string type = "GPU" // front-end visible device type
   params.params.type = type.c_str();
-  std::string name = "INTEL_GPU"; // low-level specialization device type
+  std::string name = "X_GPU"; // low-level specialization device type
   params.params.type = name.c_str();
   ...
 }
 
 void InitKernelPlugin() {
   TF_KernelBuilder* builder = TF_NewKernelBuilder(/*op_name*/"Convolution", "GPU", //"GPU" is device type
-      "INTEL_GPU", &Conv_Create, &Conv_Compute, &Conv_Delete); // "INTEL_GPU" is sub device type
+      "X_GPU", &Conv_Create, &Conv_Compute, &Conv_Delete); // "X_GPU" is sub device type
   TF_Status* status = TF_NewStatus();
   TF_RegisterKernelBuilder(/*kernel_name*/"Convolution", builder, status);
   if (TF_GetCode(status) != TF_OK) { /* handle errors */ }
diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario1.png
index 2f1af3af9f2b2ff9e8e450402ac3efbbec486d5a..a8030e52d1bea44d06a9d3b772bc6f2a84236af3 100644
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diff --git a/rfcs/20200624-pluggable-device-for-tensorflow/scenario3.png b/rfcs/20200624-pluggable-device-for-tensorflow/scenario3.png
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BIjR5v


From fd35ab1a09fd8cd6e2e33e8d6ef2e4b4f30baed6 Mon Sep 17 00:00:00 2001
From: Zhoulong <zhoulong.jiang@intel.com>
Date: Thu, 13 Aug 2020 23:32:49 -0400
Subject: [PATCH 27/28] modify front-end usage for pluggable device according
 to the review meeting

---
 ...0200624-pluggable-device-for-tensorflow.md | 35 ++++++-------------
 1 file changed, 10 insertions(+), 25 deletions(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index d49997458..6973f7222 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -73,20 +73,22 @@ This section describes the user scenarios that are supported/unsupported for Plu
 <img src=20200624-pluggable-device-for-tensorflow/scenario4.png>
 </div>
 
-### Device mapping mechanism
-This section describes the device mapping mechanism for python users, pointing at previous user scenarios.
-* **Device type && Subdevice type**  
-   Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". Subdevice type is user visible and user can specify which subdevice to use for the device type(device mapping), e.g.("NVIDIA_GPU", "INTEL_GPU", "AMD_GPU").
+### Front-end device string
+This section describes the device string for python users, pointing at previous user scenarios.
+* **Device type**  
+   Device type is user visible. User can specify the device type for the ops. e.g, "gpu", "xpu", "cpu". 
    ```
    >> with tf.device("/gpu:0"):
       ...
    >> with tf.device("/xpu:0"):
       ...
    ```
-* **Device mapping**   
-   In the case of two GPUs in the same system, e.g. NVIDIA GPU + X GPU and installing the X GPU plugin.
-  * **Option 1**: override CUDA device, only plugged gpu device visible  
-    Only plugged gpu device is visible, PluggableDevice(X GPU) overrides the default GPUDevice(CUDA GPU). If users want to use CUDA GPU, they need to manually uninstall the plugin.
+* **Subdevice type**   
+   Subdevice type is user visible, aims at differentiating gpu devices between different hardware vendors. Users can query the subdevice type if they want to know whether the GPU device is "NVIDIA_GPU", "INTEL_GPU" or "AMD_GPU", by invoking [list_physical_devices](https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices).   
+   
+   This RFC proposes that plugged gpu device will override the default gpu device. When users invoke `list_physical_devices("GPU")`, they can only see the plugged gpu device. If users want to use default GPU device(CUDA), they need to manually uninstall the plugin.  
+   
+   For example, in the case of two GPUs in the same system, e.g. NVIDIA GPU + X GPU and installing the X GPU plugin, only X GPU is visible and usable. If users want to use Nvidia GPU, they need to uninstall the X GPU plugin.
     ```
     >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
     >> print(gpu_device)
@@ -94,23 +96,6 @@ This section describes the device mapping mechanism for python users, pointing a
     >> with tf.device("/gpu:0"):
     >>   .. // place ops on PluggableDevice(X GPU)
     ```
-  * **Option 2**: both visible, but plugged gpu is default, user can set the device mapping  
-    Both plugged gpu device and default gpu device are visible, but only one gpu can work at the same time, plugged gpu device is default enabled(with higher priority), if users want to use NVIDIA GPU, they need to call device mapping API(set_sub_device_mapping()) to switch to CUDA device.
-    ```
-    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
-    >> print(gpu_device)
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `X_GPU`, enabled]
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, disabled]
- 
-    >> tf.config.set_subdevice_mapping("NVIDIA_GPU")
-    >> gpu_device = tf.config.experimental.list_physical_devices(`GPU`)
-    >> print(gpu_device)
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `X_GPU`, disabled]
-    [PhysicalDevice(name = `physical_device:GPU:0`), device_type = `GPU`, subdevice_type = `NVIDIA_GPU`, enabled]
-    
-    >> with tf.device("/gpu:0"):
-    >>   .. // place ops on GPUDevice(NVIDIA GPU)
-    ```
 * **Physical device name**  
    physical device name is user visible. User can query the physical device name(e.g. "Titan V") for the specified device instance through [tf.config.experimental.get_device_details()](https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details).
    ```

From 492ca0bb740f0f54afd1755a811b81dcadcff983 Mon Sep 17 00:00:00 2001
From: "Jiang, Zhoulong" <zhoulong.jiang@intel.com>
Date: Thu, 3 Sep 2020 01:36:14 -0400
Subject: [PATCH 28/28] update MemAllocator

---
 rfcs/20200624-pluggable-device-for-tensorflow.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/rfcs/20200624-pluggable-device-for-tensorflow.md b/rfcs/20200624-pluggable-device-for-tensorflow.md
index 6973f7222..8b798b4ba 100644
--- a/rfcs/20200624-pluggable-device-for-tensorflow.md
+++ b/rfcs/20200624-pluggable-device-for-tensorflow.md
@@ -226,7 +226,8 @@ Two sets of classes need to be defined in TensorFlow proper.
    * class `PluggableDevice`: a class represents a set of new third-party devices, its device_type attribute describes what kind of device this is. it can be "GPU" or other device type string. it also has an attribute: subdevice_type, subdevice_type is for low-level specialization of GPU device. It will be part of kernel dispatch key to avoid conflict issue with exiting GPU(CUDA) kernels. The subdevice_type is also used to check whether there is some CUDA specific logic code in grappler and common runtime when the device type is "GPU".
    * class `PluggableDeviceFactory`: a device factory to create the PluggableDevice
    * class `PluggableDeviceBFCAllocator`: a PluggableDevice memory allocator that implements a ‘best fit with coalescing’ algorithm. It extends the BFC algorithm, counter part of GPUBFCAllocator.
-   * class `PluggableDeviceAllocator`: an allocator that wraps a PluggableDevice allocator.
+   * class `PluggableDeviceMemAllocator`: Suballocator for GPU memory.
+   * class `PluggableDeviceProcessState`: manages per-process state when PluggableDevices are present
    * class `PluggableDeviceHostAllocator`: allocator for pinned CPU RAM that is made known to PluggableDevice for the purpose of efficient DMA with PluggableDevice.
    * class `PluggableDeviceEventMgr`: an object to keep track of pending Events in the StreamExecutor streams.
    * class `PluggableDeviceContext`: a wrapper of pluggable device specific context that can be passed to OpKernels.