From: 이상규/On-Device Lab(SR)/Principal Engineer/삼성전자 <sg5.lee@samsung.com>
Date: Tue, 10 Sep 2019 09:17:40 +0000 (+0900)
Subject: [nnpkg] update custom operator specification (#7337)
X-Git-Tag: accepted/tizen/unified/20190911.111615~3
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=f26a1a013a7b757e1bbafed4ac5e8d8050653cc7;p=platform%2Fcore%2Fml%2Fnnfw.git

[nnpkg] update custom operator specification (#7337)

* [nnpkg] update custom operator specification

It updates custom operator specification.

Signed-off-by: Sanggyu Lee <sg5.lee@samsung.com>

* Update custom operator specificaiton

- Remove `nncc`
- Change the sentence as requested by review

Signed-off-by: Sanggyu Lee <sg5.lee@samsung.com>
---

diff --git a/nnpackage/spec/30_custom_op.md b/nnpackage/spec/30_custom_op.md
index 7ba3823..58e0acd 100644
--- a/nnpackage/spec/30_custom_op.md
+++ b/nnpackage/spec/30_custom_op.md
@@ -1,3 +1,86 @@
 # Custom Operators
 
-To be filled
+This document explains about custom operator and how custom op is represented in nnpackage.
+
+## What is custom operator?
+
+Custom operator(hereafter custom op) is used to provide a new operator implementation.
+It can be anything that does not exist in current runtime implementation.
+
+You can use custom operator for several use cases, possible use cases are:
+
+- when an operator in tensorflow is not supported in nnfw runtime
+- when an operator is supported, however, you would like to use your own implementation
+  - it may be for optimization, by grouping several operators into one super operator.
+
+## Custom op in model
+
+nnpackage will support several kinds of models.
+Currently the only type is tflite.
+
+### tflite
+
+If you're using `tflite` format, it is same format to tensorflow lite.
+
+You can generate `tflite` model with custom op using `tflite_convert`.
+Please find the documentation in tensorflow official site.
+
+## Custom op kernel implementation
+
+You need to provide the kernel of custom op in the following form:
+
+```
+/*
+ * Custom kernel evaluation function
+ *
+ * param[in] params custom operation parameters
+ * param[in] userdata pointer to user-specified buffer( kernel instance specific )
+ */
+typedef void (*nnfw_custom_eval)(nnfw_custom_kernel_params *params, char *userdata,
+                                 size_t userdata_size);
+
+```
+
+The structures and relevant APIs are defined in nnfw APIs.
+Please see `nnfw_dev.h` for detail.
+
+You can find example in `nnfw` repository.
+
+Custom op kernel implementation is stored in nnpackage in form of prebuilt library.
+
+It is example nnpackage structure for `FillFrom`:
+
+```
+FillFrom
+├── FillFrom.tflite
+├── custom_op
+│   ├── libFillFrom.armv7l-linux.debug.a
+│   └── libFillFrom.armv7l-linux.release.a
+└── metadata
+    └── MANIFEST
+```
+
+All custom operator libraries are put under `{nnpackage_root}/custom_op/lib{customop_name}.{arch}-{os}-{buildtype}.a`.
+
+## How to use custom op in app
+
+To use custom op, the app has to register the operators with `nnfw_register_custom_op_info`.
+
+
+```
+/*
+ * custom operation registration info
+ */
+typedef struct
+{
+  nnfw_custom_eval eval_function;
+} custom_kernel_registration_info;
+
+NNFW_STATUS nnfw_register_custom_op_info(nnfw_session *session, const char *id,
+                                         custom_kernel_registration_info *info)
+```
+
+Please find sample app in `nnfw` repository
+
+The `id` should be unique in an app.
+