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diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index 972e57f73e..d979353bb3 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # How to use custom operators TensorFlow Lite currently supports a subset of TensorFlow operators. However, it @@ -89,3 +92,83 @@ builtins.AddCustom("Sin", Register_SIN()); Note that a similar process as above can be followed for supporting for a set of operations instead of a single operator. + +## Best Practices for writing custom operators + +1. Optimize memory allocations and de-allocations cautiously. It is more + efficient to allocate memory in Prepare() instead of Invoke(), and allocate + memory before a loop instead of in every iteration. Use temporary tensors + data rather than mallocing yourself (see item 2). Use pointers/references + instead of copying as much as possible. + +2. If a data structure will persist during the entire operation, we advise + pre-allocating the memory using temporary tensors. You may need to use + OpData struct to reference the tensor indices in other functions. See + example in the + [kernel for convolution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/conv.cc). + A sample code snippet is below + + ``` + auto* op_data = reinterpret_cast<OpData*>(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[0] = op_data->temp_tensor_index; + TfLiteTensor* temp_tensor = &context->tensors[op_data->temp_tensor_index]; + temp_tensor->type = kTfLiteFloat32; + temp_tensor->allocation_type = kTfLiteArenaRw; + ``` + +3. If it doesn't cost too much wasted memory, prefer using a static fixed size + array (or in Resize() pre-allocated std::vector) rather than using a + dynamically allocating std::vector every iteration of execution. + +4. Avoid instantiating standard library container templates that don't already + exist, because they affect binary size. For example, if you need a std::map + in your operation that doesn't exist in other kernels, using a std::vector + with direct indexing mapping could work while keeping the binary size small. + See what other kernels use to gain insight (or ask). + +5. Check the pointer to the memory returned by malloc. If this pointer is + nullptr, no operations should be performed using that pointer. If you + malloc() in a function and have an error exit, deallocate memory before you + exit. + +6. Use TF_LITE_ENSURE(context, condition) to check for a specific condition. + Your code must not leave memory hanging when TF_LITE_ENSURE is done, i.e., + these should be done before any resources are allocated that will leak. + +## Special TF Graph Attributes + +When Toco convertes a TF graph into TFLite format, it makes some assumption +about custom operations that might be not correct. In this case, the generated +graph can be not executable. + +It is possible to add aditional information about your custom op output to TF +graph before it is converted. The following attributes are supported: + +- **_output_quantized** a boolean attribute, true if the operation outputs are + quantized +- **_output_types** a list of types for output tensors +- **_output_shapes** a list of shapes for output tensors + +### Setting the Attributes + +This is an example how the attributes can be set: + +```python +frozen_graph_def = tf.graph_util.convert_variables_to_constants(...) +for node in frozen_graph_def.node: + if node.op == 'sin': + node.attr['_output_types'].list.type.extend([ + types_pb2.DT_FLOAT, + ]) + node.attr['_output_shapes'].list.shape.extend([ + tf.TensorShape([10]), + ]) + node.attr['_output_quantized'].b = False +tflite_model = tf.contrib.lite.toco_convert( + frozen_graph_def,...) +``` + +**Note:** After the attributes are set, the graph can not be executed by +Tensorflow, therefore it should be done just before the conversion. |