![]() Static constexpr char kOpName = "my_custom_fused_op" This name must match the name specified in the implements signature. Specified in the name attribute in the implements signature.Īn example for the op in the example is TfLiteRegistration reg Note that, the name to register the op with should be similar to the name Implement your fused operation as a TFLite Custom operation - see ![]() Implement custom op and register with TFLite Interpreter Tfl_fusable_op attribute imply this already. Note that you don't need to set allow_custom_ops on the converter as Return my_custom_fused_op(nv_1(input_a), nv_2(input_b)) Output_2 = tf.constant(0.0, dtype=tf.float32, name='second_output') Output_1 = tf.constant(0.0, dtype=tf.float32, name='first_output') # is not represented as part of the Tensorflow graph. # An empty function that represents pre/post processing example that Return ' my_custom_fused_op(input_1, input_2): # attr "tfl_fusable_op" is required to be set with true value. # 'name' will be used as a name for the operation. Operation takes attributes then pass them as part of the same Which has attribute value tfl_fusable_op with value true. Identify the part of the graph that represents a fused operation and wrap it inĪ tf.function with "experimental_implements" attribute to a tf.function, To be able to create a fused operation in TFLite, This can help with performance when writing an optimized implementationįor specific operations. In many cases, some part of the model can be mapped to a single operation in Converting from composite op to a TFLite custom operation (recommended) Wrap the composite operation in a tf.function Same fused LSTM operation in TensorFlow Lite.Īs such, conversion of fused operations has proven quite challenging. In TensorFlow (Keras, Babelfish/lingvo etc) and each of these is composed ofĭifferent primitive operations but they all could still be converted to the For example, there are many LSTM implementations There may be more than one TensorFlow implementation targeting a fused via pattern matching) the sub-graphĬorresponding to such a composite operation. It can be veryĬhallenging to identify (e.g. Primitive operations without a well defined boundary. This is because:Ĭomposite operations are represented in the TensorFlow graph as a set of Challenges with fused operationsĬonverting composite operations from TensorFlow to fused operations in Implemented in the fused LSTM operations. In TensorFlow Lite, LSTM quantization is currently only LSTM, convolution (conv2d, bias add, relu), fully connected (matmul, bias add, Include various RNN operations like Unidirectional and Bidirectional sequence TensorFlow that are implemented as a single fused operation in TensorFlow Lite Operations in the source TensorFlow program. ![]() These fused operations typically correspond to composite TensorFlow Lite has many instances of fused operations for the reasonsĪrticulated above. Transformations like quantization, which would otherwise be infeasible or very This is very valuable, especially for low-latency inference workloadsĪnd resource constrained mobile platforms.įused operations also provide a higher level interface to define complex Implementations, by optimizing the overall computation and reducing memoryįootprint. Benefits of fused operationsįused operations exist to maximize the performance of their underlying kernel Executing aĬomposite operation is equivalent to executing each of its constituent primitiveĪ fused operation corresponds to a single operation that subsumes all theĬomputation performed by each primitive operation within the correspondingĬomposite operation. Operation is a collection of nodes in the TensorFlow graph. Operation shows up as a single node in the TensorFlow graph while.a composite TensorFlow operations can either be primitive ops e.g.Ĭomposed from other primitive operations e.g. To a corresponding fused operation in TensorFlow Lite.Īn example use of this infrastructure is TensorFlow RNN operation fusion to General purpose and supports conversion of any composite operation in TensorFlow In TensorFlow to fused operations in TensorFlow Lite. This page describes the design and steps needed to convert composite operations ![]()
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