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Later when a CustomOp operator is bound/executed the functions from the shared library are executed. During the bind step, the attributes for the operator are analyzed by the customOp's parseAttrs
function in the shared library. For type and shape inference, the respective functions are also called through the inferType
and inferShape
APIs. Lastly, when executing the forward pass, the Forward
function is called for the operator from the shared library.
New MXNet APIs
These are new APIs that
...
are added to MXNet
...
C APIs
MXLoadCustomOpLib
MXLoadLib - API to load operator libraries- Checks version number
- Calls initialize on
- Load the customOp library
- Go through the operators in the library
- Check that each operator defines required functions
- ParseAttrs, InferType, InferShape, FCompute
- Register each operator found
Python APIs
load_op_lib
- API to load operator libraries- Takes a path to the operator library
- checks if the path exists and if points to file
- calls C API
MXLoadCustomOpLib
MXLoadLib
to perform actual loading - mx.operator.load_op_lib('/path/to/libtest.so')
New CustomOp Operator
- CustomOp - new operator that executes custom operators loaded from the library
- Takes op_type to identify custom operator name
- Takes any number of kwargs as attributes/parameters
- Takes any number of in-order args as input arrays
- b = mx.nd.CustomOp(a,op_type='sam',myParam='2')
APIs for implementing Custom Operators
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parseAttrs
- takes a set of key/value pairs for attributes and gives users an opportunity to validate the attributes passed to their custom operator.int parseAttrs(std::map<std::string, std::string> attrs,
int* num_in,
int* num_out);- Inputs: the map of attributes passed to the operator from the user
- Outputs: num_in, num_out - the number of input/output arrays required for this operator
- returns 1 if success, or zero if failure
inferType
- performs type inference for this operatorint inferType(std::map<std::string, std::string> attrs,
std::vector<int> &intypes,
std::vector<int> &outtypes);- Inputs: the map of attributes
- Inputs/Outputs: intypes, outtypes - the list of input/output types that should be inferred. Values of of -1 should be defined by this operator as a specific type
- returns 1 if success, or zero if failure
inferShape
- performs shape inference for this operatorint inferShape(std::map<std::string, std::string> attrs,
std::vector<std::vector<unsigned int>> &inshapes,
std::vector<std::vector<unsigned int>> &outshapes);- Inputs: the map of attributes
- Inputs: inshapes - the shapes of the input arrays
- Outputs: outshapes - the shapes of output arrays
fcompute
forward
- performs computation forward pass of this operatorint myFComputeforward(std::map<std::string, std::string> attrs,
std::vector<MXTensor> inputs,
std::vector<MXTensor> outputs,
OpResource res);- Inputs: the map of attributes
- Input data: inputs, input tensors
- Output data: outputs, output tensors
...
REGISTER_OP
- registers the operator in the libraryREGISTER_OP(sam)
.setFCompute_cpusetForward(myFCompute)
.setParseAttrs(parseAttrs)
.setInferType(inferType)
.setInferShape(inferShape);REGISTER_OP
- macro that defines an custom operator object with given namesetFCompute_cpu
- sets the FCompute function for CPU contextsetFCompute_gpu
setForward
- sets the FCompute function for GPU contextsetParseAttrs
- sets the parse attributes functionsetInferType
- sets the infer types functionsetInferShape
- sets the infer shapes function
Goals/Usecases
MXNet Java Inference API#Goals
Open Questions
Proposed Approach
Example Custom Operators
Examples of creating custom operators, building them into a library, and loading them at runtime to test them can be found here:
https://github.com/apache/incubator-mxnet/tree/master/example/extensions/lib_custom_op
The GEMM example contains two operators. The state-less operator shows a regular operator here:
Initial PoC in this branch: https://github.com/samskalickyapache/incubator-mxnet/tree//blob/master/example/extensions/lib_custom_op/
MXNet Java Inference API#ProposedApproach
MXNet Java Inference API#ClassDiagram
MXNet Java Inference API#SequenceDiagram
Addition of New APIs
Backward compatibility
Performance Considerations
Test Plan
Alternative Approaches
MXNet Scala API Usability Improvement#AlternativeApproachconsidered
Technical Challenges
MXNet Scala API Usability Improvement#TechnicalChallenges
Milestones
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The example GEMM stateful operator is here: