FrozenGene opened a new issue #5038: [RFC] Module based Model Runtime Interface
URL: https://github.com/apache/incubator-tvm/issues/5038
 
 
   As 
https://discuss.tvm.ai/t/discuss-module-based-model-runtime-interface/5025 
discussed, we want to support Module based Model Runtime Interface and solve 
the following challenges:
   
   - R1: The creation of ML model can be context dependent. For example, the 
user needs to be able to specify which GPU to run the graph runtime on.
   - R2: We start to have multiple variations of model runtime, such as 
RelayVM. While it does not makes sense to force all model runtime to have the 
same set of APIs, it would be helpful to have a same mechanism for packaging 
and loading.
   - R3: In advanced use cases, we want to be able to bundle multiple models 
into a single shared library.
   
   After discussion, we have sorted out the API and reach an agreement.  Here, 
I want to summary the API and give it an example.
   
   ```python
   # lib is a GraphRuntimeFactoryModule
   # that contains json and parameters
   lib = relay.build(...)
   
   # we could export it to shared library and load it back
   # Here, we provide one option to let user control whether we
   # want to package_params or not. The default value is true.
   lib.export_library("resnet18.so", package_params=true)
   
   # load it back
   lib = tvm.module.load("resnet18.so")
   
   # Call into the factory module to create a graph runtime
   # Having this additional factory create step solves R1
   # Note that parameters are already set
   
   # The first argument is a key that helps to solve R3, allow list of context 
in the future
   # gmod = lib["resnet18"]([tvm.cpu(0), tvm.gpu(0)])
   gmod = lib["resnet18"](tvm.cpu(0))
   
   set_input = gmod["set_input"]
   run = gmod["run"]
   get_output = gmod["get_output"]
   
   # We do not need to set the parameters here
   # as the models
   set_input(data=my_data)
   run()
   get_output()
   
   # we could use wrapper
   gmod = tvm.graph_runtime.create(lib["resnet18"], tvm.cpu(0))
   gmod.set_input(data=my_data)
   gmod.run()
   gmod.get_output()
   ```
   
   More details and the decision procedure could be seen: 
https://discuss.tvm.ai/t/discuss-module-based-model-runtime-interface/5025

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