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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