NNVM: Open Compiler for AI Frameworks
Installation | Documentation | Tutorials | Release Notes
NNVM compiler offers reusable computation graph optimization and compilation for deep learning systems. It is backed by the TVM stack and provides modules to:
- Represent deep learning workloads from front-end frameworks via a graph IR.
- Optimize computation graphs to improve performance.
- Compile into executable modules and deploy to different hardware backends with minimum dependency.
NNVM is designed to add new frontend, operators and graph optimizations in a decentralized fashion without changing the core interface. The compiled module can be deployed to server, mobile, embedded devices and browsers with minimum dependency, in languages including c++, python, javascript, java, objective-c. Checkout our release announcement
The following code snippet demonstrates the general workflow of nnvm compiler.
import tvm from tvm.contrib import graph_runtime, rpc import nnvm.frontend import nnvm.compiler # GET model from frameworks # change xyz to supported framework name. graph, params = nnvm.frontend.from_xyz(...) # OPTIMIZE and COMPILE the graph to get a deployable module # target can be "opencl", "llvm", "metal" or any target supported by tvm target = "cuda" graph, lib, params = nnvm.compiler.build(graph, target, {"data", data_shape}, params=params) # DEPLOY and run on gpu(0) module = graph_runtime.create(graph, lib, tvm.gpu(0)) module.set_input(**params) module.run(data=data_array) output = tvm.nd.empty(out_shape, ctx=tvm.gpu(0)) module.get_output(0, output) # DEPLOY to REMOTE mobile/rasp/browser with minimum tvm rpc runtime # useful for quick experiments on mobile devices remote = rpc.connect(remote_host, remote_port) lib.export_library("mylib.so") remote.upload("mylib.so") rlib = rpc.load_module("mylib.so") # run on remote device rmodule = graph_runtime.create(graph, rlib, remote.gpu(0)) rmodule.set_input(**params) rmodule.run()
License
Licensed under an Apache-2.0 license.
Links
- TinyFlow on how you can use NNVM to build a TensorFlow like API.
- Apache MXNet uses NNVM as a backend.