HTVM is a library which provides Haskell runtime and experimental frontend for
TVM the Machine Learning framework.
Both HTVM and TVM are under development. While TVM is somewhat stable, we
don't recommend to use HTVM in applications currently
GitHub repository may contain newer version of HTVM
TVM in a nutshell
TVM framework extends Halide principles to Machine
Learning domain. It offers (a) EDSLs for defining and hand-optimizing ML models
(b) export/import facilities for translating models from other frameworks such
as TensorFlow and (c) compiler to binary code for a variety of supported
platforms, including LLVM (x86, arm), CUDA, OpenCL, Vulcan, ROCm, FPGAs and even
WebAssembly (note: level of support may vary). DSLs for C++ and Python are best
supported and also there are some support for Java, Go and Rust languages.
Watch Halide introduction video
Read more on TVM site
Originally, TVM aimed at increasing speed of model's inference by providing a
rich set of optimizing primitives called
At the same time it had little support for training models. Recently,
training-related proposals were
TVM aims at compiling ML models in highly optimized binary code.
Important parts of TVM are:
tvm is a core library providing
topi is a tensor operations collection. Most of the middle-layer
primitives such as
softmax are defined there.
relay is a high-level library written in Python, providing
functional-style interface and its own typechecker. Currently, relay is
under active development and beyond the scope of HTVM.
nnvm is another high-level wrapper in Python, now deprecated in favor of
Features and goals
In HTVM we are going to provide:
- C Runtime, which makes it possible to run TVM models from Haskell.
- Experimental EDSL for building TVM programs in Haskell.
Combined TVM/HTVM-stack features are:
- Not many dependencies: TVM is much easier to build than other frameworks (hi
TensorFlow). Models are compiled to binary code, no interpreters required.
- Performance: HTVM uses TVM, which is designed with performace in mind.
- Simplicity of code.
- Experimental status
- Simplicity again. Pure ADT-based design.
- Not much type-safety yet. Expect errors in runtime. Typechecker may be
implemented in future.
Make sure you have
Build tvm from development repository located at
https://github.com/grwlf/tvm, branch autodiff
$ git clone https://github.com/grwlf/tvm
$ cd tvm
$ git checkout origin/autodiff
.. follow up with the tvm build procedure
We use development environment specified in Nix
language. In order to open it, please install the
Nix package manager.
Having Nix manager and
NIX_PATH set, enter the environment, by running Nix
development shell from the project's root folder:
It should get all the Haskell dependencies upon the first run. Alternatively,
it should be possible to run Haskell distributions like Haskell
After nix-shell or Haskell distibution is ready, run
cabal to build the
$ cabal configure --enable-tests
$ cabal build
To run tests, execute the test suite. At this point you will need
tvm of the correct version (see above).
$ cabal test
To enter the interactive shell, type
$ cabal repl htvm
*HTVM.EDSL.Types> :lo Demo
Usage examples may be found in Tests and (possibly outdated)
TODO: Update demo, write more examples
TVM C Runtime
FFI for TVM C Runtime library is a Haskell package, linked to
libtvm_runtime.so. This library contains functionality, required to load and
run ML code produced by TVM.
- The module provide wrappers to
TVMArray is the main type describing Tensors in TVM. It is represented as
ForeignPtr to internal representation and a set of accessor functions.
- Currently, HTVM can marshal data from Haskell lists. Support for
Data.Array is planned.
- No backends besides LLVM are tested. Adding them should not be hard and is
on the TODO list.
TVM Haskell EDSL
EDSL has a proof-of-concept status. It may be used to declare ML models in
Haskell, convert them to TVM IR and finally compile. Later, compiled model may be
loaded and run with Haskell FFI or with any other runtime supported by TVM.
Contrary to usual practices, we don't manipulate TVM IR by calling TVM functions
internally. Instead, we build AST in Haskell and print it to C++ program. After
that we compile the program with common instruments. This approach has its pros and
cons, which are described below.
HTVM.EDSL.Types module defines AST types which loosely corresponds to
Expr class hierarchies of TVM.
HTVM.EDSL.Monad provides monadic interface to AST builders. We favored
simplicity over type-safety. We belive that overuse of Haskell type system
ruined many good libraries. The interface relies on simple ADTs whenever
HTVM.EDSL.Print contain functions which print AST to C++ program of Model
HTVM.EDSL.Build provides instruments to compile and run the model
generator by executing
- The Model Generator program builds TVM IR and generates LLVM assembly.
In HTVM, we support LLVM target, but more targets may be added later.
- We execute
clang to compile LLVM into x86 '.so' library. Resulting
library may be loaded and executed by the Runtime code.
The whole data transformation pipeline goes as follows:
Monadic --> AST --> C++ --> Model --> LLVM --> Model --> Runtime FFI
Interface . . . Gen . asm . Library
. . . . .
. Print . Print .
Run C++ g++ clang
Known disadvantages of C++ printing approach are:
- Compilation speed is limited by the speed of
g++, which is slow. Gcc is
used to compile C++ to binary which may take as long as 5 seconds. Little may
be done about that without changing approaches. One possible way to overcome
this limitation would be to provide direct FFI to TVM IR like
Halide-hs does for Halide.
Unfortunately, this approach has its own downsides:
- Low-level IR API is not as stable as its high-level counterpart
- TVM is in its early stages and sometimes crashes. FFI to IR would provide no
isolation from this.
- Calling construction-time procedures of TVM is non-trivial. This is a
consequence of previous limitation. For example, TVM may calculate Tensor
shape in runtime and use it immediately to define new Tensors. In order to
that in Haskell we would need to compile and run C++ program which is possible
by slow. We try to avoid calling construction-time procedures.
- User may face weird C++ errors. TVM is quite a low-level library which
offers little type-checking, so user may write bad programs easily. Other high
level TVM wrappers like Relay in Python, does provide their own typecheckers
to catch errors earlier. HTVM offers no typechecker currently but it is
certainly possible to write one. Contributions are welcome!
The pros of this approach are:
- C++ printer is implemented in less than 300 lines of code. Easy to maintain.
- Easy to port to another TVM dialect such as Relay.
- Isolation from TVM crashes. Memory problems of TVM IR will be translated to error
messages in Haskell.
- We aim at supporting basic
import tvm and
import topi functionality.
- Support for Scheduling is minimal, but should be enhanced in future.
- Support for TOPI is minimal, but should be enhanced in future.
- No targets besides LLVM are supported. Adding them should be as simple as
adding them to C++ DSL.
- We plan to support Tensor-Level AD
- Adding support for Relay is also
possible but may require some efforts like writing Python printer.