DLVM: A modern compiler framework for neural network DSLs (opens in new tab)

(dlvm.org)

75 pointsprotomok8y ago17 comments

17 comments

wcrichton8y ago

Current tally of high-performance, deep-learning-oriented DSLs/IRs/compilers, in no particular order:

- TensorComprehensions (Facebook): https://github.com/facebookresearch/TensorComprehensions

- XLA (Google): https://www.tensorflow.org/performance/xla/

- taco (MIT): http://tensor-compiler.org/

- DLVM (UIUC): http://dlvm.org/

- nGraph (Intel): http://ngraph.nervanasys.com/docs/cpp/

- TVM (DMLC): https://github.com/dmlc/tvm

Honorable mention to Julia (http://julialang.org) as well.

hedgehog8y ago

As far as I know Tile/PlaidML (Vertex.AI) is the only DSL+compiler that's usable for real workloads across a variety of hardware. https://github.com/plaidml/plaidml

grandmczeb8y ago

Tensorflow + XLA seems pretty usable. Also, it's generally good practice to note that you're a cofounder of Vertex.AI in discussions like this.

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deepnotderp8y ago

Why are all the neural network DSLs JIT obsessed?

grandmczeb8y ago

Lots of modern models have very late binding variables which are hard to precompile for (sentence length in MNT, for example). That means you're going to need to do some form of specialization at runtime, so a JIT makes sense.

deepnotderp8y ago

Just treat it as an infinite loop , there's no need to JIT in an optimized version that late.

1 more reply

joe_the_user8y ago

Well, the success of neural nets over the past few years has come through harnessing massive processing power.

The problem is a lot of the programming can be low level and ad-hoc. I think the idea of the various DSLs is to allow the model to be compactly specified while having the programs go as fast as possible. A JIT may be one way to accomplish this.

deepnotderp8y ago

Relying upon a JIT often means the ability to create things which preclude the use of static compilers, which means that accelerated hardware, like ours, cannot be used efficiently.

1 more reply

stealthcat8y ago

What does it mean by modern?

2 more replies

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17 comments

wcrichton8y ago

Current tally of high-performance, deep-learning-oriented DSLs/IRs/compilers, in no particular order:

- TensorComprehensions (Facebook): https://github.com/facebookresearch/TensorComprehensions

- XLA (Google): https://www.tensorflow.org/performance/xla/

- taco (MIT): http://tensor-compiler.org/

- DLVM (UIUC): http://dlvm.org/

- nGraph (Intel): http://ngraph.nervanasys.com/docs/cpp/

- TVM (DMLC): https://github.com/dmlc/tvm

Honorable mention to Julia (http://julialang.org) as well.

hedgehog8y ago

As far as I know Tile/PlaidML (Vertex.AI) is the only DSL+compiler that's usable for real workloads across a variety of hardware. https://github.com/plaidml/plaidml

grandmczeb8y ago

Tensorflow + XLA seems pretty usable. Also, it's generally good practice to note that you're a cofounder of Vertex.AI in discussions like this.

1 more reply

deepnotderp8y ago

Why are all the neural network DSLs JIT obsessed?

grandmczeb8y ago

deepnotderp8y ago

Just treat it as an infinite loop , there's no need to JIT in an optimized version that late.

1 more reply

joe_the_user8y ago

Well, the success of neural nets over the past few years has come through harnessing massive processing power.

deepnotderp8y ago

Relying upon a JIT often means the ability to create things which preclude the use of static compilers, which means that accelerated hardware, like ours, cannot be used efficiently.

1 more reply

stealthcat8y ago

What does it mean by modern?

2 more replies

j / k navigate · click thread line to collapse