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[RFC][Relay] Dynamic Dimensions #3042

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jroesch opened this issue Apr 18, 2019 · 5 comments

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@jroesch
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commented Apr 18, 2019

Supporting Dynamic Dimensions

I recently opened an RFC proposing a new dynamic runtime (see #2810).
A missing piece of the puzzle for supporting fully dynamic models is typing, and code generation for tensors with statically unknown shapes.

There are three critical steps to supporting dynamic tensors which I believe are distinct tasks:

  • Extending the type system
  • Computing allocation sizes
  • Code generation for dynamic shape

First we should consider a simple example which exhibits the hardest version of this problem, how do we type such a function:

operator arange(%start: int32, %stop: int32, %step: int32) -> Tensor((Any,), int32)

fn (%i: int32) {
  let seq = arange(0, %i, 1);
  sum(seq);
}

This example is challenging to type check due to arange's output shape
depending on the value of %i. Currently we are able to statically determine the
output shape if it is a function of the input's shape, but not if the output shape is a function of the input itself.

*Note: we could use full dependent typing here, but this introduces new challenges. *

In the cases where the output shape is symbolic function of the input, or fully dynamic (such as arange) we must generate code which computes the precise size of the output buffer for the return value at execution time.

For example even when if we know f: (Tensor<n>, Tensor<m>) -> Tensor<n + 1, m> we must
generate a function which can allocate space based on the runtime values of n, m.
These cases are rare because most programs are closed program we know all shapes statically, but once we introduce a dynamic shape, it is possible we must defer arbitrary shape
computation until runtime.

Extending the type system

The first proposed change is finish support for symbolic shapes.

For example f can be specialized to concrete shapes, and users
can pass around functions and operations which work over symbolic
shapes.

The second change is support for an Any shape.

Any represents a tensor dimension which is not statically known.

One possible design is to use a sub-shaping rule. This rule would be
similar to the traditional subtyping rule used in popular programming
languages.

For example in C++:

class T { ... };
class U : public T { ... };

We know U <: T (that is U is a subtype of T).

In this version of Relay this would mean (1, 2, 3) <: (1, Any, 3).

Unfortunately this typing rule is not what we want. For example if
we have a function which expects a tensor of shape (1, 2, 3) we
can no longer call it with a tensor of type (1, Any, 3) we must
introduce explicit casting to properly check the runtime type.

Instead we propose borrowing ideas from gradual typing,
and introducing the notion of "gradual shaping". Any occurence
of a dynamic dimension is potentially unknown at compile time,
and when we demand a concrete dimension we must dynamically
check. This introduces the ability for runtime shape mismatches
but ensures that dynamically shaped and statically shaped code
can interact gracefully.

This is important, for example in many cases Any would pollute
the types of functions that are static, but their argument has
an unknown shape.

For example, we can optimize the below code under the assumption
that it will always have shape (10, 10), and then perform
one runtime check of shape when calling f.

fn @f(%x : Tensor[(10, 10)]) { ... }

let x: Tensor(Any, Any) = ...;
@f(x)

Computing allocation sizes

The second component is computing the allocation size required.
We must due this because TVM requires its output buffers to be preallocated.
When the output is dependent on input, or a symbolic relationship which has not
been specialized we need to generate a function at runtime which computes
the output buffer size.

Array<IndexExpr> ShapeFunc(const Array<Input>& inputs) {
  ...
}

We can then invoke this function to allocate the appropriate output storage,
during execution.

Code generation for dynamic shape

The final challenge is performing code generation, this RFC only proposes solving the
representation problem and generating naive TVM code in these cases which use fully
symbolic shapes which are not known until runtime.

I believe there are a variety of different code-generation strategies which
each may suit different situations, and we implement a mechanism for specifying
the code generation policy in the face of dynamic shapes seperately from
shape functions, and typing of dynamic shapes.

Some of the possible code generation strategies:

  • Generate placeholder code when symbolic relationship holds.
  • Generate placeholder code and make use of shape functions.
  • Implement bucketing style approach to code generation.
  • Your ideas here!

@jroesch jroesch added the status: RFC label Apr 18, 2019

@junrushao1994

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commented Apr 19, 2019

This is a great progress towards deploying real-world NLP models. Thanks Jared for proposing this!

As far as I could tell, however, in the long term, we probably need more support for more general dependent typing for more data types. I am afraid that we will have to consider composite types in the very end.

@junrushao1994

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commented Apr 19, 2019

Another immature idea is to unify composite types with operator's attributes (but maybe far long-term)

@kevinthesun

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commented Apr 23, 2019

I think for now it is okay to just support code gen with fully symbolic shape. Later if we want specific optimization strategies for different programs, we can revisit and adding more code gen strategies.

@ajtulloch

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commented May 17, 2019

This looks like a great design, and should handle all the applications I'm currently thinking of. @jroesch let us know if we can help.

@jroesch

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commented May 21, 2019

@ajtulloch @icemelon9 and I have been quietly hacking on a prototype the last few months but have been busy with other things (such as VM 😄 ). We are going to start pushing now, I opened a draft PR which will contain type checking changes, and we will follow-up with code generation next.

One thing would be to help validate and criticize the design from the standpoint of a user who is deploying models day to day. We are also generally interested in thoughts about code generation strategies.

One change that we have decided on is adding the ability to type hint when generating IR.

When generating IR from frameworks we don't necessarily have all type information up front.
That is you may know a static shape for a value, but the shape is not necessarily a stable static property.

For example imagine a loop variable which starts as a (1, 1) matrix, but will grow by one in the first dimension on every loop. Eventually the matrix will have the shape (n, 1) where n is the number of loop iterations but we can't know that statically.

Our current solution is to generate IR with unknown types, plus a type hint, allowing inference to eventually solve the most general type.

For example if we were to generate IR for a loop we can do:

def my_loop(%x: ?T, %y: ?U) {
     ...
     can_unify(%x, shape=(1, 1))
     my_loop(concat([%x, %x], axis=0))
     ...
}

These could later be used to inform code generation, but ensure that we get the correct typing.

I will also be at the Diff. Prog. Conf hosted by FB if you are around to talk more in person.

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