[RFC] Linalg on Tensors Update and Comprehensive Bufferization RFC

Linalg on Tensors Update and Comprehensive Bufferization RFC.

This post is a followup to the posts:

  1. An update on Linalg on Tensors
  2. Properly using bufferization related passes
  3. Add Linalg TileOp

Status Update

In the past few months, some of us have been exploring raising the level of abstraction of Linalg-based transformations. In a nutshell, this mostly amounts to performing more transformations in tensor-land and taking advantage of destructive update patterns on tensors, conceptually similar to LLVM’s extract/insertelement and extract/insertvalue.

In this context, tiling produces scf.for + tensor yields and SSA use-def chains are still available. This makes a certain number of transformations very natural to express:

  1. Padding and packing transformations become quite natural to write with a simple backward-slice based algorithm.
  2. Fusion + vectorization extend quite naturally to the tensor domain and the subsequent L/S forwardings become simple canonicalizations + DCE thanks to SSA use-def chains. Similarly, RAW/WAR/WAW optimizations are also quite simple to canonicalize on n-D vectors.
  3. Bufferization can now happen very late (i.e. after parallelization, fusion, tiling and vectorization have taken place). This gives extra phase-ordering flexibility and opens new tradeoffs as well as simplifies “in-place” reuse of buffers.
  4. Consequently, it became relatively simple to build an end-to-end python-driven sandbox and have it run end-to-end; and starting to connect with parallel GPU and CPU execution.

This third point is the one of interest in this post. After some initial experiments it became quite clear that the bufferization needs for Linalg on tensors and HPC codegen are quite separable from higher-level considerations related to generality of control-flow, open set of ops, composability of dialect-specific bufferization, runtime reference counting, etc… . On the other hand, in-place bufferization guarantees are highly desirable.

Back to the drawing board and experimentation, the outcome is a comprehensive bufferization pass centered around “linalg ops + scf.for with tensor yields + function calls”. This is Module pass that takes advantage of SSA use-def chains to determine which results are “inplaceable” (i.e. may end up reusing an operand/argument buffer to write a result).

It consists in the following steps.

Step 1: Inter-procedural CallOp analysis

First, perform a funcArgumentsInPlaceAnalysis which traverses all CallOps and determine whether any tensor operand could potentially bufferize to a buffer that can be updated inPlace (i.e. an in-out buffer).
Such operands are ones whose value is not read by any other subsequent op at the caller site.

As a result of this analysis, CallOp operands are marked with kInPlaceResultsAttrName.

The “meet” of all kInPlaceResultsAttrName for all CallOps to a given FuncOp determines the kInPlaceResultsAttrName for that FuncOp.

In the current implementation, a topological sort of CallOp and FuncOp is performed and recursion is disallowed.

The kInPlaceResultsAttrName is also the mechanism (ab?)used at the compiler/runtime interface to allow inplace interop with e.g. numpy and pytorch as follows:

func @main(%A : tensor<{M}x{K}xf32>, %B : tensor<{K}x{N}xf32>, %C : tensor<{M}x{N}xf32>, %iters : index) -> tensor<{M}x{N}xf32>
  attributes {{
      __writeable_func_buffer_args_attr__ = ["false", "false", "true"] }}
  %c0 = constant 0
  %c1 = constant 1
  %res = scf.for %arg0 = %c0 to %iters step %c1 iter_args(%iterC = %C) -> (tensor<{M}x{N}xf32>) {{
    %r = call @matmul_on_tensors(%A, %B, %iterC) :
      (tensor<{M}x{K}xf32>, tensor<{K}x{N}xf32>, tensor<{M}x{N}xf32>) -> (tensor<{M}x{N}xf32>)
    scf.yield %r : tensor<{M}x{N}xf32>
  return %res : tensor<{M}x{N}xf32>

argument %C is marked as “inplaceable” and the compiler may use %bufferC to write the result, assuming intra-function analysis allows it.

At the moment, may use %bufferC is implemented as must use %bufferC; this will be relaxed on a per-need basis.

Step 2: Intra-procedural bufferization

Next, traverse each FuncOp and perform bufferization within the function boundaries. Bufferization occurs by:

  1. performing an inPlace analysis inPlaceAnalysisFuncOpInternals which marks each operation within the function with the kInPlaceResultsAttrName attribute.
  2. traversing each operation in the function and rewriting it in buffer form and keeping a BlockAndValueMapping mapping of the rewrites. New allocations are introduced during this step.

The analysis uses special op knowledge of which operand / results may be inplaceable. At the moment a hardcoded enumeration is performed, in the future it seems reasonable to introduce an interface to encode this in a less adhoc fashion.

scf.for and CallOp + FuncOp are special as we additionally need to analyze how operand/argument #n flows into result #n to ensure proper inplace behavior. These operations also have special “inplaceable” semantics in combination with subtensor/subtensor_insert and vector.transfer_read/vector.transfer_write. Analyses of these patterns take SSA use-def chains (point 2. in the Status Update section).

Step 3: Function boundary and call-site bufferization

Lastly, once bufferization within function boundaries is done, the next step runs bufferizeFunctionsAndCalls, which involves:

  1. detecting function_arg -> memref.buffer_cast -> memref.tensor_load -> return patterns for each FuncOp, which determines the tiedResultMap between function args and results. In the future these will disappear as the semantics of those ops is very brittle.
  2. rewriting function arguments and returns in buffer form, skipping the tensors that appear in the tiedResultMap.
  3. bufferizing the CallOps using the callee’s tiedResultMap.

This last step is purely mechanical.


After we experimented with end-to-end paths for a few weeks and started connecting this to parallel GPU and CPU execution, a few of us are relatively confident that this should be upstreamed.
As IREE and XLA continue moving forward relying on this end-to-end codegen-on-tensors path, landing such a bufferization in core will increase reuse and reduce risk.

From a purely technical perspective, this could be landed as a separate -linalg-comprehensive-bufferize pass that can be used independently from the existing core bufferization and provide end-to-end batteries to linalg-on-tensors.

Some have expressed that IREE and XLA really want to handle allocations as well as what happens at function boundaries and keep composability with existing bufferization (XLA-only). So far, this has been a non-goal because the decisions made by -linalg-comprehensive-bufferize seem to be the complete opposite of what bufferization currently does in core.

I believe this can be sliced at the function boundary without delays by temporarily relying on the kInPlaceResultsAttrName attribute, as is done in the current implementation. Still, decoupling inter and intra function bufferization should be an implementation detail and should not preclude also having a Linalg-specific Module pass that brings end-to-end execution capabilities in core.

Once others have gotten their feet wet with this new approach, I believe we will finish connecting the pieces together so that XLA and IREE may use as much (or as little) of this new strategy as wanted.

@pifon2a @ThomasRaoux @MaheshRavishankar @_sean_silva @herhut @benvanik @stellaraccident @shabalin

Thanks for reading, please discuss!

Overall the strategy about interprocedural annotation seems sound to me.
Something that isn’t clear to me is why should this be specific to Linalg? I would expect that this can all be implemented as a core analysis/transformation based on interfaces, which Linalg and others can implement.

Nit (I hesitated to keep this for the review, but heads up :slight_smile:): we have function argument attributes which seem more suitable to model this.
Now it is not obvious to me how this fits in a value-based world (tensor) where you have immutable values…

I believe I am touching on this here:

The analysis uses special op knowledge of which operand / results may be inplaceable. 
At the moment a hardcoded enumeration is performed, in the future it seems reasonable 
to introduce an interface to encode this in a less adhoc fashion.

My thinking is once the algorithm is available, we can easily iterate on generalizing in-tree. In fact @benvanik had implemented an interface for something that seems to fit the bill in IREE and maybe some of his work can be reused. This will also likely involve discussions on how to spell all this to provide what XLA wants (and maybe even TFRT @ezhulenev ?). I certainly hope that this can be incremental followup work that can be distributed depending on folks’ needs and priorities, rather than something blocking that befalls a single pair of shoulders. For these reasons, I think confining it to a single pass in Linalg where it won’t break anyone’s flow, in a first iteration, is a reasonable first step.

Ah thanks, I’ll update indeed before sending for review.

This wants to convey the fact that “any buffer to which the tensor will be bufferized can be written into” (i.e. that at all call sites {“does not come from a global constant memref” + “has no subsequent read” + future conditions} are true).

But naming is hard :slight_smile: so please suggest a better name ?

Yes, you’re touching on it, but this isn’t going deeply enough for me to understand. You also briefly mention an interface, but that seems to indicate to me that there is an opportunity to not make anything Linalg specific here and instead decoupling this through an interface?
I’d really like to see this fleshed out and landed in a decoupled manner from Linalg op.

Let’s scratch a little deeper then and see what would be potential requirements and specific non-goals.

In the description of the steps above the mechanisms described as well as the current implementation are indeed independent. In the current implementation, LinalgOp appears in 3 places:

  1. getTiedOpResult impl: this is the place that switches on op types to determine for a given op whether its semantics allow an operand and a result to reuse the same buffer. This is an op-specific behavior that could be implemented with an interface.
  2. detectLinalgToTerminator impl looks for a specific destructive update pattern involving a Linalg op. Other patterns do not involve Linalg ops. We could imagine some hook mechanism to decouple the specification of such patterns from specific ops. I don’t think this is load bearing and anything beyond an implementation detail.
  3. convertLinalgOp bufferization impl which is the op-specific bufferization: that’s the cost of doing business and not specific to Linalg op.

The more relevant load-bearing aspects of this bufferization address the issues I raised previously in the post on Properly using bufferization related passes; mainly that it is a single pass that performs an analysis followed by a simple Module.walk and does not leak abstractions across pass boundaries.

In light of these explanations, I believe points 1. and 2. fall into the category of implementation details.

Dropped ⚙ D101693 [WIP][mlir] Add ComprehensiveBufferize pass for function and modules (step 1/n) so folks can chew on it as I disappear for 1 week.

I am going to try to read the patch (for me it is hard to read code to get all details, I understand better debugging through examples, but will give it a shot).

One thing that isnt clear to me though is if the inter-procedural and intra-procedural could be decoupled. Essentially the intra-procedural could be refactored to be Region based with expected attributes on the region arguments. At least in IREE that would be the easiest way to use it. The inter-precedural is not useful in IREEs context (and might just become a burden that might actually cause more problem).