representing dependence from producer result to consumer.
With Linalg on tensors the dependence between operations can be from
the result of the producer to the consumer. This change just does a
NFC refactoring of the LinalgDependenceGraphElem to allow representing
both OpResult and OpOperand*.
Differential Revision: https://reviews.llvm.org/D95208
This revision drops init_tensor arguments from Linalg on tensors and instead uniformizes the output buffers and output tensors to be consistent.
This significantly simplifies the usage of Linalg on tensors and is a stepping stone for
its evolution towards a mixed tensor and shape abstraction discussed in https://llvm.discourse.group/t/linalg-and-shapes/2421/19.
Differential Revision: https://reviews.llvm.org/D93469
This is to prevent assertion failures on scf.if and shape.assuming
operations where this is not enough information currently to handle any
aliasing information.
Differential Revision: https://reviews.llvm.org/D92963
This change does two main things
1) An operation might have multiple dependences to the same
producer. Not tracking them correctly can result in incorrect code
generation with fusion. To rectify this the dependence tracking
needs to also have the operand number in the consumer.
2) Improve the logic used to find the fused loops making it easier to
follow. The only constraint for fusion is that linalg ops (on
buffers) have update semantics for the result. Fusion should be
such that only one iteration of the fused loop (which is also a
tiled loop) must touch only one (disjoint) tile of the output. This
could be relaxed by allowing for recomputation that is the default
when oeprands are tensors, or can be made legal with promotion of
the fused view (in future).
Differential Revision: https://reviews.llvm.org/D90579
This revision allows the fusion of the producer of input tensors in the consumer under a tiling transformation (which produces subtensors).
Many pieces are still missing (e.g. support init_tensors, better refactor LinalgStructuredOp interface support, try to merge implementations and reuse code) but this still allows getting started.
The greedy pass itself is just for testing purposes and will be extracted in a separate test pass.
Differential revision: https://reviews.llvm.org/D89491
The methods allow to check
- if an operation has dependencies,
- if there is a dependence from one operation to another.
Differential Revision: https://reviews.llvm.org/D88993
The assertion falsely expected ranked memrefs only. Now both, ranked and
unranked memrefs are allowed.
Differential Revision: https://reviews.llvm.org/D88080
This commit marks AllocLikeOp as MemAlloc in StandardOps.
Also in Linalg dependency analysis use memory effect to detect
allocation. This allows the dependency analysis to be more
general and recognize other allocation-like operations.
Differential Revision: https://reviews.llvm.org/D78705
This can help provide a common interface for view-like
ops so that for example Linalg's dependency analysis
can avoid relying on concrete ops.
Differential Revision: https://reviews.llvm.org/D78645
Summary:
The RAW fusion happens only if the produecer block dominates the consumer block.
The WAW pattern also works with the precondition. I.e., if a producer can
dominate the consumer, they can fairly fuse together.
Since they are all tilable, we can think the pattern like this way:
Input:
```
linalg_op1 view
tile_loop
subview_2
linalg_op2 subview_2
```
Tile the first Linalg op as same as the second Linalg.
```
tile_loop
subview_1
linalg_op1 subview_1
tile_loop
subview_2
liangl_op2 subview_2
```
Since the first Linalg op is tilable in the same way and the computation are
independently, it's fair to fuse it with the second Linalg op.
```
tile_loop
subview_1
linalg_op1 subview_1
linalg_op2 subview_2
```
In short, this patch includes:
- Handling both RAW and WAW pattern.
- Adding a interface method to get input and output buffers.
- Exposing a method to get a StringRef of a dependency type.
- Fixing existing WAW tests and add one more use case: initialize the buffer
before conv op.
Differential Revision: https://reviews.llvm.org/D76897
Summary:
NFC - Moved StandardOps/Ops.h to a StandardOps/IR dir to better match surrounding
directories. This is to match other dialects, and prepare for moving StandardOps
related transforms in out for Transforms and into StandardOps/Transforms.
Differential Revision: https://reviews.llvm.org/D74940
Summary:
This diff fixes issues with the semantics of linalg.generic on tensors that appeared when converting directly from HLO to linalg.generic.
The changes are self-contained within MLIR and can be captured and tested independently of XLA.
The linalg.generic and indexed_generic are updated to:
To allow progressive lowering from the value world (a.k.a tensor values) to
the buffer world (a.k.a memref values), a linalg.generic op accepts
mixing input and output ranked tensor values with input and output memrefs.
```
%1 = linalg.generic #trait_attribute %A, %B {other-attributes} :
tensor<?x?xf32>,
memref<?x?xf32, stride_specification>
-> (tensor<?x?xf32>)
```
In this case, the number of outputs (args_out) must match the sum of (1) the
number of output buffer operands and (2) the number of tensor return values.
The semantics is that the linalg.indexed_generic op produces (i.e.
allocates and fills) its return values.
Tensor values must be legalized by a buffer allocation pass before most
transformations can be applied. Such legalization moves tensor return values
into output buffer operands and updates the region argument accordingly.
Transformations that create control-flow around linalg.indexed_generic
operations are not expected to mix with tensors because SSA values do not
escape naturally. Still, transformations and rewrites that take advantage of
tensor SSA values are expected to be useful and will be added in the near
future.
Subscribers: bmahjour, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D72555
This will enable future commits to reimplement the internal implementation of OpResult without needing to change all of the existing users. This is part of a chain of commits optimizing the size of operation results.
PiperOrigin-RevId: 286930047
This will enable future commits to reimplement the internal implementation of OpResult without needing to change all of the existing users. This is part of a chain of commits optimizing the size of operation results.
PiperOrigin-RevId: 286919966
This is an initial step to refactoring the representation of OpResult as proposed in: https://groups.google.com/a/tensorflow.org/g/mlir/c/XXzzKhqqF_0/m/v6bKb08WCgAJ
This change will make it much simpler to incrementally transition all of the existing code to use value-typed semantics.
PiperOrigin-RevId: 286844725
This type is not used anymore now that Linalg view and subview have graduated to std and that alignment is supported on alloc.
PiperOrigin-RevId: 285213424
This CL uses the now standard std.subview in linalg.
Two shortcuts are currently taken to allow this port:
1. the type resulting from a view is currently degraded to fully dynamic to pass the SubViewOp verifier.
2. indexing into SubViewOp may access out of bounds since lowering to LLVM does not currently enforce it by construction.
These will be fixed in subsequent commits after discussions.
PiperOrigin-RevId: 280250129
Now that a view op has graduated to the std dialect, we can update Linalg to use it and remove ops that have become obsolete. As a byproduct, the linalg buffer and associated ops can also disappear.
PiperOrigin-RevId: 279073591
This CL adds a simple pattern for specifying producer-consumer fusion on Linalg operations.
Implementing such an extension reveals some interesting properties.
Since Linalg operates on a buffer abstraction, the output buffers are specified as in/out parameters to the ops. As a consequence, there are no SSA use-def chains and one cannot specify complex dag input patterns with the current infrastructure.
Instead this CL uses constraints based on the existing linalg dependence analysis to focus the pattern and refine patterns based on the type of op that last wrote in a buffer.
This is a very local property and is less powerful than the generic dag specification based on SSA use-def chains.
This will be generalized in the future.
PiperOrigin-RevId: 277931503
MLIR const-correctness policy is to avoid having `const` on IR objects.
LinalgDependenceGraph is not an IR object but an auxiliary data structure.
Furthermore, it is not updated once constructed unlike IR objects. Add const
qualifiers to get* and find* methods of LinalgDependenceGraph since they are
not modifying the graph. This allows transformation functions that require the
dependence graph to take it by const-reference, clearly indicating that they
are not modifying it (and that the graph may have to be recomputed after the
transformation).
PiperOrigin-RevId: 277731608
Now that linalg.view and strided memrefs are unified, there is no reason to
disallow AllocOp in alias analysis. This CLs adds support for AllocOp which allows writing shorter tests that do not require explicitly creating a view for
each operation.
PiperOrigin-RevId: 273303060
This CL finishes the implementation of the Linalg + Affine type unification of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
As a consequence, the !linalg.view type, linalg::DimOp, linalg::LoadOp and linalg::StoreOp can now disappear and Linalg can use standard types everywhere.
PiperOrigin-RevId: 272187165
This CL modifies the linalg-fusion pass such that it does not tile anymore as part of the pass. Tiling is a separate concern that enables linalg fusion but should happen before.
This makes fusion more composable with other decisions.
In particular the fusion pass now becomes greedy and only applies the transformation on a best-effort basis.
This should also let fusion work in a multi-hop fashion with chains of producer/consumers.
Since the fusion pass does not perform tiling anymore, tests are rewritten to be in pretiled form and make the intent of the test clearer (albeit more verbose).
PiperOrigin-RevId: 271357741
Tobias Gysi