Rationale:
Narrower types for overhead storage yield a smaller memory footprint for
sparse tensors and thus needs to be supported. Also, more value types
need to be supported to deal with all kinds of kernels. Since the
"one-size-fits-all" sparse storage scheme implementation is used
instead of actual codegen, the library needs to be able to support
all combinations of desired types. With some crafty templating and
overloading, the actual code for this is kept reasonably sized though.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D96819
This revision adds support for hoisting "subtensor + vector.transfer_read" / "subtensor_insert + vector.transfer_write pairs" across scf.for.
The unit of hoisting becomes a HoistableRead / HoistableWrite struct which contains a pair of "vector.transfer_read + optional subtensor" / "vector.transfer_write + optional subtensor_insert".
scf::ForOp canonicalization patterns are applied greedily on the successful application of the transformation to cleanup the IR more eagerly and potentially expose more transformation opportunities.
Differential revision: https://reviews.llvm.org/D96731
SliceAnalysis originally was developed in the context of affine.for within mlfunc.
It predates the notion of region.
This revision updates it to not hardcode specific ops like scf::ForOp.
When rooted at an op, the behavior of the slice computation changes as it recurses into the regions of the op. This does not support gathering all values transitively depending on a loop induction variable anymore.
Additional variants rooted at a Value are added to also support the existing behavior.
Differential revision: https://reviews.llvm.org/D96702
This revision takes advantage of the newly extended `ref` directive in assembly format
to allow better region handling for LinalgOps. Specifically, FillOp and CopyOp now build their regions explicitly which allows retiring older behavior that relied on specific op knowledge in both lowering to loops and vectorization.
This reverts commit 3f22547fd1 and reland 973e133b769 with a workaround for
a gcc bug that does not accept lambda default parameters:
https://gcc.gnu.org/bugzilla/show_bug.cgi?id=59949
Differential Revision: https://reviews.llvm.org/D96598
This reverts commit 973e133b769773c89ce4b8bbfd6c77612d2ff9d4.
It triggers an issue in gcc5 that require investigation, the build is
broken with:
/tmp/ccdpj3B9.s: Assembler messages:
/tmp/ccdpj3B9.s:5821: Error: symbol `_ZNSt17_Function_handlerIFvjjEUljjE2_E9_M_invokeERKSt9_Any_dataOjS6_' is already defined
/tmp/ccdpj3B9.s:5860: Error: symbol `_ZNSt14_Function_base13_Base_managerIUljjE2_E10_M_managerERSt9_Any_dataRKS3_St18_Manager_operation' is already defined
This revision takes advantage of the newly extended `ref` directive in assembly format
to allow better region handling for LinalgOps. Specifically, FillOp and CopyOp now build their regions explicitly which allows retiring older behavior that relied on specific op knowledge in both lowering to loops and vectorization.
Differential Revision: https://reviews.llvm.org/D96598
The AffineMap in the MemRef inferred by SubViewOp may have uncompressed symbols which result in type mismatch on otherwise unused symbols. Make the computation of the AffineMap compress those unused symbols which results in better canonical types.
Additionally, improve the error message to report which inferred type was expected.
Differential Revision: https://reviews.llvm.org/D96551
The dimension order of a filter in tensorflow is
[filter_height, filter_width, in_channels, out_channels], which is different
from current definition. The current definition follows TOSA spec. Add TF
version conv ops to .tc, so we do not have to insert a transpose op around a
conv op.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D96038
This revision connects the generated sparse code with an actual
sparse storage scheme, which can be initialized from a test file.
Lacking a first-class citizen SparseTensor type (with buffer),
the storage is hidden behind an opaque pointer with some "glue"
to bring the pointer back to tensor land. Rather than generating
sparse setup code for each different annotated tensor (viz. the
"pack" methods in TACO), a single "one-size-fits-all" implementation
has been added to the runtime support library. Many details and
abstractions need to be refined in the future, but this revision
allows full end-to-end integration testing and performance
benchmarking (with on one end, an annotated Lingalg
op and, on the other end, a JIT/AOT executable).
Reviewed By: nicolasvasilache, bixia
Differential Revision: https://reviews.llvm.org/D95847
This revision fixes the indexing logic into the packed tensor that result from hoisting padding. Previously, the index was incorrectly set to the loop induction variable when in fact we need to compute the iteration count (i.e. `(iv - lb).ceilDiv(step)`).
Differential Revision: https://reviews.llvm.org/D96417
This revision fixes the fact that the padding transformation did not have enough information to set the proper type for the padding value.
Additionally, the verifier for Yield in the presence of PadTensorOp is fixed to properly report incorrect number of results or operands. Previously, the error would be silently ignored which made the core issue difficult to debug.
Differential Revision: https://reviews.llvm.org/D96264
This reverts commit 511dd4f4383b1c2873beac4dbea2df302f1f9d0c along with
a couple fixes.
Original message:
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Phabricator: https://reviews.llvm.org/D96111
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D96111
This makes ignoring a result explicit by the user, and helps to prevent accidental errors with dropped results. Marking LogicalResult as no discard was always the intention from the beginning, but got lost along the way.
Differential Revision: https://reviews.llvm.org/D95841
This revision takes advantage of recent extensions to vectorization to refactor contraction detection into a bona fide Linalg interface.
The mlit-linalg-ods-gen parser is extended to support adding such interfaces.
The detection that was originally enabling vectorization is refactored to serve as both a test on a generic LinalgOp as well as to verify ops that declare to conform to that interface.
This is plugged through Linalg transforms and strategies but it quickly becomes evident that the complexity and rigidity of the C++ class based templating does not pay for itself.
Therefore, this revision changes the API for vectorization patterns to get rid of templates as much as possible.
Variadic templates are relegated to the internals of LinalgTransformationFilter as much as possible and away from the user-facing APIs.
It is expected other patterns / transformations will follow the same path and drop as much C++ templating as possible from the class definition.
Differential revision: https://reviews.llvm.org/D95973
This revision defines a Linalg contraction in general terms:
1. Has 2 input and 1 output shapes.
2. Has at least one reduction dimension.
3. Has only projected permutation indexing maps.
4. its body computes `u5(u1(c) + u2(u3(a) * u4(b)))` on some field
(AddOpType, MulOpType), where u1, u2, u3, u4 and u5 represent scalar unary
operations that may change the type (e.g. for mixed-precision).
As a consequence, when vectorization of such an op occurs, the only special
behavior is that the (unique) MulOpType is vectorized into a
`vector.contract`. All other ops are handled in a generic fashion.
In the future, we may wish to allow more input arguments and elementwise and
constant operations that do not involve the reduction dimension(s).
A test is added to demonstrate the proper vectorization of matmul_i8_i8_i32.
Differential revision: https://reviews.llvm.org/D95939
This revision unifies Linalg vectorization and paves the way for vectorization of Linalg ops with mixed-precision operations.
The new algorithm traverses the ops in the linalg block in order and avoids recursion.
It uses a BlockAndValueMapping to keep track of vectorized operations.
The revision makes the following modifications but is otherwise NFC:
1. vector.transfer_read are created eagerly and may appear in a different order than the original order.
2. a more progressive vectorization to vector.contract results in only the multiply operation being converted to `vector.contract %a, %b, %zero`, where `%zero` is a
constant of the proper type. Later vector canonicalizations are assumed to rewrite vector.contract %a, %b, %zero + add to a proper accumulate form.
Differential revision: https://reviews.llvm.org/D95797
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
This is as same as D95615 but fixing one dep in CMakeLists.txt
Different from D95671, the fix was applied to run target.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D95785
This reverts commit d9b953d84b332a8c4751fcbf8178e32818dc718b.
This commit resulted in build bot failures and the author is away from a
computer, so I am reverting on their behalf until they have a chance to
look into this.
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95671
This is the last revision to migrate using SimplePadOp to PadTensorOp, and the
SimplePadOp is removed in the patch. Update a bit in SliceAnalysis because the
PadTensorOp takes a region different from SimplePadOp. This is not covered by
LinalgOp because it is not a structured op.
Also, remove a duplicated comment from cpp file, which is already described in a
header file. And update the pseudo-mlir in the comment.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95615
This revision creates a build method of PadTensorOp which can be mapped to
SimplePad op. The verifier is updated to accept a static custom result type,
which has the same semantic as SimplePadOp.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D95555
This revision adds a layer of SFINAE to the composable codegen strategy so it does
not have to require statically defined ops but instead can also be used with OpInterfaces, Operation* and an op name string.
A linalg.matmul_i8_i8_i32 is added to the .tc spec to demonstrate how all this works end to end.
Differential Revision: https://reviews.llvm.org/D95600
This revision improves the usage of the codegen strategy by adding a few flags that
make it easier to control for the CLI.
Usage of ModuleOp is replaced by FuncOp as this created issues in multi-threaded mode.
A simple benchmarking capability is added for linalg.matmul as well as linalg.matmul_column_major.
This latter op is also added to linalg.
Now obsolete linalg integration tests that also take too long are deleted.
Correctness checks are still missing at this point.
Differential revision: https://reviews.llvm.org/D95531
This revision starts evolving the APIs to manipulate ops with offsets, sizes and operands towards a ValueOrAttr abstraction that is already used in folding under the name OpFoldResult.
The objective, in the future, is to allow such manipulations all the way to the level of ODS to avoid all the genuflexions involved in distinguishing between values and attributes for generic constant foldings.
Once this evolution is accepted, the next step will be a mechanical OpFoldResult -> ValueOrAttr.
Differential Revision: https://reviews.llvm.org/D95310
This revision addresses a remaining comment that was overlooked in https://reviews.llvm.org/D95243:
the pad hoisting transformation is made to additionally bail out on side effecting ops other than LoopLikeOps.
This transformation anchors on a padding op whose result is only used as an input
to a Linalg op and pulls it out of a given number of loops.
The result is a packing of padded tailes of ops that is amortized just before
the outermost loop from which the pad operation is hoisted.
Differential revision: https://reviews.llvm.org/D95243
This revision allows the base Linalg tiling pattern to optionally require padding to
a constant bounding shape.
When requested, a simple analysis is performed, similar to buffer promotion.
A temporary `linalg.simple_pad` op is added to model padding for the purpose of
connecting the dots. This will be replaced by a more fleshed out `linalg.pad_tensor`
op when it is available.
In the meantime, this temporary op serves the purpose of exhibiting the necessary
properties required from a more fleshed out pad op, to compose with transformations
properly.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95149
Fusion of generic/indexed_generic operations with tensor_reshape by
expansion when the latter just adds/removes unit-dimensions is
disabled since it just adds unit-trip count loops.
Differential Revision: https://reviews.llvm.org/D94626
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
Use cases with 16- or even 8-bit pointer/index structures have been identified.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D95015
